EP4294521A1 - Verfahren und systeme zur bestimmung der eignung von zusammensetzungen zur hemmung des wachstums von polymikrobiellen proben - Google Patents

Verfahren und systeme zur bestimmung der eignung von zusammensetzungen zur hemmung des wachstums von polymikrobiellen proben

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Publication number
EP4294521A1
EP4294521A1 EP22756936.5A EP22756936A EP4294521A1 EP 4294521 A1 EP4294521 A1 EP 4294521A1 EP 22756936 A EP22756936 A EP 22756936A EP 4294521 A1 EP4294521 A1 EP 4294521A1
Authority
EP
European Patent Office
Prior art keywords
polymicrobial
sample
resistance
testing
organisms
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
EP22756936.5A
Other languages
English (en)
French (fr)
Inventor
David A. Baunoch
Miguel F. R. PENARANDA
Michael L. Opel
Maher BADIR
Natalie Luke
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Cap Diagnostics dba Pathnostics LLC
Original Assignee
Cap Diagnostics dba Pathnostics LLC
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Priority claimed from US17/178,091 external-priority patent/US20210172000A1/en
Priority claimed from PCT/US2021/027336 external-priority patent/WO2021211746A1/en
Priority claimed from US17/335,767 external-priority patent/US11746371B2/en
Application filed by Cap Diagnostics dba Pathnostics LLC filed Critical Cap Diagnostics dba Pathnostics LLC
Publication of EP4294521A1 publication Critical patent/EP4294521A1/de
Pending legal-status Critical Current

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    • A61P—SPECIFIC THERAPEUTIC ACTIVITY OF CHEMICAL COMPOUNDS OR MEDICINAL PREPARATIONS
    • A61P31/00—Antiinfectives, i.e. antibiotics, antiseptics, chemotherapeutics
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    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/02—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving viable microorganisms
    • C12Q1/04—Determining presence or kind of microorganism; Use of selective media for testing antibiotics or bacteriocides; Compositions containing a chemical indicator therefor
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    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
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    • C12Q1/04—Determining presence or kind of microorganism; Use of selective media for testing antibiotics or bacteriocides; Compositions containing a chemical indicator therefor
    • C12Q1/06—Quantitative determination
    • C12Q1/08—Quantitative determination using multifield media
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    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
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    • C12Q1/18—Testing for antimicrobial activity of a material
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    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
    • C12Q1/6876—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes
    • C12Q1/6888—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for detection or identification of organisms
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    • G—PHYSICS
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    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
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    • G16H10/40—ICT specially adapted for the handling or processing of patient-related medical or healthcare data for data related to laboratory analysis, e.g. patient specimen analysis
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    • G16H15/00—ICT specially adapted for medical reports, e.g. generation or transmission thereof
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    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/70—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H70/00—ICT specially adapted for the handling or processing of medical references
    • G16H70/40—ICT specially adapted for the handling or processing of medical references relating to drugs, e.g. their side effects or intended usage
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q2600/00—Oligonucleotides characterized by their use
    • C12Q2600/106—Pharmacogenomics, i.e. genetic variability in individual responses to drugs and drug metabolism
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
    • G01N21/59—Transmissivity
    • G01N21/5907—Densitometers
    • G01N2021/5915—Processing scan data in densitometry
    • G01N2021/593—Correcting from the background density
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/75—Systems in which material is subjected to a chemical reaction, the progress or the result of the reaction being investigated
    • G01N21/77—Systems in which material is subjected to a chemical reaction, the progress or the result of the reaction being investigated by observing the effect on a chemical indicator
    • G01N2021/7769—Measurement method of reaction-produced change in sensor
    • G01N2021/7786—Fluorescence
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
    • G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
    • G01N21/31—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/75—Systems in which material is subjected to a chemical reaction, the progress or the result of the reaction being investigated
    • G01N21/76—Chemiluminescence; Bioluminescence
    • G—PHYSICS
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    • G16H20/00—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
    • G16H20/10—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to drugs or medications, e.g. for ensuring correct administration to patients
    • G16H20/17—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to drugs or medications, e.g. for ensuring correct administration to patients delivered via infusion or injection
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
    • Y02A90/00—Technologies having an indirect contribution to adaptation to climate change
    • Y02A90/10—Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation

Definitions

  • the present application is related to methods and systems for identifying polymicrobial samples, e.g., polymicrobial infections, as well as methods of determining suitability of one or more compositions for inhibiting growth of the polymicrobial sample, e.g., providing information related to the likelihood of success of inhibiting growth of the polymicrobial sample with the one or more compositions.
  • the present application also describes methods for providing information to a user regarding the polymicrobial sample, such as but not limited to information regarding the suitability of the compositions for inhibiting growth of a polymicrobial sample.
  • Infectious diseases can affect multiple organ systems and are responsible for significant morbidity, mortality, and economic impact. Infectious agents most often present as a complex polymicrobial infections rather than as a single pathogen infection. Within the body, these polymicrobial infections cooperate with each other through various interactions changing both the type of antibiotics the organisms are susceptible to but also the level of antibiotics required to treat the infection as well as the virulence of the individual pathogens.
  • the present application describes methods for determining suitability of one or more compositions for inhibiting growth of the polymicrobial sample, e.g., providing information related to the likelihood of success of inhibiting growth of the polymicrobial sample with the one or more compositions.
  • the present application also describes methods of providing information related to the suitability of one or more compositions for inhibiting growth of the polymicrobial sample.
  • the present invention describes culture-independent approaches to detecting and identifying bacteria, such as a multiplex PCR-based method (DNA-based) (M-PCR).
  • M-PCR does not require growth and targets uropathogenic species.
  • M-PCR provides semi quantitative information that is comparable to culture and is presented in ranges such as ⁇ 9,999 cells/mL, 10,000 - 49,999 cells/mL, 50,000 - 99,999 cells/mL, and > 100,000 cells/mL Once again, the number of cells/mL is comparable to CFU’s.
  • the present invention also describes Pooled Antibiotic Susceptibility Testing (P-AST), (U.S. Pat. No. 10,160,991, the specification of which is incorporated herein in its entirety by reference), which involves simultaneously growing all detected bacteria together in the presence of antibiotics and then measuring susceptibility.
  • P-AST Pooled Antibiotic Susceptibility Testing
  • the present invention also describes genotypic antibiotic resistance (ABR) testing, wherein the bacteria of the polymicrobial infection are tested for particular genetic markers. The odds of resistance of the polymicrobial infection to particular antibiotics are applied to determine appropriate therapeutic solutions.
  • ABR genotypic antibiotic resistance
  • the present invention describes the use of said methods and systems for identifying polymicrobial infections in urine, identifying or providing therapeutic solutions for treating said polymicrobial infections in the urine.
  • the present invention provides methods and systems for allowing the rapid identification of UTIs, and the rapid identification of a treatment solution for the UTIs.
  • the present invention is not limited to polymicrobial infections associated with urine.
  • the present application also describes methods for providing information to a user regarding the polymicrobial sample, such as but not limited to information regarding the suitability of the compositions for inhibiting growth of a polymicrobial sample (e.g., statistics or information about the likelihood of success in inhibiting growth of a polymicrobial infection with particular compositions or therapeutic solutions, etc.) ⁇
  • the present invention also describes the documents, presentation, or other media for providing said information to the user.
  • the present invention also features methods for detection and identification of organisms of the polymicrobial sample (e.g., polymicrobial infection), phenotypic pooled sensitivity tests for determining the susceptibility or resistance of the polymicrobial sample (e.g., polymicrobial infection) in the sample to an antibiotic or other therapeutic agent, and identification of resistance genes, e.g., genetic markers that may indicate resistance to a particular treatment.
  • the data can be applied against databases of antibiotic/therapeutic susceptibility or resistance for particular known polymicrobial samples (e.g., polymicrobial infections) in order to provide information related to the likelihood of success of one or more therapeutic solutions for the polymicrobial sample (e.g., polymicrobial infection).
  • the present invention also features methods and systems for identifying polymicrobial infections and identifying or providing therapeutic solutions for polymicrobial infections.
  • the present invention also features methods and systems for treating polymicrobial infections.
  • the methods herein feature: (1) detection and identification of organisms (e.g., bacteria or other infectious agents) of the polymicrobial infection, (2) pooled antibiotic susceptibility tests for determining the susceptibility or resistance of the polymicrobial infection in the sample to an antibiotic or other therapeutic agent, and (3) identification of resistance genes in the infectious agents in the polymicrobial infection, e.g., genetic markers that may indicate resistance to a particular antibiotic or other therapeutic agent or treatment.
  • organisms e.g., bacteria or other infectious agents
  • pooled antibiotic susceptibility tests for determining the susceptibility or resistance of the polymicrobial infection in the sample to an antibiotic or other therapeutic agent
  • identification of resistance genes in the infectious agents in the polymicrobial infection e.g., genetic markers that may indicate resistance to a particular antibiotic or other therapeutic agent or treatment.
  • the data from (1), (2), and (3) can be applied (e.g., using databases of antibiotic/therapeutic susceptibility or resistance for particular known polymicrobial infections) in order to provide one or more therapeutic solutions for the polymicrobial infection.
  • the organisms are bacteria.
  • the present invention is not limited to bacterial infectious agents and may include viruses, fungi, protozoa, bacteria, or a combination thereof.
  • the present invention describes a method for providing a therapeutic solution to treat polymicrobial infection or suspected polymicrobial infection in a patient.
  • the method comprises subjecting a portion of a sample obtained from a source of the polymicrobial infection in the patient to genetic identification testing to detect and identify one or more organisms in the sample.
  • the method may further comprise subjecting a portion of the sample to genetic resistance marker testing to detect and identify one or more resistance genes in the organisms identified (e.g., resistance genes that confer resistance to one or more therapeutic agents).
  • the method may further comprise subjecting a portion of the sample to pooled phenotypic antibiotic resistance testing (pooled susceptibility testing), wherein phenotypic antibiotic resistance testing either or both: identifies one or more therapeutic agents to which the polymicrobial infection is resistant, and/or identifies one or more therapeutic agents to which the polymicrobial infection is susceptible.
  • the one or more organisms of the polymicrobial infection in the sample are not first isolated before phenotypic antibiotic resistance testing.
  • the method may further comprise applying the results from genetic identification testing, genetic resistance marker testing, and pooled antibiotic susceptibility testing to a database, e.g., predetermined thresholds of a database.
  • the analysis identifies one or more therapeutic agents that are effective for treating the polymicrobial infection (e.g., a “therapeutic solution”).
  • the present invention also features a method for treating a polymicrobial infection or suspected polymicrobial infection in a patient in need thereof.
  • the method comprises subjecting a portion of a sample obtained from a source of the polymicrobial infection in the patient to genetic identification testing to detect and identify one or more organisms in the sample.
  • the method may further comprise subjecting a portion of the sample to genetic resistance marker testing to detect and identify one or more resistance genes in the organisms identified (e.g., resistance genes that confer resistance to one or more therapeutic agents).
  • the method may further comprise subjecting a portion of the sample to pooled phenotypic antibiotic resistance testing, wherein phenotypic antibiotic resistance testing either or both: identifies one or more therapeutic agents to which the polymicrobial infection is resistant, and/or identifies one or more therapeutic agents to which the polymicrobial infection is susceptible.
  • the one or more organisms of the polymicrobial infection in the sample are not first isolated before phenotypic antibiotic resistance testing.
  • the method may further comprise applying the results from genetic identification testing, genetic resistance marker testing, and pooled antibiotic susceptibility testing to a database, e.g., predetermined thresholds of a database.
  • the analysis identifies one or more therapeutic agents that are effective for treating the polymicrobial infection (e.g., a “therapeutic solution”).
  • the method may further comprise administering at least one therapeutic agent identified to the patient, wherein the at least one therapeutic agent is effective for treating the polymicrobial infection.
  • the database that indicates which therapeutic agents are effective for treating a number of different polymicrobial infections may be generated by a compilation of results of phenotypic antibiotic resistance testing, genetic resistance marker testing for a plurality of different polymicrobial infections.
  • the therapeutic solution may comprise one applicable therapeutic agent. In some embodiments, the therapeutic solution comprises two or more applicable therapeutic agents. In some embodiments, the therapeutic solution comprises three or more applicable therapeutic agents. In some embodiments, the therapeutic solution comprises four or more applicable therapeutic agents. In some embodiments, the therapeutic solution comprises five or more applicable therapeutic agents.
  • the method may further comprise compiling a data set that includes one or more data points selected from: (i) results of phenotypic antibiotic resistance testing, (ii) results of genetic identification testing, (iii) results of genetic resistance marker testing, (iv) therapeutic agents to which the polymicrobial infection is expected to have increased resistance, (v) therapeutic agents to which the polymicrobial infection is expected to have decreased resistance, (vi) suggested therapeutic agents, and (vii) formulation of suggested therapeutic agents.
  • the method further comprises generating a report that communicates the data set.
  • the report features a chart and/or a table and/or a diagram.
  • the method further comprises providing the report to a medical professional, wherein the report communicates recommendations for treatment for the patient.
  • the method further comprises measuring a number or concentration of organisms present in the sample. In some embodiments, the method further comprises Extended-Spectrum Beta-lactamase (ESBL) testing. In some embodiments, the method further comprises testing for genes associated with Clostridium difficile. In some embodiments, the method further comprises determining a microbial inhibitory concentration (MIC) for organisms of the polymicrobial infection.
