US20180208997A1 - METHODS, COMPOSITIONS, AND DEVICES UTILIZING MicroRNA TO DETERMINE PHYSIOLOGICAL CONDITIONS - Google Patents

METHODS, COMPOSITIONS, AND DEVICES UTILIZING MicroRNA TO DETERMINE PHYSIOLOGICAL CONDITIONS Download PDF

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US20180208997A1
US20180208997A1 US15/926,861 US201815926861A US2018208997A1 US 20180208997 A1 US20180208997 A1 US 20180208997A1 US 201815926861 A US201815926861 A US 201815926861A US 2018208997 A1 US2018208997 A1 US 2018208997A1
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mir
hsa
mmu
microrna
sequences
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David Galas
Richard Evan Gelinas
Clay Braden Marsh
Melissa Garnet Piper
Kai Wang
Shile Zhang
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Battelle Memorial Institute Inc
Ohio State University Research Foundation
Institute for Systems Biology
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Battelle Memorial Institute Inc
Ohio State University Research Foundation
Institute for Systems Biology
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Definitions

  • microRNA such as microRNA-based markers
  • the ideal diagnostic marker has to fulfill certain key requirements including being specific, sensitive, robust, and non-invasive.
  • Current disease diagnoses are primarily based on two different but complementary approaches—physical imaging and biomolecular profiling. Both approaches currently suffer from a lack of specificity and early detection capability.
  • Tissue-specific blood biomarkers can increase the specificity to selected organs. However, the levels of these tissue-specific biomarkers are usually low in blood. In addition, the difficulty of developing suitable capture agents for proteins makes the identification and development of new molecular diagnostic markers difficult.
  • the present disclosure relates, in different embodiments, to the use of the levels of microRNA sequences (miRNA) in body fluids to establish correlations with the body's pathophysiological conditions.
  • exemplary body fluids include, but are not limited to, serum, plasma, saliva, urine, tears, amniotic fluid, sweat, cerebrospinal fluid, seminal fluid (semen), lung mucus (e.g. from bronchial lavage), pleural fluid, peritoneal fluid, colostrums, and breast milk. These levels can then provide diagnostic and/or predictive information with regard to important issues of health and disease.
  • the methods comprise: isolating microRNA sequences from a biological sample; generating a microRNA profile from the isolated microRNA sequences, the profile including the levels of expressed microRNA sequences in the biological sample; comparing the microRNA profile with a reference to identify differentially expressed microRNA sequences; and detecting the physiological condition based on the identity or the levels of the differentially expressed microRNA sequences.
  • the biological sample may be a biopsy material, tissue, or body fluid.
  • the biological sample comprises a body fluid selected from the group consisting of serum, plasma, lymph, saliva, urine, tears, sweat, semen, synovial fluid, cervical mucus, amniotic fluid, cerebrospinal fluid, and breast milk.
  • the microRNA sequences may be isolated by extracting the biological sample with an organic solvent to obtain an aqueous phase containing the microRNA sequences; and purifying the aqueous phase through a silica membrane to isolate the microRNA sequences.
  • the microRNA profile can be generated using hybridization to identify a microRNA sequences; or by using a quantitative polymerase chain reaction to identify the level of a microRNA sequences.
  • the reference can be a table of the levels of expressed microRNA sequences in a normal person, or a reference sample.
  • the biological sample may be from a microbe, such as a virus, bacterium, fungus, protozoan, or parasite.
  • the isolated microRNA sequences may be specific to a biological pathway, a cell type, or a tissue.
  • the physiological condition may be a disease, injury, or infection.
  • microRNA sequences to detect or predict a physiological condition. These methods also comprise: generating a microRNA profile from a biological sample, the profile including the levels of expressed microRNA sequences in the biological sample; and comparing the microRNA profile with a reference to identify differentially expressed microRNA sequences. The physiological condition could then be detected or predicted based on the identity or the levels of the differentially expressed microRNA sequences. Alternatively, the physiological condition can be identified, and a treatment can then be administered based on the identity of the physiological condition.
  • microRNA sequences to monitor a physiological condition, comprising: generating a first microRNA profile from a first biological sample of a patient; administering a treatment to the patient; generating a second microRNA profile from a second biological sample of the patient; comparing the second microRNA profile with the first microRNA profile to identify differentially expressed microRNA sequences; and identifying a change in the physiological condition based on the identity or the amounts of the differentially expressed microRNA sequences.
  • microRNA sequences to treat a physiological condition.
  • the methods comprise: identifying at least one microRNA sequence based on the physiological condition; and manipulating the level of the at least one microRNA sequence to treat the physiological condition.
  • Manipulating the level of the at least one microRNA sequence may comprise: constructing a specific DNA or RNA sequence related to the at least one microRNA sequence; and delivering the specific DNA or RNA sequence to a targeted cell, tissue, or organ.
  • microRNA sequences to detect, predict, or treat a physiological condition.
  • the methods comprise: generating a microRNA profile from a biological sample; identifying at least one differentially expressed microRNA sequence by comparing the microRNA profile to a reference; and detecting, predicting, or treating the physiological condition based on the identity or the levels of the at least one differentially expressed microRNA sequence.
  • at least two differentially expressed microRNA sequences are identified.
  • microRNA profile comprises at least one specific microRNA sequence; and comparing the microRNA profile to a reference to provide information useful for detecting or predicting the physiological condition.
  • the microRNA profile comprises at least two specific microRNA sequences.
  • a differentially expressed microRNA sequence can be identified by comparing the amount of a particular microRNA sequence in the microRNA profile with the amount of that particular microRNA sequence in the reference.
  • a differentially expressed microRNA sequence is identified when the ratio of the amount in the microRNA profile to the amount in the reference is at least 1.5, or at least 3.
  • the microRNA profile or the specific microRNA sequence(s) may comprise at least one microRNA sequence selected from the group consisting of mmu-miR-122, mmu-miR-486, mmu-miR-125b-5p, mmu-let-7d*, mmu-miR-101a, mmu-miR-101b, mmu-miR-1224, mmu-miR-124, mmu-miR-125a-3p, mmu-miR-125a-5p, mmu-miR-127, mmu-miR-130a, mmu-miR-133a, mmu-miR-133b, mmu-miR-135a*, mmu-miR-141, mmu-miR-193, mmu-miR-193b, mmu-miR-199a-5p,
  • the microRNA profile or the specific microRNA sequence may comprise at least one microRNA sequence selected from the group consisting of mmu-miR-122, mmu-miR-486, mmu-miR-125b-5p, mmu-let-7d*, mmu-miR-101a, mmu-miR-101b, mmu-miR-1224, mmu-miR-124, mmu-miR-125a-3p, mmu-miR-125a-5p, mmu-miR-133a, mmu-miR-133b, mmu-miR-135a*, mmu-miR-193, mmu-miR-193b, mmu-miR-199a-5p, mmu-miR-199b*, mmu-miR-202-3p, mmu-miR-291a-5p
  • the at least one differentially expressed microRNA sequence or the at least one specific sequence comprises hsa-miR-122.
  • they comprise hsa-miR-122 and either hsa-miR-486-3p or hsa-miR-486-5p (i.e. the human orthologs to mmu-miR-486).
  • the ratio of the amount of miR-122 to the amount of miR-486 may be greater than 4.0, including greater than 6.0.
  • the microRNA profile or the specific microRNA sequence may comprise at least one microRNA sequence selected from the group consisting of mmu-let-7g, mmu-miR-298, mmu-miR-1, mmu-miR-101a*, mmu-miR-101b, mmu-miR-1224, mmu-miR-126-5p, mmu-miR-127, mmu-miR-128, mmu-miR-129-3p, mmu-miR-133b, mmu-miR-136, mmu-miR-138, mmu-miR-138*, mmu-miR-139-3p, mmu-miR-140, mmu-miR-140*, mmu-miR-142-3p, mmu-miR-143, mmu-miR-146a, mmu
  • the microRNA profile or the specific microRNA sequence may comprise at least one microRNA sequence selected from the group consisting of mmu-let-7g, mmu-miR-298, mmu-miR-101a*, mmu-miR-101b, mmu-miR-1224, mmu-miR-126-5p, mmu-miR-128, mmu-miR-129-3p, mmu-miR-133b, mmu-miR-138*, mmu-miR-139-3p, mmu-miR-140*, mmu-miR-146a, mmu-miR-148b, mmu-miR-15a*, mmu-miR-15b, mmu-miR-181b, mmu-miR-181d, mmu-miR-185, mmu-miR-186,
  • the microRNA profile or the specific microRNA sequence may comprise at least one microRNA sequence selected from the group consisting of hsa-miR-135a*, hsa-miR-10b, hsa-miR-1224-3p, hsa-miR-1224-5p, hsa-miR-1225-3p, hsa-miR-1225-5p, hsa-miR-1226*, hsa-miR-1227, hsa-miR-1228, hsa-miR-1229, hsa-miR-1234, hsa-miR-1237, hsa-miR-1238, hsa-miR-124, hsa-miR-129*, hsa-miR-129-3p, hsa-miR-136*, hsa-miR-187
  • the microRNA profile or the specific microRNA sequence may comprise at least one microRNA sequence selected from the group consisting of hsa-miR-135a*, hsa-miR-1224-3p, hsa-miR-1224-5p, hsa-miR-1225-3p, hsa-miR-1225-5p, hsa-miR-1226*, hsa-miR-1227, hsa-miR-1228, hsa-miR-1229, hsa-miR-1234, hsa-miR-1237, hsa-miR-1238, hsa-miR-124, hsa-miR-129*, hsa-miR-129-3p, hsa-miR-136*, hsa-miR-187*, hsa-miR-188
  • the physiological condition may also be a lung disease or lung injury, such as chronic obstructive pulmonary disease (COPD) and idiopathic pulmonary fibrosis (IPF), also known as interstitial lung disease (ILD).
  • COPD chronic obstructive pulmonary disease
  • IPF idiopathic pulmonary fibrosis
  • ILD interstitial lung disease
  • the at least one differentially expressed microRNA sequence or at least one specific microRNA sequence is selected from the group consisting of hsa-miR-630, hsa-miR-134, hsa-miR-1225-5p, hsa-miR-135a*, hsa-miR-150*, hsa-miR-22, hsa-miR-223, hsa-miR-448, hsa-miR-451, hsa-miR-483-5p, hsa-miR-575, hsa-miR-638, hsa-miR-923, hsa-miR-92a-2*, hsa-miR-939, hsa-miR-940, hsv1-miR-H1, kshv-miR-K12-3, hsv1-miR-LAT, h
  • the biological sample is plasma and the at least one differentially expressed microRNA sequence or at least one specific microRNA sequence is selected from the group consisting of hsa-miR-630, hsa-miR-134, hsa-miR-1225-5p, hsa-miR-135a*, hsa-miR-150*, hsa-miR-22, hsa-miR-223, hsa-miR-483-5p, hsa-miR-575, hsa-miR-638, hsa-miR-923, hsa-miR-939, hsa-miR-940, hsv1-miR-H1, hsv1-miR-LAT, kshv-miR-K12-3, hcmv-miR-UL70-3p, and human orthologs thereof.
  • the biological sample is plasma and the at least one differentially expressed microRNA sequence or at least one specific microRNA sequence is selected from the group consisting of hsa-miR-630, hsa-miR-134, hcmv-miR-UL70-3p, hsa-miR-1225-5p, hsa-miR-135a*, hsa-miR-150*, hsa-miR-483-5p, hsa-miR-575, hsa-miR-638, hsv1-miR-H1, hsv1-miR-LAT, and human orthologs thereof.
  • the microRNA profile consists of only a selection of at least two of these microRNA sequences, i.e. the microRNA profile does not look at other microRNA sequences.
  • the biological sample is plasma and at least two differentially expressed microRNA sequences or specific microRNA sequences are identified. At least one of the at least two differentially expressed microRNA sequences or specific microRNA sequences is selected from the group consisting of hsa-miR-630, hcmv-miR-UL70-3p, hsa-miR-1225-5p, hsa-miR-134, hsa-miR-135a*, hsa-miR-150*, hsa-miR-483-5p, hsa-miR-575, hsa-miR-638, hsv1-miR-H1, hsv1-miR-LAT, and human orthologs thereof.
  • the other one of the at least two differentially expressed microRNA sequences or specific microRNA sequences is selected from the group consisting of hsa-miR-451, hsa-miR-448, hsa-miR-92a-2*, and human orthologs thereof.
  • the microRNA profile consists of only a selection of these microRNA sequences, i.e. the microRNA profile does not look at other microRNA sequences.
  • the biological sample is lung tissue and the at least one differentially expressed microRNA sequence is selected from the group consisting of hsa-miR-451, hsa-miR-923, hsa-miR-1225-5p, hsa-miR-22, hsa-miR-223, hsa-miR-638, kshv-miR-K12-3, and human orthologs thereof.
  • the microRNA profile consists of only a selection of these microRNA sequences.
  • the biological sample is plasma and the at least one differentially expressed microRNA sequence is selected from the group consisting of hsa-miR-940, hsa-miR-134, hsa-miR-135a*, hsa-miR-150*, hsa-miR-483-5p, hsa-miR-575, hsa-miR-939, hsv1-miR-H1, kshv-miR-K12-3, hsv1-miR-LAT, hcmv-miR-UL70-3p, and human orthologs thereof.
  • the microRNA profile consists of only a selection of these microRNA sequences, i.e. the microRNA profile does not look at other microRNA sequences.
  • Also disclosed are methods of using microRNA sequences to detect a lung condition comprising: generating a microRNA profile from a biological sample; and detecting the lung condition based on the levels of at least one overexpressed microRNA sequence and at least one underexpressed microRNA sequence.
  • the at least one overexpressed microRNA sequence is selected from the group consisting of hsa-miR-630, hcmv-miR-UL70-3p, hsa-miR-1225-5p, hsa-miR-134, hsa-miR-135a*, hsa-miR-150*, hsa-miR-483-5p, hsa-miR-575, hsa-miR-638, hsv1-miR-H1, hsv1-miR-LAT, and human orthologs thereof.
  • the at least one underexpressed microRNA sequence is selected from the group consisting of hsa-miR-451, hsa-miR-448, and hsa-miR-92a-2*, and human orthologs thereof.
  • the microRNA profile examines only a selection of these listed microRNA sequences.
  • the biological sample comprises (i) serum or plasma; and (ii) an additional body fluid specific to a particular location of the body that is relevant to the particular physiological condition.
  • the biological sample further comprises amniotic fluid and the physiological condition is the health status of a fetus being carried by the patient.
  • the biological sample further comprises urine and the physiological condition is the health status of a bladder or a kidney of the patient.
  • the biological sample further comprises breast milk and the physiological condition is the health status of a breast of the patient.
  • the biological sample further comprises saliva and the physiological condition is the health status of the head and neck region of the patient.
  • the biological sample further comprises tears and the physiological condition is the health status of an eye of the patient.
  • the biological sample further comprises semen and the physiological condition is the health status of a prostate or male reproductive organ of the patient.
  • the biological sample further comprises synovial fluid and the physiological condition is the health status of a joint of the patient.
  • the biological sample further comprises sweat and the physiological condition is the health status of the skin of the patient.
  • the biological sample further comprises cerebrospinal fluid and the physiological condition is the health status of the central nerve system of the patient.
  • the methods comprise taking a sample of a body fluid and a sample of a body tissue from a patient.
  • a first microRNA profile is generated from the body fluid sample
  • a second microRNA profile is generated from the body tissue sample.
  • At least two differentially expressed microRNA sequences are identified in the first microRNA profile by comparing the first microRNA profile to a first reference.
  • At least two differentially expressed microRNA sequences are identified in the second microRNA profile by comparing the second microRNA profile to a second reference.
  • the physiological condition is then diagnosed based on the differentially expressed microRNA sequences identified.
  • the differentially expressed microRNA sequences in the first microRNA profile are different from the differentially expressed microRNA sequences in the second microRNA profile. This difference in the differentially expressed microRNA sequences between the body fluid and the body tissue increases the probability of a correct diagnosis.
  • assays for detecting the identity and/or levels of the various combinations of microRNA sequences described above are also included.
  • FIGS. 1A-1B are electropherograms of RNA.
  • FIG. 2 is a microRNA profile showing changes in specific microRNA expression levels over time in the liver after exposing the animal to a high dose of acetaminophen.
  • FIG. 3 is a microRNA profile showing differences in specific microRNA levels between plasma samples from a treated group and a control group.
  • the text on the right-hand side of FIG. 3 reads, in order from top to bottom: mmu-miR-21, mmu-miR-122, mmu-miR-22, mmu-miR-192, mmu-miR-29a, mmu-miR-30a, mmu-miR-130a, mmu-miR-29c, mmu-miR-30a, mmu-miR-148a, mmu-miR-19b, mmu-miR-101b, mmu-miR-15a, mmu-miR-685, mmu-let-7g, mmu-miR-27b, mmu-miR-574-5p, mmu-miR-671-5p, mmu-miR-107, mmu-let-7d*, mmu-miR-29b, mmu-miR-193, mmu-m
  • FIG. 4 is a graph of intensities for two selected microRNA sequences, mir-122 and mir-486 in plasma after exposing the animal to different doses of acetaminophen.
  • FIG. 5 is a graph of the ratio between mir-122 and mir-486 (either median or average intensities) for the same data as FIG. 4 .
  • FIG. 6 is a microRNA profile showing differences in microRNA expression levels between normal brain tissue and diseased brain tissue.
  • FIG. 7 is a microRNA profile showing differences in microRNA expression levels as a disease progressed in lung tissue.
  • FIG. 8 is a microRNA profile showing differences in microRNA expression levels between serum and urine samples.
  • FIG. 9 is a graph comparing miRNA expression levels in control plasma samples with ILD plasma samples.
  • FIGS. 10A-10B are graphs showing the signal strength in the ILD and control plasma samples of FIG. 9 .
  • FIG. 11 is a graph showing the signal strength for all oligonucleotide probes used to target certain microRNA sequences.
  • FIG. 12 is a graph showing the difference in the signal strength for certain microRNA sequences in the ILD and control plasma samples of FIG. 9 .
  • FIG. 13 is a graph showing the degree of overexpression in certain microRNA sequences in the ILD and control plasma samples of FIG. 9 .
  • FIG. 14 is a graph comparing miRNA expression levels in ILD tissue samples with ILD plasma samples.
  • FIG. 15 is a graph comparing miRNA expression levels in control lung tissue samples with ILD lung tissue samples.
  • FIG. 16 is a graph showing the effect of normalization on data in a data analysis method.
  • FIGS. 17A-17B are graphs showing the effect of normalization on the quality of data.
  • FIG. 18 is a graph clustering normalized miRNA data.
  • FIG. 19 is a graph showing the p-value distribution of all miRNA in a sample.
  • FIG. 20 is a collection of charts showing the selection of panels that separates data.
  • MicroRNAs are small but potent regulatory non-coding ribonucleic acid (RNA) sequences first identified in C. elegans in 1993. miRNA may be about 21 to about 23 nucleotides in length. Through sequence complementation, microRNA interacts with messenger RNA (mRNA) and affects the stability of mRNA and/or the initiation and progression of protein translation. It has been estimated that over 30% of the mRNAs are regulated by microRNA. Like mRNA, some of the microRNAs also display restricted tissue distribution. The biological function of microRNA is yet to be fully understood; however, it has been shown that microRNA sequences are involved in various physiological and pathological conditions, including differentiation, development, cancer, and neurological disorders.
  • mRNA messenger RNA
  • microRNA is reasonably well conserved across different species.
  • a specific microRNA sequences which is shown to correlate to a particular condition, such as disease or injury, in one species should also correlate to that particular condition in other species, particularly humans (i.e. Homo sapiens ). This correlation provides useful diagnostic content.
  • MicroRNAs can also be manipulated with commonly used molecular biology techniques including complementary DNA (cDNA) synthesis, polymerase chain reactions, Northern blotting, and array based hybridization. This makes it possible to easily investigate the function(s) of a given microRNA sequences of interest.
  • cDNA complementary DNA
  • a microRNA is encoded by a gene.
  • the DNA of the gene is transcribed into RNA, the RNA is not subsequently translated into protein. Instead each primary transcript (a pri-mir) is processed into a short stem-loop structure (a pre-mir) and finally into a mature sequence, designated miR.
  • the primary transcript can form local hairpin structures, which ordinarily are processed such that a single microRNA sequence accumulates from one arm of a hairpin precursor molecule. Sometimes the primary transcript contains multiple hairpins, and different hairpins give rise to different microRNA sequences.
  • microRNA sequences discussed herein are named according the miRBase database available at http://microrna.sanger.ac.uk/ and maintained by the Wellcome Trust Sanger Institute (now redirected to http://www.miRBase.org/). Generally speaking, microRNA sequences are assigned sequential numerical identifiers, with the numerical identifier based on sequence similarity. A 3- or 4-letter prefix designates the species from which the microRNA sequence came. For example, the hsa in hsa-miR-101 refers to homo sapiens.
  • Orthologous sequences refer to microRNA sequences that are in different species but are similar (i.e. homologous) because they originated from a common ancestor. Generally speaking, orthologs have the same numerical identifier and are believed to serve a similar function. For example, mmu-miR-101 and hsa-miR-101 are in mouse and human, respectively, and are orthologs to each other. In this disclosure, microRNA sequences are referred to without the prefix designating the species, and should be construed as preferentially referring to the human microRNA sequence and the murine sequence. For example, miR-101 should be construed as referring to hsa-miR-101 and mmu-miR-101.
  • Paralogous sequences are microRNA sequences that differ from each other in only a few positions. Paralogs occur within a species. Paralogs are designated with letter suffixes. For example, mmu-miR-133a and mmu-miR-133b are paralogs.
  • Identical microRNA sequences that originate from separate genomic loci are given numerical suffixes, such as hsa-miR-26a-1 and hsa-miR-26a-2.
  • two different mature microRNA sequences are excised from opposite arms of the same hairpin precursor.
  • the two microRNA sequences can be designated in at least two ways. First, when it is possible to determine which arm gives rise to the predominantly expressed miRNA sequence, an asterisk has been used to denote the less predominant form, such as hsa-let-7b and hsa-let-7b*. Alternatively, they are named to designate whether they come from the 5′ or 3′ arm, such as hsa-miR-125a-3p and hsa-miR-125a-5p.
  • microRNA sequences have been identified in the blood that are associated with liver injuries. Thus, the levels of selected microRNA sequences can be used to detect, predict, or diagnose diseases, predict and monitor therapeutic responses, and/or predict disease outcomes.
  • MicroRNA-based blood markers offer superior properties over existing markers. Such markers are sensitive, in part because microRNA signals can be amplified using standard polymerase chain reactions (PCR) while protein-based markers cannot be easily amplified. Because the sequence and expression profile of microRNAs are largely conserved across species, discoveries made in animal models can be easily translated to and adapted for use in humans. MicroRNA assays can be quickly performed and developed with standard PCR or array based systems; therefore, beside PCR primers, there is no need to develop special detection agents. Finally, since microRNA can be easily accessed in various body fluids, obtaining such diagnostic information can be done non-invasively.
  • PCR polymerase chain reactions
  • the level of specific microRNA sequences(s) in a cell, tissue, or body fluid(s) can be used to monitor the physiopathological conditions of the body.
