WO2022174287A1 - Methods and systems for detecting a coronavirus infection - Google Patents

Methods and systems for detecting a coronavirus infection Download PDF

Info

Publication number
WO2022174287A1
WO2022174287A1 PCT/AU2022/050105 AU2022050105W WO2022174287A1 WO 2022174287 A1 WO2022174287 A1 WO 2022174287A1 AU 2022050105 W AU2022050105 W AU 2022050105W WO 2022174287 A1 WO2022174287 A1 WO 2022174287A1
Authority
WO
WIPO (PCT)
Prior art keywords
mir
mirna
sample
cov
subject
Prior art date
Application number
PCT/AU2022/050105
Other languages
French (fr)
Inventor
Cameron Stewart
Christopher COWLED
Ryan James FARR
Chwan-Hong FOO
Original Assignee
Commonwealth Scientific And Industrial Research Organisation
Exios Bio LLC
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Priority claimed from AU2021902194A external-priority patent/AU2021902194A0/en
Application filed by Commonwealth Scientific And Industrial Research Organisation, Exios Bio LLC filed Critical Commonwealth Scientific And Industrial Research Organisation
Publication of WO2022174287A1 publication Critical patent/WO2022174287A1/en

Links

Classifications

    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/80—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for detecting, monitoring or modelling epidemics or pandemics, e.g. flu
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
    • C12Q1/6876—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes
    • C12Q1/6883—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/70—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving virus or bacteriophage
    • C12Q1/701—Specific hybridization probes
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/30—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indices; for individual health risk assessment
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q2600/00—Oligonucleotides characterized by their use
    • C12Q2600/118—Prognosis of disease development
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q2600/00—Oligonucleotides characterized by their use
    • C12Q2600/158—Expression markers
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q2600/00—Oligonucleotides characterized by their use
    • C12Q2600/178—Oligonucleotides characterized by their use miRNA, siRNA or ncRNA