  • ESBL Extended-Spectrum Beta-lactamase
  • MIC microbial inhibitory concentration
  • the genetic identification testing comprises PCR, fluorescence in situ hybridization (FISH), culture, mass spectrometry, electrochemical biosensing, automated biochemical identification, flow cytometry, or a combination thereof.
  • the genetic resistance marker testing comprises PCR or sequencing.
  • the pooled phenotypic antibiotic resistance testing comprises introducing fractions of the portion of the sample to one or more media samples, each media sample comprising a therapeutic agent, incubating (e.g., under conditions suitable for growth) the media samples with the fractions, and subsequently measuring viability of organisms in the media samples after incubation.
  • the media samples are in test tubes, wells of a culture plate, an agar plate, or a microscope slide.
  • the viability of the organisms is measured by optical density (OD), fluorescence, or chemiluminescence.
  • the sample has at least one resistance marker. In certain embodiments, the sample has at least 2 resistance markers. In certain embodiments, the sample has at least 3 resistance markers. In certain embodiments, the at least one resistance marker is a mecA gene, a vanA/B gene, a TEM gene, a SHV gene, a OXA gene, a CTX-M gene, a KPC gene, a NDM gene, an OXA gene, a VIM gene, an IMP gene, or a combination thereof.
  • the one or more resistance genes is ErmA + Erm B, TEM, CTX-M group 1, SHV, VEB, OXA-1, CTX-M group 2, CTX-M group 9, CTX-M group 8/25, PER-1, PER-2, GES, blaNDM-1, VIM, KPC, IMP-2 group, IMP-1 group, OXA-23, IMP-16, IMP-7, OXA-72, OXA-40, OXA-58, OXA-48, NDM, blaOXA-48, QnrA, QnrB, mecA, ampC, FOX, ACC, DHA, MOX/CMY, BIL/LAT/CMY, vanA1, vanA2, vanB, vanC1, or vanC2-C3-2.
  • Non-limiting examples of organisms that may be tested for and/or present in the polymicrobial infection include: one or a combination of: Acinetobacter baumannii, Actinotignum schaalii, Aerococcus urinae, Aerococcus urinae, Alloscardovia omnicolens, Candida albicans, Candida glabrata, Candida parapsilosis, Candida tropicalis, Chlamydia, Citrobacter freundii, Citrobacter koseri, Clostridium difficile, Corynebacterium riegelii, Klebsiella aerogenes, Enterococcus faecalis, Escherichia coli, Klebsiella oxytoca, Klebsiella pneumoniae, Morganella morganii, Mycobacterium tuberculosis, Mycoplasma genitalium, Mycoplasma hominis, Neisseria gonorrhoeae, Pantoe
  • the therapeutic agent is one or a combination of a: penicillin, tetracycline, cephalosporin, quinolone, lincomycin, macrolide, sulfonamide, glycopeptide antibiotic, aminoglycoside, carbapenem, annamycin, lipopeptide, Fosfomycin, monobactam, nitrofuran, oxazolidinone, amphotericin B, Isavuconazole, itraconazole, micafungin, Posaconazole, voriconazole, cidofovir, vidarabine, foscarnet, acyclovir, valacyclovir, or a combination thereof.
  • the polymicrobial infection comprises 2 or more organisms In some embodiments, the polymicrobial infection comprises 3 or more organisms. In some embodiments, the polymicrobial infection comprises 4 or more organisms. In some embodiments, the polymicrobial infection comprises 5 or more organisms. In some embodiments, the polymicrobial infection comprises 6 or more organisms. In some embodiments, the polymicrobial infection comprises 7 or more organisms.
  • the presence of two or more organisms in the polymicrobial infection changes sensitivity of the polymicrobial infection to antibiotics or therapeutic agents known to be effective against at least one of the organisms present in the polymicrobial infection individually.
  • the present invention also provides a workflow method for preparing a therapeutic solution for a patient having or suspected of having a polymicrobial infection.
  • the method may comprise one or more of the steps described herein, for example subjecting a portion of the sample to a genetic identification testing, subjecting a portion of the sample to genetic resistance marker testing, and subjecting a portion of the sample to pooled antibiotic susceptibility testing.
  • the sample comprises urine, blood, plasma, cerebrospinal fluid, saliva, sputum, pulmonary lavage, vaginal secretions, wound lavage, biopsy tissue, wound swab, rectal swab, nasal swab, tissue, fecal matter, sperm sample, semen sample, or prostate fluid.
  • FIG. 1 shows a schematic view of mutualism between particular bacteria.
  • FIG. 2 shows the frequency of which certain resistance genes are found together.
  • the strength of the correlation is represented by the width of the edge connecting the genes. Only correlations greater than 0.1 are shown.
  • FIG. 4 shows an example of a report comprising one or more therapeutic solutions for a particular polymicrobial infection.
  • FIG. 5 shows a comparison of symptom resolution in patients treated based on methods of the present invention (“Guidance UTI test”), patients treated empirically, and untreated patients.
  • FIG. 7 shows bacteria detected in polymicrobial infections in the study.
  • FIG. 8 shows the distribution of bacteria detected in the 68.6% (1710/2493) of patients who were positive for bacteria showing the distribution of organisms in monomicrobial, polymicrobial, and consortia mixtures.
  • FIG. 9 shows a network diagram of the relationships among the bacteria found most frequently in consortia
  • FIG. 10 shows detection rates for Gram-positive and Gram-negative bacteria in monomicrobial and polymicrobial infections and in consortia.
  • FIG. 11 shows the odds ratios of antibiotic resistance in polymicrobial versus monomicrobial specimens, along with the odds ratio of resistance for each increase in the number of bacterial species in polymicrobial specimens.
  • FIG. 12 shows the effects of organism interactions on antibiotic resistance.
  • An upward arrow indicates an increase in resistance.
  • a downward arrow indicates a decrease in resistance (an increase in sensitivity).
  • FIG. 14 depicts an exemplary Antibiotic Source Plate with well contents and antibiotic concentration (pg/mL).
  • Nitro nitrofurantoin
  • Cipro ciprofloxacin
  • Mero meropenem
  • Ceftiaxone ceftriaxone
  • TMP/SMX trimethoprim + sulfamethoxazole
  • Pip/Tazo piperacillin + tazobactam
  • Levo levofloxacin
  • Cefoxitin cefoxitin
  • Tetra tetracycline
  • Amp/Sulb ampicillin + sulbactam
  • Amp ampicillin
  • Vanco vancomycin.
  • FIG. 15 depicts an exemplary Antibiotic Source Plate with well contents and antibiotic concentration (pg/mL).
  • Cefazolin cefazolin
  • Cefepime cefepime
  • Ceftazidime ceftazidime
  • Gentamicin gentamicin
  • Amox/Clav amoxicillin + clavulanate
  • Cefaclor cefaclor.
  • FIG. 16 shows the concordance between the presence of antibiotic resistance genes (ABR) detected by multiplex polymerase chain reaction (M-PCR) and antibiotic susceptibility detected by pooled antibiotic susceptibility testing (P-AST) of urine samples from symptomatic patients with urinary tract infection (UTI).
  • ABR antibiotic resistance genes
  • M-PCR multiplex polymerase chain reaction
  • P-AST pooled antibiotic susceptibility testing
  • the dashed line represents the weighted average concordance across all samples (60%).
  • Combo combination antibiotics, including Ampicillin/Sulbactam, Amoxicillin/Clavulanate, and Piperacillin/Tazobactam.
  • FIG. 17 shows the number of patients (with either polymicrobial infections or monomicrobial infections) having sensitive or resistant ABR genes present for meropenem, piperacillin/tazobactam, or vancomycin.
  • FIG. 18 shows antibiotic resistance by pooled antibiotic susceptibility testing (P-AST) and antibiotic resistance (ABR) gene presence for all 14 antibiotics analyzed. Also shown are detection frequencies and associated average numbers of clinical findings per patient of different consortia detected in the study. Clinical findings are defined as one or more of the following symptoms or abnormal laboratory result, including urinary incontinence, dysuria, gross hematuria, pain/pelvic discomfort, urine cloudiness or strong smell, lower urinary tract symptoms (LUTS), and abnormal urinalysis or dipstick result.
  • P-AST pooled antibiotic susceptibility testing
  • ABR antibiotic resistance
  • FIG. 19 shows the overall concordance between the presence of antibiotic resistance (ABR) genes detected by multiplex polymerase chain reaction and antibiotic susceptibility detected using pooled antibiotic susceptibility testing (P-AST) of urine samples from symptomatic patients with urinary tract infection (UTI).
  • ABR antibiotic resistance
  • P-AST pooled antibiotic susceptibility testing
  • the term “Highest Single Agent Interaction Principle” refers to a statistical model wherein the resistance of the polymicrobial infection is predicted to be the resistance of the bacteria with the highest resistance. For example, if species A is resistant with a probability 20%, and species B is resistant with a probability 50%, then the probability of resistance of the pool is 50%.
  • the term “Union Principle” refers to a statistical model wherein the polymicrobial infection of species A and B is made up of one colony (or one genetic variant) of species A and one colony (or one genetic variant) of species B, and the polymicrobial infection is resistant if either the colony of species A is resistant, or if the colony of species B is resistant.
  • an antibiotic is applied to the polymicrobial infection, it may kill off species A, but if species B survives, the polymicrobial infection is called resistant.
  • species A is resistant with a probability 20%
  • species B is resistant with a probability 50%
  • the term “Logistic Additive Model” refers to a statistical model wherein the effects of species A and species B on the resistance of the polymicrobial infection is estimated in a logistic model.
  • the effect of species A is the odds ratio of resistance when species A is present relative to when it is not present; similarly, the effect of species B is the odds ratio of resistance when species B is present relative to when it is not.
  • the additive model predicts the effect of both species as the sum of the log odds-ratio; or the product of the two individual odds-ratios. For example, if the background resistance rate is 50%, the expected polymicrobial infection (species A and B) resistance with no interactions is 20%; if the background resistance rate is 20%, the expected polymicrobial infection resistance is 50%.
  • the present methods may be conducted using a plurality of antibiotics selected from the large number available to treat patients.
  • Classes of antibiotics include, but are not limited to, penicillins, tetracyclines, cephalosporins, quinolones, lincomycins, macrolides, sulfonamides, glycopeptide antibiotics, aminoglycosides, carbapenems, ansamycins, annamycins, lipopeptides, monobactams, nitrofurans, oxazolidinones, and polypeptides.
  • Penicillin antibiotics may include, but are not limited to, penicillin, methicillin, amoxicillin, ampicillin, flucloxacillin, penicillin G, penicillin V, carbenicillin, piperacillin, ticarcillin, oxacillin, dicloxacillin, azlocillin, cloxacillin, mezlocillin, temocillin, and nafcillin. Additionally, penicillin antibiotics are often used in combination with beta-lactamase inhibitors to provide broader spectrum activity; these combination antibiotics include amoxicillin/clavulanate, ampicillin/sulbactam, piperacillin/tazobactam, and clavulanate/ticarcillin.
  • Tetracycline antibiotics include, but are not limited to, tetracycline, doxycycline, demeclocycline, minocycline, and oxytetracycline.
  • Cephalosporin antibiotics may include, but are not limited to, cefadroxil, cephradine, cefazolin, cephalexin, cefepime, ceftaroline, loracarbef, cefotetan, cefuroxime, cefprozil, cefoxitin, cefaclor, ceftibuten, cetriaxone, cefotaxime, cefpodoxime, cefdinir, cefixime, cefditoren, ceftizoxime, cefoperazone, cefalotin, cefamanadole, ceftaroline fosamil, cetobiprole, and ceftazidime.
  • Cephalosporin antibiotics are often used in combination with beta-lactamase inhibitors to provide broader spectrum activity; these combination antibiotics include, but are not limited to, avibactam/ceftazidime and ceftolozane/tazobactam.
  • Quinolone antibiotics include, but are not limited to, lomefloxacin, ofloxacin, norfloxacin, gatifloxacin, ciprofloxacin, moxifloxacin, levofloxacin, gemifloxacin, cinoxacin, nalidixic acid, trovaloxacin, enoxacin, grepafloxacin, temafloxacin, and sparfloxacin.
  • Lincomycin antibiotics may include, but are not limited to, clindamycin and lincomycin.
  • Macrolide antibiotics may include, but are not limited to, azithromycin, clarithromycin, erythromycin, telithromycin, dirithromycin, roxithromycin, troleandomycin, spiramycin, and fidazomycin.
  • Sulfonamide antibiotics may include, but are not limited to, sulfamethoxazole, sulfasalazine, mafenide, sulfacetamide, sulfadiazine, silver sulfadiazine, sulfadimethoxine, sulfanilamide, sulfisoxazole, sulfonamidochrysoidine, and sulfisoxazole. Sulfonamide antibiotics are often used in combination with trimethoprim to improve bactericidal activity.
  • Glycopeptide antibiotics may include, but are not limited to, dalbavancin, oritavancin, telavancin, teicoplanin, and vancomycin.
  • Aminoglycoside antibiotics may include, but are not limited to, paromomycin, tobramycin, gentamicin, amikacin, kanamycin, neomycin, netilmicin, streptomycin, and spectinomycin.
  • Carbapenem antibiotics include, but are not limited to, imipenem, meropenem, doripenem, ertapenem, and imipenem /cilastatin.