  • microRNA sequences in the tissue and the serum have been identified that are associated with liver injuries, lung injuries, and lung diseases.
  • the combination of information from multiple microRNA expression level changes can further enhance the sensitivity and specificity of disease/injury detection, including using the ratio of paired microRNA sequences.
  • MicroRNA profiles for example a microRNA profile of tissue-specific microRNA sequences, could be used to monitor the health status of that tissue. Those microRNA sequences could also be used as therapeutic targets for diseases associated with the tissue.
  • MicroRNA sequences from microbes or infectious agents, such as bacteria and viruses, could be used as an indication of infection.
  • Host responses could be monitored by using the combination of microRNA sequences from infectious agents and the host as measured from the host's body fluids.
  • microRNA profiles specific to a process or network. These specific microRNA sequences could also be used as therapeutic targets for diseases associated with the biological processes.
  • the methods of the present disclosure could be used to detect, predict, monitor, or treat a physiological condition such as a disease, injury, or infection.
  • the methods include: (a) isolating microRNA sequences from a biological sample; (b) generating a microRNA profile from the isolated microRNA sequences, the profile including the levels of expressed microRNA sequences in the biological sample; and (c) comparing the microRNA profile with a reference to identify differentially expressed microRNA sequences. Based on the identity or the levels of the differentially expressed microRNA sequences, the physiological condition could be detected, predicted, or monitored; or a treatment could be indicated, administered, or monitored accordingly.
  • the biological sample is generally non-invasive, and may be, for example, a biopsy material, tissue, or body fluid.
  • body fluids include serum, plasma, lymph, saliva, urine, tears, sweat, semen, synovial fluid, cervical mucus, amniotic fluid, cerebrospinal fluid, and breast milk.
  • Plasma and serum would provide some general indicators of health, while a specific body fluid could be included for specific information.
  • a specific body fluid could be included for specific information.
  • Testing the breast milk would help assess the health status of a breast of the patient providing the biological sample.
  • Testing the saliva would help assess the health status of the head and neck region.
  • Testing the tears would help assess the health status of an eye of the patient providing the biological sample.
  • testing semen would help assess the health status of a prostate or male reproductive organ.
  • testing the synovial fluid would help assess the health status of a joint of the patient providing the biological sample. Testing the sweat would help assess the health status of the skin. Testing the cerebrospinal fluid would help assess the health status of the central nerve system.
  • the term “health status” refers only to the physiological condition of the given body part, and has no specific meaning otherwise.
  • Isolating microRNA can be done by various methods.
  • the biological sample may be extracted with an organic solvent to obtain an aqueous phase containing the microRNA sequences.
  • the aqueous phase is then purified through a silica membrane to isolate the microRNA sequences.
  • a microRNA profile can then be generated from the isolated microRNA sequences.
  • the microRNA profile provides the identity of specific microRNA sequences and/or the expression level (i.e. amount) of each specific microRNA sequence.
  • An exemplary microRNA profile is seen in FIG. 2 , which shows the expression levels for several microRNA sequences from several different liver samples that have been exposed to a high dose of acetaminophen.
  • the microRNA profile of FIG. 2 has six columns, but a microRNA profile may be simply one column (along with the identifying microRNA).
  • the expression level can be displayed either as a sliding color scale or simply as numerical values.
  • the microRNA profile can be generated by using hybridization to identify the microRNA sequences and/or using quantitative PCR (qPCR) to identify the levels of one or more particular microRNA sequences.
  • qPCR quantitative PCR
  • the diagnostic information may be in the identity of the microRNA sequences themselves, or in the absolute or relative levels of the microRNA sequence, either between two microRNA sequences in a given sample or between two samples for a given microRNA sequence.
  • a reference table could be provided, for example from a reference sample taken from the patient or from a table of levels of expressed microRNA sequences in a normal (healthy) person or a table compiled from the expressed microRNA sequences over a large sample of people.
  • Differentially expressed microRNA sequences can then be identified by comparing the microRNA profile of the biological sample with the reference sample or table to obtain diagnostic information.
  • the term “differentially expressed” refers only to the fact that the amount or expression level has changed. The direction of change (i.e. upwards or downwards, overexpressed or underexpressed) is not significant, except as otherwise stated.
  • identifying at least one specific microRNA sequence as being differentially expressed would be sufficient to identify a particular physiological condition as occurring.
  • at least two differentially expressed microRNA sequences are identified. This provides for an additional degree of confirmation in the identity of the physiological condition.
  • generating and “identifying,” it is contemplated that these actions may be performed directly or indirectly.
  • a laboratory technician may perform the actions that directly “generate” a microRNA profile.
  • the physician who ordered the microRNA profile that was directly “generated” by the laboratory technician may be considered to have indirectly “generated” the microRNA profile.
  • the biological sample may be from a microbe, such as a virus, bacterium, fungus, protozoan, or parasite.
  • microRNA sequences and their expression levels can differ depending on their location in the body. In other words, they can be specific to a biological pathway, cell type, or tissue. This fact can provide powerful diagnostic information as well.
  • Table 1 lists some microRNA sequences which have been found to be specific to certain tissues in the human body.
  • microRNA sequences and their expression levels can differ depending on their location in different types of body fluid samples. In other words, they can be specific to a biological pathway, cell type, or tissue. This fact can provide powerful diagnostic information as well.
  • Table 2 lists some microRNA sequences which have been found to be highly abundant in different body fluids. The sequences in bold font are unique to the listed body fluid.
  • a physiological condition could be detected, identified, predicted, treated, and/or monitored.
  • a treatment could be administered based on the identity of the physiological condition.
  • a particular treatment could be monitored by taking a first sample, administering the treatment, taking a second sample, and comparing the microRNA profiles of the two samples to identify and/or track changes resulting from the treatment. Those changes could include the amounts of a particular microRNA sequence, or the identity of the differentially expressed microRNA sequences that have changed between the two samples.
  • microRNA level could be altered by constructing a specific DNA or RNA sequence related to the microRNA sequences, then delivering that DNA or RNA sequence to a targeted cell, tissue, or organ expressing the targeted microRNA sequences.
  • microRNA sequences are identified that may be useful in diagnosing and/or treating liver disease or injury, lung disease or injury, and neurological disease or injury.
  • Such conditions include chronic obstructive pulmonary disease (COPD) and idiopathic pulmonary fibrosis (IPF) (also known as interstitial lung disease (ILD)).
  • COPD chronic obstructive pulmonary disease
  • IPF idiopathic pulmonary fibrosis
  • ILD interstitial lung disease
  • microRNA profile comprises the amounts of specific microRNA sequences. The amounts of those specific microRNA sequences are then compared to a reference to provide information for detecting or predicting the lung condition.
  • the microRNA profile may include those specific microRNA sequences identified below in the examples, or a subset thereof. Such microRNA profiles would be smaller, faster, and provide the same diagnostic information as larger test kits.
  • microRNA can be isolated using glass filter based methods to selectively bind RNA in a high salt buffer.
  • the unwanted biomolecules can then be washed off by using high salt buffers containing at least 50% alcohol.
  • the bound pure RNA can then eluted off the glass membrane with low salt buffer or RNAse-free water.
  • the upper aqueous phase (containing the RNA) was then transferred to a new collection tube, and 1.5 volumes of ethanol was added.
  • the sample was then transferred to a cartridge containing a glass filter (i.e. silica membrane) so that RNA could attach to the glass filter.
  • the contaminants were washed off the silica membrane by applying different high salt washing buffers included in the miRNeasy kit.
  • the bound pure RNA was then eluted off the membrane with water or low salt buffer.
  • the yield of microRNA from different amount of liquid samples used in these protocols was tested. The best ratio was found to be 4 volumes of lysis buffer with 1 volume of liquid sample.
  • RNA was evaluated by RNA by NanoDrop 1000 spectrophotometer (Thermo Fisher Scientific Inc. Waltham, Mass.) and the Agilent 2100 Bioanalyzer (Agilent Inc. Santa Clara, Calif.).
  • FIG. 1A shows an electropherogram of RNA isolated from solid tissue
  • FIG. 1B shows an electropherogram of RNA isolated from a liquid sample.
  • the 18S and 28S peaks are clearly visible and marked.
  • the microRNA are located on the left of both electropherograms. This region also contains all degraded RNA.
  • Agilent's human and mouse microRNA microarray kits were used as the array platform; however, arrays from different companies including Affymetrix and Exiqon have also been used.
  • the human microRNA microarray contained probes for 723 human and 76 human viral microRNAs from the Sanger database v 10.1.
  • the mouse microRNA microarray contained probes for 567 mouse and 10 mouse herpes virus microRNA sequences from the Sanger database v 10.1. Cyanine 3-pCp labeled RNA (i.e.
  • RNA labeled with Cyanine 3-Cytidine bisphosphate for array hybridization was generated by 100 nanograms (ng) of total RNA using Agilent's microRNA complete labeling and hybridization kit. All the steps, including labeling, hybridization, washing, scanning and feature extraction were performed in accordance with the manufacturer's instructions.
  • RNA was dephosphorylated with calf intestinal alkaline phosphatase, then heat and DMSO treated to yield denatured RNA.
  • Cyanine 3-Cytidine bisphosphate was joined to the microRNA by T4 RNA ligase.
  • MicroBioSpin 6 columns were used to desalt the samples and remove any unincorporated fluorophores.
  • the samples were hybridized to 8 ⁇ 15K Agilent Human microRNA (V2) or Mouse microRNA microarrays in a rotating hybridization oven for 20 hours at 55° C. and 10 rpm. The arrays were washed for 5 minutes in Agilent GE Wash Buffer 1 with Triton X-102 and then for another 5 minutes in Agilent GE Wash Buffer 2 with Triton X-102.
  • Quantitative PCR Quantitative PCR with microRNA specific primer sets were used to confirm the results from array hybridization.
  • a SYBR Green based method, miScript real-time PCR (Qiagen Inc. Valencia, Calif., USA), or TaqMan primer set from Apply Biosystems was used with 50 ng of total RNA from each sample.
  • the first strand cDNA was generated according to the manufacturer's instruction.
  • Approximately 2.5 ng of cDNA was used in the PCR reaction.
  • the yield of 18 to 20 base pair fragments (based on SYBR Green intensity) corresponding to the specific microRNA species was monitored with the 7900HT fast real-time PCR system from Applied Biosystems (Applied Biosystems, Foster City, Calif.).
  • QPCR results were analyzed by SDS 2.2.2, with a manual CT threshold value of 0.2.
  • microRNA sequences could be used as a marker to detect liver injury. Mice were used as the experimental model.
  • mice 6-month-old male C57/B6 mice were grouped into control and treatment groups with 4 animals in each group. The mice then fasted for 24 hours prior to a single intraperitoneal injection of either (a) 300 mg/kg of acetaminophen in phosphate buffer saline (PBS) (treatment group); (b) or PBS (control group). Mice were sacrificed at different time points post-exposure (12 hr, 24 hr, 48 hr, 72 hr, and 120 hr) and plasma and liver samples were collected. Part of the liver samples were sectioned and examined by a pathologist and the serum alanine transaminase (ALT) levels were also determined to confirm as well as assess the severity of liver injury.
  • PBS phosphate buffer saline
  • microRNA profile The expression levels of various microRNA sequences in the liver tissues were used to generate a microRNA profile and used to assess tissue injury. Differentially expressed microRNA sequences were clustered using the Hierarchical clustering method and the result is shown in FIG. 2 . The different time points are indicated on the top, while the identity of individual microRNA sequences is listed on the right. (The identifying labels correspond to those in the miRNA Registry maintained at the Sanger Institute.) The hybridization intensity of individual microRNA sequences is represented in different colors as indicated on top of the figure (yellow representing the highest expression and blue representing the lowest expression signal). The microRNA profile clearly indicates that the levels of some microRNA sequences were changed by the exposure to acetaminophen.
  • mice The male C57/B6 mice were randomly grouped into two groups, a treatment group (3 animals) and control group (4 animals). They fasted for 24 hours prior to a single intraperitoneal injection of either (a) 300 mg/kg of acetaminophen in PBS (treatment group); or (b) PBS (control group). Mice were sacrificed at 24 hours post exposure, the plasma samples were collected and RNA was isolated.
  • microRNA sequences in the serum were used to make a microRNA profile.
  • the differentially expressed microRNA sequences between the treatment group and the control group were clustered with the Hierarchical clustering method and is shown in FIG. 3 .
  • mice The male C57/B6 mice were randomly grouped into nine different groups with 4 animals in each group. They fasted for 24 hours prior to a single intraperitoneal injection with either (a) 75 mg/Kg of acetaminophen in PBS (treatment 1); (b) 150 mg/Kg of acetaminophen in PBS (treatment 2); (c) 300 mg/Kg of acetaminophen in PBS (treatment 3); or (d) PBS (control group). Mice were sacrificed and plasma samples were collected at 1, 3 and 24 hours post-exposure.
  • the nine groups were: 1) 1 hour control; 2) 1 hour treatment 1; 3) 1 hour treatment 2; 4) 1 hour treatment 3; 5) 3 hour control; 6) 3 hour treatment 1; 7) 3 hour treatment 2; 8) 3 hour treatment 3; and 9) 24 hour treatment 3.
  • the group at 24 hr post-exposure received only the highest dose (300 mg/kg) to serve as a positive control.
  • the expression levels of two different microRNA sequences, mir-486 and mir-122, in the serum were profiled by quantitative polymerase chain reactions (Q-PCR).
  • the median intensities (Z-axes) from each group (X-axis) at PCR cycle number 19 were plotted. This graph is shown in FIG. 4 . Both mir-486 (red bars) and mir-122 (green bars) intensities showed dose-dependent changes at 3 hr post-exposure. The intensity of mir-122 at 300 mg/kg was almost the same between 3 hr and 24 hr post-exposure. Clear changes were observed in the samples obtained at one hour post-acetaminophen injection. The results clearly indicated that the levels of selected microRNA sequences, such as mir-122 and mir-486, in the serum could be used as an early indication of tissue injury.
  • microRNA could be used in assessing neurological disorders.
  • the microRNA expression patterns in brain tissues obtained from normal and prion infected animals were profiled as described above. The results are shown in FIG. 6 . The result clearly indicated differences between normal and diseased samples.
  • microRNA could be used in assessing the health status of lungs.
  • the microRNA expression patterns in lung tissues obtained from normal and diseased animals were profiled as described above. The results are shown in FIG. 7 . The result clearly indicated there were differences on microRNA expression as the disease progressed (from 1 to 6 where 6 has the most serious disease condition) and a number of microRNA sequences are different between normal and disease samples. Thus, specific microRNA sequences or a panel of microRNA sequences could be used as a tool to assess the health status of lungs.
  • microRNA profiles in serum and urine samples obtained from a normal mouse were profiled as described above, then compared. The result is shown in FIG. 8 .
  • the result clearly revealed a significant difference in the microRNA composition in different body fluids. This would allow the development of different biomarkers to be used in different body fluids to assess the health status of tissues.
  • microRNA sequences in a specific body fluid can be used as a reliable tool to assess the health status of tissues intimately associated with that body fluid, e.g. bladder and kidney tissues to the urine.
  • miRNA profiles from lung tissue and plasma from ILD patients and COPD patients were compared to a control set of miRNA profiles from uninvolved lung tissue obtained from lung cancer resections (controls) and a control set of miRNA profiles from plasma samples obtained from clinically normal donors (collected by the Marsh lab).
  • the miRNA profiles were compared in various pairwise combinations to determine which miRNA sequences were overexpressed and thus useful for diagnostic purposes.
  • the miRNA profiles were obtained using a microarray kit available from Agilent, which generally detected a given miRNA with usually two independent oligonucleotide (oligo) targets and four or more in some cases.
  • miRNA 1225-5p i.e. mir-1225-5p
  • mir-1225-5p was about 3-fold over expressed in ILD plasma.
  • four probe oligos were used in the Agilent array.
  • the signal strengths were within a factor of about two to each other, even though these oligos differ slightly in sequence.
  • FIG. 11 shows the signal strength for all of the oligos that targeted these 17 sequences.
  • the ILD plasma signals are shown as blue diamonds and the control plasma signals are shown as pink squares. As seen, the signal strengths for all of the independent oligo probes were reasonably close (i.e. within a factor of 2.5).
  • FIG. 12 shows the resulting graph with the mean and one standard deviation identified. Again, the ILD plasma signals are shown as blue diamonds and the control plasma signals are shown as pink squares. The expression of the displayed microRNAs was at least two-fold higher in the ILD plasma samples than in the control plasma samples (2 ⁇ was an arbitrary value). Table 3 lists the specific data of FIG. 12 .
  • the 17 miRNAs that met this criterion can be divided into three groups. 11 miRNAs (UL70-3p, 1225-5p, 134, 135a*, 150*, 483-5p, 575, 630, 638, H1, and LAT) were likely to be differentially expressed between ILD and control with high confidence. There was intermediate confidence for 4 miRNAs (mir-22, 223, 939, and 940); and lower confidence for miRNAs 923 and K12-3.
  • ILD mean mean mean control/ microRNA level St. Dev. level St. Dev. mean sequence (ILD) (ILD) (control) (control) ILD hsa-miR-451 729 1695.917 5274 5362.923 7 oligo 1 hsa-miR-451; 487 1096.762 3527.167 3421.076 7 oligo 2 hsa-miR-451; 390 375 5274 13.5 oligo 1 w/o outlier hsa-miR-451; 268 243 3527 13.1 oligo 2 w/o outlier hsa-miR-448 64 38.3 253 93 4 hsa-miR-92a-2* 78 46.94568 253.6667 109.7154 3
  • hsa-miR-448 and hiss-miR-92-a-2* were just over the threshold for inclusion and showed low absolute expression.
  • hsa-miR-451 was expressed ten times higher in control plasma relative to ILD plasma.
  • One of the miRNAs that is overexpressed in control serum relative to ILD serum, in combination with a miRNA that is overexpressed in ILD serum relative to control serum, could be used in a simple “top scoring pair” test for ILD.
  • hsa-miR-21 was present here, but not in Tables 3 or 4, while the other seven were also listed in either Table 3 or 4.
  • microRNA mean level mean level mean ILD/ sequence (ILD) control mean control hsa-miR-923 6114.7 22421.33 0.3 hsa-miR-22 5555.8 4771.83 1.2 hsa-miR-29a 4881.6 2183.67 2.2 hsa-miR-145 3759.9 1449.50 2.6 hsa-miR-26a 3187.3 1123.33 2.8 hsa-let-7c 4405.5 1336.33 3.3 hsa-miR-23a 4396.1 1144.83 3.8 hsa-miR-21 9938.7 2450.83 4.1 hsa-miR-125b 3843.2 939.17 4.1 hsa-miR-27a 3090.2 720.50 4.3 hsa-let-7a 5709.2 1039.33 5.5 hsa-let-7f 3352.5 504.83 6.6 hs
  • miRNA hsa-miR-923 was over 130-fold over-expressed in ILD plasma relative to control plasma (Table 3), but it is under-expressed in ILD lung tissue relative to control lung tissue (Table 6). This suggests the tissue or cell of origin for this miRNA may be within the blood itself, or at least not the ILD lung.
  • miRNA hsa-miR-22 is expressed three times higher in ILD plasma compared to control plasma (Table 3), but is expressed at nearly the same level in ILD lung and control lung tissue (Table 6).
  • Other miRNAs that are characteristic of ILD such as miRNA-451, were elevated in ILD lung tissue (17 ⁇ in Table 6) but not in ILD plasma (see Table 4).
  • the plasma sample data (control, COPD, and ILD) was separately analyzed using the Panorama suite of tools and consisted of the following steps: (A) Normalization; (B) Quality Control; (C) Cluster Analysis; (D) Panel Selection; and (E) Comparison. Each step is explained in more detail below.
  • FIG. 17A shows the Pearson correlation distribution before normalization. This figure correlated the score for each miRNA across the samples to the total miRNA expression level across the samples. This figure showed that the vast majority of miRNA sequences had the same expression profile across the samples, and furthermore, this expression profile is the total miRNA level per sample—this is the dominant feature of the dataset.
  • FIG. 17B shows the Pearson correlation distribution after normalization. Normalization improved the quality of the dataset. The distribution in FIG. 17B was much less skewed than that of FIG. 17A .
  • the normalized miRNA data was clustered using multi-dimensional scaling (MDS); the results are presented in FIG. 18 .
  • MDS multi-dimensional scaling
  • the samples within each group clustered together showing uniformity in miRNA expression. The exception was IPF tissue where a few outliers occurred, likely due to sample handling.
  • the tissue groups cluster away from the plasma groups.
  • the COPD and ILD plasma samples overlapped and were clustered away from the plasma control samples.
  • AOC Area Under the Curve
  • the combination rule used here was majority consensus: if the strict majority of miRNA classified a sample as diseased (i.e. ILD or IPF) then the sample was classified diseased, otherwise, the sample was classified as normal.
  • FIG. 20 is three charts showing the distribution of directional bias (upper left), the AUC distribution (upper right), and the standard deviation for the ILD group (lower left).
  • the number of miRNA higher in the control samples than the ILD samples was essentially the same as the opposite direction.
  • the distribution of AUC scores for all miRNA was centered about 0.6 which is expected.
  • a small rise around 0.95 indicated the presence of miRNA that distinguish the control and ILD samples.
  • the distribution of miRNA expression standard deviations showed that overall, variability was similar across miRNA (note that normalization is done by sample, not by miRNA).
  • the data was analyzed. Using an AUC threshold of 0.95, 57 out of 2421 (2.4%) miRNA probes were selected. Table 7 contains the oligo probe used for the miRNA, the corresponding miRNA, p-value, AUC, and number of panels of 3 miRNA above combined AUC 0.99 that each miRNA participated in. If the miRNA was expressed higher in the control sample than the ILD sample, the column “Control>ILD” was marked with a Y.
  • the claims refer to identifying “at least one” or “at least two” differentially expressed microRNA sequences in a microRNA profile, wherein the differentially expressed microRNA sequences are selected from a list. This language should be construed as meaning that the microRNA sequence selected from the list is identified as a differentially expressed microRNA sequence in the microRNA profile.
  • microRNA profiles would test for only specific microRNA sequences, such as those identified above.
  • an assay or microRNA profile tests for at least two microRNA sequences selected from the group consisting of miR-630, miR-134, hcmv-miR-UL70-3p, miR-1225-5p, miR-135a*, miR-150*, miR-22, miR-223, miR-483-5p, miR-575, miR-638, miR-923, miR-939, miR-940, hsv1-miR-H1, hsv1-miR-LAT, kshv-miR-K12-3, and human orthologs thereof.
  • at least three of these sequences is tested for.
  • all 17 of these sequences are tested for. Specific pairs of these 17 microRNA sequences include those listed in Table 8:
  • an assay or microRNA profile tests for at least two microRNA sequences selected from the group consisting of miR-630, miR-134, hcmv-miR-UL70-3p, miR-1225-5p, miR-135a*, miR-150*, miR-483-5p, miR-575, miR-638, hsv1-miR-H1, hsv1-miR-LAT, and human orthologs thereof.
  • at least three of these sequences is tested for.