Definitions

  • the present disclosure relates to methods, kits and panels for determining the likelihood of a coronavirus (CoV) infection or a severe coronavirus (CoV) infection in a subject, such as a severe acute respiratory syndrome coronavirus 2 (SARS-COV-2) virus infection.
  • the disclosure also relates to methods for monitoring CoV or severe CoV infection in a subject.
  • SARS-CoV-2 severe acute respiratory syndrome-associated coronavirus-2
  • SARS-CoV-2 severe acute respiratory syndrome-associated coronavirus-2
  • the outcome of SARS-CoV-2 infection varies widely from asymptomatic to severe disease associated with acute respiratory distress syndrome and death.
  • Several studies have established that host responses to infection play a critical role in determining disease outcome in infected patients. Technologies most commonly utilized for COVTD-19 diagnosis are virus-specific molecular assays or serology, both of which are associated with relatively high false-positive rates (Kanne et al, 2020; Ai et al., 2020).
  • MicroRNAs are a class of non-coding RNAs that regulate endogenous gene expression at the post-transcriptional level. In most instances, miRNAs function by interacting with the 3' untranslated region (3' UTR) of target mRNAs to induce degradation and translational repression.
  • miRNAs There are currently over 2,600 human miRNAs listed in the miRNA registry (miRBase, version 22) (Griffiths- Jones et al, 2006) which are estimated to collectively regulate 60% of all human protein-coding genes Friedman et al. (2009). miRNA profiles offer unique insight into cellular pathways associated with virus replication and pathogenesis.
  • TGEV Alphacoronavirus transmissible gastroenteritis virus
  • the present disclosure provides methods, kits, panels, arrays and systems for determining the likelihood of a coronavirus (CoV) infection or a severe coronavirus (CoV) infection in a subject and/or for monitoring CoV or a severe CoV infection in a subject.
  • CoV coronavirus
  • CoV severe coronavirus
  • the present disclosure provides a method of determining the likelihood of a CoV infection in a subject, the method comprising, in a biological sample from the subject, detecting a level of at least one miRNA selected from: miR-423-5p, miR-23a-3p, miR- 195-5p, miR-766-3p, miR-651-5p, let-7e-5p, miR-1275, miR-3198, miR-627-5p, miR-4662a-5p, miR-3684, let-7a-5p, miR-483-5p, miR-3125, miR-3617-5p, miR-500b-3p, miR-664b-3p, miR- 5189-3p, miR-6772-3p, miR-145-3p, miR-30a-5p, miR-27a-5p, miR-2116-3p, miR-31-5p, miR- 4772-3p, miR-1290, miR-1226-3p, miR-589-3p, miR-
  • the present disclosure provides a method for determining the likelihood of a severe CoV infection in a subject, the method comprising, in a biological sample from the subject, detecting a level of at least one miRNA selected from: let-7e-5p, miR-766-3p, miR-651-5p, miR-4433b-5p, miR-106b-5p, miR-99b-3p, miR-1273h-3p, let-7a-3p, miR-6741-5p, miR-21-3p, miR-301b-3p, miR-7-l-3p, miR-148b-5p, miR-576-5p, miR-3198, miR-4662a-5p, miR-6772-3p, miR-197-3p, miR-874-3p, miR-889-3p, miR-92a-3p, miR-500b-3p, miR-769-3p, miR-1468-5p, miR-431-3p, miR-345-5p, miR-339-3p,
  • the present disclosure provides a method of monitoring a CoV infection in a subject or evaluating the efficacy of a CoV treatment in a subject, the method comprising detecting a level of at least one miRNA selected from: miR-423-5p, miR-23a-3p, miR- 195-5p, miR-766-3p, miR-651-5p, let-7e-5p, miR-1275, miR-3198, miR-627-5p, miR-4662a-5p, miR-3684, let-7a-5p, miR-483-5p, miR-3125, miR-3617-5p, miR-500b-3p, miR-664b-3p, miR- 5189-3p, miR-6772-3p, miR-145-3p, miR-30a-5p, miR-27a-5p, miR-2116-3p, miR-31-5p, miR- 4772-3p, miR-1290, miR-1226-3p, miR-589-3p,
  • the present disclosure provides a panel or kit for determining the likelihood of a CoV infection in a subject, the panel or kit comprising one or more probes or primers for detecting at least one miRNA selected from: miR-423-5p, miR-23a-3p, miR-195-5p, miR-766-3p, miR-651-5p, let-7e-5p, miR-1275, miR-3198, miR-627-5p, miR-4662a-5p, miR- 3684, let-7a-5p, miR-483-5p, miR-3125, miR-3617-5p, miR-500b-3p, miR-664b-3p, miR-5189- 3p, miR-6772-3p, miR-145-3p, miR-30a-5p, miR-27a-5p, miR-2116-3p, miR-31-5p, miR-4772- 3p, miR-1290, miR-1226-3p, miR-589-3p, miR-210
  • the present disclosure provides a panel or kit for determining the likelihood of a severe CoV infection in a subject, the panel or kit comprising one or more probes or primers for detecting at least one miRNA selected from: let-7e-5p, miR-766-3p, miR-651-5p, miR-4433b-5p, miR-106b-5p, miR-99b-3p, miR-1273h-3p, let-7a-3p, miR-6741-5p, miR-21-3p, miR-301b-3p, miR-7-l-3p, miR-148b-5p, miR-576-5p, miR-3198, miR-4662a-5p, miR-6772-3p, miR-197-3p, miR-874-3p, miR-889-3p, miR-92a-3p, miR-500b-3p, miR-769-3p, miR-1468-5p, miR-431-3p, miR-345-5p, miR-339-3p, miRNA
  • the disclosure provides a method of treating or preventing a CoV infection or severe CoV infection in a subject, the method comprising: i) determining the likelihood of a CoV infection in a subject using the method of the disclosure, and/or determining the likelihood of a severe CoV infection in a subject using the method of the disclosure, ii) administering a treatment or preventative therapy for a CoV infection if it is determined the subject is likely to have a CoV infection or severe CoV infection.
  • an anti-coronavirus compound for the manufacture of a medicament for the treatment or prevention of a coronavirus infection or severe coronavirus infection in a subject, wherein it has been determined that it is likely the subject has a coronavirus infection using the method of the disclosure, and/or it has been determined that it is likely the subject will have a severe coronavirus infection using the method of the disclosure.
  • the disclosure provides the use of an anti-coronavirus compound for the treatment or prevention of a coronavirus infection or severe coronavirus infection in a subject, wherein it has been determined that it is likely the subject has a coronavirus infection using the method of the disclosure, and/or it has been determined that it is likely the subject will have a severe coronavirus infection using the method of the disclosure.
  • the disclosure provides a method of diagnosing a pre-symptomatic or asymptomatic subject infected with or exposed to a CoV. In some embodiments, the disclosure provides a method of detecting the presence or quantity of a CoV infection in a sample of a subject. In some embodiments, the disclosure provides a method of treating a pre-symptomatic or asymptomatic subject infected with a CoV or a subject infected with a CoV but not exhibiting clinically presented lung symptoms of CoV infection. In some embodiments, the disclosure provides a method of preventing severe CoV infection in a subject.
  • the disclosed methods comprise a) exposing a sample from the subject to a probe specific for at least one miRNA chosen from: miR-423-5p, miR-23a-3p, miR-195-5p, miR-766-3p, miR-651-5p, let- 7e-5p, miR-1275, miR-3198, miR-627-5p, miR-4662a-5p, miR-3684, let-7a-5p, miR-483-5p, miR-3125, miR-3617-5p, miR-500b-3p, miR-664b-3p, miR-5189-3p, miR-6772-3p, miR-145-3p, miR-30a-5p, miR-27a-5p, miR-2116-3p, miR-31-5p, miR-4772-3p, miR-1290, miR-1226-3p, miR-589-3p, miR-210-3p, miR-103a-3p, miR-3115, miR-769-3p
  • the method further comprises normalizing the presence, absence, or quantity of the at least one miRNA in the sample against the presence, absence or quantity of the at least one miRNA in a sample of a healthy subject. In some embodiment, the method comprises correlating the presence, absence, or quantity of the at least one miRNA in the sample to the subject being infected with a CoV. In some embodiments, the method further comprises assigning a score for the sample based on the level of the at least one miRNA. In some embodiments, the disclosed methods further comprise obtaining the sample from the subject. In some embodiments, the sample is plasma, serum, whole blood, respiratory tissue, respiratory mucosal sample, saliva or urine.
  • the disclosed methods further comprise administering a therapeutically effective amount of one or a plurality of active agents to the subject.
  • the disclosure provides a method of preparing a sample from a pre- symptomatic or asymptomatic subject infected with a CoV or a subject infected a CoV but not exhibiting clinically presented lung symptoms of CoV infection comprising: a) obtaining the sample from the subject; b) isolating total RNA from the sample; c) analysing the total RNA with a probe specific for at least one miRNA disclosed herein.
  • the method further comprises detecting the presence, absence or quantity of the at least one miRNA in the sample.
  • the method further comprises normalizing the presence, absence, or quantity of the at least one miRNA in the sample against the presence, absence or quantity of the at least one miRNA in a sample of a healthy subject. In some embodiments, the method further comprises correlating the presence, absence, or quantity of the at least one miRNA in the sample to the subject being infected with a CoV.
  • the sample is plasma, serum, whole blood, respiratory tissue, respiratory mucosal sample, saliva or urine.
  • the probe specific to the at least one miRNA is one or a plurality of primers chosen from Table 1. In some embodiments, the probe specific to the at least one miRNA comprises a nucleic acid sequence complementary to the nucleic acid sequence of the at least one miRNA.
  • the disclosed methods further comprises calculating one or more scores based upon the presence, absence, or quantity of the at least one miRNA. In some embodiments, the disclosed methods further comprises correlating the one or more scores to the presence, absence, or quantity of the at least one miRNA such that, if the amount of the at least one miRNA is greater than the quantity of the at least one miRNA in a control sample; or, if the amount of the at least one miRNA is substantially equal to the quantity of the at least one miRNA in a sample taken from a subject known to have CoV infection, then the subject is diagnosed as being infected with a CoV.
  • the at least one miRNA is detected with next generation sequencing, quantitative real-time reverse transcription-PCR (qRT-PCR), isothermal amplification, electrical interference, a CRISPR-based method, nanomaterial-based methods, nucleic acid amplification- based methods such as rolling circle amplification (RCA), loop-mediated isothermal amplification (LAMP), strand-displacement amplification (SDA), enzyme-free amplification, microarray, multiplex miRNA profiling assay, RNA-ish, or northern blotting.
  • the at least one miRNA is detected by qRT-PCR.
  • the at least one miRNA is detected with next generation sequencing.
  • the at least one miRNA is detected with electrical interference.
  • the at least one miRNA is detected with a CRISPR-based method.
  • the step of quantifying at least one quantity of the at least one miRNA in the sample comprises using a fluorescence and/or digital imaging.
  • the presence, absence, or quantity of the at least one miRNA is detected by PCR amplification using one or a plurality of primers specific for the at least one miRNA.
  • the one or plurality of primers are chosen from Table 1.
  • the presence, absence, or quantity of the at least one miRNA is detected by a probe comprising a nucleic acid sequence complementary to the nucleic acid sequence of the at least one miRNA.
  • the probe is a radioactive probe, a chemoluminescent probe, or a fluorescent probe.
  • the presence, absence, or quantity of at least 2 different miRNAs in the sample are detected, normalized and correlated. In some embodiments, the presence, absence, or quantity of at least 3 to 6 different miRNAs in the sample are detected, normalized and correlated. In some embodiments, the disclosed methods comprise detecting the presence, absence or quantity of at least one miRNA chosen from miR-423-5p, miR-195-5p and miR-23a-3p in the sample. In some embodiments, the disclosed methods comprise detecting the presence, absence or quantity of at least two miRNAs chosen from miR-423-5p, miR-195-5p and miR-23a-3p in the sample.
  • the disclosed methods comprise detecting the presence, absence or quantity of miR-423-5p, miR-195-5p and miR-23a-3p in the sample. In some embodiments, the disclosed methods comprise detecting the presence, absence or quantity of miR-23a-3p, miR-195- 5p, miR-423-5p, and miR-28-5p. In some embodiments, the disclosed methods comprise detecting the presence, absence or quantity of miR-23a-3p, miR-195-5p, miR-423-5p, miR-28-5p, and miR- 223 -5p. In some embodiments, the disclosed methods comprise detecting the presence, absence or quantity of miR-23a-3p, miR-195-5p, miR-423-5p, miR-28-5p, miR-223-5p, and miR-130b-3p.
  • the CoV infection is selected from: SARS-CoV-2, HCoV-OC43, HCoV-HKUl, HCoV-229E, HCoV-NL63, SARS-CoV or MERS-CoV, or a variant thereof.
  • the CoV infection is SARS-CoV-2 or a variant thereof.
  • the SARs-CoV-2 variant is B.1.1.529.
  • the disclosure further provides a computer program product encoded on a computer- readable storage medium, wherein the computer program product comprises instructions for: a) detecting the presence, absence or quantity of at least one miRNA in a sample of a subject; and b) correlating the presence, absence, or quantity of the at least one miRNA in the sample to a likelihood that the subject being infected with a CoV infection.
  • the method further comprises one or both of: c) normalizing the presence, absence, or quantity of the at least one miRNA in the sample against the presence, absence or quantity of the at least one miRNA in a control sample; and d) calculating a score associated with the presence, absence or quantity of the at least one miRNA in the sample and correlating the score to a likelihood that the subject being infected with a CoV infection.
  • the computer program product comprises: a) detecting and normalizing the presence, absence or quantity of a second miRNA in the sample; and b) correlating the combined score to a likelihood that the subject being infected with a CoV.
  • the method further comprises detecting and normalizing the presence, absence or quantity of a second miRNA in the sample.
  • the method further comprises calculating a combined score associated with the presence, absence or quantity of the at least one miRNA and the second miRNA in the sample.
  • the method further comprises assigning a score based on the level of the at least one miRNA.
  • the method further comprises normalizing the presence, absence, or quantity of the at least one miRNA in the sample against the presence, absence or quantity of the at least one miRNA in a control sample. In some embodiments, the method further comprises In some embodiments, the method further comprises calculating a score associated with the presence, absence or quantity of the at least one miRNA in the sample and correlating the score to a likelihood that the subject being infected with a CoV.
  • the presence, absence, or quantity of the at least one miRNA is detected by qRT-PCR amplification. In some embodiments, the presence, absence, or quantity of the at least one miRNA is detected by electrical interference. In some embodiments, the presence, absence, or quantity of the at least one miRNA is detected by a CRISPR-based method. In some embodiments, the control sample is obtained from a healthy subject. In some embodiments, the disclosure provides a system comprising: a) any of the disclosed the computer program product; and b) a processor operable to execute programs; and/or a memory associated with the processor.
  • the disclosure additionally provides a system for detecting the presence or quantity of CoV infection in a sample of a subject comprising: a processor operable to execute programs; a memory associated with the processor; a database associated with said processor and said memory; and a program stored in the memory and executable by the processor, the program being operable for: a) detecting and normalizing the presence, absence or quantity of a second miRNA in the sample; and b) correlating the combined score to a likelihood that the subject being infected with a CoV.
  • the program of the disclosed system is further operable for c) calculating a combined score associated with the presence, absence or quantity of the at least one miRNA and the second miRNA in the sample.
  • the program of the disclosed system is further operable for calculating a score associated with the presence, absence or quantity of the at least one miRNA in the sample and correlating the score to a likelihood that the subject being infected with a CoV. In some embodiments, the program of the disclosed system is further operable for: a) detecting and normalizing the presence, absence or quantity of a second miRNA in the sample; and b) correlating the combined score to a likelihood that the subject being infected with a CoV. In some embodiments, the program of the disclosed system is further operable for c) calculating a combined score associated with the presence, absence or quantity of the at least one miRNA and the second miRNA in the sample.
  • the biological sample is a blood sample. In some embodiments, the biological sample is a respiratory mucosal sample. In some embodiments, the biological sample is a nasal mucosal sample.
  • composition of matter, group of steps or group of compositions of matter shall be taken to encompass one and a plurality (i.e. one or more) of those steps, compositions of matter, groups of steps or group of compositions of matter.
  • SEQ ID NOs 178 - 354 mature DNA sequence for the miRNAs of SEQ IN NOs 1 - 177.
  • SEQ ID NOs 335 - 536 example forward primers for the miRNAs of SEQ ID NOs 1 - 177.
  • SEQ ID NOs 537 - 718 example reverse primers for the miRNAs of SEQ ID NOs 1 - 177.
  • SEQ ID NOs 719 - 734 example RT primer and probes for select miRNAs.
  • FIG. 1 SARS-CoV-2 induces circulating miRNA and cytokine changes.
  • Horizontal dotted line is the p- value cut-off (False Discovery Rate, FDR ⁇ 0.05) and the vertical lines are the fold change cut-off (>2 FC).
  • FDR ⁇ 0.05 the p- value cut-off
  • FC fold change cut-off
  • a small number of miRNAs are statistically significant but are not >2 FC. The number of statistically significant miRNAs in each section are shown. The most up-regulated, down- regulated, and statistically significant miRNAs have been labelled.
  • FIG. 1 A three miRNA signature classifies COVTD with 99.9% accuracy.
  • Boxes are the 25 th - 75 th percentile, line is the median, and whiskers are 1.5x IQR. * FDR adjusted p-value ⁇ 0.05, ** FDR adjusted p-value ⁇ 0.01. n.s. non-significant.
  • FIG. 3 Differential miRNA profiles based on COVID-19 severity.
  • Figure 4 Human miRNA signature accurately identifies influenza and SARS-CoV-2 infection in a ferret model.
  • A) Detection of SARS-CoV-2 viral genomic RNA in the retroperitoneal lymph node, nasal wash, oral swab, and turbinate tissue of infected ferrets (n 20, swab and wash samples taken from every ferret at each time point, tissue samples were analysed from the 4 euthanized ferrets at each time point). Data is presented as loglO copies per g of tissue or ml of sample.
  • Error bars are 95% Cl for 1,000 random assessments.
  • FIG. 1 Application of the VI COVID- 19 miRNA signature to subsequent time points.
  • Decision boundary graph showing the logistic regression decision point (solid black line) and the probability a person is infected with SARS-CoV-2 (dark to light shading).
  • Figure 8. A software step flow chart.
  • FIG. 9 COVID-19 alters miRNA abundance in nasal swab samples.
  • Horizontal dotted line is the p-value cut-off (False Discovery Rate, FDR ⁇ 0.05) and the vertical lines are the fold change cut-off (>2 FC).
  • the two miRNAs in the top middle section outlined by dotted lines are statistically significant but are not >2 FC. The number of statistically significant miRNAs in each section are shown. The most up-regulated, down-regulated, and statistically significant miRNAs have been labelled.
  • FIG. 10 A three miRNA signature classifies COVID with 100% accuracy.
  • nucleic acid sequence includes a plurality of nucleotides that are formed
  • nucleic acid sequence is a reference to one or more nucleic acid sequences and equivalents thereof known to those skilled in the art, and so forth.
  • X and/or Y shall be understood to mean either “X and Y” or “X or Y” and shall be taken to provide explicit support for both meanings or for either meaning.
  • the term “about” is used herein to mean within the typical ranges of tolerances in the art. For example, “about” can be understood as about 2 standard deviations from the mean.
  • a measurable value such as an amount and the like
  • “about” is meant to encompass variations of ⁇ 10%, ⁇ 5%, ⁇ 1%, ⁇ 0.9%, ⁇ 0.8%, ⁇ 0.7%, ⁇ 0.6%, ⁇ 0.5%, ⁇ 0.4%, ⁇ 0.3%, ⁇ 0.2% or ⁇ 0.1% from the specified value as such variations are appropriate to perform the disclosed methods.
  • “about” is present before a series of numbers or a range, it is understood that “about” can modify each of the numbers in the series or range.
  • accuracy refers to the ability of the method, kit or panel as described herein to discriminate between a target condition in a subject and health in a subject.
  • an “algorithm,” “formula,” or “model” is any mathematical equation, algorithmic, analytical or programmed process, or statistical technique that takes one or more continuous or categorical inputs (herein called “parameters”) and calculates an output value, sometimes referred to as an “index” or “index value.”
  • “formulas” include sums, ratios, and regression operators, such as coefficients or exponents, biomarker (e.g., miRNAs disclosed herein) value transformations and normalizations (including, without limitation, those normalization schemes based on clinical parameters, such as gender, age, or ethnicity), rules and guidelines, statistical classification models, and neural networks trained on historical populations.
  • markers Of particular use in combining markers are linear and non-linear equations and statistical classification analyses to determine the relationship between levels of the biomarkers detected in a subject sample and the subject’s risk of disease (for example).
  • panel and combination construction of particular interest are structural and syntactic statistical classification algorithms, and methods of risk index construction, utilizing pattern recognition features, including established techniques such as cross correlation, Principal Components Analysis (PCA), factor rotation, Logistic Regression (LogReg), Linear Discriminant Analysis (LDA), Eigengene Linear Discriminant Analysis (ELD A), Support Vector Machines (SVM), Random Forest (RF), Recursive Partitioning Tree (RPART), as well as other related decision tree classification techniques, Shruken Centroids (SC), StepAIC, Kth-Nearest Neighbor, Boosting, Decision Trees, Neural Networks, Bayesian Networks, Support Vector Machines, and Hidden Markov Models, among others.
  • PCA Principal Components Analysis
  • LogReg Logistic Regression
  • LDA Linear Discriminant Analysis
  • biomarker selection techniques are useful either combined with a biomarker selection technique, such as forward selection, backwards selection, or stepwise selection, complete enumeration of all potential panels of a given size, genetic algorithms, or they may themselves include biomarker selection methodologies in their own technique.
  • biomarker selection methodologies such as Akaike’s Information Criterion (AIC) or Bayes Information Criterion (BIC), in order to quantify the tradeoff between additional biomarkers and model improvement, and to aid in minimizing overfit.
  • AIC Information Criterion
  • BIC Bayes Information Criterion
  • the resulting predictive models may be validated in other studies, or cross-validated in the study they were originally trained in, using such techniques as Leave- One-Out (LOO) and 10-Fold cross-validation (10-Fold-CV).
  • LEO Leave- One-Out
  • 10-Fold cross-validation 10-Fold-CV
  • an “amplification assay” is an assay that uses purified enzymes to exponentially replicate specific nucleic acids to levels where they can be detected.
  • typically an amplification assay involves the use of oligonucleotide primers which hybridize regions flanking a target sequences, a polymerase, and numerous rounds of producing single stranded nucleic acids (usually by heat denaturation), primer annealing and primer extension using the polymerase.
  • the amplification assay is performed by using one or a plurality of oligonucleotide primers disclosed in Table 1.
  • the term “animal” includes, but is not limited to, humans and non-human vertebrates such as wild animals, rodents (such as rats), ferrets, mink, companion animals, domesticated animals, and farm animals, such as dogs, cats, horses, pigs, cows, sheep, and goats.
  • the animal is a mammal.
  • the animal is a human.
  • the animal is a non-human mammal.
  • At least prior to a number or series of numbers (e.g. “at least two”) is understood to include the number adjacent to the term “at least,” and all subsequent numbers or integers that could logically be included, as clear from context.
  • at least is present before a series of numbers or a range, it is understood that “at least” can modify each of the numbers in the series or range.
  • complementarity refers to polynucleotides (i.e., a sequence of nucleotides) related by base-pairing rules, for example, the sequence “5’-AGT-3’,” is complementary to the sequence “5’-ACT-3 ⁇ ”
  • Complementarity may be “partial,” in which only some of the nucleic acids’ bases are matched according to the base pairing rules, or there may be “complete” or “total” complementarity between the nucleic acids.
  • the degree of complementarity between nucleic acid strands can have significant effects on the efficiency and strength of hybridization between nucleic acid strands under defined conditions. This is of particular importance for methods that depend upon binding between nucleic acid bases.
  • the terms “comprising” (and any form of comprising, such as “comprise,” “comprises,” and “comprised”), “having” (and any form of having, such as “have” and “has”), “including” (and any form of including, such as “includes” and “include”), or “containing” (and any form of containing, such as “contains” and “contain”), are inclusive or open-ended and do not exclude additional, unrecited elements or method steps.
  • diagnosis refers to the identification of the nature of an illness (e.g. identification of where a subject has a CoV infection).
  • prognosis refers to the likely course of a medical condition. In some embodiments, prognosis refers to the most likely outcome, timeframes, and/or response to a particular treatment.
  • expression refers to the process by which a polynucleotide is transcribed from a DNA template into a miRNA, mRNA or other RNA transcript and/or the process by which a transcribed mRNA is subsequently translated into peptides, polypeptides, or proteins.
  • label refers to any atom or molecule that can be used to provide a detectable (preferably quantifiable) effect, and that can be attached to a nucleic acid or protein.
  • Labels include but are not limited to dyes; radiolabels such as 2 P; binding moieties such as biotin; haptens such as digoxgenin; luminogenic, phosphorescent or fluorogenic moieties; and fluorescent dyes alone or in combination with moieties that can suppress or shift emission spectra by fluorescence resonance energy transfer (FRET). Labels may provide signals detectable by fluorescence, radioactivity, colorimetry, gravimetry, X-ray diffraction or absorption, magnetism, enzymatic activity, and the like.
  • a label may be a charged moiety (positive or negative charge) or alternatively, may be charge neutral.
  • Labels can include or consist of nucleic acid or protein sequence, so long as the sequence comprising the label is detectable. In some embodiments, nucleic acids are detected directly without a label (e.g., directly reading a sequence).
  • a miRNA transcript exhibits an “increased level” when the level of the miRNA transcript is higher than a reference value as described herein. In some embodiments, a miRNA transcript exhibits a “decreased level” when the level of the miRNA transcript is lower than a reference value as described herein.
  • machine learning encompasses all possible mathematical in silico techniques for creation of useful algorithms from large data sets.
  • algorithm will be utilized in reference to the clinically useful mathematical equations or computer programs produced by the one or plurality of processes disclosed or executing the one or plurality of processes disclosed.
  • the performance of machine learning derived algorithms is independent of the specific in silico software routine used for its derivation. If the same training data set is used, techniques as different as supervised learning, unsupervised learning, association rule learning, hierarchical clustering, multiple linear and logistic regressions are likely to produce algorithms whose clinical performance is indistinguishable.
  • measuring means assessing the presence, absence, quantity or amount (which can be an effective amount) or determining a “score” as described herein of either a given substance within a clinical or subject-derived sample, including the derivation of qualitative or quantitative concentration levels of such substances.
  • detecting or “detection” may be used and is understood to cover measuring or measurement as described herein.
  • RNA transcript refers to the level of the RNA transcript, relative to the mean levels of a set or control set of reference RNA transcripts.
  • the reference RNA transcripts are based on their minimal variation across patients, tissues, or treatments.
  • the RNA transcript may be normalized to the totality of tested RNA transcripts, or a subset of such tested RNA transcripts.
  • nucleic acid refers to any nucleic acid
  • oligonucleotide refers to any nucleic acid molecules
  • polynucleotide refers to any combination of nucleic acid molecules.
  • Both terms are used to denote DNA, RNA, modified or synthetic DNA or RNA (including, but not limited to nucleic acids comprising synthetic and naturally-occurring base analogs, dideoxy or other sugars, thiols or other non-natural or natural polymer backbones), or other nucleobase containing polymers capable of hybridizing to DNA and/or RNA. Accordingly, the terms should not be construed to define or limit the length of the nucleic acids referred to and used herein, nor should the terms be used to limit the nature of the polymer backbone to which the nucleobases are attached.
  • nucleic acid sequence or “polynucleotide sequence” refers to a contiguous string of nucleotide bases and in particular contexts also refers to the particular placement of nucleotide bases in relation to each other as they appear in a polynucleotide.
  • one or more of includes at least one of the recited components, or 2, 3, 4, 5, or 5 etc. of the recited components.
  • the phase includes all of the recited components.
  • the term “or” should be understood to have the same meaning as “and/or” as defined above.
  • “or” or “and/or” shall be interpreted as being inclusive, i.e., the inclusion of at least one, but also including more than one, of a number or list of elements, and, optionally, additional unlisted items. Only terms clearly indicated to the contrary, such as “only one of’ or “exactly one of,” or, when used in the claims, “consisting of,” will refer to the inclusion of exactly one element of a number or list of elements.
  • the term “or” as used herein shall only be interpreted as indicating exclusive alternatives (i.e.
  • Ranges provided herein are understood to include all individual integer values and all subranges within the ranges.
  • performance relates to the quality and overall usefulness of, e.g., a model, algorithm, or prognostic test.
  • Factors to be considered in model or test performance include, but are not limited to, the clinical and analytical accuracy of the test, use characteristics such as stability of reagents and various components, ease of use of the model or test, health or economic value, and relative costs of various reagents and components of the test. Performing can mean the act of carrying out a function.
  • precision refers to the chance that a subject testing positive actually has the condition being tested for.
  • the terms “prevent,” “preventing,” “prevention,” “prophylactic treatment,” and the like are meant to refer to reducing the probability of developing a disease or condition in a subject, who does not have, but is at risk of or susceptible to developing a disease or condition.
  • sample refers to any biological sample that is isolated from a subject.
  • a sample can include, without limitation, a single cell or multiple cells, fragments of cells, an aliquot of body fluid, whole blood, platelets, serum, plasma, red blood cells, white blood cells or leucocytes, endothelial cells, tissue biopsies, synovial fluid, lymphatic fluid, ascites fluid, and interstitial or extracellular fluid.
  • sample also encompasses the fluid in spaces between cells, including gingival crevicular fluid, bone marrow, cerebrospinal fluid (CSF), saliva, mucous, sputum, semen, sweat, urine, or any other bodily fluids.
  • CSF cerebrospinal fluid
  • “Blood sample” can refer to whole blood or any fraction thereof, including blood cells, red blood cells, white blood cells or leucocytes, platelets, serum and plasma.
  • “Respiratory mucosal sample” refers to mucosal sample obtained from any part of the respiratory tract of the subject, which include, but not limited to, anterior nasal swabs/tissues, nasopharyngeal swabs/tissues, and oropharyngeal swabs/tissues.
  • the respiratory mucosal sample is a nasal mucosal sample.
  • the respiratory mucosal sample is an anterior nasal mucosal sample.
  • the sample is collected with a nasal swab.
  • the sample is collected with a nasal wash.
  • Samples can be obtained from a subject by means including but not limited to venipuncture, excretion, ejaculation, massage, biopsy, needle aspirate, lavage, scraping, surgical incision, or intervention or other means known in the art.
  • the sample is blood.
  • the sample is saliva.
  • the sample is mucous.
  • sequence identity is determined by using the stand-alone executable BLAST engine program for blasting two sequences (bl2seq), which can be retrieved from the National Center for Biotechnology Information (NCBI) ftp site, using the default parameters (Tatusova and Madden, 1999).
  • bl2seq stand-alone executable BLAST engine program for blasting two sequences
  • NCBI National Center for Biotechnology Information
  • % sequence identity can be determined using the EMBOSS Pairwise Alignment Algorithms tool available from The European Bioinformatics Institute (EMBL-EBI), which is part of the European Molecular Biology Laboratory (EMBL). This tool is accessible at the website ebi.ac.uk/Tools/emboss/align/.
  • This tool utilizes the Needleman-Wunsch global alignment algorithm (Needleman and Wunsch (1970); Kruskal (1983)). Default settings are utilized which include Gap Open: 10.0 and Gap Extend 0.5. The default matrix “Blosum62” is utilized for amino acid sequences and the default matrix “DNAfull” is utilized for nucleic acid sequences.
  • the term “statistically significant” means an observed alteration is greater than what would be expected to occur by chance alone (e.g., a “false positive”).
  • Statistical significance can be determined by any of various methods well-known in the art.
  • An example of a commonly used measure of statistical significance is the p-value.
  • the p-value represents the probability of obtaining a given result equivalent to a particular datapoint, where the datapoint is the result of random chance alone.
  • a result is often considered highly significant (not random chance) at a p- value less than or equal to about 0.05.
  • terapéutica means an agent utilized to treat, combat, ameliorate, prevent or improve an unwanted condition or disease of a patient.
  • a “therapeutically effective amount” or “effective amount” of a composition is a predetermined amount calculated to achieve the desired effect, i.e., to treat, combat, ameliorate, prevent or improve one or more symptoms of CoV infection.
  • the activity contemplated by the present methods includes both medical therapeutic and/or prophylactic treatment, as appropriate.
  • the specific dose of a compound administered according to the disclosure to obtain therapeutic and/or prophylactic effects will, of course, be determined by the particular circumstances surrounding the case, including, for example, the compound administered, the route of administration, and the condition being treated.
  • a therapeutically effective amount of compounds of embodiments of the disclosure is typically an amount such that when it is administered in a physiologically tolerable excipient composition, it is sufficient to achieve an effective systemic concentration or local concentration in the tissue.
  • Treat,” “treated,” “treating,” “treatment,” and the like as used herein refer to reducing or ameliorating a disorder and/or symptoms associated therewith (e.g., a viral infection).
  • Treating includes the concepts of “alleviating,” which refers to lessening the frequency of occurrence or recurrence, or the severity, of any symptoms or other ill effects related to a virus and/or the side effects associated with viral therapy.
  • Treating also encompasses the concept of “managing” which refers to reducing the severity of a particular disease or disorder in a patient or delaying its recurrence, e.g., lengthening the period of remission in a patient who had suffered from the disease.
  • a variant comprises a nucleic acid molecule having deletions (i.e., truncations) at the 5’ and/or 3’ end; deletion and/or addition of one or more nucleotides at one or more internal sites in the native polynucleotide; and/or substitution of one or more nucleotides at one or more sites in the native polynucleotide.
  • nucleic acid molecule or polypeptide comprises a naturally occurring or endogenous nucleotide sequence or amino acid sequence, respectively.
  • variants of a particular nucleic acid molecule of the disclosure will have at least about 70%, 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99% or more sequence identity to that particular polynucleotide as determined by sequence alignment programs and parameters as described elsewhere herein.
  • the nucleic acid molecules or the nucleic acid sequences comprise mutations of 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 or more nucleotides.
  • coronavirus As used herein the term “coronavirus”, “ Coronaviridae” , or “CoV” are used interchangeably and refer to viruses which are enveloped, positive sense, single-stranded RNA viruses. Coronaviruses can cause respiratory, gastrointestinal and neurological disease. There are two subfamilies of Coronaviridae, Letovirinae and Orthocoronavirinae.
  • the CoV is selected from the genera Alphacoronavirus (alphaCoV), Betacoronavirus (betaCoV), Gammacoronavirus (gammaCoV) and Deltacoronavirus (deltaCoV).
  • the coronavirus is an alphaCoV.
  • the coronavirus is a betaCoV.
  • the coronavirus is a gammaCoV.
  • the coronavirus is a deltaCoV.
  • the alphaCoV is selected from coronavirus 229E (HCoV-229E), human coronavirus NL63 (HCoV-NL63), transmissible gastroenteritis virus (TGEV), porcine epidemic diarrhea virus (PEDV), feline infectious peritonitis virus (FIPV) and canine coronavirus (CCoV).
  • the betaCoV is selected from severe acute respiratory syndrome- related coronavirus-2 (SARS-Cov-2), human coronavirus HKU1 (HCoV-HKUl), human coronavirus OC43 (HCoV-OC43), severe acute respiratory syndrome-related coronavirus (SARS- CoV), middle-east respiratory syndrome-related coronavirus (MERS-CoV), murine hepatitis virus (MHV) and/or bovine coronavirus (BCoV).
  • SARS-Cov-2 severe acute respiratory syndrome- related coronavirus-2
  • HKU1 HKU1
  • HoV-OC43 human coronavirus OC43
  • SARS- CoV severe acute respiratory syndrome-related coronavirus
  • MERS-CoV middle-east respiratory syndrome-related coronavirus
  • MHV murine hepatitis virus
  • BCoV bovine coronavirus
  • the CoV is capable of infecting a human.
  • the CoV capable of infecting a human is selected from: SARS-CoV-2, HCoV-OC43, HCoV- HKU1, HCoV-229E, HCoV-NL63, SARS-CoV, and MERS-CoV, or a subtype of variant thereof
  • the CoV is SARS-CoV-2 or a subtype or variant thereof.
  • Non-limiting examples of the SARS-CoV-2 virus, variants and subtypes thereof are described, for example, in Morais et al. (2020), Zhao et al. (2020), Shen et al. (2020), Tang et al. (2020), Phan et al. (2020) and Khan et al. (2020).
  • the SARS-CoV-2 is a SARS-CoV-2 subtype I. In some embodiments, the SARS-CoV-2 is a SARS-CoV-2 subtype II. In some embodiments, the SARS-CoV-2 is a SARS- CoV-2 subtype III.
  • the SARS-CoV-2 is a SARS-CoV-2 subtype IV. In some embodiments, the SARS-CoV-2 is a SARS-CoV-2 subtype V. In some embodiments, the SARS-CoV-2 is a SARS-CoV-2 subtype VI. In some embodiments, the SARS-CoV-2 is a SARS- CoV-2 subtype VII. In some embodiments, the SARS-CoV-2 is a SARS-CoV-2 subtype VIII. In some embodiments, the SARS-CoV-2 is a SARS-CoV-2 subtype IX. In some embodiments, the SARS-CoV-2 is a SARS-CoV-2 subtype X.
  • the SARS-CoV-2 is a SARS- CoV-2 subtype XI. In some embodiments, the SARS-CoV-2 is a SARS-CoV-2 subtype XII. In some embodiments, the SARS-CoV-2 is a SARS-CoV-2 subtype XIII. In some embodiments, the SARS-CoV-2 is a SARS-CoV-2 subtype XIV. In some embodiments, the SARS-CoV-2 is a SARS-CoV-2 subtype XV. In some embodiments, the SARS-CoV-2 is a SARS-CoV-2 subtype XVI.
  • Foster et al. (2020) have identified 3 SARS-COV-2 variants, A, B and C, based on genomic analysis.
  • the SARS-CoV-2 is SARS-CoV-2 variant A as described in Foster et al. (2020).
  • the SARS-CoV-2 is SARS-CoV-2 variant B as described in Foster et al. (2020).
  • the SARS-CoV-2 is SARS-CoV-2 variant C as described in Foster et al. (2020).
  • Tang et al. (2020) have identified two SARS-CoV-2 subtypes, subtypes S and F.
  • the SARS-CoV-2 is SARS-CoV-2 subtype F as described in Tang et al. (2020).
  • the SARS-CoV-2 is SARS-CoV-2 subtype S as described in Tang et al.
  • SARS-CoV-2 is SARS-CoV-2 hCoV-19/Australia/VIC01/2020 or a variant thereof.
  • SARS-COV-2 comprises the sequences as described in NCBI Reference Sequence: NC 045512.2 or a variant thereof.
  • SARS- CoV-2 comprises the sequence as described in GenBank: MN908947.3 or a variant thereof.
  • the SARS-Cov-2 variant is the B.l.1.7 variant, also referred to as lineage B.1.1.7, VOC 202012/01 or 20I/501Y.V1.
  • the SARS-Cov-2 variant is the B.1.351 variant, also referred to as B.1.351 lineage.
  • the SARS-Cov- 2 variant is the B.1.1.28 subclade (renamed “P.l”).
  • the SARS-Cov-2 variant is the B.l.1.7 variant, also referred to as B.l.1.7 lineage or 201/501 Y. VI.
  • the SARS-Cov-2 variant is the B.1.427 variant.
  • the SARS- Cov-2 variant is the B.1.429 variant. In some embodiments, the SARS-Cov-2 variant is the B.1.617 variant. In some embodiments, the SARS-Cov-2 variant is the B.1.618 variant. In some embodiments, the SARS-Cov-2 variant is the B.1.1.529 (omicron) variant. In an embodiment, the omicron variant is BA.1. In an embodiment, the omicron variant is BA.2.
  • the SARS-CoV-2 variant comprises a genome that is at least about 90% identical to the parental genomic sequence. In some embodiments, the SARS-CoV-2 variant comprises a genome that is at least about 91% identical to the parental genomic sequence. In some embodiments, the SARS-CoV-2 variant comprises a genome that is at least about 92% identical to the parental genomic sequence. In some embodiments, the SARS-CoV-2 variant comprises a genome that is at least about 93% identical to the parental genomic sequence. In some embodiments, the SARS-CoV-2 variant comprises a genome that is at least about 94% identical to the parental genomic sequence. In some embodiments, the SARS-CoV-2 variant comprises a genome that is at least about 95% identical to the parental genomic sequence.
  • the SARS-CoV-2 variant comprises a genome that is at least about 96% identical to the parental genomic sequence. In some embodiments, the SARS-CoV-2 variant comprises a genome that is at least about 97% identical to the parental genomic sequence. In some embodiments, the SARS-CoV-2 variant comprises a genome that is at least about 98% identical to the parental genomic sequence. In some embodiments, the SARS-CoV-2 variant comprises a genome that is at least about 99% identical to the parental genomic sequence. In some embodiments, the parental genomic sequence is from parental strain SARS-CoV-2 hCoV- 19/Australia/VIC01/2020. In some embodiments, the parental genomic sequence is from parental strain BetaCoV/Wuhan/WIV04/2019.
  • the parental genomic sequence is the sequences as described in GenBank under the accession No. NC_045512.2. In some embodiments, the parental genomic sequence is the sequences as described in GenBank under the accession No. MN908947.3. In some embodiments, the parental genomic sequence is from parental strain B.l.1.7 variant. In some embodiments, the parental genomic sequence is from parental strain B.1.351 variant. In some embodiments, the parental genomic sequence is from parental strain P.1 variant. In some embodiments, the parental genomic sequence is from parental strain B.l.1.7 variant.
  • CoV infections can cause respiratory, enteric, hepatic, and neurological diseases in different animal species, including camels, cattle, cats, and bats.
  • CoV can be transmitted from one individual to another through contact of viral droplets with mucosa.
  • viral droplets are airborne and inhaled via the respiratory tract including the nasal airway.
  • the individual is a human individual.
  • the individual is a live stock or domestic animal.
  • a CoV infection causes one or more symptoms selected from one or more of: fever, cough, sore throat, shortness of breath, viral shedding respiratory insufficiency, runny nose, nasal congestion, malaise, bronchitis, headache, muscle pain, dyspnea, moderate pneumonia, severe pneumonia, acute respiratory distress syndrome (ARDS).
  • the ARDS is selected from mild ARDS (defined as 200 mmHg ⁇ PaCh/FiCh ⁇ 300 mmHg), moderate ARDS (defined as 100 mmHg ⁇ PaCh/FiCh ⁇ 200 mmHg) and severe ARDS (defined as PaCh/FiCh ⁇ 100 mmHg).
  • the CoV infection cause no symptoms in some members of the population (an individual is asymptomatic).
  • severe coronavirus infection encompasses any factor, or a symptom thereof, considered by a medical practitioner that would warrant the subject being hospitalised, the subject’s life being at risk, or the subject requiring assistance to breath.
  • symptoms of a severe response to a CoV infection include, but are not limited to, difficulty breathing or shortness of breath, chest pain or pressure, loss of speech or loss of movement, respiratory distress, respiratory frequency > 30/min, blood oxygen saturation ⁇ 93% at rest, PaCh/FiCh ratio ⁇ 300 mmHg, lung infiltrates > 50% within 24-48 hours, respiratory failure requiring mechanical ventilation, organ failure, requiring intensive care unit monitoring and treatment.
  • a phenotype that displays a predisposition for a severe response to a CoV infection can, for example, show a higher likelihood that a severe response to a CoV infection will develop in an individual with the phenotype than in members of a relevant general population under a given set of environmental conditions (diet, physical activity regime, geographic location, etc.).
  • a subject with a severe CoV infection would benefit from an oxygenation treatment.
  • the oxygenation treatment is selected from one or more of supplemental oxygen, high-flow nasal cannula oxygen, non-invasive positive pressure ventilation, extracorporeal membrane oxygenation and intubation with mechanical ventilation.
  • the CoV infection is an early CoV infection.
  • the subject in an early CoV infection the subject is pre-symptomatic.
  • pre-symptomatic refers to occurring before symptoms of a condition (e.g., a CoV infection) occur.
  • pre-symptomatic means occurring before a subject is capable of transmitting the condition to other subjects.
  • asymptomatic refers to subject that has no symptoms of a condition. In some embodiments, an asymptomatic subject is still capable of transmitting the condition to other subjects.
  • the CoV infection in an early CoV infection, can be detected before the CoV can be detected with an amplification assay. In some embodiments, in an early CoV infection the CoV infection can be detected within 14 days or less from the infection date. In some embodiments, in an early CoV infection the CoV infection can be detected within 12 days or less from the infection date. In some embodiments, in an early CoV infection the CoV infection can be detected within 10 days or less from the infection date. In some embodiments, in an early CoV infection the CoV infection can be detected within 8 days or less from the infection date. In some embodiments, in an early CoV infection the CoV infection can be detected within 6 days or less from the infection date. In some embodiments, in an early CoV infection the CoV infection can be detected within 4 days or less from the infection date. miRNA
  • miRNA refers to miRNAs (typically 19-25 nucleotides in length) or a precursor thereof that regulate endogenous gene expression at the post- transcriptional level. miRNA play a role in gene regulation by binding to complementary target messenger RNAs (mRNAs) resulting in target mRNA degradation or translational blockade. In most instances, miRNAs function by interacting with the 3’ untranslated region (3’ UTR) of target mRNAs to induce mRNA degradation and translational repression. The sequences of miRNAs are often conserved across species.
  • the present disclosure provides a method of determining the likelihood of a CoV infection or a severe CoV infection or for monitoring thereof, the method comprising, in a biological sample from the subject, detecting a level of at least one miRNA listed in Table 1.
  • the present disclosure provides a method of determining the likelihood of a CoV infection or a severe CoV infection or for monitoring thereof, the method comprising, in a biological sample from the subject, detecting a level of at least one miRNA selected from: miR-423-5p, miR-23a-3p, miR-195-5p, miR-766-3p, miR-651-5p, let-7e-5p, miR-1275, miR-3198, miR-627-5p, miR-4662a-5p, miR-3684, let-7a-5p, miR-483-5p, miR-3125, miR-3617-5p, miR-500b-3p, miR-664b-3p, miR-5189-3p, miR-6772-3p, miR-145
  • the miRNA is a mammalian miRNA. In some embodiments, the mammal is a placental mammal. In some embodiments, the mammal is a marsupial. In some embodiments, the mammal is a monotreme. In some embodiments, the miRNA is a human miRNA. In some embodiments, the miRNA is a Mustela putorius furo miRNA. Human miRNAs are designated with the prefix “hsa”.
  • the method comprises detecting a level of an miRNA selected from one or more of: miR-423-5p, miR-23a-3p, miR-766-3p, miR-651-5p, let-7e-5p, miR-1275, miR- 3198, miR-627-5p, miR-4662a-5p, miR-3684, let-7a-5p, miR-483-5p, miR-3125, miR-3617-5p, miR-500b-3p, miR-664b-3p, miR-5189-3p, miR-6772-3p, miR-145-3p, miR-30a-5p, miR-27a-5p, miR-2116-3p, miR-31-5p, miR-4772-3p, miR-1290, miR-1226-3p, miR-589-3p, miR-210-3p, miR-103a-3p, miR-3115, miR-769-3p, miR-320b, miR-193a-5p, miR
  • the method comprises detecting a level of an miRNA selected from one or more of: miR-423-5p, miR-23a-3p, miR-195-5p, miR-766-3p, miR-651-5p, let-7e-5p, miR- 1275, miR-3198, miR-627-5p, miR-4662a-5p, miR-3684, let-7a-5p, miR-483-5p, miR-3125, miR- 3617-5p, miR-500b-3p, miR-664b-3p, miR-5189-3p, miR-6772-3p, miR-145-3p, miR-30a-5p, miR-27a-5p, miR-2116-3p, miR-31-5p, miR-4772-3p, miR-1290, miR-1226-3p, miR-589-3p, miR-210-3p, miR-103a-3p, miR-3115, miR-769-3p, miR-320b, miR-19
  • the method comprises detecting a level of an miRNA selected from one or more of: miR-423-5p, miR-23a-3p, miR-766-3p, miR-651-5p, let-7e-5p, miR-1275, miR- 3198, miR-627-5p, miR-4662a-5p, miR-3684, let-7a-5p, miR-483-5p, miR-3125, miR-3617-5p, miR-500b-3p, miR-664b-3p, miR-5189-3p, miR-6772-3p, miR-145-3p, miR-30a-5p, miR-27a-5p, miR-2116-3p, miR-31-5p, miR-4772-3p, miR-1290, miR-1226-3p, miR-589-3p, miR-210-3p, miR-103a-3p, miR-3115, miR-769-3p, miR-320b, miR-193a-5p, miR
  • the method at least comprises detecting a level of miR-423-5p. In some embodiments, the method at least comprises detecting a level of miR-195-5p. In some embodiments, the method at least comprises detecting a level of miR-23a-3p and miR-195-5p. In some embodiments, the method at least comprises detecting a level of miR-28-5p. In some embodiments, the method at least comprises detecting a level of miR-223-5p. In some embodiments, the method at least comprises detecting a level of miR-130b-3p. In some embodiments, the method at least comprises detecting a level of miR-23a-3p and miR-423-5p.
  • the method at least comprises detecting a level of miR-423-5p and miR-195- 5p. In some embodiments, the method at least comprises detecting a level of miR-423-5p, miR- 195-5p and miR-23a-3p. In some embodiments, the method at least comprises detecting a level of miR-423-5p, miR-195-5p, miR-23a-3p and miR-28-5p. In some embodiments, the method at least comprises detecting a level of miR-423-5p, miR-195-5p, miR-23a-3p and miR-223-5p.
  • the method at least comprises detecting a level of miR-423-5p, miR-195-5p, miR- 23a-3p, miR-28-5p and miR-223-5p. In some embodiments, the method at least comprises detecting a level of miR-423-5p, miR-195-5p, miR-23a-3p and miR-130b-3p. In some embodiments, the method at least comprises detecting a level of miR-423-5p, miR-195-5p, miR- 23a-3p, miR-28-5p, miR-223-5p and miR-130b-3p.
  • the method when the method comprises detecting miR-195-5p the method further comprises detecting miR-23a-3p. In some embodiments, when the method comprises detecting miR-195-5p the method further comprises detecting miR-423-5p.
  • the method comprises detecting a level of miR-423-5p, miR-195- 5p, miR-23a-3p, miR-28-5p and miR-223-5p. In some embodiments, the method comprises detecting a level of miR-423-5p, miR-195-5p, miR-23a-3p, miR-28-5p, miR-223-5p and miR- 130b-3p.
  • the method when the method comprises detecting miR-195-5p the method further comprises detecting one or more of: miR-423-5p, miR-23a-3p, miR-766-3p, miR-651-5p, let-7e-5p, miR-1275, miR-3198, miR-627-5p, miR-4662a-5p, miR-3684, let-7a-5p, miR-483-5p, miR-3125, miR-3617-5p, miR-500b-3p, miR-664b-3p, miR-5189-3p, miR-6772-3p, miR-145-3p, miR-30a-5p, miR-27a-5p, miR-2116-3p, miR-31-5p, miR-4772-3p, miR-1290, miR-1226-3p, miR-589-3p, miR-210-3p, miR-103a-3p, miR-3115, miR-769-3p, miR-320b, miR-193a-5p
  • the method when the method comprises detecting miR-21-3p the method further comprises detecting one or more of: miR-423-5p, miR-195-5p miR-766-3p, miR-651-5p, let-7e- 5p, miR-1275, miR-3198, miR-627-5p, miR-4662a-5p, miR-3684, let-7a-5p, miR-483-5p, miR- 3125, miR-3617-5p, miR-500b-3p, miR-664b-3p, miR-5189-3p, miR-6772-3p, miR-145-3p, miR- 30a-5p, miR-27a-5p, miR-2116-3p, miR-31-5p, miR-4772-3p, miR-1290, miR-1226-3p, miR- 589-3p, miR-210-3p, miR-103a-3p, miR-3115, miR-769-3p, miR-320b, miR-193a-5
  • the method when the method comprises detecting miR-423-5p the method further comprises detecting one or more of: miR-23a-3p, miR-195-5p, miR-766-3p, miR-651-5p, let-7e-5p, miR-1275, miR-3198, miR-627-5p, miR-4662a-5p, miR-3684, let-7a-5p, miR-483-5p, miR-3125, miR-3617-5p, miR-500b-3p, miR-664b-3p, miR-5189-3p, miR-6772-3p, miR-145-3p, miR-30a-5p, miR-27a-5p, miR-2116-3p, miR-31-5p, miR-4772-3p, miR-1290, miR-1226-3p, miR-589-3p, miR-210-3p, miR-103a-3p, miR-3115, miR-769-3p, miR-320b, miR-193a-5p
  • the method when the method comprises detecting miR-142-3p the method further comprises detecting one or more of: miR-423-5p, miR-23a-3p, miR-766-3p, miR-651-5p, let-7e-5p, miR-1275, miR-3198, miR-627-5p, miR-4662a-5p, miR-3684, let-7a-5p, miR-483-5p, miR-3125, miR-3617-5p, miR-500b-3p, miR-664b-3p, miR-5189-3p, miR-6772-3p, miR-145-3p, miR-30a-5p, miR-27a-5p, miR-2116-3p, miR-31-5p, miR-4772-3p, miR-1290, miR-1226-3p, miR-589-3p, miR-210-3p, miR-103a-3p, miR-3115, miR-769-3p, miR-320b, miR-193a-5p
  • the method when the method comprises detecting miR-3065-3p the method further comprises detecting one or more of: miR-423-5p, miR-23a-3p, miR-766-3p, miR-651-5p, let-7e-5p, miR-1275, miR-3198, miR-627-5p, miR-4662a-5p, miR-3684, let-7a-5p, miR-483-5p, miR-3125, miR-3617-5p, miR-500b-3p, miR-664b-3p, miR-5189-3p, miR-6772-3p, miR-145-3p, miR-30a-5p, miR-27a-5p, miR-2116-3p, miR-31-5p, miR-4772-3p, miR-1290, miR-1226-3p, miR-589-3p, miR-210-3p, miR-103a-3p, miR-3115, miR-769-3p, miR-320b, miR-193a-5p
  • the method comprises detecting a level of miR-142-3p. In some embodiments, the method comprises detecting a level of miR-3065-3p. In some embodiments, the method comprises detecting a level of miR-93-5p.
  • the method comprises detecting a level of miR-486-5p. In some embodiments, the method comprises detecting a level of miR-451a. In some embodiments, the method comprises detecting a level of miR-3065-5p. In some embodiments, the method comprises detecting a level of miR-628-3p. In some embodiments, the method comprises detecting a level of miR-19a-3p. In some embodiments, the method comprises detecting a level of miR-142-3p and miR-93- 5p. In some embodiments, the method comprises detecting a level of miR-142-3p and miR-3065- 3p.
  • the method comprises detecting a level of miR-142-3p, miR-3065- 3p, and miR-93-5p. In some embodiments, the method comprises detecting a level of miR-142- 3p, miR-3065-3p, miR-93-5p and miR-486-5p. In some embodiments, the method comprises detecting a level of miR-142-3p, miR-3065-3p, miR-93-5p and miR-451a. In some embodiments, the method comprises detecting a level of miR-142-3p, miR-3065-3p, miR-93-5p and miR-3065- 5p.
  • the method comprises detecting a level of miR-142-3p, miR-3065-3p, miR-93-5p and miR-628-3p. In some embodiments, the method comprises detecting a level of miR-142-3p, miR-3065-3p, miR-93-5p and miR-19a-3p.
  • the method comprises detecting a level of miR-142-3p, miR-3065- 3p, miR-93-5p, miR-486-5p, miR-451a, miR-3065-5p, miR-628-3p, and miR-19a-3p.
  • the method comprises detecting a level of an miRNA selected from one or more of: let-7e-5p, miR-766-3p, miR-651-5p, miR-4433b-5p, miR-106b-5p, miR-99b-3p, miR-1273h-3p, let-7a-3p, miR-6741-5p, miR-21-3p, miR-301b-3p, miR-7-l-3p, miR-148b-5p, miR-576-5p, miR-3198, miR-4662a-5p, miR-6772-3p, miR-197-3p, miR-874-3p, miR-889-3p, miR-92a-3p, miR-500b-3p, miR-769-3p, miR-1468-5p, miR-431-3p, miR-345-5p, miR-339-3p, miR-103a-3p, miR-30a-5p, miR-6503-3p, miR-542-5p, miR-6
  • the method comprises detecting a level of an miRNA selected from one or more of: let-7e-5p, miR-766-3p, miR-651-5p, miR-4433b-5p, miR-106b-5p, miR-99b-3p, miR-1273h-3p, let-7a-3p, miR-6741-5p, miR-301b-3p, miR-7-l-3p, miR-148b-5p, miR-576-5p, miR-3198, miR-4662a-5p, miR-6772-3p, miR-197-3p, miR-874-3p, miR-889-3p, miR-92a-3p, miR-500b-3p, miR-769-3p, miR-1468-5p, miR-431-3p, miR-345-5p, miR-339-3p, miR-103a-3p, miR-30a-5p, miR-6503-3p, miR-542-5p, miR-627-5p, miR
  • the method comprises detecting a level of an miRNA selected from one or more of: let-7e-5p, miR-766-3p, miR-651-5p, miR-4433b-5p, miR-106b-5p, miR-99b-3p, miR-1273h-3p, let-7a-3p, miR-6741-5p, miR-21-3p, miR-301b-3p, miR-7-l-3p, miR-148b-5p, miR-576-5p, miR-3198, miR-4662a-5p, miR-6772-3p, miR-197-3p, miR-874-3p, miR-889-3p, miR-92a-3p, miR-500b-3p, miR-769-3p, miR-1468-5p, miR-431-3p, miR-345-5p, miR-339-3p, miR-103a-3p, miR-30a-5p, miR-6503-3p, miR-542-5p, miR-6
  • the method comprises detecting a level of an miRNA selected from one or more of: let-7e-5p, miR-766-3p, miR-651-5p, miR-4433b-5p, miR-106b-5p, miR-99b-3p, miR-1273h-3p, let-7a-3p, miR-6741-5p, miR-301b-3p, miR-7-l-3p, miR-148b-5p, miR-576-5p, miR-3198, miR-4662a-5p, miR-6772-3p, miR-197-3p, miR-874-3p, miR-889-3p, miR-92a-3p, miR-500b-3p, miR-769-3p, miR-1468-5p, miR-431-3p, miR-345-5p, miR-339-3p, miR-103a-3p, miR-30a-5p, miR-6503-3p, miR-542-5p, miR-627-5p, miR
  • the method comprises detecting a level of an miRNA selected from one or more of: let-7e-5p, miR-766-3p, miR-651-5p, miR-4433b-5p, miR-10a-3p, miR-150-5p, miR-206, miR-195-5p, miR-320a-3p, miR-483-5p, miR-5189-3p, miR-98-5p, miR-200c-3p, miR- 664b-3p, and miR-96-5p.
  • the method comprises detecting a level of an miRNA selected from one or more of: let-7e-5p, miR-766-3p, miR-651-5p, miR-4433b-5p, miR-10a-3p, miR-150-5p, miR-206, miR-320a-3p, miR-483-5p, miR-5189-3p, miR-98-5p, miR-200c-3p, miR-664b-3p, and miR-96-5p.
  • the method at least comprises detecting a level of let-7e-5p, miR- 651-5p, miR-766-3p, and miR-4433b-5p. In some embodiments, the method at least comprises detecting a level of let-7e-5p, miR-651-5p, and miR-766-3p.
  • the method comprises detecting a level of an miRNA selected from one or more of: miR-3661, miR-379-5p, miR-382-5p, miR-599, miR-30a-3p, miR-130a-3p, miR- 210-3p, miR-181c-3p, miR-141-3p, miR-l-3p, miR-125a-3p, miR-1277-5p, miR-21-3p, miR- 885-5p, miR-409-3p, miR-378c, miR-340-5p, miR-195-5p, miR-219a-l-3p, miR-125b-l-3p, miR-10a-3p, miR-3064-5p, miR-200b-5p, miR-885-3p, miR-151b, let-7b-3p, miR-331-3p, miR- 574-5p, miR-432-5p, miR-1843, let-7e-5p, miR-4454, miR-147b-3
  • the method comprises detecting a level of an miRNA selected from one or more of: miR-3661, miR-379-5p, miR-382-5p, miR-599, miR-30a-3p, miR-130a-3p, miR- 210-3p, miR-181c-3p, miR-141-3p, miR-l-3p, miR-125a-3p, miR-1277-5p, miR-885-5p, miR- 409-3p, miR-378c, miR-340-5p, miR-219a-l-3p, miR-125b-l-3p, miR-10a-3p, miR-3064-5p, miR-200b-5p, miR-885-3p, miR-151b, let-7b-3p, miR-331-3p, miR-574-5p, miR-432-5p, miR- 1843, let-7e-5p, miR-4454, miR-147b-3p, miR-95-3p, miR-30e
  • the method comprises detecting a level of an miRNA selected from one or more of: miR-423-5p, miR-23a-3p, miR-766-3p, miR-651-5p, let-7e-5p, miR-1275, miR- 3198, miR-627-5p, miR-4662a-5p, miR-3684, let-7a-5p, miR-483-5p, miR-3125, miR-3617-5p, miR-500b-3p, miR-664b-3p, miR-5189-3p, miR-6772-3p, miR-145-3p, miR-30a-5p, miR-27a-5p, miR-2116-3p, miR-31-5p, miR-4772-3p, miR-1290, miR-1226-3p, miR-589-3p, miR-210-3p, miR-103a-3p, miR-3115, miR-769-3p, miR-320b, miR-193a-5p, miR
  • the method comprises detecting a level of an miRNA selected from one or more of: miR-423-5p, miR-23a-3p, miR-195-5p, miR-766-3p, miR-651-5p, let-7e-5p, miR- 1275, miR-3198, miR-627-5p, miR-4662a-5p, miR-3684, let-7a-5p, miR-483-5p, miR-3125, miR- 3617-5p, miR-500b-3p, miR-664b-3p, miR-5189-3p, miR-6772-3p, miR-145-3p, miR-30a-5p, miR-27a-5p, miR-2116-3p, miR-31-5p, miR-4772-3p, miR-1290, miR-1226-3p, miR-589-3p, miR-210-3p, miR-103a-3p, miR-3115, miR-769-3p, miR-320b, miR-19
  • the method comprises detecting a level of an miRNA selected from one or more of: miR-423-5p, miR-23a-3p, miR-766-3p, miR-651-5p, let-7e-5p, miR-1275, miR- 3198, miR-627-5p, miR-4662a-5p, miR-3684, let-7a-5p, miR-483-5p, miR-3125, miR-3617-5p, miR-500b-3p, miR-664b-3p, miR-5189-3p, miR-6772-3p, miR-145-3p, miR-30a-5p, miR-27a-5p, miR-2116-3p, miR-31-5p, miR-4772-3p, miR-1290, miR-1226-3p, miR-589-3p, miR-210-3p, miR-103a-3p, miR-3115, miR-769-3p, miR-320b, miR-193a-5p, miR
  • the method at least comprises detecting a level of miR-423-5p in a blood sample or a fraction thereof. In some embodiments, the method at least comprises detecting a level of miR-195-5p in a blood sample or a fraction thereof. In some embodiments, the method at least comprises detecting a level of miR-23a-3p and miR-195-5p in a blood sample or a fraction thereof. In some embodiments, the method at least comprises detecting a level of miR-28-5p in a blood sample or a fraction thereof. In some embodiments, the method at least comprises detecting a level of miR-223-5p in a blood sample or a fraction thereof.
  • the method at least comprises detecting a level of miR-130b-3p in a blood sample or a fraction thereof. In some embodiments, the method at least comprises detecting a level of miR-23a-3p and miR-423- 5p in a blood sample or a fraction thereof. In some embodiments, the method at least comprises detecting a level of miR-423-5p and miR-195-5p in a blood sample or a fraction thereof. In some embodiments, the method at least comprises detecting a level of miR-423-5p, miR-195-5p and miR-23a-3p in a blood sample or a fraction thereof.
  • the method at least comprises detecting a level of miR-423-5p, miR-195-5p, miR-23a-3p and miR-28-5p in a blood sample or a fraction thereof. In some embodiments, the method at least comprises detecting a level of miR-423-5p, miR-195-5p, miR-23a-3p and miR-223-5p in a blood sample or a fraction thereof. In some embodiments, the method at least comprises detecting a level of miR-423-5p, miR-195- 5p, miR-23a-3p, miR-28-5p and miR-223-5p in a blood sample or a fraction thereof.
  • the method at least comprises detecting a level of miR-423-5p, miR-195-5p, miR- 23a-3p and miR-130b-3p in a blood sample or a fraction thereof. In some embodiments, the method at least comprises detecting a level of miR-423-5p, miR-195-5p, miR-23a-3p, miR-28-5p, miR-223-5p and miR-130b-3p in a blood sample or a fraction thereof.
  • the method when the method comprises detecting miR-195-5p the method further comprises detecting miR-23a-3p in a blood sample or a fraction thereof. In some embodiments, when the method comprises detecting miR-195-5p the method further comprises detecting miR-423-5p in a blood sample or a fraction thereof.
  • the method comprises detecting a level of miR-423-5p, miR-195- 5p, miR-23a-3p, miR-28-5p and miR-223-5p in a blood sample or a fraction thereof. In some embodiments, the method comprises detecting a level of miR-423-5p, miR-195-5p, miR-23a-3p, miR-28-5p, miR-223-5p and miR-130b-3p in a blood sample or a fraction thereof.
  • the method comprises detecting a level of miR-142-3p in a nasal mucosal sample. In some embodiments, the method comprises detecting a level of miR-3065-3p in a nasal mucosal sample. In some embodiments, the method comprises detecting a level of miR- 93 -5p in a nasal mucosal sample.
  • the method comprises detecting a level of miR-486-5p in a nasal mucosal sample. In some embodiments, the method comprises detecting a level of miR-451a in a nasal mucosal sample. In some embodiments, the method comprises detecting a level of miR- 3065-5p in a nasal mucosal sample. In some embodiments, the method comprises detecting a level of miR-628-3p in a nasal mucosal sample. In some embodiments, preferably nasal mucosal samples, the method comprises detecting a level of miR-19a-3p in a nasal mucosal sample.
  • the method comprises detecting a level of miR-142-3p and miR-93- 5p in a nasal mucosal sample. In some embodiments, the method comprises detecting a level of miR-142-3p and miR-3065-3p in a nasal mucosal sample.
  • the method comprises detecting a level of miR-142-3p, miR-3065-3p, and miR-93-5p in a nasal mucosal sample. In some embodiments, the method comprises detecting a level of miR-142-3p, miR-3065-3p, miR-93-5p and miR-486-5p in a nasal mucosal sample. In some embodiments, the method comprises detecting a level of miR-142-3p, miR-3065-3p, miR-93-5p and miR-451a in a nasal mucosal sample.
  • the method comprises detecting a level of miR-142-3p, miR-3065-3p, miR- 93-5p and miR-3065-5p in a nasal mucosal sample. In some embodiments, the method comprises detecting a level of miR-142-3p, miR-3065-3p, miR-93-5p and miR-628-3p in a nasal mucosal sample. In some embodiments, the method comprises detecting a level of miR-142-3p, miR-3065- 3p, miR-93-5p and miR-19a-3p in a nasal mucosal sample.
  • the method comprises detecting a level of miR-142-3p, miR-3065- 3p, miR-93-5p, miR-486-5p, miR-451a, miR-3065-5p, miR-628-3p, and miR-19a-3p in a nasal mucosal sample.
  • detecting a level of at least one miRNA does not comprise detecting miR-155-3p. In some embodiments, detecting a level of at least one miRNA does not comprise detecting miR-223-3p. In some embodiments, detecting a level of at least one miRNA does not comprise detecting miR-449c-5p.
  • the at least one miRNA modulates IL-6 activity. In some embodiments, the at least one miRNA modulates the expression of ACE2 and TMPRSS2. In some embodiments, the miRNAs are circulating miRNAs (circulating in the blood stream).
  • the method comprises detecting not more than 50 miRNA. In some embodiments, the method comprises detecting not more than 40 miRNA. In some embodiments, the method comprises detecting not more than 30 miRNA. In some embodiments, the method comprises detecting not more than 20 miRNA. In some embodiments, the method comprises detecting not more than 10 miRNA. In some embodiments, the method comprises detecting not more than 6 miRNA. In some embodiments, the method comprises detecting not more than 5 miRNA. In some embodiments, the method comprises detecting not more than 4 miRNA. In some embodiments, the method comprises detecting not more than 3 miRNA.
  • a "subject" contemplated in the present disclosure includes humans or animals (e.g. mammals) including companion animals or livestock or laboratory or art accepted test or vehicle animals.
  • the subject is a mammal.
  • the subject is a human.
  • the subject is a ferret.
  • the subject is a ferret.
  • the companion animal is a cat or dog.
  • a biological sample refers to any sample from a subject comprising miRNA e.g. bodily fluids, biopsy, tissue, and/or waste from a patient.
  • the biological sample is selected from: plasma, serum, whole blood, lymph fluid, biopsy or tissue sample, respiratory mucosal sample, nasopharangeal sample, seminal fluid, saliva or urine.
  • the respiratory mucosal sample is a nasal mucosal sample.
  • the nasal mucosal samples is an anterior nasal mucosal sample.
  • the biological sample is, whole blood, plasma or serum.
  • the biological sample is plasma.
  • the whole blood, plasma or serum is collected in a container comprising heparin.
  • the heparin is removed from the biological sample before the samples is used in the methods as described herein.
  • the plasma may be isolated from whole blood by any method known to a person skilled in the art.
  • the method may comprise collection of whole blood in an ethylenediaminetetraacetic acid (EDTA) treated, citrate treated, potassium oxalate/sodium fluoride treated or heparinized container and centrifugation to isolate the plasma fraction.
  • EDTA ethylenediaminetetraacetic acid
  • the plasma is ethylenediaminetetraacetic acid (EDTA), citrate, potassium oxalate, sodium fluoride or heparin treated plasma.
  • the biological sample is collected in a PAXgene Blood RNA tube.
  • biological samples may be collected from a subject at more than one time point to e.g. monitor progression of a CoV infection, or to monitor, assess or optimize the efficacy of a treatment protocol.
  • the biological sample may be collected from a subject before, during and/or after treatment for a CoV infection. Samples may be collected, daily, weekly, fortnightly or monthly to monitor progression of a CoV infection or to assess the efficacy of a treatment regimen. Biological samples may be frozen for processing or analysis at a later date.
  • the biological samples are processed to extract RNA, small RNA and/or miRNA. For example, biological samples may be processed within 1 hour, or within 2 hours, or within 3 hours, or within 4 hours of collection for detection of miRNAs.
  • any of the methods disclosed herein comprise using a small volume of sample.
  • the methods disclosed comprise isolating total RNA and/or amplifying miRNA in a sample of no more than about 20 microliters of sample, about 40 microliters of sample, about 80 microliters of sample, about 100 microliters of sample, about 200 microliters of sample, about 300 microliters of sample, about 400 microliters of sample, about 500 microliters of sample, about 600 microliters of sample, about 700 microliters of sample, about 800 microliters of sample, about 900 microliters of sample, about 1 milliter of sample, about 1.1 milliters of sample, about 1.2 milliters of sample, about 1.3 milliters of sample, about 1.4 milliters of sample, about 1.5 milliters of sample, about 1.6 milliters of sample, about 1.7 milliters of sample, about 1.8 milliters of sample, about 1.9 milliters of sample, about 2.0 milliters of sample.
  • the sample size is from about 25 microliters to about 2 milliters of liquid sample in the form of subject plasma, whole blood, serum, saliva, respiratory mucosal sample, anterior nasal sample, oropharyngeal sample, or nasopharangeal sample.
  • miRNA/RNA Extraction is from about 25 microliters to about 2 milliters of liquid sample in the form of subject plasma, whole blood, serum, saliva, respiratory mucosal sample, anterior nasal sample, oropharyngeal sample, or nasopharangeal sample.
  • RNA, small RNA (cutoff approximately 200 nt) and/or miRNA fraction of the biological samples as described herein may be extracted by any method known to a person skilled in the art including for example, phenol-based techniques, combined phenol and column-based techniques or a column-based technique as described in El- Khoury et al. (2016).
  • a commercial kit may be used for RNA and/or miRNA extraction including for example, isolation with the miRNeasy Serum/Plasma kit (Qiagen, #217184), PAXgene Blood RNA Kit (Qiagen, #762174), MagnaZol cfRNA Isolation Reagent (Bioo Scientific.
  • RNA, small RNA and/or miRNA may also be determined by any method known to a person skilled in the art e.g.
  • RNA, small RNA and/or miRNA is not extracted or concentrated from the biological sample.
  • a multiplex miRNA profiling assay may be performed directly on a biological sample without prior processing to extract or concentrate the miRNA component of the sample (Tackett et al., 2017).
  • the methods disclosed comprise isolating total RNA and/or amplifying miRNA in a sample of no more than about 20 microliters, no more than about 40 microliters, no more than about 80 microliters, no more than about 100 microliters, no more than about 200 microliters, no more than about 300 microliters, no more than about 400 microliters, no more than about 500 microliters, no more than about 600 microliters, no more than about 700 microliters, no more than about 800 microliters, no more than about 900 microliters, no more than about 1 milliter, no more than about 1.1 milliters, no more than about 1.2 milliters, no more than about 1.3 milliters, no more than about 1.4 milliters, no more than about 1.5 milliters, no more than about 1.6 milliters, no more than about 1.7 milliters, no more than about 1.8 milliters, no more than about 1.9 milliters, or no more than about 2.0 milliters.
  • RNA sample may be subjected to a library preparation process.
  • the library preparation process is selected from CleanTag Small RNA Library Prep kit (TRiLink), NEXTflex Small RNA Sequence Kit v3 (Bioo Scientific) and QIAseq miRNA Library kit (Qiagen) as described for example in Wong et al. (2019).
  • the miRNA can be detected with any method known to a person skilled in the art including, for example, the methods described in or adapted from Git et al. (2010), Hunt et al. (2015), Tian et al. (2015), Blondal et al. (2017), Tackett et al. (2017), Hu et al. (2017), D’AGata et al. (2019), Aquino-Jarquin et al. (2021) and Collins et al. (2021).
  • small RNA such as miRNA
  • amplification- based methods e.g., Polymerase Chain Reaction (PCR), Real-Time Polymerase Chain Reaction (RT-PCR), Quantitative Polymerase Chain Reaction (qPCR), rolling circle amplification, etc.
  • hybridization-based methods e.g., hybridization arrays (e.g., microarrays), NanoString analysis, Northern Blot analysis, branched DNA (bDNA) signal amplification, in situ hybridization, etc.
  • sequencing-based methods e.g., next-generation sequencing methods, for example, using the Illumina or IonTorrent platforms.
  • RPA ribonuclease protection assay
  • the RNA is converted to DNA (cDNA) prior to analysis.
  • cDNA can be generated by reverse transcription of isolated miRNA using conventional techniques.
  • miRNA is amplified prior to measurement.
  • the level of miRNA is measured during the amplification process.
  • the level of miRNA is not amplified prior to measurement.
  • amplification-based methods exist for detecting the level of miRNA nucleic acid sequences, including, but not limited to, PCR, RT-PCR, qPCR, and rolling circle amplification.
  • Other amplification-based techniques include, for example, ligase chain reaction, multiplex ligatable probe amplification, in vitro transcription (IVT), strand displacement amplification, transcription-mediated amplification, RNA (Eberwine) amplification, and other methods that are known to persons skilled in the art.
  • a typical PCR reaction includes multiple steps, or cycles, that selectively amplify target nucleic acid species: a denaturing step, in which a target nucleic acid is denatured; an annealing step, in which a set of PCR primers (i.e., forward and reverse primers) anneal to complementary DNA strands, and an elongation step, in which a thermostable DNA polymerase elongates the primers. By repeating these steps multiple times, a DNA fragment is amplified to produce an amplicon, corresponding to the target sequence.
  • Typical PCR reactions include 20 or more cycles of denaturation, annealing, and elongation.
  • a reverse transcription reaction (which produces a cDNA sequence having complementarity to a miRNA) may be performed prior to PCR amplification.
  • Reverse transcription reactions include the use of, e.g., a RNA-based DNA polymerase (reverse transcriptase) and a primer.
  • Kits for quantitative real time PCR of miRNA are known, and are commercially available. Examples of suitable kits include, but are not limited to, the TaqMan miRNA Assay (Applied Biosystems) and the mirVana qRT-PCR miRNA detection kit (Ambion).
  • the miRNA can be ligated to a single stranded oligonucleotide containing universal primer sequences, a polyadenylated sequence, or adaptor sequence prior to reverse transcriptase and amplified using a primer complementary to the universal primer sequence, poly(T) primer, or primer comprising a sequence that is complementary to the adaptor sequence.
  • custom qRT-PCR assays can be developed for determination of miRNA levels.
  • Custom qRT-PCR assays to measure miRNAs in a biological sample e.g., a body fluid
  • Custom miRNA assays can be tested by running the assay on a dilution series of chemically synthesized miRNA corresponding to the target sequence. This permits determination of the limit of detection and linear range of quantitation of each assay. Furthermore, when used as a standard curve, these data permit an estimate of the absolute abundance of miRNAs measured in biological samples.
  • Amplification curves may optionally be checked to verify that Ct values are assessed in the linear range of each amplification plot. Typically, the linear range spans several orders of magnitude.
  • a chemically synthesized version of the miRNA can be obtained and analyzed in a dilution series to determine the limit of sensitivity of the assay, and the linear range of quantitation. Relative expression levels may be determined, for example, as described by Livak et al. (2001).
  • two or more miRNAs are amplified in a single reaction volume.
  • multiplex q-PCR such as qRT-PCR, enables simultaneous amplification and quantification of at least two miRNAs of interest in one reaction volume by using more than one pair of primers and/or more than one probe.
  • the primer pairs comprise at least one amplification primer that specifically binds each miRNA, and the probes are labeled such that they are distinguishable from one another, thus allowing simultaneous quantification of multiple miRNAs.
  • Rolling circle amplification is a DNA-polymerase driven reaction that can replicate circularized oligonucleotide probes with either linear or geometric kinetics under isothermal conditions (see, for example, Lizardi et al. (1998); Gusev et al. (2001); Nallur et al. (2001).
  • a complex pattern of strand displacement results in the generation of over 10 L 9 copies of each DNA molecule in 90 minutes or less.
  • Tandemly linked copies of a closed circle DNA molecule may be formed by using a single primer. The process can also be performed using a matrix-associated DNA.
  • the template used for rolling circle amplification may be reverse transcribed. This method can be used as a highly sensitive indicator of miRNA sequence and expression level at very low miRNA concentrations (see, for example, Cheng et al. (2009); Neubacher et al. (2009).
  • miRNA may be detected using hybridization-based methods, including but not limited to hybridization arrays (e.g., microarrays), NanoString analysis, Northern Blot analysis, branched DNA (bDNA) signal amplification, and in situ hybridization.
  • hybridization arrays e.g., microarrays
  • NanoString analysis e.g., NanoString analysis
  • Northern Blot analysis e.g., Northern Blot analysis
  • branched DNA (bDNA) signal amplification e.g., branched DNA (bDNA) signal amplification
  • in situ hybridization e.g., in situ hybridization.
  • Microarrays can be used to measure the expression levels of large numbers of miRNAs simultaneously.
  • Microarrays can be fabricated using a variety of technologies, including printing with fine-pointed pins onto glass slides, photolithography using pre-made masks, photolithography using dynamic micromirror devices, ink-jet printing, or electrochemistry on microelectrode arrays.
  • microfluidic TaqMan Low-Density Arrays which are based on an array of microfluidic qRT-PCR reactions, as well as related microfluidic qRT-PCR based methods.
  • Axon B-4000 scanner and Gene-Pix Pro 4.0 software or other suitable software can be used to scan images. Non-positive spots after background subtraction, and outliers detected by the ESD procedure, are removed. The resulting signal intensity values are normalized to per-chip median values and then used to obtain geometric means and standard errors for each miRNA. Each signal can be transformed to log base 2, and a one-sample t test can be conducted. Independent hybridizations for each sample can be performed on chips with each miRNA spotted multiple times to increase the robustness of the data.
  • Microarrays can be used for the expression profiling of miRNAs in diseases.
  • RNA can be extracted from a sample and, optionally, the miRNAs are size-selected from total RNA.
  • Oligonucleotide linkers can be attached to the 5' and 3' ends of the miRNAs and the resulting ligation products are used as templates for an RT-PCR reaction.
  • the sense strand PCR primer can have a fluorophore attached to its 5' end, thereby labeling the sense strand of the PCR product.
  • the PCR product is denatured and then hybridized to the microarray.
  • a PCR product referred to as the target nucleic acid that is complementary to the corresponding miRNA capture probe sequence on the array will hybridize, via base pairing, to the spot at which the, capture probes are affixed. The spot will then fluoresce when excited using a microarray laser scanner.
  • the fluorescence intensity of each spot is then evaluated in terms of the number of copies of a particular miRNA, using a number of positive and negative controls and array data normalization methods, which will result in assessment of the level of expression of a particular miRNA.
  • Total RNA containing the miRNA extracted from a body fluid sample can also be used directly without size-selection of the miRNAs.
  • the RNA can be 3' end labeled using T4 RNA ligase and a fluorophore- labeled short RNA linker.
  • Fluorophore-labeled miRNAs complementary to the corresponding miRNA capture probe sequences on the array hybridize, via base pairing, to the spot at which the capture probes are affixed. The fluorescence intensity of each spot is then evaluated in terms of the number of copies of a particular miRNA, using a number of positive and negative controls and array data normalization methods, which will result in assessment of the level of expression of a particular miRNA.
  • microarrays can be employed including, but not limited to, spotted oligonucleotide microarrays, pre-fabricated oligonucleotide microarrays or spotted long oligonucleotide arrays. miRNAs can also be detected without amplification using the nCounter Analysis System (NanoString Technologies, Seattle, Wash.). This technology employs two nucleic acid-based probes that hybridize in solution (e.g., a reporter probe and a capture probe). After hybridization, excess probes are removed, and probe/target complexes are analyzed in accordance with the manufacturer's protocol.
  • nCounter miRNA assay kits are available from NanoString Technologies, which are capable of distinguishing between highly similar miRNAs with great specificity. miRNAs can also be detected using branched DNA (bDNA) signal amplification (see, for example, Urdea (1994)). miRNA assays based on bDNA signal amplification are commercially available. One such assay is the QuantiGene R TM 2.0 miRNA Assay (Affymetrix, Santa Clara, Calif.).
  • Northern Blot and in situ hybridization may also be used to detect miRNAs. Suitable methods for performing Northern Blot and in situ hybridization are known in the art.
  • miRNAs can be detected using Illumina sequence (Solexa).
  • miRNAs can be detected using Next Generation Sequencing (massive parallel sequence or massively parallel sequencing methods that offer ultra-high throughput, scalability and speed) methods (e.g., Sequencing -By-Synthesis or TruSeq methods, Roche 454 sequenceing using, for example, the HiSeq, HiScan, GenomeAnalyzer, MiSeq systems (Illumina, Inc., San Diego, Calif.), Ion torrent and nanopores). Examples of Next Generation Sequencing technologies are described, for example, in McCombie et al. (2019). Such methods all also referred to as second generation sequencing methods. miRNAs can also be detected using Ion Torrent Sequencing (Ion Torrent Systems, Inc., Gulliford, Conn.), or other suitable methods of semiconductor sequencing.
  • CRISPR (clustered regularly interspaced short palindromic repeats) based methods can also be used for detection of the miRNAs of the invention. Such methods are described in or can be adapted from the methods e.g. in Aquino-Jarquin et al. (2021), Urban et al. (2019), Collins et al. (2021), and Makhawi et al. (2021).
  • the CRISPR-based method uses a Casl3 nuclease.
  • the CRISPR based method uses a Casl3a nuclease.
  • the CRISPR-based method uses a Casl2 nuclease.
  • the CRISPR- based method uses a Casl2a nuclease. In an embodiment, the CRISPR-based method uses a Csm6 nuclease.
  • Examples of CRISPR-based methods for miRNA detection include: a single step Cas 13a-Triggered signal amplification assay (Single-Step assay); cascade CRISPR-casl3 (casCRISPR) assay; ddDasl3a assay; DNA endonuclease-targeted CRISPR trans reporter assay (DETECTR), specific high-sensitivity enzymatic reporter unlocking assay (SHERLOCK), naked- eye-CRISPR assay; Cas 13 a-based visual detection (vCas) assay; electrochemical CRISPR/CHDC assay (EM-CRISPR); CRISPR-Biosensor X assay; CRISPR-Casl3a powered portable ECL chip (PECL-CRISPR) assay; and Cas-CHDC-Powered Electrochemical
  • the readout from the CRISPR-based assay is selected from: colometry, electrochemical, fluorescence, lateral flow or electrochemiluminescence.
  • the CRISPR-based method is an amplification free method (e.g. an electrochemical microfluidic biosensor method).
  • the method uses electrical interference to detect the miRNA, for example as described in Urban et al. (2019).
  • the CRISPR-based method determines the presence or absence of an miRNA.
  • the CRISPR-based method the level of an miRNA.
  • the CRISPR-based method determines the relative level of an miRNA.
  • the level is a relative level between two or more miRNAs which may be all associated with a CoV infection or a severe CoV infection in a subject such as those described herein, or the levels of a suitable control miRNA(s) could be used to determine the relative level, where expression of the control miRNA is not associated with a CoV infection or a severe CoV infection.
  • RNA endonucleases RNases
  • MS/MS tandem MS
  • the first approach developed utilized the on-line chromatographic separation of endonuclease digests by reversed phase HPLC coupled directly to ESI-MS.
  • the presence of posttranscriptional modifications can be revealed by mass shifts from those expected based upon the RNA sequence. Ions of anomalous mass/charge values can then be isolated for tandem MS sequencing to locate the sequence placement of the posttranscriptionally modified nucleoside.
  • MALDI-MS Matrix-assisted laser desorption/ionization mass spectrometry
  • MALDI-MS has also been used as an analytical approach for obtaining information about posttranscriptionally modified nucleosides.
  • MALDI-based approaches can be differentiated from ESI-based approaches by the separation step.
  • the mass spectrometer is used to separate the miRNA.
  • a system of capillary LC coupled with nanoESI-MS can be employed, by using a linear ion trap-orbitrap hybrid mass spectrometer (LTQ Orbitrap XL, Thermo Fisher Scientific) or a tandem-quadrupole time-of-flight mass spectrometer (QSTAR XL, Applied Biosystems) equipped with a custom-made nanospray ion source, a Nanovolume Valve (Valeo Instruments), and a splitless nano HPLC system (DiNa, KYA Technologies). Analyte/TEAA is loaded onto a nano-LC trap column, desalted, and then concentrated.
  • LTQ Orbitrap XL linear ion trap-orbitrap hybrid mass spectrometer
  • QSTAR XL tandem-quadrupole time-of-flight mass spectrometer
  • Analyte/TEAA is loaded onto a nano-LC trap column, desalted, and then concentrated.
  • Intact miRNAs are eluted from the trap column and directly injected into a Cl 8 capillary column, and chromatographed by RP-HPLC using a gradient of solvents of increasing polarity.
  • the chromatographic eluent is sprayed from a sprayer tip attached to the capillary column, using an ionization voltage that allows ions to be scanned in the negative polarity mode.
  • miRNA detection and measurement include, for example, strand invasion assay (Third Wave Technologies, Inc.), surface plasmon resonance (SPR), cDNA, MTDNA (metallic DNA; Advance Technologies, Saskatoon, SK), and single-molecule methods such as the one developed by US Genomics.
  • Multiple miRNAs can be detected in a microarray format using a novel approach that combines a surface enzyme reaction with nanoparticle- amplified SPR imaging (SPRI).
  • SPRI nanoparticle- amplified SPR imaging
  • the surface reaction of poly(A) polymerase creates poly(A) tails on miRNAs hybridized onto locked nucleic acid (LNA) microarrays. DNA-modified nanoparticles are then adsorbed onto the poly(A) tails and detected with SPRI.
  • This ultrasensitive nanoparticle- amplified SPRI methodology can be used for miRNA profiling at attamole levels.
  • the method is a CRISPR-based electrical interference method.
  • Nanomaterials e.g. gold nanoparticles (AuNPs), magnetic nanoparticles, silver nanoclusters (AgNCs), and quantum dots (QDs) (Ye et al, 2019).
  • labels, dyes, or labeled probes and/or primers are used to detect amplified or unamplified miRNAs.
  • detection methods are appropriate based on the sensitivity of the detection method and the abundance of the target.
  • amplification may or may not be required prior to detection.
  • miRNA amplification is preferred.
  • a probe or primer may include standard (A, T or U, G and C) bases, or modified bases.
  • Modified bases include, but are not limited to, the AEGIS bases (from Eragen Biosciences), which have been described, e.g., in U.S. Pat. Nos. 5,432,272, 5,965,364, and 6,001,983.
  • bases are joined by a natural phosphodiester bond or a different chemical linkage.