  • Ansamycin antibiotics may include, but are not limited to, geldanamycin, herbimycin, and rifaximin.
  • Lipopeptide antibiotics may include, but are not limited to, daptomycin.
  • Monobactam antibiotics may include, but are not limited to, aztreonam.
  • Nitrofuran antibiotics may include, but are not limited to furazolidone and nitrofurantoin.
  • Oxazolidinone antibiotics may include, but are not limited to, linezolid, posizolid, radezolid, and torezolid.
  • Polypeptide antibiotics may include, but are not limited to, bacitracin, colistin, and polymyxin B.
  • antibiotics which are not part of any of the above-mentioned groups include, but are not limited to, clofazimine, dapsone, capreomycin, cycloserine, ethambutol, ethionamide, isoniazid, pyrazinamide, rifampicin, rifabutin, rifapentine, streptomycin, arsphenamine, chloramphenicol, fosfomycin, fusidic acid, metronidazole, mupirocin, platensimycin, quinupristin/dalfopristin, thiamphenicol, tigecycline, tinidazole, and trimethoprim.
  • the scope of the presently disclosed methods encompasses the inclusion of antibiotics not yet known, or not yet approved by regulatory authorities.
  • the presently claimed assay can be performed with any anti-bacterial agent and is not limited to the antibiotics disclosed herein. DETAILED DESCRIPTION OF THE INVENTION Therapeutic Solutions for Treatment of Polymicrobial Infections
  • Disclosed herein are methods and systems for identifying polymicrobial infections and identifying or providing therapeutic solutions for polymicrobial infections or suspected polymicrobial infections in a patient.
  • the present invention also features methods and systems for treating polymicrobial infections or suspected polymicrobial infections in a patient.
  • the patient may be exhibiting symptoms of an infection.
  • symptoms include: fever, discharge, itching, dysuria, increased urinary frequency or urgency, etc.
  • the methods are applied to patients not experiencing symptoms of an infection.
  • a patient refers to a mammal (e.g., human or other appropriate mammal).
  • the methods herein feature: (1) detection and identification of organisms (e.g., bacteria or other infectious agents such as viruses, fungi, protozoa, etc., a combination thereof) of the polymicrobial infection, (2) pooled antibiotic susceptibility tests for determining the susceptibility or resistance of the polymicrobial infection in the sample to an antibiotic or other therapeutic agent, and (3) identification of resistance genes in the infectious agents in the polymicrobial infection, e.g., genetic markers that may indicate resistance to a particular antibiotic or other therapeutic agent or treatment.
  • the data from (1), (2), and (3) can be applied (e.g., using databases of antibiotic/therapeutic susceptibility or resistance for particular known polymicrobial infections) in order to provide one or more therapeutic solutions for the polymicrobial infection.
  • the present invention is not limited to any particular type of infection.
  • the present invention is not limited to bacterial infectious agents and may include viral infectious agents, fungal infectious agents, and/or protozoa as well.
  • Samples may be in the form of a fluid, biological matter, or tissue (e.g., biopsy). Samples may include but are not limited to blood samples, urine samples, plasma samples, saliva samples, pulmonary lavage samples, vaginal secretions, wound lavage, biopsy samples, wound samples, sperm samples, semen samples, prostate fluid samples, cerebrospinal fluid samples, synovial fluid samples, peritoneal fluid samples, pericardial fluid samples, pleural fluid samples, sputum samples, stool samples, mucosal samples, abscess samples, nasal samples, rectal samples, etc.
  • the present invention comprises subjecting a sample of a patient suspected of having an infection to an organism detection method for detecting and identifying organisms present in the sample. Quantitative polymerase chain reaction (PCR), which is well known to one of ordinary skill in the art, may be used to quickly detect organisms in the sample.
  • PCR Quantitative polymerase chain reaction
  • the organism detection method may allow for detection of a plurality of organisms, e.g., more than 5 organisms, more than 10 organisms, more than 20 organisms, more than 30 organisms, more than 40 organisms, more than 50 organisms, etc.
  • Samples identified as positive for infection-related organisms are then tested to determine an antibiotic susceptibility profile for the infection.
  • the sample is subjected to phenotypic antibiotic susceptibility testing, e.g., pooled antibiotic susceptibility tests (P-AST), for determining the susceptibility or resistance of the infection to one more antibiotics or therapeutic agents, and genotypic antibiotic resistance testing (G-ABR) for determining the presence of antibiotic resistance genes, which may indicate resistance to a particular antibiotic or other therapeutic agent or treatment.
  • P-AST pooled antibiotic susceptibility tests
  • G-ABR genotypic antibiotic resistance testing
  • the methods of the present invention further comprise pooled sensitivity tests wherein samples are incubated in plurality of antibiotics or other therapeutic agents.
  • the samples are not first subjected to bacterial or organism isolation.
  • a sample may be fractioned and all or a portion of the fractions may be introduced to media containing an antibiotic or other therapeutic agent.
  • P-AST tests may be performed in multi-well plates; however, the present invention is not limited to a multi-well plate format.
  • the concentrations of the infectious agents are quantitated, thereby determining the susceptibility or resistance of the polymicrobial infection in the sample to the antibiotic or other therapeutic agent.
  • the media may be read in a spectrophotometer to determine the OD to measure cell density.
  • the present invention is not limited to the use of a spectrophotometer to determine the OD to measure bacterial concentrations. Any other appropriate methods may be considered, e.g., fluorescence, chemiluminescence, etc.
  • a color matrix system is utilized, wherein color change of an indicator is observed and/or calculated.
  • the present invention uses flow cytometry (which includes the use of antibodies to the organisms).
  • an electrical system is utilized. The present invention is not limited to the aforementioned methods.
  • P-AST features testing for 1 antibiotic or therapeutic agent In certain embodiments, P-AST features testing for 2 or at least 2 antibiotics and/or therapeutic agents. In certain embodiments, P-AST features testing for 3 or at least 3 antibiotics and/or therapeutic agents. In certain embodiments, P-AST features testing for 4 or at least 4 antibiotics and/or therapeutic agents. In certain embodiments, P-AST features testing for 5 or at least 5 antibiotics and/or therapeutic agents. In certain embodiments, P-AST features testing for 6 or at least 6 antibiotics and/or therapeutic agents. In certain embodiments, P-AST features testing for 7 antibiotics and/or therapeutic agents. In certain embodiments, P-AST features testing for 8 or at least 8 antibiotics and/or therapeutic agents.
  • P-AST features testing for 9 or at least 9 antibiotics and/or therapeutic agents In certain embodiments, P-AST features testing for 10 or at least 10 antibiotics and/or therapeutic agents. In certain embodiments, P-AST features testing for 11 or at least 11 antibiotics and/or therapeutic agents. In certain embodiments, P-AST features testing for 12 or at least 12 antibiotics and/or therapeutic agents. In certain embodiments, P-AST features testing for 13 or at least 13 antibiotics and/or therapeutic agents. In certain embodiments, P-AST features testing for 14 or at least 14 antibiotics and/or therapeutic agents. In certain embodiments, P-AST features testing for 15 or at least 15 antibiotics and/or therapeutic agents. In certain embodiments, P-AST features testing for 16 or at least 16 antibiotics and/or therapeutic agents.
  • P-AST features testing for 17 or at least 17 antibiotics and/or therapeutic agents In certain embodiments, P-AST features testing for 18 or at least 18 antibiotics and/or therapeutic agents. In certain embodiments, P-AST features testing for 19 or at least 19 antibiotics and/or therapeutic agents. In certain embodiments, P-AST features testing for 20 or at least 20 antibiotics and/or therapeutic agents. In certain embodiments, P-AST features testing for 25 or at least 25 antibiotics and/or therapeutic agents. In certain embodiments, P-AST features testing for 30 or at least 30 antibiotics and/or therapeutic agents. In certain embodiments, P-AST features testing for 35 or at least 35 antibiotics and/or therapeutic agents. In certain embodiments, P-AST features testing for 40 or more than 40 antibiotics and/or therapeutic agents.
  • Non-limiting examples of antibiotics and other therapeutic agents are disclosed herein and are well known to one of ordinary skill in the art.
  • FIG. 1 describes the study of interactions of particular bacteria in polymicrobial infections subjected to pooled antibiotic sensitivity testing (P-AST).
  • P-AST pooled antibiotic sensitivity testing
  • one organism e.g., metabolite
  • G-ABR Genetic Antibiotic Resistance Testing
  • G-ABR genetic antibiotic resistance testing
  • G-ABR features testing for 1 genetic marker (e.g., antibacterial resistance gene, antiviral resistance gene, antifungal resistance gene) indicating resistance to a particular therapeutic agent.
  • G-ABR features testing for 2 or at least 2 genetic markers (e.g., antibacterial resistance genes, antiviral resistance genes, antifungal resistance genes) indicating resistance to particular therapeutic agents.
  • G-ABR features testing for 3 or at least 3 genetic markers (e.g., antibacterial resistance genes, antiviral resistance genes, antifungal resistance genes) indicating resistance to particular therapeutic agents.
  • G-ABR features testing for 4 or at least 4 genetic markers e.g., antibacterial resistance genes, antiviral resistance genes, antifungal resistance genes
  • Non-limiting examples of genetic markers e.g., antibacterial resistance genes, antiviral resistance genes, antifungal resistance genes are disclosed herein and are well known to one of ordinary skill in the art.
  • FIG. 2 shows the results of a study wherein samples were tested for the presence of resistance genes and the frequency that particular resistance genes were found together.
  • FIG. 3 shows the results of a study wherein a collection of samples were evaluated for ABR status (sensitivity or resistance) and tested for resistance markers (resistant genotype or a sensitive genotype) with respect to one or more antibiotics. Notably, there were instances where the two results were in disagreement, e.g., instances wherein the organisms are phenotypically sensitive to an antibiotic yet have a resistant genotype (see column 3) and instances wherein the organisms are phenotypically resistant yet have a sensitive genotype (see column 4).
  • the P-AST data and G-ABR data can be analyzed (e.g., applied or compared to standardized values or thresholds) for determining antibiotic/therapeutic susceptibility or resistance in order to provide one or more therapeutic solutions for the polymicrobial infection.
  • Thresholds will be specific to each test, e.g., for the genetic identification testing, pooled sensitivity testing, resistance gene testing.
  • a threshold for genetic identification testing may include a minimum number of organisms to test positive for the organism.
  • the present invention also includes the testing of resistance genes that will be identified in the future.
  • the present invention also includes the culturing of bacteria that presently cannot be cultured but may be cultured in the future as growth conditions are expanded.
  • the therapeutic agent is one or a combination of a: penicillin, tetracycline, cephalosporin, quinolone, lincomycin, macrolide, sulfonamide, glycopeptide antibiotic, aminoglycoside, carbapenem, ansamycin, annamycin, lipopeptide, Fosfomycin, monobactam, nitrofuran, oxazolidinone, and/or a polypeptide.
  • the therapeutic agent is one or a combination of cidofovir, vidarabine, foscarnet, acyclovir, and/or valacyclovir.
  • the therapeutic agent is one or a combination of amphotericin B, isavuconazole, itraconazole, micafungin, Posaconazole, and/or voriconazole.
  • Formulations of the therapeutic agents include but are not limited to oral (PO) intravenous, (IV), and/or injection.
  • FIG. 4 shows an example of a report describing the results of the methods of the present invention for a sample of a polymicrobial infection, including one or more therapeutic solutions.
  • the methods herein help provide fast results and reduce the need for empiric therapy. Without wishing to limit the present invention to any theory or mechanism, the methods of the present invention are believed to provide better accuracy compared to current standard of care practices such as standard urine culture. Referring to FIG. 5, patients treated using the methods of the present invention (“Guidance UTI test”) showed significantly better symptom resolution as compared to patients untreated or patients treated empirically.
  • Example 1 Multisite Prospective Comparison of Multiplex Polymerase Chain Reaction Testing with Urine Culture for Diagnosis of Urinary Tract Infections in Symptomatic
  • Urine culture is traditionally used for detection and identification of pathogens for diagnosis and management of urinary tract infections (UTIs).
  • UTIs urinary tract infections
  • PCR polymerase chain reaction
  • This prospective multicenter study compared PCR with traditional urine culture for detection and identification of bacteria in 2511 patients (mean age 73; range 24 - 100) presenting with symptoms of UTI. Both urine cultures and PCR were performed on samples from all patients. PCR detected bacteria in 62.7% (1575/2511) of cases, while urine culture detected bacteria in 43.7% (1098/2511) of cases.
  • Study participants were patients who presented with symptoms of UTI at urology clinics. The patients were evaluated by any of 75 physicians from 37 urology offices in seven states. A total of 2511 consecutive patients who met inclusion criteria were enrolled between July 26, 2018 and February 27, 2019. No predetermined quotas or ratios for gender participation of male and female subjects were imposed.
  • Inclusion criteria Patients presenting with symptoms of acute cystitis, complicated UTI, persistent UTI, or recurrent UTIs, or prostatitis, pyelonephritis, and/or interstitial cystitis; Symptoms of interstitial cystitis at any age; Symptoms of other conditions at > 60 years of age; Specimen volumes sufficient volume to permit urine culture and Guidance 4.0; Patient informed consent; Documented times at which the specimen samples were collected and stabilized
  • Exclusion criteria Prior participation in this study; Taking antibiotics for any reason other than UTI at the time of enrollment; Chronic (> 10 days) indwelling catheters; Self-catheterization; Patients with urinary diversion; Absence of written informed consent and/or HIPAA authorization form.