  • all 11 of these sequences are tested for. Specific pairs of these 11 microRNA sequences include those listed in Table 9:
  • an assay or microRNA profile tests for two or more microRNA sequences.
  • At least one of the microRNA sequences tested for is selected from the group consisting of miR-630, hcmv-miR-UL70-3p, miR-1225-5p, miR-134, miR-135a*, miR-150*, miR-483-5p, miR-575, miR-638, hsv1-miR-H1, hsv1-miR-LAT, and human orthologs thereof.
  • At least one of the microRNA sequences tested for is selected from the group consisting of miR-451, miR-448, and miR-92a-2*.
  • miR-451 is one of the microRNA sequences tested for. Specific pairs of these microRNA sequences include those listed in Table 10:
  • an assay or microRNA profile tests for at least two microRNA sequences selected from the group consisting of miR-451, miR-923, miR-1225-5p, miR-22, miR-223, miR-638, kshv-miR-K12-3, and human orthologs thereof. In other embodiments, at least three of these sequences is tested for. In particular embodiments, all seven of these sequences are tested for. Specific pairs of these seven microRNA sequences include those listed in Table 11:
  • an assay or microRNA profile tests for at least two microRNA sequences selected from the group consisting of miR-940, miR-134, miR-135a*, miR-150*, miR-483-5p, miR-575, miR-939, hsv1-miR-H1, kshv-miR-K12-3, hsv1-miR-LAT, hcmv-miR-UL70-3p, and human orthologs thereof.
  • at least three of these sequences is tested for.
  • all 11 of these sequences are tested for. Specific pairs of these 11 microRNA sequences include those listed in Table 12:
  • Appendix A provides a listing of the RNA sequences for all of the microRNA discussed herein, including human orthologs thereof.

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Abstract

Methods, compositions, and devices are disclosed which use microRNA to detect, predict, treat, and monitor physiological conditions such as disease or injury. microRNA are isolated and their differential expression is measured to provide diagnostic information. This information may then be utilized for evaluation and/or treatment purposes.

Description

  • This application is a divisional of U.S. patent application Ser. No. 14/300,071, filed Jun. 9, 2014, which is a continuation of U.S. patent application Ser. No. 12/615,969, now U.S. Pat. No. 8,748,101, which claims priority to U.S. Provisional Patent Application Ser. No. 61/112,985, filed Nov. 10, 2008. The contents of these applications are hereby fully incorporated by reference in its entirety.
  • BACKGROUND
  • Disclosed herein are various methods, compositions, and devices utilizing microRNA, such as microRNA-based markers, to detect, predict, treat, or monitor various physiological or pathological conditions.
  • The ideal diagnostic marker has to fulfill certain key requirements including being specific, sensitive, robust, and non-invasive. Current disease diagnoses are primarily based on two different but complementary approaches—physical imaging and biomolecular profiling. Both approaches currently suffer from a lack of specificity and early detection capability. Tissue-specific blood biomarkers can increase the specificity to selected organs. However, the levels of these tissue-specific biomarkers are usually low in blood. In addition, the difficulty of developing suitable capture agents for proteins makes the identification and development of new molecular diagnostic markers difficult.
  • It would be desirable to provide new methods, compositions, and devices for diagnosing physiological and pathological conditions.
  • BRIEF DESCRIPTION
  • The present disclosure relates, in different embodiments, to the use of the levels of microRNA sequences (miRNA) in body fluids to establish correlations with the body's pathophysiological conditions. Exemplary body fluids include, but are not limited to, serum, plasma, saliva, urine, tears, amniotic fluid, sweat, cerebrospinal fluid, seminal fluid (semen), lung mucus (e.g. from bronchial lavage), pleural fluid, peritoneal fluid, colostrums, and breast milk. These levels can then provide diagnostic and/or predictive information with regard to important issues of health and disease.
  • Disclosed are methods of using microRNA sequences to detect a physiological condition. The methods comprise: isolating microRNA sequences from a biological sample; generating a microRNA profile from the isolated microRNA sequences, the profile including the levels of expressed microRNA sequences in the biological sample; comparing the microRNA profile with a reference to identify differentially expressed microRNA sequences; and detecting the physiological condition based on the identity or the levels of the differentially expressed microRNA sequences.
  • The biological sample may be a biopsy material, tissue, or body fluid. In embodiments, the biological sample comprises a body fluid selected from the group consisting of serum, plasma, lymph, saliva, urine, tears, sweat, semen, synovial fluid, cervical mucus, amniotic fluid, cerebrospinal fluid, and breast milk.
  • The microRNA sequences may be isolated by extracting the biological sample with an organic solvent to obtain an aqueous phase containing the microRNA sequences; and purifying the aqueous phase through a silica membrane to isolate the microRNA sequences.
  • The microRNA profile can be generated using hybridization to identify a microRNA sequences; or by using a quantitative polymerase chain reaction to identify the level of a microRNA sequences.
  • The reference can be a table of the levels of expressed microRNA sequences in a normal person, or a reference sample.
  • The biological sample may be from a microbe, such as a virus, bacterium, fungus, protozoan, or parasite.
  • The isolated microRNA sequences may be specific to a biological pathway, a cell type, or a tissue.
  • The physiological condition may be a disease, injury, or infection.
  • Also disclosed are methods of using microRNA sequences to detect or predict a physiological condition. These methods also comprise: generating a microRNA profile from a biological sample, the profile including the levels of expressed microRNA sequences in the biological sample; and comparing the microRNA profile with a reference to identify differentially expressed microRNA sequences. The physiological condition could then be detected or predicted based on the identity or the levels of the differentially expressed microRNA sequences. Alternatively, the physiological condition can be identified, and a treatment can then be administered based on the identity of the physiological condition.
  • Further disclosed are methods of using microRNA sequences to monitor a physiological condition, comprising: generating a first microRNA profile from a first biological sample of a patient; administering a treatment to the patient; generating a second microRNA profile from a second biological sample of the patient; comparing the second microRNA profile with the first microRNA profile to identify differentially expressed microRNA sequences; and identifying a change in the physiological condition based on the identity or the amounts of the differentially expressed microRNA sequences.
  • Additionally disclosed are methods of using microRNA sequences to treat a physiological condition. The methods comprise: identifying at least one microRNA sequence based on the physiological condition; and manipulating the level of the at least one microRNA sequence to treat the physiological condition. Manipulating the level of the at least one microRNA sequence may comprise: constructing a specific DNA or RNA sequence related to the at least one microRNA sequence; and delivering the specific DNA or RNA sequence to a targeted cell, tissue, or organ.
  • Also disclosed are methods of using microRNA sequences to detect, predict, or treat a physiological condition. The methods comprise: generating a microRNA profile from a biological sample; identifying at least one differentially expressed microRNA sequence by comparing the microRNA profile to a reference; and detecting, predicting, or treating the physiological condition based on the identity or the levels of the at least one differentially expressed microRNA sequence. In alternative embodiments, at least two differentially expressed microRNA sequences are identified.
  • Other methods of detecting or predicting a physiological condition comprise generating a microRNA profile from a biological sample, wherein the microRNA profile comprises at least one specific microRNA sequence; and comparing the microRNA profile to a reference to provide information useful for detecting or predicting the physiological condition. In alternative embodiments, the microRNA profile comprises at least two specific microRNA sequences.
  • A differentially expressed microRNA sequence can be identified by comparing the amount of a particular microRNA sequence in the microRNA profile with the amount of that particular microRNA sequence in the reference. A differentially expressed microRNA sequence is identified when the ratio of the amount in the microRNA profile to the amount in the reference is at least 1.5, or at least 3.
  • When the physiological condition is related to liver disease or liver injury, in some embodiments, the microRNA profile or the specific microRNA sequence(s) may comprise at least one microRNA sequence selected from the group consisting of mmu-miR-122, mmu-miR-486, mmu-miR-125b-5p, mmu-let-7d*, mmu-miR-101a, mmu-miR-101b, mmu-miR-1224, mmu-miR-124, mmu-miR-125a-3p, mmu-miR-125a-5p, mmu-miR-127, mmu-miR-130a, mmu-miR-133a, mmu-miR-133b, mmu-miR-135a*, mmu-miR-141, mmu-miR-193, mmu-miR-193b, mmu-miR-199a-5p, mmu-miR-199b*, mmu-miR-200c, mmu-miR-202-3p, mmu-miR-205, mmu-miR-22, mmu-miR-23b, mmu-miR-26a, mmu-miR-27b, mmu-miR-291a-5p, mmu-miR-294*, mmu-miR-29b, mmu-miR-30a, mmu-miR-30c-1*, mmu-miR-30e, mmu-miR-320, mmu-miR-327, mmu-miR-339-3p, mmu-miR-342-3p, mmu-miR-370, mmu-miR-375, mmu-miR-451, mmu-miR-466f-3p, mmu-miR-483, mmu-miR-494, mmu-miR-574-5p, mmu-miR-652, mmu-miR-671-5p, mmu-miR-685, mmu-miR-710, mmu-miR-711, mmu-miR-712, mmu-miR-714, mmu-miR-720, mmu-miR-721, mmu-miR-877, mmu-miR-877*, mmu-miR-882, mmu-miR-93, mmu-miR-99a, and human orthologs thereof.
  • In other embodiments where the physiological condition is related to liver disease or liver injury, the microRNA profile or the specific microRNA sequence may comprise at least one microRNA sequence selected from the group consisting of mmu-miR-122, mmu-miR-486, mmu-miR-125b-5p, mmu-let-7d*, mmu-miR-101a, mmu-miR-101b, mmu-miR-1224, mmu-miR-124, mmu-miR-125a-3p, mmu-miR-125a-5p, mmu-miR-133a, mmu-miR-133b, mmu-miR-135a*, mmu-miR-193, mmu-miR-193b, mmu-miR-199a-5p, mmu-miR-199b*, mmu-miR-202-3p, mmu-miR-291a-5p, mmu-miR-294*, mmu-miR-30c-1*, mmu-miR-30e, mmu-miR-327, mmu-miR-339-3p, mmu-miR-342-3p, mmu-miR-375, mmu-miR-466f-3p, mmu-miR-483, mmu-miR-574-5p, mmu-miR-652, mmu-miR-671-5p, mmu-miR-685, mmu-miR-710, mmu-miR-711, mmu-miR-712, mmu-miR-714, mmu-miR-720, mmu-miR-721, mmu-miR-877, mmu-miR-877*, mmu-miR-882, and human orthologs thereof.
  • In particular embodiments, the at least one differentially expressed microRNA sequence or the at least one specific sequence comprises hsa-miR-122. In more specific embodiments, they comprise hsa-miR-122 and either hsa-miR-486-3p or hsa-miR-486-5p (i.e. the human orthologs to mmu-miR-486). The ratio of the amount of miR-122 to the amount of miR-486 may be greater than 4.0, including greater than 6.0.
  • When the physiological condition is neurological disease or neurological injury, in some embodiments, the microRNA profile or the specific microRNA sequence may comprise at least one microRNA sequence selected from the group consisting of mmu-let-7g, mmu-miR-298, mmu-miR-1, mmu-miR-101a*, mmu-miR-101b, mmu-miR-1224, mmu-miR-126-5p, mmu-miR-127, mmu-miR-128, mmu-miR-129-3p, mmu-miR-133b, mmu-miR-136, mmu-miR-138, mmu-miR-138*, mmu-miR-139-3p, mmu-miR-140, mmu-miR-140*, mmu-miR-142-3p, mmu-miR-143, mmu-miR-146a, mmu-miR-146b, mmu-miR-148b, mmu-miR-150, mmu-miR-15a*, mmu-miR-15b, mmu-miR-181b, mmu-miR-181d, mmu-miR-183, mmu-miR-185, mmu-miR-186, mmu-miR-191*, mmu-miR-194, mmu-miR-19a, mmu-miR-200a, mmu-miR-200b, mmu-miR-200b*, mmu-miR-202-3p, mmu-miR-206, mmu-miR-208a, mmu-miR-21, mmu-miR-211, mmu-miR-221, mmu-miR-222, mmu-miR-223, mmu-miR-27a, mmu-miR-27b*, mmu-miR-28*, mmu-miR-290-5p, mmu-miR-291a-5p, mmu-miR-297a, mmu-miR-299, mmu-miR-29b, mmu-miR-29c*, mmu-miR-301b, mmu-miR-302c*, mmu-miR-30c, mmu-miR-31, mmu-miR-322, mmu-miR-323-3p, mmu-miR-324-3p, mmu-miR-324-5p, mmu-miR-326, mmu-miR-328, mmu-miR-331-5p, mmu-miR-341, mmu-miR-34b-5p, mmu-miR-34c*, mmu-miR-369-3p, mmu-miR-374, mmu-miR-376b, mmu-miR-379, mmu-miR-380-3p, mmu-miR-382, mmu-miR-384-5p, mmu-miR-409-5p, mmu-miR-411, mmu-miR-411*, mmu-miR-423-5p, mmu-miR-425, mmu-miR-429, mmu-miR-434-5p, mmu-miR-450b-3p, mmu-miR-451, mmu-miR-455, mmu-miR-465c-3p, mmu-miR-466d-5p, mmu-miR-467e*, mmu-miR-484, mmu-miR-486, mmu-miR-487b, mmu-miR-497, mmu-miR-505, mmu-miR-511, mmu-miR-539, mmu-miR-540-3p, mmu-miR-551b, mmu-miR-568, mmu-miR-654-5p, mmu-miR-669a, mmu-miR-686, mmu-miR-688, mmu-miR-699, mmu-miR-701, mmu-miR-706, mmu-miR-708, mmu-miR-720, mmu-miR-721, mmu-miR-744*, mmu-miR-760, mmu-miR-770-5p, mmu-miR-7a, mmu-miR-7b, mmu-miR-881*, mmu-miR-93, mmu-miR-96, mghv-miR-M1-6, mghv-miR-M1-9, and human orthologs thereof.
  • In other embodiments where the physiological condition is neurological disease or neurological injury, the microRNA profile or the specific microRNA sequence may comprise at least one microRNA sequence selected from the group consisting of mmu-let-7g, mmu-miR-298, mmu-miR-101a*, mmu-miR-101b, mmu-miR-1224, mmu-miR-126-5p, mmu-miR-128, mmu-miR-129-3p, mmu-miR-133b, mmu-miR-138*, mmu-miR-139-3p, mmu-miR-140*, mmu-miR-146a, mmu-miR-148b, mmu-miR-15a*, mmu-miR-15b, mmu-miR-181b, mmu-miR-181d, mmu-miR-185, mmu-miR-186, mmu-miR-191*, mmu-miR-19a, mmu-miR-200b*, mmu-miR-202-3p, mmu-miR-208a, mmu-miR-211, mmu-miR-27b*, mmu-miR-28*, mmu-miR-290-5p, mmu-miR-291a-5p, mmu-miR-297a, mmu-miR-299, mmu-miR-29c*, mmu-miR-301b, mmu-miR-302c*, mmu-miR-322, mmu-miR-323-3p, mmu-miR-324-3p, mmu-miR-324-5p, mmu-miR-326, mmu-miR-328, mmu-miR-331-5p, mmu-miR-341, mmu-miR-34b-5p, mmu-miR-34c*, mmu-miR-369-3p, mmu-miR-374, mmu-miR-376b, mmu-miR-379, mmu-miR-380-3p, mmu-miR-382, mmu-miR-384-5p, mmu-miR-409-5p, mmu-miR-411, mmu-miR-411*, mmu-miR-423-5p, mmu-miR-425, mmu-miR-429, mmu-miR-434-5p, mmu-miR-450b-3p, mmu-miR-465c-3p, mmu-miR-466d-5p, mmu-miR-467e*, mmu-miR-505, mmu-miR-511, mmu-miR-539, mmu-miR-540-3p, mmu-miR-551b, mmu-miR-568, mmu-miR-654-5p, mmu-miR-669a, mmu-miR-686, mmu-miR-688, mmu-miR-699, mmu-miR-701, mmu-miR-706, mmu-miR-720, mmu-miR-721, mmu-miR-744*, mmu-miR-760, mmu-miR-770-5p, mmu-miR-7a, mmu-miR-7b, mmu-miR-881*, mmu-miR-96, mghv-miR-M1-6, mghv-miR-M1-9, and human orthologs thereof.
  • When the physiological condition is related to lung disease or lung injury, in some embodiments, the microRNA profile or the specific microRNA sequence may comprise at least one microRNA sequence selected from the group consisting of hsa-miR-135a*, hsa-miR-10b, hsa-miR-1224-3p, hsa-miR-1224-5p, hsa-miR-1225-3p, hsa-miR-1225-5p, hsa-miR-1226*, hsa-miR-1227, hsa-miR-1228, hsa-miR-1229, hsa-miR-1234, hsa-miR-1237, hsa-miR-1238, hsa-miR-124, hsa-miR-129*, hsa-miR-129-3p, hsa-miR-136*, hsa-miR-187*, hsa-miR-188-5p, hsa-miR-190b, hsa-miR-198, hsa-miR-22, hsa-miR-220b, hsa-miR-300, hsa-miR-301b, hsa-miR-30e, hsa-miR-338-3p, hsa-miR-33a*, hsa-miR-33b, hsa-miR-33b*, hsa-miR-34c-3p, hsa-miR-34c-5p, hsa-miR-363*, hsa-miR-371-3p, hsa-miR-371-5p, hsa-miR-375, hsa-miR-377*, hsa-miR-423-5p, hsa-miR-424, hsa-miR-424*, hsa-miR-429, hsa-miR-448, hsa-miR-449a, hsa-miR-449b, hsa-miR-450b-3p, hsa-miR-452, hsa-miR-454*, hsa-miR-455-3p, hsa-miR-483-3p, hsa-miR-483-5p, hsa-miR-491-3p, hsa-miR-491-5p, hsa-miR-493, hsa-miR-493*, hsa-miR-494, hsa-miR-497, hsa-miR-498, hsa-miR-500, hsa-miR-503, hsa-miR-505, hsa-miR-507, hsa-miR-513a-3p, hsa-miR-513a-5p, hsa-miR-513b, hsa-miR-513c, hsa-miR-515-5p, hsa-miR-518b, hsa-miR-518c*, hsa-miR-518d-3p, hsa-miR-518d-5p, hsa-miR-518e*, hsa-miR-520d-5p, hsa-miR-520h, hsa-miR-541, hsa-miR-545*, hsa-miR-548d-3p, hsa-miR-548d-5p, hsa-miR-551a, hsa-miR-551b, hsa-miR-552, hsa-miR-554, hsa-miR-556-5p, hsa-miR-557, hsa-miR-559, hsa-miR-561, hsa-miR-564, hsa-miR-572, hsa-miR-575, hsa-miR-576-3p, hsa-miR-578, hsa-miR-583, hsa-miR-586, hsa-miR-589, hsa-miR-589*, hsa-miR-591, hsa-miR-595, hsa-miR-601, hsa-miR-602, hsa-miR-609, hsa-miR-610, hsa-miR-612, hsa-miR-613, hsa-miR-614, hsa-miR-615-3p, hsa-miR-616, hsa-miR-619, hsa-miR-622, hsa-miR-623, hsa-miR-624*, hsa-miR-627, hsa-miR-633, hsa-miR-634, hsa-miR-638, hsa-miR-639, hsa-miR-640, hsa-miR-642, hsa-miR-644, hsa-miR-647, hsa-miR-648, hsa-miR-652, hsa-miR-654-5p, hsa-miR-658, hsa-miR-659, hsa-miR-662, hsa-miR-663, hsa-miR-665, hsa-miR-671-5p, hsa-miR-675, hsa-miR-708, hsa-miR-708*, hsa-miR-744*, hsa-miR-760, hsa-miR-765, hsa-miR-766, hsa-miR-767-3p, hsa-miR-802, hsa-miR-874, hsa-miR-876-3p, hsa-miR-876-5p, hsa-miR-877, hsa-miR-877*, hsa-miR-885-3p, hsa-miR-885-5p, hsa-miR-886-3p, hsa-miR-890, hsa-miR-891b, hsa-miR-892b, hsa-miR-920, hsa-miR-922, hsa-miR-923, hsa-miR-92b, hsa-miR-92b*, hsa-miR-933, hsa-miR-934, hsa-miR-935, hsa-miR-936, hsa-miR-937, hsa-miR-939, hsa-miR-940, hsv1-miR-H1, hsv1-miR-LAT, kshv-miR-K12-12, kshv-miR-K12-3, kshv-miR-K12-3*, kshv-miR-K12-4-5p, kshv-miR-K12-6-5p, kshv-miR-K12-8, kshv-miR-K12-9, kshv-miR-K12-9*, ebv-miR-BART10*, ebv-miR-BART12, ebv-miR-BART13, ebv-miR-BART13*, ebv-miR-BART15, ebv-miR-BART1-5p, ebv-miR-BART16, ebv-miR-BART18-5p, ebv-miR-BART19-3p, ebv-miR-BART19-5p, ebv-miR-BART20-5p, ebv-miR-BART2-5p, ebv-miR-BART3*, ebv-miR-BART5, ebv-miR-BART6-5p, ebv-miR-BART7, ebv-miR-BART7*, ebv-miR-BHRF1-1, ebv-miR-BHRF1-3, hcmv-miR-UL148D, hcmv-miR-UL22A, hcmv-miR-UL22A*, hcmv-miR-UL70-3p, hcmv-miR-UL70-5p, hcmv-miR-US25-1, hcmv-miR-US25-2-3p, hcmv-miR-US25-2-5p, hcmv-miR-US4, hiv1-miR-H1, hiv1-miR-N367, and human orthologs thereof.