  • Different chemical linkages include, but are not limited to, a peptide bond or a Locked Nucleic Acid (LNA) linkage, which is described, e.g., in U.S. Pat. No. 7,060,809.
  • LNA Locked Nucleic Acid
  • oligonucleotide probes or primers present in an amplification reaction are suitable for monitoring the amount of amplification product produced as a function of time.
  • probes having different single stranded versus double stranded character are used to detect the nucleic acid.
  • Probes include, but are not limited to, the 5'-exonuclease assay (e.g., TAQMAN) probes (see U.S. Pat. No. 5,538,848), stem-loop molecular beacons (see, e.g., U.S. Pat. Nos. 6,103,476 and 5,925,517), stemless or linear beacons (see, e.g., WO 9921881, U.S.
  • one or more of the primers in an amplification reaction can include a label.
  • different probes or primers comprise detectable labels that are distinguishable from one another.
  • a nucleic acid, such as the probe or primer may be labeled with two or more distinguishable labels.
  • a label is attached to one or more probes and has one or more of the following properties: (i) provides a detectable signal; (ii) interacts with a second label to modify the detectable signal provided by the second label, e.g., FRET (Fluorescent Resonance Energy Transfer); (iii) stabilizes hybridization, e.g., duplex formation; and (iv) provides a member of a binding complex or affinity set, e.g., affinity, antibody-antigen, ionic complexes, hapten-ligand (e.g., biotin-avidin).
  • FRET Fluorescent Resonance Energy Transfer
  • miRNAs can be detected by direct or indirect methods.
  • a direct detection method one or more miRNAs are detected by a detectable label that is linked to a nucleic acid molecule.
  • the miRNAs may be labeled prior to binding to the probe. Therefore, binding is detected by screening for the labeled miRNA that is bound to the probe.
  • the probe is optionally linked to a bead in the reaction volume.
  • nucleic acids are detected by direct binding with a labeled probe, and the probe is subsequently detected.
  • the nucleic acids such as amplified miRNAs, are detected using FlexMAP Microspheres (Luminex) conjugated with probes to capture the desired nucleic acids.
  • FlexMAP Microspheres Luminex
  • Some methods may involve detection with polynucleotide probes modified with fluorescent labels or branched DNA (bDNA) detection, for example.
  • biomarker expression is determined using a PCR-based assay comprising specific primers and/or probes for each biomarker.
  • probe refers to any molecule that is capable of selectively binding a specifically intended target biomolecule.
  • the term “probe” refers to any molecule that may bind or associate, indirectly or directly, covalently or non-covalently, to any of the substrates and/or reaction products and/or proteases disclosed herein and whose association or binding is detectable using the methods disclosed herein.
  • the probe is a fluorogenic probe, antibody or absorbance-based probes.
  • the chromophore pNA may be used as a probe for detection and/or quantification of a target nucleic acid sequence disclosed herein.
  • the probe may be a nucleic acid sequence comprising a fluoregnic molecule or a substrate that when exposed to an enzyme becomes fluoregenic and the nucleic acid sequence is complementary or substantially complementary to the nucleic acid sequence comprising at least about 70%, 80%, 81%, 82%, 83%, 84, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99% or 100% sequence identity to any of the miRNAs provided in Table 1.
  • the target molecule could be any one or combination of nucleic acid sequences identified in Table 1.
  • the target molecule is a nucleic acid sequence comprising at least about 70%, 80%, 81%, 82%, 83%, 84, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% sequence identity to any one or combination of nucleic acid sequences provided in Table 1.
  • Probes can be synthesized by one of skill in the art using known techniques, or derived from biological preparations. Probes may include but are not limited to, RNA, DNA, proteins, peptides, aptamers, antibodies, and organic molecules.
  • the term “primer” or “probe” encompasses oligonucleotides that have a specific sequence that is complimentary or substantially complimentary to any one or combination of nucleic acid sequences identified in Table 1.
  • the target molecule is any amplified fragment of any one or combination of nucleic acid sequences identified in Table 1 and/or any one or combination of nucleic acid sequence comprising at least about 70%, 80%, 81%, 82%, 83%, 84, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% sequence identity to any one or combination of nucleic acid sequences in Table 1.
  • nucleic acids are detected by indirect detection methods.
  • a biotinylated probe may be combined with a streptavidin-conjugated dye to detect the bound nucleic acid.
  • the streptavidin molecule binds a biotin label on amplified miRNA, and the bound miRNA is detected by detecting the dye molecule attached to the streptavidin molecule.
  • the streptavidin- conjugated dye molecule comprises PHYCOLINK. Streptavidin R-Phycoerythrin (PROzyme). Other conjugated dye molecules are known to persons skilled in the art.
  • Labels include, but are not limited to: light-emitting, light-scattering, and light-absorbing compounds which generate or quench a detectable fluorescent, chemiluminescent, or bioluminescent signal (see, e.g., Kricka, 1992; Garman, 1997).
  • a dual labeled fluorescent probe that includes a reporter fluorophore and a quencher fluorophore is used in some embodiments. It will be appreciated that pairs of fluorophores are chosen that have distinct emission spectra so that they can be easily distinguished.
  • labels are hybridization-stabilizing moieties which serve to enhance, stabilize, or influence hybridization of duplexes, e.g., intercalators and intercalating dyes (including, but not limited to, ethidium bromide and SYBR-Green), minor-groove binders, and cross-linking functional groups (see, e.g., Blackburn et al., 1996).
  • intercalators and intercalating dyes including, but not limited to, ethidium bromide and SYBR-Green
  • minor-groove binders include, but not limited to, ethidium bromide and SYBR-Green
  • cross-linking functional groups see, e.g., Blackburn et al., 1996.
  • methods relying on hybridization and/or ligation to quantify miRNAs may be used, including oligonucleotide ligation (OLA) methods and methods that allow a distinguishable probe that hybridizes to the target nucleic acid sequence to be separated from an unbound probe.
  • OLA oligonucleotide ligation
  • HARP-like probes as disclosed in U.S. 2006/0078894 may be used to measure the quantity of miRNAs.
  • the probe after hybridization between a probe and the targeted nucleic acid, the probe is modified to distinguish the hybridized probe from the unhybridized probe. Thereafter, the probe may be amplified and/or detected.
  • a probe inactivation region comprises a subset of nucleotides within the target hybridization region of the probe.
  • a post-hybridization probe inactivation step is carried out using an agent which is able to distinguish between a HARP probe that is hybridized to its targeted nucleic acid sequence and the corresponding unhybridized HARP probe.
  • the agent is able to inactivate or modify the unhybridized HARP probe such that it cannot be amplified.
  • a probe ligation reaction may also be used to quantify miRNAs.
  • MLPA Multiplex Ligation-dependent Probe Amplification
  • the miRNA can be detected using an isothermal exponential amplification method, a rolling cycle amplification based method, a cleavage based method, a gold particle (AuNPs)-based method, a duplex specific nuclease (DSN) and AuNPs-based system quantum dot-based method or capillary-electrophoreses-based assay method, an AuNPs-based method, an DSN and AuNPs-based system quantum dot-based method or capillary- electrophoreses-based as described in Tian et al. (2015).
  • AuNPs gold particle
  • DSN duplex specific nuclease
  • AuNPs-based system quantum dot-based method or capillary-electrophoreses-based assay method an AuNPs-based method, an DSN and AuNPs-based system quantum dot-based method or capillary- electrophoreses-based as described in Tian et al. (2015).
  • the method comprises performing an assay on a sample from a subject to determine the miRNA expression profile of the subject.
  • the miRNA can be detected using real-time reverse transcription- PCR (qRT-PCR), microarray hybridization, a multiplex miRNA profiling assay, massively parallel/next generation sequencing also referred to as “NGS sequencing,” RNA-ish, northern blotting or colorimetric sensor based analysis.
  • qRT-PCR real-time reverse transcription- PCR
  • microarray hybridization a multiplex miRNA profiling assay
  • massively parallel/next generation sequencing also referred to as “NGS sequencing”
  • RNA-ish RNA-ish
  • northern blotting or colorimetric sensor based analysis.
  • the next generation sequencing is selected from: RNA-seq, small RNA-seq, and miRNA-seq.
  • Detection includes methods comprising direct labelling of a miRNA (e.g. with a modified nucleotide, labelled nucleotide or tag incorporated into the miRNA) or binding of the miRNA with a binding molecule which binds a miRNA or a truncated version thereof forming a miRNA- binding molecule complex.
  • a miRNA e.g. with a modified nucleotide, labelled nucleotide or tag incorporated into the miRNA
  • binding of the miRNA with a binding molecule which binds a miRNA or a truncated version thereof forming a miRNA- binding molecule complex.
  • the binding molecule is selected from: i) a polynucleotide, ii) an aptamer, iii) an antibody.
  • the polynucleotide is complementary to the miRNA or a truncated version thereof or detects a tag attached to the miRNA.
  • the polynucleotide is a primer.
  • the binding molecule is detectably labelled or capable of binding a detectable label.
  • the binding molecule is linked to an enzyme, enzyme substrate, a fluorescent or fluorescent substrate, chemiluminescent molecule, chemiluminescent substrate, purification tag and/or a solid support.
  • the miRNA-binding complex is directly or indirectly detected.
  • the detection method is a reverse transcription and quantitative PCR (RT-qPCR or qRT-PCR) assay.
  • the primer for reverse transcription can be a stem-loop specific RT primer or a universal primer if the miRNAs have undergone prior 3’ poly- A tailing and 5’ adaptor ligation, for example the TaqMan Advanced miRNA cDNA Synthesis kit from Applied Biosystems.
  • the detection method is fluorescence. In some embodiments, the detection method uses one or more of the primers provided in Table 1.
  • the method comprises determining a coronavirus infection in a subject, the method comprising i) obtaining a sample from a subject, and ii) assaying the sample for the level of at least one miRNA selected from: miR-423-5p, miR-23a-3p, miR-195-5p, miR- 766-3p, miR-651-5p, let-7e-5p, miR-1275, miR-3198, miR-627-5p, miR-4662a-5p, miR-3684, let-7a-5p, miR-483-5p, miR-3125, miR-3617-5p, miR-500b-3p, miR-664b-3p, miR-5189-3p, miR-6772-3p, miR-145-3p, miR-30a-5p, miR-27a-5p, miR-2116-3p, miR-31-5p, miR-4772-3p, miR-1290, miR-1226-3p, miR-589-3p
  • data that are generated using samples can then be used to “train” a classification model.
  • a “known sample” is a sample that has been pre classified, e.g., classified as being derived from a normal subject, from a subject known to have CoV infection, or a subject known to have severe CoV infection.
  • the data that are derived from a range of sources and are used to form the classification model can be referred to as a “training data set.”
  • the classification model can recognize patterns in data derived from spectra generated using unknown samples.
  • the classification model can then be used to classify the unknown samples into classes. This can be useful, for example, in predicting whether or not a particular biological sample is associated with a certain biological condition (e.g., diseased versus non-diseased).
  • data for the training data set that is used to form the classification model can be obtained from any established method for nucleic acid quantitation.
  • the data can come directly from quantitative PCR (for example, Ct values obtained using the double delta Ct method), or from high-throughput expression profiling, such as microarray analysis (for example, total counts or normalized counts from a miRNA RNA expression assay).
  • Classification models can be formed using any suitable statistical classification (or “learning”) method that attempts to segregate bodies of data into classes based on objective parameters present in the data.
  • Classification methods may be either supervised or unsupervised. Examples of supervised and unsupervised classification processes are described in Jain, "Statistical Pattern Recognition: A Review", IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 22, No. 1, January 2000.
  • supervised classification training data containing examples of known categories are presented to a learning mechanism, which learns one or more sets of relationships that define each of the known classes. New data may then be applied to the learning mechanism, which then classifies the new data using the learned relationships.
  • supervised classification processes include linear regression processes (e.g., multiple linear regression (MLR), partial least squares (PLS) regression and principal components regression (PCR)), binary decision trees (e.g., recursive partitioning processes such as CART— classification and regression trees), artificial neural networks such as back propagation networks, discriminant analyses (e.g., Bayesian classifier or Fischer analysis), logistic classifiers, and support vector classifiers (support vector machines).
  • linear regression processes e.g., multiple linear regression (MLR), partial least squares (PLS) regression and principal components regression (PCR)
  • binary decision trees e.g., recursive partitioning processes such as CART— classification and regression trees
  • artificial neural networks such as back propagation networks
  • discriminant analyses e.g., Baye
  • the classification models that are created can be formed using unsupervised learning methods.
  • Unsupervised classification attempts to learn classifications based on similarities in the training data set, without pre-classifying the spectra from which the training data set was derived.
  • Unsupervised learning methods include cluster analyses. A cluster analysis attempts to divide the data into ’’clusters” or groups that ideally should have members that are very similar to each other, and very dissimilar to members of other clusters. Similarity is then measured using some distance metric, which measures the distance between data items, and clusters together data items that are closer to each other.
  • Clustering techniques include the MacQueen's K-means algorithm and the Kohonen's Self-Organizing Map algorithm. Learning algorithms asserted for use in classifying biological information are described, for example, in. WO 01/31580, U.S. 2002 0193950, U.S. 2003 0004402 Al, and U.S. 2003 0055615 Al.
  • the classification models can be formed on and used on any suitable digital computer.
  • Suitable digital computers include micro, mini, or large computers using any standard or specialized operating system, such as a Unix, WINDOWS or LINUX based operating system.
  • the training data set(s) and the classification models can be embodied by computer code that is executed or used by a digital computer.
  • the computer code can be stored on any suitable computer readable media including optical or magnetic disks, sticks, tapes, etc., and can be written in any suitable computer programming language including C, C++, visual basic, etc.
  • the learning algorithms described above can be used for developing classification algorithms for miRNAs specific for CoV infection.
  • the classification algorithms can, in turn, be used in diagnostic tests by providing diagnostic values (e.g., cut-off points) for miRNAs used singly or in combination.
  • a method of the invention comprises comparing the level of the at least one miRNA to a reference value.
  • the reference value can be determined, or predetermined, using a wide variety of procedures known in the art including, but not limited to, the reference value is a predetermined level of the at least one miRNA, a predetermined score, the level of the at least one miRNA in a control sample, or the level of the at least one miRNA in a subject who does not have one or more of a CoV infection, an influenza virus infection and a respiratory infection.
  • the miRNA analysis algorithm can assign the sample a score using the level of the at least one miRNA in the sample. In an embodiment, the miRNA analysis algorithm can assign the sample a score using the level of at least one miRNA in Table 1. In an embodiment, the miRNA analysis algorithm can assign the sample a score using the level of at least two miRNA in Table 1. In an embodiment, the miRNA analysis algorithm can assign the sample a score using the level of at least three miRNA in Table 1.
  • the miRNA analysis algorithm can assign the sample a score using the level of miR-423-5p in the sample. In an embodiment, the miRNA analysis algorithm can assign the sample a score using the level of miR-195-5p in the sample. In an embodiment, the miRNA analysis algorithm can assign the sample a score using the level of miR-23a-3p in the sample. In an embodiment, the miRNA analysis algorithm can assign the sample a score using the level of miR-28-5p in the sample. In an embodiment, the miRNA analysis algorithm can assign the sample a score using the level of miR-223-5p in the sample. In an embodiment, the miRNA analysis algorithm can assign the sample a score using the level of miR-130b-3p in the sample.
  • the miRNA analysis algorithm can assign the sample a score using the level of miR- 423-5p, miR-195-5p and miR-23a-3p in the sample. In an embodiment, the miRNA analysis algorithm can assign the sample a score using the level of miR-423-5p, miR-195-5p, miR-23a-3p, miR-28-5p and miR-223-5p in the sample. In an embodiment, the miRNA analysis algorithm can assign the sample a score using the level of miR-423-5p, miR-195-5p, miR-23a-3p, miR-28-5p, miR-223-5p miR-130b-3p in the sample.
  • the miRNA analysis algorithm can assign the sample a score using the level of miR-142-3p in the sample. In an embodiment, the miRNA analysis algorithm can assign the sample a score using the level of miR-3065-3p in the sample. In an embodiment, the miRNA analysis algorithm can assign the sample a score using the level of miR-93-5p in the sample. In an embodiment, the miRNA analysis algorithm can assign the sample a score using the level of miR-142-3p and 3065-3p in the sample. In an embodiment, the miRNA analysis algorithm can assign the sample a score using the level of miR-142-3p and 3065-3p in the sample.
  • the miRNA analysis algorithm can assign the sample a score using the level of miR-142-3p, miR-3065-3p, and miR-93-5p in the sample.
  • a predetermined score may also be considered a threshold value. More specifically, based on the analysis of a sufficient number of subjects with and without a CoV infection or a severe CoV infection, a value (threshold) can be determined such that if the subject has a score based on the level of the at least one (typically two or more) miRNA at or above the threshold it is determined they have a CoV infection or a severe CoV infection. Processes for determining suitable scores and thresholds for a given diagnostic test are well known in the art.
  • the reference value is a threshold determined by the computer based miRNA analysis algorithm on training and/or validation data.
  • the algorithm is based on the level of 2 or 3 or 4 or 5 or 6 or 7 or 8 or 9 or 10 or more miRNA in the sample.
  • the reference value is based upon a machine learning classification algorithm (such as logistic regression) that has been trained using the expression of, for example, miR-423-5p, miR-195-5p and miR-23a-3p. Training provides the parameters, or coefficients, to the algorithm to allow it to make a prediction.
  • a machine learning classification algorithm such as logistic regression
  • Training provides the parameters, or coefficients, to the algorithm to allow it to make a prediction.
  • the resulting data for the, for example, three-miR signature is fed into the algorithm and the probability of that sample coming from someone infected with SARS-CoV-2 is produced. If the probability is greater than 0.5 or 50%, than the sample is classified as being COVTD-19 positive.
  • the level is an absolute level. In an embodiment, the level is a relative level between two or more miRNAs which may be all associated with a CoV infection or a severe CoV infection in a subject such as those described herein, or the levels of a suitable control miRNA(s) could be used to determine the relative level, where expression of the control miRNA is not associated with a CoV infection or a severe CoV infection.
  • the score factors in patient characteristics such as age, gender, other health conditions.
  • the reference value is the level of the at least one miRNA in a control sample.
  • the reference value may be a standard level of an RNA or miRNA synthetically produced or from a normal control biological sample from one or more subjects.
  • the normal control biological sample is age, gender and/or ethnicity matched to the subject being evaluated by the methods as described herein.
  • the reference value is the level of the at least one miRNA in a normal control biological sample.
  • the reference value is the level of the at least one miRNA in subject not having one or more of a CoV infection, an influenza virus infection and a respiratory infection. In an embodiment, the reference value is the level of the at least one miRNA in subject not having a CoV infection. In an embodiment, the reference value is the level of the at least one miRNA in subject not having an influenza infection. In an embodiment, the reference value is the level of the at least one miRNA in subject not having a respiratory infection. In an embodiment, the respiratory infection is a viral respiratory infection. In an embodiment, the reference value is the level of the at least one miRNA in subject not having cancer. In an embodiment, the reference value is the level of the at least one miRNA in subject not having an inflammatory disorder.
  • an altered level of the at least one miRNA in the biological sample compared to a predetermined reference value indicates the presence of a CoV infection.
  • the level of the at least one miRNA is a higher level compared to the reference value, and a higher level of the at least one miRNA is indicative of CoV infection in the subject.
  • the level of the at least one miRNA is a lower level compared to the reference value, and the lower level of the at least one miRNA is indicative of CoV infection in the subject.
  • the method has an accuracy of one or more of at least 80%, or at least 85%, or at least 90%, or at least 95%, or at least 99%. In an embodiment, the method has an accuracy of at least 90%. In an embodiment, the method has an accuracy of at least 95%. In an embodiment, the method has an accuracy of at least 99%.
  • the method can distinguish a CoV infection from another respiratory infection.
  • the method can distinguish between a CoV infection and an influenza A infection with at least 95% accuracy. In an embodiment, the method can distinguish between a CoV infection and an influenza A infection with at least 97% accuracy. In an embodiment, the method can distinguish between a CoV infection and an influenza A infection with at least 99% accuracy.
  • the method can identify a CoV infection with at least 95% precision. In an embodiment, the method can identify a CoV infection with at least 97% precision. In an embodiment, the method can identify a CoV infection with at least 99% precision.
  • the area under the curve (AUC) of the at least one miRNA is at least 0.65. In an embodiment, the area under the curve (AUC) of the at least one miRNA is at least 0.7. In an embodiment, the area under the curve (AUC) of the at least one miRNA is at least 0.75. In an embodiment, the area under the curve (AUC) of the at least one miRNA is at least 0.80. In an embodiment, the area under the curve (AUC) of the at least one miRNA is at least 0.85. In an embodiment, the area under the curve (AUC) of the at least one miRNA is at least 0.90.
  • the method comprises detecting the level of at least three miRNAs the method has one or more of i) an accuracy of at least about 99%, ii) a precision of at least about 99% and iii) a AUC of about 1.
  • the level of the at least one miRNA may be normalized. In an embodiment, the level of the at least one miRNA is normalised against a control.
  • the method comprises normalizing the level of the at least one miRNA to obtain a normalized level of the at least one miRNA, and wherein the method comprises comparing the normalised level of the at least one miRNA to the reference value of the at least one miRNA.
  • control is an endogenous control.
  • the endogenous control is a small RNA, for example, a miRNA, small non-coding RNA (ncRNA), transfer RNA (tRNA), small nuclear RNA (snRNA), or small nuceloalar RNA (snoRNA).
  • the endogenous control is a miRNA.
  • the control is an exogenous control, for example an exogenous RNA added to the biological sample before miRNA extraction (a spike-in control).
  • Spike-in controls may be added to a sample before RNA, small RNA and/or miRNA is recovered, the amount of the spike-in control recovered after RNA, small RNA and/or miRNA extraction is directly correlated with the amount of total RNA recovered.
  • the exogenous RNA is isolated from a host source or is synthetic. Synthetic spike- in controls are available from a number of commercial manufactures including for example, Qiagen and Norgen Biotek Corporation and Life Technologies.
  • the miRNAs described herein can be used individually or in combination in diagnostic tests to assess the status, degree, or severity of a CoV infection in a subject.
  • the CoV infection status includes the presence or absence of a CoV virus in the subject.
  • the CoV infection status may also include monitoring the course of the viral infection, for example, monitoring disease progression. Based on the CoV infection status of a subject, additional procedures may be indicated, including, for example, additional diagnostic tests or therapeutic procedures.
  • the power of a diagnostic test to correctly predict disease status is commonly measured in terms of the accuracy of the assay, the sensitivity of the assay, the specificity of the assay, or the “Area Under a Curve” (AUC), for example, the area under a Receiver Operating Characteristic (ROC) curve.
  • accuracy is a measure of the fraction of misclassified samples. Accuracy may be calculated as the total number of correctly classified samples divided by the total number of samples, e.g., in a test population.
  • Sensitivity is a measure of the “true positives” that are predicted by a test to be positive, and may be calculated as the number of correctly identified breast cancer samples divided by the total number of breast cancer samples.
  • Specificity is a measure of the "true negatives" that are predicted by a test to be negative, and may be calculated as the number of correctly identified normal samples divided by the total number of normal samples.
  • AUC is a measure of the area under a Receiver Operating Characteristic curve, which is a plot of sensitivity vs. the false positive rate (1 -specificity). The greater the AUC, the more powerful the predictive value of the test.
  • Other useful measures of the utility of a test include the “positive predictive value,” which is the percentage of actual positives who test as positives, and the “negative predictive value,” which is the percentage of actual negatives who test as negatives.
  • diagnostic tests that use miRNAs described herein individually or in combination show an accuracy of at least about 75%, e.g., an accuracy of at least about 75%, about 80%, about 85%, about 90%, about 95%, about 97%, about 99% or about 100%.
  • diagnostic tests that use miRNAs described herein individually or in combination show a specificity of at least about 75%, e.g., a specificity of at least about 75%, about 80%, about 85%, about 90%, about 95%, about 97%, about 99% or about 100%.
  • diagnostic tests that use miRNA described herein individually or in combination show a sensitivity of at least about 75%, e.g., a sensitivity of at least about 75%, about 80%, about 85%, about 90%, about 95%, about 97%, about 99% or about 100%.
  • diagnostic tests that use miRNAs described herein individually or in combination show a specificity and sensitivity of at least about 75% each, e.g., a specificity and sensitivity of at least about 75%, about 80%, about 85%, about 90%, about 95%, about 97%, about 99% or about 100% (for example, a specificity of at least about 80% and sensitivity of at least about 80%, or for example, a specificity of at least about 80% and sensitivity of at least about 95%).
  • Determining the level of the miRNA in a sample may include measuring, detecting, or assaying the level of the miRNA in the sample using any suitable method, for example, the methods described herein elsewhere. Determining the level of the miRNA in a sample may also include examining the results of an assay that measured, detected, or assayed the level of the miRNA in the sample. In some embodiments, the method may also involve comparing the level of the miRNA in a sample with a reference value.
  • a change in the level of the miRNA relative to that in a normal subject as assessed using a suitable reference value is indicative of the CoV infection status of the subject.
  • a diagnostic amount of a miRNA that represents an amount of the miRNA above or below which a subject is classified as having a particular CoV infection status can be used. For example, if the miRNA is upregulated in samples derived from an individual having CoV infection as compared to a normal individual (or a reference value), a measured amount above the diagnostic cutoff provides a diagnosis of CoV infection.
  • adjusting the particular diagnostic cut-off used in an assay allows one to adjust the sensitivity and/or specificity of the diagnostic assay as desired.
  • the particular diagnostic cut-off can be determined, for example, by measuring the amount of the miRNA in a statistically significant number of samples from subjects with different CoV infection statuses, and drawing the cut-off at the desired level of accuracy, sensitivity, and/or specificity.
  • the diagnostic cut-off can be determined with the assistance of a classification algorithm, as described herein.
  • the at least one miRNA preferably includes one or more miRNAs provided in Table 1.
  • the present disclosure provides a method of determining the level of at least one miRNA in a sample containing small RNAs derived from the subject, wherein an increase in the level of the at least one miRNA relative to a reference value is indicative of CoV infection in the subject.
  • the method may further comprise providing a diagnosis that the subject has or does not CoV infection based on the level of at least one miRNA in the sample.
  • the method may further comprise correlating a difference in the level or levels of at least one miRNA relative to a reference value with a diagnosis of CoV infection in the subject.
  • a diagnosis may be provided directly to the subject, or it may be provided to another party involved in the subject’s care.
  • a combination of miRNAs may provide greater predictive value of CoV infection status than a single miRNA when used alone.
  • the detection of a plurality of miRNAs can increase the accuracy, sensitivity, and/or specificity of a diagnostic test.
  • Exemplary miRNAs are shown in Table 1. Exemplary miRNA combinations are disclosed herein elsewhere. The disclosure includes the individual miRNA alone and miRNA combinations as set forth herein, and their use in methods and kits described herein.
  • methods are provided for diagnosing CoV infection in a subject, by determining the level of two or more miRNAs as described herein in a sample containing small RNA from the subject.
  • Comparison of the sample from the subject with the set of data may be assisted by a classification algorithm, which computes whether or not a statistically significant difference exists between the collective levels of the two or more miRNAs in the sample.
  • the methods of the disclosure detect the presence, absence or quantity of at least two miRNAs chosen from Table 1. In some embodiments, the methods of the disclosure detect the presence, absence or quantity of at least three miRNAs chosen from Table 1. In some embodiments, the methods of the disclosure detect the presence, absence or quantity of at least four miRNAs chosen from Table 1. In some embodiments, the methods of the disclosure detect the presence, absence or quantity of at least five miRNAs chosen from Table 1. In some embodiments, the methods of the disclosure detect the presence, absence or quantity of at least six miRNAs chosen from Table 1. In some embodiments, the methods of the disclosure detect the presence, absence or quantity of at least seven miRNAs chosen from Table 1. In some embodiments, the methods of the disclosure detect the presence, absence or quantity of at least eight miRNAs chosen from Table 1.
  • the methods of the disclosure detect the presence, absence or quantity of at least two miRNAs chosen from miR-23a-3p, miR-195-5p, miR-423-5p, miR-28-5p, miR-223-5p, and miR-130b-3p. In some embodiments, the methods of the disclosure detect the presence, absence or quantity of at least three miRNAs chosen from miR-23a-3p, miR-195-5p, miR-423-5p, miR-28-5p, miR-223-5p, and miR-130b-3p.
  • the methods of the disclosure detect the presence, absence or quantity of at least four miRNAs chosen from miR- 23a-3p, miR-195-5p, miR-423-5p, miR-28-5p, miR-223-5p, and miR-130b-3p. In some embodiments, the methods of the disclosure detect the presence, absence or quantity of at least five miRNAs chosen from miR-23a-3p, miR-195-5p, miR-423-5p, miR-28-5p, miR-223-5p, and miR-130b-3p. In some embodiments, the methods of the disclosure detect the presence, absence or quantity of miR-23a-3p, miR-195-5p, miR-423-5p, miR-28-5p, miR-223-5p, and miR-130b-3p.
  • the methods of the disclosure detect the presence, absence or quantity of miR-23a-3p, miR-195-5p, and miR-423-5p. In some embodiments, the methods of the disclosure detect the presence, absence or quantity of miR-23a-3p, miR-195-5p, miR-423-5p, and miR-28-5p. In some embodiments, the methods of the disclosure detect the presence, absence or quantity of miR-23a-3p, miR-195-5p, miR-423-5p, and miR-223-5p. In some embodiments, the methods of the disclosure detect the presence, absence or quantity of miR-23a-3p, miR-195-5p, miR-423-5p, and miR-130b-3p.
  • the methods of the disclosure detect the presence, absence or quantity of miR-23a-3p, miR-195-5p, miR-423-5p, , miR-28-5p, and miR- 223 -5p. In some embodiments, the methods of the disclosure detect the presence, absence or quantity of miR-23a-3p, miR-195-5p, miR-423-5p, miR-28-5p, and miR-130b-3p.
  • a combined score that integrates the level of the multiple miRNA biomarkers within a signature serves as the basis of the prediction of infection.
  • the combined score is calculated based on the miRNA level using Equation I below:
  • the score is compared to a reference value as described herein.
  • a combined score that integrates the RT-PCR Ct values of the multiple miRNA biomarkers within a signature serves as the basis of the prediction of infection.
  • Such combined score may be a combining function that can be as simple as sum of the Ct values of specific set of miRNAs.
  • the combined score may be determined by logistic regression, other regression techniques, support vector machines, random forests, neural networks, genetic algorithms, annealing algorithms, weighted sums, additive models, linear models, nearest neighbors or probabilistic models.
  • the combined score is the combination of the Ct values of the miRNAs calculated by Equation II below:
  • X, Y and Z refer to different miRNAs.
  • the X, Y and Z miRNAs contribute equally to the score (in such embodiments they are all weighted 1).
  • one or more of the X, Y and Z miRNAs do not contribute equally to the score (in such embodiments one or more of the miRNAs are assigned a different weight value from one or more of the other miRNA).
  • the score is compared to a reference value as described herein.
  • the combined score is the linear combination of the Ct values of the miRNAs calculated by Equation III below:
  • the Ct value of each of the miRNAs used in the prediction ranges from about 10 to about 50. In some embodiments, the Ct value of each of the miRNAs used in the prediction ranges from about 15 to about 45. In some embodiments, the Ct value of each of the miRNAs used in the prediction ranges from about 20 to about 40. In some embodiments, the Ct value of each of the miRNAs used in the prediction ranges from about 25 to about 35. In some embodiments, the Ct value of each of the miRNAs used in the prediction is about 15. In some embodiments, the Ct value of each of the miRNAs used in the prediction is about 20. In some embodiments, the Ct value of each of the miRNAs used in the prediction is about 25.
  • the Ct value of each of the miRNAs used in the prediction is about 30. In some embodiments, the Ct value of each of the miRNAs used in the prediction is about 35. In some embodiments, the Ct value of each of the miRNAs used in the prediction is about 40. In some embodiments, the Ct value of each of the miRNAs used in the prediction is about 45. In some embodiments, the Ct value of each of the miRNAs used in the prediction is about 50.
  • the maximum value of the combining function or combined score for the prediction is from about 10 to about 60. In some embodiments, the maximum value of the combining function or combined score for the prediction is from about 12 to about 55. In some embodiments, the maximum value of the combining function or combined score for the prediction is from about 14 to about 50. In some embodiments, the maximum value of the combining function or combined score for the prediction is from about 15 to about 45. In some embodiments, the maximum value of the combining function or combined score for the prediction is from about 20 to about 40. In some embodiments, the maximum value of the combining function or combined score for the prediction is about 10. In some embodiments, the maximum value of the combining function or combined score for the prediction is about 15.
  • the maximum value of the combining function or combined score for the prediction is about 20. In some embodiments, the maximum value of the combining function or combined score for the prediction is about 25. In some embodiments, the maximum value of the combining function or combined score for the prediction is about 30. In some embodiments, the maximum value of the combining function or combined score for the prediction is about 35. In some embodiments, the maximum value of the combining function or combined score for the prediction is about 40. In some embodiments, the maximum value of the combining function or combined score for the prediction is about 45. In some embodiments, the maximum value of the combining function or combined score for the prediction is about 50.
  • the combined score can be calculated for a set of about 60 patient samples and the scores bucketed into different groups based on the combined score.
  • the combined score of from about 10 to about 21 is used as the diagnostic threshold.
  • the combined score of from about 15 to about 21 is used as the diagnostic threshold.
  • the combined score of from about 21 to about 27 is used as the diagnostic threshold.
  • the combined score of from about 27 to about 39 is used as the diagnostic threshold.
  • the combined score of from about 39 to about 45 is used as the diagnostic threshold.
  • the embodiments may be implemented using a computer program product (i.e. software), hardware, software or a combination thereof.
  • the software code can be executed on any suitable processor or collection of processors, whether provided in a single computer or distributed among multiple computers.
  • a computer may be embodied in any of a number of forms, such as a rack-mounted computer, a desktop computer, a laptop computer, or a tablet computer. Additionally, a computer may be embedded in a device not generally regarded as a computer but with suitable processing capabilities, including a Personal Digital Assistant (PDA), a smart phone or any other suitable portable or fixed electronic device.
  • PDA Personal Digital Assistant
  • a computer may have one or more input and output devices. These devices can be used, among other things, to present a user interface. Examples of output devices that can be used to provide a user interface include printers or display screens for visual presentation of output and speakers or other sound generating devices for audible presentation of output. Examples of input devices that can be used for a user interface include keyboards, and pointing devices, such as mice, touch pads, and digitizing tablets. As another example, a computer may receive input information through speech recognition or in other audible format.
  • Such computers may be interconnected by one or more networks in any suitable form, including a local area network or a wide area network, such as an enterprise network, and intelligent network (IN) or the Internet.
  • networks may be based on any suitable technology and may operate according to any suitable protocol and may include wireless networks, wired networks or fiber optic networks.
  • a computer employed to implement at least a portion of the functionality described herein may include a memory, coupled to one or more processing units (also referred to herein simply as “processors”), one or more communication interfaces, one or more display units, and one or more user input devices.
  • the memory may include any computer-readable media, and may store computer instructions (also referred to herein as “processor-executable instructions”) for implementing the various functionalities described herein.
  • the processing unit(s) may be used to execute the instructions.
  • the communication interface(s) may be coupled to a wired or wireless network, bus, or other communication means and may therefore allow the computer to transmit communications to and/or receive communications from other devices.
  • the display unit(s) may be provided, for example, to allow a user to view various information in connection with execution of the instructions.
  • the user input device(s) may be provided, for example, to allow the user to make manual adjustments, make selections, enter data or various other information, and/or interact in any of a variety of manners with the processor during execution of the instructions.
  • the various methods or processes outlined herein may be coded as software that is executable on one or more processors that employ any one of a variety of operating systems or platforms. Additionally, such software may be written using any of a number of suitable programming languages and/or programming or scripting tools, and also may be compiled as executable machine language code or intermediate code that is executed on a framework or virtual machine.
  • inventive concepts may be embodied as a computer readable storage medium (or multiple computer readable storage media) (e.g., a computer memory, one or more floppy discs, compact discs, optical discs, magnetic tapes, flash memories, circuit configurations in Field Programmable Gate Arrays or other semiconductor devices, or other non-transitory medium or tangible computer storage medium) encoded with one or more programs that, when executed on one or more computers or other processors, perform methods that implement the various embodiments of the invention disclosed herein.
  • the computer readable medium or media can be transportable, such that the program or programs stored thereon can be loaded onto one or more different computers or other processors to implement various aspects of the present invention as discussed above.
  • the system comprises cloud-based software that executes one or all of the steps of each disclosed method instruction.
  • program or “software” are used herein in a generic sense to refer to any type of computer code or set of computer-executable instructions that can be employed to program a computer or other processor to implement various aspects of embodiments as discussed above. Additionally, it should be appreciated that according to one aspect, one or more computer programs that when executed perform methods of the present disclosure need not reside on a single computer or processor, but may be distributed in a modular fashion amongst a number of different computers or processors to implement various aspects of the present invention.
  • Computer-executable instructions may be in many forms, such as program modules, executed by one or more computers or other devices.
  • program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types.
  • functionality of the program modules may be combined or distributed as desired in various embodiments.
  • data structures may be stored in computer-readable media in any suitable form.
  • data structures may be shown to have fields that are related through location in the data structure. Such relationships may likewise be achieved by assigning storage for the fields with locations in a computer-readable medium that convey relationship between the fields.
  • any suitable mechanism may be used to establish a relationship between information in fields of a data structure, including through the use of pointers, tags or other mechanisms that establish relationship between data elements.
  • the disclosure relates to various embodiments in which one or more disclosed methods are used.
  • the acts performed as part of the method may be ordered in any suitable way. Accordingly, embodiments may be constructed in which acts are performed in an order different than illustrated, which may include performing some acts simultaneously, even though shown as sequential acts in illustrative embodiments.
  • the disclosure relates to a system that comprises at least one processor, a program storage, such as memory, for storing program code executable on the processor, and one or more input/output devices and/or interfaces, such as data communication and/or peripheral devices and/or interfaces.
  • the user device and computer system or systems are communicably connected by a data communication network, such as a Local Area Network (LAN), the Internet, or the like, which may also be connected to a number of other client and/or server computer systems.
  • the user device and client and/or server computer systems may further include appropriate operating system software.
  • components and/or units of the devices described herein may be able to interact through one or more communication channels or mediums or links, for example, a shared access medium, a global communication network, the Internet, the World Wide Web, a wired network, a wireless network, a combination of one or more wired networks and/or one or more wireless networks, one or more communication networks, an a-synchronic or asynchronous wireless network, a synchronic wireless network, a managed wireless network, a non-managed wireless network, a burstable wireless network, a non-burstable wireless network, a scheduled wireless network, a non-scheduled wireless network, or the like.
  • a shared access medium for example, a shared access medium, a global communication network, the Internet, the World Wide Web, a wired network, a wireless network, a combination of one or more wired networks and/or one or more wireless networks, one or more communication networks, an a-synchronic or asynchronous wireless network, a synchronic wireless network, a managed wireless network
  • Discussions herein utilizing terms such as, for example, “processing,” “computing,” “calculating,” “determining,” or the like, may refer to operation(s) and/or process(es) of a computer, a computing platform, a computing system, or other electronic computing device, that manipulate and/or transform data represented as physical (e.g., electronic) quantities within the computer's registers and/or memories into other data similarly represented as physical quantities within the computer’s registers and/or memories or other information storage medium that may store instructions to perform operations and/or processes.
  • Some embodiments may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment including both hardware and software elements. Some embodiments may be implemented in software, which includes but is not limited to firmware, resident software, microcode, or the like.
  • some embodiments may take the form of a computer program product accessible from a computer-usable or computer-readable medium providing program code for use by or in connection with a computer or any instruction execution system.
  • a computer-usable or computer-readable medium may be or may include any apparatus that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device.
  • the medium may be or may include an electronic, magnetic, optical, electromagnetic, InfraRed (IR), or semiconductor system (or apparatus or device) or a propagation medium.
  • a computer-readable medium may include a semiconductor or solid state memory, magnetic tape, a removable computer diskette, a Random Access Memory (RAM), a Read-Only Memory (ROM), a rigid magnetic disk, an optical disk, or the like.
  • RAM Random Access Memory
  • ROM Read-Only Memory
  • optical disks include Compact Disk-Read-Only Memory (CD-ROM), Compact Disk-Read/Write (CD-R/W), DVD, or the like.
  • a data processing system suitable for storing and/or executing program code may include at least one processor coupled directly or indirectly to memory elements, for example, through a system bus.
  • the memory elements may include, for example, local memory employed during actual execution of the program code, bulk storage, and cache memories which may provide temporary storage of at least some program code in order to reduce the number of times code must be retrieved from bulk storage during execution.
  • input/output or I/O devices may be coupled to the system either directly or through intervening I/O controllers.
  • network adapters may be coupled to the system to enable the data processing system to become coupled to other data processing systems or remote printers or storage devices, for example, through intervening private or public networks.
  • modems, cable modems and Ethernet cards are demonstrative examples of types of network adapters.
  • Other suitable components may be used.
  • Some embodiments may be implemented by software, by hardware, or by any combination of software and/or hardware as may be suitable for specific applications or in accordance with specific design requirements.
  • Some embodiments may include units and/or sub-units, which may be separate of each other or combined together, in whole or in part, and may be implemented using specific, multi-purpose or general processors or controllers.
  • Some embodiments may include buffers, registers, stacks, storage units and/or memory units, for temporary or long-term storage of data or in order to facilitate the operation of particular implementations.
  • Some embodiments may be implemented, for example, using a machine-readable medium or article which may store an instruction or a set of instructions that, if executed by a machine, cause the machine to perform a method steps and/or operations described herein.
  • Such machine may include, for example, any suitable processing platform, computing platform, computing device, processing device, electronic device, electronic system, computing system, processing system, computer, processor, or the like, and may be implemented using any suitable combination of hardware and/or software.
  • the machine-readable medium or article may include, for example, any suitable type of memory unit, memory device, memory article, memory medium, storage device, storage article, storage medium and/or storage unit; for example, memory, removable or non-removable media, erasable or non-erasable media, writeable or re-writeable media, digital or analog media, hard disk drive, floppy disk, Compact Disk Read Only Memory (CD-ROM), Compact Disk Recordable (CD-R), Compact Disk Re-Writeable (CD-RW), optical disk, magnetic media, various types of Digital Versatile Disks (DVDs), a tape, a cassette, or the like.
  • any suitable type of memory unit for example, any suitable type of memory unit, memory device, memory article, memory medium, storage device, storage article, storage medium and/or storage unit; for example, memory, removable or non-removable media, erasable or non-erasable media, writeable or re-writeable media, digital or analog media, hard disk drive, floppy disk, Compact Dis
  • the instructions may include any suitable type of code, for example, source code, compiled code, interpreted code, executable code, static code, dynamic code, or the like, and may be implemented using any suitable high-level, low-level, object-oriented, visual, compiled and/or interpreted programming language, e.g., C, C++, JavaTM, BASIC, Pascal, Fortran, Cobol, assembly language, machine code, or the like.
  • code for example, source code, compiled code, interpreted code, executable code, static code, dynamic code, or the like
  • suitable high-level, low-level, object-oriented, visual, compiled and/or interpreted programming language e.g., C, C++, JavaTM, BASIC, Pascal, Fortran, Cobol, assembly language, machine code, or the like.
  • a circuit may be implemented as a hardware circuit comprising custom very-large-scale integration (VLSI) circuits or gate arrays, off-the-shelf semiconductors such as logic chips, transistors, or other discrete components.
  • VLSI very-large-scale integration
  • a circuit may also be implemented in programmable hardware devices such as field programmable gate arrays, programmable array logic, programmable logic devices or the like.
  • the circuits may also be implemented in machine-readable medium for execution by various types of processors.
  • An identified circuit of executable code may, for instance, comprise one or more physical or logical blocks of computer instructions, which may, for instance, be organized as an object, procedure, or function. Nevertheless, the executables of an identified circuit need not be physically located together, but may comprise disparate instructions stored in different locations which, when joined logically together, comprise the circuit and achieve the stated purpose for the circuit.
  • a circuit of computer readable program code may be a single instruction, or many instructions, and may even be distributed over several different code segments, among different programs, and across several memory devices.
  • operational data may be identified and illustrated herein within circuits, and may be embodied in any suitable form and organized within any suitable type of data structure.
  • the operational data may be collected as a single data set, or may be distributed over different locations including over different storage devices, and may exist, at least partially, merely as electronic signals on a system or network.
  • the computer readable medium (also referred to herein as machine-readable media or machine-readable content) may be a tangible computer readable storage medium storing the computer readable program code.
  • the computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, holographic, micromechanical, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing.
  • examples of the computer readable storage medium may include but are not limited to a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), a portable compact disc read-only memory (CD-ROM), a digital versatile disc (DVD), an optical storage device, a magnetic storage device, a holographic storage medium, a micromechanical storage device, or any suitable combination of the foregoing.
  • a computer readable storage medium may be any tangible medium that can contain, and/or store computer readable program code for use by and/or in connection with an instruction execution system, apparatus, or device.
  • the computer readable medium may also be a computer readable signal medium.
  • a computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated signal may take any of a variety of forms, including, but not limited to, electrical, electro-magnetic, magnetic, optical, or any suitable combination thereof.
  • a computer readable signal medium may be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport computer readable program code for use by or in connection with an instruction execution system, apparatus, or device.
  • computer readable program code embodied on a computer readable signal medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, Radio Frequency (RF), or the like, or any suitable combination of the foregoing.
  • the computer readable medium may comprise a combination of one or more computer readable storage mediums and one or more computer readable signal mediums.
  • computer readable program code may be both propagated as an electro-magnetic signal through a fiber optic cable for execution by a processor and stored on RAM storage device for execution by the processor.
  • Computer readable program code for carrying out operations for aspects of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C++ or the like and conventional procedural programming languages, such as the “C” programming language or similar programming languages.
  • the computer readable program code may execute entirely on a user's computer, partly on the user’s computer, as a stand-alone computer-readable package, partly on the user’s computer and partly on a remote computer or entirely on the remote computer or server.
  • the remote computer may be connected to the user’s computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet Service Provider).
  • LAN local area network
  • WAN wide area network
  • Internet Service Provider for example, AT&T, MCI, Sprint, EarthLink, MSN, GTE, etc.
  • the program code may also be stored in a computer readable medium that can direct a computer, other programmable data processing apparatus, or other devices to function in a particular manner, such that the instructions stored in the computer readable medium produce an article of manufacture including instructions which implement the function/act specified in the schematic flowchart diagrams and/or schematic block diagrams block or blocks.
  • kits for determining the likelihood of a CoV infection in a subject or for determining the likelihood of a severe CoV infection will preferably comprise a nucleotide array comprising miRNA-specific probes and/or oligonucleotides for amplifying at least one miRNA described herein.
  • the disclosure provides a panel or kit for determining the likelihood of a CoV infection in a subject, the panel or kit comprising one or more probes or primers for detecting at least one miRNA as described herein.
  • the disclosure provides a panel or kit for determining the likelihood of a severe CoV infection in a subject, the panel or kit comprising one or more probes or primers for detecting at least one miRNA described in Table 1.
  • the kit comprises one or more reagents for detecting miR-423-5p. In some embodiments, the kit comprises one or more reagents for detecting miR-195-5p. In some embodiments, the kit comprises one or more reagents for detecting miR-23a-3p. In some embodiments, the kit comprises one or more reagents for detecting miR-28-5p. In some embodiments, the kit comprises one or more reagents for detecting miR-223-5p. In some embodiments, the kit comprises one or more reagents for detecting miR-130b-3p. In some embodiments, the kit comprises one or more reagents for detecting miR-423-5p, miR-195-5p, and miR-23a-3p.
  • the kit comprises one or more reagents for detecting miR- 423-5p, miR-195-5p, miR-23a-3p and miR-28-5p. In some embodiments, the kit comprises one or more reagents for detecting miR-423-5p, miR-195-5p, miR-23a-3p and miR-223-5p. In some embodiments, the kit comprises one or more reagents for detecting miR-423-5p, miR-195-5p, miR-23a-3p, miR-28-5p and miR-223-5p.
  • the kit comprises one or more reagents for detecting miR-423-5p, miR-195-5p, miR-23a-3p and miR-130b-3p. In some embodiments, the kit comprises one or more reagents for detecting miR-423-5p, miR-195-5p, miR-23a-3p, miR-28-5p, miR-223-5p and miR-130b-3p.
  • the kit comprises one or more reagents for detecting miR-142-3p. In some embodiments, the kit comprises one or more reagents for detecting miR-3065-3p. In some embodiments, the kit comprises one or more reagents for detecting miR-93-5p. In some embodiments, the kit comprises one or more reagents for detecting miR-486-5p. In some embodiments, the kit comprises one or more reagents for detecting miR-451a. In some embodiments, the kit comprises one or more reagents for detecting 3065-5p. In some embodiments, the kit comprises one or more reagents for detecting 628-3p.
  • the kit comprises one or more reagents for detecting miR-19a-3p. In some embodiments, the kit comprises one or more reagents for detecting miR-142-3p and miR-3065-3p. In some embodiments, the kit comprises one or more reagents for detecting miR-142-3p and miR-93-5p. In some embodiments, the kit comprises one or more reagents for detecting miR-142-3p and miR- 3065-3p. In some embodiments, the kit comprises one or more reagents for detecting miR-142-3p, miR-3065-3p, and miR-93-5p.
  • the kit comprises one or more reagents for detecting miR-142-3p, miR-3065-3p, miR-93-5p, miR-486-5p, miR-451a, miR-3065-5p, miR- 628-3p, and miR-19a-3p.
  • the one or more reagents for detecting at least one at least one miRNA comprises a binding molecule which binds a miRNA or a truncated version thereof.
  • the binding molecule is a polynucleotide, aptamer or antibody.
  • the binding molecule is detectably labelled.
  • the panel or kit further comprises a reference value as described herein. In some embodiments, the reference value comprises a standard curve of at least one miRNA as described herein. In some embodiments, the panel or kit further comprises a control as described herein. In some embodiments, the panel or kit further comprises a standard curve of the control as described herein. In some embodiments, the panel or kit further comprises one or more reagents for detecting the level of a control. In some embodiments, the one or more reagents in a binding molecule which binds an exogenous control as described herein. In some embodiments, the binding molecule is detectably labelled.
  • the reference value comprises a standard curve of miR-423-5p. In some embodiments, the reference value comprises a standard curve of miR-195-5p. In some embodiments, the reference value comprises a standard curve of miR-23a-3p. In some embodiments, the reference value comprises a standard curve of miR-28-5p. In some embodiments, the reference value comprises a standard curve of miR-223-5p. In some embodiments, the reference value comprises a standard curve of miR-130b-3p. In some embodiments, the reference value comprises a standard curve of miR-423-5p, miR-195-5p, and miR-23a-3p.
  • the reference value comprises a standard curve of miR-23a- 3p, miR-195-5p, miR-423-5p, and miR-28-5p. In some embodiments, the reference value comprises a standard curve of miR-23a-3p, miR-195-5p, miR-423-5p, miR-28-5p, and miR-223- 5p. In some embodiments, the reference value comprises a standard curve of miR-23a-3p, miR- 195-5p, miR-423-5p, miR-28-5p, miR-223-5p, and miR-130b-3p. In some embodiments, the reference value comprises a standard curve of miR-423-5p.
  • the reference value comprises a standard curve of mir-195-5p. In some embodiments, the reference value comprises a standard curve of mir-23a-3p. In some embodiments, the reference value comprises a standard curve of miR-28-5p. In some embodiments, the reference value comprises a standard curve of miR-223-5p. In some embodiments, the reference value comprises a standard curve of miR-130b-3p.
  • the reference value comprises a standard curve of miR-142-3p. In some embodiments, the reference value comprises a standard curve of miR-3065-3p. In some embodiments, the reference value comprises a standard curve of miR-93-5p. In some embodiments, the reference value comprises a standard curve of miR-486-5p. In some embodiments, the reference value comprises a standard curve of miR-451a. In some embodiments, the reference value comprises a standard curve of miR-3065-5p. In some embodiments, the reference value comprises a standard curve of miR-628-3p. In some embodiments, the reference value comprises a standard curve of miR-19a-3p.
  • the reference value comprises a standard curve of miR-142-3p and miR-3065-3p. In some embodiments, the reference value comprises a standard curve of miR-142-3p and miR-93-5p. In some embodiments, the reference value comprises a standard curve of miR-142-3p and miR-3065-3p. In some embodiments, the reference value comprises a standard curve of miR-142-3p, miR-3065-3p, and miR-93-5p. In some embodiments, the reference value comprises a standard curve of miR-142-3p, miR-3065-3p, miR-93-5p, miR-486-5p, miR-451a, miR-3065-5p, miR-628-3p, and miR-19a-3p.
  • the reference value comprises a predetermined threshold of a miRNA described in Table 1. In some embodiments, the reference value comprises a predetermined threshold level of miR-423-5p. In some embodiments, the reference value comprises a predetermined threshold level of miR-195-5p. In some embodiments, the reference value comprises a predetermined threshold level of miR-23a-3p. In some embodiments, the reference value comprises a predetermined threshold level of miR-28-5p. In some embodiments, the reference value comprises a predetermined threshold level of miR-223-5p. In some embodiments, the reference value comprises a predetermined threshold level of miR-130b-3p.
  • the reference value comprises a predetermined threshold level of one or more miRNAs selected from: miR-23a-3p, miR-195-5p and miR-423-5p. In some embodiments, the reference value comprises a predetermined threshold level of one or more miRNAs selected from: miR-23a-3p, miR-195-5p, miR-423-5p, and miR-28-5p. In some embodiments, the reference value comprises a predetermined threshold level of one or more miRNAs selected from: miR-23a- 3p, miR-195-5p, miR-423-5p, miR-28-5p, and miR-223-5p.
  • the reference value comprises a predetermined threshold level of one or more miRNAs selected from: miR-23a- 3p, miR-195-5p, miR-423-5p, miR-28-5p, miR-223-5p, and miR-130b-3p.
  • the kit comprises one or more reagents for detecting miR-142-3p. In some embodiments, the kit comprises one or more reagents for detecting miR-3065-3p. In some embodiments, the kit comprises one or more reagents for detecting miR-93-5p. In some embodiments, the kit comprises one or more reagents for detecting miR-486-5p. In some embodiments, the kit comprises one or more reagents for detecting miR-451a. In some embodiments, the kit comprises one or more reagents for detecting 3065-5p. In some embodiments, the kit comprises one or more reagents for detecting 628-3p.
  • the kit comprises one or more reagents for detecting miR-19a-3p. In some embodiments, the kit comprises one or more reagents for detecting miR-142-3p and miR-3065-3p. In some embodiments, the kit comprises one or more reagents for detecting miR-142-3p and miR-93-5p. In some embodiments, the kit comprises one or more reagents for detecting miR-142-3p and miR- 3065-3p. In some embodiments, the kit comprises one or more reagents for detecting miR-142-3p, miR-3065-3p, and miR-93-5p.
  • the kit comprises one or more reagents for detecting miR-142-3p, miR-3065-3p, miR-93-5p, miR-486-5p, miR-451a, miR-3065-5p, miR- 628-3p, and miR-19a-3p.
  • the disclosure provides a nucleotide array for determining the likelihood of a CoV infection in a subject, the nucleotide array comprising miRNA-specific probes for at least one miRNA as described herein.
  • the disclosure provides a nucleotide array for determining the likelihood of a severe CoV infection in a subject, the nucleotide array comprising miRNA-specific probes for at least one miRNA as described herein.
  • the panel or kit as described herein is for next generation sequencing, quantitative real-time reverse transcription-PCR (qRT-PCR), isothermal amplification, electrical interference, CRISPR-based method, nanomaterial-based methods, nucleic acid amplification-based methods such as rolling circle amplification (RCA), loop- mediated isothermal amplification (LAMP), strand-displacement amplification (SDA), enzyme- free amplification, a microarray, a multiplex miRNA profiling assay, RNA-ish, or northern blotting.
  • qRT-PCR quantitative real-time reverse transcription-PCR
  • the panel or kit uses qRT-PCT to detect the miRNA. In some embodiments, the panel or kit uses electrical interference to detect the miRNA. In some embodiments, the panel or kit uses a CRISPR-based method to detect the miRNA. In some embodiment, the kit comprises a Casl3 nuclease. In some embodiments, the kit comprises a Casl3a nuclease.
  • the panel or kit comprises not more than about 50 miRNA.
  • the panel or kit comprises not more than about 40 miRNA. In some embodiments, the panel or kit comprises not more than about 30 miRNA. In some embodiments, the panel or kit comprises not more than about 20 miRNA. In some embodiments, the panel or kit comprises not more than about 10 miRNA. In some embodiments, the panel or kit comprises not more than about 5 miRNA. In some embodiments, the panel or kit comprises not more than about 3 miRNA. In some embodiments, the panel or kit as described herein is for ex vivo analysis. In some embodiments, the kit is suitable for use with blood samples or a fraction there of e.g. blood or serum.
  • the panel or kit as described herein is suitable for high-throughput screening.
  • high-throughput screening refers to screening methods that can be used to test or assess more than one sample at a time and that can reduce the time for testing multiple samples.
  • the methods are suitable for testing or assessing at least about 5 samples, at least about 10, at least about 20, at least about 30, at least about 50, at least about 70, at least about 90, at least about 150, at least about 200, at least about 300 samples at a time.
  • Such high-throughput screening methods can analyse more than one sample rapidly e.g.
  • High-throughput screening may also involve the use of liquid handling devices.
  • high-throughput analysis may be automated.
  • the present disclosure provides a method of monitoring a CoV infection in a subject or evaluating the efficacy of a CoV treatment in a subject, the method comprising detecting a level of at least one miRNA as described herein in a biological sample from the subject at a first time point and at least one subsequent time point. In some embodiments, the method comprises determining whether a subject has recovered from a CoV infection.
  • the present disclosure further provides methods of treating such subjects identified to have CoV infection.
  • Active agents suitable for treating CoV infection may include, for example, substances approved by the U.S. Food and Drug Administration for the treatment of CoV infection.
  • Other active agents known to be used for treating CoV infection such as antivirals, targeted drug therapies, convalescent plasma, and oxygenation therapy, can also be used.
  • the oxygenation therapy is selected from one or more of: supplemental oxygen, high-flow nasal cannula oxygen, non-invasive positive pressure ventilation, extracorporeal membrane oxygenation and intubation with mechanical ventilation.
  • Non-limiting examples of active agents for treating CoV infection may include those provided in Table 2.
  • Table 2 List of active agents for treating coronavirus infection.
  • the active agents may be administered to a subject using a pharmaceutical composition.
  • suitable pharmaceutical compositions comprise a pharmaceutically effective amount of such active agent (or a pharmaceutically acceptable salt or ester thereof), and optionally comprise a pharmaceutically acceptable carrier). In certain embodiments, these compositions optionally further comprise one or more additional therapeutic agents.
  • the term "pharmaceutically acceptable salt” refers to those salts which are, within the scope of sound medical judgment, suitable for use in contact with the tissues of humans and lower animals without undue toxicity, irritation, allergic response and the like, and are commensurate with a reasonable benefit/risk ratio.
  • Pharmaceutically acceptable salts of amines, carboxylic acids, and other types of compounds are well known in the art. For example, S. M. Berge et al. (1977).
  • the salts can be prepared in situ during the final isolation and purification of the compounds of the invention, or separately by reacting a free base or free acid function with a suitable reagent. For example, a free base function can be reacted with a suitable acid.
  • suitable pharmaceutically acceptable salts thereof may, include metal salts such as alkali metal salts, e.g. sodium or potassium salts; and alkaline earth metal salts, e.g. calcium or magnesium salts.
  • ester refers to esters that hydrolyze in vivo and include those that break down readily in the human body to leave the parent compound or a salt thereof.
  • Suitable ester groups include, for example, those derived from pharmaceutically acceptable aliphatic carboxylic acids, particularly alkanoic, alkenoic, cycloalkanoic and alkanedioic acids, in which each alkyl or alkenyl moiety advantageously has not more than 6 carbon atoms.
  • the pharmaceutical compositions may additionally comprise a pharmaceutically acceptable carrier.
  • carrier includes any and all solvents, diluents, or other liquid vehicle, dispersion or suspension aids, surface active agents, isotonic agents, thickening or emulsifying agents, preservatives, solid binders, lubricants and the like, suitable for preparing the particular dosage form desired.
  • Remington's Pharmaceutical Sciences, Sixteenth Edition, E. W. Martin (Mack Publishing Co., Easton, Pa., 1980) discloses various carriers used in formulating pharmaceutical compositions and known techniques for the preparation thereof.
  • materials which can serve as pharmaceutically acceptable carriers include, but are not limited to, sugars such as lactose, glucose and sucrose; starches such as corn starch and potato starch; cellulose and its derivatives such as sodium carboxymethyl cellulose, ethyl cellulose and cellulose acetate; powdered tragacanth; malt; gelatine; talc; excipients such as cocoa butter and suppository waxes; oils such as peanut oil, cottonseed oil; safflower oil, sesame oil; olive oil; corn oil and soybean oil; glycols; such as propylene glycol; esters such as ethyl oleate and ethyl laurate; agar; buffering agents such as magnesium hydroxide and aluminium hydroxide; alginic acid; pyrogen free water; isotonic saline; Ringer's solution; ethyl alcohol, and phosphate buffer solutions, as well as other non-toxic compatible lubricants such as
  • Viral loads in tissues, swabs and nasal wash samples were assessed by qRT-PCR (Corman et al., 2020). Eleven ferrets (aged 4-6 months) were exposed to 1 x 10 5 TCID50 of influenza A (H1N1) virus as described (Rockman et al., 2012).
  • glycogen 10 pg, Sigma Aldrich, G1757
  • Adapters were trimmed using cutadapt (Martin et al., 2011) with a read length parameter (18-26 nucleotides). The remaining reads were examined using FastQC (www.bioinformatics.babraham.ac.uk/projects/fastqc/) to ensure high-quality data.
  • miRDeep2 (Friedlander et al, 2012) was used to map and quantify reads against the latest miRBase human reference (version 22) (Kozomara et al, 2019). Raw read counts were normalized and differential expression analysis was completed using the DESeq2 (Love et al., 2014) package in R. An adjusted False Discovery Rate (FDR) p- value of ⁇ 0.05 was used to identify differentially expressed miRNAs.
  • a logistic regression model was used for binary classification.
  • a linear support vector classifier was used for multiclass classification.
  • Each model underwent hyperparameter tuning using GridSearchCV.
  • GridSearchCV To assess the performance of the classification model, the data was randomly split into 70% labelled training data and 30% unlabelled test data, and the predicted classes of the test data samples were compared to the true classes. This process was repeated 1,000 times to ensure confidence in the classification performance.
  • the machine learning models were assessed on their accuracy (how many of the predictions were correct), precision (how many of the predicted positives were true positives), and recall (how many of the true positives were found by the model).
  • ROC AUC receiver operating characteristic area under the curve
  • Example 2 Host miRNA responses to SARS-COV2 infection
  • Plasma samples were obtained from ten COVID-19 patients and ten age- and gender- matched healthy controls. Longitudinal samples were available for some COVID-19 patients, categorized by visit (V), with VI representing the plasma sample first taken following hospital admission. Plasma samples were first obtained from COVID-19 patients 2-15 days (average 8 days) post symptomatic disease onset. Small RNA deep sequencing resulted in 23-50 million (average 34 million) raw reads per sample. Reads were trimmed of adaptors and filtered on length and quality, resulting in a loss of 29-74% (average 56%) of raw reads, leaving 8-35 million (average 15.2 million) reads per sample for further analysis (Figure 5). The majority of sequences were deemed high quality by FASTQC (data not shown).
  • MiRDeep2 was used to identify all known miRNA transcripts amongst the 29 samples and read counts were determined for each mature miRNA transcript. Total counts included all reads that mapped to a locus (as opposed to reads matching the canonical/consensus sequence only). A total of 985 different mature miRNA transcripts were detected, corresponding to 756 different precursors (5p and 3p miRNAs were counted separately). A significant difference in the total number of miRNAs identified in infected versus uninfected patients was not observed (data not shown). The most abundant miRNA in the plasma dataset were miR-16-5p, followed by miR-223- 3p, let-7b-5p and miR-146a-5p.
  • DESeq2 By using DESeq2 to perform count- based differential expression (DE) testing, a subset of miRNAs that were up- or down-regulated in infected patients relative to uninfected controls were identified ( Figure la, Table 3).
  • Table 3 also provides the results for has-miR-23a-3p. It is downregulated, but not to an extent outlined by the above parameters.
  • Table 3 provides the results for hsa-miR-130b-3p hsa-miR- 223-5p and hsa-miR-223 -3p.
  • miRNA-766-3p One significantly reduced miRNA, miR-766-3p, has been shown to reduce IL-6 expression in a dose-dependent manner, while another, miR 4662a-5p (Figure lc), is predicted to target IL-6 mRNA (TargetScan, release 7.2).
  • the miRNAs may play a role in the hyperinflammatory state commonly seen in patients with COVID-19.
  • Example 3 miRNA biomarkers for COVID-19 detection
  • ROC AUC receiver operating characteristic area under the curve
  • Measuring three miRNA targets (miR-423-5p, miR-23a-3p and miR- 195-5p) in combination in blood samples gave a model with 99.9% accuracy, 99.8% precision and 99.9% recall, with a ROC AUC of 1.0 ( Figure 2b).
  • the biomarker is comprised of two miRNAs DE in COVID-19 patients (miR-423-5p and miR-195-5p, both upregulated) and miR-23a-3p, which was not DE.
  • a decision boundary graph showed clear distinctions between healthy and infected patients based on these three miRNAs ( Figure 2c).
  • the decision boundary graph also clearly shows that each sample’s grouping was predicted with a high degree of confidence (0% probability of healthy samples being identified as infected with SARS-CoV-2, and 100% probability of COVTD-19 samples being detected as infected).
  • the probability of a sample being infected with SARS-CoV- 2 is determined by its distance from the decision boundary. The absence of points close to the boundary supports the high predictive accuracy of this miRNA signature.
  • RNA from serum samples were profiled for miRNAs using the same methodology as the patient samples. Sera from 12 uninfected ferrets were included as controls.
  • the previously identified biomarker signature miR-423-5p, miR-23a-3p and miR-195-5p
  • the decision boundary graph displayed high confidence in the predicted groupings (Figure 4c).
  • the miRNA biomarker still identified SARS-CoV-2 infection at 14 d.p.e., by which time ferrets were SARS-CoV-2 negative by nasal wash qRT-PCR, but with virus replication observed in the retroperitoneal lymph node tissue of 3 out of 4 ferrets (Figure 4a).
  • the biomarker could distinguish SARS-CoV-2 infection from influenza infection and healthy control ferrets with 95 % accuracy, 95.5 % precision and 94.6 % recall (Figure 4d).
  • the decision boundary graph comparing predicted grouping and true grouping is shown in Figure 4e.
  • miR-423-5p, miR-23a-3p and miR-195-5p have not been defined previously as a biomarker for a specific disease
  • increased expression of circulating miR-423-5p is observed in other conditions such as during heart failure and pulmonary tuberculosis.
  • Increases in circulating mil95-5p are associated with osteosarcoma, autism and gestational diabetes mellitus.
  • host responses to infection are known to be critical in differential outcomes of S ARS-
  • miR-31- 5p was the most strongly up-regulated miRNA in COVID-19 patients, which may be related to its role in modulating inflammation.
  • miR-27a-5p (also up-regulated in VI COVID-19 samples), is elevated in animal models of enterocolitis.
  • the up-regulation of miR-31-5p and miR-27a-5p in COVID-19 patients may reflect SARS-CoV-2 mediated gastrointestinal tract infection or inflammation.
  • the most statistically significant down-regulated miRNA was miR- 766-3p, a previously identified anti-inflammatory miRNA.
  • This miRNA was shown to reduce the expression of IL-6 in TNF-a stimulated MH7a cells and so its reduction may be partially responsible for the characteristic IL-6 increase seen in COVID-19 patients.
  • miR-31- 5p, miR-27a-5p, and miR-766-3p several miRNAs DE in COVID-19 patients that are poorly characterized from a functional perspective were identified.
  • Many miRNAs upregulated (miR- 3125, miR-4742-3p, miR 2116-3p) or down-regulated (miR-3617-5p, miR-500b-3p, miR-3684) in COVTD 19 patients have not been functionally characterized or previously observed in studies of miRNA responses to viral infection.
  • SARS-CoV-2 infection induces a miRNA response during the early stages of disease that involves three miRNAs (miR-423-5p, miR-23a-3p and miR-195-5p) that can independently identify COVID-19 cases and distinguish SARS-CoV-2 from influenza infections.
  • miRNAs miR-423-5p, miR-23a-3p and miR-195-5p
  • Host molecules correlating with COVID-19 severity such as the proinflammatory cytokine IL-6, are hypothesized to contribute to adverse COVID-19 outcomes and are the focus of ongoing clinical trials to assess treatments for severe COVID-19.
  • the study revealed differential miRNA responses in blood samples patients suffering moderate versus more severe COVID-19.
  • Four miRNAs (let-7e-5p, miR-651-5p, miR-766-3p, and miR-4433b-5p) were differentially expressed in both groups, suggesting that these molecules might be potential candidates for stratifying patients based on severity.
  • miRNAs are predicted to target HIF1AN (hypoxia inducible factor 1, alpha subunit inhibitor) (TargetScan, release 7.2) and so may play a role in the hypoxic response during COVID-19. Furthermore, previous studies have demonstrated altered let-7e-5p expression during hypoxic damage to the heart and retina supporting its role in the molecular response to oxygen deprivation. While the other three miRNAs have yet to be linked with hypoxia, miR-766-3p has an established anti-inflammatory role, and miR-4433b-5p is part of a biomarker signature of multi-drug resistant tuberculosis (an indicator of patient prognosis).
  • HIF1AN hypooxia inducible factor 1, alpha subunit inhibitor
  • This study exemplifies how analysis of miRNA responses to SARS-CoV-2 infection presents novel avenues in the characterization of cellular factors aiding in COVID-19 pathogenesis. It also presents novel opportunities for treatment and diagnosis of viral diseases. Targeting of pro- inflammatory miRNAs could present novel therapeutic opportunities against COVD-19, while miRNA profiling may aid in the disease detection and surveillance.
  • Nasal mucosa samples were collected from 8 healthy controls and 12 COVID-19 patients. COVID-19 infection was confirmed by performing qRT-PCR for viral genomic RNA on nasopharyngeal swabs using the method described in Example 1 (data not shown).
  • RNA samples were collected using nasopharyngeal swabs and were stored in saline solution or viral transport media at the time of collection.
  • RNA was isolated from 200 m ⁇ of the saline solution or viral transport media and 5 m ⁇ of the eluted RNA was used for library prep as described in Example 1.
  • Small RNA from nasal mucosal samples were profiled for miRNAs using the same methodology as described for the human blood samples..
  • miRNAs were upregulated (hsa-miR-142-3p, hsa-miR-93-5p, hsa-miR-486-5p, and hsa-miR-451a) in COVID- 19 samples and 4 miRNAs were downregulated (hsa-miR-3065-3p, hsa-miR-3065-5p, hsa-miR- 628-3p and hsa-miR-19a-3p) in COVID-19 samples compared to healthy controls.
  • miR-628-3p identifies COVID with 82.62% accuracy, 87.58% precision, 90.23% recall and a 95.43% ROC AUC.
  • miR-628-3p and miR-93-5p identifies COVID with 90.92% accuracy, 95.29% precision, 92.33% recall and 99.54% ROC AUC.
  • Example 1 The supervised machine learning method as described in Example 1 was implemented for the identification of the most predictive miRNAs in nasal mucosal samples and refined to identify the minimum targets necessary for accurate prediction and classification between healthy control and COVID-19.
  • a logistic regression model was implemented that randomly split the data into discovery and validation sets, trained and tested the model, which was repeated 1,000 times to determine reproducibility.
  • the most predictive miRNAs were selected using recursive feature elimination.
  • a three miRNA signature (miR-142-3p, miR-3065-3p and miR-93-5p) in human nasal mucosal samples was identified that classifies COVID with 100% accuracy (Figure 10).
  • the biomarker is comprised of two miRNAs that are upregulated in COVID-19 patients (miR-142-3p, miR-3065-3p and miR-93-5p) and hsa-miR-3065-3p, which is downregulated in COVID-19 subjects.
  • the EarlyDx Analysis and Interpretation software (120) is designed to work with one of the most common RT-PCR systems- the Applied BiosystemsTM (ABI) 7500 Fast Dx Real-Time PCR instrument.
  • the ABI-7500 Fast Dx is FDA-cleared and is commonly used in many CLIA-regulated diagnostic laboratories.
  • this RT-PCR instrument is connected to a PC computer, which has an ABI RT-PCR software (110) installed, e.g. 7500 Fast Dx software.
  • a GUI-friendly EarlyDx Analysis and Interpretation software (120) is also installed on the same computer and uses the standard computer keyboard for data entry.
  • a software step flow chart is shown in Figure 8.
  • the operator at the clinical laboratory enters patient Sample IDs on the setup screen of the 7500 Fast Dx software (110). This is done by typing in the Sample IDs using the keyboard connected to the ABI-7500 Fast Dx. If the laboratory uses bar code readers as a data input device with its ABI-7500 Fast Dx, the operator may use an attached reader.
  • the ABI-7500 Fast Dx (110) generates the real-time RT-qPCR data (e.g. Ct values of the miRNA analytes), and automatically writes the data on an output SDS file (11 IN).
  • the patient Sample IDs are also automatically embedded in this SDS file (11 IN).
  • the SDS file (11 IN) is automatically saved, often on the local computer hard-drive.
  • the laboratory operator opens the output SDS file (11 IN) using the 7500 Fast Dx software (110), and manually exports the SDS file (11 IN).
  • the operator then securely logs onto the EarlyDx Analysis and Interpretation software (120) using their personal password phrase, and manually imports the SDS file (11 IN) from the run.
  • the EarlyDx software (121) next reads the SDS file (11 IN) and parses its contents, populating them on a temporary data file readable by the EarlyDx software (120).
  • the operator executes a command to run the miRNA biomarker analysis using a predictive algorithm (122) (see description in Section 2 below).
  • This proprietary algorithm (122) uses the real-time RT-qPCR data (e.g.
  • the EarlyDx Analysis and Interpretation software (121) reads the SDS file (11 IN) and parses its contents, using each embedded patient Sample ID to search the local HL7 (Health Level 7) directory (130) for the first occurrence of an HL7 file that has the corresponding Sample ID tag within the relevant HL7 file element. Once the corresponding HL7 file is located by the EarlyDx Analysis and Interpretation software (121), it reads the contents of this file, and then populates the appropriate fields on the patient medical report (123N) with the following patient information: Name, Sample ID, Medical Report Number, Biospecimen Collection Date, and Date of Symptom Onset.
  • the operator can use the EarlyDx Analysis and Interpretation software (121) to generate an independent report (123N) with only the Sample ID field and no additional patient demographic information. The laboratory operator can then manually link the relevant patient information to this report after its generation.
  • the EarlyDx Analysis and Interpretation software (121) can import patient demographic data from the LIMS (Laboratory Information Management System) software system (130) via file system transfer.
  • the EarlyDx software (121) can also export patient data and reports to the LIMS system (130), or a secure 21 CFR part 11 -compliant cloud (140).
  • a combining function termed the EarlyDx-score integrates the RT-PCR Ct values of multiple biomarkers within a signature and serves as the basis of the prediction of infection.
  • the EarlyDx-score may be a combining function that can be as simple as sum of the Ct values of specific set of miRNAs. Or the EarlyDx-score may be determined by logistic regression, other regression techniques, support vector machines, random forests, neural networks, genetic algorithms, annealing algorithms, weighted sums, additive models, linear models, nearest neighbors or probabilistic models.
  • an EarlyDx-score which provides a high-level of predictive value is the linear combination of the Ct values of miR-423-5p, miR195-5p, miR-23a-3p, (Equation 1).
  • the specific EarlyDx-score is noted in equation 1 below:
  • Ct values of the miRNAs ranged between 25 and 35 so the maximum value of the EarlyDx-score is 45 giving a total score range of 15 to 45.
  • Early Dx-scores were calculated for a set of 60 patient samples and the scores bucketed in Table 8 below.
  • a EarlyDx- score of 21.0 was selected as the diagnostic threshold because 92% of samples with EarlyDx- scores 21.0 or less were COVID-positive.