  • Urine samples were obtained from patients either by self-administered clean catch or by catheterization. The samples were collected and transported to Pathnostics (Irvine, California) for testing by culture. For culture, urine was vortexed, and a sterile plastic loop (1 uL) was used to inoculate blood agar plates. A sterile plastic loop (1 uL) was used also to inoculate colistin and nalidixic acid agar/MacConkey agar (CNA/MAC) plates, one loop-full of urine on the CNA side of the plate and another full loop-full on the MAC side of the plate. All plates were incubated at 35° C in 5% C0 2 for > 18 hours, then examined for evidence of growth.
  • CNA/MAC colistin and nalidixic acid agar/MacConkey agar
  • Plates with ⁇ 10 4 CFU/ml were reported as normal urogenital flora. For plates with growth (> 10 4 CFU/ml), the quantity and morphology of each organism was recorded. The maximum readable colony count using the 1 uL loop is > 10 5 CFU/ml. Colony counts were performed on the blood agar plates. Species identification and colony counts were performed on CNA/MAC plates. For plates with ⁇ 2 pathogens, species identification and colony counts were reported for each pathogen with > 10 4 CFU/ml. If > 3 pathogens were present, and one or two were predominant, species identification and colony counts were reported. If > 3 pathogens were present without predominant species, a mixed morphotype was reported.
  • Pathogen identification was confirmed with the VITEK 2 Compact System (bioMerieux, Durham, NC) in accordance with standard operating procedures. Briefly, a sterile swab was used to transfer morphologically similar colonies from positive blood agar plates to prepared polystyrene test tubes containing 3.0 mL of sterile saline. The sample was adjusted for density (equivalent to McFarland No. 0.50 to 0.63). The sample tube and an appropriate identification card were placed into the cassette and inserted into the VITEK 2 instrument. The identity of the bacteria was used to determine Gram status, and a GN card was used for Gram-negative bacteria, and a GP card was used for Gram-positive bacteria. A YST card was used for yeast. Pathogen identification was read from the VITEK 2 instrument.
  • PK Mix Proteinase K Mix
  • Lysis buffer 125 pL/well
  • DNA Binding Bead Mix 40 L/well
  • the 96-well plate was loaded into the KingFisher/MagMAX Automated DNA Extraction instrument, which was operated in accordance with standard operating procedures.
  • DNA samples were analyzed with the Pathnostics GuidanceTM UTI Test.
  • the samples were mixed with universal PCR master mix and amplified with TaqMan technology on a Life Technologies 12K Flex Open Array System.
  • DNA samples were spotted in duplicate on 112-format OpenArray chips. Plasmids for each organism being tested for were used as positive controls.
  • Candida tropicalis was used as an inhibition control.
  • a data analysis tool developed by Pathnostics was used to sort data, assess the quality of data, summarize control sample data, identify positive assays, calculate concentrations, and generate draft reports.
  • Probes and primers were used for the following pathogens: Bacteria: Acinetobacter baumannii, Actinotignum schaalii, Aerococcus urinae, Alloscardovia omnicolens, Citrobacter freundii, Clostridium difficile, Citrobacter koseri, Corynebacterium riegelii, Enterobacter aerogenes, Enterococcus faecalis, Escherichia coli, Klebsiella oxytoca, Klebsiella pneumoniae, Morganella morganii, Mycobacterium tuberculosis, Mycoplasma genitalium, Mycoplasma hominis, Pantoea agglomerans, Proteus mirabilis, Providencia stuartii, Pseudomonas aeruginosa, Serratia marcescens, Staphylococcus aureus, Streptococcus agalactiae, and Urea
  • Bacterial groups Coagulase negative staphylococci ( Staphylococcus epidermidis, Staphylococcus haemolyticus, Staphylococcus lugdunensis, Staphylococcus saprophyticus ); Viridans group streptococci ( Streptococcus anginosus, Streptococcus oralis, Streptococcus pasteuranus).
  • urinary frequency and urgency were significantly more common in women, whereas nocturia and microhematuria were more common in men. Women had significantly more of these symptoms or signs than did men: acute change in mentation; increased falls or clumsiness; tiredness; feeling ill; and decline in activities of daily living. Increased numbers of leukocytes and concentrations of nitrites were present in the urine of women more often than in the urine of men, and UTIs were treated with antibiotics significantly more often in women than in men.
  • Table 2 illustrates the percentage agreement (or disagreement) between traditional urine culture and PCR in patients with UTI symptoms.
  • results of the two tests were in agreement; culture and PCR were both positive in 1018/2511 (40.5%) cases and both were negative in 856/2511 (34.1%) cases.
  • PCR was positive whereas culture was negative in 557/2511 (22.2%) cases, and PCR was negative whereas culture was positive in 80/2511 (3.2%) of cases.
  • Culture did not detect six bacterial species that were detected by PCR: Actinotignum schaalii, Alloscardovia omnicolens, Corynebacterium riegelii, Mycoplasma genitalium, Mycoplasma hominis, and Ureaplasma urealyticum). These six bacteria constituted 9.0% (67/741) of monomicrobial infections, but were detected in 60.7% (523/861) of polymicrobial infections.
  • PCR did not detect three bacteria that were detected by culture (FIG. 6). That PCR did not detect these bacteria is not a failure of the PCR because the PCR panel did not include probes for them.
  • E. coli is the most frequently detected bacterium for both.
  • E. coli constituted 42.1% of bacteria detected by culture
  • E. coli constituted only 18% of bacteria detected by PCR.
  • McNemar’s test p-value 0.0002
  • PCR since PCR detected more different types of bacteria and a larger number of each type of bacteria, E. coli constituted a smaller percentage. For instance, K.
  • PCR detected 834 In 861 total cases, PCR detected 834, whereas culture detected 168. Culture is inherently limited in its ability to detect and resolve the identity of pathogens in polymicrobial infections. Although there are several reasons for the limitations of culture, a significant factor is the inability of some bacteria to be cultured. In this study, six bacteria that were detected by PCR were not detected by culture. Three of these bacteria ( Actinotignum schaalii, Alloscardovia omnicolens, and Corynebacterium riegelii) were among the 10 most frequently detected bacteria, combining for 20.6% of all bacterial detections. Even more, Actinotignum schaalii was the bacterium involved in polymicrobial infections more often than any other and was involved in 53.0% (442/834) of all polymicrobial infections.
  • culture detects Klebsiella pneumoniae, but it does not detect Actinotignum schaalii; in this study, both bacteria are ranked second in occurrence in culture as well as PCR even though the two bacteria have very different resistance and susceptibility profiles; in some ways, their resistance/susceptibility profiles are mirror images of each other, and detection of K. pneumoniae but not A. schaalii could lead to selection of inappropriate antibiotics. For instance, culture detected Klebsiella pneumoniae, but not Actinotignum schaalii. Because K. pneumoniae and A. schaalii share no commonalities with respect to antibiotic resistance and susceptibility, selection of an antibiotic regimen on the basis of detection of K pneumoniae alone would result in treating with an antibiotic that A. schaalii is resistant to.
  • UTI Failure to appropriately treat UTI increases the risk of developing additional medical problems, some of which may be even more severe than recurrent UTI. Although many kinds of infections are associated with acute ischemic stroke, UTI presents the greatest risk, with an odds ratio of 5.32. The seven bacteria that were missed by culture but detected by PCR in this study have been associated with increased risk for sepsis and bacteremia, endocarditis, Fournier’s gangrene, and abdominal abscess.
  • Urine is not sterile and has a unique microbiome, referred to as the urobiome.
  • Dysbiosis can take three forms, all of which involve a shift in the balance of bacteria in a microbiome.
  • One type of dysbiosis is referred to as gain of function dysbiosis, in which there is an overgrowth of pathogens that can cause disease.
  • a second type is known as loss of function, in which bacteria that would otherwise function to protect health are lost. Loss of function dysbiosis is often associated with use of antibiotics, resulting in the loss of both beneficial and pathogenic organisms.
  • the third type of dysbiosis is a mixture of gain and loss of function.
  • This analysis investigated patterns of gain of function dysbiosis that may contribute to the pathogenesis of polymicrobial UTI. Formation of bacterial consortia and biofilms can cause gain of function dysbiosis. Consortia are non-random polymicrobial communities that interact synergistically, providing community members with growth and survival advantages over planktonic, free-floating, microbes. Like biofilms, consortia are self-organizing structures, and consortia can attach to surfaces to become biofilms. Multiple consortia may be found within individual biofilms, separated by interstitial spaces filled with fluid. However, biofilms can be monomicrobial, whereas consortia are inherently polymicrobial.
  • Consortia may be defined as non-random patterns of bacterial communities that are found together in symptomatic patients.
  • Inclusion criteria included: >60 years of age; symptoms of acute cystitis, complicated UTI, persistent UTI, recurrent UTI, prostatitis, pyelonephritis, or interstitial cystitis; specimen volumes sufficient to permit urine culture and multiplex polymerase chain reaction (M-PCR); documented times at which the specimens were collected and stabilized with boric acid in grey-top tubes.
  • Exclusion criteria included antibiotics taken for any reason other than UTI at the time of enrollment, chronic (>10 days) indwelling catheters, self-catheterization, and urinary diversion. Physicians recorded ICD-10 codes for each clinical encounter, as well as the patients’ presenting UTI symptoms and urinalysis dipstick results. Catheterized and clean catch urine specimens were collected, depending on the patients’ ability.
  • Lysis buffer 125 pL/well
  • DNA Binding Bead Mix 40 pL/well
  • the 96-well plate was loaded into the KingFisher/MagMAX Automated DNA Extraction instrument, which was operated in accordance with standard operating procedures.
  • DNA samples were analyzed with the Pathnostics GuidanceTM UTI Test.
  • the samples were mixed with universal PCR master mix and amplified with TaqMan technology on a Life Technologies 12K Flex Open Array System.
  • DNA samples were spotted in duplicate on 112-format OpenArray chips. Plasmids for each organism being tested for were used as positive controls.
  • Candida tropicalis was used as an inhibition control.
  • a data analysis tool developed by Pathnostics was used to organize data, assess the quality of data, summarize control sample data, identify samples with positive assay results, calculate concentrations, and generate draft reports. Probes and primers were used for the following pathogens:
  • Bacteria Acinetobacter baumannii, Actinotignum schaalii, Aerococcus urinae, Alloscardovia omnicolens, Citrobacter freundii, Clostridium difficile, Citrobacter koseri, Corynebacterium riegelii, Enterobacter aerogenes, Enterococcus faecalis, Escherichia coli, Klebsiella oxytoca, Klebsiella pneumoniae, Morganella morganii, Mycobacterium tuberculosis, Mycoplasma genitalium, Mycoplasma hominis, Pantoea agglomerans, Proteus mirabilis, Providencia stuartii, Pseudomonas aeruginosa, Serratia marcescens, Staphylococcus aureus, Streptococcus agalactiae, and Ureaplasma urealyticum.
  • Bacterial groups Coagulase negative staphylococci ( Staphylococcus epidermidis, Staphylococcus haemolyticus, Staphylococcus lugdunesis, Staphylococcus saprophyticus ); Viridans group streptococci ( Streptococcus anginosus, Streptococcus oralis, Streptococcus pasteuranus).
  • a cutoff of 10 was applied to the frequency of occurrence.
  • the present invention is not limited to a cutoff of 10.
  • Summary statistics are provided for patient demographics (age and gender), diagnosis group by ICD-10 coding, method of sample collection, frequency of the total number of symptoms and each of the 12 symptoms for the study samples. The overall prevalence and individual prevalence of monomicrobial infection and polymicrobial infection for each of the 24 bacteria were calculated. To differentiate between consortia and random associations of bacteria, a cutoff of 10 patients was applied to the frequency of occurrence. The present invention is not limited to a cutoff of 10.
  • Phi coefficient (or mean square contingency coefficient) for every pair of the bacteria is calculated as
  • Phi coefficient is similar to Pearson correlation coefficient, it ranges from -1 (perfect negative association) to 1 (perfect positive association).
  • a network diagram was used to visually depict the relationships among the bacteria found most frequently in consortia, and to illustrate the frequency of consortia detected most often and the associated count of symptoms presented in the patients. Lastly, summary statistics and ANOVA comparisons were made for the mean number of symptoms across groups with different numbers of bacteria in consortia. The analyses were performed using SAS version 9.4 (SAS Institute, Inc., Cary, NC) and R 3.5.3
  • FIG. 8 shows the distribution of bacteria detected in the 68.6% (1710/2493) of patients who were positive for bacteria.
  • E coli E coli (7.7%, 192/2493)
  • Coagulase Negative Staphylococci 4.1%, 102/2493
  • a urinae 3.0%, 75/2493
  • a schaalii 2.3%, 57/2493
  • Viridans Group Streptococci 2.0%, 51/2493
  • Polymicrobial infections were found in 41.2% (1027/2493) of all patients and 60.1% (1027/1710) of patients who tested positive for bacteria. More than 90% of C riegelli (95.4%), A omnicolens (94.4%), and A schaalii (91.7%) were found in polymicrobial infections. Other than M genitalium, which was not found in polymicrobial infections, P aeruginosa (53.8%) and S aureus (51.6%) were the least likely to be detected in polymicrobial infections.