  • In other embodiments where the physiological condition is related to lung disease or lung injury, the microRNA profile or the specific microRNA sequence may comprise at least one microRNA sequence selected from the group consisting of hsa-miR-135a*, hsa-miR-1224-3p, hsa-miR-1224-5p, hsa-miR-1225-3p, hsa-miR-1225-5p, hsa-miR-1226*, hsa-miR-1227, hsa-miR-1228, hsa-miR-1229, hsa-miR-1234, hsa-miR-1237, hsa-miR-1238, hsa-miR-124, hsa-miR-129*, hsa-miR-129-3p, hsa-miR-136*, hsa-miR-187*, hsa-miR-188-5p, hsa-miR-190b, hsa-miR-220b, hsa-miR-300, hsa-miR-301b, hsa-miR-30e, hsa-miR-338-3p, hsa-miR-33a*, hsa-miR-33b, hsa-miR-33b*, hsa-miR-34c-3p, hsa-miR-34c-5p, hsa-miR-363*, hsa-miR-371-3p, hsa-miR-371-5p, hsa-miR-375, hsa-miR-377*, hsa-miR-423-5p, hsa-miR-424*, hsa-miR-429, hsa-miR-448, hsa-miR-449a, hsa-miR-449b, hsa-miR-450b-3p, hsa-miR-452, hsa-miR-454*, hsa-miR-455-3p, hsa-miR-483-3p, hsa-miR-483-5p, hsa-miR-491-3p, hsa-miR-491-5p, hsa-miR-493, hsa-miR-493*, hsa-miR-500, hsa-miR-505, hsa-miR-507, hsa-miR-513a-3p, hsa-miR-513a-5p, hsa-miR-513b, hsa-miR-513c, hsa-miR-515-5p, hsa-miR-518c*, hsa-miR-518d-3p, hsa-miR-518d-5p, hsa-miR-518e*, hsa-miR-520d-5p, hsa-miR-541, hsa-miR-545*, hsa-miR-548d-3p, hsa-miR-548d-5p, hsa-miR-551b, hsa-miR-552, hsa-miR-554, hsa-miR-556-5p, hsa-miR-557, hsa-miR-559, hsa-miR-561, hsa-miR-564, hsa-miR-575, hsa-miR-576-3p, hsa-miR-578, hsa-miR-583, hsa-miR-586, hsa-miR-589, hsa-miR-589*, hsa-miR-591, hsa-miR-595, hsa-miR-602, hsa-miR-609, hsa-miR-610, hsa-miR-612, hsa-miR-613, hsa-miR-614, hsa-miR-615-3p, hsa-miR-616, hsa-miR-619, hsa-miR-623, hsa-miR-624*, hsa-miR-633, hsa-miR-638, hsa-miR-639, hsa-miR-640, hsa-miR-642, hsa-miR-644, hsa-miR-647, hsa-miR-652, hsa-miR-654-5p, hsa-miR-658, hsa-miR-659, hsa-miR-665, hsa-miR-671-5p, hsa-miR-675, hsa-miR-708*, hsa-miR-744*, hsa-miR-760, hsa-miR-765, hsa-miR-766, hsa-miR-767-3p, hsa-miR-768-3p, hsa-miR-768-5p, hsa-miR-801, hsa-miR-802, hsa-miR-874, hsa-miR-876-3p, hsa-miR-876-5p, hsa-miR-877, hsa-miR-877*, hsa-miR-885-3p, hsa-miR-885-5p, hsa-miR-886-3p, hsa-miR-890, hsa-miR-891b, hsa-miR-892b, hsa-miR-920, hsa-miR-922, hsa-miR-923, hsa-miR-92b*, hsv1-miR-H1, hsv1-miR-LAT, kshv-miR-K12-12, kshv-miR-K12-3, kshv-miR-K12-3*, kshv-miR-K12-4-5p, kshv-miR-K12-6-5p, kshv-miR-K12-8, kshv-miR-K12-9, kshv-miR-K12-9*, ebv-miR-BART10*, ebv-miR-BART12, ebv-miR-BART13, ebv-miR-BART13*, ebv-miR-BART15, ebv-miR-BART1-5p, ebv-miR-BART16, ebv-miR-BART18-5p, ebv-miR-BART19-3p, ebv-miR-BART19-5p, ebv-miR-BART20-5p, ebv-miR-BART2-5p, ebv-miR-BART3*, ebv-miR-BART5, ebv-miR-BART6-5p, ebv-miR-BART7, ebv-miR-BART7*, ebv-miR-BHRF1-1, ebv-miR-BHRF1-3, hcmv-miR-UL148D, hcmv-miR-UL22A, hcmv-miR-UL22A*, hcmv-miR-UL70-3p, hcmv-miR-UL70-5p, hcmv-miR-US25-1, hcmv-miR-US25-2-3p, hcmv-miR-US25-2-5p, hcmv-miR-US4, hiv1-miR-H1, hiv1-miR-N367, and human orthologs thereof.
  • The physiological condition may also be a lung disease or lung injury, such as chronic obstructive pulmonary disease (COPD) and idiopathic pulmonary fibrosis (IPF), also known as interstitial lung disease (ILD).
  • In embodiments, the at least one differentially expressed microRNA sequence or at least one specific microRNA sequence is selected from the group consisting of hsa-miR-630, hsa-miR-134, hsa-miR-1225-5p, hsa-miR-135a*, hsa-miR-150*, hsa-miR-22, hsa-miR-223, hsa-miR-448, hsa-miR-451, hsa-miR-483-5p, hsa-miR-575, hsa-miR-638, hsa-miR-923, hsa-miR-92a-2*, hsa-miR-939, hsa-miR-940, hsv1-miR-H1, kshv-miR-K12-3, hsv1-miR-LAT, hcmv-miR-UL70-3p, hsv1-miR-H1, hsv1-miR-LAT, kshv-miR-K12-3, hcmv-miR-UL70-3p, and human orthologs thereof.
  • In other embodiments, the biological sample is plasma and the at least one differentially expressed microRNA sequence or at least one specific microRNA sequence is selected from the group consisting of hsa-miR-630, hsa-miR-134, hsa-miR-1225-5p, hsa-miR-135a*, hsa-miR-150*, hsa-miR-22, hsa-miR-223, hsa-miR-483-5p, hsa-miR-575, hsa-miR-638, hsa-miR-923, hsa-miR-939, hsa-miR-940, hsv1-miR-H1, hsv1-miR-LAT, kshv-miR-K12-3, hcmv-miR-UL70-3p, and human orthologs thereof. In some particular embodiments, the microRNA profile consists of only a selection of at least two of these microRNA sequences, i.e. the microRNA profile does not look at other microRNA sequences.
  • In yet other embodiments, the biological sample is plasma and the at least one differentially expressed microRNA sequence or at least one specific microRNA sequence is selected from the group consisting of hsa-miR-630, hsa-miR-134, hcmv-miR-UL70-3p, hsa-miR-1225-5p, hsa-miR-135a*, hsa-miR-150*, hsa-miR-483-5p, hsa-miR-575, hsa-miR-638, hsv1-miR-H1, hsv1-miR-LAT, and human orthologs thereof. In some particular embodiments, the microRNA profile consists of only a selection of at least two of these microRNA sequences, i.e. the microRNA profile does not look at other microRNA sequences.
  • In some alternate embodiments, the biological sample is plasma and at least two differentially expressed microRNA sequences or specific microRNA sequences are identified. At least one of the at least two differentially expressed microRNA sequences or specific microRNA sequences is selected from the group consisting of hsa-miR-630, hcmv-miR-UL70-3p, hsa-miR-1225-5p, hsa-miR-134, hsa-miR-135a*, hsa-miR-150*, hsa-miR-483-5p, hsa-miR-575, hsa-miR-638, hsv1-miR-H1, hsv1-miR-LAT, and human orthologs thereof. The other one of the at least two differentially expressed microRNA sequences or specific microRNA sequences is selected from the group consisting of hsa-miR-451, hsa-miR-448, hsa-miR-92a-2*, and human orthologs thereof. In some particular embodiments, the microRNA profile consists of only a selection of these microRNA sequences, i.e. the microRNA profile does not look at other microRNA sequences.
  • In yet other embodiments, the biological sample is lung tissue and the at least one differentially expressed microRNA sequence is selected from the group consisting of hsa-miR-451, hsa-miR-923, hsa-miR-1225-5p, hsa-miR-22, hsa-miR-223, hsa-miR-638, kshv-miR-K12-3, and human orthologs thereof. In some particular embodiments, the microRNA profile consists of only a selection of these microRNA sequences.
  • In still other embodiments, the biological sample is plasma and the at least one differentially expressed microRNA sequence is selected from the group consisting of hsa-miR-940, hsa-miR-134, hsa-miR-135a*, hsa-miR-150*, hsa-miR-483-5p, hsa-miR-575, hsa-miR-939, hsv1-miR-H1, kshv-miR-K12-3, hsv1-miR-LAT, hcmv-miR-UL70-3p, and human orthologs thereof. In some particular embodiments, the microRNA profile consists of only a selection of these microRNA sequences, i.e. the microRNA profile does not look at other microRNA sequences.
  • Also disclosed are methods of using microRNA sequences to detect a lung condition, comprising: generating a microRNA profile from a biological sample; and detecting the lung condition based on the levels of at least one overexpressed microRNA sequence and at least one underexpressed microRNA sequence. The at least one overexpressed microRNA sequence is selected from the group consisting of hsa-miR-630, hcmv-miR-UL70-3p, hsa-miR-1225-5p, hsa-miR-134, hsa-miR-135a*, hsa-miR-150*, hsa-miR-483-5p, hsa-miR-575, hsa-miR-638, hsv1-miR-H1, hsv1-miR-LAT, and human orthologs thereof. The at least one underexpressed microRNA sequence is selected from the group consisting of hsa-miR-451, hsa-miR-448, and hsa-miR-92a-2*, and human orthologs thereof. In some particular embodiments, the microRNA profile examines only a selection of these listed microRNA sequences.
  • Also disclosed are methods of detecting or predicting certain physiological conditions in a patient. Those methods comprise generating a microRNA profile from a biological sample provided by the patient; identifying at least one differentially expressed microRNA sequence by comparing the microRNA profile to a reference; and detecting or predicting the physiological condition based on the identity or the amounts of the at least one differentially expressed microRNA sequence. The biological sample comprises (i) serum or plasma; and (ii) an additional body fluid specific to a particular location of the body that is relevant to the particular physiological condition. In a first embodiment, the biological sample further comprises amniotic fluid and the physiological condition is the health status of a fetus being carried by the patient. In a second embodiment, the biological sample further comprises urine and the physiological condition is the health status of a bladder or a kidney of the patient. In a third embodiment, the biological sample further comprises breast milk and the physiological condition is the health status of a breast of the patient. In a fourth embodiment, the biological sample further comprises saliva and the physiological condition is the health status of the head and neck region of the patient. In a fifth embodiment, the biological sample further comprises tears and the physiological condition is the health status of an eye of the patient. In a sixth embodiment, the biological sample further comprises semen and the physiological condition is the health status of a prostate or male reproductive organ of the patient. In a seventh embodiment, the biological sample further comprises synovial fluid and the physiological condition is the health status of a joint of the patient. In an eighth embodiment, the biological sample further comprises sweat and the physiological condition is the health status of the skin of the patient. In a ninth embodiment, the biological sample further comprises cerebrospinal fluid and the physiological condition is the health status of the central nerve system of the patient.
  • Also disclosed are methods of diagnosing a physiological condition. The methods comprise taking a sample of a body fluid and a sample of a body tissue from a patient. A first microRNA profile is generated from the body fluid sample, and a second microRNA profile is generated from the body tissue sample. At least two differentially expressed microRNA sequences are identified in the first microRNA profile by comparing the first microRNA profile to a first reference. At least two differentially expressed microRNA sequences are identified in the second microRNA profile by comparing the second microRNA profile to a second reference. The physiological condition is then diagnosed based on the differentially expressed microRNA sequences identified. In particular, the differentially expressed microRNA sequences in the first microRNA profile are different from the differentially expressed microRNA sequences in the second microRNA profile. This difference in the differentially expressed microRNA sequences between the body fluid and the body tissue increases the probability of a correct diagnosis.
  • Also included are assays for detecting the identity and/or levels of the various combinations of microRNA sequences described above.
  • These and other non-limiting aspects and/or objects of the disclosure are more particularly described below.
  • BRIEF DESCRIPTION OF THE DRAWINGS
  • The following is a brief description of the drawings, which are presented for the purposes of illustrating the disclosure set forth herein and not for the purposes of limiting the same.
  • FIGS. 1A-1B are electropherograms of RNA.
  • FIG. 2 is a microRNA profile showing changes in specific microRNA expression levels over time in the liver after exposing the animal to a high dose of acetaminophen.
  • For reference, the text on the right-hand side of FIG. 2 reads, in order from top to bottom: mmu-miR-720, mmu-miR-1224, mmu-miR-122, mmu-miR-494, mmu-miR-609, mmu-miR-21, mmu-miR-22, mmu-miR-451, mmu-miR-466f-3p, mmu-miR-574-5p, mmu-let-7a, mmu-let-7f, mmu-miR-192, mmu-miR-194, mmu-miR-212, mmu-let-7g, mmu-miR-29b, mmu-miR-26a, mmu-miR-30c, mmu-miR-29a, mmu-miR-188-5p, mmu-miR-709, mmu-miR-466g, mmu-miR-574-3p, mmu-miR-125a-3p, mmu-miR-125b-5p, mmu-miR-29c, mmu-miR-483, mmu-miR-600, mmu-miR-705, mmu-miR-721, mmu-miR-376b, mmu-miR-706, mmu-miR-710, mmu-miR-711, mmu-let-7c-2*, mmu-miR-376a, mmu-miR-891, mmu-miR-452, mmu-miR-467a*, mmu-miR-718, mmu-miR-500, mmu-miR-669c, mmu-miR-714, mmu-miR-290-5p, mmu-miR-134, mmu-miR-27b, mmu-miR-671-5p, mmu-miR-135a*, and mmu-miR-877*.
  • FIG. 3 is a microRNA profile showing differences in specific microRNA levels between plasma samples from a treated group and a control group.
  • For reference, the text on the right-hand side of FIG. 3 reads, in order from top to bottom: mmu-miR-21, mmu-miR-122, mmu-miR-22, mmu-miR-192, mmu-miR-29a, mmu-miR-30a, mmu-miR-130a, mmu-miR-29c, mmu-miR-30a, mmu-miR-148a, mmu-miR-19b, mmu-miR-101b, mmu-miR-15a, mmu-miR-685, mmu-let-7g, mmu-miR-27b, mmu-miR-574-5p, mmu-miR-671-5p, mmu-miR-107, mmu-let-7d*, mmu-miR-29b, mmu-miR-193, mmu-miR-194, mmu-miR-101a, mmu-miR-185, mmu-miR-221, mmu-miR-294*, mmu-miR-877, mmu-miR-291a-5p, mmu-miR-877*, mmu-miR-339-3p, mmu-miR-466f-3p, mmu-miR-30c-1*, mmu-miR-199b, mmu-miR-199a-5p, mmu-miR-193b, mmu-miR-370, mmu-miR-882, mmu-miR-327, mmu-miR-127, mmu-miR-714, mmu-miR-150, mmu-miR-125a-5p, mmu-miR-141, mmu-miR-23b, mmu-miR-145, mmu-miR-320, mmu-miR-342-3p, mmu-miR-200c, mmu-miR-223, mmu-miR-99a, mmu-miR-202-3p, mmu-miR-494, mmu-miR-652, mmu-miR-375, mmu-miR-125a-3p, mmu-miR-124, mmu-miR-721, mmu-miR-93, mmu-miR-483, mmu-miR-205, mmu-miR-712, mmu-miR-26a, mmu-miR-710, mmu-miR-23a, mmu-miR-135a*, mmu-miR-711, mmu-miR-720, mmu-miR-125b-5p, mmu-miR-133a, mmu-miR-133b, mmu-miR-451, mmu-miR-486, and mmu-miR-1224.
  • FIG. 4 is a graph of intensities for two selected microRNA sequences, mir-122 and mir-486 in plasma after exposing the animal to different doses of acetaminophen.
  • FIG. 5 is a graph of the ratio between mir-122 and mir-486 (either median or average intensities) for the same data as FIG. 4.
  • FIG. 6 is a microRNA profile showing differences in microRNA expression levels between normal brain tissue and diseased brain tissue.
  • FIG. 7 is a microRNA profile showing differences in microRNA expression levels as a disease progressed in lung tissue.
  • FIG. 8 is a microRNA profile showing differences in microRNA expression levels between serum and urine samples.
  • FIG. 9 is a graph comparing miRNA expression levels in control plasma samples with ILD plasma samples.
  • FIGS. 10A-10B are graphs showing the signal strength in the ILD and control plasma samples of FIG. 9.
  • FIG. 11 is a graph showing the signal strength for all oligonucleotide probes used to target certain microRNA sequences.
  • FIG. 12 is a graph showing the difference in the signal strength for certain microRNA sequences in the ILD and control plasma samples of FIG. 9.
  • FIG. 13 is a graph showing the degree of overexpression in certain microRNA sequences in the ILD and control plasma samples of FIG. 9.
  • FIG. 14 is a graph comparing miRNA expression levels in ILD tissue samples with ILD plasma samples.
  • FIG. 15 is a graph comparing miRNA expression levels in control lung tissue samples with ILD lung tissue samples.
  • FIG. 16 is a graph showing the effect of normalization on data in a data analysis method.
  • FIGS. 17A-17B are graphs showing the effect of normalization on the quality of data.
  • FIG. 18 is a graph clustering normalized miRNA data.
  • FIG. 19 is a graph showing the p-value distribution of all miRNA in a sample.
  • FIG. 20 is a collection of charts showing the selection of panels that separates data.
  • DETAILED DESCRIPTION
  • A more complete understanding of the processes and apparatuses disclosed herein can be obtained by reference to the accompanying drawings. These figures are merely schematic representations based on convenience and the ease of demonstrating the existing art and/or the present development, and are, therefore, not intended to indicate relative size and dimensions of the assemblies or components thereof.
  • Although specific terms are used in the following description for the sake of clarity, these terms are intended to refer only to the particular structure of the embodiments selected for illustration in the drawings, and are not intended to define or limit the scope of the disclosure. In the drawings and the following description below, it is to be understood that like numeric designations refer to components of like function.
  • MicroRNAs (also known as miRNA) are small but potent regulatory non-coding ribonucleic acid (RNA) sequences first identified in C. elegans in 1993. miRNA may be about 21 to about 23 nucleotides in length. Through sequence complementation, microRNA interacts with messenger RNA (mRNA) and affects the stability of mRNA and/or the initiation and progression of protein translation. It has been estimated that over 30% of the mRNAs are regulated by microRNA. Like mRNA, some of the microRNAs also display restricted tissue distribution. The biological function of microRNA is yet to be fully understood; however, it has been shown that microRNA sequences are involved in various physiological and pathological conditions, including differentiation, development, cancer, and neurological disorders. Unlike mRNA and proteins, microRNA is reasonably well conserved across different species. Thus, a specific microRNA sequences which is shown to correlate to a particular condition, such as disease or injury, in one species, should also correlate to that particular condition in other species, particularly humans (i.e. Homo sapiens). This correlation provides useful diagnostic content.
  • MicroRNAs can also be manipulated with commonly used molecular biology techniques including complementary DNA (cDNA) synthesis, polymerase chain reactions, Northern blotting, and array based hybridization. This makes it possible to easily investigate the function(s) of a given microRNA sequences of interest.
  • A microRNA is encoded by a gene. When the DNA of the gene is transcribed into RNA, the RNA is not subsequently translated into protein. Instead each primary transcript (a pri-mir) is processed into a short stem-loop structure (a pre-mir) and finally into a mature sequence, designated miR. The primary transcript can form local hairpin structures, which ordinarily are processed such that a single microRNA sequence accumulates from one arm of a hairpin precursor molecule. Sometimes the primary transcript contains multiple hairpins, and different hairpins give rise to different microRNA sequences.
  • The microRNA sequences discussed herein are named according the miRBase database available at http://microrna.sanger.ac.uk/ and maintained by the Wellcome Trust Sanger Institute (now redirected to http://www.miRBase.org/). Generally speaking, microRNA sequences are assigned sequential numerical identifiers, with the numerical identifier based on sequence similarity. A 3- or 4-letter prefix designates the species from which the microRNA sequence came. For example, the hsa in hsa-miR-101 refers to homo sapiens.
  • Orthologous sequences, or orthologs, refer to microRNA sequences that are in different species but are similar (i.e. homologous) because they originated from a common ancestor. Generally speaking, orthologs have the same numerical identifier and are believed to serve a similar function. For example, mmu-miR-101 and hsa-miR-101 are in mouse and human, respectively, and are orthologs to each other. In this disclosure, microRNA sequences are referred to without the prefix designating the species, and should be construed as preferentially referring to the human microRNA sequence and the murine sequence. For example, miR-101 should be construed as referring to hsa-miR-101 and mmu-miR-101.
  • Paralogous sequences, or paralogs, are microRNA sequences that differ from each other in only a few positions. Paralogs occur within a species. Paralogs are designated with letter suffixes. For example, mmu-miR-133a and mmu-miR-133b are paralogs.
  • Identical microRNA sequences that originate from separate genomic loci are given numerical suffixes, such as hsa-miR-26a-1 and hsa-miR-26a-2.
  • Sometimes, two different mature microRNA sequences are excised from opposite arms of the same hairpin precursor. The two microRNA sequences can be designated in at least two ways. First, when it is possible to determine which arm gives rise to the predominantly expressed miRNA sequence, an asterisk has been used to denote the less predominant form, such as hsa-let-7b and hsa-let-7b*. Alternatively, they are named to designate whether they come from the 5′ or 3′ arm, such as hsa-miR-125a-3p and hsa-miR-125a-5p.
  • Specific microRNA sequences have been identified in the blood that are associated with liver injuries. Thus, the levels of selected microRNA sequences can be used to detect, predict, or diagnose diseases, predict and monitor therapeutic responses, and/or predict disease outcomes.
  • MicroRNA-based blood markers offer superior properties over existing markers. Such markers are sensitive, in part because microRNA signals can be amplified using standard polymerase chain reactions (PCR) while protein-based markers cannot be easily amplified. Because the sequence and expression profile of microRNAs are largely conserved across species, discoveries made in animal models can be easily translated to and adapted for use in humans. MicroRNA assays can be quickly performed and developed with standard PCR or array based systems; therefore, beside PCR primers, there is no need to develop special detection agents. Finally, since microRNA can be easily accessed in various body fluids, obtaining such diagnostic information can be done non-invasively.
  • The level of specific microRNA sequences(s) in a cell, tissue, or body fluid(s) can be used to monitor the physiopathological conditions of the body.
  • Sets of microRNA sequences in the tissue and the serum have been identified that are associated with liver injuries, lung injuries, and lung diseases. The combination of information from multiple microRNA expression level changes can further enhance the sensitivity and specificity of disease/injury detection, including using the ratio of paired microRNA sequences.
  • MicroRNA profiles, for example a microRNA profile of tissue-specific microRNA sequences, could be used to monitor the health status of that tissue. Those microRNA sequences could also be used as therapeutic targets for diseases associated with the tissue.
  • MicroRNA sequences from microbes or infectious agents, such as bacteria and viruses, could be used as an indication of infection. Host responses could be monitored by using the combination of microRNA sequences from infectious agents and the host as measured from the host's body fluids.
  • Biological processes occurring in a number of cell types or tissues could be monitored by the use of microRNA profiles specific to a process or network. These specific microRNA sequences could also be used as therapeutic targets for diseases associated with the biological processes.
  • The methods of the present disclosure could be used to detect, predict, monitor, or treat a physiological condition such as a disease, injury, or infection. Generally, the methods include: (a) isolating microRNA sequences from a biological sample; (b) generating a microRNA profile from the isolated microRNA sequences, the profile including the levels of expressed microRNA sequences in the biological sample; and (c) comparing the microRNA profile with a reference to identify differentially expressed microRNA sequences. Based on the identity or the levels of the differentially expressed microRNA sequences, the physiological condition could be detected, predicted, or monitored; or a treatment could be indicated, administered, or monitored accordingly.
  • The biological sample is generally non-invasive, and may be, for example, a biopsy material, tissue, or body fluid. Exemplary body fluids include serum, plasma, lymph, saliva, urine, tears, sweat, semen, synovial fluid, cervical mucus, amniotic fluid, cerebrospinal fluid, and breast milk.