Landscapes

  • Health & Medical Sciences (AREA)
  • Chemical & Material Sciences (AREA)
  • Engineering & Computer Science (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Organic Chemistry (AREA)
  • Public Health (AREA)
  • Proteomics, Peptides & Aminoacids (AREA)
  • Medical Informatics (AREA)
  • General Health & Medical Sciences (AREA)
  • Zoology (AREA)
  • Wood Science & Technology (AREA)
  • Pathology (AREA)
  • Analytical Chemistry (AREA)
  • Immunology (AREA)
  • Genetics & Genomics (AREA)
  • Biomedical Technology (AREA)
  • Biochemistry (AREA)
  • Data Mining & Analysis (AREA)
  • General Engineering & Computer Science (AREA)
  • Molecular Biology (AREA)
  • Biophysics (AREA)
  • Physics & Mathematics (AREA)
  • Biotechnology (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • Databases & Information Systems (AREA)
  • Primary Health Care (AREA)
  • Microbiology (AREA)
  • Epidemiology (AREA)
  • Virology (AREA)
  • Measuring Or Testing Involving Enzymes Or Micro-Organisms (AREA)

Abstract

The present disclosure relates to methods, kits, and panels for determining the likelihood of a coronavirus (CoV) infection or a severe coronavirus (CoV) infection in a subject, such as a severe acute respiratory syndrome coronavirus 2 (SARS-COV-2) virus infection. The disclosure also relates to methods for monitoring CoV or severe CoV infection in a subject.