  • FIG. 9 shows a network diagram of the relationships among the bacteria found most frequently in consortia. Consortia were found in 17.4% (433/2493) of all patients and 42.2% (433/1027) of polymicrobial infections. Eight bacteria formed 18 different consortia, which ranged in count from 2 to 4 organisms. Eleven of the consortia consisted of two organisms, six consortia contained three organisms, and one consortium involved four organisms.
  • Clinical findings are a combination of urinalysis results and UTI symptoms.
  • a schaalii and A urinae constituted the most frequently detected bacteria and the most common combination of bacteria in consortia, being found in 9.7% (242/2493) of all patients, in 38.9% (7/18) of types of consortia, and in 55.9% (242/433) of the instances of consortia.
  • some bacteria such as P aeruginosa and S aureus, were detected in polymicrobial infections but were not found in consortia.
  • Other bacteria such as C riegelli and E faecalis, appear very often in polymicrobial infections but rarely form consortia.
  • C riegelli exhibited an overall prevalence of 4.4% (109/2493); 95.4% (104/109) of those were found in polymicrobial infections, but only 10.1% (11/109) existed in consortia.
  • E faecalis displayed an overall prevalence of 9.0% (224/2493); 81.7% (183/224) of those were found in polymicrobial infections, while only 4.5% (10/224) were in consortia.
  • Gram negative bacteria constituted 55.1% (376/683) of bacteria detected in monomicrobial infections and 76.4% (2369/3100) of bacteria detected in polymicrobial infections.
  • Gram positive bacteria dominated consortia: 80.3% (813/1013) of bacteria detected in consortia were Gram positive.
  • 41.1% (422/1027) of polymicrobial infections contained only Gram-positive bacteria
  • 53.8% (233/433) of consortia contained only Gram-positive bacteria.
  • a relationship between Gram stain classification and symptom presentation also became evident during data analysis.
  • 10 were associated with a clinical presentation of three or more clinical findings.
  • 8 included a Gram-negative bacterium; 6 included E coli, and 2 included K pneumoniae.
  • Another 8 consortia were associated with no more than 2 clinical findings reported during clinical presentation; only one of these included a Gram-negative bacterium, which was E coli.
  • a urinae another Gram-positive bacterium, was detected in 67.7% of consortia, but in only 3.0% of monomicrobial infections and 50.2% of non-consortia polymicrobial infections.
  • the most common combination of bacteria in both consortia and non-consortia polymicrobial infections was A schaalii and A urinae occurring in 55.9% of consortia but on only 33.0% of non-consortia polymicrobial infections.
  • the balance of bacteria in consortia is shifted not only toward Gram positive bacteria, but toward specific Gram-positive bacteria.
  • Gram negative bacteria constitute a minority of bacteria in consortia, they appear to be important factors in symptom presentation: patients with consortia containing Gram negative bacteria presented to clinic with more symptoms than patients with consortia that did not contain Gram negative bacteria. Ten of the 18 types of defined consortia were associated with a larger number of clinical findings, and 8 of those 10 consortia contained Gram negative bacteria. Six of those 8 contained E coli and 2 contained K pneumoniae. Of the 8 consortia associated with fewer clinical findings, only one contained a Gram-negative bacterium, which was E coli.
  • Microbes form consortia and biofilms because doing so confers growth and survival advantages.
  • a possible contributing factor to these four bacteria forming consortia is that they are not closely related. Previous results have shown that relation at the genus level inhibits the formation of consortia.
  • Genetic diversity within consortia and biofilms is known to increase the overall fitness of the community, producing a more resilient community.
  • Community structures, referred to as urotypes have been identified in the normal human urobiome. Each normal urotype is dominated by a single genus, with the most common being Lactobacillus, Streptococcus, Gardnerella, and E coli.
  • Urotypes dominated by either A schaalii or A urinae have not been reported.
  • a schaalii or A urinae in 14 of the 18 defined consortia may suggest that they are keystone bacteria in formation of pathogenic consortia. Keystone bacteria often have the capacity to initiate degradation of critical energy substrates in the environment, releasing energy to other members of the consortia. Some have suggested that strategic removal of a keystone bacterium from consortia would cause the consortia to collapse, but evidence indicates that is not likely to be the case. Functional redundancy within consortia may prevent them from collapsing on removal of individual species; complex metabolic networks in bacterial consortia may provide this type of redundancy. Further, functional redundancy may involve other types of networks, including metagenomic, antibiotic resistance and substrate modification.
  • the findings provide evidence that formation of consortia constitutes dysbiosis in the urobiome.
  • the type of dysbiosis observed is a gain of function, consisting of overgrowth of specific Gram-positive bacteria, A schaalii and A urinae.
  • Gram positive bacteria in general, are fastidious and not amenable to growth in standard culture.
  • Accumulating evidence that SUC is inadequate because of poor sensitivity has led to an increasing consensus regarding the clinical value of molecular methods for diagnosis of UTI.
  • Recent evidence indicates that A schaalii and A urinae are not detected by culture, but are detected by PCR.
  • the emergence of molecular methods such as PCR as clinical diagnostic tools has been critical for advancing the understanding of polymicrobial UTIs, which tend to be more antibiotic-resistant, and more commonly to lead to urosepsis and increased mortality.
  • UTIs urinary tract infections
  • SUC standard urine culture
  • antimicrobial susceptibility testing has served to guide treatment since the early 1950s.
  • the methodology relies on an “Escherichia coli (E. coli)- centric” view that perceives UTIs as caused by one or two pathogens.
  • E. coli E. coli- centric
  • antibiotic resistance has been well-studied in monomicrobial infections, but is less characterized in polymicrobial infections. Yet, interactions between bacteria can alter responses to antibiotics.
  • AST antibiotic susceptibility testing
  • the present invention discusses the method called Pooled Antibiotic Susceptibility Testing (P-AST), which involves simultaneously growing all detected bacteria together in the presence of antibiotics and then measuring susceptibility.
  • P-AST considers interactions between cohabiting bacterial species.
  • Urine specimens were obtained from patients presenting with UTI-like symptoms to 37 urology clinics.
  • the odds of resistance to 18 antibiotics relative to increasing numbers of bacterial species in a specimen were estimated.
  • antimicrobial susceptibility patterns in polymicrobial specimens differed from those observed in monomicrobial specimens. Since standard of care relies on assessment of antibiotic susceptibility in monomicrobial infections, these findings show that P-AST could serve as a more accurate predictor of antibiotic susceptibility.
  • This study combines data from two studies of antibiotic resistance patterns in elderly patients presenting with symptoms consistent with a UTI.
  • Retrospective data and patient information (Western IRB number 20171870) were obtained from a single site (Comprehensive Urology, Royal Oak, Ml) for 613 patients who presented between March and July 2018.
  • Prospective data and patient information (Western IRB number 20181661) were obtained for 2,511 patients who presented at any of 37 geographically disparate clinics in the United States between July 2018 and February 2019. All subjects met the following inclusion and exclusion criteria.
  • Inclusion criteria included: symptoms of acute cystitis, complicated UTI, persistent UTI, recurrent UTI, prostatitis, pyelonephritis, interstitial cystitis (at any age), symptoms of other conditions at >60 years of age, specimen volumes sufficient to permit urine culture and Multiplex Polymerase Chain Reaction (M-PCR) combined with Pooled Antibiotic Sensitivity Testing (P-AST), patient informed consent, documented times at which the specimens were collected and stabilized with boric acid in grey-top tubes.
  • Exclusion criteria included prior participation in this study, antibiotics taken for any reason other than UTI at the time of enrollment, chronic (>10 days) indwelling catheters, self-catheterization, and urinary diversion. Antibiotic susceptibility data were available for 1,352 of the 3,124 patients (43.3%).
  • DNA extraction was performed using the KingFisher/MagMAXTM Automated DNA Extraction instrument and the MagMAXTM DNA Multi-Sample Ultra Kit (ThermoFisher, Carlsbad, CA). 400 mI_ of urine were transferred to 96-well deep-well plates, sealed, and centrifuged to concentrate the samples, and then the supernatant was removed. Enzyme Lysis Mix (220 pL/well) was added to the samples, which were then incubated for 20 min at 65°C. Proteinase K Mix (PK Mix) was added (50 pL/well) and incubated for 30 min at 65°C.
  • PK Mix Proteinase K Mix
  • Lysis buffer 125 pL/well
  • DNA Binding Bead Mix 40 pL/well
  • the samples were vortexed for a minimum of 5 min.
  • Each 96-well plate was loaded into the KingFisher/MagMAX Automated DNA Extraction instrument, which was operated in accordance with standard operating procedures.
  • DNA analysis was conducted using the Guidance ® UTI Test (Pathnostics, Irvine, CA), which consists of both M-PCR and P-AST. Samples were mixed with universal PCR master mix and amplified using TaqMan technology on the Life Technologies 12K Flex OpenArray SystemTM (Life Technologies, Carlsbad, CA). DNA samples were spotted in duplicate on 112-format OpenArray chips. Plasmids unique to each bacterial species being tested were used as positive controls. Candida tropicaiis was used as an inhibition control. A data analysis tool developed by Pathnostics was used to sort data, assess the quality of data, summarize control sample data, identify positive assays, calculate concentrations, and generate draft reports.
  • Probes and primers were used to detect the following pathogenic bacteria: Acinetobacter baumannii, Actinotignum schaalii, Aerococcus urinae, Alloscardovia omnicolens, Citrobacter freundii, Citrobacter koseri, Corynebacterium riegelii, Enterobacter aerogenes, Enterococcus faecalis, Escherichia coli, Klebsiella oxytoca, Klebsiella pneumoniae, Morganella morganii, Mycobacterium tuberculosis, Mycoplasma genitalium, Mycoplasma hominis, Pantoea agglomerans, Proteus mirabilis, Providencia stuartii, Pseudomonas aeruginosa, Serratia marcescens, Staphylococcus aureus, Streptococcus agalactiae, and Ureaplasma urealyticum.
  • CoNS Coagulase negative staphylococci
  • VNS Viridans group streptococci
  • P-AST Pooled Antibiotic Susceptibility Testing
  • Logistic regression was used to compare resistance rates in monomicrobial and polymicrobial infections. Specifically, 18 different logistic regression models were fit to the data: the response variable was an indicator of whether the specimen was resistant to the specific antibiotic or not and the predictor variable was an indicator of whether the infection was monomicrobial or polymicrobial. Specimens were classified as monomicrobial if a single bacterial species was detected above the 10000 cells/mL threshold; they were classified as polymicrobial if two or more distinct bacteria species were detected above that threshold. Similar logistic regression models also were run, using the number of distinct bacterial species as the predictor variable.
  • This model was used to predict resistance rates when a specific bacterial species was present or when a specific pair of species was present.
  • HSAP Highest Single Agent Principle
  • the UP assumes a pair of bacteria (species A and B) is made up of one genetic variant of species A and one genetic variant of species B, and that the pool is resistant if either species A is resistant or if species B is resistant. If species A is resistant with probability P(A , and species B is resistant with probability P(E), then the probability of resistance of the pool is:
  • P(pool resistance ) P A + P(B ) — P(A P(B )
  • Odds ratios of antibiotic resistance in polymicrobial versus monomicrobial specimens are shown in FIG. 11, along with the odds ratio of resistance for each increase in the number of bacterial species in polymicrobial specimens.
  • the resistance rates of polymicrobial samples were generally higher than the rates of monomicrobial samples; 10 of 18 antibiotics had statistically higher resistance rates for polymicrobial samples.
  • FIG. 12 shows the effect of specific species interactions on the probability of increased or decreased resistance to each antibiotic tested. No interactions were detected for nitrofurantoin and piperacillin/tazobactam. Whereas the odds of resistance to ampicillin, amoxicillin/clavulanate, 6 different cephalosporins, vancomycin, and tetracycline increased with increasing number of detected species, there were 19 instances for which 11 of the 13 bacterial pairs resulted in reduced susceptibility to the same antibiotics.
  • the UP model identified 49 statistically significant interactions, all of which showed decreased probability of resistance to the antibiotics tested.
  • FIG. 13 shows the predicted probabilities of resistance to ampicillin/sulbactam, cefaclor, and tetracycline by monomicrobial positive cultures for E. coli and K. pneumoniae and a polymicrobial culture positive for both E. coli and K. pneumoniae.
  • the pairing of E. coli and K. pneumoniae resulted in either a significant increase or significant decrease in the probability of resistance depending on the antibiotic tested.
  • the resistance rate was higher than either E. coli or K. pneumoniae alone.
  • the resistance rate to tetracycline of same combination of species, E. coli and K. pneumoniae was intermediate between the resistance rates to each species alone.
  • pneumoniae resulted in increased resistance to amoxicillin/clavulanate and ampicillin/sulbactam, but decreased resistance to levofloxacin, meropenem, and tetracycline.
  • E. faecalis combined with S. agalactiae produced an increase in resistance to tetracycline, but decreased resistance to ampicillin and vancomycin.
  • the combination of CoNS and E. coli produced an increased probability in resistance to levofloxacin, but the same combination produced a decreased probability in resistance to amoxicillin/clavulanate, ceftriaxone, tetracycline, and trimethoprim/sulfamethoxazole.