  • Combinations of different biological samples are also contemplated for providing more specific diagnoses. For example, plasma and serum would provide some general indicators of health, while a specific body fluid could be included for specific information. For example, if one wanted to assess the health status of a fetus being carried by the mother, one might test the amniotic fluid along with the mother's plasma or serum. As another example, one might test the urine to assess the health status of a bladder or a kidney. Testing the breast milk would help assess the health status of a breast of the patient providing the biological sample. Testing the saliva would help assess the health status of the head and neck region. Testing the tears would help assess the health status of an eye of the patient providing the biological sample. Testing semen would help assess the health status of a prostate or male reproductive organ. Testing the synovial fluid would help assess the health status of a joint of the patient providing the biological sample. Testing the sweat would help assess the health status of the skin. Testing the cerebrospinal fluid would help assess the health status of the central nerve system. The term “health status” refers only to the physiological condition of the given body part, and has no specific meaning otherwise.
  • Isolating microRNA can be done by various methods. For example, the biological sample may be extracted with an organic solvent to obtain an aqueous phase containing the microRNA sequences. The aqueous phase is then purified through a silica membrane to isolate the microRNA sequences.
  • A microRNA profile can then be generated from the isolated microRNA sequences. Generally speaking, the microRNA profile provides the identity of specific microRNA sequences and/or the expression level (i.e. amount) of each specific microRNA sequence. An exemplary microRNA profile is seen in FIG. 2, which shows the expression levels for several microRNA sequences from several different liver samples that have been exposed to a high dose of acetaminophen. The microRNA profile of FIG. 2 has six columns, but a microRNA profile may be simply one column (along with the identifying microRNA). The expression level can be displayed either as a sliding color scale or simply as numerical values. The microRNA profile can be generated by using hybridization to identify the microRNA sequences and/or using quantitative PCR (qPCR) to identify the levels of one or more particular microRNA sequences. It should be noted that the diagnostic information may be in the identity of the microRNA sequences themselves, or in the absolute or relative levels of the microRNA sequence, either between two microRNA sequences in a given sample or between two samples for a given microRNA sequence. A reference table could be provided, for example from a reference sample taken from the patient or from a table of levels of expressed microRNA sequences in a normal (healthy) person or a table compiled from the expressed microRNA sequences over a large sample of people. Differentially expressed microRNA sequences can then be identified by comparing the microRNA profile of the biological sample with the reference sample or table to obtain diagnostic information. The term “differentially expressed” refers only to the fact that the amount or expression level has changed. The direction of change (i.e. upwards or downwards, overexpressed or underexpressed) is not significant, except as otherwise stated.
  • In particular embodiments, it is contemplated that identifying at least one specific microRNA sequence as being differentially expressed would be sufficient to identify a particular physiological condition as occurring. In other embodiments, at least two differentially expressed microRNA sequences are identified. This provides for an additional degree of confirmation in the identity of the physiological condition.
  • In using the terms “generating” and “identifying,” it is contemplated that these actions may be performed directly or indirectly. For example, a laboratory technician may perform the actions that directly “generate” a microRNA profile. The physician who ordered the microRNA profile that was directly “generated” by the laboratory technician may be considered to have indirectly “generated” the microRNA profile.
  • Because microRNA sequences and expression levels are generally conserved across species, it is contemplated that sequences and levels from other species would contain useful diagnostic information. For example, the biological sample may be from a microbe, such as a virus, bacterium, fungus, protozoan, or parasite.
  • It has been found that microRNA sequences and their expression levels can differ depending on their location in the body. In other words, they can be specific to a biological pathway, cell type, or tissue. This fact can provide powerful diagnostic information as well.
  • Table 1 lists some microRNA sequences which have been found to be specific to certain tissues in the human body.
  • TABLE 1
    Human tissue Human tissue
    Tissue specific miRNA Tissue specific miRNA
    Adipose hsa-miR-452 Placenta hsa-miR-527
    Adipose hsa-miR-196a Placenta hsa-miR-377
    Adipose hsa-miR-224 Placenta hsa-miR-526c
    Adipose hsa-miR-335 Placenta hsa-miR-524*
    Adipose hsa-miR-452* Placenta hsa-miR-517*
    Adipose hsa-miR-432* Placenta hsa-miR-450
    Adrenal hsa-miR-409-5p Placenta hsa-miR-503
    Adrenal hsa-miR-494 Placenta hsa-miR-526b*
    Adrenal hsa-miR-485-5p Placenta hsa-miR-371
    Adrenal hsa-miR-360-5p Placenta hsa-miR-519b
    Adrenal hsa-miR-154 Placenta hsa-miR-516-3p
    Adrenal hsa-miR-370 Placenta hsa-miR-526a
    Adrenal hsa-miR-381 Placenta hsa-miR-523
    Adrenal hsa-miR-369 Placenta hsa-miR-518a-2*
    Adrenal hsa-miR-485-3p Placenta hsa-miR-518c*
    Adrenal hsa-miR-134 Placenta hsa-miR-520b
    Adrenal hsa-miR-323 Placenta hsa-miR-518d
    Adrenal hsa-miR-7N Placenta hsa-miR-524
    Adrenal hsa-miR-382 Placenta hsa-miR-519a
    Adrenal hsa-miR-7 Placenta hsa-miR-520a
    Adrenal hsa-miR-405 Placenta hsa-miR-521
    Adrenal hsa-miR-127 Placenta hsa-miR-522
    Adrenal hsa-miR-493 Placenta hsa-miR-520d
    Adrenal hsa-miR-379 Placenta hsa-miR-525
    Adrenal hsa-miR-432 Placenta hsa-miR-512-5p
    Adrenal hsa-miR-299 Placenta hsa-miR-520a*
    Adrenal hsa-miR-433 Placenta hsa-miR-519a*
    Adrenal hsa-miR-376a Placenta hsa-miR-517a
    Adrenal hsa-miR-202* Placenta hsa-miR-517b
    Adrenal hsa-miR-137 Placenta hsa-miR-515-5p
    Adrenal hsa-miR-501 Placenta hsa-miR-525*
    Adrenal hsa-miR-202 Placenta hsa-miR-518
    Adrenal hsa-miR-491 Placenta hsa-miR-512-3p
    Bladder hsa-miR-451 Placenta hsa-miR-517c
    Brain hsa-miR-330 Placenta hsa-miR-518a
    Brain hsa-miR-219 Placenta hsa-miR-519d
    Brain hsa-miR-124 Placenta hsa-miR-518c
    Brain hsa-miR-9 Placenta hsa-miR-518e
    Brain hsa-miR-9* Placenta hsa-miR-520g
    Brain hsa-miR-124a Placenta hsa-miR-519c
    Brain hsa-miR-129 Placenta hsa-miR-515-3p
    Brain hsa-miR-124b Placenta hsa-miR-520b
    Brain hsa-miR-137 Placenta hsa-miR-372
    Brain hsa-miR-383 Placenta hsa-miR-520a
    Brain hsa-miR-433 Placenta hsa-miR-520c
    Brain hsa-miR-348 Placenta hsa-miR-373
    Brain hsa-miR-323 Placenta hsa-miR-520b
    Brain hsa-miR-153 Placenta hsa-miR-154*
    Brain hsa-miR-128b Placenta hsa-miR-520c
    Brain hsa-miR-128a Placenta hsa-miR-493
    Brain hsa-miR-485-5p Placenta hsa-miR-381
    Brain hsa-miR-370 Placenta hsa-miR-151
    Brain hsa-miR-485-3p Placenta hsa-miR-495
    Brain hsa-miR-181b Placenta hsa-miR-474
    Brain hsa-miR-338 Placenta hsa-miR-369-5p
    Brain hsa-miR-154* Placenta hsa-miR-184
    Brain hsa-miR-149 Placenta hsa-miR-489
    Brain hsa-miR-213 Placenta hsa-miR-376a
    Brain hsa-miR-340 Placenta hsa-miR-500
    Brain hsa-miR-181bN Placenta hsa-miR-369
    Brain hsa-miR-181d Placenta hsa-miR-135b
    Brain hsa-miR-491 Placenta hsa-miR-432
    Brain hsa-miR-184 Placenta hsa-miR-27aN
    Brain hsa-miR-138 Placenta hsa-miR-198
    Brain hsa-miR-132 Placenta hsa-miR-224
    Brain hsa-miR-181c Placenta hsa-miR-452*
    Brain hsa-miR-204 Placenta hsa-miR-433
    Brain hsa-miR-328 Placenta hsa-miR-193b
    Brain hsa-miR-181a Placenta hsa-miR-494
    Brain hsa-miR-432 Placenta hsa-miR-502
    Brain hsa-miR-379 Placenta hsa-miR-335
    Brain hsa-miR-324-5p Placenta hsa-miR-299
    Brain hsa-miR-122 Placenta hsa-miR-149
    Brain hsa-miR-134 Placenta hsa-miR-213
    Brain hsa-miR-342 Placenta hsa-miR-30d
    Breast hsa-miR-452 Placenta hsa-miR-141
    Breast hsa-miR-205 Placenta hsa-miR-301
    Breast hsa-miR-489 Placenta hsa-miR-485-3p
    Colon hsa-miR-490 Placenta hsa-miR-141N
    Colon hsa-miR-363 Placenta hsa-miR-379
    Colon hsa-miR-338 Placenta hsa-miR-130a
    Colon hsa-miR-31 Placenta hsa-miR-382
    Colon hsa-miR-215 Placenta hsa-miR-99b
    Colon hsa-miR-200a* Placenta hsa-miR-370
    Colon hsa-miR-200a Placenta hsa-miR-130b
    Colon hsa-miR-196b Placenta hsa-miR-27a
    Colon hsa-miR-196a Placenta hsa-miR-200cN
    Colon hsa-miR-194 Placenta hsa-miR-24
    Colon hsa-miR-192 Placenta hsa-miR-30a-5p
    Colon hsa-miR-141N Placenta hsa-miR-30bN
    Colon hsa-miR-141 Placenta hsa-miR-221
    Small hsa-miR-490 Placenta hsa-miR-200c
    Intestine
    Small hsa-miR-451 Placenta hsa-miR-320
    Intestine
    Small hsa-miR-429 Placenta hsa-miR-127
    Intestine
    Small hsa-miR-31 Placenta hsa-miR-485-5p
    Intestine
    Small hsa-miR-215 Placenta hsa-miR-30b
    Intestine
    Small hsa-miR-200bN Placenta hsa-miR-90a-3p
    Intestine
    Small hsa-miR-200b Placenta hsa-miR-181a
    Intestine
    Small hsa-miR-200a* Placenta hsa-miR-222
    Intestine
    Small hsa-miR-198 Placenta hsa-miR-362
    Intestine
    Small hsa-miR-194 Placenta hsa-miR-125a
    Intestine
    Small hsa-miR-192 Placenta hsa-miR-323
    Intestine
    Small hsa-miR-138 Placenta hsa-miR-451
    Intestine
    Cervix hsa-miR-196b Placenta hsa-miR-409-5p
    Cervix hsa-miR-99a Placenta hsa-miR-452
    Heart hsa-miR-1 Placenta hsa-miR-518b
    Heart hsa-miR-107 Placenta hsa-miR-515-5p
    Heart hsa-miR-133a Placenta hsa-miR-130aN
    Heart hsa-miR-189 Skeletal hsa-miR-206
    Muscle
    Heart hsa-miR-221 Skeletal hsa-miR-95
    Muscle
    Heart hsa-miR-23bN Skeletal hsa-miR-133b
    Muscle
    Heart hsa-miR-302a Skeletal hsa-miR-133a
    Muscle
    Heart hsa-miR-302b Skeletal hsa-miR-128b
    Muscle
    Heart hsa-miR-302c Skeletal hsa-miR-1
    Muscle
    Heart hsa-miR-302d Skeletal hsa-miR-489
    Muscle
    Heart hsa-miR-300-3p Skeletal hsa-miR-378
    Muscle
    Heart hsa-miR-367 Skeletal hsa-miR-422a
    Muscle
    Heart hsa-miR-378 Skeletal hsa-miR-128a
    Muscle
    Heart hsa-miR-422a Skeletal hsa-miR-196a
    Muscle
    Heart hsa-miR-422b Skeletal hsa-miR-502
    Muscle
    Heart hsa-miR-452 Spleen hsa-miR-223
    Heart hsa-miR-490 Spleen hsa-miR-139
    Heart hsa-miR-491 Lymph Node hsa-miR-150
    Heart hsa-miR-409 Lymph Node hsa-miR-142-3p
    Heart hsa-miR-7a Lymph Node hsa-miR-146b
    Pericardium hsa-miR-188 Lymph Node hsa-miR-146
    Pericardium hsa-miR-369 Lymph Node hsa-miR-155
    Pericardium hsa-miR-305 Lymph Node hsa-miR-363
    Pericardium hsa-miR-452 PBMC hsa-miR-128a
    Pericardium hsa-miR-224 PBMC hsa-miR-124b
    Pericardium hsa-miR-511 PBMC hsa-miR-124a
    Pericardium hsa-miR-199b PBMC hsa-miR-137
    Kidney hsa-miR-500 PBMC hsa-miR-431
    Kidney hsa-miR-204 PBMC hsa-miR-129
    Kidney hsa-miR-480 PBMC hsa-miR-128b
    Kidney hsa-miR-190 PBMC hsa-miR-138
    Kidney hsa-miR-501 Thymus hsa-miR-183
    Kidney hsa-miR-196a Thymus hsa-miR-96
    Kidney hsa-miR-211 Thymus hsa-miR-128b
    Kidney hsa-miR-363 Thymus hsa-miR-213
    Kidney hsa-miR-502 Thymus hsa-miR-205
    Kidney hsa-miR-184 Thymus hsa-miR-128a
    Liver hsa-miR-122a Thymus hsa-miR-181bN
    Liver hsa-miR-30a-3p Thymus hsa-miR-182
    Lung hsa-miR-223 Thymus hsa-miR-181b
    Esophagus hsa-miR-203 Thymus hsa-miR-181d
    Esophagus hsa-miR-205 Thymus hsa-miR-181a
    Esophagus hsa-miR-145 Thymus hsa-miR-181c
    Esophagus hsa-miR-210N Thymus hsa-miR-20b
    Esophagus hsa-miR-143 Thymus hsa-miR-383
    Esophagus hsa-miR-31 Thymus hsa-miR-17-5p
    Esophagus hsa-miR-187 Thymus hsa-miR-142-3p
    Trachea hsa-miR-34b Stomach hsa-miR-211
    Trachea hsa-miR-205 Stomach hsa-miR-188
    Trachea hsa-miR-34cN Stomach hsa-miR-346
    Trachea hsa-miR-34c Stomach hsa-miR-200a*
    Prostate hsa-miR-363 Stomach hsa-miR-375
    Prostate hsa-miR-205 Stomach hsa-miR-148a
    Prostate hsa-miR-196b Stomach hsa-miR-200a
    Ovary hsa-miR-502 Stomach hsa-miR-200b
    Ovary hsa-miR-383 Stomach hsa-miR-200c
    Fallopian hsa-miR-34bN Stomach hsa-miR-200bN
    Tube
    Fallopian hsa-miR-34b Stomach hsa-miR-212
    Tube
    Fallopian hsa-mi-34cN Stomach hsa-miR-31
    Tube
    Fallopian hsa-miR-449 Stomach hsa-miR-7
    Tube
    Fallopian hsa-miR-34c Stomach hsa-miR-153
    Tube
    Fallopian hsa-miR-135a Stomach hsa-miR-429
    Tube
    Pancreas hsa-miR-217 Stomach hsa-miR-107
    Pancreas hsa-miR-216 Stomach hsa-miR-200cN
    Pancreas hsa-miR-375 Stomach hsa-miR-502
    Pancreas hsa-miR-98 Stomach hsa-miR-203
    Pancreas hsa-miR-163 Testicle hsa-miR-202
    Pancreas hsa-miR-141N Testicle hsa-miR-506
    Pancreas hsa-miR-148a Testicle hsa-miR-507
    Pancreas hsa-miR-141 Testicle hsa-miR-510
    Pancreas hsa-miR-7N Testicle hsa-miR-514
    Pancreas hsa-miR-494 Testicle hsa-miR-513
    Pancreas hsa-miR-130b Testicle hsa-miR-508
    Pancreas hsa-miR-200cN Testicle hsa-miR-509
    Pancreas hsa-miR-148b Testicle hsa-miR-202*
    Pancreas hsa-miR-182 Testicle hsa-miR-449
    Pancreas hsa-miR-200a Testicle hsa-miR-34c
    Thyroid hsa-miR-138 Testicle hsa-miR-432*
    Thyroid hsa-miR-135a Testicle hsa-miR-184
    Thyroid hsa-miR-206 Testicle hsa-miR-520c
    Thyroid hsa-miR-95 Testicle hsa-miR-520l
    Thyroid hsa-miR-1 Testicle hsa-miR-34cN
    Thyroid hsa-miR-7 Testicle hsa-miR-34b
    Uterus hsa-miR-10b Testicle hsa-miR-520b
    Uterus hsa-miR-196b Testicle hsa-miR-135b
    Uterus hsa-miR-502 Testicle hsa-miR-383
    Testicle hsa-miR-204 Testicle hsa-miR-34bN
  • It has been found that microRNA sequences and their expression levels can differ depending on their location in different types of body fluid samples. In other words, they can be specific to a biological pathway, cell type, or tissue. This fact can provide powerful diagnostic information as well.
  • Table 2 lists some microRNA sequences which have been found to be highly abundant in different body fluids. The sequences in bold font are unique to the listed body fluid.
  • TABLE 2
    Tears Urine Breast Milk Seminal Fluid Saliva Amniotic Fluid
    miR-518e miR-515-3p miR-518e miR-518e miR-335* miR-518e
    miR-335* miR-335* miR-26a-2* miR-590-3p miR-515-3p miR-335*
    miR-137 miR-892a miR-335* miR-588 miR-545* miR-302c
    miR-515-3p miR-509-5p miR-490-5p miR-873 miR-492 miR-515-3p
    miR-509-5p miR-223* miR-181d miR-590-5p miR-892a miR-452
    miR-873 miR-302d miR-26a-1* miR-137 miR-518e miR-892a
    miR-223* miR-873 miR-137 miR-197 miR-27a miR-671-5p
    miR-892a miR-923 miR-524-5p miR-515-5p miR-923 miR-515-5p
    miR-590-3p miR-616* miR-509-5p miR-515-3p miR-509-5p miR-590-3p
    miR-302d miR-483-5p miR-513c miR-218 miR-873 miR-593*
    miR-616* miR-134 miR-595 miR-20b miR-483-5p miR-873
    miR-590-5p miR-589 miR-515-3p miR-410 miR-616* miR-137
    miR-101* miR-556-3p miR-515-5p miR-335* miR-580 miR-410
    miR-130a miR-101* miR-598 miR-617 miR-609 miR-548d-5p
    miR-410 miR-138 miR-130a miR-671-5p miR-302d miR-223*
    miR-195 miR-652 miR-181b miR-524-5p miR-25* miR-590-5p
    miR-675 miR-325 miR-671-5p miR-892a miR-134 miR-616*
    miR-325 let-7i miR-892a miR-181d miR-92b miR-302d
    miR-134 miR-377* miR-578 miR-545* miR-598 miR-509-5p
    miR-29b miR-545* miR-580 miR-1 let-7a miR-210
    Bronchial Lavage CSF Pleural Fluid Peritoneal Fluid Colostrum Plasma
    miR-515-3p miR-515-3p miR-515-3p miR-892a miR-509-5p miR-335*
    miR-335* miR-335* miR-892a miR-518e miR-181d miR-325
    miR-509-5p miR-892a miR-509-5p miR-515-3p miR-335* miR-377*
    miR-483-5p miR-223* miR-134 miR-134 miR-518e miR-586
    miR-892a miR-873 miR-590-5p miR-509-5p miR-515-5p miR-518e
    miR-223* miR-509-5p miR-515-5p miR-223* miR-223* let-7i
    miR-873 miR-302d miR-873 miR-515-5p miR-671-5p miR-539
    miR-1225-3p miR-616* miR-335* miR-616* miR-873 miR-616*
    miR-302d miR-134 miR-920 miR-137 miR-483-5p miR-302d
    miR-545* miR-483-5p miR-616* miR-873 miR-186 miR-589
    miR-324-3p miR-325 miR-302d miR-483-5p miR-515-3p miR-556-3p
    miR-616* miR-151-5p miR-518e miR-518c miR-616* miR-151-3p
    miR-92b miR-589 miR-923 miR-92b miR-134 miR-548b-3p
    miR-25* miR-377* miR-589 miR-923 miR-892a miR-192
    miR-539 miR-923 miR-377* miR-302d miR-590-5p miR-151-5p
    miR-923 miR-652 miR-410 miR-374a miR-590-3p miR-598
    miR-192 miR-518e miR-137 miR-598 miR-425 miR-187
    miR-134 miR-556-3p miR-671-5p miR-937 miR-454 miR-873
    miR-371-3p miR-767-3p miR-151-5p miR-335* miR-101* miR-218
    miR-580 miR-505 miR-223* miR-885-5p miR-132 miR-923
  • With the diagnostic information obtained, a physiological condition could be detected, identified, predicted, treated, and/or monitored. For example, a treatment could be administered based on the identity of the physiological condition. A particular treatment could be monitored by taking a first sample, administering the treatment, taking a second sample, and comparing the microRNA profiles of the two samples to identify and/or track changes resulting from the treatment. Those changes could include the amounts of a particular microRNA sequence, or the identity of the differentially expressed microRNA sequences that have changed between the two samples.
  • It is also contemplated that manipulating the levels of microRNA sequences might itself be a treatment for a physiological condition. The microRNA level could be altered by constructing a specific DNA or RNA sequence related to the microRNA sequences, then delivering that DNA or RNA sequence to a targeted cell, tissue, or organ expressing the targeted microRNA sequences.
  • As discussed below, specific microRNA sequences are identified that may be useful in diagnosing and/or treating liver disease or injury, lung disease or injury, and neurological disease or injury. Such conditions include chronic obstructive pulmonary disease (COPD) and idiopathic pulmonary fibrosis (IPF) (also known as interstitial lung disease (ILD)).
  • Other methods embodied herein include generating a microRNA profile from a biological sample. The microRNA profile comprises the amounts of specific microRNA sequences. The amounts of those specific microRNA sequences are then compared to a reference to provide information for detecting or predicting the lung condition. In this regard, the microRNA profile may include those specific microRNA sequences identified below in the examples, or a subset thereof. Such microRNA profiles would be smaller, faster, and provide the same diagnostic information as larger test kits.
  • The following examples are provided to illustrate the devices and methods of the present disclosure. The examples are merely illustrative and are not intended to limit the disclosure to the materials, conditions, or process parameters set forth therein.
  • EXAMPLES Isolation of microRNA
  • microRNA can be isolated using glass filter based methods to selectively bind RNA in a high salt buffer. The unwanted biomolecules can then be washed off by using high salt buffers containing at least 50% alcohol. The bound pure RNA can then eluted off the glass membrane with low salt buffer or RNAse-free water.