Description

METHODS AND SYSTEMS FOR DETECTING A CORONA VIRUS INFECTION
FIELD OF THE INVENTION
The present disclosure relates to methods, kits and panels for determining the likelihood of a coronavirus (CoV) infection or a severe coronavirus (CoV) infection in a subject, such as a severe acute respiratory syndrome coronavirus 2 (SARS-COV-2) virus infection. The disclosure also relates to methods for monitoring CoV or severe CoV infection in a subject.
BACKGROUND OF THE INVENTON
As of December 2020, the COVTD-19 pandemic, caused by infection with severe acute respiratory syndrome-associated coronavirus-2 (SARS-CoV-2) has caused over 74.4 million cases and 1.65 million deaths worldwide. The outcome of SARS-CoV-2 infection varies widely from asymptomatic to severe disease associated with acute respiratory distress syndrome and death. Several studies have established that host responses to infection play a critical role in determining disease outcome in infected patients. Technologies most commonly utilized for COVTD-19 diagnosis are virus-specific molecular assays or serology, both of which are associated with relatively high false-positive rates (Kanne et al, 2020; Ai et al., 2020).
Current COVTD-19 molecular tests target viral RNA for detection. Unfortunately, even the most advanced current molecular diagnostic tests (i.e. PCR or LAMP amplifying viral RNA) for SARS-CoV-2 require a relatively high viral load to accurately detect infection (Zhang et al., 2020). Thus, their sensitivity during the early pre-symptomatic phase of disease (i.e. incubation period), when the viral load is still low, is poor. Overall sensitivity of current PCR tests has been estimated to be as low as 30-70% (Del Valle et al., 2020; Xu et al, 2020), making it difficult to diagnose infections in many pre-symptomatic and some asymptomatic cases.
Host biomarkers have shown promise in the quest to improve the infectious disease detection and diagnosis. MicroRNAs (miRNAs) are a class of non-coding RNAs that regulate endogenous gene expression at the post-transcriptional level. In most instances, miRNAs function by interacting with the 3' untranslated region (3' UTR) of target mRNAs to induce degradation and translational repression. There are currently over 2,600 human miRNAs listed in the miRNA registry (miRBase, version 22) (Griffiths- Jones et al, 2006) which are estimated to collectively regulate 60% of all human protein-coding genes Friedman et al. (2009). miRNA profiles offer unique insight into cellular pathways associated with virus replication and pathogenesis. Host miRNA responses have been reported to differ based on virus type and pathogenicity. For instance, the human coronavirus OC43 potentiates NF-kB activation during infection by binding and sequestering miR-9, a negative regulator of NF-kB (Lai et al., 2014). There is also evidence that coronaviruses co-opt the host miRNAs response to subvert antiviral immune responses. Infection by the Alphacoronavirus transmissible gastroenteritis virus (TGEV) downregulates miR-30a-5p expression, which disrupts the type I interferon response against TGEV.
As there are currently no curative treatments for COVED- 19, the characterisation of host factors associated with SARS-CoV-2 pathogenesis is critically important for the design of novel therapies for preventing and focusing treatments for COVED- 19. Thus, there is a need to develop new methods, panels, kits, arrays and systems for determining the likelihood of a coronavirus infection or a severe coronavirus infection in a subject.
SUMMARY OF THE INVENTION
The present disclosure provides methods, kits, panels, arrays and systems for determining the likelihood of a coronavirus (CoV) infection or a severe coronavirus (CoV) infection in a subject and/or for monitoring CoV or a severe CoV infection in a subject.
In some embodiments, the present disclosure provides a method of determining the likelihood of a CoV infection in a subject, the method comprising, in a biological sample from the subject, detecting a level of at least one miRNA selected from: miR-423-5p, miR-23a-3p, miR- 195-5p, miR-766-3p, miR-651-5p, let-7e-5p, miR-1275, miR-3198, miR-627-5p, miR-4662a-5p, miR-3684, let-7a-5p, miR-483-5p, miR-3125, miR-3617-5p, miR-500b-3p, miR-664b-3p, miR- 5189-3p, miR-6772-3p, miR-145-3p, miR-30a-5p, miR-27a-5p, miR-2116-3p, miR-31-5p, miR- 4772-3p, miR-1290, miR-1226-3p, miR-589-3p, miR-210-3p, miR-103a-3p, miR-3115, miR-769- 3p, miR-320b, miR-193a-5p, miR-206, miR-873-5p, miR-148a-3p, miR-3913-5p, miR-320a-3p, miR-576-5p, miR-197-3p, miR-4742-3p, miR-28-5p, miR-320c, miR-551b-3p, miR-142-3p, let- 7i-3p, miR-92a-3p, miR-548k, miR-18a-3p, miR-491-5p, miR-6721-5p, miR-6503-3p, miR-3065- 3p, miR-150-5p, let-7f-5p, miR-106b-5p, miR-99b-3p, miR-1273h-3p, let-7a-3p, miR-6741-5p, miR-21-3p, miR-301b-3p, miR-7-l-3p, miR-148b-5p, miR-874-3p, miR-889-3p, miR-1468-5p, miR-431-3p, miR-345-5p, miR-339-3p, miR-4433b-5p, miR-542-5p, miR-4750-5p, miR-132-3p, miR-877-5p, miR-1255b-5p, miR-450b-5p, let-7f-2-3p, miR-30a-3p, miR-23a-5p, miR-215-5p, miR-10a-3p, miR-98-5p, miR-200c-3p, miR-96-5p, miR-486-5p, miR-30e-5p, miR-141-3p, miR- 128-3p, miR-l-3p, miR-500a-3p, let-7b-5p, miR-22-3p, miR-3661, miR-24-3p, miR-340-5p, miR- 574-5p, miR-378c, miR-221-3p, miR-181c-3p, miR-130a-3p, miR-1306-3p, miR-95-3p, miR- 146a-5p, miR-27b-3p, miR-425-5p, miR-9-5p, miR-4454, miR-148b-3p, miR-599, miR-379-5p, miR-142-5p, miR-451a, miR-191-3p, miR-223-5p, miR-196b-5p, miR-194-5p, let-7d-5p, miR- 324-3p, miR-186-5p, miR-502-3p, miR-423-3p, miR-151b, miR-760, miR-93-3p, miR-382-5p, miR-25-3p, miR-331-3p, miR-29c-3p, miR-1277-5p, miR-320d, miR-769-5p, miR-3064-5p, miR- 6529-5p, miR-146b-5p, miR-151a-3p, miR-378a-5p, miR-27a-3p, miR-503-5p, miR-192-5p, miR-147b-3p, miR-182-5p, miR-409-3p, miR-885-3p, miR-106b-3p, miR-125a-3p, miR-542-3p, miR-30d-5p, miR-432-5p, miR-342-3p, miR-200a-3p, miR-30e-3p, miR-1843, miR-26b-3p, miR- 26b-5p, miR-499a-5p, miR-200b-5p, miR-125b-l-3p, miR-10b-5p, miR-219a-l-3p, miR-885-5p, miR-574-3p, miR-15b-3p, miR-449a, let-7b-3p, miR-32-5p, miR-339-5p, miR-130b-3p, miR- 2114-5p, miR-19a-3p, miR-155-3p, miR-223-3p, miR-449c-5p, miR-93-5p, miR-3065-5p, miR- 628-3p and miR-19a-3p.
In some embodiments, the present disclosure provides a method for determining the likelihood of a severe CoV infection in a subject, the method comprising, in a biological sample from the subject, detecting a level of at least one miRNA selected from: let-7e-5p, miR-766-3p, miR-651-5p, miR-4433b-5p, miR-106b-5p, miR-99b-3p, miR-1273h-3p, let-7a-3p, miR-6741-5p, miR-21-3p, miR-301b-3p, miR-7-l-3p, miR-148b-5p, miR-576-5p, miR-3198, miR-4662a-5p, miR-6772-3p, miR-197-3p, miR-874-3p, miR-889-3p, miR-92a-3p, miR-500b-3p, miR-769-3p, miR-1468-5p, miR-431-3p, miR-345-5p, miR-339-3p, miR-103a-3p, miR-30a-5p, miR-6503-3p, miR-542-5p, miR-627-5p, miR-4750-5p, miR-132-3p, miR-877-5p, miR-1255b-5p, miR-1290, miR-450b-5p, let-7f-2-3p, miR-30a-3p, miR-23a-5p, miR-215-5p, miR-10a-3p, miR-150-5p, miR-206, miR-195-5p, miR-320a-3p, miR-483-5p, miR-5189-3p, miR-98-5p, miR-200c-3p, miR- 664b-3p, and miR-96-5p.
In some embodiments, the present disclosure provides a method of monitoring a CoV infection in a subject or evaluating the efficacy of a CoV treatment in a subject, the method comprising detecting a level of at least one miRNA selected from: miR-423-5p, miR-23a-3p, miR- 195-5p, miR-766-3p, miR-651-5p, let-7e-5p, miR-1275, miR-3198, miR-627-5p, miR-4662a-5p, miR-3684, let-7a-5p, miR-483-5p, miR-3125, miR-3617-5p, miR-500b-3p, miR-664b-3p, miR- 5189-3p, miR-6772-3p, miR-145-3p, miR-30a-5p, miR-27a-5p, miR-2116-3p, miR-31-5p, miR- 4772-3p, miR-1290, miR-1226-3p, miR-589-3p, miR-210-3p, miR-103a-3p, miR-3115, miR-769- 3p, miR-320b, miR-193a-5p, miR-206, miR-873-5p, miR-148a-3p, miR-3913-5p, miR-320a-3p, miR-576-5p, miR-197-3p, miR-4742-3p, miR-28-5p, miR-320c, miR-551b-3p, miR-142-3p, let- 7i-3p, miR-92a-3p, miR-548k, miR-18a-3p, miR-491-5p, miR-6721-5p, miR-6503-3p, miR-3065- 3p, miR-150-5p, let-7f-5p, miR-106b-5p, miR-99b-3p, miR-1273h-3p, let-7a-3p, miR-6741-5p, miR-21-3p, miR-301b-3p, miR-7-l-3p, miR-148b-5p, miR-874-3p, miR-889-3p, miR-1468-5p, miR-431-3p, miR-345-5p, miR-339-3p, miR-4433b-5p, miR-542-5p, miR-4750-5p, miR-132-3p, miR-877-5p, miR-1255b-5p, miR-450b-5p, let-7f-2-3p, miR-30a-3p, miR-23a-5p, miR-215-5p, miR-10a-3p, miR-98-5p, miR-200c-3p, miR-96-5p, miR-486-5p, miR-30e-5p, miR-141-3p, miR- 128-3p, miR-l-3p, miR-500a-3p, let-7b-5p, miR-22-3p, miR-3661, miR-24-3p, miR-340-5p, miR- 574-5p, miR-378c, miR-221-3p, miR-181c-3p, miR-130a-3p, miR-1306-3p, miR-95-3p, miR- 146a-5p, miR-27b-3p, miR-425-5p, miR-9-5p, miR-4454, miR-148b-3p, miR-599, miR-379-5p, miR-142-5p, miR-451a, miR-191-3p, miR-223-5p, miR-196b-5p, miR-194-5p, let-7d-5p, miR- 324-3p, miR-186-5p, miR-502-3p, miR-423-3p, miR-151b, miR-760, miR-93-3p, miR-382-5p, miR-25-3p, miR-331-3p, miR-29c-3p, miR-1277-5p, miR-320d, miR-769-5p, miR-3064-5p, miR- 6529-5p, miR-146b-5p, miR-151a-3p, miR-378a-5p, miR-27a-3p, miR-503-5p, miR-192-5p, miR-147b-3p, miR-182-5p, miR-409-3p, miR-885-3p, miR-106b-3p, miR-125a-3p, miR-542-3p, miR-30d-5p, miR-432-5p, miR-342-3p, miR-200a-3p, miR-30e-3p, miR-1843, miR-26b-3p, miR- 26b-5p, miR-499a-5p, miR-200b-5p, miR-125b-l-3p, miR-10b-5p, miR-219a-l-3p, miR-885-5p, miR-574-3p, miR-15b-3p, miR-449a, let-7b-3p, miR-32-5p, miR-339-5p, miR-130b-3p, miR- 2114-5p, miR-19a-3p, miR-155-3p, miR-223-3p, miR-449c-5p, miR-93-5p, miR-3065-5p, miR- 628-3p and miR-19a-3p.; in a biological sample from the subject at a first time point and at least one subsequent time point.
In some embodiments, the present disclosure provides a panel or kit for determining the likelihood of a CoV infection in a subject, the panel or kit comprising one or more probes or primers for detecting at least one miRNA selected from: miR-423-5p, miR-23a-3p, miR-195-5p, miR-766-3p, miR-651-5p, let-7e-5p, miR-1275, miR-3198, miR-627-5p, miR-4662a-5p, miR- 3684, let-7a-5p, miR-483-5p, miR-3125, miR-3617-5p, miR-500b-3p, miR-664b-3p, miR-5189- 3p, miR-6772-3p, miR-145-3p, miR-30a-5p, miR-27a-5p, miR-2116-3p, miR-31-5p, miR-4772- 3p, miR-1290, miR-1226-3p, miR-589-3p, miR-210-3p, miR-103a-3p, miR-3115, miR-769-3p, miR-320b, miR-193a-5p, miR-206, miR-873-5p, miR-148a-3p, miR-3913-5p, miR-320a-3p, miR-576-5p, miR-197-3p, miR-4742-3p, miR-28-5p, miR-320c, miR-551b-3p, miR-142-3p, let- 7i-3p, miR-92a-3p, miR-548k, miR-18a-3p, miR-491-5p, miR-6721-5p, miR-6503-3p, miR-3065- 3p, miR-150-5p, let-7f-5p, miR-106b-5p, miR-99b-3p, miR-1273h-3p, let-7a-3p, miR-6741-5p, miR-21-3p, miR-301b-3p, miR-7-l-3p, miR-148b-5p, miR-874-3p, miR-889-3p, miR-1468-5p, miR-431-3p, miR-345-5p, miR-339-3p, miR-4433b-5p, miR-542-5p, miR-4750-5p, miR-132-3p, miR-877-5p, miR-1255b-5p, miR-450b-5p, let-7f-2-3p, miR-30a-3p, miR-23a-5p, miR-215-5p, miR-10a-3p, miR-98-5p, miR-200c-3p, miR-96-5p, miR-486-5p, miR-30e-5p, miR-141-3p, miR- 128-3p, miR-l-3p, miR-500a-3p, let-7b-5p, miR-22-3p, miR-3661, miR-24-3p, miR-340-5p, miR- 574-5p, miR-378c, miR-221-3p, miR-181c-3p, miR-130a-3p, miR-1306-3p, miR-95-3p, miR- 146a-5p, miR-27b-3p, miR-425-5p, miR-9-5p, miR-4454, miR-148b-3p, miR-599, miR-379-5p, miR-142-5p, miR-451a, miR-191-3p, miR-223-5p, miR-196b-5p, miR-194-5p, let-7d-5p, miR- 324-3p, miR-186-5p, miR-502-3p, miR-423-3p, miR-151b, miR-760, miR-93-3p, miR-382-5p, miR-25-3p, miR-331-3p, miR-29c-3p, miR-1277-5p, miR-320d, miR-769-5p, miR-3064-5p, miR- 6529-5p, miR-146b-5p, miR-151a-3p, miR-378a-5p, miR-27a-3p, miR-503-5p, miR-192-5p, miR-147b-3p, miR-182-5p, miR-409-3p, miR-885-3p, miR-106b-3p, miR-125a-3p, miR-542-3p, miR-30d-5p, miR-432-5p, miR-342-3p, miR-200a-3p, miR-30e-3p, miR-1843, miR-26b-3p, miR- 26b-5p, miR-499a-5p, miR-200b-5p, miR-125b-l-3p, miR-10b-5p, miR-219a-l-3p, miR-885-5p, miR-574-3p, miR-15b-3p, miR-449a, let-7b-3p, miR-32-5p, miR-339-5p, miR-130b-3p, miR- 2114-5p, miR-19a-3p, miR-155-3p, miR-223-3p, miR-449c-5p, miR-93-5p, miR-3065-5p, miR- 628-3p and miR-19a-3p.
In some embodiments, the present disclosure provides a panel or kit for determining the likelihood of a severe CoV infection in a subject, the panel or kit comprising one or more probes or primers for detecting at least one miRNA selected from: let-7e-5p, miR-766-3p, miR-651-5p, miR-4433b-5p, miR-106b-5p, miR-99b-3p, miR-1273h-3p, let-7a-3p, miR-6741-5p, miR-21-3p, miR-301b-3p, miR-7-l-3p, miR-148b-5p, miR-576-5p, miR-3198, miR-4662a-5p, miR-6772-3p, miR-197-3p, miR-874-3p, miR-889-3p, miR-92a-3p, miR-500b-3p, miR-769-3p, miR-1468-5p, miR-431-3p, miR-345-5p, miR-339-3p, miR-103a-3p, miR-30a-5p, miR-6503-3p, miR-542-5p, miR-627-5p, miR-4750-5p, miR-132-3p, miR-877-5p, miR-1255b-5p, miR-1290, miR-450b-5p, let-7f-2-3p, miR-30a-3p, miR-23a-5p, miR-215-5p, miR-10a-3p, miR-150-5p, miR-206, miR- 195-5p, miR-320a-3p, miR-483-5p, miR-5189-3p, miR-98-5p, miR-200c-3p, miR-664b-3p, and miR-96-5p. In some embodiments, the disclosure provides a method of treating or preventing a CoV infection or severe CoV infection in a subject, the method comprising: i) determining the likelihood of a CoV infection in a subject using the method of the disclosure, and/or determining the likelihood of a severe CoV infection in a subject using the method of the disclosure, ii) administering a treatment or preventative therapy for a CoV infection if it is determined the subject is likely to have a CoV infection or severe CoV infection.
Also provided is the use of an anti-coronavirus compound for the manufacture of a medicament for the treatment or prevention of a coronavirus infection or severe coronavirus infection in a subject, wherein it has been determined that it is likely the subject has a coronavirus infection using the method of the disclosure, and/or it has been determined that it is likely the subject will have a severe coronavirus infection using the method of the disclosure.
In some embodiments, the disclosure provides the use of an anti-coronavirus compound for the treatment or prevention of a coronavirus infection or severe coronavirus infection in a subject, wherein it has been determined that it is likely the subject has a coronavirus infection using the method of the disclosure, and/or it has been determined that it is likely the subject will have a severe coronavirus infection using the method of the disclosure.
In some embodiments, the disclosure provides a method of diagnosing a pre-symptomatic or asymptomatic subject infected with or exposed to a CoV. In some embodiments, the disclosure provides a method of detecting the presence or quantity of a CoV infection in a sample of a subject. In some embodiments, the disclosure provides a method of treating a pre-symptomatic or asymptomatic subject infected with a CoV or a subject infected with a CoV but not exhibiting clinically presented lung symptoms of CoV infection. In some embodiments, the disclosure provides a method of preventing severe CoV infection in a subject. In some embodiments, the disclosed methods comprise a) exposing a sample from the subject to a probe specific for at least one miRNA chosen from: miR-423-5p, miR-23a-3p, miR-195-5p, miR-766-3p, miR-651-5p, let- 7e-5p, miR-1275, miR-3198, miR-627-5p, miR-4662a-5p, miR-3684, let-7a-5p, miR-483-5p, miR-3125, miR-3617-5p, miR-500b-3p, miR-664b-3p, miR-5189-3p, miR-6772-3p, miR-145-3p, miR-30a-5p, miR-27a-5p, miR-2116-3p, miR-31-5p, miR-4772-3p, miR-1290, miR-1226-3p, miR-589-3p, miR-210-3p, miR-103a-3p, miR-3115, miR-769-3p, miR-320b, miR-193a-5p, miR- 206, miR-873-5p, miR-148a-3p, miR-3913-5p, miR-320a-3p, miR-576-5p, miR-197-3p, miR- 4742-3p, miR-28-5p, miR-320c, miR-551b-3p, miR-142-3p, let-7i-3p, miR-92a-3p, miR-548k, miR-18a-3p, miR-491-5p, miR-6721-5p, miR-6503-3p, miR-3065-3p, miR-150-5p, let-7f-5p, miR-106b-5p, miR-99b-3p, miR-1273h-3p, let-7a-3p, miR-6741-5p, miR-21-3p, miR-301b-3p, miR-7-l-3p, miR-148b-5p, miR-874-3p, miR-889-3p, miR-1468-5p, miR-431-3p, miR-345-5p, miR-339-3p, miR-4433b-5p, miR-542-5p, miR-4750-5p, miR-132-3p, miR-877-5p, miR-1255b- 5p, miR-450b-5p, let-7f-2-3p, miR-30a-3p, miR-23a-5p, miR-215-5p, miR-10a-3p, miR-98-5p, miR-200c-3p, miR-96-5p, miR-486-5p, miR-30e-5p, miR-141-3p, miR-128-3p, miR-l-3p, miR- 500a-3p, let-7b-5p, miR-22-3p, miR-3661, miR-24-3p, miR-340-5p, miR-574-5p, miR-378c, miR-221-3p, miR-181c-3p, miR-130a-3p, miR-1306-3p, miR-95-3p, miR-146a-5p, miR-27b-3p, miR-425-5p, miR-9-5p, miR-4454, miR-148b-3p, miR-599, miR-379-5p, miR-142-5p, miR-451a, miR-191-3p, miR-223-5p, miR-196b-5p, miR-194-5p, let-7d-5p, miR-324-3p, miR-186-5p, miR- 502-3p, miR-423-3p, miR-151b, miR-760, miR-93-3p, miR-382-5p, miR-25-3p, miR-331-3p, miR-29c-3p, miR-1277-5p, miR-320d, miR-769-5p, miR-3064-5p, miR-6529-5p, miR-146b-5p, miR-151a-3p, miR-378a-5p, miR-27a-3p, miR-503-5p, miR-192-5p, miR-147b-3p, miR-182-5p, miR-409-3p, miR-885-3p, miR-106b-3p, miR-125a-3p, miR-542-3p, miR-30d-5p, miR-432-5p, miR-342-3p, miR-200a-3p, miR-30e-3p, miR-1843, miR-26b-3p, miR-26b-5p, miR-499a-5p, miR-200b-5p, miR-125b-l-3p, miR-10b-5p, miR-219a-l-3p, miR-885-5p, miR-574-3p, miR-15b- 3p, miR-449a, let-7b-3p, miR-32-5p, miR-339-5p, miR-130b-3p, miR-2114-5p, miR-19a-3p, miR-155-3p, miR-223-3p, miR-449c-5p, miR-93-5p, miR-3065-5p, miR-628-3p and miR-19a- 3p; and b) detecting the presence, absence or quantity of the at least one miRNA in the sample.
In some embodiments, the method further comprises normalizing the presence, absence, or quantity of the at least one miRNA in the sample against the presence, absence or quantity of the at least one miRNA in a sample of a healthy subject. In some embodiment, the method comprises correlating the presence, absence, or quantity of the at least one miRNA in the sample to the subject being infected with a CoV. In some embodiments, the method further comprises assigning a score for the sample based on the level of the at least one miRNA. In some embodiments, the disclosed methods further comprise obtaining the sample from the subject. In some embodiments, the sample is plasma, serum, whole blood, respiratory tissue, respiratory mucosal sample, saliva or urine. In some embodiments, the disclosed methods further comprise administering a therapeutically effective amount of one or a plurality of active agents to the subject. In some embodiments, the disclosure provides a method of preparing a sample from a pre- symptomatic or asymptomatic subject infected with a CoV or a subject infected a CoV but not exhibiting clinically presented lung symptoms of CoV infection comprising: a) obtaining the sample from the subject; b) isolating total RNA from the sample; c) analysing the total RNA with a probe specific for at least one miRNA disclosed herein. In some embodiments, the method further comprises detecting the presence, absence or quantity of the at least one miRNA in the sample. In some embodiments, the method further comprises normalizing the presence, absence, or quantity of the at least one miRNA in the sample against the presence, absence or quantity of the at least one miRNA in a sample of a healthy subject. In some embodiments, the method further comprises correlating the presence, absence, or quantity of the at least one miRNA in the sample to the subject being infected with a CoV. In some embodiments, the sample is plasma, serum, whole blood, respiratory tissue, respiratory mucosal sample, saliva or urine. In some embodiments, the probe specific to the at least one miRNA is one or a plurality of primers chosen from Table 1. In some embodiments, the probe specific to the at least one miRNA comprises a nucleic acid sequence complementary to the nucleic acid sequence of the at least one miRNA.
In some embodiments, the disclosed methods further comprises calculating one or more scores based upon the presence, absence, or quantity of the at least one miRNA. In some embodiments, the disclosed methods further comprises correlating the one or more scores to the presence, absence, or quantity of the at least one miRNA such that, if the amount of the at least one miRNA is greater than the quantity of the at least one miRNA in a control sample; or, if the amount of the at least one miRNA is substantially equal to the quantity of the at least one miRNA in a sample taken from a subject known to have CoV infection, then the subject is diagnosed as being infected with a CoV.
In some embodiments, the at least one miRNA is detected with next generation sequencing, quantitative real-time reverse transcription-PCR (qRT-PCR), isothermal amplification, electrical interference, a CRISPR-based method, nanomaterial-based methods, nucleic acid amplification- based methods such as rolling circle amplification (RCA), loop-mediated isothermal amplification (LAMP), strand-displacement amplification (SDA), enzyme-free amplification, microarray, multiplex miRNA profiling assay, RNA-ish, or northern blotting. In some embodiments, the at least one miRNA is detected by qRT-PCR. In some embodiments, the at least one miRNA is detected with next generation sequencing. In some embodiments, the at least one miRNA is detected with electrical interference. In some embodiments, the at least one miRNA is detected with a CRISPR-based method. In some embodiments, the step of quantifying at least one quantity of the at least one miRNA in the sample comprises using a fluorescence and/or digital imaging. In some embodiments, the presence, absence, or quantity of the at least one miRNA is detected by PCR amplification using one or a plurality of primers specific for the at least one miRNA. In some embodiments, the one or plurality of primers are chosen from Table 1. In some embodiments, the presence, absence, or quantity of the at least one miRNA is detected by a probe comprising a nucleic acid sequence complementary to the nucleic acid sequence of the at least one miRNA. In some embodiments, the probe is a radioactive probe, a chemoluminescent probe, or a fluorescent probe.
In some embodiments, the presence, absence, or quantity of at least 2 different miRNAs in the sample are detected, normalized and correlated. In some embodiments, the presence, absence, or quantity of at least 3 to 6 different miRNAs in the sample are detected, normalized and correlated. In some embodiments, the disclosed methods comprise detecting the presence, absence or quantity of at least one miRNA chosen from miR-423-5p, miR-195-5p and miR-23a-3p in the sample. In some embodiments, the disclosed methods comprise detecting the presence, absence or quantity of at least two miRNAs chosen from miR-423-5p, miR-195-5p and miR-23a-3p in the sample. In some embodiments, the disclosed methods comprise detecting the presence, absence or quantity of miR-423-5p, miR-195-5p and miR-23a-3p in the sample. In some embodiments, the disclosed methods comprise detecting the presence, absence or quantity of miR-23a-3p, miR-195- 5p, miR-423-5p, and miR-28-5p. In some embodiments, the disclosed methods comprise detecting the presence, absence or quantity of miR-23a-3p, miR-195-5p, miR-423-5p, miR-28-5p, and miR- 223 -5p. In some embodiments, the disclosed methods comprise detecting the presence, absence or quantity of miR-23a-3p, miR-195-5p, miR-423-5p, miR-28-5p, miR-223-5p, and miR-130b-3p.
In some embodiments, the CoV infection is selected from: SARS-CoV-2, HCoV-OC43, HCoV-HKUl, HCoV-229E, HCoV-NL63, SARS-CoV or MERS-CoV, or a variant thereof. In some embodiments, the CoV infection is SARS-CoV-2 or a variant thereof. In some embodiments, the SARs-CoV-2 variant is B.1.1.529.
The disclosure further provides a computer program product encoded on a computer- readable storage medium, wherein the computer program product comprises instructions for: a) detecting the presence, absence or quantity of at least one miRNA in a sample of a subject; and b) correlating the presence, absence, or quantity of the at least one miRNA in the sample to a likelihood that the subject being infected with a CoV infection. In some embodiment, the method further comprises one or both of: c) normalizing the presence, absence, or quantity of the at least one miRNA in the sample against the presence, absence or quantity of the at least one miRNA in a control sample; and d) calculating a score associated with the presence, absence or quantity of the at least one miRNA in the sample and correlating the score to a likelihood that the subject being infected with a CoV infection.
In some embodiments, the computer program product comprises: a) detecting and normalizing the presence, absence or quantity of a second miRNA in the sample; and b) correlating the combined score to a likelihood that the subject being infected with a CoV. In some embodiments, the method further comprises detecting and normalizing the presence, absence or quantity of a second miRNA in the sample. In some embodiments, the method further comprises calculating a combined score associated with the presence, absence or quantity of the at least one miRNA and the second miRNA in the sample. In some embodiments, the method further comprises assigning a score based on the level of the at least one miRNA. In some embodiments, the method further comprises normalizing the presence, absence, or quantity of the at least one miRNA in the sample against the presence, absence or quantity of the at least one miRNA in a control sample. In some embodiments, the method further comprises In some embodiments, the method further comprises calculating a score associated with the presence, absence or quantity of the at least one miRNA in the sample and correlating the score to a likelihood that the subject being infected with a CoV.
In some embodiments, at least 3 to 6 different miRNAs in the sample are detected, normalized and correlated. In some embodiments, the presence, absence, or quantity of the at least one miRNA is detected by qRT-PCR amplification. In some embodiments, the presence, absence, or quantity of the at least one miRNA is detected by electrical interference. In some embodiments, the presence, absence, or quantity of the at least one miRNA is detected by a CRISPR-based method. In some embodiments, the control sample is obtained from a healthy subject. In some embodiments, the disclosure provides a system comprising: a) any of the disclosed the computer program product; and b) a processor operable to execute programs; and/or a memory associated with the processor. The disclosure additionally provides a system for detecting the presence or quantity of CoV infection in a sample of a subject comprising: a processor operable to execute programs; a memory associated with the processor; a database associated with said processor and said memory; and a program stored in the memory and executable by the processor, the program being operable for: a) detecting and normalizing the presence, absence or quantity of a second miRNA in the sample; and b) correlating the combined score to a likelihood that the subject being infected with a CoV. In some embodiments, the program of the disclosed system is further operable for c) calculating a combined score associated with the presence, absence or quantity of the at least one miRNA and the second miRNA in the sample. In some embodiments, the program of the disclosed system is further operable for calculating a score associated with the presence, absence or quantity of the at least one miRNA in the sample and correlating the score to a likelihood that the subject being infected with a CoV. In some embodiments, the program of the disclosed system is further operable for: a) detecting and normalizing the presence, absence or quantity of a second miRNA in the sample; and b) correlating the combined score to a likelihood that the subject being infected with a CoV. In some embodiments, the program of the disclosed system is further operable for c) calculating a combined score associated with the presence, absence or quantity of the at least one miRNA and the second miRNA in the sample.
In some embodiments, the biological sample is a blood sample. In some embodiments, the biological sample is a respiratory mucosal sample. In some embodiments, the biological sample is a nasal mucosal sample.
Any embodiment herein shall be taken to apply mutatis mutandis to any other embodiment unless specifically stated otherwise. For instance, as the skilled person would understand examples of miRNA outlined above for the methods of the disclosure equally apply to panels and kits of the disclosure.
The present disclosure is not to be limited in scope by the specific embodiments described herein, which are intended for the purpose of exemplification only. Functionally-equivalent products, compositions and methods are clearly within the scope of the disclosure, as described herein.
Throughout this specification, unless specifically stated otherwise or the context requires otherwise, reference to a single step, composition of matter, group of steps or group of compositions of matter shall be taken to encompass one and a plurality (i.e. one or more) of those steps, compositions of matter, groups of steps or group of compositions of matter.
The disclosure is hereinafter described by way of the following non-limiting Examples and with reference to the accompanying figures.
KEY TO SEQUENCE LISTING
SEQ ID NOs 1 - 177 mature miRNA sequences.
SEQ ID NOs 178 - 354 mature DNA sequence for the miRNAs of SEQ IN NOs 1 - 177.
SEQ ID NOs 335 - 536 example forward primers for the miRNAs of SEQ ID NOs 1 - 177.
SEQ ID NOs 537 - 718 example reverse primers for the miRNAs of SEQ ID NOs 1 - 177.
SEQ ID NOs 719 - 734 example RT primer and probes for select miRNAs.
BRIEF DESCRIPTION OF THE DRAWINGS
Figure 1. SARS-CoV-2 induces circulating miRNA and cytokine changes. A) Volcano plot showing the increased (right) and decreased (left) differentially expressed (DE) miRNAs in visit 1 (VI) COVTD-19 patients when compared to healthy controls. Horizontal dotted line is the p- value cut-off (False Discovery Rate, FDR<0.05) and the vertical lines are the fold change cut-off (>2 FC). A small number of miRNAs are statistically significant but are not >2 FC. The number of statistically significant miRNAs in each section are shown. The most up-regulated, down- regulated, and statistically significant miRNAs have been labelled. B) PCA plot showing the separation of healthy (n=10) and COVTD-19 VI (n=7) samples using the 55 DE miRNAs. Boxplots of C) select qRT-PCR validated miRNAs and D) IL-6 expression in healthy (n=10) and COVTD- 19 VI (n=8) samples. Boxes are the 25th - 75th percentile, line is the median, and whiskers are 1 5x IQR. * p-value < 0.05, ** p-value < 0.01.
Figure 2. A three miRNA signature classifies COVTD with 99.9% accuracy. A) Feature (miRNA) selection lineplot showing the impact of increasing numbers of miRNAs on the performance of a logistic regression model. miRNAs were selected using recursive feature elimination to identify the most important miRNAs. Each combination of miRNAs was randomly assessed 1,000 times. Shaded areas are the 95% Cl, and the dotted line is a perfect (100%) score. B) Barplot showing the average score of the three- miRNA signature in predicting healthy controls and COVTD-19 patients. Error bars are the 95% Cl after 1,000 random iterative assessments. C) Decision boundary graph showing the logistic regression decision point (solid black line) and the probability a person is infected with SARS-CoV-2 (dark to light shading). Data points are healthy (circles, n=10) and COVID-19 VI (crosses, n=7) samples. D) PCA plot based on the three miRNA signature showing all healthy (n=10) and COVID-19 (n=19) samples. Subsequent visit 2 (V2) (crosses, n=5), visit 3 (V3) (squares, n=4), and visit 4 (V4) (plus signs, n=3) samples cluster with the healthy controls, apart from those denoted with a hash (#) - these all came from one participant that was treated in ICU and had not recovered at any visit. E) Boxplots of each of the signature miRNAs in healthy (n=10) and COVID-19 VI (n=7) samples. Boxes are the 25th - 75th percentile, line is the median, and whiskers are 1.5x IQR. * FDR adjusted p-value < 0.05, ** FDR adjusted p-value < 0.01. n.s. non-significant.
Figure 3. Differential miRNA profiles based on COVID-19 severity. A) Venn diagram of COVID- 19 and COVID-19 + O2 VI DE miRNAs when compared to healthy controls. B) PCA plot based on the four common DE miRNAs. Healthy (n=10), COVID-19 (n=3) and COVID-19 + O2 (n=4) VI samples.
Figure 4. Human miRNA signature accurately identifies influenza and SARS-CoV-2 infection in a ferret model. A) Detection of SARS-CoV-2 viral genomic RNA in the retroperitoneal lymph node, nasal wash, oral swab, and turbinate tissue of infected ferrets (n=20, swab and wash samples taken from every ferret at each time point, tissue samples were analysed from the 4 euthanized ferrets at each time point). Data is presented as loglO copies per g of tissue or ml of sample. B) Final metrics of the trained logistic regression model to identify uninfected or SARS-CoV-2 infected ferrets. Dotted line is a perfect score (or 100%). Error bars are 95% Cl for 1,000 random assessments. C) Decision boundary graph showing the logistic regression decision point (solid black line) and the probability a sample is infected with SARS-CoV-2 (dark to light shading). Data points are uninfected (circles, n=ll) and SARS-CoV-2 infected (crosses, n=20) ferrets. D) Final metrics of the trained linear support vector classifier model to identify uninfected, influenza A (H1N1) virus, or SARS-CoV-2 infected ferrets. Dotted line is a perfect score (or 100%). Error bars are 95% Cl for 1,000 random assessments. As ROC AUC is a measure of binary classification (two groups) it is omitted here. E) Decision boundary graph showing the linear support vector classifier decision points and predicted groups: uninfected (n=ll), influenza A (H1N1) virus infected (n=ll) or SARS-CoV-2 infected (n=20) ferrets. Figure 5. Small RNA deep sequencing resulted in 23-50 million (average 34 million) raw reads. After adaptor trimming, reads that fell outside the expected size range for miRNAs (18-26 nt) were filtered out, as were reads that failed to map to a miRNA precursor.
Figure 6. Application of the VI COVID- 19 miRNA signature to subsequent time points. Decision boundary graph showing the logistic regression decision point (solid black line) and the probability a person is infected with SARS-CoV-2 (dark to light shading). Data points are COVID- 19 patients at V2 (circles, n=5), V3 (crosses, n=4), and V4 (squares, n=3).
Figure 7. Detection of influenza (H1N1) genomic RNA in lung tissue, nasal swab, nasal wash and serum of infected ferrets (2-4 ferrets per time point). Data is presented as normalized CT on a reverse y-axis. Undetectable results are plotted as CT=40.
Figure 8. A software step flow chart.
Figure 9. COVID-19 alters miRNA abundance in nasal swab samples. A) Volcano plot showing the increased (a fold change greater than one; right side) and decreased (a fold change below -1; left side) DE miRNAs in COVID-19 patients when compared to controls. Horizontal dotted line is the p-value cut-off (False Discovery Rate, FDR<0.05) and the vertical lines are the fold change cut-off (>2 FC). The two miRNAs in the top middle section outlined by dotted lines are statistically significant but are not >2 FC. The number of statistically significant miRNAs in each section are shown. The most up-regulated, down-regulated, and statistically significant miRNAs have been labelled. B) PCA plot showing the separation of healthy (n=8) and COVID-19 (n=12) samples using the 8 DE miRNAs. C) Boxplots of select miRNAs in healthy (n=8) and COVID-19 (n=12) samples. Boxes are the 25th - 75th percentile, line is the median, and whiskers are 1.5x IQR. ** adjusted p-value < 0.01, *** adjusted p-value <0.001.
Figure 10. A three miRNA signature classifies COVID with 100% accuracy. A) Feature (miRNA) selection lineplot showing the impact of increasing numbers of miRNAs on the performance of a logistic regression model. miRNAs were selected using recursive feature elimination to identify the most important miRNAs. Each combination of miRNAs was randomly assessed 1,000 times. Shaded areas are the 95% Cl, and the dotted line is a perfect (100%) score. B) Barplot showing the average score of the three- miRNA signature in predicting healthy controls and COVID-19 patients. Error bars are the 95% Cl after 1 ,000 random iterative assessments. C) Decision boundary graph showing the machine learning decision point between Control (shaded area on the right) and COVID-19 (shaded area on the left) groups. Datapoints are healthy (circles, n=8) and COVID-19 (crosses, n=12) samples. D) Boxplots of each of the signature miRNAs in healthy (n=8) and COVID-19 (n=12) samples. Boxes are the 25th - 75th percentile, line is the median, and whiskers are 1.5xIQR. * FDR adjusted p-value< 0.05, ** FDR adjusted p-value< 0.01, n.s. non-significant.
DETAILED DESCRIPTION OF THE INVENTION
Unless specifically defined otherwise, all technical and scientific terms used herein shall be taken to have the same meaning as commonly understood by one of ordinary skill in the art (e.g., in cell culture, molecular genetics, miRNA and detection thereof, immunology, immunohistochemistry, protein chemistry, and biochemistry).
Unless otherwise indicated, the molecular biology techniques utilized in the present disclosure are standard procedures, well known to those skilled in the art. Such techniques are described and explained throughout the literature in sources such as, J. Perbal, A Practical Guide to Molecular Cloning, John Wiley and Sons (1984), J. Sambrook et al, Molecular Cloning: A Laboratory Manual, Cold Spring Harbour Laboratory Press (1989), T.A. Brown (editor), Essential Molecular Biology: A Practical Approach, Volumes 1 and 2, IRL Press (1991), D.M. Glover and B.D. Hames (editors), DNA Cloning: A Practical Approach, Volumes 1-4, IRL Press (1995 and 1996), and F.M. Ausubel et al. (editors), Current Protocols in Molecular Biology, Greene Pub. Associates and Wiley -Interscience (1988, including all updates until present), Ed Harlow and David Lane (editors) Antibodies: A Laboratory Manual, Cold Spring Harbour Laboratory, (1988), J.E. Coligan et al. (editors) Current Protocols in Immunology, John Wiley & Sons (including all updates until present) and Shao-Yao Ying (editor), MicroRNA Protocols, Methods in Molecular Biology, Humana Press (2013).
It must be noted that as used herein and in the appended claims, the singular forms “a”, “an,” and “the” include plural reference unless the context clearly dictates otherwise. Thus, for example, reference to “a nucleic acid sequence” includes a plurality of nucleotides that are formed, reference to “the nucleic acid sequence” is a reference to one or more nucleic acid sequences and equivalents thereof known to those skilled in the art, and so forth.
The term “and/or”, e.g., “X and/or Y” shall be understood to mean either “X and Y” or “X or Y” and shall be taken to provide explicit support for both meanings or for either meaning. The term “about” is used herein to mean within the typical ranges of tolerances in the art. For example, “about” can be understood as about 2 standard deviations from the mean. According to certain embodiments, when referring to a measurable value such as an amount and the like, “about” is meant to encompass variations of ±10%, ±5%, ±1%, ±0.9%, ±0.8%, ±0.7%, ±0.6%, ±0.5%, ±0.4%, ±0.3%, ±0.2% or ±0.1% from the specified value as such variations are appropriate to perform the disclosed methods. When “about” is present before a series of numbers or a range, it is understood that “about” can modify each of the numbers in the series or range.
As used herein, “accuracy” refers to the ability of the method, kit or panel as described herein to discriminate between a target condition in a subject and health in a subject.
An “algorithm,” “formula,” or “model” is any mathematical equation, algorithmic, analytical or programmed process, or statistical technique that takes one or more continuous or categorical inputs (herein called “parameters”) and calculates an output value, sometimes referred to as an “index” or “index value.” Non-limiting examples of “formulas” include sums, ratios, and regression operators, such as coefficients or exponents, biomarker (e.g., miRNAs disclosed herein) value transformations and normalizations (including, without limitation, those normalization schemes based on clinical parameters, such as gender, age, or ethnicity), rules and guidelines, statistical classification models, and neural networks trained on historical populations. Of particular use in combining markers are linear and non-linear equations and statistical classification analyses to determine the relationship between levels of the biomarkers detected in a subject sample and the subject’s risk of disease (for example). In panel and combination construction, of particular interest are structural and syntactic statistical classification algorithms, and methods of risk index construction, utilizing pattern recognition features, including established techniques such as cross correlation, Principal Components Analysis (PCA), factor rotation, Logistic Regression (LogReg), Linear Discriminant Analysis (LDA), Eigengene Linear Discriminant Analysis (ELD A), Support Vector Machines (SVM), Random Forest (RF), Recursive Partitioning Tree (RPART), as well as other related decision tree classification techniques, Shruken Centroids (SC), StepAIC, Kth-Nearest Neighbor, Boosting, Decision Trees, Neural Networks, Bayesian Networks, Support Vector Machines, and Hidden Markov Models, among others. Many of these techniques are useful either combined with a biomarker selection technique, such as forward selection, backwards selection, or stepwise selection, complete enumeration of all potential panels of a given size, genetic algorithms, or they may themselves include biomarker selection methodologies in their own technique. These may be coupled with information criteria, such as Akaike’s Information Criterion (AIC) or Bayes Information Criterion (BIC), in order to quantify the tradeoff between additional biomarkers and model improvement, and to aid in minimizing overfit. The resulting predictive models may be validated in other studies, or cross-validated in the study they were originally trained in, using such techniques as Leave- One-Out (LOO) and 10-Fold cross-validation (10-Fold-CV).
As used herein, an “amplification assay” is an assay that uses purified enzymes to exponentially replicate specific nucleic acids to levels where they can be detected. As the skilled person will appreciate, typically an amplification assay involves the use of oligonucleotide primers which hybridize regions flanking a target sequences, a polymerase, and numerous rounds of producing single stranded nucleic acids (usually by heat denaturation), primer annealing and primer extension using the polymerase. In some embodiments, the amplification assay is performed by using one or a plurality of oligonucleotide primers disclosed in Table 1.
As used herein, the term “animal” includes, but is not limited to, humans and non-human vertebrates such as wild animals, rodents (such as rats), ferrets, mink, companion animals, domesticated animals, and farm animals, such as dogs, cats, horses, pigs, cows, sheep, and goats. In some embodiments, the animal is a mammal. In some embodiments, the animal is a human. In some embodiments, the animal is a non-human mammal.
The term “at least” prior to a number or series of numbers (e.g. “at least two”) is understood to include the number adjacent to the term “at least,” and all subsequent numbers or integers that could logically be included, as clear from context. When “at least” is present before a series of numbers or a range, it is understood that “at least” can modify each of the numbers in the series or range.
The terms “complementary” or “complementarity” refer to polynucleotides (i.e., a sequence of nucleotides) related by base-pairing rules, for example, the sequence “5’-AGT-3’,” is complementary to the sequence “5’-ACT-3\” Complementarity may be “partial,” in which only some of the nucleic acids’ bases are matched according to the base pairing rules, or there may be “complete” or “total” complementarity between the nucleic acids. The degree of complementarity between nucleic acid strands can have significant effects on the efficiency and strength of hybridization between nucleic acid strands under defined conditions. This is of particular importance for methods that depend upon binding between nucleic acid bases. As used herein, the terms “comprising” (and any form of comprising, such as “comprise,” “comprises,” and “comprised”), “having” (and any form of having, such as “have” and “has”), “including” (and any form of including, such as “includes” and “include”), or “containing” (and any form of containing, such as “contains” and “contain”), are inclusive or open-ended and do not exclude additional, unrecited elements or method steps.
The term “diagnosis” as used herein refers to the identification of the nature of an illness (e.g. identification of where a subject has a CoV infection).
The term “prognosis” as used herein refers to the likely course of a medical condition. In some embodiments, prognosis refers to the most likely outcome, timeframes, and/or response to a particular treatment.
As used herein, “expression” refers to the process by which a polynucleotide is transcribed from a DNA template into a miRNA, mRNA or other RNA transcript and/or the process by which a transcribed mRNA is subsequently translated into peptides, polypeptides, or proteins.
The term “label” as used herein refers to any atom or molecule that can be used to provide a detectable (preferably quantifiable) effect, and that can be attached to a nucleic acid or protein. Labels include but are not limited to dyes; radiolabels such as 2P; binding moieties such as biotin; haptens such as digoxgenin; luminogenic, phosphorescent or fluorogenic moieties; and fluorescent dyes alone or in combination with moieties that can suppress or shift emission spectra by fluorescence resonance energy transfer (FRET). Labels may provide signals detectable by fluorescence, radioactivity, colorimetry, gravimetry, X-ray diffraction or absorption, magnetism, enzymatic activity, and the like. A label may be a charged moiety (positive or negative charge) or alternatively, may be charge neutral. Labels can include or consist of nucleic acid or protein sequence, so long as the sequence comprising the label is detectable. In some embodiments, nucleic acids are detected directly without a label (e.g., directly reading a sequence).
The term “level” as used herein refers to qualitative or quantitative determination of the number of copies of a miRNA. In some embodiments, a miRNA transcript exhibits an “increased level” when the level of the miRNA transcript is higher than a reference value as described herein. In some embodiments, a miRNA transcript exhibits a “decreased level” when the level of the miRNA transcript is lower than a reference value as described herein.
The term “machine learning” as used herein encompasses all possible mathematical in silico techniques for creation of useful algorithms from large data sets. The term “algorithm” will be utilized in reference to the clinically useful mathematical equations or computer programs produced by the one or plurality of processes disclosed or executing the one or plurality of processes disclosed. In some embodiments, the performance of machine learning derived algorithms is independent of the specific in silico software routine used for its derivation. If the same training data set is used, techniques as different as supervised learning, unsupervised learning, association rule learning, hierarchical clustering, multiple linear and logistic regressions are likely to produce algorithms whose clinical performance is indistinguishable.
The term “measuring” or “measurement” means assessing the presence, absence, quantity or amount (which can be an effective amount) or determining a “score” as described herein of either a given substance within a clinical or subject-derived sample, including the derivation of qualitative or quantitative concentration levels of such substances. Alternatively, the term “detecting” or “detection” may be used and is understood to cover measuring or measurement as described herein.
The term “normalizing” or “normalized” as used herein with regard to RNA transcript, refers to the level of the RNA transcript, relative to the mean levels of a set or control set of reference RNA transcripts. The reference RNA transcripts are based on their minimal variation across patients, tissues, or treatments. Alternatively, the RNA transcript may be normalized to the totality of tested RNA transcripts, or a subset of such tested RNA transcripts.
The particular use of terms “nucleic acid,” “oligonucleotide,” and “polynucleotide” should in no way be considered limiting and may be used interchangeably herein. “Oligonucleotide” is used when the relevant nucleic acid molecules typically comprise less than about 100 bases. “Polynucleotide” is used when the relevant nucleic acid molecules typically comprise more than about 100 bases. Both terms are used to denote DNA, RNA, modified or synthetic DNA or RNA (including, but not limited to nucleic acids comprising synthetic and naturally-occurring base analogs, dideoxy or other sugars, thiols or other non-natural or natural polymer backbones), or other nucleobase containing polymers capable of hybridizing to DNA and/or RNA. Accordingly, the terms should not be construed to define or limit the length of the nucleic acids referred to and used herein, nor should the terms be used to limit the nature of the polymer backbone to which the nucleobases are attached. The term “nucleic acid sequence” or “polynucleotide sequence” refers to a contiguous string of nucleotide bases and in particular contexts also refers to the particular placement of nucleotide bases in relation to each other as they appear in a polynucleotide.
As used herein “one or more of’ includes at least one of the recited components, or 2, 3, 4, 5, or 5 etc. of the recited components. In some embodiments, the phase includes all of the recited components.
As used herein in the specification and in the claims, the term “or” should be understood to have the same meaning as “and/or” as defined above. For example, when separating items in a list, “or” or “and/or” shall be interpreted as being inclusive, i.e., the inclusion of at least one, but also including more than one, of a number or list of elements, and, optionally, additional unlisted items. Only terms clearly indicated to the contrary, such as “only one of’ or “exactly one of,” or, when used in the claims, “consisting of,” will refer to the inclusion of exactly one element of a number or list of elements. In general, the term “or” as used herein shall only be interpreted as indicating exclusive alternatives (i.e. “one or the other but not both”) when preceded by terms of exclusivity, “either,” “one of,” “only one of,” or “exactly one of.” “Consisting essentially of,” when used in the claims, shall have its ordinary meaning as used in the field of patent law.
Ranges provided herein are understood to include all individual integer values and all subranges within the ranges.
As used herein, the term “performance” relates to the quality and overall usefulness of, e.g., a model, algorithm, or prognostic test. Factors to be considered in model or test performance include, but are not limited to, the clinical and analytical accuracy of the test, use characteristics such as stability of reagents and various components, ease of use of the model or test, health or economic value, and relative costs of various reagents and components of the test. Performing can mean the act of carrying out a function.
As used herein, “precision” refers to the chance that a subject testing positive actually has the condition being tested for.
As used herein, the terms “prevent,” “preventing,” “prevention,” “prophylactic treatment,” and the like, are meant to refer to reducing the probability of developing a disease or condition in a subject, who does not have, but is at risk of or susceptible to developing a disease or condition.
As used herein, the term “sample” or “biological sample” refers to any biological sample that is isolated from a subject. A sample can include, without limitation, a single cell or multiple cells, fragments of cells, an aliquot of body fluid, whole blood, platelets, serum, plasma, red blood cells, white blood cells or leucocytes, endothelial cells, tissue biopsies, synovial fluid, lymphatic fluid, ascites fluid, and interstitial or extracellular fluid. The term “sample” also encompasses the fluid in spaces between cells, including gingival crevicular fluid, bone marrow, cerebrospinal fluid (CSF), saliva, mucous, sputum, semen, sweat, urine, or any other bodily fluids. “Blood sample” can refer to whole blood or any fraction thereof, including blood cells, red blood cells, white blood cells or leucocytes, platelets, serum and plasma. “Respiratory mucosal sample” refers to mucosal sample obtained from any part of the respiratory tract of the subject, which include, but not limited to, anterior nasal swabs/tissues, nasopharyngeal swabs/tissues, and oropharyngeal swabs/tissues. In an embodiment, the respiratory mucosal sample is a nasal mucosal sample. In an embodiment, the respiratory mucosal sample is an anterior nasal mucosal sample. In an embodiment, the sample is collected with a nasal swab. In an embodiment, the sample is collected with a nasal wash. Samples can be obtained from a subject by means including but not limited to venipuncture, excretion, ejaculation, massage, biopsy, needle aspirate, lavage, scraping, surgical incision, or intervention or other means known in the art. In some embodiments, the sample is blood. In some embodiments, the sample is saliva. In some embodiments, the sample is mucous.
As used herein, “sequence identity” is determined by using the stand-alone executable BLAST engine program for blasting two sequences (bl2seq), which can be retrieved from the National Center for Biotechnology Information (NCBI) ftp site, using the default parameters (Tatusova and Madden, 1999). Alternatively, “% sequence identity” can be determined using the EMBOSS Pairwise Alignment Algorithms tool available from The European Bioinformatics Institute (EMBL-EBI), which is part of the European Molecular Biology Laboratory (EMBL). This tool is accessible at the website ebi.ac.uk/Tools/emboss/align/. This tool utilizes the Needleman-Wunsch global alignment algorithm (Needleman and Wunsch (1970); Kruskal (1983)). Default settings are utilized which include Gap Open: 10.0 and Gap Extend 0.5. The default matrix “Blosum62” is utilized for amino acid sequences and the default matrix “DNAfull” is utilized for nucleic acid sequences.
As used herein, the term “statistically significant” means an observed alteration is greater than what would be expected to occur by chance alone (e.g., a “false positive”). Statistical significance can be determined by any of various methods well-known in the art. An example of a commonly used measure of statistical significance is the p-value. The p-value represents the probability of obtaining a given result equivalent to a particular datapoint, where the datapoint is the result of random chance alone. A result is often considered highly significant (not random chance) at a p- value less than or equal to about 0.05.
As used herein, the term “therapeutic” means an agent utilized to treat, combat, ameliorate, prevent or improve an unwanted condition or disease of a patient.
A “therapeutically effective amount” or “effective amount” of a composition is a predetermined amount calculated to achieve the desired effect, i.e., to treat, combat, ameliorate, prevent or improve one or more symptoms of CoV infection. The activity contemplated by the present methods includes both medical therapeutic and/or prophylactic treatment, as appropriate. The specific dose of a compound administered according to the disclosure to obtain therapeutic and/or prophylactic effects will, of course, be determined by the particular circumstances surrounding the case, including, for example, the compound administered, the route of administration, and the condition being treated. It will be understood that the effective amount administered will be determined by the physician in the light of the relevant circumstances including the condition to be treated, the choice of compound to be administered, and the chosen route of administration, and therefore the above dosage ranges are not intended to limit the scope of the present disclosure in any way. A therapeutically effective amount of compounds of embodiments of the disclosure is typically an amount such that when it is administered in a physiologically tolerable excipient composition, it is sufficient to achieve an effective systemic concentration or local concentration in the tissue.
The terms “treat,” “treated,” “treating,” “treatment,” and the like as used herein refer to reducing or ameliorating a disorder and/or symptoms associated therewith (e.g., a viral infection). “Treating” includes the concepts of “alleviating,” which refers to lessening the frequency of occurrence or recurrence, or the severity, of any symptoms or other ill effects related to a virus and/or the side effects associated with viral therapy. The term “treating” also encompasses the concept of “managing” which refers to reducing the severity of a particular disease or disorder in a patient or delaying its recurrence, e.g., lengthening the period of remission in a patient who had suffered from the disease. It is appreciated that, although not precluded, treating a disorder or condition does not require that the disorder, condition, or symptoms associated therewith be completely eliminated. The term “variant” or “variants” as used herein is intended to mean substantially similar sequences. For nucleic acid molecules, a variant comprises a nucleic acid molecule having deletions (i.e., truncations) at the 5’ and/or 3’ end; deletion and/or addition of one or more nucleotides at one or more internal sites in the native polynucleotide; and/or substitution of one or more nucleotides at one or more sites in the native polynucleotide.
As used herein, a “native” nucleic acid molecule or polypeptide comprises a naturally occurring or endogenous nucleotide sequence or amino acid sequence, respectively. Generally, variants of a particular nucleic acid molecule of the disclosure will have at least about 70%, 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99% or more sequence identity to that particular polynucleotide as determined by sequence alignment programs and parameters as described elsewhere herein. In some embodiments, the nucleic acid molecules or the nucleic acid sequences comprise mutations of 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 or more nucleotides.
Coronavirus
As used herein the term "coronavirus", " Coronaviridae" , or "CoV" are used interchangeably and refer to viruses which are enveloped, positive sense, single-stranded RNA viruses. Coronaviruses can cause respiratory, gastrointestinal and neurological disease. There are two subfamilies of Coronaviridae, Letovirinae and Orthocoronavirinae. In some embodiments, the CoV is selected from the genera Alphacoronavirus (alphaCoV), Betacoronavirus (betaCoV), Gammacoronavirus (gammaCoV) and Deltacoronavirus (deltaCoV). In some embodiments, the coronavirus is an alphaCoV. In some embodiments, the coronavirus is a betaCoV. In some embodiments, the coronavirus is a gammaCoV. In some embodiments, the coronavirus is a deltaCoV.
In some embodiments, the alphaCoV is selected from coronavirus 229E (HCoV-229E), human coronavirus NL63 (HCoV-NL63), transmissible gastroenteritis virus (TGEV), porcine epidemic diarrhea virus (PEDV), feline infectious peritonitis virus (FIPV) and canine coronavirus (CCoV). In some embodiments, the betaCoV is selected from severe acute respiratory syndrome- related coronavirus-2 (SARS-Cov-2), human coronavirus HKU1 (HCoV-HKUl), human coronavirus OC43 (HCoV-OC43), severe acute respiratory syndrome-related coronavirus (SARS- CoV), middle-east respiratory syndrome-related coronavirus (MERS-CoV), murine hepatitis virus (MHV) and/or bovine coronavirus (BCoV). In some embodiments, the CoV is capable of infecting a human. In some embodiments, the CoV capable of infecting a human is selected from: SARS-CoV-2, HCoV-OC43, HCoV- HKU1, HCoV-229E, HCoV-NL63, SARS-CoV, and MERS-CoV, or a subtype of variant thereof In some embodiments, the CoV is SARS-CoV-2 or a subtype or variant thereof. Non-limiting examples of the SARS-CoV-2 virus, variants and subtypes thereof are described, for example, in Morais et al. (2020), Zhao et al. (2020), Shen et al. (2020), Tang et al. (2020), Phan et al. (2020) and Khan et al. (2020).
Morais et al. (2020) have identified 6 subtypes: subtype I, subtype II, subtype III, subtype IV, subtype V and subtype VI and 10 tentative subtypes: subtype VII, subtype VIII, subtype IX, subtype X, subtype XI, subtype XII, subtype XIII, subtype XIV, subtype XV and subtype XVI. In some embodiments, the SARS-CoV-2 is a SARS-CoV-2 subtype I. In some embodiments, the SARS-CoV-2 is a SARS-CoV-2 subtype II. In some embodiments, the SARS-CoV-2 is a SARS- CoV-2 subtype III. In some embodiments, the SARS-CoV-2 is a SARS-CoV-2 subtype IV. In some embodiments, the SARS-CoV-2 is a SARS-CoV-2 subtype V. In some embodiments, the SARS-CoV-2 is a SARS-CoV-2 subtype VI. In some embodiments, the SARS-CoV-2 is a SARS- CoV-2 subtype VII. In some embodiments, the SARS-CoV-2 is a SARS-CoV-2 subtype VIII. In some embodiments, the SARS-CoV-2 is a SARS-CoV-2 subtype IX. In some embodiments, the SARS-CoV-2 is a SARS-CoV-2 subtype X. In some embodiments, the SARS-CoV-2 is a SARS- CoV-2 subtype XI. In some embodiments, the SARS-CoV-2 is a SARS-CoV-2 subtype XII. In some embodiments, the SARS-CoV-2 is a SARS-CoV-2 subtype XIII. In some embodiments, the SARS-CoV-2 is a SARS-CoV-2 subtype XIV. In some embodiments, the SARS-CoV-2 is a SARS-CoV-2 subtype XV. In some embodiments, the SARS-CoV-2 is a SARS-CoV-2 subtype XVI.
Foster et al. (2020) have identified 3 SARS-COV-2 variants, A, B and C, based on genomic analysis. In some embodiments, the SARS-CoV-2 is SARS-CoV-2 variant A as described in Foster et al. (2020). In some embodiments, the SARS-CoV-2 is SARS-CoV-2 variant B as described in Foster et al. (2020). In some embodiments, the SARS-CoV-2 is SARS-CoV-2 variant C as described in Foster et al. (2020).
Tang et al. (2020) have identified two SARS-CoV-2 subtypes, subtypes S and F. In some embodiments, the SARS-CoV-2 is SARS-CoV-2 subtype F as described in Tang et al. (2020). In some embodiments, the SARS-CoV-2 is SARS-CoV-2 subtype S as described in Tang et al.
(2020).
In some embodiments, SARS-CoV-2 is SARS-CoV-2 hCoV-19/Australia/VIC01/2020 or a variant thereof. In some embodiments, SARS-COV-2 comprises the sequences as described in NCBI Reference Sequence: NC 045512.2 or a variant thereof. In some embodiments, SARS- CoV-2 comprises the sequence as described in GenBank: MN908947.3 or a variant thereof.
In some embodiments, the SARS-Cov-2 variant is the B.l.1.7 variant, also referred to as lineage B.1.1.7, VOC 202012/01 or 20I/501Y.V1. In some embodiments, the SARS-Cov-2 variant is the B.1.351 variant, also referred to as B.1.351 lineage. In some embodiments, the SARS-Cov- 2 variant is the B.1.1.28 subclade (renamed “P.l”). In some embodiments, the SARS-Cov-2 variant is the B.l.1.7 variant, also referred to as B.l.1.7 lineage or 201/501 Y. VI. In some embodiments, the SARS-Cov-2 variant is the B.1.427 variant. In some embodiments, the SARS- Cov-2 variant is the B.1.429 variant. In some embodiments, the SARS-Cov-2 variant is the B.1.617 variant. In some embodiments, the SARS-Cov-2 variant is the B.1.618 variant. In some embodiments, the SARS-Cov-2 variant is the B.1.1.529 (omicron) variant. In an embodiment, the omicron variant is BA.1. In an embodiment, the omicron variant is BA.2.
In some embodiments, the SARS-CoV-2 variant comprises a genome that is at least about 90% identical to the parental genomic sequence. In some embodiments, the SARS-CoV-2 variant comprises a genome that is at least about 91% identical to the parental genomic sequence. In some embodiments, the SARS-CoV-2 variant comprises a genome that is at least about 92% identical to the parental genomic sequence. In some embodiments, the SARS-CoV-2 variant comprises a genome that is at least about 93% identical to the parental genomic sequence. In some embodiments, the SARS-CoV-2 variant comprises a genome that is at least about 94% identical to the parental genomic sequence. In some embodiments, the SARS-CoV-2 variant comprises a genome that is at least about 95% identical to the parental genomic sequence. In some embodiments, the SARS-CoV-2 variant comprises a genome that is at least about 96% identical to the parental genomic sequence. In some embodiments, the SARS-CoV-2 variant comprises a genome that is at least about 97% identical to the parental genomic sequence. In some embodiments, the SARS-CoV-2 variant comprises a genome that is at least about 98% identical to the parental genomic sequence. In some embodiments, the SARS-CoV-2 variant comprises a genome that is at least about 99% identical to the parental genomic sequence. In some embodiments, the parental genomic sequence is from parental strain SARS-CoV-2 hCoV- 19/Australia/VIC01/2020. In some embodiments, the parental genomic sequence is from parental strain BetaCoV/Wuhan/WIV04/2019. In some embodiments, the parental genomic sequence is the sequences as described in GenBank under the accession No. NC_045512.2. In some embodiments, the parental genomic sequence is the sequences as described in GenBank under the accession No. MN908947.3. In some embodiments, the parental genomic sequence is from parental strain B.l.1.7 variant. In some embodiments, the parental genomic sequence is from parental strain B.1.351 variant. In some embodiments, the parental genomic sequence is from parental strain P.1 variant. In some embodiments, the parental genomic sequence is from parental strain B.l.1.7 variant.
CoV infections can cause respiratory, enteric, hepatic, and neurological diseases in different animal species, including camels, cattle, cats, and bats. CoV can be transmitted from one individual to another through contact of viral droplets with mucosa. Typically, viral droplets are airborne and inhaled via the respiratory tract including the nasal airway. In some embodiments, the individual is a human individual. In some embodiments, the individual is a live stock or domestic animal.
In some embodiments, a CoV infection causes one or more symptoms selected from one or more of: fever, cough, sore throat, shortness of breath, viral shedding respiratory insufficiency, runny nose, nasal congestion, malaise, bronchitis, headache, muscle pain, dyspnea, moderate pneumonia, severe pneumonia, acute respiratory distress syndrome (ARDS). In some embodiments, the ARDS is selected from mild ARDS (defined as 200 mmHg < PaCh/FiCh < 300 mmHg), moderate ARDS (defined as 100 mmHg < PaCh/FiCh < 200 mmHg) and severe ARDS (defined as PaCh/FiCh < 100 mmHg). In some embodiments, the CoV infection cause no symptoms in some members of the population (an individual is asymptomatic).
As used herein, the term “severe coronavirus infection” encompasses any factor, or a symptom thereof, considered by a medical practitioner that would warrant the subject being hospitalised, the subject’s life being at risk, or the subject requiring assistance to breath. Examples of symptoms of a severe response to a CoV infection include, but are not limited to, difficulty breathing or shortness of breath, chest pain or pressure, loss of speech or loss of movement, respiratory distress, respiratory frequency > 30/min, blood oxygen saturation <93% at rest, PaCh/FiCh ratio < 300 mmHg, lung infiltrates > 50% within 24-48 hours, respiratory failure requiring mechanical ventilation, organ failure, requiring intensive care unit monitoring and treatment. A phenotype that displays a predisposition for a severe response to a CoV infection, can, for example, show a higher likelihood that a severe response to a CoV infection will develop in an individual with the phenotype than in members of a relevant general population under a given set of environmental conditions (diet, physical activity regime, geographic location, etc.). In some embodiments, a subject with a severe CoV infection would benefit from an oxygenation treatment. In some embodiments, the oxygenation treatment is selected from one or more of supplemental oxygen, high-flow nasal cannula oxygen, non-invasive positive pressure ventilation, extracorporeal membrane oxygenation and intubation with mechanical ventilation.
In some embodiments, the CoV infection is an early CoV infection. In some embodiments, in an early CoV infection the subject is pre-symptomatic. As used herein, “pre-symptomatic” refers to occurring before symptoms of a condition (e.g., a CoV infection) occur. In some embodiments, pre-symptomatic means occurring before a subject is capable of transmitting the condition to other subjects. In some embodiments, in an early CoV infection the subject is asymptomatic. As used herein, “asymptomatic” refers to subject that has no symptoms of a condition. In some embodiments, an asymptomatic subject is still capable of transmitting the condition to other subjects. In some embodiments, in an early CoV infection, the CoV infection can be detected before the CoV can be detected with an amplification assay. In some embodiments, in an early CoV infection the CoV infection can be detected within 14 days or less from the infection date. In some embodiments, in an early CoV infection the CoV infection can be detected within 12 days or less from the infection date. In some embodiments, in an early CoV infection the CoV infection can be detected within 10 days or less from the infection date. In some embodiments, in an early CoV infection the CoV infection can be detected within 8 days or less from the infection date. In some embodiments, in an early CoV infection the CoV infection can be detected within 6 days or less from the infection date. In some embodiments, in an early CoV infection the CoV infection can be detected within 4 days or less from the infection date. miRNA
As used herein “microRNA” or “miRNA” refers to miRNAs (typically 19-25 nucleotides in length) or a precursor thereof that regulate endogenous gene expression at the post- transcriptional level. miRNA play a role in gene regulation by binding to complementary target messenger RNAs (mRNAs) resulting in target mRNA degradation or translational blockade. In most instances, miRNAs function by interacting with the 3’ untranslated region (3’ UTR) of target mRNAs to induce mRNA degradation and translational repression. The sequences of miRNAs are often conserved across species.
The present disclosure provides a method of determining the likelihood of a CoV infection or a severe CoV infection or for monitoring thereof, the method comprising, in a biological sample from the subject, detecting a level of at least one miRNA listed in Table 1. The present disclosure provides a method of determining the likelihood of a CoV infection or a severe CoV infection or for monitoring thereof, the method comprising, in a biological sample from the subject, detecting a level of at least one miRNA selected from: miR-423-5p, miR-23a-3p, miR-195-5p, miR-766-3p, miR-651-5p, let-7e-5p, miR-1275, miR-3198, miR-627-5p, miR-4662a-5p, miR-3684, let-7a-5p, miR-483-5p, miR-3125, miR-3617-5p, miR-500b-3p, miR-664b-3p, miR-5189-3p, miR-6772-3p, miR-145-3p, miR-30a-5p, miR-27a-5p, miR-2116-3p, miR-31-5p, miR-4772-3p, miR-1290, miR- 1226-3p, miR-589-3p, miR-210-3p, miR-103a-3p, miR-3115, miR-769-3p, miR-320b, miR-193a- 5p, miR-206, miR-873-5p, miR-148a-3p, miR-3913-5p, miR-320a-3p, miR-576-5p, miR-197-3p, miR-4742-3p, miR-28-5p, miR-320c, miR-551b-3p, miR-142-3p, let-7i-3p, miR-92a-3p, miR- 548k, miR-18a-3p, miR-491-5p, miR-6721-5p, miR-6503-3p, miR-3065-3p, miR-150-5p, let-7f- 5p, miR-106b-5p, miR-99b-3p, miR-1273h-3p, let-7a-3p, miR-6741-5p, miR-21-3p, miR-301b- 3p, miR-7-l-3p, miR-148b-5p, miR-874-3p, miR-889-3p, miR-1468-5p, miR-431-3p, miR-345- 5p, miR-339-3p, miR-4433b-5p, miR-542-5p, miR-4750-5p, miR-132-3p, miR-877-5p, miR- 1255b-5p, miR-450b-5p, let-7f-2-3p, miR-30a-3p, miR-23a-5p, miR-215-5p, miR-10a-3p, miR- 98-5p, miR-200c-3p, miR-96-5p, miR-486-5p, miR-30e-5p, miR-141-3p, miR-128-3p, miR-l-3p, miR-500a-3p, let-7b-5p, miR-22-3p, miR-3661, miR-24-3p, miR-340-5p, miR-574-5p, miR-378c, miR-221-3p, miR-181c-3p, miR-130a-3p, miR-1306-3p, miR-95-3p, miR-146a-5p, miR-27b-3p, miR-425-5p, miR-9-5p, miR-4454, miR-148b-3p, miR-599, miR-379-5p, miR-142-5p, miR-451a, miR-191-3p, miR-223-5p, miR-196b-5p, miR-194-5p, let-7d-5p, miR-324-3p, miR-186-5p, miR- 502-3p, miR-423-3p, miR-151b, miR-760, miR-93-3p, miR-382-5p, miR-25-3p, miR-331-3p, miR-29c-3p, miR-1277-5p, miR-320d, miR-769-5p, miR-3064-5p, miR-6529-5p, miR-146b-5p, miR-151a-3p, miR-378a-5p, miR-27a-3p, miR-503-5p, miR-192-5p, miR-147b-3p, miR-182-5p, miR-409-3p, miR-885-3p, miR-106b-3p, miR-125a-3p, miR-542-3p, miR-30d-5p, miR-432-5p, miR-342-3p, miR-200a-3p, miR-30e-3p, miR-1843, miR-26b-3p, miR-26b-5p, miR-499a-5p, miR-200b-5p, miR-125b-l-3p, miR-10b-5p, miR-219a-l-3p, miR-885-5p, miR-574-3p, miR-15b- 3p, miR-449a, let-7b-3p, miR-32-5p, miR-339-5p, miR-130b-3p, miR-2114-5p, miR-19a-3p, miR-155-3p, miR-223-3p, miR-449c-5p, miR-93-5p, miR-3065-5p, miR-628-3p and miR-19a-3p. The sequences of the disclosed miRNAs are provided in Table 1.
In some embodiments, the miRNA is a mammalian miRNA. In some embodiments, the mammal is a placental mammal. In some embodiments, the mammal is a marsupial. In some embodiments, the mammal is a monotreme. In some embodiments, the miRNA is a human miRNA. In some embodiments, the miRNA is a Mustela putorius furo miRNA. Human miRNAs are designated with the prefix “hsa”.
In some embodiments, the method comprises detecting a level of an miRNA selected from one or more of: miR-423-5p, miR-23a-3p, miR-766-3p, miR-651-5p, let-7e-5p, miR-1275, miR- 3198, miR-627-5p, miR-4662a-5p, miR-3684, let-7a-5p, miR-483-5p, miR-3125, miR-3617-5p, miR-500b-3p, miR-664b-3p, miR-5189-3p, miR-6772-3p, miR-145-3p, miR-30a-5p, miR-27a-5p, miR-2116-3p, miR-31-5p, miR-4772-3p, miR-1290, miR-1226-3p, miR-589-3p, miR-210-3p, miR-103a-3p, miR-3115, miR-769-3p, miR-320b, miR-193a-5p, miR-206, miR-873-5p, miR- 148a-3p, miR-3913-5p, miR-320a-3p, miR-576-5p, miR-197-3p, miR-4742-3p, miR-28-5p, miR- 320c, miR-551b-3p, miR-142-3p, let-7i-3p, miR-92a-3p, miR-548k, miR-18a-3p, miR-491-5p, miR-6721-5p, miR-6503-3p, miR-3065-3p, miR-150-5p, let-7f-5p, miR-106b-5p, miR-99b-3p, miR-1273h-3p, let-7a-3p, miR-6741-5p, miR-301b-3p, miR-7-l-3p, miR-148b-5p, miR-874-3p, miR-889-3p, miR-1468-5p, miR-431-3p, miR-345-5p, miR-339-3p, miR-4433b-5p, miR-542-5p, miR-4750-5p, miR-132-3p, miR-877-5p, miR-1255b-5p, miR-450b-5p, let-7f-2-3p, miR-30a-3p, miR-23a-5p, miR-215-5p, miR-10a-3p, miR-98-5p, miR-200c-3p, miR-96-5p, miR-486-5p, miR- 30e-5p, miR-141-3p, miR-128-3p, miR-l-3p, miR-500a-3p, let-7b-5p, miR-22-3p, miR-3661, miR-24-3p, miR-340-5p, miR-574-5p, miR-378c, miR-221-3p, miR-181c-3p, miR-130a-3p, miR- 1306-3p, miR-95-3p, miR-146a-5p, miR-27b-3p, miR-425-5p, miR-9-5p, miR-4454, miR-148b- 3p, miR-599, miR-379-5p, miR-142-5p, miR-451a, miR-191-3p, miR-223-5p, miR-196b-5p, miR-194-5p, let-7d-5p, miR-324-3p, miR-186-5p, miR-502-3p, miR-423-3p, miR-151b, miR- 760, miR-93-3p, miR-382-5p, miR-25-3p, miR-331-3p, miR-29c-3p, miR-1277-5p, miR-320d, miR-769-5p, miR-3064-5p, miR-6529-5p, miR-146b-5p, miR-151a-3p, miR-378a-5p, miR-27a- 3p, miR-503-5p, miR-192-5p, miR-147b-3p, miR-182-5p, miR-409-3p, miR-885-3p, miR-106b- 3p, miR-125a-3p, miR-542-3p, miR-30d-5p, miR-432-5p, miR-342-3p, miR-200a-3p, miR-30e- 3p, miR-1843, miR-26b-3p, miR-26b-5p, miR-499a-5p, miR-200b-5p, miR-125b-l-3p, miR-lOb- 5p, miR-219a-l-3p, miR-885-5p, miR-574-3p, miR-15b-3p, miR-449a, let-7b-3p, miR-32-5p, miR-339-5p, miR-130b-3p, miR-2114-5p, miR-19a-3p, miR-155-3p, miR-223-3p, miR-449c-5p, miR-93-5p, miR-3065-5p, miR-628-3p and miR-19a-3p.
In some embodiments, the method comprises detecting a level of an miRNA selected from one or more of: miR-423-5p, miR-23a-3p, miR-195-5p, miR-766-3p, miR-651-5p, let-7e-5p, miR- 1275, miR-3198, miR-627-5p, miR-4662a-5p, miR-3684, let-7a-5p, miR-483-5p, miR-3125, miR- 3617-5p, miR-500b-3p, miR-664b-3p, miR-5189-3p, miR-6772-3p, miR-145-3p, miR-30a-5p, miR-27a-5p, miR-2116-3p, miR-31-5p, miR-4772-3p, miR-1290, miR-1226-3p, miR-589-3p, miR-210-3p, miR-103a-3p, miR-3115, miR-769-3p, miR-320b, miR-193a-5p, miR-206, miR- 873-5p, miR-148a-3p, miR-3913-5p, miR-320a-3p, miR-576-5p, miR-197-3p, miR-4742-3p, miR-28-5p, miR-320c, miR-551b-3p, miR-142-3p, let-7i-3p, miR-92a-3p, miR-548k, miR-18a- 3p, miR-491-5p, miR-6721-5p, miR-6503-3p, miR-3065-3p, miR-150-5p, and let-7f-5p.
In some embodiments, the method comprises detecting a level of an miRNA selected from one or more of: miR-423-5p, miR-23a-3p, miR-766-3p, miR-651-5p, let-7e-5p, miR-1275, miR- 3198, miR-627-5p, miR-4662a-5p, miR-3684, let-7a-5p, miR-483-5p, miR-3125, miR-3617-5p, miR-500b-3p, miR-664b-3p, miR-5189-3p, miR-6772-3p, miR-145-3p, miR-30a-5p, miR-27a-5p, miR-2116-3p, miR-31-5p, miR-4772-3p, miR-1290, miR-1226-3p, miR-589-3p, miR-210-3p, miR-103a-3p, miR-3115, miR-769-3p, miR-320b, miR-193a-5p, miR-206, miR-873-5p, miR- 148a-3p, miR-3913-5p, miR-320a-3p, miR-576-5p, miR-197-3p, miR-4742-3p, miR-28-5p, miR- 320c, miR-551b-3p, miR-142-3p, let-7i-3p, miR-92a-3p, miR-548k, miR-18a-3p, miR-491-5p, miR-6721-5p, miR-6503-3p, miR-3065-3p, miR-150-5p, and let-7f-5p.
In some embodiments, the method at least comprises detecting a level of miR-423-5p. In some embodiments, the method at least comprises detecting a level of miR-195-5p. In some embodiments, the method at least comprises detecting a level of miR-23a-3p and miR-195-5p. In some embodiments, the method at least comprises detecting a level of miR-28-5p. In some embodiments, the method at least comprises detecting a level of miR-223-5p. In some embodiments, the method at least comprises detecting a level of miR-130b-3p. In some embodiments, the method at least comprises detecting a level of miR-23a-3p and miR-423-5p. In some embodiments, the method at least comprises detecting a level of miR-423-5p and miR-195- 5p. In some embodiments, the method at least comprises detecting a level of miR-423-5p, miR- 195-5p and miR-23a-3p. In some embodiments, the method at least comprises detecting a level of miR-423-5p, miR-195-5p, miR-23a-3p and miR-28-5p. In some embodiments, the method at least comprises detecting a level of miR-423-5p, miR-195-5p, miR-23a-3p and miR-223-5p. In some embodiments, the method at least comprises detecting a level of miR-423-5p, miR-195-5p, miR- 23a-3p, miR-28-5p and miR-223-5p. In some embodiments, the method at least comprises detecting a level of miR-423-5p, miR-195-5p, miR-23a-3p and miR-130b-3p. In some embodiments, the method at least comprises detecting a level of miR-423-5p, miR-195-5p, miR- 23a-3p, miR-28-5p, miR-223-5p and miR-130b-3p.
In some embodiments, when the method comprises detecting miR-195-5p the method further comprises detecting miR-23a-3p. In some embodiments, when the method comprises detecting miR-195-5p the method further comprises detecting miR-423-5p.
In some embodiments, the method comprises detecting a level of miR-423-5p, miR-195- 5p, miR-23a-3p, miR-28-5p and miR-223-5p. In some embodiments, the method comprises detecting a level of miR-423-5p, miR-195-5p, miR-23a-3p, miR-28-5p, miR-223-5p and miR- 130b-3p.
In some embodiments, when the method comprises detecting miR-195-5p the method further comprises detecting one or more of: miR-423-5p, miR-23a-3p, miR-766-3p, miR-651-5p, let-7e-5p, miR-1275, miR-3198, miR-627-5p, miR-4662a-5p, miR-3684, let-7a-5p, miR-483-5p, miR-3125, miR-3617-5p, miR-500b-3p, miR-664b-3p, miR-5189-3p, miR-6772-3p, miR-145-3p, miR-30a-5p, miR-27a-5p, miR-2116-3p, miR-31-5p, miR-4772-3p, miR-1290, miR-1226-3p, miR-589-3p, miR-210-3p, miR-103a-3p, miR-3115, miR-769-3p, miR-320b, miR-193a-5p, miR- 206, miR-873-5p, miR-148a-3p, miR-3913-5p, miR-320a-3p, miR-576-5p, miR-197-3p, miR- 4742-3p, miR-28-5p, miR-320c, miR-551b-3p, miR-142-3p, let-7i-3p, miR-92a-3p, miR-548k, miR-18a-3p, miR-491-5p, miR-6721-5p, miR-6503-3p, miR-3065-3p, miR-150-5p, let-7f-5p, miR-106b-5p, miR-99b-3p, miR-1273h-3p, let-7a-3p, miR-6741-5p, miR-21-3p, miR-301b-3p, miR-7-l-3p, miR-148b-5p, miR-874-3p, miR-889-3p, miR-1468-5p, miR-431-3p, miR-345-5p, miR-339-3p, miR-4433b-5p, miR-542-5p, miR-4750-5p, miR-132-3p, miR-877-5p, miR-1255b- 5p, miR-450b-5p, let-7f-2-3p, miR-30a-3p, miR-23a-5p, miR-215-5p, miR-10a-3p, miR-98-5p, miR-200c-3p, miR-96-5p, miR-486-5p, miR-30e-5p, miR-141-3p, miR-128-3p, miR-l-3p, miR- 500a-3p, let-7b-5p, miR-22-3p, miR-3661, miR-24-3p, miR-340-5p, miR-574-5p, miR-378c, miR-221-3p, miR-181c-3p, miR-130a-3p, miR-1306-3p, miR-95-3p, miR-146a-5p, miR-27b-3p, miR-425-5p, miR-9-5p, miR-4454, miR-148b-3p, miR-599, miR-379-5p, miR-142-5p, miR-451a, miR-191-3p, miR-223-5p, miR-196b-5p, miR-194-5p, let-7d-5p, miR-324-3p, miR-186-5p, miR- 502-3p, miR-423-3p, miR-151b, miR-760, miR-93-3p, miR-382-5p, miR-25-3p, miR-331-3p, miR-29c-3p, miR-1277-5p, miR-320d, miR-769-5p, miR-3064-5p, miR-6529-5p, miR-146b-5p, miR-151a-3p, miR-378a-5p, miR-27a-3p, miR-503-5p, miR-192-5p, miR-147b-3p, miR-182-5p, miR-409-3p, miR-885-3p, miR-106b-3p, miR-125a-3p, miR-542-3p, miR-30d-5p, miR-432-5p, miR-342-3p, miR-200a-3p, miR-30e-3p, miR-1843, miR-26b-3p, miR-26b-5p, miR-499a-5p, miR-200b-5p, miR-125b-l-3p, miR-10b-5p, miR-219a-l-3p, miR-885-5p, miR-574-3p, miR-15b- 3p, miR-449a, let-7b-3p, miR-32-5p, miR-339-5p, miR-130b-3p, miR-2114-5p, miR-19a-3p, miR-155-3p, miR-223-3p, and miR-449c-5p.
In some embodiments, when the method comprises detecting miR-21-3p the method further comprises detecting one or more of: miR-423-5p, miR-195-5p miR-766-3p, miR-651-5p, let-7e- 5p, miR-1275, miR-3198, miR-627-5p, miR-4662a-5p, miR-3684, let-7a-5p, miR-483-5p, miR- 3125, miR-3617-5p, miR-500b-3p, miR-664b-3p, miR-5189-3p, miR-6772-3p, miR-145-3p, miR- 30a-5p, miR-27a-5p, miR-2116-3p, miR-31-5p, miR-4772-3p, miR-1290, miR-1226-3p, miR- 589-3p, miR-210-3p, miR-103a-3p, miR-3115, miR-769-3p, miR-320b, miR-193a-5p, miR-206, miR-873-5p, miR-148a-3p, miR-3913-5p, miR-320a-3p, miR-576-5p, miR-197-3p, miR-4742-3p, miR-28-5p, miR-320c, miR-551b-3p, miR-142-3p, let-7i-3p, miR-92a-3p, miR-548k, miR-18a- 3p, miR-491-5p, miR-6721-5p, miR-6503-3p, miR-3065-3p, miR-150-5p, let-7f-5p, miR-106b- 5p, miR-99b-3p, miR-1273h-3p, let-7a-3p, miR-6741-5p, miR-301b-3p, miR-7-l-3p, miR-148b- 5p, miR-874-3p, miR-889-3p, miR-1468-5p, miR-431-3p, miR-345-5p, miR-339-3p, miR-4433b- 5p, miR-542-5p, miR-4750-5p, miR-132-3p, miR-877-5p, miR-1255b-5p, miR-450b-5p, let-7f-2- 3p, miR-30a-3p, miR-23a-5p, miR-215-5p, miR-10a-3p, miR-98-5p, miR-200c-3p, miR-96-5p, miR-486-5p, miR-30e-5p, miR-141-3p, miR-128-3p, miR-l-3p, miR-500a-3p, let-7b-5p, miR-22- 3p, miR-3661, miR-24-3p, miR-340-5p, miR-574-5p, miR-378c, miR-221-3p, miR-181c-3p, miR- 130a-3p, miR-1306-3p, miR-95-3p, miR-146a-5p, miR-27b-3p, miR-425-5p, miR-9-5p, miR- 4454, miR-148b-3p, miR-599, miR-379-5p, miR-142-5p, miR-451a, miR-191-3p, miR-223-5p, miR-196b-5p, miR-194-5p, let-7d-5p, miR-324-3p, miR-186-5p, miR-502-3p, miR-423-3p, miR- 151b, miR-760, miR-93-3p, miR-382-5p, miR-25-3p, miR-331-3p, miR-29c-3p, miR-1277-5p, miR-320d, miR-769-5p, miR-3064-5p, miR-6529-5p, miR-146b-5p, miR-151a-3p, miR-378a-5p, miR-27a-3p, miR-503-5p, miR-192-5p, miR-147b-3p, miR-182-5p, miR-409-3p, miR-885-3p, miR-106b-3p, miR-125a-3p, miR-542-3p, miR-30d-5p, miR-432-5p, miR-342-3p, miR-200a-3p, miR-30e-3p, miR-1843, miR-26b-3p, miR-26b-5p, miR-499a-5p, miR-200b-5p, miR-125b-l-3p, miR-10b-5p, miR-219a-l-3p, miR-885-5p, miR-574-3p, miR-15b-3p, miR-449a, let-7b-3p, miR- 32-5p, miR-339-5p, miR-130b-3p, miR-2114-5p, miR-19a-3p, miR-155-3p, miR-223-3p, and miR-449c-5p.