  • the observed effects on antibiotic resistance in polymicrobial infections may be due to cooperative and/or competitive interactions between bacteria.
  • Resistant bacteria can cooperatively protect susceptible bacteria by degrading antibiotics, as occurs when secreted beta-lactamase degrades beta-lactam antibiotics.
  • Antibiotic resistance can be conferred by one bacterium on another bacterium by means of horizontal gene transfer (HGT) of antibiotic resistance genes.
  • HGT horizontal gene transfer
  • Bacterial interactions with host macrophages can promote HGT.
  • P. aeruginosa when present in biofilms, produces extracellular DNA that induces neutrophils to produce pro-inflammatory cytokines (IL-8 and IL-1 beta). The ensuing inflammation can promote HGT involving E. coli.
  • antibiotics can also promote HGT: antibiotics that cause bacterial lysis release DNA and proteins that can be taken up by other bacteria.
  • one bacterium can stimulate gene expression in another bacterium, resulting in upregulation of efflux pumps leading to increased antibiotic resistance.
  • Bacterial community spatial structuring within a polymicrobial biofilm may also affect the efficacy of antibiotics.
  • Type VI secretion systems secrete proteases that digest IgA, surface receptors that bind the constant region of IgG, and virulence factor/adhesin proteins that promote colonization.
  • Type VI secretion systems allow Gram-negative bacteria to secrete antibacterial toxins directly into other bacteria.
  • Type VI systems mediate DNA acquisition via HGT; an example is the capacity for A. baumannii to rapidly acquire resistance genes from E. coli by means of Type VI transfer systems.
  • Bacteria are social organisms that interact within and between species. Key interactions play critical roles in the growth, pathogenesis, and virulence of bacterial species. Because of these key and specific interactions, correct identification of bacterial species increases in significance.
  • P-AST a pooled antibiotic susceptibility test. Using this methodology, we observed both increased and decreased antibiotic susceptibility based on the type of species observed, as well as the class of antibiotic administered. Elucidation of the molecular mechanisms by which alterations in antibiotic response occur in polymicrobial infections will require additional research. Based on these findings, P-AST testing might more closely approximate the polymicrobial environment in the patient and possibly provide more clinically important information regarding antibiotic susceptibility.
  • Example 4 Utilization of M-PCR and P-AST for Diagnosis and Management of UTIs in Home-Based Primary Care
  • the present invention describes utilization of M-PCR in addition to P-AST to determine antibiotic susceptibility and resistance of the organisms identified by M-PCR.
  • Antibiotic susceptibility assays that test individual pathogens against antibiotics may miss interactions between bacteria in polymicrobial environments.
  • the P-AST tests all pathogens within a urine specimen simultaneously against antibiotics and can be informative about changes in antibiotic response due to bacterial interactions.
  • Inventors have recently shown that bacterial interactions in polymicrobial infections could alter antibiotic susceptibilities. These changes can significantly affect the type and dosage of antibiotics required to treat the patient.
  • the use of the M-PCR/P-AST test significantly reduces turnaround time, providing results in 24 hours, with SUC taking up to 5 days to result.
  • M-PCR/P-AST assay quickly detects 2 organisms or more while also providing susceptibility information in polymicrobial samples.
  • M-PCR/P-AST was superior to SUC using ED utilization and hospitalization rates as proxy measures of outpatient treatment effectiveness: more effective outpatient treatment should result in lower rates for ED utilization and hospitalization.
  • DNA extraction was performed using the KingFisher/MagMAXTM Automated DNA Extraction instrument and the MagMAXTM DNA Multi-Sample Ultra Kit (ThermoFisher, Carlsbad, CA). 400 pL of urine were transferred to 96-well deep-well plates, sealed, and centrifuged to concentrate the samples, and then the supernatant was removed. Enzyme Lysis Mix (220 L/well) was added to the samples, which were then incubated for 20 min at 65°C. Proteinase K Mix (PK Mix) was added (50 pL/well) and incubated for 30 min at 65°C.
  • PK Mix Proteinase K Mix
  • Lysis buffer 125 pL/well
  • DNA Binding Bead Mix 40 pL/well
  • the samples were vortexed for a minimum of 5 min.
  • Each 96-well plate was loaded into the KingFisher/MagMAX Automated DNA Extraction instrument, which was operated in accordance with standard operating procedures.
  • DNA analysis was conducted using the Guidance ® UTI Test (Pathnostics, Irvine, CA), which consists of both M-PCR and P-AST. Samples were mixed with universal PCR master mix and amplified using TaqMan technology on the Life Technologies 12K Flex OpenArray SystemTM (Life Technologies, Carlsbad, CA). DNA samples were spotted in duplicate on 112-format OpenArray chips. Plasmids unique to each bacterial species being tested were used as positive controls. Candida tropicalis was used as an inhibition control. A data analysis tool developed by Pathnostics was used to sort data, assess the quality of data, summarize control sample data, identify positive assays, calculate concentrations, and generate draft reports.
  • Probes and primers were used to detect the following pathogenic bacteria: Acinetobacter baumannii, Actinotignum schaalii, Aerococcus urinae, Alloscardovia omnicolens, Citrobacter freundii, Citrobacter koseri, Corynebacterium riegelii, Enterobacter aerogenes, Enterococcus faecalis, Escherichia coli, Klebsiella oxytoca, Klebsiella pneumoniae, Morganella morganii, Mycobacterium tuberculosis, Mycoplasma genitalium, Mycoplasma hominis, Pantoea agglomerans, Proteus mirabilis, Providencia stuartii, Pseudomonas aeruginosa, Serratia marcescens, Staphylococcus aureus, Streptococcus agalactiae, and Ureaplasma urealyticum.
  • Probes and primers also were used to detect the following bacterial groups: Coagulase negative staphylococci (CoNS) ( Staphylococcus epidermidis, Staphylococcus haemolyticus, Staphylococcus lugdunensis, Staphylococcus saprophyticus ); Viridans group streptococci (VGS) (Streptococcus anginosus, Streptococcus oralis, Streptococcus pasteuranus). Reporting included both the name of the organism identified and semi quantified counts of organisms. Counts were reported in cells/mL and correlated to Colony Forming Units (CFU).
  • CoNS Coagulase negative staphylococci
  • VVS Viridans group streptococci
  • Reporting included both the name of the organism identified and semi quantified counts of organisms. Counts were reported in cells/mL and correlated to Colony Forming Units (CFU).
  • P-AST was performed by aliquoting 1 mL of patient urine specimen into a 1.7 mL microcentrifuge tube. After centrifugation, the supernatant was aspirated and discarded, leaving approximately 500 pL of patient sample in the microcentrifuge tube.
  • One mL of Mueller Hinton Growth Media was then aliquoted into the patient sample in the microcentrifuge tube and the tubes were incubated at 35°C in a non-C0 2 incubator for 6 hours.
  • Mueller Hinton Agar was used as a negative control. Those samples that reached a minimum threshold of 10,000 cells/mL were then diluted by aliquoting 0.5 mL of sample into a 50 mL conical tube containing Mueller Hinton Growth Media.
  • 96-well plates pre-loaded with antibiotics were then inoculated with diluted samples and incubated along with control plates for 12-16 hours at 35°C in a single layer. Optical density of samples was then read on a DensiCHEK plate readerTM (BioMerieux, Marcy-l'Etoile, France).
  • Patient demographic, comorbidity, and facility-level data were collected for both groups of patients using the Health Catalyst (data warehouse). Demographic variables included age and gender. Additionally, the Charlson/Deyo (CD) Index Score was collected along with the number of per-patient physician visits.
  • CD Charlson/Deyo
  • ICD-9 diagnosis codes include 5990 (Urinary tract infection), 788.1 (Dysuria), 590.10 (Acute pyelonephritis), 590.80 (Pyelonephritis), 5999 (Urinary tract disease), and 59000 (Chronic pyelonephritis).
  • ICD-10 diagnosis codes include N39.0 (Urinary tract infection, site not specified), R30.0 (Dysuria), R35.0 (Frequency of micturition), R32 (Unspecified urinary incontinence), Z87.440 (Personal history of urinary (tract) infections). Cases were excluded if records indicated that the National Provider Identifier (NPI) did not match that of a listed investigator participating in the study, if a patient resided in hospice, or if the diagnosis code was missing.
  • NPI National Provider Identifier
  • SD standardized difference
  • the average number of ED visits and/or hospitalizations for UTI for each cohort was calculated.
  • a generalized linear model was used to compare the number of ED visits and/or hospitalizations between the two cohorts, using the negative binomial distribution with log link to account for over-dispersion of the count data.
  • the dependent variable was the count data for the number of ED visits and/or hospitalizations per patient per cohort, and the independent variable was the cohort. Multiple UTI events per patient could confound the number of ED visits and/or hospitalizations.
  • Also conducted was a patient-level analysis by summarizing the frequencies and the proportion of patients with any ED or hospitalization event for UTI.
  • a logistic regression model with logit link was used for this binary outcome to assess the difference between the two groups.
  • the dependent variable was the dichotomized variable of any hospitalization and/or ER visit per patient per cohort; the independent variable was the cohort.
  • samples may be collected from subjects according to standard collection protocols in sterile containers and are transported to the testing facility.
  • ABR antibiotic resistance testing plates
  • An example of the preparation of the antibiotic resistance (ABR) testing plates involves two steps. First is preparation of antibiotic solutions and the second is preparation of the bacterial growth medium plate.
  • the antibiotics to be tested for any given sample include antibiotics known to be useful for treating the tissue having the suspected infection, or any antibiotics requested by a medical or laboratory professional having knowledge of the particular patient sample. It is anticipated that most assays will be performed with a standard panel of antibiotics based on the type and location of infection suspected by a medical professional.
  • the standard panel of antibiotics comprises one or a combination of nitrofurantoin, ciprofloxacin, meropenem, ceftriaxone, trimethoprim/sulfamethoxazole, piperacillin/tazobactam, levofloxacin, cefoxitin, tetracycline, ampicillin/sulbactam, ampicillin, tetracyline, celfaclor, cefazolin, amoxicillin/clavulant, ceftazidime, fosfomycin, cefuroxime, cephalalexine, vancomycin, any other antibiotic disclosed herein including the figures, the like, or a combination thereof.
  • patients with known antibiotic allergies or sensitivities, or with a history of antibiotic resistance may require customized panels of antibiotics.
  • the assay can be performed simultaneous with an unlimited number of antibiotics.
  • Antibiotic stock solutions are prepared using solvents suitable for each antibiotic and then 10x solutions are prepared and stored in multi-well plates to allow efficient transfer to testing plates. Each antibiotic is tested at a minimum of concentrations. In some embodiments, three concentrations, four concentrations, five concentrations, six concentrations, seven concentrations, eight concentrations, nine concentrations, or ten concentrations of an antibiotic, or antibiotic combination, are included in the assay. Typically serial dilutions of the antibiotics are prepared wherein each dilution represents half the concentration of the higher concentration.
  • the 10x antibiotic solutions are stored in the multi-well plate according to a plate plan established for the antibiotic panel chosen for the assay. Exemplary plate plans are depicted in the Antibiotic Source Plates in FIG. 14 and FIG. 15. Antibiotic stocks and 10x solutions are stored at 2-8°C until needed.
  • the ABR testing plates may be multi-well plates (e.g., 6-well, 12-well, 24-well, 48-well, 96-well, 384-well plates, or any multi-well plate suitable for this purpose) capable of containing bacterial growth medium and culturing bacteria.
  • the plates are 96-well plates.
  • sterile agar-bacterial growth medium is dispensed into each well of the plate.
  • Exemplary agar-bacterial growth medium include, but are not limited to Mueller-Hinton agar, blood agar, trypticase soy agar, etc.
  • the agar After the agar has solidified at room temperature, 1/10 volume (of bacterial growth medium) of 10x antibiotic solution is added to each well of the test plate according to the predetermined plate plan. After the antibiotics have been introduced to the bacterial growth medium, the plates are allowed to rest for at least one hour. For long-term storage, the antibiotic-containing ABR plates are stored at 2-8°C. In some embodiments, sterile liquid broth bacterial growth medium mixed with sample is dispensed into each well of the plate containing 1/10 volume (of bacterial growth medium) of 10x antibiotic solution arrayed according to a predetermined plate plan. Multi-well plates containing 1/10 volume (of final well volume of bacterial growth medium and antibiotic solution) are stored at 2-8°C for later use or long-term storage.
  • Samples for the disclosed antibiotic resistance testing may be optionally diluted in sterile aqueous solution or mixed with bacterial growth medium.
  • a volume of sample for the disclosed antibiotic resistance testing are first mixed with a growth medium and incubated for 0-24 hours at an incubation temperature of 35 ⁇ 4°C.
  • the samples are then diluted with saline and then mixed with growth medium and added to room temperature ABR testing plates at 9/10 volume of each well in the multi-well plate.
  • samples are added to room temperature ABR plates at 1/20 volume of bacterial growth medium present in the well.
  • a single patient specimen is used for each ABR plate. If multiple patient specimens are to be tested, each specimen is assayed in its own plate.
  • the plates can be used to culture either anaerobic or aerobic bacteria.
  • anaerobic bacteria the plates are incubated at a temperature and in a reduced-oxygen environment to encourage growth of anaerobic bacteria.
  • aerobic bacteria the plates are incubated at a temperature and in an oxygen-containing environment to encourage growth of aerobic bacteria.