  • 1). Isolating microRNA from Solid Tissues
  • Briefly, total RNA, including microRNA, was isolated using commercial kits such as miRNeasy mini kit (Qiagen Inc. Valencia, Calif.). Approximately 5 mg to 50 mg tissue samples were excised from flash-frozen tissue. After placing the tissue sample into a Dounce tissue grinder, 700 microliter (μl) QIAzol lysis reagent was added to the grinder and the tissue was homogenized immediately. For every 700 μl QIAzol lysis reagent used, 140 μl chloroform was added to the tissue lysate to extract the water soluble content. After mixing for 15 seconds, the lysate was placed in a centrifuge and spun at 12000×g for 15 minutes at room temperature. The upper aqueous phase (containing the RNA) was then transferred to a new collection tube, and 1.5 volumes of ethanol was added. The sample was then transferred to a cartridge containing a glass filter (i.e. silica membrane) so that RNA could attach to the glass filter. The contaminants were washed off the silica membrane by applying different high salt washing buffers included in the miRNeasy kit. The bound pure RNA was then eluted off the membrane with water or low salt buffer.
  • 2). Isolating microRNA from Liquid Samples
  • Approximately, 800 μl of QIAzol lysis reagent was added to 200 μl liquid sample. The sample was mixed in a tube followed by adding 200 μl of chloroform. After mixing rigorously for 15 seconds, the sample was then centrifuged at 12,000×g for 15 minutes. The upper aqueous phase was carefully transferred to a new collection tube, and 1.5 total volumes of ethanol was added. The sample was then applied directly to a glass membrane containing column and the RNA was bound and purified by three contiguous washing to remove unwanted contamination. The immobilized RNA was then collected from the membrane with a low salt elution buffer.
  • The yield of microRNA from different amount of liquid samples used in these protocols was tested. The best ratio was found to be 4 volumes of lysis buffer with 1 volume of liquid sample.
  • The quality and quantity of RNA isolated was evaluated by RNA by NanoDrop 1000 spectrophotometer (Thermo Fisher Scientific Inc. Waltham, Mass.) and the Agilent 2100 Bioanalyzer (Agilent Inc. Santa Clara, Calif.).
  • FIG. 1A shows an electropherogram of RNA isolated from solid tissue, while FIG. 1B shows an electropherogram of RNA isolated from a liquid sample. The 18S and 28S peaks are clearly visible and marked. The microRNA are located on the left of both electropherograms. This region also contains all degraded RNA.
  • Array Hybridization and Quantitative PCR
  • Agilent's human and mouse microRNA microarray kits (Agilent Inc. Santa Clara, Calif.) were used as the array platform; however, arrays from different companies including Affymetrix and Exiqon have also been used. The human microRNA microarray contained probes for 723 human and 76 human viral microRNAs from the Sanger database v 10.1. The mouse microRNA microarray contained probes for 567 mouse and 10 mouse herpes virus microRNA sequences from the Sanger database v 10.1. Cyanine 3-pCp labeled RNA (i.e. RNA labeled with Cyanine 3-Cytidine bisphosphate) for array hybridization was generated by 100 nanograms (ng) of total RNA using Agilent's microRNA complete labeling and hybridization kit. All the steps, including labeling, hybridization, washing, scanning and feature extraction were performed in accordance with the manufacturer's instructions.
  • In brief, 100 ng of total RNA was dephosphorylated with calf intestinal alkaline phosphatase, then heat and DMSO treated to yield denatured RNA. Cyanine 3-Cytidine bisphosphate was joined to the microRNA by T4 RNA ligase. MicroBioSpin 6 columns were used to desalt the samples and remove any unincorporated fluorophores. The samples were hybridized to 8×15K Agilent Human microRNA (V2) or Mouse microRNA microarrays in a rotating hybridization oven for 20 hours at 55° C. and 10 rpm. The arrays were washed for 5 minutes in Agilent GE Wash Buffer 1 with Triton X-102 and then for another 5 minutes in Agilent GE Wash Buffer 2 with Triton X-102.
  • After washing, all slides were immediately scanned using a PerkinElmer ScanArray Express at 5 micron resolution. The resulting images were quantified using Agilent's Feature Extraction software. The differentially expressed microRNA sequences were then identified using a standard protocol developed for gene array data processing. The sample or gene clustering and array hybridization heatmap were generated using MeV4 software package from The Institute for Genomic Research (TIGR) (available at http://www.tigr.org/tdb/microarray/).
  • Quantitative PCR (QPCR) with microRNA specific primer sets were used to confirm the results from array hybridization. In brief, a SYBR Green based method, miScript real-time PCR (Qiagen Inc. Valencia, Calif., USA), or TaqMan primer set from Apply Biosystems, was used with 50 ng of total RNA from each sample. The first strand cDNA was generated according to the manufacturer's instruction. Approximately 2.5 ng of cDNA was used in the PCR reaction. The yield of 18 to 20 base pair fragments (based on SYBR Green intensity) corresponding to the specific microRNA species was monitored with the 7900HT fast real-time PCR system from Applied Biosystems (Applied Biosystems, Foster City, Calif.). QPCR results were analyzed by SDS 2.2.2, with a manual CT threshold value of 0.2.
  • Example 1
  • This example showed that microRNA sequences could be used as a marker to detect liver injury. Mice were used as the experimental model.
  • 6-month-old male C57/B6 mice were grouped into control and treatment groups with 4 animals in each group. The mice then fasted for 24 hours prior to a single intraperitoneal injection of either (a) 300 mg/kg of acetaminophen in phosphate buffer saline (PBS) (treatment group); (b) or PBS (control group). Mice were sacrificed at different time points post-exposure (12 hr, 24 hr, 48 hr, 72 hr, and 120 hr) and plasma and liver samples were collected. Part of the liver samples were sectioned and examined by a pathologist and the serum alanine transaminase (ALT) levels were also determined to confirm as well as assess the severity of liver injury.
  • Total RNA was isolated from collected samples to conduct comprehensive microRNA analyses. To assess the level of microRNA in liver tissues, a microRNA array from Agilent was used. The RNA samples were labeled and processed according to the manufacturer's recommended protocols. The data from each array were extracted, normalized and compared following a standard gene expression microarray method.
  • The expression levels of various microRNA sequences in the liver tissues were used to generate a microRNA profile and used to assess tissue injury. Differentially expressed microRNA sequences were clustered using the Hierarchical clustering method and the result is shown in FIG. 2. The different time points are indicated on the top, while the identity of individual microRNA sequences is listed on the right. (The identifying labels correspond to those in the miRNA Registry maintained at the Sanger Institute.) The hybridization intensity of individual microRNA sequences is represented in different colors as indicated on top of the figure (yellow representing the highest expression and blue representing the lowest expression signal). The microRNA profile clearly indicates that the levels of some microRNA sequences were changed by the exposure to acetaminophen.
  • Example 2
  • This example showed that the levels of specific microRNA sequences in the serum or plasma could be used to assess drug-induced liver injury.
  • The male C57/B6 mice were randomly grouped into two groups, a treatment group (3 animals) and control group (4 animals). They fasted for 24 hours prior to a single intraperitoneal injection of either (a) 300 mg/kg of acetaminophen in PBS (treatment group); or (b) PBS (control group). Mice were sacrificed at 24 hours post exposure, the plasma samples were collected and RNA was isolated.
  • The expression levels of microRNA sequences in the serum were used to make a microRNA profile. The differentially expressed microRNA sequences between the treatment group and the control group were clustered with the Hierarchical clustering method and is shown in FIG. 3. The result clearly indicated that the levels of certain microRNA sequences in the serum could be used as an indication of the acetaminophen toxicity.
  • Example 3
  • This example showed that the levels of specific microRNA sequences in the serum or plasma could be used as an early indication of drug-induced liver injury.
  • The male C57/B6 mice were randomly grouped into nine different groups with 4 animals in each group. They fasted for 24 hours prior to a single intraperitoneal injection with either (a) 75 mg/Kg of acetaminophen in PBS (treatment 1); (b) 150 mg/Kg of acetaminophen in PBS (treatment 2); (c) 300 mg/Kg of acetaminophen in PBS (treatment 3); or (d) PBS (control group). Mice were sacrificed and plasma samples were collected at 1, 3 and 24 hours post-exposure. The nine groups were: 1) 1 hour control; 2) 1 hour treatment 1; 3) 1 hour treatment 2; 4) 1 hour treatment 3; 5) 3 hour control; 6) 3 hour treatment 1; 7) 3 hour treatment 2; 8) 3 hour treatment 3; and 9) 24 hour treatment 3. The group at 24 hr post-exposure received only the highest dose (300 mg/kg) to serve as a positive control. The expression levels of two different microRNA sequences, mir-486 and mir-122, in the serum were profiled by quantitative polymerase chain reactions (Q-PCR).
  • The median intensities (Z-axes) from each group (X-axis) at PCR cycle number 19 were plotted. This graph is shown in FIG. 4. Both mir-486 (red bars) and mir-122 (green bars) intensities showed dose-dependent changes at 3 hr post-exposure. The intensity of mir-122 at 300 mg/kg was almost the same between 3 hr and 24 hr post-exposure. Clear changes were observed in the samples obtained at one hour post-acetaminophen injection. The results clearly indicated that the levels of selected microRNA sequences, such as mir-122 and mir-486, in the serum could be used as an early indication of tissue injury.
  • Next, the ratios of the median intensities (green bars) and average intensities (blue bars) from each group at PCR cycle number 19 were plotted. This graph is shown as FIG. 5. As expected, the ratios of both median and average intensities showed dose-dependent changes at 3 hr post-exposure. The ratio also clearly indicated the difference between 3 hr and 24 hr post-exposure. This result clearly indicated the ratio of selected microRNA sequences, such as mir-122 and mir-486, in the serum could be used as an early indication of tissue injury.
  • Example 4
  • This example showed that microRNA could be used in assessing neurological disorders. The microRNA expression patterns in brain tissues obtained from normal and prion infected animals were profiled as described above. The results are shown in FIG. 6. The result clearly indicated differences between normal and diseased samples.
  • Example 5
  • This example showed that microRNA could be used in assessing the health status of lungs. The microRNA expression patterns in lung tissues obtained from normal and diseased animals were profiled as described above. The results are shown in FIG. 7. The result clearly indicated there were differences on microRNA expression as the disease progressed (from 1 to 6 where 6 has the most serious disease condition) and a number of microRNA sequences are different between normal and disease samples. Thus, specific microRNA sequences or a panel of microRNA sequences could be used as a tool to assess the health status of lungs.
  • Example 6
  • This example showed that different biological pathways or compartments had very different microRNA profiles. The microRNA profiles in serum and urine samples obtained from a normal mouse were profiled as described above, then compared. The result is shown in FIG. 8. The result clearly revealed a significant difference in the microRNA composition in different body fluids. This would allow the development of different biomarkers to be used in different body fluids to assess the health status of tissues. In addition, microRNA sequences in a specific body fluid can be used as a reliable tool to assess the health status of tissues intimately associated with that body fluid, e.g. bladder and kidney tissues to the urine.
  • Example 7
  • miRNA profiles from lung tissue and plasma from ILD patients and COPD patients were compared to a control set of miRNA profiles from uninvolved lung tissue obtained from lung cancer resections (controls) and a control set of miRNA profiles from plasma samples obtained from clinically normal donors (collected by the Marsh lab). The miRNA profiles were compared in various pairwise combinations to determine which miRNA sequences were overexpressed and thus useful for diagnostic purposes. The miRNA profiles were obtained using a microarray kit available from Agilent, which generally detected a given miRNA with usually two independent oligonucleotide (oligo) targets and four or more in some cases.
  • FIG. 9 shows the graph comparing miRNA expression levels in control plasma with ILD plasma. Note the log scale. MiRNA with expression values that differ substantially between the two samples reside away from the diagonal line (i.e. y=x) that would represent equivalent expression in the two samples. This graph indicated that many miRNAs in ILD plasma are expressed at substantially higher levels than in the control plasma. There also appear to be a few miRNAs in the control profile that were expressed at relatively lower levels than in the ILD profile.
  • Next, in order to reduce the complexity of the data, the similarity of the signals returned by the different oligos that were present on the Agilent array and designed to detect a given miRNA were examined. For example, miRNA 1225-5p (i.e. mir-1225-5p) was about 3-fold over expressed in ILD plasma. For mir-1225-5p, four probe oligos were used in the Agilent array. FIG. 10A shows the signal in the ILD plasma samples (n=24). FIG. 10B shows the signal in the control plasma samples (n=6). As seen, all four probe oligos gave signals in the ILD and control plasma samples. In addition, the signal strengths were within a factor of about two to each other, even though these oligos differ slightly in sequence.
  • 17 miRNA sequences were identified that appeared to be overexpressed in the ILD plasma samples. FIG. 11 shows the signal strength for all of the oligos that targeted these 17 sequences. The ILD plasma signals are shown as blue diamonds and the control plasma signals are shown as pink squares. As seen, the signal strengths for all of the independent oligo probes were reasonably close (i.e. within a factor of 2.5).
  • Since the signals from the independent probes were close, the data from all probes was combined. Then, the mean expression level for each miRNA was calculated and miRNA sequences which were relatively overexpressed in the ILD plasma samples were identified. (An alternative analysis path could have been to choose the data from one or two of the independent probes and identify overexpressed miRNA sequences based on that data.)
  • FIG. 12 shows the resulting graph with the mean and one standard deviation identified. Again, the ILD plasma signals are shown as blue diamonds and the control plasma signals are shown as pink squares. The expression of the displayed microRNAs was at least two-fold higher in the ILD plasma samples than in the control plasma samples (2× was an arbitrary value). Table 3 lists the specific data of FIG. 12.
  • TABLE 3
    mean
    mean mean ILD/
    microRNA level St. Dev. level St. Dev. mean
    sequence (ILD) (ILD) (control) (control) control
    hsv1-miR-H1 371 100 207 13.9 1.8
    hsa-miR-223 688 317 338 105 2.0
    hsa-miR-575 416 17 198 21.4 2.1
    hsa-miR-483-5p 589 179 259 70.1 2.3
    hsa-miR-150* 659 142 267 35.6 2.5
    hsa-miR-22 958 589 376 91 2.5
    hsa-miR-1225-5p 3361 873 1201 324.4 2.8
    hsa-miR-939 644 463 224 36.5 2.9
    hsa-miR-135a* 499 127 172 33.2 2.9
    hsa-miR-940 1316 906 341 45.6 3.9
    hsa-miR-134 830 9 201 50 4.1
    hcmv-miR-UL70-3p 721 129 166 25.2 4.3
    hsa-miR-630 3349 65 683 100.3 4.9
    hsv1-miR-LAT 1250 490 223 64.2 5.6
    kshv-miR-K12-3 3542 2912 588 422.3 6.0
    hsa-miR-638 18055 11123 1670 716.2 10.8
    hsa-miR-923 42215 40796 310 9883.1 136.2
  • Based on the standard deviations, the 17 miRNAs that met this criterion can be divided into three groups. 11 miRNAs (UL70-3p, 1225-5p, 134, 135a*, 150*, 483-5p, 575, 630, 638, H1, and LAT) were likely to be differentially expressed between ILD and control with high confidence. There was intermediate confidence for 4 miRNAs (mir-22, 223, 939, and 940); and lower confidence for miRNAs 923 and K12-3.
  • The degree of over expression displayed by these miRNAs varied over 100-fold, as shown in FIG. 13. Note the log scale.
  • Next, the miRNA that were expressed at a higher level in control plasma than ILD plasma were investigated. An arbitrary expression level of 250 or greater and 3.0 fold or greater relative overexpression as used to screen out marginal miRNA candidates. Three miRNA sequences passed this screen as shown in Table 4.
  • TABLE 4
    mean
    mean mean control/
    microRNA level St. Dev. level St. Dev. mean
    sequence (ILD) (ILD) (control) (control) ILD
    hsa-miR-451 729 1695.917 5274 5362.923 7
    oligo 1
    hsa-miR-451; 487 1096.762 3527.167 3421.076 7
    oligo 2
    hsa-miR-451; 390 375 5274 13.5
    oligo 1
    w/o outlier
    hsa-miR-451; 268 243 3527 13.1
    oligo 2
    w/o outlier
    hsa-miR-448 64 38.3 253 93 4
    hsa-miR-92a-2* 78 46.94568 253.6667 109.7154 3
  • hsa-miR-448 and hiss-miR-92-a-2* were just over the threshold for inclusion and showed low absolute expression. The standard deviation for hsa-miR-451 was rather large when all samples (n=24) were used. However, when one outlier was removed (n=23), the standard deviations improved, as did the ratios. hsa-miR-451 was expressed ten times higher in control plasma relative to ILD plasma.
  • One of the miRNAs that is overexpressed in control serum relative to ILD serum, in combination with a miRNA that is overexpressed in ILD serum relative to control serum, could be used in a simple “top scoring pair” test for ILD.
  • Example 8
  • Using the same data as in Example 7, the expression of miRNA in both ILD plasma and ILD tissue was examined. That graph is shown in FIG. 14. While many miRNAs that were expressed in ILD plasma had little or no expression in ILD tissue, most of those that were expressed in tissue had at least some expression in plasma. Those miRNA sequences that had signal strength of at least 1000 in both tissue and plasma (an arbitrarily chosen value) are listed in Table 5. The ratio of the expression for the miRNA sequence was also compared to the average expression of all the miRNAs in the sample and is labeled as “overexpression ratio.”
  • TABLE 5
    mean mean
    microRNA level overexpression level overexpression
    sequence (plasma) ratio (tissue) ratio
    hsa-miR-1225-5p 4043 9.8 1189.0 3.1
    hsa-miR-21 455 1.1 9938.7 26.0
    hsa-miR-22 1374 3.3 5555.8 14.5
    hsa-miR-223 835 2.0 1303.4 3.4
    hsa-miR-451 729 1.8 6564.4 17.2
    hsa-miR-638 25920 62.6 1084.6 2.8
    hsa-miR-923 71062 171.7 6114.7 16.0
    kshv-miR-K12-3 5602 13.5 498.0 1.3
  • hsa-miR-21 was present here, but not in Tables 3 or 4, while the other seven were also listed in either Table 3 or 4.
  • Example 9
  • Using the same data as in Example 7, the expression of miRNA in both ILD lung tissue and control lung tissue was examined. That graph is shown in FIG. 15. The expression levels for certain miRNA sequences, as well as those overexpressed in ILD lung tissue, are listed below in Table 6.
  • TABLE 6
    microRNA mean level mean level mean ILD/
    sequence (ILD) (control) mean control
    hsa-miR-923 6114.7 22421.33 0.3
    hsa-miR-22 5555.8 4771.83 1.2
    hsa-miR-29a 4881.6 2183.67 2.2
    hsa-miR-145 3759.9 1449.50 2.6
    hsa-miR-26a 3187.3 1123.33 2.8
    hsa-let-7c 4405.5 1336.33 3.3
    hsa-miR-23a 4396.1 1144.83 3.8
    hsa-miR-21 9938.7 2450.83 4.1
    hsa-miR-125b 3843.2 939.17 4.1
    hsa-miR-27a 3090.2 720.50 4.3
    hsa-let-7a 5709.2 1039.33 5.5
    hsa-let-7f 3352.5 504.83 6.6
    hsa-miR-451 6564.4 383.33 17.1
  • miRNA hsa-miR-923 was over 130-fold over-expressed in ILD plasma relative to control plasma (Table 3), but it is under-expressed in ILD lung tissue relative to control lung tissue (Table 6). This suggests the tissue or cell of origin for this miRNA may be within the blood itself, or at least not the ILD lung. Similarly, miRNA hsa-miR-22 is expressed three times higher in ILD plasma compared to control plasma (Table 3), but is expressed at nearly the same level in ILD lung and control lung tissue (Table 6). Other miRNAs that are characteristic of ILD, such as miRNA-451, were elevated in ILD lung tissue (17× in Table 6) but not in ILD plasma (see Table 4).
  • In total 17 miRNA sequences were identified as containing diagnostic information related to ILD. Those 17 sequences are listed in Table 3.
  • Example 10
  • The plasma sample data (control, COPD, and ILD) was separately analyzed using the Panorama suite of tools and consisted of the following steps: (A) Normalization; (B) Quality Control; (C) Cluster Analysis; (D) Panel Selection; and (E) Comparison. Each step is explained in more detail below.
  • In Normalization, the following steps occurred. First, missing values were left unchanged instead of imputing a value. Second, each sample was normalized independently of other samples. Third, the natural log was applied to the values for each sample; then the values were adjusted by the median and standard deviation. FIG. 16 shows the results of normalization.
  • In Quality Control, the quality of the data was assessed before and after normalization. FIG. 17A shows the Pearson correlation distribution before normalization. This figure correlated the score for each miRNA across the samples to the total miRNA expression level across the samples. This figure showed that the vast majority of miRNA sequences had the same expression profile across the samples, and furthermore, this expression profile is the total miRNA level per sample—this is the dominant feature of the dataset. FIG. 17B shows the Pearson correlation distribution after normalization. Normalization improved the quality of the dataset. The distribution in FIG. 17B was much less skewed than that of FIG. 17A.
  • In Cluster Analysis, the normalized miRNA data was clustered using multi-dimensional scaling (MDS); the results are presented in FIG. 18. This was an unsupervised analysis without samples being identified by group or miRNA selected that differentiated the groups. There were several notable features of this plot. First, the samples within each group clustered together showing uniformity in miRNA expression. The exception was IPF tissue where a few outliers occurred, likely due to sample handling. Second, the tissue groups cluster away from the plasma groups. Third, within the plasma groups, the COPD and ILD plasma samples overlapped and were clustered away from the plasma control samples.
  • Performing a T-test at significance level 0.01, 194 miRNA were found to separate the plasma control samples from the ILD plasma samples. Performing 50 permutation tests revealed that the expected number of miRNA, by chance alone, was 21.5, yielding a false discovery rate of 11%. The p-value distribution of all miRNA is shown in FIG. 19. There was a uniform distribution over most p-values, except an increase below 0.05. This was consistent with the hypothesis that there are miRNA that segregate the two sample groups.
  • In Panel Selection, panels that segregated the control plasma samples from the ILD plasma samples were selected using Area Under the Curve (AOC). AOC is a measure of diagnostic segregation. It ranges from 0 to 1 where 1 indicates perfect segregation. The AUC of individual miRNA can be determined independently of each other allowing for straightforward selection of the best segregating miRNA. In addition, the combined AUC of panels of miRNA can be calculated to assess how well groups of miRNA work together to segregate control plasma samples from ILD plasma samples.
  • To calculate the combined AUC, a combination rule must be established. The combination rule used here was majority consensus: if the strict majority of miRNA classified a sample as diseased (i.e. ILD or IPF) then the sample was classified diseased, otherwise, the sample was classified as normal.
  • FIG. 20 is three charts showing the distribution of directional bias (upper left), the AUC distribution (upper right), and the standard deviation for the ILD group (lower left). The number of miRNA higher in the control samples than the ILD samples was essentially the same as the opposite direction. The distribution of AUC scores for all miRNA was centered about 0.6 which is expected. A small rise around 0.95 indicated the presence of miRNA that distinguish the control and ILD samples. The distribution of miRNA expression standard deviations showed that overall, variability was similar across miRNA (note that normalization is done by sample, not by miRNA).