In some embodiments, when the method comprises detecting miR-423-5p the method further comprises detecting one or more of: miR-23a-3p, miR-195-5p, miR-766-3p, miR-651-5p, let-7e-5p, miR-1275, miR-3198, miR-627-5p, miR-4662a-5p, miR-3684, let-7a-5p, miR-483-5p, miR-3125, miR-3617-5p, miR-500b-3p, miR-664b-3p, miR-5189-3p, miR-6772-3p, miR-145-3p, miR-30a-5p, miR-27a-5p, miR-2116-3p, miR-31-5p, miR-4772-3p, miR-1290, miR-1226-3p, miR-589-3p, miR-210-3p, miR-103a-3p, miR-3115, miR-769-3p, miR-320b, miR-193a-5p, miR- 206, miR-873-5p, miR-148a-3p, miR-3913-5p, miR-320a-3p, miR-576-5p, miR-197-3p, miR- 4742-3p, miR-28-5p, miR-320c, miR-551b-3p, miR-142-3p, let-7i-3p, miR-92a-3p, miR-548k, miR-18a-3p, miR-491-5p, miR-6721-5p, miR-6503-3p, miR-3065-3p, miR-150-5p, let-7f-5p, miR-106b-5p, miR-99b-3p, miR-1273h-3p, let-7a-3p, miR-6741-5p, miR-21-3p, miR-301b-3p, miR-7-l-3p, miR-148b-5p, miR-874-3p, miR-889-3p, miR-1468-5p, miR-431-3p, miR-345-5p, miR-339-3p, miR-4433b-5p, miR-542-5p, miR-4750-5p, miR-132-3p, miR-877-5p, miR-1255b- 5p, miR-450b-5p, let-7f-2-3p, miR-30a-3p, miR-23a-5p, miR-215-5p, miR-10a-3p, miR-98-5p, miR-200c-3p, miR-96-5p, miR-486-5p, miR-30e-5p, miR-141-3p, miR-128-3p, miR-l-3p, miR- 500a-3p, let-7b-5p, miR-22-3p, miR-3661, miR-24-3p, miR-340-5p, miR-574-5p, miR-378c, miR-221-3p, miR-181c-3p, miR-130a-3p, miR-1306-3p, miR-95-3p, miR-146a-5p, miR-27b-3p, miR-425-5p, miR-9-5p, miR-4454, miR-148b-3p, miR-599, miR-379-5p, miR-142-5p, miR-451a, miR-191-3p, miR-223-5p, miR-196b-5p, miR-194-5p, let-7d-5p, miR-324-3p, miR-186-5p, miR- 502-3p, miR-423-3p, miR-151b, miR-760, miR-93-3p, miR-382-5p, miR-25-3p, miR-331-3p, miR-29c-3p, miR-1277-5p, miR-320d, miR-769-5p, miR-3064-5p, miR-6529-5p, miR-146b-5p, miR-151a-3p, miR-378a-5p, miR-27a-3p, miR-503-5p, miR-192-5p, miR-147b-3p, miR-182-5p, miR-409-3p, miR-885-3p, miR-106b-3p, miR-125a-3p, miR-542-3p, miR-30d-5p, miR-432-5p, miR-342-3p, miR-200a-3p, miR-30e-3p, miR-1843, miR-26b-3p, miR-26b-5p, miR-499a-5p, miR-200b-5p, miR-125b-l-3p, miR-10b-5p, miR-219a-l-3p, miR-885-5p, miR-574-3p, miR-15b- 3p, miR-449a, let-7b-3p, miR-32-5p, miR-339-5p, miR-130b-3p, miR-2114-5p, miR-19a-3p, miR-155-3p, miR-223-3p, and miR-449c-5p. In some embodiments, the method comprises detecting the level of miR-142-3p and 3065- 3p.
In some embodiments, when the method comprises detecting miR-142-3p the method further comprises detecting one or more of: miR-423-5p, miR-23a-3p, miR-766-3p, miR-651-5p, let-7e-5p, miR-1275, miR-3198, miR-627-5p, miR-4662a-5p, miR-3684, let-7a-5p, miR-483-5p, miR-3125, miR-3617-5p, miR-500b-3p, miR-664b-3p, miR-5189-3p, miR-6772-3p, miR-145-3p, miR-30a-5p, miR-27a-5p, miR-2116-3p, miR-31-5p, miR-4772-3p, miR-1290, miR-1226-3p, miR-589-3p, miR-210-3p, miR-103a-3p, miR-3115, miR-769-3p, miR-320b, miR-193a-5p, miR- 206, miR-873-5p, miR-148a-3p, miR-3913-5p, miR-320a-3p, miR-576-5p, miR-197-3p, miR- 4742-3p, miR-28-5p, miR-320c, miR-551b-3p, let-7i-3p, miR-92a-3p, miR-548k, miR-18a-3p, miR-491-5p, miR-6721-5p, miR-6503-3p, miR-3065-3p, miR-150-5p, let-7f-5p, miR-106b-5p, miR-99b-3p, miR-1273h-3p, let-7a-3p, miR-6741-5p, miR-301b-3p, miR-7-l-3p, miR-148b-5p, miR-874-3p, miR-889-3p, miR-1468-5p, miR-431-3p, miR-345-5p, miR-339-3p, miR-4433b-5p, miR-542-5p, miR-4750-5p, miR-132-3p, miR-877-5p, miR-1255b-5p, miR-450b-5p, let-7f-2-3p, miR-30a-3p, miR-23a-5p, miR-215-5p, miR-10a-3p, miR-98-5p, miR-200c-3p, miR-96-5p, miR- 486-5p, miR-30e-5p, miR-141-3p, miR-128-3p, miR-l-3p, miR-500a-3p, let-7b-5p, miR-22-3p, miR-3661, miR-24-3p, miR-340-5p, miR-574-5p, miR-378c, miR-221-3p, miR-181c-3p, miR- 130a-3p, miR-1306-3p, miR-95-3p, miR-146a-5p, miR-27b-3p, miR-425-5p, miR-9-5p, miR- 4454, miR-148b-3p, miR-599, miR-379-5p, miR-142-5p, miR-451a, miR-191-3p, miR-223-5p, miR-196b-5p, miR-194-5p, let-7d-5p, miR-324-3p, miR-186-5p, miR-502-3p, miR-423-3p, miR- 151b, miR-760, miR-93-3p, miR-382-5p, miR-25-3p, miR-331-3p, miR-29c-3p, miR-1277-5p, miR-320d, miR-769-5p, miR-3064-5p, miR-6529-5p, miR-146b-5p, miR-151a-3p, miR-378a-5p, miR-27a-3p, miR-503-5p, miR-192-5p, miR-147b-3p, miR-182-5p, miR-409-3p, miR-885-3p, miR-106b-3p, miR-125a-3p, miR-542-3p, miR-30d-5p, miR-432-5p, miR-342-3p, miR-200a-3p, miR-30e-3p, miR-1843, miR-26b-3p, miR-26b-5p, miR-499a-5p, miR-200b-5p, miR-125b-l-3p, miR-10b-5p, miR-219a-l-3p, miR-885-5p, miR-574-3p, miR-15b-3p, miR-449a, let-7b-3p, miR- 32-5p, miR-339-5p, miR-130b-3p, miR-2114-5p, miR-19a-3p, miR-155-3p, miR-223-3p, miR- 449c-5p, miR-93-5p, miR-3065-5p, miR-628-3p and miR-19a-3p.
In some embodiments, when the method comprises detecting miR-3065-3p the method further comprises detecting one or more of: miR-423-5p, miR-23a-3p, miR-766-3p, miR-651-5p, let-7e-5p, miR-1275, miR-3198, miR-627-5p, miR-4662a-5p, miR-3684, let-7a-5p, miR-483-5p, miR-3125, miR-3617-5p, miR-500b-3p, miR-664b-3p, miR-5189-3p, miR-6772-3p, miR-145-3p, miR-30a-5p, miR-27a-5p, miR-2116-3p, miR-31-5p, miR-4772-3p, miR-1290, miR-1226-3p, miR-589-3p, miR-210-3p, miR-103a-3p, miR-3115, miR-769-3p, miR-320b, miR-193a-5p, miR- 206, miR-873-5p, miR-148a-3p, miR-3913-5p, miR-320a-3p, miR-576-5p, miR-197-3p, miR- 4742-3p, miR-28-5p, miR-320c, miR-551b-3p, miR-142-3p, let-7i-3p, miR-92a-3p, miR-548k, miR-18a-3p, miR-491-5p, miR-6721-5p, miR-6503-3p, miR-150-5p, let-7f-5p, miR-106b-5p, miR-99b-3p, miR-1273h-3p, let-7a-3p, miR-6741-5p, miR-301b-3p, miR-7-l-3p, miR-148b-5p, miR-874-3p, miR-889-3p, miR-1468-5p, miR-431-3p, miR-345-5p, miR-339-3p, miR-4433b-5p, miR-542-5p, miR-4750-5p, miR-132-3p, miR-877-5p, miR-1255b-5p, miR-450b-5p, let-7f-2-3p, miR-30a-3p, miR-23a-5p, miR-215-5p, miR-10a-3p, miR-98-5p, miR-200c-3p, miR-96-5p, miR- 486-5p, miR-30e-5p, miR-141-3p, miR-128-3p, miR-l-3p, miR-500a-3p, let-7b-5p, miR-22-3p, miR-3661, miR-24-3p, miR-340-5p, miR-574-5p, miR-378c, miR-221-3p, miR-181c-3p, miR- 130a-3p, miR-1306-3p, miR-95-3p, miR-146a-5p, miR-27b-3p, miR-425-5p, miR-9-5p, miR- 4454, miR-148b-3p, miR-599, miR-379-5p, miR-142-5p, miR-451a, miR-191-3p, miR-223-5p, miR-196b-5p, miR-194-5p, let-7d-5p, miR-324-3p, miR-186-5p, miR-502-3p, miR-423-3p, miR- 151b, miR-760, miR-93-3p, miR-382-5p, miR-25-3p, miR-331-3p, miR-29c-3p, miR-1277-5p, miR-320d, miR-769-5p, miR-3064-5p, miR-6529-5p, miR-146b-5p, miR-151a-3p, miR-378a-5p, miR-27a-3p, miR-503-5p, miR-192-5p, miR-147b-3p, miR-182-5p, miR-409-3p, miR-885-3p, miR-106b-3p, miR-125a-3p, miR-542-3p, miR-30d-5p, miR-432-5p, miR-342-3p, miR-200a-3p, miR-30e-3p, miR-1843, miR-26b-3p, miR-26b-5p, miR-499a-5p, miR-200b-5p, miR-125b-l-3p, miR-10b-5p, miR-219a-l-3p, miR-885-5p, miR-574-3p, miR-15b-3p, miR-449a, let-7b-3p, miR- 32-5p, miR-339-5p, miR-130b-3p, miR-2114-5p, miR-19a-3p, miR-155-3p, miR-223-3p, miR- 449c-5p, miR-93-5p, miR-3065-5p, miR-628-3p and miR-19a-3p.
In some embodiments, the method comprises detecting a level of miR-142-3p. In some embodiments, the method comprises detecting a level of miR-3065-3p. In some embodiments, the method comprises detecting a level of miR-93-5p.
In some embodiments, the method comprises detecting a level of miR-486-5p. In some embodiments, the method comprises detecting a level of miR-451a. In some embodiments, the method comprises detecting a level of miR-3065-5p. In some embodiments, the method comprises detecting a level of miR-628-3p. In some embodiments, the method comprises detecting a level of miR-19a-3p. In some embodiments, the method comprises detecting a level of miR-142-3p and miR-93- 5p. In some embodiments, the method comprises detecting a level of miR-142-3p and miR-3065- 3p.
In some embodiments, the method comprises detecting a level of miR-142-3p, miR-3065- 3p, and miR-93-5p. In some embodiments, the method comprises detecting a level of miR-142- 3p, miR-3065-3p, miR-93-5p and miR-486-5p. In some embodiments, the method comprises detecting a level of miR-142-3p, miR-3065-3p, miR-93-5p and miR-451a. In some embodiments, the method comprises detecting a level of miR-142-3p, miR-3065-3p, miR-93-5p and miR-3065- 5p. In some embodiments, the method comprises detecting a level of miR-142-3p, miR-3065-3p, miR-93-5p and miR-628-3p. In some embodiments, the method comprises detecting a level of miR-142-3p, miR-3065-3p, miR-93-5p and miR-19a-3p.
In some embodiments, the method comprises detecting a level of miR-142-3p, miR-3065- 3p, miR-93-5p, miR-486-5p, miR-451a, miR-3065-5p, miR-628-3p, and miR-19a-3p.
In some embodiments, the method comprises detecting a level of an miRNA selected from one or more of: let-7e-5p, miR-766-3p, miR-651-5p, miR-4433b-5p, miR-106b-5p, miR-99b-3p, miR-1273h-3p, let-7a-3p, miR-6741-5p, miR-21-3p, miR-301b-3p, miR-7-l-3p, miR-148b-5p, miR-576-5p, miR-3198, miR-4662a-5p, miR-6772-3p, miR-197-3p, miR-874-3p, miR-889-3p, miR-92a-3p, miR-500b-3p, miR-769-3p, miR-1468-5p, miR-431-3p, miR-345-5p, miR-339-3p, miR-103a-3p, miR-30a-5p, miR-6503-3p, miR-542-5p, miR-627-5p, miR-4750-5p, miR-132-3p, miR-877-5p, miR-1255b-5p, miR-1290, miR-450b-5p, let-7f-2-3p, miR-30a-3p, miR-23a-5p, miR-215-5p, miR-10a-3p, miR-150-5p, miR-206, miR-195-5p, miR-320a-3p, miR-483-5p, miR- 5189-3p, miR-98-5p, miR-200c-3p, miR-664b-3p, miR-19a-3p, miR-155-3p, miR-223-3p, and miR-449c-5p.
In some embodiments, the method comprises detecting a level of an miRNA selected from one or more of: let-7e-5p, miR-766-3p, miR-651-5p, miR-4433b-5p, miR-106b-5p, miR-99b-3p, miR-1273h-3p, let-7a-3p, miR-6741-5p, miR-301b-3p, miR-7-l-3p, miR-148b-5p, miR-576-5p, miR-3198, miR-4662a-5p, miR-6772-3p, miR-197-3p, miR-874-3p, miR-889-3p, miR-92a-3p, miR-500b-3p, miR-769-3p, miR-1468-5p, miR-431-3p, miR-345-5p, miR-339-3p, miR-103a-3p, miR-30a-5p, miR-6503-3p, miR-542-5p, miR-627-5p, miR-4750-5p, miR-132-3p, miR-877-5p, miR-1255b-5p, miR-1290, miR-450b-5p, let-7f-2-3p, miR-30a-3p, miR-23a-5p, miR-215-5p, miR-10a-3p, miR-150-5p, miR-206, miR-320a-3p, miR-483-5p, miR-5189-3p, miR-98-5p, miR- 200c-3p, miR-664b-3p, and miR-96-5p.
In some embodiments, the method comprises detecting a level of an miRNA selected from one or more of: let-7e-5p, miR-766-3p, miR-651-5p, miR-4433b-5p, miR-106b-5p, miR-99b-3p, miR-1273h-3p, let-7a-3p, miR-6741-5p, miR-21-3p, miR-301b-3p, miR-7-l-3p, miR-148b-5p, miR-576-5p, miR-3198, miR-4662a-5p, miR-6772-3p, miR-197-3p, miR-874-3p, miR-889-3p, miR-92a-3p, miR-500b-3p, miR-769-3p, miR-1468-5p, miR-431-3p, miR-345-5p, miR-339-3p, miR-103a-3p, miR-30a-5p, miR-6503-3p, miR-542-5p, miR-627-5p, miR-4750-5p, miR-132-3p, miR-877-5p, miR-1255b-5p, miR-1290, miR-450b-5p, let-7f-2-3p, miR-30a-3p, miR-23a-5p, and miR-215-5p.
In some embodiments, the method comprises detecting a level of an miRNA selected from one or more of: let-7e-5p, miR-766-3p, miR-651-5p, miR-4433b-5p, miR-106b-5p, miR-99b-3p, miR-1273h-3p, let-7a-3p, miR-6741-5p, miR-301b-3p, miR-7-l-3p, miR-148b-5p, miR-576-5p, miR-3198, miR-4662a-5p, miR-6772-3p, miR-197-3p, miR-874-3p, miR-889-3p, miR-92a-3p, miR-500b-3p, miR-769-3p, miR-1468-5p, miR-431-3p, miR-345-5p, miR-339-3p, miR-103a-3p, miR-30a-5p, miR-6503-3p, miR-542-5p, miR-627-5p, miR-4750-5p, miR-132-3p, miR-877-5p, miR-1255b-5p, miR-1290, miR-450b-5p, let-7f-2-3p, miR-30a-3p, miR-23a-5p, and miR-215-5p.
In some embodiments, the method comprises detecting a level of an miRNA selected from one or more of: let-7e-5p, miR-766-3p, miR-651-5p, miR-4433b-5p, miR-10a-3p, miR-150-5p, miR-206, miR-195-5p, miR-320a-3p, miR-483-5p, miR-5189-3p, miR-98-5p, miR-200c-3p, miR- 664b-3p, and miR-96-5p.
In some embodiments, the method comprises detecting a level of an miRNA selected from one or more of: let-7e-5p, miR-766-3p, miR-651-5p, miR-4433b-5p, miR-10a-3p, miR-150-5p, miR-206, miR-320a-3p, miR-483-5p, miR-5189-3p, miR-98-5p, miR-200c-3p, miR-664b-3p, and miR-96-5p.
In some embodiments, the method at least comprises detecting a level of let-7e-5p, miR- 651-5p, miR-766-3p, and miR-4433b-5p. In some embodiments, the method at least comprises detecting a level of let-7e-5p, miR-651-5p, and miR-766-3p.
In some embodiments, the method comprises detecting a level of an miRNA selected from one or more of: miR-3661, miR-379-5p, miR-382-5p, miR-599, miR-30a-3p, miR-130a-3p, miR- 210-3p, miR-181c-3p, miR-141-3p, miR-l-3p, miR-125a-3p, miR-1277-5p, miR-21-3p, miR- 885-5p, miR-409-3p, miR-378c, miR-340-5p, miR-195-5p, miR-219a-l-3p, miR-125b-l-3p, miR-10a-3p, miR-3064-5p, miR-200b-5p, miR-885-3p, miR-151b, let-7b-3p, miR-331-3p, miR- 574-5p, miR-432-5p, miR-1843, let-7e-5p, miR-4454, miR-147b-3p, miR-95-3p, miR-30e-3p, miR-26b-3p, miR-542-3p, miR-22-3p, miR-32-5p, miR-500a-3p, miR-182-5p, miR-378a-5p, miR-223-5p, miR-30e-5p, miR-191-3p, miR-196b-5p, miR-499a-5p, miR-30a-5p, miR-574-3p, miR-146b-5p, miR-24-3p, miR-200a-3p, miR-760, miR-502-3p, miR-192-5p, miR-221-3p, let- 7d-5p, miR-194-5p, miR-320d, miR-146a-5p, miR-27a-3p, miR-320c, miR-142-5p, miR-27b-3p, miR-19a-3p, miR-215-5p, miR-6529-5p, miR-130b-3p, miR-151a-3p, miR-26b-5p, miR-320b, miR-320a-3p, let-7f-5p, miR-339-5p, miR-30d-5p, miR-449a, miR-9-5p, miR-2114-5p, miR-486- 5p, miR-18a-3p, miR-92a-3p, miR-450b-5p, miR-423-5p, miR-93-3p, miR-451a, miR-1306-3p, miR-197-3p, miR-324-3p, miR-128-3p, miR-769-5p, miR-150-5p, miR-503-5p, miR-148b-3p, miR-425-5p, let-7b-5p, miR-10b-5p, miR-29c-3p, miR-423-3p, miR-106b-3p, miR-25-3p, miR- 15b-3p, miR-342-3p and miR-186-5p.
In some embodiments, the method comprises detecting a level of an miRNA selected from one or more of: miR-3661, miR-379-5p, miR-382-5p, miR-599, miR-30a-3p, miR-130a-3p, miR- 210-3p, miR-181c-3p, miR-141-3p, miR-l-3p, miR-125a-3p, miR-1277-5p, miR-885-5p, miR- 409-3p, miR-378c, miR-340-5p, miR-219a-l-3p, miR-125b-l-3p, miR-10a-3p, miR-3064-5p, miR-200b-5p, miR-885-3p, miR-151b, let-7b-3p, miR-331-3p, miR-574-5p, miR-432-5p, miR- 1843, let-7e-5p, miR-4454, miR-147b-3p, miR-95-3p, miR-30e-3p, miR-26b-3p, miR-542-3p, miR-22-3p, miR-32-5p, miR-500a-3p, miR-182-5p, miR-378a-5p, miR-223-5p, miR-30e-5p, miR-191-3p, miR-196b-5p, miR-499a-5p, miR-30a-5p, miR-574-3p, miR-146b-5p, miR-24-3p, miR-200a-3p, miR-760, miR-502-3p, miR-192-5p, miR-221-3p, let-7d-5p, miR-194-5p, miR- 320d, miR-146a-5p, miR-27a-3p, miR-320c, miR-142-5p, miR-27b-3p, miR-19a-3p, miR-215-5p, miR-6529-5p, miR-130b-3p, miR-151a-3p, miR-26b-5p, miR-320b, miR-320a-3p, let-7f-5p, miR-339-5p, miR-30d-5p, miR-449a, miR-9-5p, miR-2114-5p, miR-486-5p, miR-18a-3p, miR- 92a-3p, miR-450b-5p, miR-423-5p, miR-93-3p, miR-451a, miR-1306-3p, miR-197-3p, miR-324- 3p, miR-128-3p, miR-769-5p, miR-150-5p, miR-503-5p, miR-148b-3p, miR-425-5p, let-7b-5p, miR-10b-5p, miR-29c-3p, miR-423-3p, miR-106b-3p, miR-25-3p, miR-15b-3p, miR-342-3p and miR-186-5p.
In some embodiments, the method comprises detecting a level of an miRNA selected from one or more of: miR-423-5p, miR-23a-3p, miR-766-3p, miR-651-5p, let-7e-5p, miR-1275, miR- 3198, miR-627-5p, miR-4662a-5p, miR-3684, let-7a-5p, miR-483-5p, miR-3125, miR-3617-5p, miR-500b-3p, miR-664b-3p, miR-5189-3p, miR-6772-3p, miR-145-3p, miR-30a-5p, miR-27a-5p, miR-2116-3p, miR-31-5p, miR-4772-3p, miR-1290, miR-1226-3p, miR-589-3p, miR-210-3p, miR-103a-3p, miR-3115, miR-769-3p, miR-320b, miR-193a-5p, miR-206, miR-873-5p, miR- 148a-3p, miR-3913-5p, miR-320a-3p, miR-576-5p, miR-197-3p, miR-4742-3p, miR-28-5p, miR- 320c, miR-551b-3p, miR-142-3p, let-7i-3p, miR-92a-3p, miR-548k, miR-18a-3p, miR-491-5p, miR-6721-5p, miR-6503-3p, miR-3065-3p, miR-150-5p, let-7f-5p, miR-106b-5p, miR-99b-3p, miR-1273h-3p, let-7a-3p, miR-6741-5p, miR-301b-3p, miR-7-l-3p, miR-148b-5p, miR-874-3p, miR-889-3p, miR-1468-5p, miR-431-3p, miR-345-5p, miR-339-3p, miR-4433b-5p, miR-542-5p, miR-4750-5p, miR-132-3p, miR-877-5p, miR-1255b-5p, miR-450b-5p, let-7f-2-3p, miR-30a-3p, miR-23a-5p, miR-215-5p, miR-10a-3p, miR-98-5p, miR-200c-3p, miR-96-5p, miR-486-5p, miR- 30e-5p, miR-141-3p, miR-128-3p, miR-l-3p, miR-500a-3p, let-7b-5p, miR-22-3p, miR-3661, miR-24-3p, miR-340-5p, miR-574-5p, miR-378c, miR-221-3p, miR-181c-3p, miR-130a-3p, miR- 1306-3p, miR-95-3p, miR-146a-5p, miR-27b-3p, miR-425-5p, miR-9-5p, miR-4454, miR-148b- 3p, miR-599, miR-379-5p, miR-142-5p, miR-451a, miR-191-3p, miR-223-5p, miR-196b-5p, miR-194-5p, let-7d-5p, miR-324-3p, miR-186-5p, miR-502-3p, miR-423-3p, miR-151b, miR- 760, miR-93-3p, miR-382-5p, miR-25-3p, miR-331-3p, miR-29c-3p, miR-1277-5p, miR-320d, miR-769-5p, miR-3064-5p, miR-6529-5p, miR-146b-5p, miR-151a-3p, miR-378a-5p, miR-27a- 3p, miR-503-5p, miR-192-5p, miR-147b-3p, miR-182-5p, miR-409-3p, miR-885-3p, miR-106b- 3p, miR-125a-3p, miR-542-3p, miR-30d-5p, miR-432-5p, miR-342-3p, miR-200a-3p, miR-30e- 3p, miR-1843, miR-26b-3p, miR-26b-5p, miR-499a-5p, miR-200b-5p, miR-125b-l-3p, miR-lOb- 5p, miR-219a-l-3p, miR-885-5p, miR-574-3p, miR-15b-3p, miR-449a, let-7b-3p, miR-32-5p, miR-339-5p, miR-130b-3p, miR-2114-5p, miR-19a-3p, miR-155-3p, miR-223-3p, miR-449c-5p, miR-93-5p, miR-3065-5p, miR-628-3p and miR-19a-3p in a blood sample or a fraction thereof.
In some embodiments, the method comprises detecting a level of an miRNA selected from one or more of: miR-423-5p, miR-23a-3p, miR-195-5p, miR-766-3p, miR-651-5p, let-7e-5p, miR- 1275, miR-3198, miR-627-5p, miR-4662a-5p, miR-3684, let-7a-5p, miR-483-5p, miR-3125, miR- 3617-5p, miR-500b-3p, miR-664b-3p, miR-5189-3p, miR-6772-3p, miR-145-3p, miR-30a-5p, miR-27a-5p, miR-2116-3p, miR-31-5p, miR-4772-3p, miR-1290, miR-1226-3p, miR-589-3p, miR-210-3p, miR-103a-3p, miR-3115, miR-769-3p, miR-320b, miR-193a-5p, miR-206, miR- 873-5p, miR-148a-3p, miR-3913-5p, miR-320a-3p, miR-576-5p, miR-197-3p, miR-4742-3p, miR-28-5p, miR-320c, miR-551b-3p, miR-142-3p, let-7i-3p, miR-92a-3p, miR-548k, miR-18a- 3p, miR-491-5p, miR-6721-5p, miR-6503-3p, miR-3065-3p, miR-150-5p, and let-7f-5p in a blood sample or a fraction thereof.
In some embodiments, the method comprises detecting a level of an miRNA selected from one or more of: miR-423-5p, miR-23a-3p, miR-766-3p, miR-651-5p, let-7e-5p, miR-1275, miR- 3198, miR-627-5p, miR-4662a-5p, miR-3684, let-7a-5p, miR-483-5p, miR-3125, miR-3617-5p, miR-500b-3p, miR-664b-3p, miR-5189-3p, miR-6772-3p, miR-145-3p, miR-30a-5p, miR-27a-5p, miR-2116-3p, miR-31-5p, miR-4772-3p, miR-1290, miR-1226-3p, miR-589-3p, miR-210-3p, miR-103a-3p, miR-3115, miR-769-3p, miR-320b, miR-193a-5p, miR-206, miR-873-5p, miR- 148a-3p, miR-3913-5p, miR-320a-3p, miR-576-5p, miR-197-3p, miR-4742-3p, miR-28-5p, miR- 320c, miR-551b-3p, miR-142-3p, let-7i-3p, miR-92a-3p, miR-548k, miR-18a-3p, miR-491-5p, miR-6721-5p, miR-6503-3p, miR-3065-3p, miR-150-5p, and let-7f-5p in a blood sample or a fraction thereof.
In some embodiments, the method at least comprises detecting a level of miR-423-5p in a blood sample or a fraction thereof. In some embodiments, the method at least comprises detecting a level of miR-195-5p in a blood sample or a fraction thereof. In some embodiments, the method at least comprises detecting a level of miR-23a-3p and miR-195-5p in a blood sample or a fraction thereof. In some embodiments, the method at least comprises detecting a level of miR-28-5p in a blood sample or a fraction thereof. In some embodiments, the method at least comprises detecting a level of miR-223-5p in a blood sample or a fraction thereof. In some embodiments, the method at least comprises detecting a level of miR-130b-3p in a blood sample or a fraction thereof. In some embodiments, the method at least comprises detecting a level of miR-23a-3p and miR-423- 5p in a blood sample or a fraction thereof. In some embodiments, the method at least comprises detecting a level of miR-423-5p and miR-195-5p in a blood sample or a fraction thereof. In some embodiments, the method at least comprises detecting a level of miR-423-5p, miR-195-5p and miR-23a-3p in a blood sample or a fraction thereof. In some embodiments, the method at least comprises detecting a level of miR-423-5p, miR-195-5p, miR-23a-3p and miR-28-5p in a blood sample or a fraction thereof. In some embodiments, the method at least comprises detecting a level of miR-423-5p, miR-195-5p, miR-23a-3p and miR-223-5p in a blood sample or a fraction thereof. In some embodiments, the method at least comprises detecting a level of miR-423-5p, miR-195- 5p, miR-23a-3p, miR-28-5p and miR-223-5p in a blood sample or a fraction thereof. In some embodiments, the method at least comprises detecting a level of miR-423-5p, miR-195-5p, miR- 23a-3p and miR-130b-3p in a blood sample or a fraction thereof. In some embodiments, the method at least comprises detecting a level of miR-423-5p, miR-195-5p, miR-23a-3p, miR-28-5p, miR-223-5p and miR-130b-3p in a blood sample or a fraction thereof.
In some embodiments, when the method comprises detecting miR-195-5p the method further comprises detecting miR-23a-3p in a blood sample or a fraction thereof. In some embodiments, when the method comprises detecting miR-195-5p the method further comprises detecting miR-423-5p in a blood sample or a fraction thereof.
In some embodiments, the method comprises detecting a level of miR-423-5p, miR-195- 5p, miR-23a-3p, miR-28-5p and miR-223-5p in a blood sample or a fraction thereof. In some embodiments, the method comprises detecting a level of miR-423-5p, miR-195-5p, miR-23a-3p, miR-28-5p, miR-223-5p and miR-130b-3p in a blood sample or a fraction thereof.
In some embodiments, the method comprises detecting a level of miR-142-3p in a nasal mucosal sample. In some embodiments, the method comprises detecting a level of miR-3065-3p in a nasal mucosal sample. In some embodiments, the method comprises detecting a level of miR- 93 -5p in a nasal mucosal sample.
In some embodiments, the method comprises detecting a level of miR-486-5p in a nasal mucosal sample. In some embodiments, the method comprises detecting a level of miR-451a in a nasal mucosal sample. In some embodiments, the method comprises detecting a level of miR- 3065-5p in a nasal mucosal sample. In some embodiments, the method comprises detecting a level of miR-628-3p in a nasal mucosal sample. In some embodiments, preferably nasal mucosal samples, the method comprises detecting a level of miR-19a-3p in a nasal mucosal sample.
In some embodiments, the method comprises detecting a level of miR-142-3p and miR-93- 5p in a nasal mucosal sample. In some embodiments, the method comprises detecting a level of miR-142-3p and miR-3065-3p in a nasal mucosal sample.
In some embodiments, preferably nasal mucosal samples, the method comprises detecting a level of miR-142-3p, miR-3065-3p, and miR-93-5p in a nasal mucosal sample. In some embodiments, the method comprises detecting a level of miR-142-3p, miR-3065-3p, miR-93-5p and miR-486-5p in a nasal mucosal sample. In some embodiments, the method comprises detecting a level of miR-142-3p, miR-3065-3p, miR-93-5p and miR-451a in a nasal mucosal sample. In some embodiments, the method comprises detecting a level of miR-142-3p, miR-3065-3p, miR- 93-5p and miR-3065-5p in a nasal mucosal sample. In some embodiments, the method comprises detecting a level of miR-142-3p, miR-3065-3p, miR-93-5p and miR-628-3p in a nasal mucosal sample. In some embodiments, the method comprises detecting a level of miR-142-3p, miR-3065- 3p, miR-93-5p and miR-19a-3p in a nasal mucosal sample.
In some embodiments, the method comprises detecting a level of miR-142-3p, miR-3065- 3p, miR-93-5p, miR-486-5p, miR-451a, miR-3065-5p, miR-628-3p, and miR-19a-3p in a nasal mucosal sample.
In some embodiments, detecting a level of at least one miRNA does not comprise detecting miR-155-3p. In some embodiments, detecting a level of at least one miRNA does not comprise detecting miR-223-3p. In some embodiments, detecting a level of at least one miRNA does not comprise detecting miR-449c-5p.
In some embodiments, the at least one miRNA modulates IL-6 activity. In some embodiments, the at least one miRNA modulates the expression of ACE2 and TMPRSS2. In some embodiments, the miRNAs are circulating miRNAs (circulating in the blood stream).
In some embodiments, the method comprises detecting not more than 50 miRNA. In some embodiments, the method comprises detecting not more than 40 miRNA. In some embodiments, the method comprises detecting not more than 30 miRNA. In some embodiments, the method comprises detecting not more than 20 miRNA. In some embodiments, the method comprises detecting not more than 10 miRNA. In some embodiments, the method comprises detecting not more than 6 miRNA. In some embodiments, the method comprises detecting not more than 5 miRNA. In some embodiments, the method comprises detecting not more than 4 miRNA. In some embodiments, the method comprises detecting not more than 3 miRNA.
Figure imgf000043_0001
A "subject" contemplated in the present disclosure includes humans or animals (e.g. mammals) including companion animals or livestock or laboratory or art accepted test or vehicle animals. In some embodiments, the subject is a mammal. In some embodiments, the subject is a human. In some embodiments, the subject is a ferret. In some embodiments, the subject is a ferret. In some embodiments, the companion animal is a cat or dog.
As described herein a biological sample refers to any sample from a subject comprising miRNA e.g. bodily fluids, biopsy, tissue, and/or waste from a patient. In some embodiments, the biological sample is selected from: plasma, serum, whole blood, lymph fluid, biopsy or tissue sample, respiratory mucosal sample, nasopharangeal sample, seminal fluid, saliva or urine. In some embodiments, the respiratory mucosal sample is a nasal mucosal sample. In some embodiments, the nasal mucosal samples is an anterior nasal mucosal sample.
In some embodiments, the biological sample is, whole blood, plasma or serum. In some embodiments, the biological sample is plasma. In some embodiments, the whole blood, plasma or serum is collected in a container comprising heparin. In some embodiments, the heparin is removed from the biological sample before the samples is used in the methods as described herein. A person skilled in the art would appreciate that the plasma may be isolated from whole blood by any method known to a person skilled in the art. For example, the method may comprise collection of whole blood in an ethylenediaminetetraacetic acid (EDTA) treated, citrate treated, potassium oxalate/sodium fluoride treated or heparinized container and centrifugation to isolate the plasma fraction. Thus, in some embodiments, the plasma is ethylenediaminetetraacetic acid (EDTA), citrate, potassium oxalate, sodium fluoride or heparin treated plasma. In some embodiments, the biological sample is collected in a PAXgene Blood RNA tube.
In some embodiments, biological samples may be collected from a subject at more than one time point to e.g. monitor progression of a CoV infection, or to monitor, assess or optimize the efficacy of a treatment protocol. In some embodiments, the biological sample may be collected from a subject before, during and/or after treatment for a CoV infection. Samples may be collected, daily, weekly, fortnightly or monthly to monitor progression of a CoV infection or to assess the efficacy of a treatment regimen. Biological samples may be frozen for processing or analysis at a later date. In some embodiments, of the methods as described herein the biological samples are processed to extract RNA, small RNA and/or miRNA. For example, biological samples may be processed within 1 hour, or within 2 hours, or within 3 hours, or within 4 hours of collection for detection of miRNAs.
In some embodiments, any of the methods disclosed herein comprise using a small volume of sample. In some embodiments, the methods disclosed comprise isolating total RNA and/or amplifying miRNA in a sample of no more than about 20 microliters of sample, about 40 microliters of sample, about 80 microliters of sample, about 100 microliters of sample, about 200 microliters of sample, about 300 microliters of sample, about 400 microliters of sample, about 500 microliters of sample, about 600 microliters of sample, about 700 microliters of sample, about 800 microliters of sample, about 900 microliters of sample, about 1 milliter of sample, about 1.1 milliters of sample, about 1.2 milliters of sample, about 1.3 milliters of sample, about 1.4 milliters of sample, about 1.5 milliters of sample, about 1.6 milliters of sample, about 1.7 milliters of sample, about 1.8 milliters of sample, about 1.9 milliters of sample, about 2.0 milliters of sample. In some embodiments, the sample size is from about 25 microliters to about 2 milliters of liquid sample in the form of subject plasma, whole blood, serum, saliva, respiratory mucosal sample, anterior nasal sample, oropharyngeal sample, or nasopharangeal sample. miRNA/RNA Extraction
A person skilled in the art will appreciate that the RNA, small RNA (cutoff approximately 200 nt) and/or miRNA fraction of the biological samples as described herein may be extracted by any method known to a person skilled in the art including for example, phenol-based techniques, combined phenol and column-based techniques or a column-based technique as described in El- Khoury et al. (2016). A commercial kit may be used for RNA and/or miRNA extraction including for example, isolation with the miRNeasy Serum/Plasma kit (Qiagen, #217184), PAXgene Blood RNA Kit (Qiagen, #762174), MagnaZol cfRNA Isolation Reagent (Bioo Scientific. #NOVA- 3830-01), mirPrimer (Sigmaaldrich #SNC10), miRCURY RNA Isolation Kit - Cell and Plant (Exiqon, #300110) or miRCURY RNA Isolation Kit - Biofluids (Exiqon, #300112). In some embodiments, for miRNeasy extraction glycogen (10 pg, Sigma Aldrich, G1767) may be added as a carrier to each sample after lysis with Qiazol. In some embodiments, the quality and/or quantity of the extracted RNA, small RNA and/or miRNA may also be determined by any method known to a person skilled in the art e.g. spectrophotometrically at 260, 280 and 230 nm, agarose gel electrophoreses, fluorometrically (for example, using the Qubit Fluorometer (Invitrogen)), or Bioanalyzer analysis (Agilent). In some embodiments, RNA, small RNA and/or miRNA is not extracted or concentrated from the biological sample. For example, a multiplex miRNA profiling assay may be performed directly on a biological sample without prior processing to extract or concentrate the miRNA component of the sample (Tackett et al., 2017).
In some embodiments, the methods disclosed comprise isolating total RNA and/or amplifying miRNA in a sample of no more than about 20 microliters, no more than about 40 microliters, no more than about 80 microliters, no more than about 100 microliters, no more than about 200 microliters, no more than about 300 microliters, no more than about 400 microliters, no more than about 500 microliters, no more than about 600 microliters, no more than about 700 microliters, no more than about 800 microliters, no more than about 900 microliters, no more than about 1 milliter, no more than about 1.1 milliters, no more than about 1.2 milliters, no more than about 1.3 milliters, no more than about 1.4 milliters, no more than about 1.5 milliters, no more than about 1.6 milliters, no more than about 1.7 milliters, no more than about 1.8 milliters, no more than about 1.9 milliters, or no more than about 2.0 milliters.
Library Preparation
For detection methods comprising next generation sequencing a person skilled in the art would appreciate that an RNA sample may be subjected to a library preparation process. In some embodiments, the library preparation process is selected from CleanTag Small RNA Library Prep kit (TRiLink), NEXTflex Small RNA Sequence Kit v3 (Bioo Scientific) and QIAseq miRNA Library kit (Qiagen) as described for example in Wong et al. (2019).
Detection Methods
A person skilled in the art will appreciate that the miRNA can be detected with any method known to a person skilled in the art including, for example, the methods described in or adapted from Git et al. (2010), Hunt et al. (2015), Tian et al. (2015), Blondal et al. (2017), Tackett et al. (2017), Hu et al. (2017), D’AGata et al. (2019), Aquino-Jarquin et al. (2021) and Collins et al. (2021). Generally, small RNA, such as miRNA, can be detected and quantified from a sample (including fractions thereof), such as samples of isolated RNA by various methods known for mRNA, including, for example, amplification- based methods (e.g., Polymerase Chain Reaction (PCR), Real-Time Polymerase Chain Reaction (RT-PCR), Quantitative Polymerase Chain Reaction (qPCR), rolling circle amplification, etc.), hybridization-based methods (e.g., hybridization arrays (e.g., microarrays), NanoString analysis, Northern Blot analysis, branched DNA (bDNA) signal amplification, in situ hybridization, etc.), and sequencing-based methods (e.g., next-generation sequencing methods, for example, using the Illumina or IonTorrent platforms). Other exemplary techniques include ribonuclease protection assay (RPA), mass spectroscopy, electrical interference, or a CRISPR-based method.
In some embodiments, the RNA is converted to DNA (cDNA) prior to analysis. cDNA can be generated by reverse transcription of isolated miRNA using conventional techniques. In some embodiments, miRNA is amplified prior to measurement. In other embodiments, the level of miRNA is measured during the amplification process. In still other embodiments, the level of miRNA is not amplified prior to measurement. Some exemplary methods suitable for determining the level of miRNA in a sample are described in greater detail below. These methods are provided by way of illustration only, and it will be apparent to a skilled person that other suitable methods may likewise be used.
Amplification-Based Methods
Many amplification-based methods exist for detecting the level of miRNA nucleic acid sequences, including, but not limited to, PCR, RT-PCR, qPCR, and rolling circle amplification. Other amplification-based techniques include, for example, ligase chain reaction, multiplex ligatable probe amplification, in vitro transcription (IVT), strand displacement amplification, transcription-mediated amplification, RNA (Eberwine) amplification, and other methods that are known to persons skilled in the art.
A typical PCR reaction includes multiple steps, or cycles, that selectively amplify target nucleic acid species: a denaturing step, in which a target nucleic acid is denatured; an annealing step, in which a set of PCR primers (i.e., forward and reverse primers) anneal to complementary DNA strands, and an elongation step, in which a thermostable DNA polymerase elongates the primers. By repeating these steps multiple times, a DNA fragment is amplified to produce an amplicon, corresponding to the target sequence. Typical PCR reactions include 20 or more cycles of denaturation, annealing, and elongation. In many cases, the annealing and elongation steps can be performed concurrently, in which case the cycle contains only two steps. A reverse transcription reaction (which produces a cDNA sequence having complementarity to a miRNA) may be performed prior to PCR amplification. Reverse transcription reactions include the use of, e.g., a RNA-based DNA polymerase (reverse transcriptase) and a primer.
Kits for quantitative real time PCR of miRNA are known, and are commercially available. Examples of suitable kits include, but are not limited to, the TaqMan miRNA Assay (Applied Biosystems) and the mirVana qRT-PCR miRNA detection kit (Ambion). The miRNA can be ligated to a single stranded oligonucleotide containing universal primer sequences, a polyadenylated sequence, or adaptor sequence prior to reverse transcriptase and amplified using a primer complementary to the universal primer sequence, poly(T) primer, or primer comprising a sequence that is complementary to the adaptor sequence.
In some instances, custom qRT-PCR assays can be developed for determination of miRNA levels. Custom qRT-PCR assays to measure miRNAs in a biological sample, e.g., a body fluid, can be developed using, for example, methods that involve an extended reverse transcription primer and locked nucleic acid modified PCR. Custom miRNA assays can be tested by running the assay on a dilution series of chemically synthesized miRNA corresponding to the target sequence. This permits determination of the limit of detection and linear range of quantitation of each assay. Furthermore, when used as a standard curve, these data permit an estimate of the absolute abundance of miRNAs measured in biological samples.
Amplification curves may optionally be checked to verify that Ct values are assessed in the linear range of each amplification plot. Typically, the linear range spans several orders of magnitude. For each candidate miRNA assayed, a chemically synthesized version of the miRNA can be obtained and analyzed in a dilution series to determine the limit of sensitivity of the assay, and the linear range of quantitation. Relative expression levels may be determined, for example, as described by Livak et al. (2001).
In some embodiments, two or more miRNAs are amplified in a single reaction volume. For example, multiplex q-PCR, such as qRT-PCR, enables simultaneous amplification and quantification of at least two miRNAs of interest in one reaction volume by using more than one pair of primers and/or more than one probe. The primer pairs comprise at least one amplification primer that specifically binds each miRNA, and the probes are labeled such that they are distinguishable from one another, thus allowing simultaneous quantification of multiple miRNAs.
Rolling circle amplification is a DNA-polymerase driven reaction that can replicate circularized oligonucleotide probes with either linear or geometric kinetics under isothermal conditions (see, for example, Lizardi et al. (1998); Gusev et al. (2001); Nallur et al. (2001). In the presence of two primers, one hybridizing to the (+) strand of DNA, and the other hybridizing to the (-) strand, a complex pattern of strand displacement results in the generation of over 10L9 copies of each DNA molecule in 90 minutes or less. Tandemly linked copies of a closed circle DNA molecule may be formed by using a single primer. The process can also be performed using a matrix-associated DNA. The template used for rolling circle amplification may be reverse transcribed. This method can be used as a highly sensitive indicator of miRNA sequence and expression level at very low miRNA concentrations (see, for example, Cheng et al. (2009); Neubacher et al. (2009).
Hybridization-Based Methods miRNA may be detected using hybridization-based methods, including but not limited to hybridization arrays (e.g., microarrays), NanoString analysis, Northern Blot analysis, branched DNA (bDNA) signal amplification, and in situ hybridization.
Microarrays can be used to measure the expression levels of large numbers of miRNAs simultaneously. Microarrays can be fabricated using a variety of technologies, including printing with fine-pointed pins onto glass slides, photolithography using pre-made masks, photolithography using dynamic micromirror devices, ink-jet printing, or electrochemistry on microelectrode arrays. Also useful are microfluidic TaqMan Low-Density Arrays, which are based on an array of microfluidic qRT-PCR reactions, as well as related microfluidic qRT-PCR based methods.
Axon B-4000 scanner and Gene-Pix Pro 4.0 software or other suitable software can be used to scan images. Non-positive spots after background subtraction, and outliers detected by the ESD procedure, are removed. The resulting signal intensity values are normalized to per-chip median values and then used to obtain geometric means and standard errors for each miRNA. Each signal can be transformed to log base 2, and a one-sample t test can be conducted. Independent hybridizations for each sample can be performed on chips with each miRNA spotted multiple times to increase the robustness of the data.
Microarrays can be used for the expression profiling of miRNAs in diseases. For example, RNA can be extracted from a sample and, optionally, the miRNAs are size-selected from total RNA. Oligonucleotide linkers can be attached to the 5' and 3' ends of the miRNAs and the resulting ligation products are used as templates for an RT-PCR reaction. The sense strand PCR primer can have a fluorophore attached to its 5' end, thereby labeling the sense strand of the PCR product. The PCR product is denatured and then hybridized to the microarray. A PCR product, referred to as the target nucleic acid that is complementary to the corresponding miRNA capture probe sequence on the array will hybridize, via base pairing, to the spot at which the, capture probes are affixed. The spot will then fluoresce when excited using a microarray laser scanner.
The fluorescence intensity of each spot is then evaluated in terms of the number of copies of a particular miRNA, using a number of positive and negative controls and array data normalization methods, which will result in assessment of the level of expression of a particular miRNA.
Total RNA containing the miRNA extracted from a body fluid sample can also be used directly without size-selection of the miRNAs. For example, the RNA can be 3' end labeled using T4 RNA ligase and a fluorophore- labeled short RNA linker. Fluorophore-labeled miRNAs complementary to the corresponding miRNA capture probe sequences on the array hybridize, via base pairing, to the spot at which the capture probes are affixed. The fluorescence intensity of each spot is then evaluated in terms of the number of copies of a particular miRNA, using a number of positive and negative controls and array data normalization methods, which will result in assessment of the level of expression of a particular miRNA.
Several types of microarrays can be employed including, but not limited to, spotted oligonucleotide microarrays, pre-fabricated oligonucleotide microarrays or spotted long oligonucleotide arrays. miRNAs can also be detected without amplification using the nCounter Analysis System (NanoString Technologies, Seattle, Wash.). This technology employs two nucleic acid-based probes that hybridize in solution (e.g., a reporter probe and a capture probe). After hybridization, excess probes are removed, and probe/target complexes are analyzed in accordance with the manufacturer's protocol. nCounter miRNA assay kits are available from NanoString Technologies, which are capable of distinguishing between highly similar miRNAs with great specificity. miRNAs can also be detected using branched DNA (bDNA) signal amplification (see, for example, Urdea (1994)). miRNA assays based on bDNA signal amplification are commercially available. One such assay is the QuantiGeneR™ 2.0 miRNA Assay (Affymetrix, Santa Clara, Calif.).
Northern Blot and in situ hybridization may also be used to detect miRNAs. Suitable methods for performing Northern Blot and in situ hybridization are known in the art.
Sequencing-Based Methods
Advanced sequencing methods can likewise be used as available. For example, miRNAs can be detected using Illumina sequence (Solexa). For example, miRNAs can be detected using Next Generation Sequencing (massive parallel sequence or massively parallel sequencing methods that offer ultra-high throughput, scalability and speed) methods (e.g., Sequencing -By-Synthesis or TruSeq methods, Roche 454 sequenceing using, for example, the HiSeq, HiScan, GenomeAnalyzer, MiSeq systems (Illumina, Inc., San Diego, Calif.), Ion torrent and nanopores). Examples of Next Generation Sequencing technologies are described, for example, in McCombie et al. (2019). Such methods all also referred to as second generation sequencing methods. miRNAs can also be detected using Ion Torrent Sequencing (Ion Torrent Systems, Inc., Gulliford, Conn.), or other suitable methods of semiconductor sequencing.
CRISPR-Based Methods
CRISPR (clustered regularly interspaced short palindromic repeats) based methods can also be used for detection of the miRNAs of the invention. Such methods are described in or can be adapted from the methods e.g. in Aquino-Jarquin et al. (2021), Urban et al. (2019), Collins et al. (2021), and Makhawi et al. (2021). In an embodiment, the CRISPR-based method uses a Casl3 nuclease. In an embodiment, the CRISPR based method uses a Casl3a nuclease. In an embodiment, the CRISPR-based method uses a Casl2 nuclease. In an embodiment, the CRISPR- based method uses a Casl2a nuclease. In an embodiment, the CRISPR-based method uses a Csm6 nuclease. Examples of CRISPR-based methods for miRNA detection include: a single step Cas 13a-Triggered signal amplification assay (Single-Step assay); cascade CRISPR-casl3 (casCRISPR) assay; ddDasl3a assay; DNA endonuclease-targeted CRISPR trans reporter assay (DETECTR), specific high-sensitivity enzymatic reporter unlocking assay (SHERLOCK), naked- eye-CRISPR assay; Cas 13 a-based visual detection (vCas) assay; electrochemical CRISPR/CHDC assay (EM-CRISPR); CRISPR-Biosensor X assay; CRISPR-Casl3a powered portable ECL chip (PECL-CRISPR) assay; and Cas-CHDC-Powered Electrochemical RNA Sensing Technology (COMET) assay. In an embodiment, the readout from the CRISPR-based assay is selected from: colometry, electrochemical, fluorescence, lateral flow or electrochemiluminescence. In an embodiment, the CRISPR-based method is an amplification free method (e.g. an electrochemical microfluidic biosensor method). In an embodiment, the method uses electrical interference to detect the miRNA, for example as described in Urban et al. (2019). In an embodiment, the CRISPR-based method determines the presence or absence of an miRNA. In an embodiment, the CRISPR-based method the level of an miRNA. In an embodiment, the CRISPR-based method determines the relative level of an miRNA. In an embodiment, the level is a relative level between two or more miRNAs which may be all associated with a CoV infection or a severe CoV infection in a subject such as those described herein, or the levels of a suitable control miRNA(s) could be used to determine the relative level, where expression of the control miRNA is not associated with a CoV infection or a severe CoV infection.
Additional miRNA Detection Tools
Mass spectroscopy can be used to quantify miRNA using RNase mapping. Isolated RNAs can be enzymatically digested with RNA endonucleases (RNases) having high specificity (e.g., RNase Tl, which cleaves at the 3 '-side of all unmodified guanosine residues) prior to their analysis by MS or tandem MS (MS/MS) approaches. The first approach developed utilized the on-line chromatographic separation of endonuclease digests by reversed phase HPLC coupled directly to ESI-MS. The presence of posttranscriptional modifications can be revealed by mass shifts from those expected based upon the RNA sequence. Ions of anomalous mass/charge values can then be isolated for tandem MS sequencing to locate the sequence placement of the posttranscriptionally modified nucleoside.
Matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS) has also been used as an analytical approach for obtaining information about posttranscriptionally modified nucleosides. MALDI-based approaches can be differentiated from ESI-based approaches by the separation step. In MALDI-MS, the mass spectrometer is used to separate the miRNA.
To analyze a limited quantity of intact miRNAs, a system of capillary LC coupled with nanoESI-MS can be employed, by using a linear ion trap-orbitrap hybrid mass spectrometer (LTQ Orbitrap XL, Thermo Fisher Scientific) or a tandem-quadrupole time-of-flight mass spectrometer (QSTAR XL, Applied Biosystems) equipped with a custom-made nanospray ion source, a Nanovolume Valve (Valeo Instruments), and a splitless nano HPLC system (DiNa, KYA Technologies). Analyte/TEAA is loaded onto a nano-LC trap column, desalted, and then concentrated. Intact miRNAs are eluted from the trap column and directly injected into a Cl 8 capillary column, and chromatographed by RP-HPLC using a gradient of solvents of increasing polarity. The chromatographic eluent is sprayed from a sprayer tip attached to the capillary column, using an ionization voltage that allows ions to be scanned in the negative polarity mode.
Additional methods for miRNA detection and measurement include, for example, strand invasion assay (Third Wave Technologies, Inc.), surface plasmon resonance (SPR), cDNA, MTDNA (metallic DNA; Advance Technologies, Saskatoon, SK), and single-molecule methods such as the one developed by US Genomics. Multiple miRNAs can be detected in a microarray format using a novel approach that combines a surface enzyme reaction with nanoparticle- amplified SPR imaging (SPRI). The surface reaction of poly(A) polymerase creates poly(A) tails on miRNAs hybridized onto locked nucleic acid (LNA) microarrays. DNA-modified nanoparticles are then adsorbed onto the poly(A) tails and detected with SPRI. This ultrasensitive nanoparticle- amplified SPRI methodology can be used for miRNA profiling at attamole levels. In some embodiments, the method is a CRISPR-based electrical interference method.
Other methods include nanomaterials e.g. gold nanoparticles (AuNPs), magnetic nanoparticles, silver nanoclusters (AgNCs), and quantum dots (QDs) (Ye et al, 2019).
Detection of Amplified or Non- Amplified miRNAs
In certain embodiments, labels, dyes, or labeled probes and/or primers are used to detect amplified or unamplified miRNAs. The skilled artisan will recognize which detection methods are appropriate based on the sensitivity of the detection method and the abundance of the target. Depending on the sensitivity of the detection method and the abundance of the target, amplification may or may not be required prior to detection. One skilled in the art will recognize the detection methods where miRNA amplification is preferred.
A probe or primer may include standard (A, T or U, G and C) bases, or modified bases. Modified bases include, but are not limited to, the AEGIS bases (from Eragen Biosciences), which have been described, e.g., in U.S. Pat. Nos. 5,432,272, 5,965,364, and 6,001,983. In certain aspects, bases are joined by a natural phosphodiester bond or a different chemical linkage. Different chemical linkages include, but are not limited to, a peptide bond or a Locked Nucleic Acid (LNA) linkage, which is described, e.g., in U.S. Pat. No. 7,060,809.
In a further aspect, oligonucleotide probes or primers present in an amplification reaction are suitable for monitoring the amount of amplification product produced as a function of time. In certain aspects, probes having different single stranded versus double stranded character are used to detect the nucleic acid. Probes include, but are not limited to, the 5'-exonuclease assay (e.g., TAQMAN) probes (see U.S. Pat. No. 5,538,848), stem-loop molecular beacons (see, e.g., U.S. Pat. Nos. 6,103,476 and 5,925,517), stemless or linear beacons (see, e.g., WO 9921881, U.S. Pat. Nos. 6,485,901 and 6,649,349), peptide nucleic acid (PNA) Molecular Beacons (see, e.g., U.S. Pat. Nos. 6,355,421 and 6,593,091), linear PNA beacons (see, e.g. U.S. Pat. No. 6,329,144), non- FRET probes (see, e.g., U.S. Pat. No. 6,150,097), Sunrise. TM./Amplif uorB.TM. probes (see, e.g., U.S. Pat. No. 6,548,250), stem-loop and duplex SCORPION probes (see, e.g., U.S. Pat. No. 6,589,743), bulge loop probes (see, e.g., U.S. Pat. No. 6,590,091), pseudo knot probes (see, e.g., U.S. Pat. No. 6,548,250), cyclicons (see, e.g., U.S. Pat. No. 6,383,752), MGB Eclipse.TM. probe (Epoch Biosciences), hairpin probes (see, e.g., U.S. Pat. No. 6,596,490), PNA light-up probes, antiprimer quench probes (Li et al., 2006), self-assembled nanoparticle probes, and ferrocene- modified probes described, for example, in U.S. Pat. No. 6,485,901.
In certain embodiments, one or more of the primers in an amplification reaction can include a label. In yet further embodiments, different probes or primers comprise detectable labels that are distinguishable from one another. In some embodiments a nucleic acid, such as the probe or primer, may be labeled with two or more distinguishable labels.
In some aspects, a label is attached to one or more probes and has one or more of the following properties: (i) provides a detectable signal; (ii) interacts with a second label to modify the detectable signal provided by the second label, e.g., FRET (Fluorescent Resonance Energy Transfer); (iii) stabilizes hybridization, e.g., duplex formation; and (iv) provides a member of a binding complex or affinity set, e.g., affinity, antibody-antigen, ionic complexes, hapten-ligand (e.g., biotin-avidin). In still other aspects, use of labels can be accomplished using any one of a large number of known techniques employing known labels, linkages, linking groups, reagents, reaction conditions, and analysis and purification methods. miRNAs can be detected by direct or indirect methods. In a direct detection method, one or more miRNAs are detected by a detectable label that is linked to a nucleic acid molecule. In such methods, the miRNAs may be labeled prior to binding to the probe. Therefore, binding is detected by screening for the labeled miRNA that is bound to the probe. The probe is optionally linked to a bead in the reaction volume.
In certain embodiments, nucleic acids are detected by direct binding with a labeled probe, and the probe is subsequently detected. In some embodiments, the nucleic acids, such as amplified miRNAs, are detected using FlexMAP Microspheres (Luminex) conjugated with probes to capture the desired nucleic acids. Some methods may involve detection with polynucleotide probes modified with fluorescent labels or branched DNA (bDNA) detection, for example.
In some embodiments, biomarker expression is determined using a PCR-based assay comprising specific primers and/or probes for each biomarker. As used herein, the term “probe” refers to any molecule that is capable of selectively binding a specifically intended target biomolecule. In some embodiments, herein, the term “probe” refers to any molecule that may bind or associate, indirectly or directly, covalently or non-covalently, to any of the substrates and/or reaction products and/or proteases disclosed herein and whose association or binding is detectable using the methods disclosed herein. In some embodiments, the probe is a fluorogenic probe, antibody or absorbance-based probes. If an absorbance-based probe, the chromophore pNA (para- nitroanaline) may be used as a probe for detection and/or quantification of a target nucleic acid sequence disclosed herein. In some embodiments the probe may be a nucleic acid sequence comprising a fluoregnic molecule or a substrate that when exposed to an enzyme becomes fluoregenic and the nucleic acid sequence is complementary or substantially complementary to the nucleic acid sequence comprising at least about 70%, 80%, 81%, 82%, 83%, 84, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99% or 100% sequence identity to any of the miRNAs provided in Table 1.
The target molecule could be any one or combination of nucleic acid sequences identified in Table 1. In some embodiments, the target molecule is a nucleic acid sequence comprising at least about 70%, 80%, 81%, 82%, 83%, 84, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% sequence identity to any one or combination of nucleic acid sequences provided in Table 1. Probes can be synthesized by one of skill in the art using known techniques, or derived from biological preparations. Probes may include but are not limited to, RNA, DNA, proteins, peptides, aptamers, antibodies, and organic molecules. The term “primer” or “probe” encompasses oligonucleotides that have a specific sequence that is complimentary or substantially complimentary to any one or combination of nucleic acid sequences identified in Table 1. In some embodiments, the target molecule is any amplified fragment of any one or combination of nucleic acid sequences identified in Table 1 and/or any one or combination of nucleic acid sequence comprising at least about 70%, 80%, 81%, 82%, 83%, 84, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% sequence identity to any one or combination of nucleic acid sequences in Table 1.
In other embodiments, nucleic acids are detected by indirect detection methods. For example, a biotinylated probe may be combined with a streptavidin-conjugated dye to detect the bound nucleic acid. The streptavidin molecule binds a biotin label on amplified miRNA, and the bound miRNA is detected by detecting the dye molecule attached to the streptavidin molecule. In one embodiment, the streptavidin- conjugated dye molecule comprises PHYCOLINK. Streptavidin R-Phycoerythrin (PROzyme). Other conjugated dye molecules are known to persons skilled in the art.
Table 1. Sequences of the miRNAs of the disclosure and corresponding primer sequences for detection.
Figure imgf000057_0001
Figure imgf000058_0001
Figure imgf000059_0001
Figure imgf000060_0001
Figure imgf000061_0001
Figure imgf000062_0001
Labels include, but are not limited to: light-emitting, light-scattering, and light-absorbing compounds which generate or quench a detectable fluorescent, chemiluminescent, or bioluminescent signal (see, e.g., Kricka, 1992; Garman, 1997). A dual labeled fluorescent probe that includes a reporter fluorophore and a quencher fluorophore is used in some embodiments. It will be appreciated that pairs of fluorophores are chosen that have distinct emission spectra so that they can be easily distinguished.
In certain embodiments, labels are hybridization-stabilizing moieties which serve to enhance, stabilize, or influence hybridization of duplexes, e.g., intercalators and intercalating dyes (including, but not limited to, ethidium bromide and SYBR-Green), minor-groove binders, and cross-linking functional groups (see, e.g., Blackburn et al., 1996).
In other embodiments, methods relying on hybridization and/or ligation to quantify miRNAs may be used, including oligonucleotide ligation (OLA) methods and methods that allow a distinguishable probe that hybridizes to the target nucleic acid sequence to be separated from an unbound probe. As an example, HARP-like probes, as disclosed in U.S. 2006/0078894 may be used to measure the quantity of miRNAs. In such methods, after hybridization between a probe and the targeted nucleic acid, the probe is modified to distinguish the hybridized probe from the unhybridized probe. Thereafter, the probe may be amplified and/or detected. In general, a probe inactivation region comprises a subset of nucleotides within the target hybridization region of the probe. To reduce or prevent amplification or detection of a HARP probe that is not hybridized to its target nucleic acid, and thus allow detection of the target nucleic acid, a post-hybridization probe inactivation step is carried out using an agent which is able to distinguish between a HARP probe that is hybridized to its targeted nucleic acid sequence and the corresponding unhybridized HARP probe. The agent is able to inactivate or modify the unhybridized HARP probe such that it cannot be amplified. A probe ligation reaction may also be used to quantify miRNAs. In a Multiplex Ligation-dependent Probe Amplification (MLPA) technique (Schouten et al, 2002), pairs of probes which hybridize immediately adjacent to each other on the target nucleic acid are ligated to each other driven by the presence of the target nucleic acid. In some aspects, MLPA probes have flanking PCR primer binding sites. MLPA probes are specifically amplified when ligated, thus allowing for detection and quantification of miRNA biomarkers.
In other embodiments, the miRNA can be detected using an isothermal exponential amplification method, a rolling cycle amplification based method, a cleavage based method, a gold particle (AuNPs)-based method, a duplex specific nuclease (DSN) and AuNPs-based system quantum dot-based method or capillary-electrophoreses-based assay method, an AuNPs-based method, an DSN and AuNPs-based system quantum dot-based method or capillary- electrophoreses-based as described in Tian et al. (2015).
In some embodiments, the method comprises performing an assay on a sample from a subject to determine the miRNA expression profile of the subject.
In some embodiments, the miRNA can be detected using real-time reverse transcription- PCR (qRT-PCR), microarray hybridization, a multiplex miRNA profiling assay, massively parallel/next generation sequencing also referred to as “NGS sequencing,” RNA-ish, northern blotting or colorimetric sensor based analysis.
In some embodiments, the next generation sequencing is selected from: RNA-seq, small RNA-seq, and miRNA-seq.
Detection includes methods comprising direct labelling of a miRNA (e.g. with a modified nucleotide, labelled nucleotide or tag incorporated into the miRNA) or binding of the miRNA with a binding molecule which binds a miRNA or a truncated version thereof forming a miRNA- binding molecule complex.
In some embodiments, the binding molecule is selected from: i) a polynucleotide, ii) an aptamer, iii) an antibody. In some embodiments, the polynucleotide is complementary to the miRNA or a truncated version thereof or detects a tag attached to the miRNA. In some embodiments, the polynucleotide is a primer.
In some embodiments, the binding molecule is detectably labelled or capable of binding a detectable label. In some embodiments, the binding molecule is linked to an enzyme, enzyme substrate, a fluorescent or fluorescent substrate, chemiluminescent molecule, chemiluminescent substrate, purification tag and/or a solid support. In some embodiments the miRNA-binding complex is directly or indirectly detected.
In some embodiments, the detection method is a reverse transcription and quantitative PCR (RT-qPCR or qRT-PCR) assay. In some embodiments, the primer for reverse transcription can be a stem-loop specific RT primer or a universal primer if the miRNAs have undergone prior 3’ poly- A tailing and 5’ adaptor ligation, for example the TaqMan Advanced miRNA cDNA Synthesis kit from Applied Biosystems. In some embodiments, the detection method is fluorescence. In some embodiments, the detection method uses one or more of the primers provided in Table 1. In some embodiments, the method comprises determining a coronavirus infection in a subject, the method comprising i) obtaining a sample from a subject, and ii) assaying the sample for the level of at least one miRNA selected from: miR-423-5p, miR-23a-3p, miR-195-5p, miR- 766-3p, miR-651-5p, let-7e-5p, miR-1275, miR-3198, miR-627-5p, miR-4662a-5p, miR-3684, let-7a-5p, miR-483-5p, miR-3125, miR-3617-5p, miR-500b-3p, miR-664b-3p, miR-5189-3p, miR-6772-3p, miR-145-3p, miR-30a-5p, miR-27a-5p, miR-2116-3p, miR-31-5p, miR-4772-3p, miR-1290, miR-1226-3p, miR-589-3p, miR-210-3p, miR-103a-3p, miR-3115, miR-769-3p, miR- 320b, miR-193a-5p, miR-206, miR-873-5p, miR-148a-3p, miR-3913-5p, miR-320a-3p, miR-576- 5p, miR-197-3p, miR-4742-3p, miR-28-5p, miR-320c, miR-551b-3p, miR-142-3p, let-7i-3p, miR- 92a-3p, miR-548k, miR-18a-3p, miR-491-5p, miR-6721-5p, miR-6503-3p, miR-3065-3p, miR- 150-5p, let-7f-5p, miR-106b-5p, miR-99b-3p, miR-1273h-3p, let-7a-3p, miR-6741-5p, miR-21- 3p, miR-301b-3p, miR-7-l-3p, miR-148b-5p, miR-874-3p, miR-889-3p, miR-1468-5p, miR-431- 3p, miR-345-5p, miR-339-3p, miR-4433b-5p, miR-542-5p, miR-4750-5p, miR-132-3p, miR-877- 5p, miR-1255b-5p, miR-450b-5p, let-7f-2-3p, miR-30a-3p, miR-23a-5p, miR-215-5p, miR-lOa- 3p, miR-98-5p, miR-200c-3p, miR-96-5p, miR-486-5p, miR-30e-5p, miR-141-3p, miR-128-3p, miR-l-3p, miR-500a-3p, let-7b-5p, miR-22-3p, miR-3661, miR-24-3p, miR-340-5p, miR-574-5p, miR-378c, miR-221-3p, miR-181c-3p, miR-130a-3p, miR-1306-3p, miR-95-3p, miR-146a-5p, miR-27b-3p, miR-425-5p, miR-9-5p, miR-4454, miR-148b-3p, miR-599, miR-379-5p, miR-142- 5p, miR-451a, miR-191-3p, miR-223-5p, miR-196b-5p, miR-194-5p, let-7d-5p, miR-324-3p, miR-186-5p, miR-502-3p, miR-423-3p, miR-151b, miR-760, miR-93-3p, miR-382-5p, miR-25- 3p, miR-331-3p, miR-29c-3p, miR-1277-5p, miR-320d, miR-769-5p, miR-3064-5p, miR-6529- 5p, miR-146b-5p, miR-151a-3p, miR-378a-5p, miR-27a-3p, miR-503-5p, miR-192-5p, miR- 147b-3p, miR-182-5p, miR-409-3p, miR-885-3p, miR-106b-3p, miR-125a-3p, miR-542-3p, miR- 30d-5p, miR-432-5p, miR-342-3p, miR-200a-3p, miR-30e-3p, miR-1843, miR-26b-3p, miR-26b- 5p, miR-499a-5p, miR-200b-5p, miR-125b-l-3p, miR-10b-5p, miR-219a-l-3p, miR-885-5p, miR-574-3p, miR-15b-3p, miR-449a, let-7b-3p, miR-32-5p, miR-339-5p, miR-130b-3p, miR- 2114-5p, miR-19a-3p, miR-155-3p, miR-223-3p, miR-449c-5p, miR-93-5p, miR-3065-5p, miR- 628-3p and miR-19a-3p. In some embodiments, the method further comprises comparing the measured level of the at least one miRNA to a reference value. miRNA Analysis Algorithms
In some embodiments, data that are generated using samples such as “known samples” can then be used to “train” a classification model. A “known sample” is a sample that has been pre classified, e.g., classified as being derived from a normal subject, from a subject known to have CoV infection, or a subject known to have severe CoV infection. The data that are derived from a range of sources and are used to form the classification model can be referred to as a “training data set.” Once trained, the classification model can recognize patterns in data derived from spectra generated using unknown samples. The classification model can then be used to classify the unknown samples into classes. This can be useful, for example, in predicting whether or not a particular biological sample is associated with a certain biological condition (e.g., diseased versus non-diseased).
In some embodiments, data for the training data set that is used to form the classification model can be obtained from any established method for nucleic acid quantitation. In some embodiments, the data can come directly from quantitative PCR (for example, Ct values obtained using the double delta Ct method), or from high-throughput expression profiling, such as microarray analysis (for example, total counts or normalized counts from a miRNA RNA expression assay).
Classification models can be formed using any suitable statistical classification (or “learning”) method that attempts to segregate bodies of data into classes based on objective parameters present in the data. Classification methods may be either supervised or unsupervised. Examples of supervised and unsupervised classification processes are described in Jain, "Statistical Pattern Recognition: A Review", IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 22, No. 1, January 2000.
In supervised classification, training data containing examples of known categories are presented to a learning mechanism, which learns one or more sets of relationships that define each of the known classes. New data may then be applied to the learning mechanism, which then classifies the new data using the learned relationships. Examples of supervised classification processes include linear regression processes (e.g., multiple linear regression (MLR), partial least squares (PLS) regression and principal components regression (PCR)), binary decision trees (e.g., recursive partitioning processes such as CART— classification and regression trees), artificial neural networks such as back propagation networks, discriminant analyses (e.g., Bayesian classifier or Fischer analysis), logistic classifiers, and support vector classifiers (support vector machines).
In other embodiments, the classification models that are created can be formed using unsupervised learning methods. Unsupervised classification attempts to learn classifications based on similarities in the training data set, without pre-classifying the spectra from which the training data set was derived. Unsupervised learning methods include cluster analyses. A cluster analysis attempts to divide the data into ’’clusters” or groups that ideally should have members that are very similar to each other, and very dissimilar to members of other clusters. Similarity is then measured using some distance metric, which measures the distance between data items, and clusters together data items that are closer to each other. Clustering techniques include the MacQueen's K-means algorithm and the Kohonen's Self-Organizing Map algorithm. Learning algorithms asserted for use in classifying biological information are described, for example, in. WO 01/31580, U.S. 2002 0193950, U.S. 2003 0004402 Al, and U.S. 2003 0055615 Al.
The classification models can be formed on and used on any suitable digital computer. Suitable digital computers include micro, mini, or large computers using any standard or specialized operating system, such as a Unix, WINDOWS or LINUX based operating system.
The training data set(s) and the classification models can be embodied by computer code that is executed or used by a digital computer. The computer code can be stored on any suitable computer readable media including optical or magnetic disks, sticks, tapes, etc., and can be written in any suitable computer programming language including C, C++, visual basic, etc.
The learning algorithms described above can be used for developing classification algorithms for miRNAs specific for CoV infection. The classification algorithms can, in turn, be used in diagnostic tests by providing diagnostic values (e.g., cut-off points) for miRNAs used singly or in combination.
Reference Value
In an embodiment, a method of the invention comprises comparing the level of the at least one miRNA to a reference value.
The reference value can be determined, or predetermined, using a wide variety of procedures known in the art including, but not limited to, the reference value is a predetermined level of the at least one miRNA, a predetermined score, the level of the at least one miRNA in a control sample, or the level of the at least one miRNA in a subject who does not have one or more of a CoV infection, an influenza virus infection and a respiratory infection.
In an embodiment, the miRNA analysis algorithm can assign the sample a score using the level of the at least one miRNA in the sample. In an embodiment, the miRNA analysis algorithm can assign the sample a score using the level of at least one miRNA in Table 1. In an embodiment, the miRNA analysis algorithm can assign the sample a score using the level of at least two miRNA in Table 1. In an embodiment, the miRNA analysis algorithm can assign the sample a score using the level of at least three miRNA in Table 1.
In an embodiment, the miRNA analysis algorithm can assign the sample a score using the level of miR-423-5p in the sample. In an embodiment, the miRNA analysis algorithm can assign the sample a score using the level of miR-195-5p in the sample. In an embodiment, the miRNA analysis algorithm can assign the sample a score using the level of miR-23a-3p in the sample. In an embodiment, the miRNA analysis algorithm can assign the sample a score using the level of miR-28-5p in the sample. In an embodiment, the miRNA analysis algorithm can assign the sample a score using the level of miR-223-5p in the sample. In an embodiment, the miRNA analysis algorithm can assign the sample a score using the level of miR-130b-3p in the sample. In an embodiment, the miRNA analysis algorithm can assign the sample a score using the level of miR- 423-5p, miR-195-5p and miR-23a-3p in the sample. In an embodiment, the miRNA analysis algorithm can assign the sample a score using the level of miR-423-5p, miR-195-5p, miR-23a-3p, miR-28-5p and miR-223-5p in the sample. In an embodiment, the miRNA analysis algorithm can assign the sample a score using the level of miR-423-5p, miR-195-5p, miR-23a-3p, miR-28-5p, miR-223-5p miR-130b-3p in the sample. In an embodiment, the miRNA analysis algorithm can assign the sample a score using the level of miR-142-3p in the sample. In an embodiment, the miRNA analysis algorithm can assign the sample a score using the level of miR-3065-3p in the sample. In an embodiment, the miRNA analysis algorithm can assign the sample a score using the level of miR-93-5p in the sample. In an embodiment, the miRNA analysis algorithm can assign the sample a score using the level of miR-142-3p and 3065-3p in the sample. In an embodiment, the miRNA analysis algorithm can assign the sample a score using the level of miR-142-3p and 3065-3p in the sample. In an embodiment, the miRNA analysis algorithm can assign the sample a score using the level of miR-142-3p, miR-3065-3p, and miR-93-5p in the sample. As is known in the art, a predetermined score may also be considered a threshold value. More specifically, based on the analysis of a sufficient number of subjects with and without a CoV infection or a severe CoV infection, a value (threshold) can be determined such that if the subject has a score based on the level of the at least one (typically two or more) miRNA at or above the threshold it is determined they have a CoV infection or a severe CoV infection. Processes for determining suitable scores and thresholds for a given diagnostic test are well known in the art. In an embodiment, the reference value is a threshold determined by the computer based miRNA analysis algorithm on training and/or validation data. In some embodiments, the algorithm is based on the level of 2 or 3 or 4 or 5 or 6 or 7 or 8 or 9 or 10 or more miRNA in the sample.
In an embodiment, the reference value is based upon a machine learning classification algorithm (such as logistic regression) that has been trained using the expression of, for example, miR-423-5p, miR-195-5p and miR-23a-3p. Training provides the parameters, or coefficients, to the algorithm to allow it to make a prediction. When a new sample is tested, the resulting data for the, for example, three-miR signature is fed into the algorithm and the probability of that sample coming from someone infected with SARS-CoV-2 is produced. If the probability is greater than 0.5 or 50%, than the sample is classified as being COVTD-19 positive.
In an embodiment, the level is an absolute level. In an embodiment, the level is a relative level between two or more miRNAs which may be all associated with a CoV infection or a severe CoV infection in a subject such as those described herein, or the levels of a suitable control miRNA(s) could be used to determine the relative level, where expression of the control miRNA is not associated with a CoV infection or a severe CoV infection.
In an embodiment, the score factors in patient characteristics such as age, gender, other health conditions.
In an embodiment, the reference value is the level of the at least one miRNA in a control sample. The reference value may be a standard level of an RNA or miRNA synthetically produced or from a normal control biological sample from one or more subjects. In an embodiment, the normal control biological sample is age, gender and/or ethnicity matched to the subject being evaluated by the methods as described herein. In an embodiment, the reference value is the level of the at least one miRNA in a normal control biological sample.
In an embodiment, the reference value is the level of the at least one miRNA in subject not having one or more of a CoV infection, an influenza virus infection and a respiratory infection. In an embodiment, the reference value is the level of the at least one miRNA in subject not having a CoV infection. In an embodiment, the reference value is the level of the at least one miRNA in subject not having an influenza infection. In an embodiment, the reference value is the level of the at least one miRNA in subject not having a respiratory infection. In an embodiment, the respiratory infection is a viral respiratory infection. In an embodiment, the reference value is the level of the at least one miRNA in subject not having cancer. In an embodiment, the reference value is the level of the at least one miRNA in subject not having an inflammatory disorder.
In an embodiment, an altered level of the at least one miRNA in the biological sample compared to a predetermined reference value indicates the presence of a CoV infection.
In an embodiment, the level of the at least one miRNA is a higher level compared to the reference value, and a higher level of the at least one miRNA is indicative of CoV infection in the subject.
In an embodiment, the level of the at least one miRNA is a lower level compared to the reference value, and the lower level of the at least one miRNA is indicative of CoV infection in the subject.
In an embodiment, the method has an accuracy of one or more of at least 80%, or at least 85%, or at least 90%, or at least 95%, or at least 99%. In an embodiment, the method has an accuracy of at least 90%. In an embodiment, the method has an accuracy of at least 95%. In an embodiment, the method has an accuracy of at least 99%.
In an embodiment, the method can distinguish a CoV infection from another respiratory infection.
In an embodiment, the method can distinguish between a CoV infection and an influenza A infection with at least 95% accuracy. In an embodiment, the method can distinguish between a CoV infection and an influenza A infection with at least 97% accuracy. In an embodiment, the method can distinguish between a CoV infection and an influenza A infection with at least 99% accuracy.
In an embodiment, the method can identify a CoV infection with at least 95% precision. In an embodiment, the method can identify a CoV infection with at least 97% precision. In an embodiment, the method can identify a CoV infection with at least 99% precision.
In an embodiment, the area under the curve (AUC) of the at least one miRNA is at least 0.65. In an embodiment, the area under the curve (AUC) of the at least one miRNA is at least 0.7. In an embodiment, the area under the curve (AUC) of the at least one miRNA is at least 0.75. In an embodiment, the area under the curve (AUC) of the at least one miRNA is at least 0.80. In an embodiment, the area under the curve (AUC) of the at least one miRNA is at least 0.85. In an embodiment, the area under the curve (AUC) of the at least one miRNA is at least 0.90.
In an embodiment, the method comprises detecting the level of at least three miRNAs the method has one or more of i) an accuracy of at least about 99%, ii) a precision of at least about 99% and iii) a AUC of about 1.
In an embodiment, of the methods described herein the level of the at least one miRNA may be normalized. In an embodiment, the level of the at least one miRNA is normalised against a control.
In an embodiment, the method comprises normalizing the level of the at least one miRNA to obtain a normalized level of the at least one miRNA, and wherein the method comprises comparing the normalised level of the at least one miRNA to the reference value of the at least one miRNA.
In an embodiment, the control is an endogenous control. In an embodiment, the endogenous control is a small RNA, for example, a miRNA, small non-coding RNA (ncRNA), transfer RNA (tRNA), small nuclear RNA (snRNA), or small nuceloalar RNA (snoRNA). In an embodiment, the endogenous control is a miRNA.
In an embodiment, the control is an exogenous control, for example an exogenous RNA added to the biological sample before miRNA extraction (a spike-in control). Spike-in controls may be added to a sample before RNA, small RNA and/or miRNA is recovered, the amount of the spike-in control recovered after RNA, small RNA and/or miRNA extraction is directly correlated with the amount of total RNA recovered. In an embodiment, the exogenous RNA is isolated from a host source or is synthetic. Synthetic spike- in controls are available from a number of commercial manufactures including for example, Qiagen and Norgen Biotek Corporation and Life Technologies.
Diagnostic Tests
The miRNAs described herein can be used individually or in combination in diagnostic tests to assess the status, degree, or severity of a CoV infection in a subject. The CoV infection status includes the presence or absence of a CoV virus in the subject. The CoV infection status may also include monitoring the course of the viral infection, for example, monitoring disease progression. Based on the CoV infection status of a subject, additional procedures may be indicated, including, for example, additional diagnostic tests or therapeutic procedures.
The power of a diagnostic test to correctly predict disease status is commonly measured in terms of the accuracy of the assay, the sensitivity of the assay, the specificity of the assay, or the “Area Under a Curve” (AUC), for example, the area under a Receiver Operating Characteristic (ROC) curve. As used herein, accuracy is a measure of the fraction of misclassified samples. Accuracy may be calculated as the total number of correctly classified samples divided by the total number of samples, e.g., in a test population. Sensitivity is a measure of the “true positives” that are predicted by a test to be positive, and may be calculated as the number of correctly identified breast cancer samples divided by the total number of breast cancer samples. Specificity is a measure of the "true negatives" that are predicted by a test to be negative, and may be calculated as the number of correctly identified normal samples divided by the total number of normal samples. AUC is a measure of the area under a Receiver Operating Characteristic curve, which is a plot of sensitivity vs. the false positive rate (1 -specificity). The greater the AUC, the more powerful the predictive value of the test. Other useful measures of the utility of a test include the “positive predictive value,” which is the percentage of actual positives who test as positives, and the “negative predictive value,” which is the percentage of actual negatives who test as negatives. In some embodiments, the level of one or more miRNAs disclosed herein in samples derived from subjects having different CoV infection statuses show a statistically significant difference of at least p = 0.05, e.g., p = 0.05, p = 0.01, p = 0.005, p = 0.001, etc. relative to normal subjects, as determined relative to a suitable control. In other preferred embodiments, diagnostic tests that use miRNAs described herein individually or in combination show an accuracy of at least about 75%, e.g., an accuracy of at least about 75%, about 80%, about 85%, about 90%, about 95%, about 97%, about 99% or about 100%. In other embodiments, diagnostic tests that use miRNAs described herein individually or in combination show a specificity of at least about 75%, e.g., a specificity of at least about 75%, about 80%, about 85%, about 90%, about 95%, about 97%, about 99% or about 100%. In other embodiments, diagnostic tests that use miRNA described herein individually or in combination show a sensitivity of at least about 75%, e.g., a sensitivity of at least about 75%, about 80%, about 85%, about 90%, about 95%, about 97%, about 99% or about 100%. In other embodiments, diagnostic tests that use miRNAs described herein individually or in combination show a specificity and sensitivity of at least about 75% each, e.g., a specificity and sensitivity of at least about 75%, about 80%, about 85%, about 90%, about 95%, about 97%, about 99% or about 100% (for example, a specificity of at least about 80% and sensitivity of at least about 80%, or for example, a specificity of at least about 80% and sensitivity of at least about 95%).
Determining the level of the miRNA in a sample may include measuring, detecting, or assaying the level of the miRNA in the sample using any suitable method, for example, the methods described herein elsewhere. Determining the level of the miRNA in a sample may also include examining the results of an assay that measured, detected, or assayed the level of the miRNA in the sample. In some embodiments, the method may also involve comparing the level of the miRNA in a sample with a reference value.