  • the incubation temperature can vary depending on the expected types of bacteria but will most likely be in a range of 35-40°C.
  • the plates containing samples are incubated for 12-48 hours, 12-24 hours, 24-28 hours, 12-36 hours, 14-30 hours, 16-24 hours, 16-20 hours, or 16-18 hours, or any range bounded by these numbers.
  • bacteria present in each well are recovered by resuspension in an aqueous liquid.
  • suitable liquids include, but are not limited to, water, saline, culture medium, etc.
  • the aqueous liquid should be sterile, or at least free from bacterial growth.
  • a volume of liquid equal to 100% of the volume of bacterial growth medium is carefully added to the wells of the ABR plate and allowed to sit for at least 30 minutes. In some embodiments, the plates are allowed to sit for 35 minutes, 40 minutes, 45 minutes, 50 minutes, or 60 minutes.
  • the resulting suspension is then carefully removed from each well into individual wells of a clean multi-well plate according to the predetermined plate plan.
  • the plates are optionally agitated to cause mixing of the bacteria with the liquid prior to removal of the suspension.
  • the multi-well plate will be applied to ODgoo measurement immediately after incubation.
  • the multi-well plate containing the bacteria-containing suspension is then read in a spectrophotometer.
  • the optical density of the recovered liquid is measured at OD 600 multiple times to correct for uneven distribution of bacteria particles in the suspension.
  • the plates are read one time, two times, three times, four times, five times, six times, seven times, or eight times. The multiple plate reads occur in sequence without allowing the suspension to settle in the wells.
  • each well contains a blend of antibiotics (AB-blend).
  • this no-growth well contains sodium azide (Na-Azide).
  • the blanked value is representative of the ability of bacteria to grow in the presence of the particular antibiotic in the well.
  • the blanked results are then converted into a “resistance” (R) or “sensitive” (S) score based on a threshold value.
  • R resistance
  • S sensitive
  • the threshold value is for an agar-containing medium.
  • a threshold value has been determined at 0.010 to 1.000, 0.010-0.090, 0.015 to 0.035, or 0.020 to 0.030 based on correlations to a standard reference method.
  • the threshold value as been determined at about 0.010, about 0.015, about 0.020, about 0.025, about 0.030, about 0.035, about 0.040, about 0.045, about 0.050, about 0.055, about 0.060, about 0.065, about 0.070, about 0.075, about 0.080, about 0.085, or about 0.090 based on correlations to a standard reference method.
  • a threshold value has been determined at 0.025 based on correlations to a standard reference method.
  • the threshold value is for a liquid medium. In some embodiments, a threshold value has been determined at 0.010-1.000, 0.020-0.090, 0.050-0.080, 0.055 to 0.075, or 0.060 to 0.070 based on correlation to a consensus score between two standard reference methods.
  • the threshold value as been determined at about 0.010, about 0.015, about 0.020, about 0.025, about 0.030, about 0.035, about 0.040, about 0.045, about 0.050, about 0.055, about 0.060, about 0.065, about 0.070, about 0.075, about 0.080, about 0.085, about 0.090, or about 0.095 based on correlation to a consensus score between two standard reference methods.
  • a threshold value has been determined at 0.065 based on correlation to a consensus score between two standard reference methods.
  • any adjusted OD 600 measurement greater than blank OD 600 measurement can be determined as indicative of bacterial growth and applied as a threshold value by correlation to a standard reference method or combination of reference methods.
  • Results of the antibiotic resistance assay disclosed herein are transmitted to the appropriate medical professional who then has the option of prescribing an antibiotic, or antibiotics, shown to be active against the patient’s infection, changing the antibiotic to a more effective antibiotic, or ordering additional testing.
  • Urine samples suitable for processing with this assay are collected, transported, and stored using BD Vacutainer (gray top) tubes or other suitable leak-proof sterile container. Urine samples may be held at room temperature for 48 hours before test results are compromised.
  • Antibiotics not received in ready-made solutions were dissolved in appropriate solvent and according to their individual solubility (e.g., at 10x the concentration desired in the assay as antibiotic stocks). Antibiotic stocks are stored at 2-8°C and protected from direct sunlight. Prepared antibiotic stock solutions were aliquoted into one or more 96-deep well plates (Thermo Fisher Scientific) to form an Antibiotic Source Plate, e.g., shown in FIG. 14 and identified by antibiotic name and concentration (pg/mL; 10x final concentration).
  • Antibiotics may include nitrofurantoin, ciprofloxacin, meropenem, ceftriaxone, trimethoprim, sulfamethoxazole, trimethoprim/sulfamethoxazole, piperacillin, tazobactam, piperacillin/tazobactam, levofloxacin, cefoxitin, tetracycline, ampicillin/sulbactam, ampicillin, sulbactam, amoxicillin, amoxicillin/clavulanic acid, cefaclor, cefazolin, cefepime, ceftazidime, fosfomycin, gentamicin, and/or vancomycin, either singly or in combination.
  • one or more wells are designated AB-blend which contained a combination of antibiotics to ensure there was no bacterial growth.
  • Mueller-Hinton agar medium e.g., 100 microliters
  • VIS 96/F-PS VIS 96/F-PS, Eppendorf
  • the medium was allowed to solidify at room temperature for at least 10 min.
  • the antibiotics (10 pL) at various concentrations were then aliquoted into desired wells from the Antibiotic Source Plate. After the antibiotics were introduced to the agar medium, the ABR microplates were allowed to sit for at least 1 hr before use. If long-term storage is required, ABR microplates containing antibiotic-infuse agar are stored at 2-8°C in the dark.
  • urine samples were diluted 1 :20 in sterile saline and vortexed. Each patient sample utilized a single ABR microplate. Five microliters of diluted patient sample were added to each well of the room temperature microplate, the plate was sealed and incubated for 16-18 hr at 37°C.
  • the plate was removed from the incubator and carefully uncovered. Two-hundred microliters of deionized water were added to each well to suspend cells present above the agar and the plates incubated at room temperature for 30 min. After 30 min, 100 pi from each well was removed to a new plate and the OD 600 was determined in a spectrophotometer. Five separate reads were taken of each plate and a mean OD 600 measurement calculated.
  • the present invention is not limited to the use of OD to determine antibiotic resistance.
  • Controls include: No-antibiotic control, Negative control plate, and AB-Blend.
  • No antibiotic control Any well containing medium that is not infused with antibiotics to ensure viability of bacterial cells present in patient urine samples and included in each plate. If the no-antibiotic control for any given patient does not yield growth, a secondary test is performed using the same patient sample without dilution.
  • Negative control plate Microplate containing antibiotic-infused agar medium without addition of patient sample or cultured bacterial organisms to ensure non-contamination of reagents.
  • AB-Blend One or more wells containing a combination of antibiotics to ensure there is no bacterial growth.
  • Table 7 shows the antibiotics, well positions, mean OD (raw data), blanked OD, and an assessment of resistance (R) or susceptibility (S) to the antibiotic.
  • Each well position corresponds to a particular antibiotic at a certain concentration.
  • the wells in Table 7 are arranged by sorting like antibiotics together.
  • the blanked column refers to the raw data being blanked by using the measurement obtained from the AB-Blend well, as depicted in Table 7.
  • Raw data collected is shown as “mean” OD.
  • blanked OD readings were compared to a threshold OD 600 of 0.025.
  • Resistant meaning bacterial organisms present in patient sample were resistant to that particular antibiotic at that certain concentration.
  • Sensitive meaning bacterial organisms present in patient sample were sensitive to that particular antibiotic at that certain concentration.
  • the sample contains bacteria sensitive to nitrofurantoin, ciprofloxacin, meropenem, ceftriaxone, piperacillin/tazobactam, and cefoxitin.
  • the results for levo are equivocal.
  • the MIC for each drug can then be provided.
  • the minimum inhibitory concentration (MIC) is the minimum test antibiotic concentration to which the sample is sensitive.
  • An exemplary MIC determination for meropenem based on the results above is depicted in Table 8.
  • Inter-assay precision was evaluated by testing three samples from the “Accuracy” sample set over three days. Intra-assay precision was evaluated by testing each of these samples in triplicate in one batch. Precision for each sample was assessed by determining the consensus result of all 5 replicates and then counting the number of replicates that match the consensus. This number was then divided by the sum of all measurements (sum of measurements for all drugs) to determine the % precision. The overall precision was calculated by dividing the sum of all correct matches by the total number of measurements from all samples. The assay demonstrated very good precision. The total matched of all precision samples was 643 out of 690 measured, a percentage of 93%.
  • Analytic sensitivity or the limit of detection (LOD) was assessed by determining the lowest bacterial concentration that yielded accurate results. In certain cases, bacterial concentrations lower than 10,000 cells/mL are not considered positive for UTI and therefore the lowest concentration tested was 10,000 cells/mL. Consistent results (>98%) correlation to the consensus results were obtained at the lowest bacterial concentrations tested. The LOD of this assay was 10,000 cells/mL. Note, the present invention is not limited to a concentration of 10,000 cells/mL.
  • the analytic specificity of this assay was assessed by testing samples at bacterial concentrations of 100,000,000 cells/mL. Such concentrations are not typically observed in routine UTI patient samples but were achieved in saturated overnight bacterial cultures. Assessment of analytic measurement range (AMR) was then performed by testing three samples from the “Accuracy” sample set each diluted as follows: 100,000,000 cells/mL, 1,000,000 cells/mL, 100,000 cells/mL and 10,000 cells/mL. Consistent results (>94%) correlation to the consensus results were obtained at all bacterial concentrations tested.
  • the assay is specific at bacterial concentration up to 100,000,000 cells/mL.
  • the present invention is not limited to a concentration of 10,000 cells/mL.
  • Urine samples suitable for processing with this assay are collected, transported, and stored using BD Vacutainer tubes or other suitable leak-proof sterile containers. Urine samples may be held at room temperature for 48 hours before test results are compromised.
  • Antibiotics not received in ready-made solutions were dissolved in appropriate solvents and according to their individual solubility to 50x the concentration desired in the assay and stored as antibiotic stocks. Antibiotic stocks are stored at 2-8°C and protected from direct sunlight. Prepared antibiotic stock solutions were aliquoted into a 96-deep well plate (ThermoFisher Scientific) to form a 50x Antibiotic Source Plate and then diluted 1 :5 to form a 10x Antibiotic Source Plate, as shown in FIG. 15 where each well is identified by antibiotic name and concentration (pg/mL; 10x final concentration).
  • Antibiotics included in this assay were amoxicillin, clavulanate, ampicillin, sulbactam, cefaclor, cefazolin, cefepime, cefoxitin, ceftazidime, ceftriaxone, ciprofloxacin, fosfomycin, gentamicin, levofloxacin, meropenem, nitrofurantoin, piperacillin, tazobactam, tetracycline, trimethoprim, sulfamethoxazole, and vancomycin, either singly or in combination.
  • One well was assigned sodium azide to ensure no bacterial growth would be observed in that well.
  • urine samples were centrifuged to concentrate any bacterial cells and then mixed with liquid Mueller-Hinton medium and incubated for 6-16 hours at 37°C. After this initial incubation, the sample is diluted to 0.5-0.6 McF in saline and then 500 pi of that suspension was added to 29.5 pi of Mueller-Hinton medium. One-hundred and eighty microliters of the diluted sample is then aliquoted to each well of the ABR microplate already containing 10x antibiotic solution, bringing all of the antibiotics to the desired final concentration. The plate is then sealed and incubated for 12-16 hours at 37°C.
  • Controls include (1) no-antibiotic control: Any well containing medium that is not infused with antibiotics to ensure viability of bacterial cells present in patient urine samples and included in each plate; If the no-antibiotic control for any given patient does not yield growth, the sample is repeated on the assay and reported as quantity not sufficient if repeat testing still does not yield satisfactory results; (2) negative control plate: Microplate containing antibiotic-infused agar medium without addition of patient sample or cultured bacterial organisms to ensure non-contamination of reagents; and (3) Na Azide: One or more wells containing a dilute concentration of sodium azide to ensure no bacterial growth will occur.
  • Table 9 shows the antibiotics, well positions, mean OD (raw data), blanked OD, and an assessment of resistance (R) or susceptibility (S) to the antibiotic.
  • Each well position corresponds to a particular antibiotic at a certain concentration.
  • the wells are arranged by sorting like antibiotics together.
  • the blanked column refers to the raw data being blanked by using the measurement obtained from the Na Azide well.
  • Raw data collected is shown as “mean” OD.
  • blanked OD readings were compared to a threshold OD 600 of 0.065.
  • Resistant meaning bacterial organisms present in patient sample were resistant to that particular antibiotic at that certain concentration.
  • Sensitive meaning bacterial organisms present in patient sample were sensitive to that particular antibiotic at that certain concentration.
  • the sample contains bacteria sensitive to amoxicillin/clavulanate, ampicillin, ampicillin/sulbactam, ciprofloxacin, gentamicin, levofloxacin, nitrofurantoin, piperacillin/tazobactam, and vancomycin.
  • the MIC for each drug can then be provided.
  • the minimum inhibitory concentration (MIC) is the minimum test antibiotic concentration to which the sample is sensitive.
  • An exemplary MIC determination for meropenem based on the results above is depicted in Table 10.