  • In Comparison, the data was analyzed. Using an AUC threshold of 0.95, 57 out of 2421 (2.4%) miRNA probes were selected. Table 7 contains the oligo probe used for the miRNA, the corresponding miRNA, p-value, AUC, and number of panels of 3 miRNA above combined AUC 0.99 that each miRNA participated in. If the miRNA was expressed higher in the control sample than the ILD sample, the column “Control>ILD” was marked with a Y.
  • TABLE 7
    Probe miRNA Control > ILD P-value AUC St. Dev. # Panels
    A_25_P00010804 hsa-miR-518d-3p Y 9.96E−06 1.00 0.42 945
    A_25_P00013406 hsa-miR-135a* N 1.56E−09 1.00 0.55 681
    A_25_P00013825 hiv1-miR-H1 N 4.98E−06 1.00 0.66 656
    A_25_P00011724 hcmv-miR-UL70-3p N 1.23E−13 1.00 0.62 621
    A_25_P00013407 hsa-miR-135a* N 1.41E−12 1.00 0.62 615
    A_25_P00013090 hsa-miR-940 N 8.13E−09 1.00 0.88 581
    A_25_P00012074 hsa-miR-139-3p N 3.27E−07 1.00 0.63 572
    A_25_P00013689 kshv-miR-K12-3 N 1.09E−11 1.00 0.60 572
    A_25_P00012231 hsa-miR-134 N 9.11E−11 1.00 0.64 548
    A_25_P00012230 hsa-miR-134 N 6.22E−13 1.00 0.65 539
    A_25_P00010345 hsa-miR-557 N 2.47E−06 1.00 0.55 534
    A_25_P00013829 hsv1-miR-LAT N 1.52E−11 1.00 0.77 500
    A_25_P00011725 hcmv-miR-UL70-3p N 6.62E−13 1.00 0.65 463
    A_25_P00013830 hsv1-miR-LAT N 1.42E−12 1.00 0.65 449
    A_25_P00013831 hsv1-miR-LAT N 2.62E−09 1.00 0.84 449
    A_25_P00013087 hsa-miR-939 N 3.19E−13 1.00 0.53 362
    A_25_P00013453 hsa-miR-150* N 1.67E−08 1.00 0.58 362
    A_25_P00014907 hsa-miR-1224-5p N 6.43E−07 1.00 0.47 344
    A_25_P00013326 hsa-miR-187* N 1.24E−06 1.00 0.70 324
    A_25_P00013828 hsv1-miR-LAT N 1.33E−12 1.00 0.80 299
    A_25_P00011853 ebv-miR-BART13 N 4.09E−08 0.99 0.47 435
    A_25_P00015004 hsa-miR-1226* N 2.14E−06 0.99 0.66 362
    A_25_P00010687 hsa-miR-498 N 7.82E−08 0.99 0.46 498
    A_25_P00011096 hsa-miR-572 N 3.25E−08 0.99 0.81 420
    A_25_P00010808 hsa-miR-575 N 2.42E−07 0.99 0.85 415
    A_25_P00014908 hsa-miR-1224-5p N 4.81E−07 0.99 0.60 344
    A_25_P00014896 hsa-miR-575 N 4.23E−07 0.99 0.82 316
    A_25_P00010641 hsa-miR-601 N 2.63E−08 0.99 0.49 218
    A_25_P00013086 hsa-miR-939 N 1.09E−07 0.99 0.49 179
    A_25_P00013450 hsa-miR-150* N 2.91E−08 0.99 0.67 178
    A_25_P00010344 hsa-miR-557 N 5.45E−06 0.98 0.65 684
    A_25_P00013327 hsa-miR-187* N 1.06E−05 0.98 0.65 347
    A_25_P00015003 hsa-miR-1226* N 2.15E−06 0.98 0.61 179
    A_25_P00013451 hsa-miR-150* N 1.39E−06 0.98 0.67 178
    A_25_P00014906 hsa-miR-1224-5p N 2.03E−07 0.97 0.55 330
    A_25_P00012059 hsa-miR-198 N 1.13E−05 0.97 0.67 296
    A_25_P00011799 hsv1-miR-H1 N 6.61E−07 0.97 0.98 268
    A_25_P00011097 hsa-miR-572 N   6E−05 0.97 0.60 203
    A_25_P00013452 hsa-miR-150* N 8.41E−07 0.97 0.62 179
    A_25_P00010669 hsa-miR-326 N 7.84E−05 0.97 0.83 177
    A_25_P00014892 hsa-miR-539 N 0.000543 0.97 0.59 722
    A_25_P00010444 hsa-miR-448 N 6.72E−05 0.97 0.58 581
    A_25_P00012030 hsa-miR-92a N 1.18E−05 0.97 0.86 343
    A_25_P00013448 hsa-miR-149* N 1.95E−05 0.97 0.58 260
    A_25_P00014861 hsa-miR-483-5p N  2.9E−07 0.97 0.62 144
    A_25_P00010228 hsa-miR-623 N  5.7E−05 0.96 0.79 356
    A_25_P00012419 hsa-miR-423-5p N 0.000569 0.96 0.87 336
    A_25_P00011796 hsv1-miR-H1 N 2.96E−06 0.96 0.69 268
    A_25_P00011854 ebv-miR-BART13 N 0.000141 0.96 0.73 268
    A_25_P00011719 ebv-miR-BART7 N 7.87E−05 0.96 0.57 224
    A_25_P00012459 hsa-miR-483-5p N 2.03E−07 0.96 0.66 178
    A_25_P00013449 hsa-miR-149* N 1.55E−06 0.96 0.66 164
    A_25_P00012262 hsa-miR-320 N 5.41E−05 0.96 0.73  84
    A_25_P00011342 hsa-miR-765 N 5.08E−06 0.96 0.38  57
    A_25_P00013324 hsa-miR-187* N 5.29E−05 0.95 0.68 477
    A_25_P00010227 hsa-miR-623 N 4.89E−05 0.95 0.97 362
    A_25_P00012031 hsa-miR-92a N 2.59E−05 0.95 1.07 343
  • Interestingly, only 3 of the 57 miRNA were higher in the control samples than the ILD samples, despite the near equivalence of miRNA higher in control samples over ILD samples, as compared to the opposite among all miRNA (see FIG. 20). 20 of the 57 miRNA had a perfect AUC score of 1.00. Not shown here is the fact that there were also many panels of three miRNA that had a perfect AUC score of 1.00.
  • There were also unique miRNA among the 57 miRNA probes, which illustrated a strong redundancy among probes. This redundancy could be used as a selection criterion.
  • 11 of the 17 miRNA sequences listed in Table 3 of Example 7 also appear in Table 7. They are shown in bold text in Table 7.
  • The claims refer to identifying “at least one” or “at least two” differentially expressed microRNA sequences in a microRNA profile, wherein the differentially expressed microRNA sequences are selected from a list. This language should be construed as meaning that the microRNA sequence selected from the list is identified as a differentially expressed microRNA sequence in the microRNA profile.
  • It is contemplated that assays or microRNA profiles would test for only specific microRNA sequences, such as those identified above.
  • In some embodiments, an assay or microRNA profile tests for at least two microRNA sequences selected from the group consisting of miR-630, miR-134, hcmv-miR-UL70-3p, miR-1225-5p, miR-135a*, miR-150*, miR-22, miR-223, miR-483-5p, miR-575, miR-638, miR-923, miR-939, miR-940, hsv1-miR-H1, hsv1-miR-LAT, kshv-miR-K12-3, and human orthologs thereof. In other embodiments, at least three of these sequences is tested for. In particular embodiments, all 17 of these sequences are tested for. Specific pairs of these 17 microRNA sequences include those listed in Table 8:
  • TABLE 8
    miR-630, miR-134 miR-630, hcmv-miR-UL70-3p
    miR-630, miR-1225-5p miR-630, miR-135a*
    miR-630, miR-150* miR-630, miR-22
    miR-630, miR-223 miR-630, miR-483-5p
    miR-630, miR-575 miR-630, miR-638
    miR-630, miR-923 miR-630, miR-939
    miR-630, miR-940 miR-630, hsv1-miR-H1
    miR-630, hsv1-miR-LAT miR-630, kshv-miR-K12-3
    miR-134, hcmv-miR-UL70-3p miR-134, miR-1225-5p
    miR-134, miR-135a* miR-134, miR-150*
    miR-134, miR-22 miR-134, miR-223
    miR-134, miR-483-5p miR-134, miR-575
    miR-134, miR-638 miR-134, miR-923
    miR-134, miR-939 miR-134, miR-940
    miR-134, hsv1-miR-H1 miR-134, hsv1-miR-LAT
    miR-134, kshv-miR-K12-3 hcmv-miR-UL70-3p, miR-1225-5p
    hcmv-miR-UL70-3p, miR-135a* hcmv-miR-UL70-3p, miR-150*
    hcmv-miR-UL70-3p, miR-22 hcmv-miR-UL70-3p, miR-223
    hcmv-miR-UL70-3p, miR-483-5p hcmv-miR-UL70-3p, miR-575
    hcmv-miR-UL70-3p, miR-638 hcmv-miR-UL70-3p, miR-923
    hcmv-miR-UL70-3p, miR-939 hcmv-miR-UL70-3p, miR-940
    hcmv-miR-UL70-3p, hsv1-miR-H1 hcmv-miR-UL70-3p, hsv1-miR-LAT
    hcmv-miR-UL70-3p, kshv-miR- miR-1225-5p, miR-135a*
    K12-3
    miR-1225-5p, miR-150* miR-1225-5p, miR-22
    miR-1225-5p, miR-223 miR-1225-5p, miR-483-5p
    miR-1225-5p, miR-575 miR-1225-5p, miR-638
    miR-1225-5p, miR-923 miR-1225-5p, miR-939
    miR-1225-5p, miR-940 miR-1225-5p, hsv1-miR-H1
    miR-1225-5p, hsv1-miR-LAT miR-1225-5p, kshv-miR-K12-3
    miR-135a*, miR-150* miR-135a*, miR-22
    miR-135a*, miR-223 miR-135a*, miR-483-5p
    miR-135a*, miR-575 miR-135a*, miR-638
    miR-135a*, miR-923 miR-135a*, miR-939
    miR-135a*, miR-940 miR-135a*, hsv1-miR-H1
    miR-135a*, hsv1-miR-LAT miR-135a*, kshv-miR-K12-3
    miR-150*, miR-22 miR-150*, miR-223
    miR-150*, miR-483-5p miR-150*, miR-575
    miR-150*, miR-638 miR-150*, miR-923
    miR-150*, miR-939 miR-150*, miR-940
    miR-150*, hsv1-miR-H1 miR-150*, hsv1-miR-LAT
    miR-150*, kshv-miR-K12-3 miR-22, miR-223
    miR-22, miR-483-5p miR-22, miR-575
    miR-22, miR-638 miR-22, miR-923
    miR-22, miR-939 miR-22, miR-940
    miR-22, hsv1-miR-H1 miR-22, hsv1-miR-LAT
    miR-22, kshv-miR-K12-3 miR-223, miR-483-5p
    miR-223, miR-575 miR-223, miR-638
    miR-223, miR-923 miR-223, miR-939
    miR-223, miR-940 miR-223, hsv1-miR-H1
    miR-223, hsv1-miR-LAT miR-223, kshv-miR-K12-3
    miR-483-5p, miR-575 miR-483-5p, miR-638
    miR-483-5p, miR-923 miR-483-5p, miR-939
    miR-483-5p, miR-940 miR-483-5p, hsv1-miR-H1
    miR-483-5p, hsv1-miR-LAT miR-483-5p, kshv-miR-K12-3
    miR-575, miR-638 miR-575, miR-923
    miR-575, miR-939 miR-575, miR-940
    miR-575, hsv1-miR-H1 miR-575, hsv1-miR-LAT
    miR-575, kshv-miR-K12-3 miR-638, miR-923
    miR-638, miR-939 miR-638, miR-940
    miR-638, hsv1-miR-H1 miR-638, hsv1-miR-LAT
    miR-638, kshv-miR-K12-3 miR-923, miR-939
    miR-923, miR-940 miR-923, hsv1-miR-H1
    miR-923, hsv1-miR-LAT miR-923, kshv-miR-K12-3
    miR-939, miR-940 miR-939, hsv1-miR-H1
    miR-939, hsv1-miR-LAT miR-939, kshv-miR-K12-3
    miR-940, hsv1-miR-H1 miR-940, hsv1-miR-LAT
    miR-940, kshv-miR-K12-3 hsv1-miR-H1, hsv1-miR-LAT
    hsv1-miR-H1, kshv-miR-K12-3 hsv1-miR-LAT, kshv-miR-K12-3
  • In other embodiments, an assay or microRNA profile tests for at least two microRNA sequences selected from the group consisting of miR-630, miR-134, hcmv-miR-UL70-3p, miR-1225-5p, miR-135a*, miR-150*, miR-483-5p, miR-575, miR-638, hsv1-miR-H1, hsv1-miR-LAT, and human orthologs thereof. In other embodiments, at least three of these sequences is tested for. In particular embodiments, all 11 of these sequences are tested for. Specific pairs of these 11 microRNA sequences include those listed in Table 9:
  • TABLE 9
    miR-630, miR-134 miR-630, hcmv-miR-UL70-3p
    miR-630, miR-1225-5p miR-630, miR-135a*
    miR-630, miR-150* miR-630, miR-483-5p
    miR-630, miR-575 miR-630, miR-638
    miR-630, hsv1-miR-H1 miR-630, hsv1-miR-LAT
    miR-134, hcmv-miR-UL70-3p miR-134, miR-1225-5p
    miR-134, miR-135a* miR-134, miR-150*
    miR-134, miR-483-5p miR-134, miR-575
    miR-134, miR-638 miR-134, hsv1-miR-H1
    miR-134, hsv1-miR-LAT hcmv-miR-UL70-3p, miR-1225-5p
    hcmv-miR-UL70-3p, miR-135a* hcmv-miR-UL70-3p, miR-150*
    hcmv-miR-UL70-3p, miR-483-5p hcmv-miR-UL70-3p, miR-575
    hcmv-miR-UL70-3p, miR-638 hcmv-miR-UL70-3p, hsv1-miR-H1
    hcmv-miR-UL70-3p, hsv1-miR-LAT miR-1225-5p, miR-135a*
    miR-1225-5p, miR-150* miR-1225-5p, miR-483-5p
    miR-1225-5p, miR-575 miR-1225-5p, miR-638
    miR-1225-5p, hsv1-miR-H1 miR-1225-5p, hsv1-miR-LAT
    miR-135a*, miR-150* miR-135a*, miR-483-5p
    miR-135a*, miR-575 miR-135a*, miR-638
    miR-135a*, hsv1-miR-H1 miR-135a*, hsv1-miR-LAT
    miR-150*, miR-483-5p miR-150*, miR-575
    miR-150*, miR-638 miR-150*, hsv1-miR-H1
    miR-150*, hsv1-miR-LAT miR-483-5p, miR-575
    miR-483-5p, miR-638 miR-483-5p, hsv1-miR-H1
    miR-483-5p, hsv1-miR-LAT miR-575, miR-638
    miR-575, hsv1-miR-H1 miR-575, hsv1-miR-LAT
    miR-638, hsv1-miR-H1 miR-638, hsv1-miR-LAT
    hsv1-miR-H1, hsv1-miR-LAT
  • In some embodiments, an assay or microRNA profile tests for two or more microRNA sequences. At least one of the microRNA sequences tested for is selected from the group consisting of miR-630, hcmv-miR-UL70-3p, miR-1225-5p, miR-134, miR-135a*, miR-150*, miR-483-5p, miR-575, miR-638, hsv1-miR-H1, hsv1-miR-LAT, and human orthologs thereof. At least one of the microRNA sequences tested for is selected from the group consisting of miR-451, miR-448, and miR-92a-2*. In particular embodiments, miR-451 is one of the microRNA sequences tested for. Specific pairs of these microRNA sequences include those listed in Table 10:
  • TABLE 10
    miR-630, miR-451 miR-630, miR-448
    miR-630, miR-92a-2* hcmv-miR-UL70-3p, miR-451
    hcmv-miR-UL70-3p, miR-448 hcmv-miR-UL70-3p, miR-92a-2*
    miR-1225-5p, miR-451 miR-1225-5p, miR-448
    miR-1225-5p, miR-92a-2* miR-134, miR-451
    miR-134, miR-448 miR-134, miR-92a-2*
    miR-135a*, miR-451 miR-135a*, miR-448
    miR-135a*, miR-92a-2* miR-150*, miR-451
    miR-150*, miR-448 miR-150*, miR-92a-2*
    miR-483-5p, miR-451 miR-483-5p, miR-448
    miR-483-5p, miR-92a-2* miR-575, miR-451
    miR-575, miR-448 miR-575, miR-92a-2*
    miR-638, miR-451 miR-638, miR-448
    miR-638, miR-92a-2* hsv1-miR-H1, miR-451
    hsv1-miR-H1, miR-448 hsv1-miR-H1, miR-92a-2*
    hsv1-miR-LAT, miR-451 hsv1-miR-LAT, miR-448
    hsv1-miR-LAT, miR-92a-2*
  • In some embodiments, an assay or microRNA profile tests for at least two microRNA sequences selected from the group consisting of miR-451, miR-923, miR-1225-5p, miR-22, miR-223, miR-638, kshv-miR-K12-3, and human orthologs thereof. In other embodiments, at least three of these sequences is tested for. In particular embodiments, all seven of these sequences are tested for. Specific pairs of these seven microRNA sequences include those listed in Table 11:
  • TABLE 11
    miR-451, miR-923 miR-451, miR-1225-5p
    miR-451, miR-22 miR-451, miR-223
    miR-451, miR-638 miR-451, kshv-miR-K12-3
    miR-923, miR-1225-5p miR-923, miR-22
    miR-923, miR-223 miR-923, miR-638
    miR-923, kshv-miR-K12-3 miR-1225-5p, miR-22
    miR-1225-5p, miR-223 miR-1225-5p, miR-638
    miR-1225-5p, kshv-miR-K12-3 miR-22, miR-223
    miR-22, miR-638 miR-22, kshv-miR-K12-3
    miR-223, miR-638 miR-223, kshv-miR-K12-3
    miR-638, kshv-miR-K12-3
  • In some embodiments, an assay or microRNA profile tests for at least two microRNA sequences selected from the group consisting of miR-940, miR-134, miR-135a*, miR-150*, miR-483-5p, miR-575, miR-939, hsv1-miR-H1, kshv-miR-K12-3, hsv1-miR-LAT, hcmv-miR-UL70-3p, and human orthologs thereof. In other embodiments, at least three of these sequences is tested for. In particular embodiments, all 11 of these sequences are tested for. Specific pairs of these 11 microRNA sequences include those listed in Table 12:
  • TABLE 12
    miR-940, miR-134 miR-940, miR-135a*
    miR-940, miR-150* miR-940, miR-483-5p
    miR-940, miR-575 miR-940, miR-939
    miR-940, hsv1-miR-H1 miR-940, kshv-miR-K12-3
    miR-940, hsv1-miR-LAT miR-940, hcmv-miR-UL70-3p
    miR-134, miR-135a* miR-134, miR-150*
    miR-134, miR-483-5p miR-134, miR-575
    miR-134, miR-939 miR-134, hsv1-miR-H1
    miR-134, kshv-miR-K12-3 miR-134, hsv1-miR-LAT
    miR-134, hcmv-miR-UL70-3p miR-135a*, miR-150*
    miR-135a*, miR-483-5p miR-135a*, miR-575
    miR-135a*, miR-939 miR-135a*, hsv1-miR-H1
    miR-135a*, kshv-miR-K12-3 miR-135a*, hsv1-miR-LAT
    miR-135a*, hcmv-miR-UL70-3p miR-150*, miR-483-5p
    miR-150*, miR-575 miR-150*, miR-939
    miR-150*, hsv1-miR-H1 miR-150*, kshv-miR-K12-3
    miR-150*, hsv1-miR-LAT miR-150*, hcmv-miR-UL70-3p
    miR-483-5p, miR-575 miR-483-5p, miR-939
    miR-483-5p, hsv1-miR-H1 miR-483-5p, kshv-miR-K12-3
    miR-483-5p, hsv1-miR-LAT miR-483-5p, hcmv-miR-UL70-3p
    miR-575, miR-939 miR-575, hsv1-miR-H1
    miR-575, kshv-miR-K12-3 miR-575, hsv1-miR-LAT
    miR-575, hcmv-miR-UL70-3p miR-939, hsv1-miR-H1
    miR-939, kshv-miR-K12-3 miR-939, hsv1-miR-LAT
    miR-939, hcmv-miR-UL70-3p hsv1-miR-H1, kshv-miR-K12-3
    hsv1-miR-H1, hsv1-miR-LAT hsv1-miR-H1, hcmv-miR-UL70-3p
    kshv-miR-K12-3, hsv1-miR-LAT kshv-miR-K12-3, hcmv-miR-UL70-3p
    hsv1-miR-LAT, hcmv-miR-UL70-3p
  • Appendix A provides a listing of the RNA sequences for all of the microRNA discussed herein, including human orthologs thereof.