A change in the level of the miRNA relative to that in a normal subject as assessed using a suitable reference value is indicative of the CoV infection status of the subject. A diagnostic amount of a miRNA that represents an amount of the miRNA above or below which a subject is classified as having a particular CoV infection status can be used. For example, if the miRNA is upregulated in samples derived from an individual having CoV infection as compared to a normal individual (or a reference value), a measured amount above the diagnostic cutoff provides a diagnosis of CoV infection. As is well-understood in the art, adjusting the particular diagnostic cut-off used in an assay allows one to adjust the sensitivity and/or specificity of the diagnostic assay as desired. The particular diagnostic cut-off can be determined, for example, by measuring the amount of the miRNA in a statistically significant number of samples from subjects with different CoV infection statuses, and drawing the cut-off at the desired level of accuracy, sensitivity, and/or specificity. In certain embodiments, the diagnostic cut-off can be determined with the assistance of a classification algorithm, as described herein.
Accordingly, methods are provided for diagnosing CoV infection or identifying the status thereof in a subject, by determining the level of at least one miRNA in a sample containing small RNAs from the subject, wherein a difference in the level of the at least one disclosed miRNA versus that in a reference value is indicative of CoV infection in the subject. In some embodiments, the at least one miRNA preferably includes one or more miRNAs provided in Table 1. For example, the present disclosure provides a method of determining the level of at least one miRNA in a sample containing small RNAs derived from the subject, wherein an increase in the level of the at least one miRNA relative to a reference value is indicative of CoV infection in the subject. Optionally, the method may further comprise providing a diagnosis that the subject has or does not CoV infection based on the level of at least one miRNA in the sample. In addition or alternatively, the method may further comprise correlating a difference in the level or levels of at least one miRNA relative to a reference value with a diagnosis of CoV infection in the subject. In some embodiments, such a diagnosis may be provided directly to the subject, or it may be provided to another party involved in the subject’s care.
While individual miRNA are useful in diagnostic applications for CoV infection, as shown herein, a combination of miRNAs may provide greater predictive value of CoV infection status than a single miRNA when used alone. Specifically, the detection of a plurality of miRNAs can increase the accuracy, sensitivity, and/or specificity of a diagnostic test. Exemplary miRNAs are shown in Table 1. Exemplary miRNA combinations are disclosed herein elsewhere. The disclosure includes the individual miRNA alone and miRNA combinations as set forth herein, and their use in methods and kits described herein.
Accordingly, methods are provided for diagnosing CoV infection in a subject, by determining the level of two or more miRNAs as described herein in a sample containing small RNA from the subject.
Also provided is a method of diagnosing CoV infection in a subject by determining the levels of two or more miRNAs as described herein in a sample containing small RNA from the subject.
Comparison of the sample from the subject with the set of data may be assisted by a classification algorithm, which computes whether or not a statistically significant difference exists between the collective levels of the two or more miRNAs in the sample.
In some embodiments, the methods of the disclosure detect the presence, absence or quantity of at least two miRNAs chosen from Table 1. In some embodiments, the methods of the disclosure detect the presence, absence or quantity of at least three miRNAs chosen from Table 1. In some embodiments, the methods of the disclosure detect the presence, absence or quantity of at least four miRNAs chosen from Table 1. In some embodiments, the methods of the disclosure detect the presence, absence or quantity of at least five miRNAs chosen from Table 1. In some embodiments, the methods of the disclosure detect the presence, absence or quantity of at least six miRNAs chosen from Table 1. In some embodiments, the methods of the disclosure detect the presence, absence or quantity of at least seven miRNAs chosen from Table 1. In some embodiments, the methods of the disclosure detect the presence, absence or quantity of at least eight miRNAs chosen from Table 1.
In some embodiments, the methods of the disclosure detect the presence, absence or quantity of at least two miRNAs chosen from miR-23a-3p, miR-195-5p, miR-423-5p, miR-28-5p, miR-223-5p, and miR-130b-3p. In some embodiments, the methods of the disclosure detect the presence, absence or quantity of at least three miRNAs chosen from miR-23a-3p, miR-195-5p, miR-423-5p, miR-28-5p, miR-223-5p, and miR-130b-3p. In some embodiments, the methods of the disclosure detect the presence, absence or quantity of at least four miRNAs chosen from miR- 23a-3p, miR-195-5p, miR-423-5p, miR-28-5p, miR-223-5p, and miR-130b-3p. In some embodiments, the methods of the disclosure detect the presence, absence or quantity of at least five miRNAs chosen from miR-23a-3p, miR-195-5p, miR-423-5p, miR-28-5p, miR-223-5p, and miR-130b-3p. In some embodiments, the methods of the disclosure detect the presence, absence or quantity of miR-23a-3p, miR-195-5p, miR-423-5p, miR-28-5p, miR-223-5p, and miR-130b-3p.
In some embodiments, the methods of the disclosure detect the presence, absence or quantity of miR-23a-3p, miR-195-5p, and miR-423-5p. In some embodiments, the methods of the disclosure detect the presence, absence or quantity of miR-23a-3p, miR-195-5p, miR-423-5p, and miR-28-5p. In some embodiments, the methods of the disclosure detect the presence, absence or quantity of miR-23a-3p, miR-195-5p, miR-423-5p, and miR-223-5p. In some embodiments, the methods of the disclosure detect the presence, absence or quantity of miR-23a-3p, miR-195-5p, miR-423-5p, and miR-130b-3p. In some embodiments, the methods of the disclosure detect the presence, absence or quantity of miR-23a-3p, miR-195-5p, miR-423-5p, , miR-28-5p, and miR- 223 -5p. In some embodiments, the methods of the disclosure detect the presence, absence or quantity of miR-23a-3p, miR-195-5p, miR-423-5p, miR-28-5p, and miR-130b-3p.
In some embodiments wherein more than one miRNA is used in the methods of the disclosure, a combined score that integrates the level of the multiple miRNA biomarkers within a signature serves as the basis of the prediction of infection.
In some embodiments, the combined score is calculated based on the miRNA level using Equation I below:
(miR-X level *miR-X weight value) +/- (miR-Y level * miR-Y weight value) +/- (miR-Z level * miR-Z weight value).
In some embodiment, the score is compared to a reference value as described herein. In some embodiments wherein more than one miRNA is used in the methods of the disclosure, a combined score that integrates the RT-PCR Ct values of the multiple miRNA biomarkers within a signature serves as the basis of the prediction of infection. Such combined score may be a combining function that can be as simple as sum of the Ct values of specific set of miRNAs. Alternatively, the combined score may be determined by logistic regression, other regression techniques, support vector machines, random forests, neural networks, genetic algorithms, annealing algorithms, weighted sums, additive models, linear models, nearest neighbors or probabilistic models.
In some embodiments, the combined score is the combination of the Ct values of the miRNAs calculated by Equation II below:
(miR-X CT *miR-X weight value) +/- (miR-Y CT * miR-Y weight value) +/- (miR-Z CT * miR-Z weight value).
In Equation I and II, X, Y and Z refer to different miRNAs. In some embodiments, the X, Y and Z miRNAs contribute equally to the score (in such embodiments they are all weighted 1). In some embodiments, one or more of the X, Y and Z miRNAs do not contribute equally to the score (in such embodiments one or more of the miRNAs are assigned a different weight value from one or more of the other miRNA). In some embodiment, the score is compared to a reference value as described herein.
In some embodiments, the combined score is the linear combination of the Ct values of the miRNAs calculated by Equation III below:
Ct(miRNAl) + Ct(miRNA2) - Ct(miRNA3)
In some embodiments, the Ct value of each of the miRNAs used in the prediction ranges from about 10 to about 50. In some embodiments, the Ct value of each of the miRNAs used in the prediction ranges from about 15 to about 45. In some embodiments, the Ct value of each of the miRNAs used in the prediction ranges from about 20 to about 40. In some embodiments, the Ct value of each of the miRNAs used in the prediction ranges from about 25 to about 35. In some embodiments, the Ct value of each of the miRNAs used in the prediction is about 15. In some embodiments, the Ct value of each of the miRNAs used in the prediction is about 20. In some embodiments, the Ct value of each of the miRNAs used in the prediction is about 25. In some embodiments, the Ct value of each of the miRNAs used in the prediction is about 30. In some embodiments, the Ct value of each of the miRNAs used in the prediction is about 35. In some embodiments, the Ct value of each of the miRNAs used in the prediction is about 40. In some embodiments, the Ct value of each of the miRNAs used in the prediction is about 45. In some embodiments, the Ct value of each of the miRNAs used in the prediction is about 50.
In some embodiments, the maximum value of the combining function or combined score for the prediction is from about 10 to about 60. In some embodiments, the maximum value of the combining function or combined score for the prediction is from about 12 to about 55. In some embodiments, the maximum value of the combining function or combined score for the prediction is from about 14 to about 50. In some embodiments, the maximum value of the combining function or combined score for the prediction is from about 15 to about 45. In some embodiments, the maximum value of the combining function or combined score for the prediction is from about 20 to about 40. In some embodiments, the maximum value of the combining function or combined score for the prediction is about 10. In some embodiments, the maximum value of the combining function or combined score for the prediction is about 15. In some embodiments, the maximum value of the combining function or combined score for the prediction is about 20. In some embodiments, the maximum value of the combining function or combined score for the prediction is about 25. In some embodiments, the maximum value of the combining function or combined score for the prediction is about 30. In some embodiments, the maximum value of the combining function or combined score for the prediction is about 35. In some embodiments, the maximum value of the combining function or combined score for the prediction is about 40. In some embodiments, the maximum value of the combining function or combined score for the prediction is about 45. In some embodiments, the maximum value of the combining function or combined score for the prediction is about 50.
In some embodiments, to establish a set of interpretive ranges and a diagnostic threshold, the combined score can be calculated for a set of about 60 patient samples and the scores bucketed into different groups based on the combined score. In some embodiments, the combined score of from about 10 to about 21 is used as the diagnostic threshold. In some embodiments, the combined score of from about 15 to about 21 is used as the diagnostic threshold. In some embodiments, the combined score of from about 21 to about 27 is used as the diagnostic threshold. In some embodiments, the combined score of from about 27 to about 39 is used as the diagnostic threshold. In some embodiments, the combined score of from about 39 to about 45 is used as the diagnostic threshold. Systems
The above described methods can be implemented in any of numerous ways. For example, the embodiments may be implemented using a computer program product (i.e. software), hardware, software or a combination thereof. When implemented in software, the software code can be executed on any suitable processor or collection of processors, whether provided in a single computer or distributed among multiple computers.
Further, it should be appreciated that a computer may be embodied in any of a number of forms, such as a rack-mounted computer, a desktop computer, a laptop computer, or a tablet computer. Additionally, a computer may be embedded in a device not generally regarded as a computer but with suitable processing capabilities, including a Personal Digital Assistant (PDA), a smart phone or any other suitable portable or fixed electronic device.
Also, a computer may have one or more input and output devices. These devices can be used, among other things, to present a user interface. Examples of output devices that can be used to provide a user interface include printers or display screens for visual presentation of output and speakers or other sound generating devices for audible presentation of output. Examples of input devices that can be used for a user interface include keyboards, and pointing devices, such as mice, touch pads, and digitizing tablets. As another example, a computer may receive input information through speech recognition or in other audible format.
Such computers may be interconnected by one or more networks in any suitable form, including a local area network or a wide area network, such as an enterprise network, and intelligent network (IN) or the Internet. Such networks may be based on any suitable technology and may operate according to any suitable protocol and may include wireless networks, wired networks or fiber optic networks.
A computer employed to implement at least a portion of the functionality described herein may include a memory, coupled to one or more processing units (also referred to herein simply as “processors”), one or more communication interfaces, one or more display units, and one or more user input devices. The memory may include any computer-readable media, and may store computer instructions (also referred to herein as “processor-executable instructions”) for implementing the various functionalities described herein. The processing unit(s) may be used to execute the instructions. The communication interface(s) may be coupled to a wired or wireless network, bus, or other communication means and may therefore allow the computer to transmit communications to and/or receive communications from other devices. The display unit(s) may be provided, for example, to allow a user to view various information in connection with execution of the instructions. The user input device(s) may be provided, for example, to allow the user to make manual adjustments, make selections, enter data or various other information, and/or interact in any of a variety of manners with the processor during execution of the instructions.
The various methods or processes outlined herein may be coded as software that is executable on one or more processors that employ any one of a variety of operating systems or platforms. Additionally, such software may be written using any of a number of suitable programming languages and/or programming or scripting tools, and also may be compiled as executable machine language code or intermediate code that is executed on a framework or virtual machine.
In this respect, various inventive concepts may be embodied as a computer readable storage medium (or multiple computer readable storage media) (e.g., a computer memory, one or more floppy discs, compact discs, optical discs, magnetic tapes, flash memories, circuit configurations in Field Programmable Gate Arrays or other semiconductor devices, or other non-transitory medium or tangible computer storage medium) encoded with one or more programs that, when executed on one or more computers or other processors, perform methods that implement the various embodiments of the invention disclosed herein. The computer readable medium or media can be transportable, such that the program or programs stored thereon can be loaded onto one or more different computers or other processors to implement various aspects of the present invention as discussed above. In some embodiments, the system comprises cloud-based software that executes one or all of the steps of each disclosed method instruction.
The terms “program” or “software” are used herein in a generic sense to refer to any type of computer code or set of computer-executable instructions that can be employed to program a computer or other processor to implement various aspects of embodiments as discussed above. Additionally, it should be appreciated that according to one aspect, one or more computer programs that when executed perform methods of the present disclosure need not reside on a single computer or processor, but may be distributed in a modular fashion amongst a number of different computers or processors to implement various aspects of the present invention.
Computer-executable instructions may be in many forms, such as program modules, executed by one or more computers or other devices. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. Typically, the functionality of the program modules may be combined or distributed as desired in various embodiments.
Also, data structures may be stored in computer-readable media in any suitable form. For simplicity of illustration, data structures may be shown to have fields that are related through location in the data structure. Such relationships may likewise be achieved by assigning storage for the fields with locations in a computer-readable medium that convey relationship between the fields. However, any suitable mechanism may be used to establish a relationship between information in fields of a data structure, including through the use of pointers, tags or other mechanisms that establish relationship between data elements.
Also, the disclosure relates to various embodiments in which one or more disclosed methods are used. The acts performed as part of the method may be ordered in any suitable way. Accordingly, embodiments may be constructed in which acts are performed in an order different than illustrated, which may include performing some acts simultaneously, even though shown as sequential acts in illustrative embodiments.
In some embodiments, the disclosure relates to a system that comprises at least one processor, a program storage, such as memory, for storing program code executable on the processor, and one or more input/output devices and/or interfaces, such as data communication and/or peripheral devices and/or interfaces. In some embodiments, the user device and computer system or systems are communicably connected by a data communication network, such as a Local Area Network (LAN), the Internet, or the like, which may also be connected to a number of other client and/or server computer systems. The user device and client and/or server computer systems may further include appropriate operating system software.
In some embodiments, components and/or units of the devices described herein may be able to interact through one or more communication channels or mediums or links, for example, a shared access medium, a global communication network, the Internet, the World Wide Web, a wired network, a wireless network, a combination of one or more wired networks and/or one or more wireless networks, one or more communication networks, an a-synchronic or asynchronous wireless network, a synchronic wireless network, a managed wireless network, a non-managed wireless network, a burstable wireless network, a non-burstable wireless network, a scheduled wireless network, a non-scheduled wireless network, or the like. Discussions herein utilizing terms such as, for example, “processing,” “computing,” “calculating,” “determining,” or the like, may refer to operation(s) and/or process(es) of a computer, a computing platform, a computing system, or other electronic computing device, that manipulate and/or transform data represented as physical (e.g., electronic) quantities within the computer's registers and/or memories into other data similarly represented as physical quantities within the computer’s registers and/or memories or other information storage medium that may store instructions to perform operations and/or processes.
Some embodiments may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment including both hardware and software elements. Some embodiments may be implemented in software, which includes but is not limited to firmware, resident software, microcode, or the like.
Furthermore, some embodiments may take the form of a computer program product accessible from a computer-usable or computer-readable medium providing program code for use by or in connection with a computer or any instruction execution system. For example, a computer-usable or computer-readable medium may be or may include any apparatus that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device.
In some embodiments, the medium may be or may include an electronic, magnetic, optical, electromagnetic, InfraRed (IR), or semiconductor system (or apparatus or device) or a propagation medium. Some demonstrative examples of a computer-readable medium may include a semiconductor or solid state memory, magnetic tape, a removable computer diskette, a Random Access Memory (RAM), a Read-Only Memory (ROM), a rigid magnetic disk, an optical disk, or the like. Some demonstrative examples of optical disks include Compact Disk-Read-Only Memory (CD-ROM), Compact Disk-Read/Write (CD-R/W), DVD, or the like.
In some embodiments, a data processing system suitable for storing and/or executing program code may include at least one processor coupled directly or indirectly to memory elements, for example, through a system bus. The memory elements may include, for example, local memory employed during actual execution of the program code, bulk storage, and cache memories which may provide temporary storage of at least some program code in order to reduce the number of times code must be retrieved from bulk storage during execution. In some embodiments, input/output or I/O devices (including but not limited to keyboards, displays, pointing devices, etc.) may be coupled to the system either directly or through intervening I/O controllers. In some embodiments, network adapters may be coupled to the system to enable the data processing system to become coupled to other data processing systems or remote printers or storage devices, for example, through intervening private or public networks. In some embodiments, modems, cable modems and Ethernet cards are demonstrative examples of types of network adapters. Other suitable components may be used.
Some embodiments may be implemented by software, by hardware, or by any combination of software and/or hardware as may be suitable for specific applications or in accordance with specific design requirements. Some embodiments may include units and/or sub-units, which may be separate of each other or combined together, in whole or in part, and may be implemented using specific, multi-purpose or general processors or controllers. Some embodiments may include buffers, registers, stacks, storage units and/or memory units, for temporary or long-term storage of data or in order to facilitate the operation of particular implementations.
Some embodiments may be implemented, for example, using a machine-readable medium or article which may store an instruction or a set of instructions that, if executed by a machine, cause the machine to perform a method steps and/or operations described herein. Such machine may include, for example, any suitable processing platform, computing platform, computing device, processing device, electronic device, electronic system, computing system, processing system, computer, processor, or the like, and may be implemented using any suitable combination of hardware and/or software. The machine-readable medium or article may include, for example, any suitable type of memory unit, memory device, memory article, memory medium, storage device, storage article, storage medium and/or storage unit; for example, memory, removable or non-removable media, erasable or non-erasable media, writeable or re-writeable media, digital or analog media, hard disk drive, floppy disk, Compact Disk Read Only Memory (CD-ROM), Compact Disk Recordable (CD-R), Compact Disk Re-Writeable (CD-RW), optical disk, magnetic media, various types of Digital Versatile Disks (DVDs), a tape, a cassette, or the like. The instructions may include any suitable type of code, for example, source code, compiled code, interpreted code, executable code, static code, dynamic code, or the like, and may be implemented using any suitable high-level, low-level, object-oriented, visual, compiled and/or interpreted programming language, e.g., C, C++, Java™, BASIC, Pascal, Fortran, Cobol, assembly language, machine code, or the like.
In some embodiments, the methods described herein comprise a circuit. For example, a circuit may be implemented as a hardware circuit comprising custom very-large-scale integration (VLSI) circuits or gate arrays, off-the-shelf semiconductors such as logic chips, transistors, or other discrete components. A circuit may also be implemented in programmable hardware devices such as field programmable gate arrays, programmable array logic, programmable logic devices or the like.
In some embodiment, the circuits may also be implemented in machine-readable medium for execution by various types of processors. An identified circuit of executable code may, for instance, comprise one or more physical or logical blocks of computer instructions, which may, for instance, be organized as an object, procedure, or function. Nevertheless, the executables of an identified circuit need not be physically located together, but may comprise disparate instructions stored in different locations which, when joined logically together, comprise the circuit and achieve the stated purpose for the circuit. Indeed, a circuit of computer readable program code may be a single instruction, or many instructions, and may even be distributed over several different code segments, among different programs, and across several memory devices. Similarly, operational data may be identified and illustrated herein within circuits, and may be embodied in any suitable form and organized within any suitable type of data structure. The operational data may be collected as a single data set, or may be distributed over different locations including over different storage devices, and may exist, at least partially, merely as electronic signals on a system or network.
The computer readable medium (also referred to herein as machine-readable media or machine-readable content) may be a tangible computer readable storage medium storing the computer readable program code. The computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, holographic, micromechanical, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. As alluded to above, examples of the computer readable storage medium may include but are not limited to a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), a portable compact disc read-only memory (CD-ROM), a digital versatile disc (DVD), an optical storage device, a magnetic storage device, a holographic storage medium, a micromechanical storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, and/or store computer readable program code for use by and/or in connection with an instruction execution system, apparatus, or device.
The computer readable medium may also be a computer readable signal medium. A computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated signal may take any of a variety of forms, including, but not limited to, electrical, electro-magnetic, magnetic, optical, or any suitable combination thereof. A computer readable signal medium may be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport computer readable program code for use by or in connection with an instruction execution system, apparatus, or device. As also alluded to above, computer readable program code embodied on a computer readable signal medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, Radio Frequency (RF), or the like, or any suitable combination of the foregoing. In one embodiment, the computer readable medium may comprise a combination of one or more computer readable storage mediums and one or more computer readable signal mediums. For example, computer readable program code may be both propagated as an electro-magnetic signal through a fiber optic cable for execution by a processor and stored on RAM storage device for execution by the processor.
Computer readable program code for carrying out operations for aspects of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C++ or the like and conventional procedural programming languages, such as the “C” programming language or similar programming languages. The computer readable program code may execute entirely on a user's computer, partly on the user’s computer, as a stand-alone computer-readable package, partly on the user’s computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user’s computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet Service Provider).
The program code may also be stored in a computer readable medium that can direct a computer, other programmable data processing apparatus, or other devices to function in a particular manner, such that the instructions stored in the computer readable medium produce an article of manufacture including instructions which implement the function/act specified in the schematic flowchart diagrams and/or schematic block diagrams block or blocks.
Functions, operations, components and/or features described herein with reference to one or more embodiments, may be combined with, or may be utilized in combination with, one or more other functions, operations, components and/or features described herein with reference to one or more other embodiments, or vice versa.
Panels and Kits
The present disclosure provides panels or kits for determining the likelihood of a CoV infection in a subject or for determining the likelihood of a severe CoV infection. The kits of the disclosure will preferably comprise a nucleotide array comprising miRNA-specific probes and/or oligonucleotides for amplifying at least one miRNA described herein.
In an aspect, the disclosure provides a panel or kit for determining the likelihood of a CoV infection in a subject, the panel or kit comprising one or more probes or primers for detecting at least one miRNA as described herein.
In an aspect, the disclosure provides a panel or kit for determining the likelihood of a severe CoV infection in a subject, the panel or kit comprising one or more probes or primers for detecting at least one miRNA described in Table 1.
In some embodiments, the kit comprises one or more reagents for detecting miR-423-5p. In some embodiments, the kit comprises one or more reagents for detecting miR-195-5p. In some embodiments, the kit comprises one or more reagents for detecting miR-23a-3p. In some embodiments, the kit comprises one or more reagents for detecting miR-28-5p. In some embodiments, the kit comprises one or more reagents for detecting miR-223-5p. In some embodiments, the kit comprises one or more reagents for detecting miR-130b-3p. In some embodiments, the kit comprises one or more reagents for detecting miR-423-5p, miR-195-5p, and miR-23a-3p. In some embodiments, the kit comprises one or more reagents for detecting miR- 423-5p, miR-195-5p, miR-23a-3p and miR-28-5p. In some embodiments, the kit comprises one or more reagents for detecting miR-423-5p, miR-195-5p, miR-23a-3p and miR-223-5p. In some embodiments, the kit comprises one or more reagents for detecting miR-423-5p, miR-195-5p, miR-23a-3p, miR-28-5p and miR-223-5p. In some embodiments, the kit comprises one or more reagents for detecting miR-423-5p, miR-195-5p, miR-23a-3p and miR-130b-3p. In some embodiments, the kit comprises one or more reagents for detecting miR-423-5p, miR-195-5p, miR-23a-3p, miR-28-5p, miR-223-5p and miR-130b-3p.
In some embodiments, the kit comprises one or more reagents for detecting miR-142-3p. In some embodiments, the kit comprises one or more reagents for detecting miR-3065-3p. In some embodiments, the kit comprises one or more reagents for detecting miR-93-5p. In some embodiments, the kit comprises one or more reagents for detecting miR-486-5p. In some embodiments, the kit comprises one or more reagents for detecting miR-451a. In some embodiments, the kit comprises one or more reagents for detecting 3065-5p. In some embodiments, the kit comprises one or more reagents for detecting 628-3p. In some embodiments, the kit comprises one or more reagents for detecting miR-19a-3p. In some embodiments, the kit comprises one or more reagents for detecting miR-142-3p and miR-3065-3p. In some embodiments, the kit comprises one or more reagents for detecting miR-142-3p and miR-93-5p. In some embodiments, the kit comprises one or more reagents for detecting miR-142-3p and miR- 3065-3p. In some embodiments, the kit comprises one or more reagents for detecting miR-142-3p, miR-3065-3p, and miR-93-5p. In some embodiments, the kit comprises one or more reagents for detecting miR-142-3p, miR-3065-3p, miR-93-5p, miR-486-5p, miR-451a, miR-3065-5p, miR- 628-3p, and miR-19a-3p.
In some embodiments, the one or more reagents for detecting at least one at least one miRNA comprises a binding molecule which binds a miRNA or a truncated version thereof. In some embodiments, the binding molecule is a polynucleotide, aptamer or antibody. In some embodiments, the binding molecule is detectably labelled.
In some embodiments, the panel or kit further comprises a reference value as described herein. In some embodiments, the reference value comprises a standard curve of at least one miRNA as described herein. In some embodiments, the panel or kit further comprises a control as described herein. In some embodiments, the panel or kit further comprises a standard curve of the control as described herein. In some embodiments, the panel or kit further comprises one or more reagents for detecting the level of a control. In some embodiments, the one or more reagents in a binding molecule which binds an exogenous control as described herein. In some embodiments, the binding molecule is detectably labelled.
In some embodiments, the reference value comprises a standard curve of miR-423-5p. In some embodiments, the reference value comprises a standard curve of miR-195-5p. In some embodiments, the reference value comprises a standard curve of miR-23a-3p. In some embodiments, the reference value comprises a standard curve of miR-28-5p. In some embodiments, the reference value comprises a standard curve of miR-223-5p. In some embodiments, the reference value comprises a standard curve of miR-130b-3p. In some embodiments, the reference value comprises a standard curve of miR-423-5p, miR-195-5p, and miR-23a-3p. In some embodiments, the reference value comprises a standard curve of miR-23a- 3p, miR-195-5p, miR-423-5p, and miR-28-5p. In some embodiments, the reference value comprises a standard curve of miR-23a-3p, miR-195-5p, miR-423-5p, miR-28-5p, and miR-223- 5p. In some embodiments, the reference value comprises a standard curve of miR-23a-3p, miR- 195-5p, miR-423-5p, miR-28-5p, miR-223-5p, and miR-130b-3p. In some embodiments, the reference value comprises a standard curve of miR-423-5p. In some embodiments, the reference value comprises a standard curve of mir-195-5p. In some embodiments, the reference value comprises a standard curve of mir-23a-3p. In some embodiments, the reference value comprises a standard curve of miR-28-5p. In some embodiments, the reference value comprises a standard curve of miR-223-5p. In some embodiments, the reference value comprises a standard curve of miR-130b-3p.
In some embodiments, the reference value comprises a standard curve of miR-142-3p. In some embodiments, the reference value comprises a standard curve of miR-3065-3p. In some embodiments, the reference value comprises a standard curve of miR-93-5p. In some embodiments, the reference value comprises a standard curve of miR-486-5p. In some embodiments, the reference value comprises a standard curve of miR-451a. In some embodiments, the reference value comprises a standard curve of miR-3065-5p. In some embodiments, the reference value comprises a standard curve of miR-628-3p. In some embodiments, the reference value comprises a standard curve of miR-19a-3p. In some embodiments, the reference value comprises a standard curve of miR-142-3p and miR-3065-3p. In some embodiments, the reference value comprises a standard curve of miR-142-3p and miR-93-5p. In some embodiments, the reference value comprises a standard curve of miR-142-3p and miR-3065-3p. In some embodiments, the reference value comprises a standard curve of miR-142-3p, miR-3065-3p, and miR-93-5p. In some embodiments, the reference value comprises a standard curve of miR-142-3p, miR-3065-3p, miR-93-5p, miR-486-5p, miR-451a, miR-3065-5p, miR-628-3p, and miR-19a-3p.
In some embodiments, the reference value comprises a predetermined threshold of a miRNA described in Table 1. In some embodiments, the reference value comprises a predetermined threshold level of miR-423-5p. In some embodiments, the reference value comprises a predetermined threshold level of miR-195-5p. In some embodiments, the reference value comprises a predetermined threshold level of miR-23a-3p. In some embodiments, the reference value comprises a predetermined threshold level of miR-28-5p. In some embodiments, the reference value comprises a predetermined threshold level of miR-223-5p. In some embodiments, the reference value comprises a predetermined threshold level of miR-130b-3p. In some embodiments, the reference value comprises a predetermined threshold level of one or more miRNAs selected from: miR-23a-3p, miR-195-5p and miR-423-5p. In some embodiments, the reference value comprises a predetermined threshold level of one or more miRNAs selected from: miR-23a-3p, miR-195-5p, miR-423-5p, and miR-28-5p. In some embodiments, the reference value comprises a predetermined threshold level of one or more miRNAs selected from: miR-23a- 3p, miR-195-5p, miR-423-5p, miR-28-5p, and miR-223-5p. In some embodiments, the reference value comprises a predetermined threshold level of one or more miRNAs selected from: miR-23a- 3p, miR-195-5p, miR-423-5p, miR-28-5p, miR-223-5p, and miR-130b-3p.
In some embodiments, the kit comprises one or more reagents for detecting miR-142-3p. In some embodiments, the kit comprises one or more reagents for detecting miR-3065-3p. In some embodiments, the kit comprises one or more reagents for detecting miR-93-5p. In some embodiments, the kit comprises one or more reagents for detecting miR-486-5p. In some embodiments, the kit comprises one or more reagents for detecting miR-451a. In some embodiments, the kit comprises one or more reagents for detecting 3065-5p. In some embodiments, the kit comprises one or more reagents for detecting 628-3p. In some embodiments, the kit comprises one or more reagents for detecting miR-19a-3p. In some embodiments, the kit comprises one or more reagents for detecting miR-142-3p and miR-3065-3p. In some embodiments, the kit comprises one or more reagents for detecting miR-142-3p and miR-93-5p. In some embodiments, the kit comprises one or more reagents for detecting miR-142-3p and miR- 3065-3p. In some embodiments, the kit comprises one or more reagents for detecting miR-142-3p, miR-3065-3p, and miR-93-5p. In some embodiments, the kit comprises one or more reagents for detecting miR-142-3p, miR-3065-3p, miR-93-5p, miR-486-5p, miR-451a, miR-3065-5p, miR- 628-3p, and miR-19a-3p.
In an aspect, the disclosure provides a nucleotide array for determining the likelihood of a CoV infection in a subject, the nucleotide array comprising miRNA-specific probes for at least one miRNA as described herein.
In an aspect, the disclosure provides a nucleotide array for determining the likelihood of a severe CoV infection in a subject, the nucleotide array comprising miRNA-specific probes for at least one miRNA as described herein.
In some embodiments, the panel or kit as described herein is for next generation sequencing, quantitative real-time reverse transcription-PCR (qRT-PCR), isothermal amplification, electrical interference, CRISPR-based method, nanomaterial-based methods, nucleic acid amplification-based methods such as rolling circle amplification (RCA), loop- mediated isothermal amplification (LAMP), strand-displacement amplification (SDA), enzyme- free amplification, a microarray, a multiplex miRNA profiling assay, RNA-ish, or northern blotting.
In some embodiment, the panel or kit uses qRT-PCT to detect the miRNA. In some embodiments, the panel or kit uses electrical interference to detect the miRNA. In some embodiments, the panel or kit uses a CRISPR-based method to detect the miRNA. In some embodiment, the kit comprises a Casl3 nuclease. In some embodiments, the kit comprises a Casl3a nuclease.
In some embodiments, the panel or kit comprises not more than about 50 miRNA.
In some embodiments, the panel or kit comprises not more than about 40 miRNA. In some embodiments, the panel or kit comprises not more than about 30 miRNA. In some embodiments, the panel or kit comprises not more than about 20 miRNA. In some embodiments, the panel or kit comprises not more than about 10 miRNA. In some embodiments, the panel or kit comprises not more than about 5 miRNA. In some embodiments, the panel or kit comprises not more than about 3 miRNA. In some embodiments, the panel or kit as described herein is for ex vivo analysis. In some embodiments, the kit is suitable for use with blood samples or a fraction there of e.g. blood or serum.
In some embodiments, the panel or kit as described herein is suitable for high-throughput screening. The term “high-throughput screening” refers to screening methods that can be used to test or assess more than one sample at a time and that can reduce the time for testing multiple samples. In some embodiments, the methods are suitable for testing or assessing at least about 5 samples, at least about 10, at least about 20, at least about 30, at least about 50, at least about 70, at least about 90, at least about 150, at least about 200, at least about 300 samples at a time. Such high-throughput screening methods can analyse more than one sample rapidly e.g. in at least about 30 minutes, in at least about 1 hour, in at least about 2 hours, in at least about 3 hours, in at least about 4 hours, in at least about 5 hours, in at least about 6 hours, in at least about 7 hours, in at least about 8 hours, in at least about 9 hours or in at least about 10 hours. High-throughput screening may also involve the use of liquid handling devices. In some embodiments, high- throughput analysis may be automated.
Methods of Treatment
In some embodiments, the present disclosure provides a method of monitoring a CoV infection in a subject or evaluating the efficacy of a CoV treatment in a subject, the method comprising detecting a level of at least one miRNA as described herein in a biological sample from the subject at a first time point and at least one subsequent time point. In some embodiments, the method comprises determining whether a subject has recovered from a CoV infection.
In some embodiments, where a subject is diagnosed with having CoV infection by the methods described herein, the present disclosure further provides methods of treating such subjects identified to have CoV infection.
Active agents suitable for treating CoV infection may include, for example, substances approved by the U.S. Food and Drug Administration for the treatment of CoV infection. Other active agents known to be used for treating CoV infection, such as antivirals, targeted drug therapies, convalescent plasma, and oxygenation therapy, can also be used. In some embodiments, the oxygenation therapy is selected from one or more of: supplemental oxygen, high-flow nasal cannula oxygen, non-invasive positive pressure ventilation, extracorporeal membrane oxygenation and intubation with mechanical ventilation. Non-limiting examples of active agents for treating CoV infection may include those provided in Table 2.
Table 2. List of active agents for treating coronavirus infection.
Figure imgf000091_0001
The active agents may be administered to a subject using a pharmaceutical composition. Suitable pharmaceutical compositions comprise a pharmaceutically effective amount of such active agent (or a pharmaceutically acceptable salt or ester thereof), and optionally comprise a pharmaceutically acceptable carrier). In certain embodiments, these compositions optionally further comprise one or more additional therapeutic agents.
As used herein, the term "pharmaceutically acceptable salt" refers to those salts which are, within the scope of sound medical judgment, suitable for use in contact with the tissues of humans and lower animals without undue toxicity, irritation, allergic response and the like, and are commensurate with a reasonable benefit/risk ratio. Pharmaceutically acceptable salts of amines, carboxylic acids, and other types of compounds, are well known in the art. For example, S. M. Berge et al. (1977). The salts can be prepared in situ during the final isolation and purification of the compounds of the invention, or separately by reacting a free base or free acid function with a suitable reagent. For example, a free base function can be reacted with a suitable acid. Furthermore, where the compounds carry an acidic moiety, suitable pharmaceutically acceptable salts thereof may, include metal salts such as alkali metal salts, e.g. sodium or potassium salts; and alkaline earth metal salts, e.g. calcium or magnesium salts.
The term "pharmaceutically acceptable ester", as used herein, refers to esters that hydrolyze in vivo and include those that break down readily in the human body to leave the parent compound or a salt thereof. Suitable ester groups include, for example, those derived from pharmaceutically acceptable aliphatic carboxylic acids, particularly alkanoic, alkenoic, cycloalkanoic and alkanedioic acids, in which each alkyl or alkenyl moiety advantageously has not more than 6 carbon atoms.
As described above, the pharmaceutical compositions may additionally comprise a pharmaceutically acceptable carrier. The term carrier includes any and all solvents, diluents, or other liquid vehicle, dispersion or suspension aids, surface active agents, isotonic agents, thickening or emulsifying agents, preservatives, solid binders, lubricants and the like, suitable for preparing the particular dosage form desired. Remington's Pharmaceutical Sciences, Sixteenth Edition, E. W. Martin (Mack Publishing Co., Easton, Pa., 1980) discloses various carriers used in formulating pharmaceutical compositions and known techniques for the preparation thereof. Some examples of materials which can serve as pharmaceutically acceptable carriers include, but are not limited to, sugars such as lactose, glucose and sucrose; starches such as corn starch and potato starch; cellulose and its derivatives such as sodium carboxymethyl cellulose, ethyl cellulose and cellulose acetate; powdered tragacanth; malt; gelatine; talc; excipients such as cocoa butter and suppository waxes; oils such as peanut oil, cottonseed oil; safflower oil, sesame oil; olive oil; corn oil and soybean oil; glycols; such as propylene glycol; esters such as ethyl oleate and ethyl laurate; agar; buffering agents such as magnesium hydroxide and aluminium hydroxide; alginic acid; pyrogen free water; isotonic saline; Ringer's solution; ethyl alcohol, and phosphate buffer solutions, as well as other non-toxic compatible lubricants such as sodium lauryl sulfate and magnesium stearate, as well as coloring agents, releasing agents, coating agents, sweetening, flavoring and perfuming agents, preservatives and antioxidants can also be present in the composition, according to the judgment of the formulator. Compositions for use in the present disclosure may be formulated to have any concentration of the active agents for treating CoV infection as desired. In some embodiments, the composition is formulated such that it comprises a therapeutically effective amount of such active agent.
EXAMPLES
Example 1 - Materials and methods
Ferret Infection Trials
Twenty ferrets (approximately four months of age) were exposed to 4.64 x 104 TCID50 of SARS-COV-2 (hCoV-19/Australia/VIC01/2020) by the intranasal route. Prior to any manipulations, animals were immobilised with a mixture of ketamine HC1 (5 mg/kg) and medetomidine (0.05 mg/kg); atimepazole was administered for reversal at a dose of 0.25 mg/kg. After virus exposure, animals were monitored for clinical signs of disease, and fever. They were randomly assigned to euthanasia on post-exposure days 3, 5, 7, 9 or 14, when clinical samples including nasal washes, serum and urine were collected together with multiple tissue specimens. Viral loads in tissues, swabs and nasal wash samples were assessed by qRT-PCR (Corman et al., 2020). Eleven ferrets (aged 4-6 months) were exposed to 1 x 105 TCID50 of influenza A (H1N1) virus as described (Rockman et al., 2012).
RNA Isolation and Heparinase Treatment
Total RNA was isolated from 200 pL of human plasma, 150 pL of ferret serum or 200 pL viral transport media for nasal mucosal samples using the miRNeasy micro kit (Qiagen) as per the manufacturer’s instructions with one modification: glycogen (10 pg, Sigma Aldrich, G1767) was added as a carrier to each sample after lysis with Qiazol. As the human plasma samples were originally obtained using sodium heparin vacutainers, the eluted RNA was treated with 1U heparinase I (Sigma Aldrich, H2519) at 25°C for 30 min to remove any remaining heparin.
Next-Generation Sequencing
Complementary DNA libraries were generated using the QIAseq miRNA Library Kit and QIAseq miRNA NGS 48 Index IL (Qiagen) as per the manufacturer’s protocol, with slight modifications: 5 pL of eluted RNA was used as the template and the libraries underwent 24 cycles of amplification. All libraries were analysed on the Bioanalyser 2100 using the High Sensitivity DNA Kit (Agilent) to ensure correct insert size and minimal adapter or primer carryover. Libraries were then sent to the Australian Genome Research Facility (AGRF) for 100 bp single end sequencing on the NovaSeq 6000 (Illumina). Due to technical issues, 1 VI COVTD samples could not be sequenced but was used in qRT-PCR validation.
Figure imgf000094_0001
Adapters were trimmed using cutadapt (Martin et al., 2011) with a read length parameter (18-26 nucleotides). The remaining reads were examined using FastQC (www.bioinformatics.babraham.ac.uk/projects/fastqc/) to ensure high-quality data. miRDeep2 (Friedlander et al, 2012) was used to map and quantify reads against the latest miRBase human reference (version 22) (Kozomara et al, 2019). Raw read counts were normalized and differential expression analysis was completed using the DESeq2 (Love et al., 2014) package in R. An adjusted False Discovery Rate (FDR) p- value of <0.05 was used to identify differentially expressed miRNAs.
Machine Learning
All machine learning analysis was conducted using the scikit-learn (Pedregosa et al, 2011) module in python. Normalized reads were first examined for highly correlated miRNAs; any pairs with a Pearson R of >0.8 or <-0.8 had one member removed. Highly correlated features (miRNAs) can impact the performance of machine learning algorithms. Multicollinearity can cause skewed or misleading results, especially in models such as logistic regression. The remaining normalized miRNA counts were scaled using either a standard z-score transformation or a robust scaler (where the median is removed and the data is scaled according to the interquartile range). Feature selection was performed using recursive feature elimination (RFE) to identify the miRNAs that contributed the most to the classification model. For binary classification, a logistic regression model was used. For multiclass classification, a linear support vector classifier was used. Each model underwent hyperparameter tuning using GridSearchCV. To assess the performance of the classification model, the data was randomly split into 70% labelled training data and 30% unlabelled test data, and the predicted classes of the test data samples were compared to the true classes. This process was repeated 1,000 times to ensure confidence in the classification performance. The machine learning models were assessed on their accuracy (how many of the predictions were correct), precision (how many of the predicted positives were true positives), and recall (how many of the true positives were found by the model). The logistic regression model was also assessed using the receiver operating characteristic area under the curve (ROC AUC), which is a succinct metric to describe a binary classification model (Tribolet et al, 2020). miRNA cDNA was generated using the TaqMan Advanced miRNA cDNA Synthesis Kit (Applied Biosystems) with 2 pL of input RNA as per manufacturer’s instructions. Quantitative PCT (qPCR) was conducted using IX TaqMan Advanced miRNA Assay, IX TaqMan Fast Universal PCR Master Mix, no AmpErase UNG (Applied Biosystems) and 5 mΐ of 1:10 diluted cDNA product, using standard PCR cycling conditions (95 °C for 20 sec, 40 cycles of 95 °C for 1 sec, 60 °C for 20 sec). Cycle threshold for all assays was set to 0.1. Data is presented as fold over detectable, as previously described (Hardikar et al., 2014), with a detectability cut off of CT = 40.
Cytokine Analysis
Plasma was diluted 1:2 and cytokine abundance measured using the LEGENDplexTM Human Inflammation Panel 1 kit, as per manufacturer’s instructions (BioLegend).
Statistics
Differences in qRT-PCR results were examined using a Mann- Whitney U test, while differences in IL-6 expression was assessed using a t-test. A p-value <0.05 was considered significant.
Example 2 - Host miRNA responses to SARS-COV2 infection
Plasma samples were obtained from ten COVID-19 patients and ten age- and gender- matched healthy controls. Longitudinal samples were available for some COVID-19 patients, categorized by visit (V), with VI representing the plasma sample first taken following hospital admission. Plasma samples were first obtained from COVID-19 patients 2-15 days (average 8 days) post symptomatic disease onset. Small RNA deep sequencing resulted in 23-50 million (average 34 million) raw reads per sample. Reads were trimmed of adaptors and filtered on length and quality, resulting in a loss of 29-74% (average 56%) of raw reads, leaving 8-35 million (average 15.2 million) reads per sample for further analysis (Figure 5). The majority of sequences were deemed high quality by FASTQC (data not shown).
MiRDeep2 was used to identify all known miRNA transcripts amongst the 29 samples and read counts were determined for each mature miRNA transcript. Total counts included all reads that mapped to a locus (as opposed to reads matching the canonical/consensus sequence only). A total of 985 different mature miRNA transcripts were detected, corresponding to 756 different precursors (5p and 3p miRNAs were counted separately). A significant difference in the total number of miRNAs identified in infected versus uninfected patients was not observed (data not shown). The most abundant miRNA in the plasma dataset were miR-16-5p, followed by miR-223- 3p, let-7b-5p and miR-146a-5p.
DESeq2 was used to identify miRNAs with significantly altered expression levels between healthy control (n=10) and COVID-19 VI (n=7) samples. By using DESeq2 to perform count- based differential expression (DE) testing, a subset of miRNAs that were up- or down-regulated in infected patients relative to uninfected controls were identified (Figure la, Table 3). Table 3 also provides the results for has-miR-23a-3p. It is downregulated, but not to an extent outlined by the above parameters. In addition the Table 3 provides the results for hsa-miR-130b-3p hsa-miR- 223-5p and hsa-miR-223 -3p. Using a False Discovery Rate (FDR) adjusted p-value <0.05, log2 fold change (FC) >1 and baseMean >5, this dataset consisted of 50 miRNAs, of which 20 were up-regulated (elevated in infected patients) and 30 were down- regulated. An additional 5 miRNAs were significantly DE in COVTD-19 patients with log2FC values <1. The most highly up-regulated candidates in COVID-19 patients were miR-31-5p reported to be associated with inflammatory disorders (Figure la), miR-3125-5p and miR- 4742-3p, while the most down-regulated were miR- 1275 (Figure la), miR-3617-5p and miR-500b-5p. The most statistically significant change was seen in miR-766-3p (Figure la), a known anti-inflammatory miRNA. Unsupervised analysis of variance using principal components analysis (PCA) involving the 55 DE miRNAs showed tight clustering of patient groups (Figure lb). qRT-PCR was employed to validate select DE miRNA expression (Figure lc). Quantitation of circulating cytokines highlighted a significant increase in IL-6 (Figure Id) during acute COVID-19 illness. Other patient cytokine data is shown in Table 4. One significantly reduced miRNA, miR-766-3p, has been shown to reduce IL-6 expression in a dose-dependent manner, while another, miR 4662a-5p (Figure lc), is predicted to target IL-6 mRNA (TargetScan, release 7.2). The miRNAs may play a role in the hyperinflammatory state commonly seen in patients with COVID-19.
Table 3. Differentially expressed miRNAs in VI COVID-19 patients.
Figure imgf000097_0001
Figure imgf000098_0001
Table 4. Human cytokine data.
Figure imgf000098_0002
Figure imgf000099_0001
Example 3 - miRNA biomarkers for COVID-19 detection
Technologies most commonly utilized for COVID-19 diagnosis are virus-targeting molecular assays or serology, both of which can be associated with relatively high false-positive rates (Kanne et al, 2020; Ai et al., 2020). It was assessed whether, during the early stages of COVID-19, infected patients displayed a miRNA profile that could independently identify SARS- CoV-2 infection. A supervised machine learning method was implemented for the identification of the most predictive miRNAs and refined to identify the minimum targets necessary for accurate prediction and classification between healthy control and COVID-19 (VI) samples. A logistic regression model was implemented that randomly split the data into discovery and validation sets, trained and tested the model, which was repeated 1,000 times to determine reproducibility. The most predictive miRNAs were selected using recursive feature elimination (Figure 2a). Measuring a single miRNA in blood samples (miR-195-5p) identified COVID-19 (VI) cases with -90% accuracy, 95% precision, and 72% recall with a receiver operating characteristic area under the curve (ROC AUC) of 0.9. Measuring three miRNA targets (miR-423-5p, miR-23a-3p and miR- 195-5p) in combination in blood samples gave a model with 99.9% accuracy, 99.8% precision and 99.9% recall, with a ROC AUC of 1.0 (Figure 2b). The biomarker is comprised of two miRNAs DE in COVID-19 patients (miR-423-5p and miR-195-5p, both upregulated) and miR-23a-3p, which was not DE. Increasing candidates within the biomarker signature to more than three miRNAs did not improve test performance. A decision boundary graph showed clear distinctions between healthy and infected patients based on these three miRNAs (Figure 2c). The decision boundary graph also clearly shows that each sample’s grouping was predicted with a high degree of confidence (0% probability of healthy samples being identified as infected with SARS-CoV-2, and 100% probability of COVTD-19 samples being detected as infected). The probability of a sample being infected with SARS-CoV- 2 is determined by its distance from the decision boundary. The absence of points close to the boundary supports the high predictive accuracy of this miRNA signature. Samples taken at successive timepoints (V2, V3 and V4) cluster with the healthy controls, indicating a return to normal baseline and suggesting that the three-miRNA signature is associated with the early stages of COVTD-19 (Figure 2d). An exception to this is one sample from V2, V3, and V4 that clusters closer to the VI COVTD-19 samples (indicated by # in Figure 2d); these samples all came from the same participant who was treated in the intensive care unit (ICU) and had not recovered from COVTD-19 at any of the timepoints sampled. Testing this model on the later time points reduced the accuracy of the biomarker signal to 27.3%.
Recent studies have shown that pro-inflammatory cytokines (IL-6, IL-8, TNF-a, IL-Ib) are differentially expressed in COVTD-19 patients according to severity. In this study prospective differences between miRNA profiles in moderate and severe COVTD-19 cases was investigated. In this analysis the need for infected patients to receive supplemental oxygen (O2) or intubation was used as a proxy marker for severe disease. This metric has been previously used to categorise COVTD-19 severity. COVTD-19 VI patient samples were split into two groups (COVTD-19 and COVTD-19 + O2) based on the need for supplemental oxygen or intubation and then compared to the healthy controls. Analysis revealed that COVTD-19 patients requiring oxygenation had fewer DE miRNAs in circulation (15 vs 42) compared to patients not requiring oxygenation (Figure 3a, Table 5). Four miRNAs (let-7e-5p, miR-651-5p, miR-766-3p, and miR-4433b-5p) were differentially expressed in both groups, suggesting that these molecules might be potential candidates for stratifying patients based on severity. Indeed, the healthy, COVTD-19 and COVTD- 19 + O2 groups clustered based on the expression of these four miRNAs (Figure 3b).
Table 5. Differentially expressed miRNAs in COVTD patients with and without oxygen therapy compared to healthy controls.
Figure imgf000101_0001
Figure imgf000102_0001
indicates DE miRNAs common in both comparisons (Table 3 and Table 5).
Example 4: Application of biomarkers in the ferret model of respiratory infections
It was investigated whether the biomarker of early-stage COVTD-19 was robust in an animal model and could distinguish between different viral respiratory infections. To address this, infection studies were performed in domestic ferrets ( Mustla putorius furo ), a well-established model for human respiratory viruses, including SARS-CoV-2 and influenza virus. Twenty adult ferrets were exposed to SARS-CoV-2 via the intranasal route and monitored for clinical signs, with four ferrets euthanized at 3, 5, 7, 9, and 14 days post-exposure (d.p.e.). The establishment of infection was confirmed by performing qRT-PCR for viral genomic RNA on tissues and swabs (Figure 4a). High viral load was detected in nasal wash samples from day 3, which declined over time and was negative in all ferrets by 14 d.p.e. Eleven ferrets were infected with influenza A(H1N1) virus via the intranasal route, with animals euthanized at days 1, 2, 3, 5, 6 and 7 d.p.e. Influenza virology data in tissue and swab samples is shown in Figure 6. Viral load was detected in nasal wash samples from 1 to 7 d.p.e.
Small RNA from serum samples were profiled for miRNAs using the same methodology as the patient samples. Sera from 12 uninfected ferrets were included as controls. In the ferret model, the previously identified biomarker signature (miR-423-5p, miR-23a-3p and miR-195-5p) could independently distinguish uninfected ferrets from COVID-19 infected ferrets with 99.7 % accuracy, 99.5 % precision, 100 % recall, and a ROC AUC of 1.0 (Figure 4b). As with the human plasma samples, the decision boundary graph displayed high confidence in the predicted groupings (Figure 4c). The miRNA biomarker still identified SARS-CoV-2 infection at 14 d.p.e., by which time ferrets were SARS-CoV-2 negative by nasal wash qRT-PCR, but with virus replication observed in the retroperitoneal lymph node tissue of 3 out of 4 ferrets (Figure 4a). In addition, the biomarker could distinguish SARS-CoV-2 infection from influenza infection and healthy control ferrets with 95 % accuracy, 95.5 % precision and 94.6 % recall (Figure 4d). The decision boundary graph comparing predicted grouping and true grouping is shown in Figure 4e.
Results
In plasma samples obtained soon after the onset of disease symptoms (VI), a total of 55 miRNAs were DE, with several miRNAs more than 50-fold up-regulated (miR-31-5p, miR-3125- 5p, miR-4742-3p) or down-regulated (miR-1275-5p, miR-3617-5p, miR-500b-3p) compared to basal miRNA expression levels in healthy donors. A response involving three miRNAs (miR- 423, 5p, miR-23a-3p and miR-195-5p) was consistently observed in COVID-19 patients at VI and could independently classify SARS-CoV-2 infection with >99% accuracy. Ferret infection trials showed that this signature response was robust across species and was still valid during time points where SARS-CoV-2 replication was observed in internal organs but not in nasal wash samples.
This signature was not determined based on FC differences in miRNA expression between infected and control groups, as miR-423-5p and miR-195-5p were relatively mildly up-regulated COVID-19 patients (log2FC 2.35 and 0.86, respectively), while miR-23-3p was non-significantly down-regulated (log2FC -0.64, adjusted p-value=0.103). It was hypothesized that a biomarker consisting of multiple miRNAs is more robust than one based on absolute or relative levels of a single miRNA. The three-miRNA signature was robust in humans and ferrets, despite a relatively poor overlap in DE miRNAs observed in human and ferret COVID-19 samples (Table 6). While miR-423-5p, miR-23a-3p and miR-195-5p have not been defined previously as a biomarker for a specific disease, increased expression of circulating miR-423-5p is observed in other conditions such as during heart failure and pulmonary tuberculosis. Increases in circulating mil95-5p are associated with osteosarcoma, autism and gestational diabetes mellitus. While host responses to infection are known to be critical in differential outcomes of S ARS-
CoV-2 infection, the role of miRNAs in COVID-19 pathogenesis is poorly understood. miR-31- 5p was the most strongly up-regulated miRNA in COVID-19 patients, which may be related to its role in modulating inflammation. miR-27a-5p (also up-regulated in VI COVID-19 samples), is elevated in animal models of enterocolitis. The up-regulation of miR-31-5p and miR-27a-5p in COVID-19 patients may reflect SARS-CoV-2 mediated gastrointestinal tract infection or inflammation. Furthermore, the most statistically significant down-regulated miRNA was miR- 766-3p, a previously identified anti-inflammatory miRNA. This miRNA was shown to reduce the expression of IL-6 in TNF-a stimulated MH7a cells and so its reduction may be partially responsible for the characteristic IL-6 increase seen in COVID-19 patients. In addition to miR-31- 5p, miR-27a-5p, and miR-766-3p, several miRNAs DE in COVID-19 patients that are poorly characterized from a functional perspective were identified. Many miRNAs upregulated (miR- 3125, miR-4742-3p, miR 2116-3p) or down-regulated (miR-3617-5p, miR-500b-3p, miR-3684) in COVTD 19 patients have not been functionally characterized or previously observed in studies of miRNA responses to viral infection.
Table 6. Differentially expressed miRNAs in ferrets infected with SARS-CoV-2 compared to uninfected ferrets.
Figure imgf000104_0001
Figure imgf000105_0001
Figure imgf000106_0001
Figure imgf000107_0001
indicates differentially expressed miRNAs that were also identified as differentially expressed in human patient plasma samples.
Current COVID-19 molecular tests target viral RNA for detection. Unfortunately, even the most advanced current molecular diagnostic tests (i.e. PCR or LAMP amplifying viral RNA) for SARS-CoV-2 require a relatively high viral load to accurately detect infection (Zhang et al., 2020). Thus, their sensitivity during the early pre-symptomatic phase of disease (incubation period), when the viral load is still low, is poor. Overall sensitivity of current PCR tests has been estimated to be as low as 30-70% (Del Valle et al., 2020; Xu et al, 2020), making it difficult to diagnose infections in many pre-symptomatic and some asymptomatic cases. The study suggests that SARS-CoV-2 infection induces a miRNA response during the early stages of disease that involves three miRNAs (miR-423-5p, miR-23a-3p and miR-195-5p) that can independently identify COVID-19 cases and distinguish SARS-CoV-2 from influenza infections. Further studies involving larger patient groups, including pre-symptomatic, asymptomatic and mild (non-hospitalised) patients, are planned to assess whether this miRNA biomarker can improve COVID-19 detection rates.
Host molecules correlating with COVID-19 severity, such as the proinflammatory cytokine IL-6, are hypothesized to contribute to adverse COVID-19 outcomes and are the focus of ongoing clinical trials to assess treatments for severe COVID-19. The study revealed differential miRNA responses in blood samples patients suffering moderate versus more severe COVID-19. Four miRNAs (let-7e-5p, miR-651-5p, miR-766-3p, and miR-4433b-5p) were differentially expressed in both groups, suggesting that these molecules might be potential candidates for stratifying patients based on severity. All four miRNAs are predicted to target HIF1AN (hypoxia inducible factor 1, alpha subunit inhibitor) (TargetScan, release 7.2) and so may play a role in the hypoxic response during COVID-19. Furthermore, previous studies have demonstrated altered let-7e-5p expression during hypoxic damage to the heart and retina supporting its role in the molecular response to oxygen deprivation. While the other three miRNAs have yet to be linked with hypoxia, miR-766-3p has an established anti-inflammatory role, and miR-4433b-5p is part of a biomarker signature of multi-drug resistant tuberculosis (an indicator of patient prognosis).
This study exemplifies how analysis of miRNA responses to SARS-CoV-2 infection presents novel avenues in the characterization of cellular factors aiding in COVID-19 pathogenesis. It also presents novel opportunities for treatment and diagnosis of viral diseases. Targeting of pro- inflammatory miRNAs could present novel therapeutic opportunities against COVD-19, while miRNA profiling may aid in the disease detection and surveillance.
Example 5: miRNA Biomarkers for COVID-19 Detection in Nasal Mucosal Samples
Nasal mucosa samples were collected from 8 healthy controls and 12 COVID-19 patients. COVID-19 infection was confirmed by performing qRT-PCR for viral genomic RNA on nasopharyngeal swabs using the method described in Example 1 (data not shown).
Mucosal samples were collected using nasopharyngeal swabs and were stored in saline solution or viral transport media at the time of collection. RNA was isolated from 200 mΐ of the saline solution or viral transport media and 5 mΐ of the eluted RNA was used for library prep as described in Example 1. Small RNA from nasal mucosal samples were profiled for miRNAs using the same methodology as described for the human blood samples..
It was found that COVID-19 alters miRNA abundance in nasal swab samples compared to healthy controls (Figure 9). A total of 8 miRNA were identified as differentially expressed in the nasal mucosal samples from COVID-19 patients compared to nasal mucosal samples from normal controls (Table 7). Of these, 2 of the miRNA (hsa-miR-142-3p and hsa-miR-3065-3p) were the same as those identified as differentially expressed in human blood samples. 4 miRNAs were upregulated (hsa-miR-142-3p, hsa-miR-93-5p, hsa-miR-486-5p, and hsa-miR-451a) in COVID- 19 samples and 4 miRNAs were downregulated (hsa-miR-3065-3p, hsa-miR-3065-5p, hsa-miR- 628-3p and hsa-miR-19a-3p) in COVID-19 samples compared to healthy controls. miR-628-3p identifies COVID with 82.62% accuracy, 87.58% precision, 90.23% recall and a 95.43% ROC AUC. miR-628-3p and miR-93-5p identifies COVID with 90.92% accuracy, 95.29% precision, 92.33% recall and 99.54% ROC AUC.
Table 7. Differentially expressed miRNAs in nasal mucosal samples from COVID-19 patients compared to controls.
Figure imgf000109_0001
*Indicates differentially expressed miRNAs that were also identified as differentially expressed in human patient plasma samples.
The supervised machine learning method as described in Example 1 was implemented for the identification of the most predictive miRNAs in nasal mucosal samples and refined to identify the minimum targets necessary for accurate prediction and classification between healthy control and COVID-19. A logistic regression model was implemented that randomly split the data into discovery and validation sets, trained and tested the model, which was repeated 1,000 times to determine reproducibility. The most predictive miRNAs were selected using recursive feature elimination. A three miRNA signature (miR-142-3p, miR-3065-3p and miR-93-5p) in human nasal mucosal samples was identified that classifies COVID with 100% accuracy (Figure 10). The biomarker is comprised of two miRNAs that are upregulated in COVID-19 patients (miR-142-3p, miR-3065-3p and miR-93-5p) and hsa-miR-3065-3p, which is downregulated in COVID-19 subjects.
Example 6: A Software System for the Prediction of Coronavirus Infection
Overview and Description of Clinical Software Workflow (EarlyDx) The methods and systems as described herein can be incorporated into current workflows in diagnostic laboratories with a software application. To meet this end, an Analysis and Interpretation software application (Figure 8, 120) has been designed that meets the regulatory standards necessary for use in such a setting and is easy to use. The software (120) meets FDA 21 CFR part 11 regulatory requirements including secure login security, data integrity checks, audit trails and e-signatures. This will protect patient data while ensuring compliance.
Referring to Figure 8, from an ease-of-use perspective, the EarlyDx Analysis and Interpretation software (120) is designed to work with one of the most common RT-PCR systems- the Applied Biosystems™ (ABI) 7500 Fast Dx Real-Time PCR instrument. The ABI-7500 Fast Dx is FDA-cleared and is commonly used in many CLIA-regulated diagnostic laboratories. During use, this RT-PCR instrument is connected to a PC computer, which has an ABI RT-PCR software (110) installed, e.g. 7500 Fast Dx software. A GUI-friendly EarlyDx Analysis and Interpretation software (120) is also installed on the same computer and uses the standard computer keyboard for data entry. A software step flow chart is shown in Figure 8.
Figure imgf000110_0001
The operator at the clinical laboratory enters patient Sample IDs on the setup screen of the 7500 Fast Dx software (110). This is done by typing in the Sample IDs using the keyboard connected to the ABI-7500 Fast Dx. If the laboratory uses bar code readers as a data input device with its ABI-7500 Fast Dx, the operator may use an attached reader.
During the RT-PCR run of the miRNA test
The ABI-7500 Fast Dx (110) generates the real-time RT-qPCR data (e.g. Ct values of the miRNA analytes), and automatically writes the data on an output SDS file (11 IN). The patient Sample IDs are also automatically embedded in this SDS file (11 IN). The SDS file (11 IN) is automatically saved, often on the local computer hard-drive.
Figure imgf000110_0002
The laboratory operator opens the output SDS file (11 IN) using the 7500 Fast Dx software (110), and manually exports the SDS file (11 IN). On the same computer, the operator then securely logs onto the EarlyDx Analysis and Interpretation software (120) using their personal password phrase, and manually imports the SDS file (11 IN) from the run. The EarlyDx software (121) next reads the SDS file (11 IN) and parses its contents, populating them on a temporary data file readable by the EarlyDx software (120). The operator then executes a command to run the miRNA biomarker analysis using a predictive algorithm (122) (see description in Section 2 below). This proprietary algorithm (122) uses the real-time RT-qPCR data (e.g. Ct values) from the SDS file (11 IN) as inputs and outputs an unique EarlyDx-score. The EarlyDx Analysis and Interpretation software (121) then uses this EarlyDx-score to interpret the probability of SARS-CoV-2 infection in the patient. The results, i.e. the EarlyDx-score and the Probability of Infection, are displayed to the laboratory operator on the software’s (121) graphical user interface (GUI) where they can also be printed.
Figure imgf000111_0001
The EarlyDx Analysis and Interpretation software (121) reads the SDS file (11 IN) and parses its contents, using each embedded patient Sample ID to search the local HL7 (Health Level 7) directory (130) for the first occurrence of an HL7 file that has the corresponding Sample ID tag within the relevant HL7 file element. Once the corresponding HL7 file is located by the EarlyDx Analysis and Interpretation software (121), it reads the contents of this file, and then populates the appropriate fields on the patient medical report (123N) with the following patient information: Name, Sample ID, Medical Report Number, Biospecimen Collection Date, and Date of Symptom Onset.
If no HL7 file exists in the local directory (130), the operator can use the EarlyDx Analysis and Interpretation software (121) to generate an independent report (123N) with only the Sample ID field and no additional patient demographic information. The laboratory operator can then manually link the relevant patient information to this report after its generation.
The EarlyDx Analysis and Interpretation software (121) can import patient demographic data from the LIMS (Laboratory Information Management System) software system (130) via file system transfer. The EarlyDx software (121) can also export patient data and reports to the LIMS system (130), or a secure 21 CFR part 11 -compliant cloud (140).
Computational Prediction of Infection (122) A combining function termed the EarlyDx-score integrates the RT-PCR Ct values of multiple biomarkers within a signature and serves as the basis of the prediction of infection.
The EarlyDx-score may be a combining function that can be as simple as sum of the Ct values of specific set of miRNAs. Or the EarlyDx-score may be determined by logistic regression, other regression techniques, support vector machines, random forests, neural networks, genetic algorithms, annealing algorithms, weighted sums, additive models, linear models, nearest neighbors or probabilistic models.
One example of an EarlyDx-score which provides a high-level of predictive value is the linear combination of the Ct values of miR-423-5p, miR195-5p, miR-23a-3p, (Equation 1). The specific EarlyDx-score is noted in equation 1 below:
EarlyDx-score: Ct(miR-423-5p) + Ct(miR195-5p) - Ct(miR-23a-3p) Equation 1
In this example Ct values of the miRNAs ranged between 25 and 35 so the maximum value of the EarlyDx-score is 45 giving a total score range of 15 to 45.
To establish a set of interpretive ranges and a diagnostic threshold, Early Dx-scores were calculated for a set of 60 patient samples and the scores bucketed in Table 8 below. A EarlyDx- score of 21.0 was selected as the diagnostic threshold because 92% of samples with EarlyDx- scores 21.0 or less were COVID-positive.
Table 8. Ranges of Early Dx-scores for 60 patient samples.
Figure imgf000112_0001
It will be appreciated by persons skilled in the art that numerous variations and/or modifications may be made to the disclosure as shown in the specific embodiments without
Il l departing from the spirit or scope of the disclosure as broadly described. The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive.
This application claims priority from United States Provisional Application No. 63/150,068 entitled “Methods and systems for detecting a coronavirus infection” filed on 16 February 2021, United States Provisional Application No. 63/215408 entitled “Methods and systems for detecting a coronavirus infection” filed on 25 June 2021, and Australian Provisional Application No. 2021902194 entitled “Methods and systems for detecting a coronavirus infection” filed on 16 July 2021, the entire contents of each of which are hereby incorporated by reference.
All publications discussed and/or referenced herein are incorporated by reference herein in their entirety.
Any discussion of documents, acts, materials, devices, articles or the like which has been included in the present specification is solely for the purpose of providing a context for the present disclosure. It is not to be taken as an admission that any or all of these matters form part of the prior art base or were common general knowledge in the field relevant to the present disclosure as it existed before the priority date of each claim of this application.
The steps, features, integers, compositions and/or compounds disclosed herein or indicated in the specification of this application individually or collectively, and any and all combinations of two or more of said steps or features.
REFERENCES
Ai et al. (2020) Radiology 296: https://doi.org/10.1148/radiol.2020200642.
Aquino- Jarquin et al. (2021) Cells 10(7): 1655.
Berge et al. (1977) 66: 1-19.
Blackburn et al. (1996) eds. "DNA and RNA Structure" in Nucleic Acids in Chem and Bio. Blondal et al. (2017) Methods Mol Biol 1580: 21-44.
Cheng et al. (2009) Chem Int Ed Engl 48: 3268-72.
Collins et al. (2021) NatBiom Eng (5): 643-656.
Corman et al. (2020) Euro Surveill 25: https://doi.Org/10.2807/15607917.ES.2020.25.3.2000045. D'Agata et al. (2019) Ana and Bio Chem 411: 4425-44.
Del Valle et al. (2020) Nat Med 26: 1636-43.
El-Khoury et al. (2016) Scientific Reports 6: 19529.
Foster et al. (2020) PNAS 17: 9241-43.
Friedlander et al. (2012) Nucleic Acids Res 40: 37-52.
Friedman et al. (2009) Genome Res 19: 92-105.
Garman (1997) Non-Radioactive Labeling, Academic Press.
Git et al. (2010) RNA 16: 1991-1006.
Griffiths-Jones et al. (2006) Nucleic Acids Res 34(Database issue): D140-4.
Gusev et al. (2001) Am J Pathol 159: 63-69.
Hardikar et al. (2014) J Am Heart Assoc 3: e000792.
Hu et al. (2017) Methods Mol Bil 1617: 169-77.
Hunt et al. (2015) Annu Rev Anal Chem 8: 217-37.
Kanne et al. (2020) Radiology 296: https://doi.org/10.1148/radiol.2020200527.
Khan et al. (2020) PloS One 3;15:e0238344.
Khoury et al. (2016) Scientific Reports 20: 6:19529. doi: 10.1038/srepl9529.
Kozomara et al. (2019) Nucleic Acids Res 47(D1): D155-D62.
Kruskal (1983) SIAM review 25: 201-37.
Lai et al. (2014) J Virol 88: 54-65.
Li et al. (2006) Clin. Chem. 52: 624-633.
Livak et al. (2001) Methods 25: 402-8.
Lizardi et al. (1998) Nat Gen 19: 225-32. Love et al. (2014) Genome Biol. 15: 550.
Makhawi et al. (2021) J Clin Microbiol. 59(3) e00745-20.
Martin (2011) EMBnet. journal 17: 3.
McCombie et al. (2019) Cold Spring Harb Perspect Med 9(11): a036798. Morais et al. (2020) Sci Rep 10: 18289.
Nallur et al. (2001) Nucleic Acids Res 29: El 18.
Needleman and Wunsch (1970) 48: 443-453.
Neubacher et al. (2009) Chembiochem 10: 1289-91.
Pedregosa et al. (2011) J Machine Learning Res 12: 2825-30. Phan et al. (2020) Infection, Genetics and Evolution 81: 104260. Rockman et al. (2012) Vaccine 30: 3618-23.
Schouten et al. (2002) Nucleic Acids Research 30:e57 Shen et al. (2020) Clin Infect Dis. 71: 7 13-20.
Tackett et al. (2017) Methods Mol Biol 1654: 209-19. Tang et al. (2020) National Science Review 7: 1012-23.
Tatusova and Madden (1999) 174: 247-50.
Tian et al. (2015) Org Biomol Chem 13: 2226.
Urban et al. (2019) Advan Mat 31: 1905311.
Urdea (1994) Nature Biotechnology 12: 926-28. Tribolet et al. (2020) Front Microbiol 11: bl 197.
Wong et al. (2019) BMC Genomics 20: 446.
Xu et al. (2020) Lancet Respir Med 8: 420-22.
Zhang et al. (2020) Nature 583: 437-40.
Zhao et al. (2020) PLos Comput Biol 16: el 008269. Ye et al. (2019) J Pharm Analysis 9(4): 207-26.

Claims

1. A method of determining the likelihood of a coronavirus (CoV) infection in a subject, the method comprising, in a biological sample from the subject, detecting a level of at least one miRNA selected from: miR-423-5p, miR-23a-3p, miR-195-5p, miR-766-3p, miR-651-5p, let-7e- 5p, miR-1275, miR-3198, miR-627-5p, miR-4662a-5p, miR-3684, let-7a-5p, miR-483-5p, miR- 3125, miR-3617-5p, miR-500b-3p, miR-664b-3p, miR-5189-3p, miR-6772-3p, miR-145-3p, miR- 30a-5p, miR-27a-5p, miR-2116-3p, miR-31-5p, miR-4772-3p, miR-1290, miR-1226-3p, miR- 589-3p, miR-210-3p, miR-103a-3p, miR-3115, miR-769-3p, miR-320b, miR-193a-5p, miR-206, miR-873-5p, miR-148a-3p, miR-3913-5p, miR-320a-3p, miR-576-5p, miR-197-3p, miR-4742-3p, miR-28-5p, miR-320c, miR-551b-3p, miR-142-3p, let-7i-3p, miR-92a-3p, miR-548k, miR-18a- 3p, miR-491-5p, miR-6721-5p, miR-6503-3p, miR-3065-3p, miR-150-5p, let-7f-5p, miR-106b- 5p, miR-99b-3p, miR-1273h-3p, let-7a-3p, miR-6741-5p, miR-21-3p, miR-301b-3p, miR-7-l-3p, miR-148b-5p, miR-874-3p, miR-889-3p, miR-1468-5p, miR-431-3p, miR-345-5p, miR-339-3p, miR-4433b-5p, miR-542-5p, miR-4750-5p, miR-132-3p, miR-877-5p, miR-1255b-5p, miR-450b- 5p, let-7f-2-3p, miR-30a-3p, miR-23a-5p, miR-215-5p, miR-10a-3p, miR-98-5p, miR-200c-3p, miR-96-5p, miR-486-5p, miR-30e-5p, miR-141-3p, miR-128-3p, miR-l-3p, miR-500a-3p, let- 7b- 5p, miR-22-3p, miR-3661, miR-24-3p, miR-340-5p, miR-574-5p, miR-378c, miR-221-3p, miR- 181c-3p, miR-130a-3p, miR-1306-3p, miR-95-3p, miR-146a-5p, miR-27b-3p, miR-425-5p, miR- 9-5p, miR-4454, miR-148b-3p, miR-599, miR-379-5p, miR-142-5p, miR-451a, miR-191-3p, miR-223-5p, miR-196b-5p, miR-194-5p, let-7d-5p, miR-324-3p, miR-186-5p, miR-502-3p, miR- 423-3p, miR-151b, miR-760, miR-93-3p, miR-382-5p, miR-25-3p, miR-331-3p, miR-29c-3p, miR-1277-5p, miR-320d, miR-769-5p, miR-3064-5p, miR-6529-5p, miR-146b-5p, miR-151a-3p, miR-378a-5p, miR-27a-3p, miR-503-5p, miR-192-5p, miR-147b-3p, miR-182-5p, miR-409-3p, miR-885-3p, miR-106b-3p, miR-125a-3p, miR-542-3p, miR-30d-5p, miR-432-5p, miR-342-3p, miR-200a-3p, miR-30e-3p, miR-1843, miR-26b-3p, miR-26b-5p, miR-499a-5p, miR-200b-5p, miR-125b-l-3p, miR-10b-5p, miR-219a-l-3p, miR-885-5p, miR-574-3p, miR-15b-3p, miR-449a, let-7b-3p, miR-32-5p, miR-339-5p, miR-130b-3p, miR-2114-5p, miR-155-3p, miR-223-3p, miR- 449c-5p, miR-93-5p, miR-3065-5p, miR-628-3p and miR-19a-3p.
2. The method of claim 1, wherein the method comprises detecting a level of at least one miRNA selected from miR-142-3p, miR-3065-3p, miR-93-5p, miR-486-5p, miR-451a, miR- 3065-5p, miR-628-3p, and miR-19a-3p.
3. The method of claim 1, wherein the method comprises detecting a level of at least one miRNA selected from: miR-423-5p, miR-23a-3p, miR-195-5p, miR-766-3p, miR-651-5p, let-7e- 5p, miR-1275, miR-3198, miR-627-5p, miR-4662a-5p, miR-3684, let-7a-5p, miR-483-5p, miR- 3125, miR-3617-5p, miR-500b-3p, miR-664b-3p, miR-5189-3p, miR-6772-3p, miR-145-3p, miR- 30a-5p, miR-27a-5p, miR-2116-3p, miR-31-5p, miR-4772-3p, miR-1290, miR-1226-3p, miR- 589-3p, miR-210-3p, miR-103a-3p, miR-3115, miR-769-3p, miR-320b, miR-193a-5p, miR-206, miR-873-5p, miR-148a-3p, miR-3913-5p, miR-320a-3p, miR-576-5p, miR-197-3p, miR-4742-3p, miR-28-5p, miR-320c, miR-551b-3p, miR-142-3p, let-7i-3p, miR-92a-3p, miR-548k, miR-18a- 3p, miR-491-5p, miR-6721-5p, miR-6503-3p, miR-3065-3p, miR-150-5p, and let-7f-5p.
4. The method of any one of claims 1 to 3, wherein the method comprises detecting a level of miR-423-5p.
5. The method of any one of claims 1 to 4, wherein the method comprises detecting a level of miR-195-5p.
6. The method of any one of claims 1 to 5, wherein the method comprises detecting a level of miR-23a-3p.
7. The method of any one of claims 1 to 6, wherein the method comprises detecting a level of miR-142-3p.
8. The method any one of claims 1 to 7, wherein the method at least comprises detecting a level of miR-3065-3p.
9. The method of any one of claims 1 to 8, wherein the method at least comprises detecting a level of miR-93-5p.
10. The method of any one of claims 1 to 9, wherein the method comprises comparing the level of the at least one miRNA to a reference value.
11. The method of claim 10, wherein the reference value is a predetermined level of the at least one miRNA, a predetermined score, the level of the at least one miRNA in a control sample, or the level of the at least one miRNA in a subject who does not have one or more of a CoV infection, an influenza virus infection and a respiratory infection.
12. The method of any one of claims 1 to 11, wherein the method comprises assigning a score for the sample based on the level of the at least one miRNA.
13. The method of claim 12, wherein the score is assigned using an miRNA analysis algorithm.
14. The method of claim 13, wherein the miRNA analysis algorithm assigns the sample a score using one or more of: i) the level of the at least one miRNA in the sample; ii) the level of at least two miRNA listed in claim 1 in the sample; iii) the level of at least three miRNA listed in claim 1 in the sample; iv) the level of miR-423-5p in the sample; v) the level of miR-195-5p in the sample; vi) the level of miR-23a-3p in the sample; vii) the level of miR-28-5p in the sample; viii) the level of miR-223-5p in the sample; ix) the level of miR-130b-3p in the sample; x) the level of miR-423-5p, miR-195-5p and miR-23a-3p in the sample; xi) the level of miR-423-5p, miR-195-5p, miR-23a-3p, miR-28-5p and miR-223-5p in the sample; xii) the level of miR-423-5p, miR-195-5p, miR-23a-3p, miR-28-5p, miR-223-5p miR- 130b-3 p in the sample; xiii) the level of miR-142-3p in the sample; xiv) the level of miR-3065-3p in the sample; xv) the level of miR-93-5p in the sample; xvi) the level of miR-142-3p and 3065-3p in the sample; and xvii) the level of miR-142-3p, miR-3065-3p, and miR-93-5p in the sample.
15. The method of any one of claims 1 to 12, which further comprises normalizing the level of the at least one miRNA to obtain a normalized level of the at least one miRNA, and wherein the method comprises comparing the normalised level of the at least one miRNA to the reference value of the at least one miRNA.
16. The method of any one of claims 1 to 15, which has an accuracy of one or more of or at least 80%, or at least 85%, or at least 90%, or at least 95%, or at least 99%.
17. The method of any one of claims 1 to 16, wherein the method can distinguish between a CoV infection and an influenza A infection with at least 95% accuracy.
18. The method of any one of claims 1 to 17, wherein the method can identify a CoV infection with at least 95% precision.
19. The method of any one of claims 1 to 18, wherein the area under the curve (AUC) of the at least one miRNA is one or more of at least 0.65, or at least 0.7, or at least 0.75, or at least 0.80, or at least 0.85, or at least 0.90.
20. The method of any one of claims 1 to 19, wherein, when the method comprises detecting the level of at least three miRNAs, the method has one or more of i) an accuracy of at least about 99%, ii) a precision of at least about 99% and iii) a AUC of about 1.
21. The method of any one of claims 1 to 20, wherein the CoV is capable of infecting a human.
22. The method of any one of claims 1 to 21, wherein the CoV infection is selected from: SARS-CoV-2, HCoV-OC43, HCoV-HKUl, HCoV-229E, HCoV-NL63, SARS-CoV and MERS- CoV or a variant thereof.
23. The method of any one of claims 1 to 22, wherein the CoV infection is SARS-CoV-2.
24. The method of any one of claims 1 to 23, wherein the method comprises detecting the level of miR-23a-3p, miR-195-5p and miR-423-5p in a biological sample from a subject.
25. The method of any one of claims 1 to 24, wherein the method comprises detecting the level of miR-142-3p, miR-3065-3p, and miR-93-5p in a biological sample from a subject.
26. The method of any one of claims 1 to 25, wherein the CoV infection can be detected within 14 days or less from the infection date.
27. The method of any one of claims 1 to 26, wherein the CoV infection can be detected before the CoV can be detected with an amplification assay.
28. The method of any one of claims 1 to 27, wherein the subject is pre-symptomatic or asymptomatic for the CoV infection.
29. The method of any one of claims 1 to 28, wherein the subject is mammal.
30. The method of any one of claims 1 to 29, wherein the mammal is a human.
31. The method of any one of claims 1 to 30, wherein the method comprises detecting not more than 50 miRNA, or not more than 40 miRNA, or not more than 30 miRNA, or not more than 20 miRNA, or not more than 10 miRNA, or not more than 6 miRNA, or not more than 5 miRNA, or not more than 3 miRNA.
32. The method of any one of claims 1 to 31, wherein the biological sample is selected from: plasma, serum, whole blood, respiratory tissue, respiratory mucosal sample, saliva or urine.
33. The method of claim 32, wherein the biological sample is plasma.
34. The method of claim 33, wherein the biological sample is a respiratory mucosal sample.
35. The method of claim 34, wherein the biological sample is a nasal mucosal sample.
36. The method of any one of claims 1 to 35, wherein the at least one miRNA is detected by next generation sequencing, quantitative real-time reverse transcription-PCR (qRT-PCR), isothermal amplification, electrical interference, CRISPR-based method, nanomaterial-based methods, nucleic acid amplification-based methods such as rolling circle amplification (RCA), loop-mediated isothermal amplification (LAMP), strand-displacement amplification (SDA), enzyme-free amplification, a microarray, a multiplex miRNA profiling assay, RNA-ish, or northern blotting.
37. The method of claim 36, wherein the next generation sequencing is RNA-seq, small RNA- seq, miRNA-seq or targeted next generation sequencing.
38. A method for determining the likelihood of a severe coronavirus (CoV) infection in a subject, the method comprising, in a biological sample from the subject, detecting a level of at least one miRNA selected from: let-7e-5p, miR-766-3p, miR-651-5p, miR-4433b-5p, miR-106b- 5p, miR-99b-3p, miR-1273h-3p, let-7a-3p, miR-6741-5p, miR-21-3p, miR-301b-3p, miR-7-l-3p, miR-148b-5p, miR-576-5p, miR-3198, miR-4662a-5p, miR-6772-3p, miR-197-3p, miR-874-3p, miR-889-3p, miR-92a-3p, miR-500b-3p, miR-769-3p, miR-1468-5p, miR-431-3p, miR-345-5p, miR-339-3p, miR-103a-3p, miR-30a-5p, miR-6503-3p, miR-542-5p, miR-627-5p, miR-4750-5p, miR-132-3p, miR-877-5p, miR-1255b-5p, miR-1290, miR-450b-5p, let-7f-2-3p, miR-30a-3p, miR-23a-5p, miR-215-5p, miR-10a-3p, miR-150-5p, miR-206, miR-195-5p, miR-320a-3p, miR- 483-5p, miR-5189-3p, miR-98-5p, miR-200c-3p, miR-664b-3p, and miR-96-5p.
39. The method of claim 38, wherein the method comprises comparing the level of the at least one miRNA to a reference value.
40. The method of any one of claims 1 to 39, wherein the method comprises detecting a level of at least one miRNA selected from: let-7e-5p, miR-766-3p, miR-651-5p, miR-4433b-5p, miR- 106b-5p, miR-99b-3p, miR-1273h-3p, let-7a-3p, miR-6741-5p, miR-21-3p, miR-301b-3p, miR-7- l-3p, miR-148b-5p, miR-576-5p, miR-3198, miR-4662a-5p, miR-6772-3p, miR-197-3p, miR- 874-3p, miR-889-3p, miR-92a-3p, miR-500b-3p, miR-769-3p, miR-1468-5p, miR-431-3p, miR- 345-5p, miR-339-3p, miR-103a-3p, miR-30a-5p, miR-6503-3p, miR-542-5p, miR-627-5p, miR- 4750-5p, miR-132-3p, miR-877-5p, miR-1255b-5p, miR-1290, miR-450b-5p, let-7f-2-3p, miR- 30a-3p, miR-23a-5p, and miR-215-5p.
41. The method of any one of claims 1 to 40, wherein the method comprises detecting a level of at least one miRNA selected from: let-7e-5p, miR-766-3p, miR-651-5p, miR-4433b-5p, miR- 10a-3p, miR-150-5p, miR-206, miR-195-5p, miR-320a-3p, miR-483-5p, miR-5189-3p, miR-98- 5p, miR-200c-3p, miR-664b-3p, and miR-96-5p.
42. The method of any one of claims 1 to 41, wherein the method comprises detecting a level of at least one miRNA selected from: let-7e-5p, miR-651-5p, miR-766-3p, and miR-4433b-5p.
43. A method of monitoring a coronavirus (CoV) infection in a subject or evaluating the efficacy of a CoV treatment in a subject, the method comprising detecting a level of at least one miRNA selected from: miR-423-5p, miR-23a-3p, miR-195-5p, miR-766-3p, miR-651-5p, let-7e- 5p, miR-1275, miR-3198, miR-627-5p, miR-4662a-5p, miR-3684, let-7a-5p, miR-483-5p, miR- 3125, miR-3617-5p, miR-500b-3p, miR-664b-3p, miR-5189-3p, miR-6772-3p, miR-145-3p, miR- 30a-5p, miR-27a-5p, miR-2116-3p, miR-31-5p, miR-4772-3p, miR-1290, miR-1226-3p, miR- 589-3p, miR-210-3p, miR-103a-3p, miR-3115, miR-769-3p, miR-320b, miR-193a-5p, miR-206, miR-873-5p, miR-148a-3p, miR-3913-5p, miR-320a-3p, miR-576-5p, miR-197-3p, miR-4742-3p, miR-28-5p, miR-320c, miR-551b-3p, miR-142-3p, let-7i-3p, miR-92a-3p, miR-548k, miR-18a- 3p, miR-491-5p, miR-6721-5p, miR-6503-3p, miR-3065-3p, miR-150-5p, let-7f-5p, miR-106b- 5p, miR-99b-3p, miR-1273h-3p, let-7a-3p, miR-6741-5p, miR-21-3p, miR-301b-3p, miR-7-l-3p, miR-148b-5p, miR-874-3p, miR-889-3p, miR-1468-5p, miR-431-3p, miR-345-5p, miR-339-3p, miR-4433b-5p, miR-542-5p, miR-4750-5p, miR-132-3p, miR-877-5p, miR-1255b-5p, miR-450b- 5p, let-7f-2-3p, miR-30a-3p, miR-23a-5p, miR-215-5p, miR-10a-3p, miR-98-5p, miR-200c-3p, miR-96-5p, miR-486-5p, miR-30e-5p, miR-141-3p, miR-128-3p, miR-l-3p, miR-500a-3p, let- 7b- 5p, miR-22-3p, miR-3661, miR-24-3p, miR-340-5p, miR-574-5p, miR-378c, miR-221-3p, miR- 181c-3p, miR-130a-3p, miR-1306-3p, miR-95-3p, miR-146a-5p, miR-27b-3p, miR-425-5p, miR- 9-5p, miR-4454, miR-148b-3p, miR-599, miR-379-5p, miR-142-5p, miR-451a, miR-191-3p, miR-223-5p, miR-196b-5p, miR-194-5p, let-7d-5p, miR-324-3p, miR-186-5p, miR-502-3p, miR- 423-3p, miR-151b, miR-760, miR-93-3p, miR-382-5p, miR-25-3p, miR-331-3p, miR-29c-3p, miR-1277-5p, miR-320d, miR-769-5p, miR-3064-5p, miR-6529-5p, miR-146b-5p, miR-151a-3p, miR-378a-5p, miR-27a-3p, miR-503-5p, miR-192-5p, miR-147b-3p, miR-182-5p, miR-409-3p, miR-885-3p, miR-106b-3p, miR-125a-3p, miR-542-3p, miR-30d-5p, miR-432-5p, miR-342-3p, miR-200a-3p, miR-30e-3p, miR-1843, miR-26b-3p, miR-26b-5p, miR-499a-5p, miR-200b-5p, miR-125b-l-3p, miR-10b-5p, miR-219a-l-3p, miR-885-5p, miR-574-3p, miR-15b-3p, miR-449a, let-7b-3p, miR-32-5p, miR-339-5p, miR-130b-3p, miR-2114-5p, miR-19a-3p, miR-155-3p, miR- 223 -3p, and miR-449c-5p in a biological sample from the subject at a first time point and at least one subsequent time point.
44. A panel or kit for determining the likelihood of a coronavirus (CoV) infection in a subject, the panel or kit comprising one or more probes or primers for detecting at least one miRNA selected from: miR-423-5p, miR-23a-3p, miR-195-5p, miR-766-3p, miR-651-5p, let-7e-5p, miR- 1275, miR-3198, miR-627-5p, miR-4662a-5p, miR-3684, let-7a-5p, miR-483-5p, miR-3125, miR- 3617-5p, miR-500b-3p, miR-664b-3p, miR-5189-3p, miR-6772-3p, miR-145-3p, miR-30a-5p, miR-27a-5p, miR-2116-3p, miR-31-5p, miR-4772-3p, miR-1290, miR-1226-3p, miR-589-3p, miR-210-3p, miR-103a-3p, miR-3115, miR-769-3p, miR-320b, miR-193a-5p, miR-206, miR- 873-5p, miR-148a-3p, miR-3913-5p, miR-320a-3p, miR-576-5p, miR-197-3p, miR-4742-3p, miR-28-5p, miR-320c, miR-551b-3p, miR-142-3p, let-7i-3p, miR-92a-3p, miR-548k, miR-18a- 3p, miR-491-5p, miR-6721-5p, miR-6503-3p, miR-3065-3p, miR-150-5p, let-7f-5p, miR-106b- 5p, miR-99b-3p, miR-1273h-3p, let-7a-3p, miR-6741-5p, miR-21-3p, miR-301b-3p, miR-7-l-3p, miR-148b-5p, miR-874-3p, miR-889-3p, miR-1468-5p, miR-431-3p, miR-345-5p, miR-339-3p, miR-4433b-5p, miR-542-5p, miR-4750-5p, miR-132-3p, miR-877-5p, miR-1255b-5p, miR-450b- 5p, let-7f-2-3p, miR-30a-3p, miR-23a-5p, miR-215-5p, miR-10a-3p, miR-98-5p, miR-200c-3p, miR-96-5p, miR-486-5p, miR-30e-5p, miR-141-3p, miR-128-3p, miR-l-3p, miR-500a-3p, let- 7b- 5p, miR-22-3p, miR-3661, miR-24-3p, miR-340-5p, miR-574-5p, miR-378c, miR-221-3p, miR- 181c-3p, miR-130a-3p, miR-1306-3p, miR-95-3p, miR-146a-5p, miR-27b-3p, miR-425-5p, miR- 9-5p, miR-4454, miR-148b-3p, miR-599, miR-379-5p, miR-142-5p, miR-451a, miR-191-3p, miR-223-5p, miR-196b-5p, miR-194-5p, let-7d-5p, miR-324-3p, miR-186-5p, miR-502-3p, miR- 423-3p, miR-151b, miR-760, miR-93-3p, miR-382-5p, miR-25-3p, miR-331-3p, miR-29c-3p, miR-1277-5p, miR-320d, miR-769-5p, miR-3064-5p, miR-6529-5p, miR-146b-5p, miR-151a-3p, miR-378a-5p, miR-27a-3p, miR-503-5p, miR-192-5p, miR-147b-3p, miR-182-5p, miR-409-3p, miR-885-3p, miR-106b-3p, miR-125a-3p, miR-542-3p, miR-30d-5p, miR-432-5p, miR-342-3p, miR-200a-3p, miR-30e-3p, miR-1843, miR-26b-3p, miR-26b-5p, miR-499a-5p, miR-200b-5p, miR-125b-l-3p, miR-10b-5p, miR-219a-l-3p, miR-885-5p, miR-574-3p, miR-15b-3p, miR-449a, let-7b-3p, miR-32-5p, miR-339-5p, miR-130b-3p, miR-2114-5p, miR-19a-3p, miR-155-3p, miR- 223-3p, miR-449c-5p, miR-93-5p, miR-3065-5p, miR-628-3p and miR-19a-3p.
45. The panel or kit of claim 44, wherein the panel or kit comprises one or more probes or primers for detecting one or more or all of miR-142-3p, miR-3065-3p, miR-93-5p, miR-486-5p, miR-451a, miR-3065-5p, miR-628-3p, and miR-19a-3p.
46. The panel or kit of claim 45, where the panel or kit further comprises one or more probes or primers for detecting one or more or all of miR-142-3p, miR-3065-3p, and miR-93-5p.
47. A panel or kit for determining the likelihood of a severe coronavirus (CoV) infection in a subject, the panel or kit comprising one or more probes or primers for detecting at least one miRNA selected from: let-7e-5p, miR-766-3p, miR-651-5p, miR-4433b-5p, miR-106b-5p, miR-99b-3p, miR-1273h-3p, let-7a-3p, miR-6741-5p, miR-21-3p, miR-301b-3p, miR-7-l-3p, miR-148b-5p, miR-576-5p, miR-3198, miR-4662a-5p, miR-6772-3p, miR-197-3p, miR-874-3p, miR-889-3p, miR-92a-3p, miR-500b-3p, miR-769-3p, miR-1468-5p, miR-431-3p, miR-345-5p, miR-339-3p, miR-103a-3p, miR-30a-5p, miR-6503-3p, miR-542-5p, miR-627-5p, miR-4750-5p, miR-132-3p, miR-877-5p, miR-1255b-5p, miR-1290, miR-450b-5p, let-7f-2-3p, miR-30a-3p, miR-23a-5p, miR-215-5p, miR-10a-3p, miR-150-5p, miR-206, miR-195-5p, miR-320a-3p, miR-483-5p, miR- 5189-3p, miR-98-5p, miR-200c-3p, miR-664b-3p, and miR-96-5p.
48. The panel or kit of claim 44 or claim 47, where the panel or kit further comprises one or more probes or primers for detecting one or more or all of miR-23a-3p, miR-195-5p, and miR- 423-5p.
49. The panel or kit of any one of claims 44 to 48, further comprising a control.
50. The panel or kit of any one of claims 44 to 49, further comprising a reference value.
51. The panel or kit of any one of claims 44 to 50, wherein the panel or kit comprises a nucleotide array.
52. The panel or kit of any one of claims 44 to 51, wherein the primers are chosen from Table 1
53. A method of treating or preventing a coronavirus (CoV) infection or severe CoV infection in a subject, the method comprising i) determining the likelihood of a CoV infection in a subject using the method of any one of claims 1 to 37 or 40 to 42, and/or determining the likelihood of a severe CoV infection in a subject using the method of any one of claims 38 to 42; and ii) administering a treatment or preventative therapy for a CoV infection if it is determined the subject is likely to have a CoV infection or severe CoV infection.
54. The method of claim 53, wherein treatment or preventative therapy comprises administering one or more of an antiviral, targeted drug therapy, convalescent plasma, and an oxygenation therapy.
55. The method of claim 54, wherein the oxygenation therapy is selected from one or more of: supplemental oxygen, high-flow nasal cannula oxygen, non-invasive positive pressure ventilation, extracorporeal membrane oxygenation and intubation with mechanical ventilation.
56. Use of an anti-coronavirus compound for the manufacture of a medicament for the treatment or prevention of a coronavirus infection or severe coronavirus infection in a subject, wherein it has been determined that it is likely the subject has a coronavirus infection using the method of any one of claims 1 to 37 or 40 to 42, and/or it has been determined that it is likely the subject will have a severe coronavirus infection using the method of any one of claims 38 to 42.
57. Use of an anti-coronavirus compound for the treatment or prevention of a coronavirus infection or severe coronavirus infection in a subject, wherein it has been determined that it is likely the subject has a coronavirus infection using the method of any one of claims 1 to 37 or 40 to 42, and/or it has been determined that it is likely the subject will have a severe coronavirus infection using the method of any one of claims 38 to 42.
58. A method of diagnosing a pre-symptomatic or asymptomatic subject infected with or exposed to a coronavirus (CoV) comprising: a) exposing a sample from the subject to a probe specific for at least one miRNA chosen from: miR-423-5p, miR-23a-3p, miR-195-5p, miR-766-3p, miR-651-5p, let-7e-5p, miR-1275, miR-3198, miR-627-5p, miR-4662a-5p, miR-3684, let-7a-5p, miR-483-5p, miR-3125, miR-3617- 5p, miR-500b-3p, miR-664b-3p, miR-5189-3p, miR-6772-3p, miR-145-3p, miR-30a-5p, miR- 27a-5p, miR-2116-3p, miR-31-5p, miR-4772-3p, miR-1290, miR-1226-3p, miR-589-3p, miR- 210-3p, miR-103a-3p, miR-3115, miR-769-3p, miR-320b, miR-193a-5p, miR-206, miR-873-5p, miR-148a-3p, miR-3913-5p, miR-320a-3p, miR-576-5p, miR-197-3p, miR-4742-3p, miR-28-5p, miR-320c, miR-551b-3p, miR-142-3p, let-7i-3p, miR-92a-3p, miR-548k, miR-18a-3p, miR-491- 5p, miR-6721-5p, miR-6503-3p, miR-3065-3p, miR-150-5p, let-7f-5p, miR-106b-5p, miR-99b- 3p, miR-1273h-3p, let-7a-3p, miR-6741-5p, miR-21-3p, miR-301b-3p, miR-7-l-3p, miR-148b- 5p, miR-874-3p, miR-889-3p, miR-1468-5p, miR-431-3p, miR-345-5p, miR-339-3p, miR-4433b- 5p, miR-542-5p, miR-4750-5p, miR-132-3p, miR-877-5p, miR-1255b-5p, miR-450b-5p, let-7f-2- 3p, miR-30a-3p, miR-23a-5p, miR-215-5p, miR-10a-3p, miR-98-5p, miR-200c-3p, miR-96-5p, miR-486-5p, miR-30e-5p, miR-141-3p, miR-128-3p, miR-l-3p, miR-500a-3p, let-7b-5p, miR-22- 3p, miR-3661, miR-24-3p, miR-340-5p, miR-574-5p, miR-378c, miR-221-3p, miR-181c-3p, miR- 130a-3p, miR-1306-3p, miR-95-3p, miR-146a-5p, miR-27b-3p, miR-425-5p, miR-9-5p, miR- 4454, miR-148b-3p, miR-599, miR-379-5p, miR-142-5p, miR-451a, miR-191-3p, miR-223-5p, miR-196b-5p, miR-194-5p, let-7d-5p, miR-324-3p, miR-186-5p, miR-502-3p, miR-423-3p, miR- 151b, miR-760, miR-93-3p, miR-382-5p, miR-25-3p, miR-331-3p, miR-29c-3p, miR-1277-5p, miR-320d, miR-769-5p, miR-3064-5p, miR-6529-5p, miR-146b-5p, miR-151a-3p, miR-378a-5p, miR-27a-3p, miR-503-5p, miR-192-5p, miR-147b-3p, miR-182-5p, miR-409-3p, miR-885-3p, miR-106b-3p, miR-125a-3p, miR-542-3p, miR-30d-5p, miR-432-5p, miR-342-3p, miR-200a-3p, miR-30e-3p, miR-1843, miR-26b-3p, miR-26b-5p, miR-499a-5p, miR-200b-5p, miR-125b-l-3p, miR-10b-5p, miR-219a-l-3p, miR-885-5p, miR-574-3p, miR-15b-3p, miR-449a, let-7b-3p, miR- 32-5p, miR-339-5p, miR-130b-3p, miR-2114-5p, miR-19a-3p, miR-155-3p, miR-223-3p, miR- 449c-5p, miR-93-5p, miR-3065-5p, miR-628-3p and miR-19a-3p; and b) detecting the presence, absence or quantity of the at least one miRNA in the sample.
59. A method of detecting the presence or quantity of a coronavirus (CoV) in a sample of a subject comprising: a) exposing the sample from the subject to a probe specific for at least one miRNA chosen from: miR-423-5p, miR-23a-3p, miR-195-5p, miR-766-3p, miR-651-5p, let-7e-5p, miR-1275, miR-3198, miR-627-5p, miR-4662a-5p, miR-3684, let-7a-5p, miR-483-5p, miR-3125, miR-3617- 5p, miR-500b-3p, miR-664b-3p, miR-5189-3p, miR-6772-3p, miR-145-3p, miR-30a-5p, miR- 27a-5p, miR-2116-3p, miR-31-5p, miR-4772-3p, miR-1290, miR-1226-3p, miR-589-3p, miR- 210-3p, miR-103a-3p, miR-3115, miR-769-3p, miR-320b, miR-193a-5p, miR-206, miR-873-5p, miR-148a-3p, miR-3913-5p, miR-320a-3p, miR-576-5p, miR-197-3p, miR-4742-3p, miR-28-5p, miR-320c, miR-551b-3p, miR-142-3p, let-7i-3p, miR-92a-3p, miR-548k, miR-18a-3p, miR-491- 5p, miR-6721-5p, miR-6503-3p, miR-3065-3p, miR-150-5p, let-7f-5p, miR-106b-5p, miR-99b- 3p, miR-1273h-3p, let-7a-3p, miR-6741-5p, miR-21-3p, miR-301b-3p, miR-7-l-3p, miR-148b- 5p, miR-874-3p, miR-889-3p, miR-1468-5p, miR-431-3p, miR-345-5p, miR-339-3p, miR-4433b- 5p, miR-542-5p, miR-4750-5p, miR-132-3p, miR-877-5p, miR-1255b-5p, miR-450b-5p, let-7f-2- 3p, miR-30a-3p, miR-23a-5p, miR-215-5p, miR-10a-3p, miR-98-5p, miR-200c-3p, miR-96-5p, miR-486-5p, miR-30e-5p, miR-141-3p, miR-128-3p, miR-l-3p, miR-500a-3p, let-7b-5p, miR-22- 3p, miR-3661, miR-24-3p, miR-340-5p, miR-574-5p, miR-378c, miR-221-3p, miR-181c-3p, miR- 130a-3p, miR-1306-3p, miR-95-3p, miR-146a-5p, miR-27b-3p, miR-425-5p, miR-9-5p, miR- 4454, miR-148b-3p, miR-599, miR-379-5p, miR-142-5p, miR-451a, miR-191-3p, miR-223-5p, miR-196b-5p, miR-194-5p, let-7d-5p, miR-324-3p, miR-186-5p, miR-502-3p, miR-423-3p, miR- 151b, miR-760, miR-93-3p, miR-382-5p, miR-25-3p, miR-331-3p, miR-29c-3p, miR-1277-5p, miR-320d, miR-769-5p, miR-3064-5p, miR-6529-5p, miR-146b-5p, miR-151a-3p, miR-378a-5p, miR-27a-3p, miR-503-5p, miR-192-5p, miR-147b-3p, miR-182-5p, miR-409-3p, miR-885-3p, miR-106b-3p, miR-125a-3p, miR-542-3p, miR-30d-5p, miR-432-5p, miR-342-3p, miR-200a-3p, miR-30e-3p, miR-1843, miR-26b-3p, miR-26b-5p, miR-499a-5p, miR-200b-5p, miR-125b-l-3p, miR-10b-5p, miR-219a-l-3p, miR-885-5p, miR-574-3p, miR-15b-3p, miR-449a, let-7b-3p, miR- 32-5p, miR-339-5p, miR-130b-3p, miR-2114-5p, miR-19a-3p, miR-155-3p, miR-223-3p, miR- 449c-5p, miR-93-5p, miR-3065-5p, miR-628-3p and miR-19a-3p; and b) detecting the presence, absence or quantity of the at least one miRNA in the sample.
60. A method of treating a pre-symptomatic or asymptomatic subject infected with a coronavirus (CoV) or a subject infected with a CoV but not exhibiting clinically presented lung symptoms of CoV infection comprising: a) exposing the sample from the subject to a probe specific for at least one miRNA chosen from: miR-423-5p, miR-23a-3p, miR-195-5p, miR-766-3p, miR-651-5p, let-7e-5p, miR-1275, miR-3198, miR-627-5p, miR-4662a-5p, miR-3684, let-7a-5p, miR-483-5p, miR-3125, miR-3617- 5p, miR-500b-3p, miR-664b-3p, miR-5189-3p, miR-6772-3p, miR-145-3p, miR-30a-5p, miR- 27a-5p, miR-2116-3p, miR-31-5p, miR-4772-3p, miR-1290, miR-1226-3p, miR-589-3p, miR- 210-3p, miR-103a-3p, miR-3115, miR-769-3p, miR-320b, miR-193a-5p, miR-206, miR-873-5p, miR-148a-3p, miR-3913-5p, miR-320a-3p, miR-576-5p, miR-197-3p, miR-4742-3p, miR-28-5p, miR-320c, miR-551b-3p, miR-142-3p, let-7i-3p, miR-92a-3p, miR-548k, miR-18a-3p, miR-491- 5p, miR-6721-5p, miR-6503-3p, miR-3065-3p, miR-150-5p, let-7f-5p, miR-106b-5p, miR-99b- 3p, miR-1273h-3p, let-7a-3p, miR-6741-5p, miR-21-3p, miR-301b-3p, miR-7-l-3p, miR-148b- 5p, miR-874-3p, miR-889-3p, miR-1468-5p, miR-431-3p, miR-345-5p, miR-339-3p, miR-4433b- 5p, miR-542-5p, miR-4750-5p, miR-132-3p, miR-877-5p, miR-1255b-5p, miR-450b-5p, let-7f-2- 3p, miR-30a-3p, miR-23a-5p, miR-215-5p, miR-10a-3p, miR-98-5p, miR-200c-3p, miR-96-5p, miR-486-5p, miR-30e-5p, miR-141-3p, miR-128-3p, miR-l-3p, miR-500a-3p, let-7b-5p, miR-22- 3p, miR-3661, miR-24-3p, miR-340-5p, miR-574-5p, miR-378c, miR-221-3p, miR-181c-3p, miR- 130a-3p, miR-1306-3p, miR-95-3p, miR-146a-5p, miR-27b-3p, miR-425-5p, miR-9-5p, miR- 4454, miR-148b-3p, miR-599, miR-379-5p, miR-142-5p, miR-451a, miR-191-3p, miR-223-5p, miR-196b-5p, miR-194-5p, let-7d-5p, miR-324-3p, miR-186-5p, miR-502-3p, miR-423-3p, miR- 151b, miR-760, miR-93-3p, miR-382-5p, miR-25-3p, miR-331-3p, miR-29c-3p, miR-1277-5p, miR-320d, miR-769-5p, miR-3064-5p, miR-6529-5p, miR-146b-5p, miR-151a-3p, miR-378a-5p, miR-27a-3p, miR-503-5p, miR-192-5p, miR-147b-3p, miR-182-5p, miR-409-3p, miR-885-3p, miR-106b-3p, miR-125a-3p, miR-542-3p, miR-30d-5p, miR-432-5p, miR-342-3p, miR-200a-3p, miR-30e-3p, miR-1843, miR-26b-3p, miR-26b-5p, miR-499a-5p, miR-200b-5p, miR-125b-l-3p, miR-10b-5p, miR-219a-l-3p, miR-885-5p, miR-574-3p, miR-15b-3p, miR-449a, let-7b-3p, miR- 32-5p, miR-339-5p, miR-130b-3p, miR-2114-5p, miR-19a-3p, miR-155-3p, miR-223-3p, miR- 449c-5p, miR-93-5p, miR-3065-5p, miR-628-3p and miR-19a-3p; and b) detecting the presence, absence or quantity of the at least one miRNA in the sample.
61. A method of preventing a severe coronavirus (CoV) infection in a subject comprising: a) exposing a sample from the subject to a probe specific for at least one miRNA chosen from: miR-423-5p, miR-23a-3p, miR-195-5p, miR-766-3p, miR-651-5p, let-7e-5p, miR-1275, miR-3198, miR-627-5p, miR-4662a-5p, miR-3684, let-7a-5p, miR-483-5p, miR-3125, miR-3617- 5p, miR-500b-3p, miR-664b-3p, miR-5189-3p, miR-6772-3p, miR-145-3p, miR-30a-5p, miR- 27a-5p, miR-2116-3p, miR-31-5p, miR-4772-3p, miR-1290, miR-1226-3p, miR-589-3p, miR- 210-3p, miR-103a-3p, miR-3115, miR-769-3p, miR-320b, miR-193a-5p, miR-206, miR-873-5p, miR-148a-3p, miR-3913-5p, miR-320a-3p, miR-576-5p, miR-197-3p, miR-4742-3p, miR-28-5p, miR-320c, miR-551b-3p, miR-142-3p, let-7i-3p, miR-92a-3p, miR-548k, miR-18a-3p, miR-491- 5p, miR-6721-5p, miR-6503-3p, miR-3065-3p, miR-150-5p, let-7f-5p, miR-106b-5p, miR-99b- 3p, miR-1273h-3p, let-7a-3p, miR-6741-5p, miR-21-3p, miR-301b-3p, miR-7-l-3p, miR-148b- 5p, miR-874-3p, miR-889-3p, miR-1468-5p, miR-431-3p, miR-345-5p, miR-339-3p, miR-4433b- 5p, miR-542-5p, miR-4750-5p, miR-132-3p, miR-877-5p, miR-1255b-5p, miR-450b-5p, let-7f-2- 3p, miR-30a-3p, miR-23a-5p, miR-215-5p, miR-10a-3p, miR-98-5p, miR-200c-3p, miR-96-5p, miR-486-5p, miR-30e-5p, miR-141-3p, miR-128-3p, miR-l-3p, miR-500a-3p, let-7b-5p, miR-22- 3p, miR-3661, miR-24-3p, miR-340-5p, miR-574-5p, miR-378c, miR-221-3p, miR-181c-3p, miR- 130a-3p, miR-1306-3p, miR-95-3p, miR-146a-5p, miR-27b-3p, miR-425-5p, miR-9-5p, miR- 4454, miR-148b-3p, miR-599, miR-379-5p, miR-142-5p, miR-451a, miR-191-3p, miR-223-5p, miR-196b-5p, miR-194-5p, let-7d-5p, miR-324-3p, miR-186-5p, miR-502-3p, miR-423-3p, miR- 151b, miR-760, miR-93-3p, miR-382-5p, miR-25-3p, miR-331-3p, miR-29c-3p, miR-1277-5p, miR-320d, miR-769-5p, miR-3064-5p, miR-6529-5p, miR-146b-5p, miR-151a-3p, miR-378a-5p, miR-27a-3p, miR-503-5p, miR-192-5p, miR-147b-3p, miR-182-5p, miR-409-3p, miR-885-3p, miR-106b-3p, miR-125a-3p, miR-542-3p, miR-30d-5p, miR-432-5p, miR-342-3p, miR-200a-3p, miR-30e-3p, miR-1843, miR-26b-3p, miR-26b-5p, miR-499a-5p, miR-200b-5p, miR-125b-l-3p, miR-10b-5p, miR-219a-l-3p, miR-885-5p, miR-574-3p, miR-15b-3p, miR-449a, let-7b-3p, miR- 32-5p, miR-339-5p, miR-130b-3p, miR-2114-5p, miR-19a-3p, miR-155-3p, miR-223-3p, miR- 449c-5p, miR-93-5p, miR-3065-5p, miR-628-3p and miR-19a-3p; and b) detecting the presence, absence or quantity of the at least one miRNA in the sample.
62. A method of preparing a sample from a pre-symptomatic or asymptomatic subject infected with a coronavirus (CoV) or a subject infected with a CoV but not exhibiting clinically presented lung symptoms of CoV infection comprising: a) obtaining the sample from the subject; b) isolating total RNA from the sample; and c) analysing the total RNA with a probe specific for at least one miRNA chosen from: miR- 423-5p, miR-23a-3p, miR-195-5p, miR-766-3p, miR-651-5p, let-7e-5p, miR-1275, miR-3198, miR-627-5p, miR-4662a-5p, miR-3684, let-7a-5p, miR-483-5p, miR-3125, miR-3617-5p, miR- 500b-3p, miR-664b-3p, miR-5189-3p, miR-6772-3p, miR-145-3p, miR-30a-5p, miR-27a-5p, miR-2116-3p, miR-31-5p, miR-4772-3p, miR-1290, miR-1226-3p, miR-589-3p, miR-210-3p, miR-103a-3p, miR-3115, miR-769-3p, miR-320b, miR-193a-5p, miR-206, miR-873-5p, miR- 148a-3p, miR-3913-5p, miR-320a-3p, miR-576-5p, miR-197-3p, miR-4742-3p, miR-28-5p, miR- 320c, miR-551b-3p, miR-142-3p, let-7i-3p, miR-92a-3p, miR-548k, miR-18a-3p, miR-491-5p, miR-6721-5p, miR-6503-3p, miR-3065-3p, miR-150-5p, let-7f-5p, miR-106b-5p, miR-99b-3p, miR-1273h-3p, let-7a-3p, miR-6741-5p, miR-21-3p, miR-301b-3p, miR-7-l-3p, miR-148b-5p, miR-874-3p, miR-889-3p, miR-1468-5p, miR-431-3p, miR-345-5p, miR-339-3p, miR-4433b-5p, miR-542-5p, miR-4750-5p, miR-132-3p, miR-877-5p, miR-1255b-5p, miR-450b-5p, let-7f-2-3p, miR-30a-3p, miR-23a-5p, miR-215-5p, miR-10a-3p, miR-98-5p, miR-200c-3p, miR-96-5p, miR- 486-5p, miR-30e-5p, miR-141-3p, miR-128-3p, miR-l-3p, miR-500a-3p, let-7b-5p, miR-22-3p, miR-3661, miR-24-3p, miR-340-5p, miR-574-5p, miR-378c, miR-221-3p, miR-181c-3p, miR- 130a-3p, miR-1306-3p, miR-95-3p, miR-146a-5p, miR-27b-3p, miR-425-5p, miR-9-5p, miR- 4454, miR-148b-3p, miR-599, miR-379-5p, miR-142-5p, miR-451a, miR-191-3p, miR-223-5p, miR-196b-5p, miR-194-5p, let-7d-5p, miR-324-3p, miR-186-5p, miR-502-3p, miR-423-3p, miR- 151b, miR-760, miR-93-3p, miR-382-5p, miR-25-3p, miR-331-3p, miR-29c-3p, miR-1277-5p, miR-320d, miR-769-5p, miR-3064-5p, miR-6529-5p, miR-146b-5p, miR-151a-3p, miR-378a-5p, miR-27a-3p, miR-503-5p, miR-192-5p, miR-147b-3p, miR-182-5p, miR-409-3p, miR-885-3p, miR-106b-3p, miR-125a-3p, miR-542-3p, miR-30d-5p, miR-432-5p, miR-342-3p, miR-200a-3p, miR-30e-3p, miR-1843, miR-26b-3p, miR-26b-5p, miR-499a-5p, miR-200b-5p, miR-125b-l-3p, miR-10b-5p, miR-219a-l-3p, miR-885-5p, miR-574-3p, miR-15b-3p, miR-449a, let-7b-3p, miR- 32-5p, miR-339-5p, miR-130b-3p, miR-2114-5p, miR-19a-3p, miR-155-3p, miR-223-3p, miR- 449c-5p, miR-93-5p, miR-3065-5p, miR-628-3p and miR-19a-3p.
63. The method of any one of claims 58 to 61, further comprising one or more of: c) normalizing the presence, absence, or quantity of the at least one miRNA in the sample against the presence, absence or quantity of the at least one miRNA in a sample of a healthy subject; d) correlating the presence, absence, or quantity of the at least one miRNA in the sample to the subject being infected with a coronavirus; and e) assigning a score for the sample based on the level of the at least one miRNA.
64. The method of claim 63, wherein when the method comprises administering a therapeutically effective amount of one or a plurality of active agents to the subject.
65. The method of any one of claims 58 to 64, wherein the sample is plasma, serum, whole blood, respiratory tissue, respiratory mucosal sample, saliva or urine.
66. The method of claim 65, wherein the respiratory mucosal sample is a nasal mucosal sample.
67. The method of any one of claims 63 to 66, wherein step (d) further comprises correlating the one or more scores to the presence, absence, or quantity of the at least one miRNA such that, if the amount of the at least one miRNA is greater than the quantity of the at least one miRNA in a control sample; or, if the amount of the at least one miRNA is substantially equal to the quantity of the at least one miRNA in a sample taken from a subject known to have a coronavirus infection, then the subject is diagnosed as being infected with a coronavirus.
68. The method of any one of claims 58 to 67, wherein the at least one miRNA is detected by next generation sequencing, quantitative real-time reverse transcription-PCR (qRT-PCR), isothermal amplification, electrical interference, CRISPR-based method, nanomaterial-based methods, nucleic acid amplification-based methods such as rolling circle amplification (RCA), loop-mediated isothermal amplification (LAMP), strand-displacement amplification (SDA), enzyme-free amplification, microarray, multiplex miRNA profiling assay, RNA-ish, or northern blotting.
69. The method of any one of claims 58 to 68, wherein the at least one miRNA is detected by qRT-PCR, electrical interference or a CRISPR-based method.
70. The method of any one of claims 58 to 69, wherein the step of quantifying at least one quantity of the at least one miRNA in the sample comprises using a fluorescence and/or digital imaging.
71. The method of any one of claims 58 to 61 or 63 to 70, wherein the presence, absence, or quantity of at least 2 different miRNAs in the sample are detected, normalized and correlated.
72. The method of any one of claims 58 to 61 or 63 to 71, wherein the presence, absence, or quantity of at least 3 to 6 different miRNAs in the sample are detected, normalized and correlated.
73. The method of any one of claims 58 to 61 or 63 to 72, wherein the presence, absence, or quantity of the at least one miRNA is detected by PCR amplification using one or a plurality of primers specific for the at least one miRNA.
74. The method of any one of claims 58 to 73, wherein the probe specific to the at least one miRNA is one or a plurality of primers chosen from Table 1.
75. The method of any one of claims 58 to 74, wherein the probe specific to the at least one miRNA comprises a nucleic acid sequence complementary to the nucleic acid sequence of the at least one miRNA.
76. The method of claim 74 or claim 75, wherein the probe is a radioactive probe, a chemoluminescent probe, or a fluorescent probe.
77. The method of claim 62, further comprising:
1) detecting the presence, absence or quantity of the at least one miRNA in the sample;
2) normalizing the presence, absence, or quantity of the at least one miRNA in the sample against the presence, absence or quantity of the at least one miRNA in a sample of a healthy subject;
3) correlating the presence, absence, or quantity of the at least one miRNA in the sample to the subject being infected with a CoV; and
4) assigning a score for the sample based on the level of the at least one miRNA.
78. The method of any one of claims 58 to 77, wherein the CoV infection is selected from: SARS-CoV-2, HCoV-OC43, HCoV-HKUl, HCoV-229E, HCoV-NL63, SARS-CoV and MERS- CoV or a variant thereof.
79. The method of any one of claims 58 to 78, wherein the CoV infection is SARS-CoV-2 or a variant thereof.
80. The method of any one of claims 58 to 79, comprising detecting the presence, absence or quantity of at least one miRNA chosen from miR-423-5p, miR-195-5p and miR-23a-3p in the sample.
81. The method of any one of claims 58 to 80, comprising detecting the presence, absence or quantity of miR-142-3p and 3065-3p in the sample.
82. The method of any one of claims 58 to 81, comprising detecting the presence, absence or quantity of miR-142-3p, miR-3065-3p, and miR-93-5p in the sample.
83. The method of any one of claims 1 to 43, 53 to 55, and 58 to 82, wherein the method comprises detecting: a) the presence, absence or quantity of miR-23a-3p, miR-195-5p, miR-423-5p, and miR-
28-5p; b) the presence, absence or quantity of miR-23a-3p, miR-195-5p, miR-423-5p, miR-28-5p, and miR-223-5p; or c) the presence, absence or quantity of miR-23a-3p, miR-195-5p, miR-423-5p, miR-28-5p, miR-223-5p, and miR-130b-3p.
84. The method of any one of claims 1 to 43 and 53 to 55, wherein the at least one miRNA is detected by qRT-PCR, electrical interference or a CRISPR-based method.
85. The panel or kit of any one of claims 44 to 52, where the panel or kit further comprises one or more probes or primers for detecting: a) the presence, absence or quantity of miR-23a-3p, miR-195-5p, miR-423-5p, and miR-
28-5p; b) the presence, absence or quantity of miR-23a-3p, miR-195-5p, miR-423-5p, miR-28-5p, and miR-223-5p; or c) the presence, absence or quantity of miR-23a-3p, miR-195-5p, miR-423-5p, miR-28-5p, miR-223-5p, and miR-130b-3p.
86. A computer program product encoded on a computer-readable storage medium, wherein the computer program product comprises instructions for: a) detecting the presence, absence or quantity of at least one miRNA in a sample of a subject; and b) correlating the presence, absence, or quantity of the at least one miRNA in the sample to a likelihood that the subject being infected with a CoV.
87. The computer program product of claim 133 further comprising: a) detecting and normalizing the presence, absence or quantity of a second miRNA in the sample; b) calculating a combined score associated with the presence, absence or quantity of the at least one miRNA and the second miRNA in the sample; and c) assigning a score based on the level of the at least one miRNA.
88. The computer program product of claim 86 or claim 87 further comprising
1) normalizing the presence, absence, or quantity of the at least one miRNA in the sample against the presence, absence or quantity of the at least one miRNA in a control sample and/or
2) calculating a score associated with the presence, absence or quantity of the at least one miRNA in the sample and correlating the score to a likelihood that the subject being infected with a CoV.
89. The computer program product of any one of claims 86 to 88, wherein at least 3 to 6 different miRNAs in the sample are detected, normalized and correlated.
90. The computer program product of any one of claims 86 to 89, wherein the presence, absence, or quantity of the at least one miRNA is detected by qRT-PCR amplification, electrical interference or a CRISPR-based method.
91. The computer program product of any one of claims 86 to 90, wherein the control sample is obtained from a healthy subject.
92. A system comprising: a) the computer program product of any one of claims 86 to 91 ; and b) a processor operable to execute programs; and/or a memory associated with the processor.
93. A system for detecting the presence or quantity of a coronavirus (CoV) in a sample of a subject comprising: a processor operable to execute programs; a memory associated with the processor; a database associated with said processor and said memory; and a program stored in the memory and executable by the processor, the program being operable for: a) detecting the presence, absence or quantity of at least one miRNA in a sample of a subject; and b) correlating the presence, absence, or quantity of the at least one miRNA in the sample to a likelihood that the subject being infected with a CoV.
94. The system of claim 93, wherein the program is further operable for one or both of: a) normalizing the presence, absence, or quantity of the at least one miRNA in the sample against the presence, absence or quantity of the at least one miRNA in a control sample; and b) assigning a score for the sample based on the level of the at least one miRNA and correlating the score to a likelihood that the subject being infected with a CoV.
95. The system of claim 93 or claim 94, wherein the program is further operable for: a) detecting and normalizing the presence, absence or quantity of a second miRNA in the sample; b) calculating a combined score associated with the presence, absence or quantity of the at least one miRNA and the second miRNA in the sample; and c) correlating the combined score to a likelihood that the subject being infected with CoV.
PCT/AU2022/050105 2021-02-16 2022-02-16 Methods and systems for detecting a coronavirus infection WO2022174287A1 (en)

Applications Claiming Priority (6)

Application Number Priority Date Filing Date Title
US202163150068P 2021-02-16 2021-02-16
US63/150,068 2021-02-16
US202163215408P 2021-06-25 2021-06-25
US63/215,408 2021-06-25
AU2021902194 2021-07-16
AU2021902194A AU2021902194A0 (en) 2021-07-16 Methods and systems for detecting a coronavirus infection

Publications (1)

Publication Number Publication Date
WO2022174287A1 true WO2022174287A1 (en) 2022-08-25

Family

ID=82932160

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/AU2022/050105 WO2022174287A1 (en) 2021-02-16 2022-02-16 Methods and systems for detecting a coronavirus infection

Country Status (1)

Country Link
WO (1) WO2022174287A1 (en)

Non-Patent Citations (11)

* Cited by examiner, † Cited by third party
Title
"Applied Biosystems™ 7500 Fast Dx Real-Time PCR Instrument, with tower computer", THERMOFISCHER, 18 May 2010 (2010-05-18), Retrieved from the Internet <URL:https://www.thermofisher.com/order/catalog/product/4406985#/4406985> [retrieved on 20210823] *
"COVID-19 Treatment Guidelines: Oxygenation and Ventilation", 17 December 2020 (2020-12-17), Retrieved from the Internet <URL:https://www.covid19treatmentguidelines.nih.gov/management/critical-care/oxygenation-and-ventilation> [retrieved on 20210824] *
"Medicines management - COVID-19", 22 April 2020 (2020-04-22), Retrieved from the Internet <URL:https://www.safetyandquality.gov.au/our-work/medication-safety/medicines-management-covid-19> [retrieved on 20210824] *
"miRNA List of Mouse miRNome qPCR Arrays 18.0Version(384-well) for QM042by GeneCopoeia", 20 October 2012 (2012-10-20), Retrieved from the Internet <URL:https://www.genecopoeia.com/wp-content/uploads/oldpdfs/product/qpcr-arrays/mima/docs/miRNA%20List%20of%20QM043.pdf> [retrieved on 20210823] *
AISHWARYA S., GUNASEKARAN K., MARGRET A. ANITA: "Computational gene expression profiling in the exploration of biomarkers, non-coding functional RNAs and drug perturbagens for COVID-19", JOURNAL OF BIOMOLECULAR STRUCTURE & DYNAMICS, ADENINE PRESS, NEW YORK, NY, US, vol. 40, no. 8, 24 May 2022 (2022-05-24), US , pages 3681 - 3696, XP055965628, ISSN: 0739-1102, DOI: 10.1080/07391102.2020.1850360 *
ALINE DE SOUZA NICOLETTI, MARÍLIA UNICAMP, VISACRI BERLOFA, CARLA UNICAMP, DA REGINA, CORREA SILVA, UNICAMP RONDA, EDUARDO PEDRO, : "Differentially expressed circulating microRNAs in Brazilian patients with COVID-19: a preliminary study on potential biomarkers for diagnosis and severity", RESEARCH SQUARE, 24 June 2021 (2021-06-24), pages 1 - 27, XP055965643, Retrieved from the Internet <URL:https://assets.researchsquare.com/files/rs-630726/v1_covered.pdf?c=1631871428> [retrieved on 20220928] *
CHOW JONATHAN TAK-SUM, SALMENA LEONARDO: "Prediction and Analysis of SARS-CoV-2-Targeting MicroRNA in Human Lung Epithelium", GENES, vol. 11, no. 9, 26 August 2020 (2020-08-26), pages 1002, XP055965634, DOI: 10.3390/genes11091002 *
KIM WOO RYUNG, PARK EUN GYUNG, KANG KYUNG-WON, LEE SANG-MYEONG, KIM BUMSEOK, KIM HEUI-SOO: "Expression Analyses of MicroRNAs in Hamster Lung Tissues Infected by SARS-CoV-2", MOLECULES AND CELLS, vol. 43, no. 11, 30 November 2020 (2020-11-30), pages 953 - 963, XP055965631, DOI: 10.14348/molcells.2020.0177 *
LI CAIXIA, HU XIAO, LI LEILEI, LI JIN‐HUI: "Differential microRNA expression in the peripheral blood from human patients with COVID‐19", JOURNAL OF CLINICAL LABORATORY ANALYSIS., NEW YORK, NY., US, vol. 34, no. 10, 1 October 2020 (2020-10-01), US , pages 3334 - 3340, XP055887433, ISSN: 0887-8013, DOI: 10.1002/jcla.23590 *
PIERCE JACOB B., SIMION VIOREL, ICLI BASAK, PÉREZ-CREMADES DANIEL, CHENG HENRY S., FEINBERG MARK W.: "Computational Analysis of Targeting SARS-CoV-2, Viral Entry Proteins ACE2 and TMPRSS2, and Interferon Genes by Host MicroRNAs", GENES, vol. 11, no. 11, 16 November 2020 (2020-11-16), pages 1354, XP055965644, DOI: 10.3390/genes11111354 *
TANG HAO, GAO YUEHAN, LI ZHAOHUAI, MIAO YUSHAN, HUANG ZHAOHAO, LIU XIUXING, XIE LIHUI, LI HE, WEN WEN, ZHENG YINGFENG, SU WENRU: "The noncoding and coding transcriptional landscape of the peripheral immune response in patients with COVID‐19", CLINICAL AND TRANSLATIONAL MEDICINE, INTERNATIONAL SOCIETY FOR TRANSLATIONAL MEDICINE, SE, vol. 10, no. 6, 1 October 2020 (2020-10-01), SE , pages e200, XP055965626, ISSN: 2001-1326, DOI: 10.1002/ctm2.200 *

Similar Documents

Publication Publication Date Title
EP2971128B1 (en) Biomarkers for diagnosis of lung diseases and methods of use thereof
US9771617B2 (en) Microrna biomarkers indicative of alzheimer&#39;s disease
US9850539B2 (en) Biomarkers for the molecular classification of bacterial infection
CN106795565B (en) Methods for assessing lung cancer status
US8821876B2 (en) Methods of identifying infectious disease and assays for identifying infectious disease
US20140113978A1 (en) Multifocal hepatocellular carcinoma microrna expression patterns and uses thereof
US20180245154A1 (en) Methods to diagnose and treat acute respiratory infections
US20200131586A1 (en) Methods and compositions for diagnosing or detecting lung cancers
US11661632B2 (en) Compositions and methods for diagnosing lung cancers using gene expression profiles
US20230227914A1 (en) Biomarkers of oral, pharyngeal and laryngeal cancers
CN111344409A (en) Non-coding RNA for detecting cancer
Torres et al. Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS) proteomic profiling of cerebrospinal fluid in the diagnosis of enteroviral meningitis: A proof-of-principle study
US20180371553A1 (en) Methods and compositions for the analysis of cancer biomarkers
US20240150829A1 (en) System and methods of detection of oncrnas for cancer diagnosis
WO2022174287A1 (en) Methods and systems for detecting a coronavirus infection
KR102229647B1 (en) MiRNA bio-marker for non-invasive differential diagnosis of acute rejection in kidney transplanted patients and uses thereof
US20240071622A1 (en) Clinical classifiers and genomic classifiers and uses thereof
WO2024008955A1 (en) Method of screening for severe covid-19 susceptibility
JP2024529488A (en) Compositions and methods for TET-assisted pyridine borane sequencing for cell-free DNA
CN116694752A (en) Application of lncRNA 046173 as marker in developing tuberculosis patient screening reagent
Bruder et al. Research article Transcriptome sequencing and development of an expression microarray platform for the domestic ferret
AU2013204118A1 (en) MicroRNA biomarkers indicative of Alzheimer&#39;s Disease

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 22755396

Country of ref document: EP

Kind code of ref document: A1

NENP Non-entry into the national phase

Ref country code: DE

122 Ep: pct application non-entry in european phase

Ref document number: 22755396

Country of ref document: EP

Kind code of ref document: A1