  • APPENDIX A
    Accession SEQ ID
    miRNA name Number RNA Sequence No:
    ebv-miR-BART10* MIMAT0004817 gccaccucuuugguucuguaca 1
    ebv-miR-BART12 MIMAT0003423 uccugugguguuuggugugguu 2
    ebv-miR-BART13 MIMAT0003424 uguaacuugccagggacggcuga 3
    ebv-miR-BART13* MIMAT0004818 aaccggcucguggcucguacag 4
    ebv-miR-BART15 MIMAT0003713 gucagugguuuuguuuccuuga 5
    ebv-miR-BART1-5p MIMAT0000999 ucuuaguggaagugacgugcugug 6
    ebv-miR-BART16 MIMAT0003714 uuagauagagugggugugugcucu 7
    ebv-miR-BART18- MIMAT0003717 ucaaguucgcacuuccuauaca 8
    5p
    ebv-miR-BART19- MIMAT0003718 uuuuguuugcuugggaaugcu 9
    3p
    ebv-miR-BART19- MIMAT0004836 acauuccccgcaaacaugacaug 10
    5p
    ebv-miR-BART20- MIMAT0003719 uagcaggcaugucuucauucc 11
    5p
    ebv-miR-BART2-5p MIMAT0001000 uauuuucugcauucgcccuugc 12
    ebv-miR-BART3* MIMAT0003410 accuaguguuaguguugugcu 13
    ebv-miR-BART5 MIMAT0003413 caaggugaauauagcugcccaucg 14
    ebv-miR-BART6-5p MIMAT0003414 uaagguugguccaauccauagg 15
    ebv-miR-BART7 MIMAT0003416 caucauaguccaguguccaggg 16
    ebv-miR-BART7* MIMAT0004815 ccuggaccuugacuaugaaaca 17
    ebv-miR-BHRF1-1 MIMAT0000995 uaaccugaucagccccggaguu 18
    ebv-miR-BHRF1-3 MIMAT0000998 uaacgggaaguguguaagcaca 19
    hcmv-miR-UL148D MIMAT0001578 ucguccuccccuucuucaccg 20
    hcmv-miR-UL22A MIMAT0001574 uaacuagccuucccgugaga 21
    hcmv-miR-UL22A* MIMAT0001575 ucaccagaaugcuaguuuguag 22
    hcmv-miR-UL70-3p MIMAT0003343 ggggaugggcuggcgcgcgg 23
    hcmv-miR-UL70-5p MIMAT0003342 ugcgucucggccucguccaga 24
    hcmv-miR-US25-1 MIMAT0001581 aaccgcucaguggcucggacc 25
    hcmv-miR-US25-2- MIMAT0001583 auccacuuggagagcucccgcgg 26
    3p
    hcmv-miR-US25-2- MIMAT0001582 agcggucuguucagguggauga 27
    5p
    hcmv-miR-US4 MIMAT0003341 cgacauggacgugcagggggau 28
    hiv1-miR-H1 MIMAT0004480 ccagggaggcgugccugggc 29
    hiv1-miR-N367 MIMAT0004478 acugaccuuuggauggugcuucaa 30
    hsa-miR-1 MIMAT0000416 ggaauguaaagaaguauguau 31
    hsa-miR-10b MIMAT0000254 uacccuguagaaccgaauuugug 32
    hsa-miR-122 MIMAT0000421 uggagugugacaaugguguuug 33
    hsa-miR-1224-3p MIMAT0005459 ccccaccuccucucuccucag 34
    hsa-miR-1224-5p MIMAT0005458 gugaggacucgggaggugg 35
    hsa-miR-1225-3p MIMAT0005573 ugagccccugugccgcccccag 36
    hsa-miR-1225-5p MIMAT0005572 guggguacggcccagugggggg 37
    hsa-miR-1226* MIMAT0005576 gugagggcaugcaggccuggaugggg 38
    hsa-miR-1227 MIMAT0005580 cgugccacccuuuuccccag 39
    hsa-miR-1228 MIMAT0005583 ucacaccugccucgcccccc 40
    hsa-miR-1229 MIMAT0005584 cucucaccacugcccucccacag 41
    hsa-miR-1234 MIMAT0005589 ucggccugaccacccaccccac 42
    hsa-miR-1237 MIMAT0005592 uccuucugcuccgucccccag 43
    hsa-miR-1238 MIMAT0005593 cuuccucgucugucugcccc 44
    hsa-miR-124 MIMAT0000422 uaaggcacgcggugaaugcc 45
    hsa-miR-125a-3p MIMAT0004602 acaggugagguucuugggagcc 46
    hsa-miR-125a-5p MIMAT0000443 ucccugagacccuuuaaccuguga 47
    hsa-miR-127-3p MIMAT0000446 ucggauccgucugagcuuggcu 48
    hsa-miR-127-5p MIMAT0004604 cugaagcucagagggcucugau 49
    hsa-miR-128 MIMAT0000424 ucacagugaaccggucucuuu 50
    hsa-miR-129* MIMAT0004548 aagcccuuaccccaaaaaguau 51
    hsa-miR-129-3p MIMAT0004605 aagcccuuaccccaaaaagcau 52
    hsa-miR-130a MIMAT0000425 cagugcaauguuaaaagggcau 53
    hsa-miR-133a MIMAT0000427 uuugguccccuucaaccagcug 54
    hsa-miR-133b MIMAT0000770 uuugguccccuucaaccagcua 55
    hsa-miR-134 MIMAT0000447 ugugacugguugaccagagggg 56
    hsa-miR-135a* MIMAT0004595 uauagggauuggagccguggcg 57
    hsa-miR-136 MIMAT0000448 acuccauuuguuuugaugaugga 58
    hsa-miR-136* MIMAT0004606 caucaucgucucaaaugagucu 59
    hsa-miR-138 MIMAT0000430 agcugguguugugaaucaggccg 60
    hsa-miR-139-3p MIMAT0004552 ggagacgcggcccuguuggagu 61
    hsa-miR-140-3p MIMAT0004597 uaccacaggguagaaccacgg 62
    hsa-miR-140-5p MIMAT0000431 cagugguuuuacccuaugguag 63
    hsa-miR-141 MIMAT0000432 uaacacugucugguaaagaugg 64
    hsa-miR-142-3p MIMAT0000434 uguaguguuuccuacuuuaugga 65
    hsa-miR-143 MIMAT0000435 ugagaugaagcacuguagcuc 66
    hsa-miR-146a MIMAT0000449 ugagaacugaauuccauggguu 67
    hsa-miR-146b-3p MIMAT0004766 ugcccuguggacucaguucugg 68
    hsa-miR-146b-5p MIMAT0002809 ugagaacugaauuccauaggcu 69
    hsa-miR-148b MIMAT0000759 ucagugcaucacagaacuuugu 70
    hsa-miR-150 MIMAT0000451 ucucccaacccuuguaccagug 71
    hsa-miR-150* MIMAT0004610 cugguacaggccugggggacag 72
    hsa-miR-15a* MIMAT0004488 caggccauauugugcugccuca 73
    hsa-miR-15b MIMAT0000417 uagcagcacaucaugguuuaca 74
    hsa-miR-181b MIMAT0000257 aacauucauugcugucggugggu 75
    hsa-miR-181d MIMAT0002821 aacauucauuguugucggugggu 76
    hsa-miR-183 MIMAT0000261 uauggcacugguagaauucacu 77
    hsa-miR-185 MIMAT0000455 uggagagaaaggcaguuccuga 78
    hsa-miR-186 MIMAT0000456 caaagaauucuccuuuugggcu 79
    hsa-miR-187* MIMAT0004561 ggcuacaacacaggacccgggc 80
    hsa-miR-188-5p MIMAT0000457 caucccuugcaugguggaggg 81
    hsa-miR-190b MIMAT0004929 ugauauguuugauauuggguu 82
    hsa-miR-191* MIMAT0001618 gcugcgcuuggauuucgucccc 83
    hsa-miR-193b MIMAT0002819 aacuggcccucaaagucccgcu 84
    hsa-miR-194 MIMAT0000460 uguaacagcaacuccaugugga 85
    hsa-miR-198 MIMAT0000228 gguccagaggggagauagguuc 86
    hsa-miR-199a-5p MIMAT0000231 cccaguguucagacuaccuguuc 87
    hsa-miR-19a MIMAT0000073 ugugcaaaucuaugcaaaacuga 88
    hsa-miR-200a MIMAT0000682 uaacacugucugguaacgaugu 89
    hsa-miR-200b MIMAT0000318 uaauacugccugguaaugauga 90
    hsa-miR-200b* MIMAT0004571 caucuuacugggcagcauugga 91
    hsa-miR-200c MIMAT0000617 uaauacugccggguaaugaugga 92
    hsa-miR-205 MIMAT0000266 uccuucauuccaccggagucug 93
    hsa-miR-206 MIMAT0000462 uggaauguaaggaagugugugg 94
    hsa-miR-208a MIMAT0000241 auaagacgagcaaaaagcuugu 95
    hsa-miR-21 MIMAT0000076 uagcuuaucagacugauguuga 96
    hsa-miR-211 MIMAT0000268 uucccuuugucauccuucgccu 97
    hsa-miR-22 MIMAT0000077 aagcugccaguugaagaacugu 98
    hsa-miR-220b MIMAT0004908 ccaccaccgugucugacacuu 99
    hsa-miR-221 MIMAT0000278 agcuacauugucugcuggguuuc 100
    hsa-miR-222 MIMAT0000279 agcuacaucuggcuacugggu 101
    hsa-miR-223 MIMAT0000280 ugucaguuugucaaauacccca 102
    hsa-miR-23b MIMAT0000418 aucacauugccagggauuacc 103
    hsa-miR-26a MIMAT0000082 uucaaguaauccaggauaggcu 104
    hsa-miR-27a MIMAT0000084 uucacaguggcuaaguuccgc 105
    hsa-miR-27b MIMAT0000419 uucacaguggcuaaguucugc 106
    hsa-miR-27b* MIMAT0004588 agagcuuagcugauuggugaac 107
    hsa-miR-299-3p MIMAT0000687 uaugugggaugguaaaccgcuu 108
    hsa-miR-299-5p MIMAT0002890 ugguuuaccgucccacauacau 109
    hsa-miR-29b MIMAT0000100 uagcaccauuugaaaucaguguu 110
    hsa-miR-29c* MIMAT0004673 ugaccgauuucuccugguguuc 111
    hsa-miR-300 MIMAT0004903 uauacaagggcagacucucucu 112
    hsa-miR-301b MIMAT0004958 cagugcaaugauauugucaaagc 113
    hsa-miR-302c* MIMAT0000716 uuuaacauggggguaccugcug 114
    hsa-miR-30a MIMAT0000087 uguaaacauccucgacuggaag 115
    hsa-miR-30c MIMAT0000244 uguaaacauccuacacucucagc 116
    hsa-miR-30c-1* MIMAT0004674 cugggagaggguuguuuacucc 117
    hsa-miR-30e MIMAT0000692 uguaaacauccuugacuggaag 118
    hsa-miR-31 MIMAT0000089 aggcaagaugcuggcauagcu 119
    hsa-miR-323-3p MIMAT0000755 cacauuacacggucgaccucu 120
    hsa-miR-324-3p MIMAT0000762 acugccccaggugcugcugg 121
    hsa-miR-324-5p MIMAT0000761 cgcauccccuagggcauuggugu 122
    hsa-miR-326 MIMAT0000756 ccucugggcccuuccuccag 123
    hsa-miR-328 MIMAT0000752 cuggcccucucugcccuuccgu 124
    hsa-miR-331-5p MIMAT0004700 cuagguauggucccagggaucc 125
    hsa-miR-338-3p MIMAT0000763 uccagcaucagugauuuuguug 126
    hsa-miR-339-3p MIMAT0004702 ugagcgccucgacgacagagccg 127
    hsa-miR-33a* MIMAT0004506 caauguuuccacagugcaucac 128
    hsa-miR-33b MIMAT0003301 gugcauugcuguugcauugc 129
    hsa-miR-33b* MIMAT0004811 cagugccucggcagugcagccc 130
    hsa-miR-342-3p MIMAT0000753 ucucacacagaaaucgcacccgu 131
    hsa-miR-34c-3p MIMAT0004677 aaucacuaaccacacggccagg 132
    hsa-miR-34c-5p MIMAT0000686 aggcaguguaguuagcugauugc 133
    hsa-miR-363* MIMAT0003385 cggguggaucacgaugcaauuu 134
    hsa-miR-369-3p MIMAT0000721 aauaauacaugguugaucuuu 135
    hsa-miR-370 MIMAT0000722 gccugcugggguggaaccuggu 136
    hsa-miR-371-3p MIMAT0000723 aagugccgccaucuuuugagugu 137
    hsa-miR-371-5p MIMAT0004687 acucaaacugugggggcacu 138
    hsa-miR-375 MIMAT0000728 uuuguucguucggcucgcguga 139
    hsa-miR-376b MIMAT0002172 aucauagaggaaaauccauguu 140
    hsa-miR-377* MIMAT0000730 aucacacaaaggcaacuuuugu 141
    hsa-miR-379 MIMAT0000733 ugguagacuauggaacguagg 142
    hsa-miR-382 MIMAT0000737 gaaguuguucgugguggauucg 143
    hsa-miR-409-5p MIMAT0001638 agguuacccgagcaacuuugcau 144
    hsa-miR-411 MIMAT0003329 uaguagaccguauagcguacg 145
    hsa-miR-411* MIMAT0004813 uauguaacacgguccacuaacc 146
    hsa-miR-423-5p MIMAT0004748 ugaggggcagagagcgagacuuu 147
    hsa-miR-424 MIMAT0001341 cagcagcaauucauguuuugaa 148
    hsa-miR-424* MIMAT0004749 caaaacgugaggcgcugcuau 149
    hsa-miR-425 MIMAT0003393 aaugacacgaucacucccguuga 150
    hsa-miR-429 MIMAT0001536 uaauacugucugguaaaaccgu 151
    hsa-miR-448 MIMAT0001532 uugcauauguaggaugucccau 152
    hsa-miR-449a MIMAT0001541 uggcaguguauuguuagcuggu 153
    hsa-miR-449b MIMAT0003327 aggcaguguauuguuagcuggc 154
    hsa-miR-450b-3p MIMAT0004910 uugggaucauuuugcauccaua 155
    hsa-miR-451 MIMAT0001631 aaaccguuaccauuacugaguu 156
    hsa-miR-452 MIMAT0001635 aacuguuugcagaggaaacuga 157
    hsa-miR-454* MIMAT0003884 acccuaucaauauugucucugc 158
    hsa-miR-455-3p MIMAT0004784 gcaguccaugggcauauacac 159
    hsa-miR-455-5p MIMAT0003150 uaugugccuuuggacuacaucg 160
    hsa-miR-483-3p MIMAT0002173 ucacuccucuccucccgucuu 161
    hsa-miR-483-5p MIMAT0004761 aagacgggaggaaagaagggag 162
    hsa-miR-484 MIMAT0002174 ucaggcucaguccccucccgau 163
    hsa-miR-486-3p MIMAT0004762 cggggcagcucaguacaggau 164
    hsa-miR-486-5p MIMAT0002177 uccuguacugagcugccccgag 165
    hsa-miR-487b MIMAT0003180 aaucguacagggucauccacuu 166
    hsa-miR-491-3p MIMAT0004765 cuuaugcaagauucccuucuac 167
    hsa-miR-491-5p MIMAT0002807 aguggggaacccuuccaugagg 168
    hsa-miR-493 MIMAT0003161 ugaaggucuacugugugccagg 169
    hsa-miR-493* MIMAT0002813 uuguacaugguaggcuuucauu 170
    hsa-miR-494 MIMAT0002816 ugaaacauacacgggaaaccuc 171
    hsa-miR-497 MIMAT0002820 cagcagcacacugugguuugu 172
    hsa-miR-498 MIMAT0002824 uuucaagccagggggcguuuuuc 173
    hsa-miR-500 MIMAT0004773 uaauccuugcuaccugggugaga 174
    hsa-miR-503 MIMAT0002874 uagcagcgggaacaguucugcag 175
    hsa-miR-505 MIMAT0002876 cgucaacacuugcugguuuccu 176
    hsa-miR-507 MIMAT0002879 uuuugcaccuuuuggagugaa 177
    hsa-miR-511 MIMAT0002808 gugucuuuugcucugcaguca 178
    hsa-miR-513a-3p MIMAT0004777 uaaauuucaccuuucugagaagg 179
    hsa-miR-513a-5p MIMAT0002877 uucacagggaggugucau 180
    hsa-miR-513b MIMAT0005788 uucacaaggaggugucauuuau 181
    hsa-miR-513c MIMAT0005789 uucucaaggaggugucguuuau 182
    hsa-miR-515-5p MIMAT0002826 uucuccaaaagaaagcacuuucug 183
    hsa-miR-518b MIMAT0002844 caaagcgcuccccuuuagaggu 184
    hsa-miR-518c* MIMAT0002847 ucucuggagggaagcacuuucug 185
    hsa-miR-518d-3p MIMAT0002864 caaagcgcuucccuuuggagc 186
    hsa-miR-518d-5p MIMAT0005456 cucuagagggaagcacuuucug 187
    hsa-miR-518e* MIMAT0005450 cucuagagggaagcgcuuucug 188
    hsa-miR-520d-5p MIMAT0002855 cuacaaagggaagcccuuuc 189
    hsa-miR-520h MIMAT0002867 acaaagugcuucccuuuagagu 190
    hsa-miR-539 MIMAT0003163 ggagaaauuauccuuggugugu 191
    hsa-miR-541 MIMAT0004920 uggugggcacagaaucuggacu 192
    hsa-miR-545* MIMAT0004785 ucaguaaauguuuauuagauga 193
    hsa-miR-548d-3p MIMAT0003323 caaaaaccacaguuucuuuugc 194
    hsa-miR-548d-5p MIMAT0004812 aaaaguaauugugguuuuugcc 195
    hsa-miR-551a MIMAT0003214 gcgacccacucuugguuucca 196
    hsa-miR-551b MIMAT0003233 gcgacccauacuugguuucag 197
    hsa-miR-552 MIMAT0003215 aacaggugacugguuagacaa 198
    hsa-miR-554 MIMAT0003217 gcuaguccugacucagccagu 199
    hsa-miR-556-5p MIMAT0003220 gaugagcucauuguaauaugag 200
    hsa-miR-557 MIMAT0003221 guuugcacgggugggccuugucu 201
    hsa-miR-559 MIMAT0003223 uaaaguaaauaugcaccaaaa 202
    hsa-miR-561 MIMAT0003225 caaaguuuaagauccuugaagu 203
    hsa-miR-564 MIMAT0003228 aggcacggugucagcaggc 204
    hsa-miR-568 MIMAT0003232 auguauaaauguauacacac 205
    hsa-miR-572 MIMAT0003237 guccgcucggcgguggccca 206
    hsa-miR-574-5p MIMAT0004795 ugagugugugugugugagugugu 207
    hsa-miR-575 MIMAT0003240 gagccaguuggacaggagc 208
    hsa-miR-576-3p MIMAT0004796 aagauguggaaaaauuggaauc 209
    hsa-miR-578 MIMAT0003243 cuucuugugcucuaggauugu 210
    hsa-miR-583 MIMAT0003248 caaagaggaaggucccauuac 211
    hsa-miR-586 MIMAT0003252 uaugcauuguauuuuuaggucc 212
    hsa-miR-589 MIMAT0004799 ugagaaccacgucugcucugag 213
    hsa-miR-589* MIMAT0003256 ucagaacaaaugccgguucccaga 214
    hsa-miR-591 MIMAT0003259 agaccauggguucucauugu 215
    hsa-miR-595 MIMAT0003263 gaagugugccguggugugucu 216
    hsa-miR-601 MIMAT0003269 uggucuaggauuguuggaggag 217
    hsa-miR-602 MIMAT0003270 gacacgggcgacagcugcggccc 218
    hsa-miR-609 MIMAT0003277 aggguguuucucucaucucu 219
    hsa-miR-610 MIMAT0003278 ugagcuaaaugugugcuggga 220
    hsa-miR-612 MIMAT0003280 gcugggcagggcuucugagcuccuu 221
    hsa-miR-613 MIMAT0003281 aggaauguuccuucuuugcc 222
    hsa-miR-614 MIMAT0003282 gaacgccuguucuugccaggugg 223
    hsa-miR-615-3p MIMAT0003283 uccgagccugggucucccucuu 224
    hsa-miR-616 MIMAT0004805 agucauuggaggguuugagcag 225
    hsa-miR-619 MIMAT0003288 gaccuggacauguuugugcccagu 226
    hsa-miR-622 MIMAT0003291 acagucugcugagguuggagc 227
    hsa-miR-623 MIMAT0003292 aucccuugcaggggcuguugggu 228
    hsa-miR-624* MIMAT0003293 uaguaccaguaccuuguguuca 229
    hsa-miR-627 MIMAT0003296 gugagucucuaagaaaagagga 230
    hsa-miR-630 MIMAT0003299 aguauucuguaccagggaaggu 231
    hsa-miR-633 MIMAT0003303 cuaauaguaucuaccacaauaaa 232
    hsa-miR-634 MIMAT0003304 aaccagcaccccaacuuuggac 233
    hsa-miR-638 MIMAT0003308 agggaucgcgggcggguggcggccu 234
    hsa-miR-639 MIMAT0003309 aucgcugcgguugcgagcgcugu 235
    hsa-miR-640 MIMAT0003310 augauccaggaaccugccucu 236
    hsa-miR-642 MIMAT0003312 gucccucuccaaaugugucuug 237
    hsa-miR-644 MIMAT0003314 aguguggcuuucuuagagc 238
    hsa-miR-647 MIMAT0003317 guggcugcacucacuuccuuc 239
    hsa-miR-648 MIMAT0003318 aagugugcagggcacuggu 240
    hsa-miR-652 MIMAT0003322 aauggcgccacuaggguugug 241
    hsa-miR-654-5p MIMAT0003330 uggugggccgcagaacaugugc 242
    hsa-miR-658 MIMAT0003336 ggcggagggaaguagguccguuggu 243
    hsa-miR-659 MIMAT0003337 cuugguucagggagggucccca 244
    hsa-miR-662 MIMAT0003325 ucccacguuguggcccagcag 245
    hsa-miR-663 MIMAT0003326 aggcggggcgccgcgggaccgc 246
    hsa-miR-665 MIMAT0004952 accaggaggcugaggccccu 247
    hsa-miR-671-5p MIMAT0003880 aggaagcccuggaggggcuggag 248
    hsa-miR-675 MIMAT0004284 uggugcggagagggcccacagug 249
    hsa-miR-708 MIMAT0004926 aaggagcuuacaaucuagcuggg 250
    hsa-miR-708* MIMAT0004927 caacuagacugugagcuucuag 251
    hsa-miR-711 MIMAT0012734 gggacccagggagagacguaag 252
    hsa-miR-720 MIMAT0005954 ucucgcuggggccucca 253
    hsa-miR-744* MIMAT0004946 cuguugccacuaaccucaaccu 254
    hsa-miR-760 MIMAT0004957 cggcucugggucugugggga 255
    hsa-miR-765 MIMAT0003945 uggaggagaaggaaggugaug 256
    hsa-miR-766 MIMAT0003888 acuccagccccacagccucagc 257
    hsa-miR-767-3p MIMAT0003883 ucugcucauaccccaugguuucu 258
    hsa-miR-770-5p MIMAT0003948 uccaguaccacgugucagggcca 259
    hsa-miR-802 MIMAT0004185 caguaacaaagauucauccuugu 260
    hsa-miR-874 MIMAT0004911 cugcccuggcccgagggaccga 261
    hsa-miR-876-3p MIMAT0004925 uggugguuuacaaaguaauuca 262
    hsa-miR-876-5p MIMAT0004924 uggauuucuuugugaaucacca 263
    hsa-miR-877 MIMAT0004949 guagaggagauggcgcaggg 264
    hsa-miR-877* MIMAT0004950 uccucuucucccuccucccag 265
    hsa-miR-885-3p MIMAT0004948 aggcagcgggguguaguggaua 266
    hsa-miR-885-5p MIMAT0004947 uccauuacacuacccugccucu 267
    hsa-miR-886-3p MIMAT0004906 cgcgggugcuuacugacccuu 268
    hsa-miR-890 MIMAT0004912 uacuuggaaaggcaucaguug 269
    hsa-miR-891b MIMAT0004913 ugcaacuuaccugagucauuga 270
    hsa-miR-892b MIMAT0004918 cacuggcuccuuucuggguaga 271
    hsa-miR-920 MIMAT0004970 ggggagcuguggaagcagua 272
    hsa-miR-922 MIMAT0004972 gcagcagagaauaggacuacguc 273
    hsa-miR-923 none GUCAGCGGAGGAAAAGAAA 274
    CU
    hsa-miR-92a-2* MIMAT0004508 ggguggggauuuguugcauuac 275
    hsa-miR-92b MIMAT0003218 uauugcacucgucccggccucc 276
    hsa-miR-92b* MIMAT0004792 agggacgggacgcggugcagug 277
    hsa-miR-93 MIMAT0000093 caaagugcuguucgugcagguag 278
    hsa-miR-933 MIMAT0004976 ugugcgcagggagaccucuccc 279
    hsa-miR-934 MIMAT0004977 ugucuacuacuggagacacugg 280
    hsa-miR-935