  • Inter-Assay precision was evaluated by testing five samples over three different days. Intra-Assay precision was evaluated by testing the same five samples in triplicate in a single day. Percent concordance was calculated to measure the precision of results obtained by this assay. The assay demonstrated very good precision. In the Intra-assay, the number of matches was 841 out of 855 measurements (98% concordance); for the Inter-assay, the number of matches was 1388 out of 1425 measured (97% concordance).
  • Analytic sensitivity was evaluated by creating a dilution series of E. coll and E. faecalis with the lowest bacterial concentration at less than 100 cells/mL for each organism. Each dilution level for each isolate was tested to show reproducibility of results down to the lowest concentration. 98% correlation was observed across all dilution levels for both isolates, indicating the limit of detection (LOD) of this assay is less than 100 cells/ml.
  • LOD limit of detection
  • Analytic specificity was evaluated in the context of inhibitory effect of overloading the assay with too many bacterial cells. Lower accuracy (due to false-resistant results) was observed for samples inoculated at high bacterial concentration. This indicates that all samples must be diluted to the specified cell density post pre-culture and before ABR inoculation.
  • This assay utilizes a pre-culture step prior to introducing samples to antibiotics.
  • the duration of this pre-culture incubation was tested at 6 and 16 hours for 2 isolates (E. coli and E. faecalis).
  • Good accuracy for each isolate was observed after both 6 and 16 hour pre-culture incubations, indicating a pre-culture window of 6 to 16 hours for this assay.
  • the number of matches was 81 out of 83 measurements (98% accuracy).
  • samples are introduced to antibiotics, they are incubated for 12 to 16 hours. This incubation length was determined by obtaining OD measurements for Precision samples after 12 and 16 hours of incubation. Good percent concordance was observed for all samples across within a 12 to 16 hour incubation window. The number of matches was 2758 out of 2850 measurements (97% accuracy).
  • Example 8 Concordance Between the Presence of Antibiotic Resistance Genes by Multiplex PCR and Susceptibility Testing in Symptomatic Patients with Urinary Tract Infection
  • antibiotic resistance There are two mechanisms of antibiotic resistance: innate resistance and acquired resistance. Innate antibiotic resistance is usually chromosome-encoded, such as the non-specific efflux pumps, antibiotic inactivating enzymes, or permeability barriers. Horizontally transferred resistant genes provide acquired resistance and include plasmid encoded ABR genes for specific efflux pumps and enzymes that can modify targeted antibiotics. [00238] While an increasing number of ABR genes are known, detection of an ABR gene does not guarantee the activity of that gene. Since the regulation of many of these ABR genes has not been fully understood, further research is needed to understand how frequently the presence of an ABR gene is correlated with its activity. The present invention analyzes the concordant and discordant rates between the presence or absence of ABR genes and antibiotic susceptibility testing in urine samples collected from UTI-symptomatic patients.
  • This concordance study was based on a subset of a prospective UTI study cohort of 2,512 consecutive patients enrolled between July 26, 2018, and February 27, 2019. Briefly, patients presenting with UTI symptoms were evaluated by 75 physicians from 37 urology clinics across the United States. The study included patients 60 years or older presenting at the urology office with a suspicion of acute cystitis, complicated UTI, persistent UTI, recurrent UTIs, prostatitis, or pyelonephritis. Additionally, the study also included patients at any age, presenting with a history of interstitial cystitis.
  • M-PCR bacterial detection by M-PCR (methods below).
  • M-PCR detected bacteria in a total of 1,579 patients.
  • Three hundred and seventy-two of the patient samples contained exclusively fastidious bacteria that were deemed unculturable by lab standards; as a result, susceptibility testing could not be performed.
  • the presence of bacteria was determined using the Pathnostics Guidance ® UTI Test, as described previously. Briefly, the DNA extracted from patient samples were mixed with a universal PCR master mix and amplified with TaqMan technology on a Life Technologies 12K Flex Open Array System. DNA samples were spotted in duplicate on 112-format OpenArray chips. Positive controls were included in the form of plasmids containing bacterial target DNA. Candida tropicalis was used as an inhibition control. A data analysis tool developed by Pathnostics was used to sort data, assess the quality of data, summarize control sample data, identify positive assays, calculate concentrations, and generate results.
  • the quantities of each of the bacterial species were determined using the standard curve method, as described previously. Briefly, standard curves of each of the bacteria were generated from testing replicates of dilution series of DNA/culture at known concentrations; constants necessary for the quantitation of each of the bacterial species in unknown samples were established from the standard curves. PCR cycle values of a target bacterium from a patient sample were compared to the standard curve, and the concentration of the target bacterial species (cells/mL) present in the samples was extrapolated and determined. A bacterium with a quantity of >10,000 cells/mL was defined as “positive” or “detected.” and bacteria with quantity ⁇ 10,000 cells/mL were defined as “negative”. The present invention is not limited to these thresholds.
  • a total of 33 ABR genes linking to the resistances of 6 classes of antibiotics were tested in using the Pathnostics Guidance ® UTI Test including carbapenem resistance genes (VIM, KPC, IMP-1 group, IMP-7, OXA-72, OXA-23, OXA-40, OXA-48, OXA-58), ampicillin resistance genes (DHA, MOX/CMY, BIL/LAT/CMY, AmpC, FOX, ACC), fluoroquinolone resistance genes (QnrA, QnrB), vancomycin resistance genes (vanA1 , vanA2, vanB), extended-spectrum beta-lactamases (ESBL) resistance genes (CTX-M group 1 , CTX-M group 2, CTX-M group 8/25, CTX-M group 9, PER-1, PER-2, NDM-1, OXA-1, GES, SHV, TEM, and VEB), and one methicillin resistance gene (mecA).
  • carbapenem resistance genes VIM, K
  • Each spot of a 112-format OpenArray chip was coated with a probe for a single gene with the exception of three groups which were combined to coat a single spot (spot 1: DHA/MOX/CMY; spot 2: BIL/LAT/CMY; and spot there: AmpC/FOX/ACC).
  • spot 1 DHA/MOX/CMY
  • spot 2 BIL/LAT/CMY
  • spot there: AmpC/FOX/ACC spot there: AmpC/FOX/ACC.
  • Two additional ABR genes, ErmA and ErmB, associated with resistance to macrolide class of antibiotics were also included on the OpenArray chip as part of the ABR gene testing. However, results from these two genes were not included in the concordance analysis as no macrolide antibiotics were part of the P-AST testing.
  • a bacterium was determined to be positive for an ABR gene if the cycle number (Ct) of that ABR gene was above a particular threshold. That threshold was determined by comparing a series of negative samples, extraction control samples, and specificity samples (genomic DNA of non-target organism/gene). The lowest Ct from these assays was determined. Second, a plasmid dilution series was tested (ThermoFisher provided plasmids for each target ABR gene). The lowest plasmid concentration in which 50% or more of the replicates were detected with a Ct value below the cycle number determined in the first step was established as the lower limit of detection (LLOD) for an assay.
  • LLOD lower limit of detection
  • the threshold Ct for any particular target ABR gene assay was then set at the cycle equivalent of the determined LLOD for that gene.
  • ABR genes with Ct value no higher than the Ct threshold of the gene was defined as “positive” or “detected”, and ABR genes with Ct value higher than the threshold was defined as “negative” or “not detected.”
  • mecA gene “positive” or “detected” status for mecA was only limited to patients with S. aureus detection.
  • a total of 18 antibiotics representing six antibiotic classes were evaluated in the P-AST assay.
  • antibiotics were not associated with any of the ABR genes on the Pathnostics Guidance ® UTI Test detection panel. Therefore, 14 of the 18 antibiotics were evaluated in the concordance analysis.
  • the antibiotics were purchased from Sigma Aldrich: amoxicillin/clavulanate (Cat. # A8523-1G/33454-100MG), ampicillin (Cat. # A5354-10ML), ampicillin/sulbactam (Cat.
  • P-AST was performed as described previously. Briefly, 1 ml_ of patient urine sample was aliquot into a 1.7 mL microcentrifuge tube. After centrifugation, the supernatant was aspirated and discarded, leaving approximately 500 pl_ of the patient sample in the microcentrifuge tube. One mL of Mueller Hinton Growth Media was then aliquot into the sample in the microcentrifuge tube and the tubes were incubated at 35°C in a non-C0 2 incubator for 6 hours. Samples that reached a minimum threshold of 10,000 cells/mL were diluted by aliquoting 0.5 mL of sample into a 50 mL conical tube containing Mueller Hinton Growth Media.
  • 96-well plates pre-loaded with dilution series of different antibiotics were inoculated with samples and incubated along with control plates for 12-16 hours at 35°C aerobically/anaerobically in a single layer.
  • a DensiCHEK plate readerTM BioMerieux, Marcy-l’Etoile, France was used to measure optical densities every X hours/min. Growth curves of cultures grown with antibiotcs were compared to standard growth to determine the resistance to a given antibiotic.
  • M-PCR detected a polymicrobial infection, defined as two or more bacteria, in 23.3% (269) of patients, and 886 (76.7%) patients contained one microorganism. Overall, the most detected bacteria in patients’ urine samples were E. coli (564/1,155, 48.8%), CoNS group (282/1,155, 24.4%), and E. faecalis (242/1,155, 21.0%). Data not shown: the detection frequency of each of the bacteria detected.
  • the resistance rates for each antibiotic ranged from 6.2% (for piperacillin/tazobactam) to 50.9% (for ceftriaxone) (see Table 12).
  • the detection rate for the ABR genes range from 0.3% (for meropenem, levofloxacin, and ciprofloxacin-associated ABR genes) to 36.8% (for amoxicillin/clavulanate-associated ABR genes)(see Table 12).
  • M-PCR detected 24 ABR genes 470 times in 36.2% (419/1155) of patient samples, the majority of whom [379/419 (90.5%)] had only one ABR gene detected. M-PCR detected two ABR genes in 29/419 (6.92%) of samples, and 3 ABR genes in 11/419 (2.63%) of samples. The most frequently identified ABR genes were TEM, SHV, and CTX-M group 1 gene, in 205 (17.7%), 100 (8.7%), and 47 (4.1%) patients, respectively.
  • ABR genes detected in the samples are associated with resistance to the following classes of antibiotics: aminopenicillins, beta-lactamase inhibitor/antibiotic combinations, glycopeptides, fluoquinolones, carbapenems, and cephalosporins. Data not shown: the prevalence of each of the ABR genes in the 372 patients.
  • antibiotic categories showed higher concordance than the overall concordance rate. For example, aminopenicillins, beta-lactamase inhibitor/antibiotic combinations, fluoroquinolones, and carbapenems had concordance rates >67.2%. By contrast, cephalosporins only exhibited a concordance rate of 48.5% (see FIG. 16).
  • a total of 14 antibiotics were involved with the concordance analysis. The overall concordance rates for each of the antibiotics range from 44.7% (ceftriaxone) to 78.4% (ampicillin). Most antibiotics were associated with similar concordance rates for monomicrobial and polymicrobial infections.
  • Antibiotic resistance is an increasingly important issue. Reliable and rapid microbial identification with resistance information is essential for the management of UTIs and antibiotic stewardship. M-PCR-based tests have been developed for clinical use for UTI in the detections of ABR genes. However, the detection of an ABR gene may not translate into phenotypic resistance, nor can their absence indicate susceptibility.
  • the study used an M-PCR-based test to detect the presence of different bacteria and ABR genes, evaluated the antimicrobial susceptibility with a P-AST test, and measured the concordance between the two test results, e.g., ABR gene status (present or absent) and the antimicrobial susceptibility results from the P-AST test.
  • the 40% discordance included a 25% lack of ABR genes and resistant P-AST results and 15% of the presence of ABR genes and sensitive P-AST results.
  • ABR genes did not confer phenotypic resistance. The discordance could be due to several reasons.
  • the PCR assay detects the presence of the ABR gene at the DNA level.
  • the bacterium must first transcribe the gene into messenger ribonucleic acid (mRNA).
  • mRNA messenger ribonucleic acid
  • the ribosomes translate the mRNA into a protein; then in some cases, the protein must be activated when signaled to do so. If mutations, for example, are in the gene promoter region, it may not produce a protein, thus, not yielding antibiotic resistance.
  • mutational changes in the coding region of an ABR gene may also fail to produce a downstream protein product of the ABR gene, preventing the bacteria from generating the antibiotic-resistant phenotype. Therefore, this study demonstrates that the presence or absence of the ABR gene alone is not entirely reliable in predicting bacterial antibiotic response and supports the clinical utility of antimicrobial susceptibility in aiding clinical treatment decision-making.
  • the concordance rate differed by antibiotic classes. For example, the concordance rates were as high as 78% for single-agent penicillin’s, and as low 48.4% for cephalosporins.
  • the concordance rates were as high as 78% for single-agent penicillin’s, and as low 48.4% for cephalosporins.
  • At the individual antibiotic level there was a significant mismatch between ABR genes and P-AST results for five antibiotics, four of which included: meropenem, ciprofloxacin, levofloxacin, and vancomycin.
  • the number of ABR genes targeted for detection for ciprofloxacin, levofloxacin, meropenem, and vancomycin were less than those targeted at the individual level for cephalosporins, beta-lactamase inhibitory combinations, and penicillin.
  • other unknown ABR genes associated with resistance to ciprofloxacin, levofloxacin, meropenem, and vancomycin are not included in
  • Piperacillin/tazobactam was the fifth antibiotic to display a high rate of discordance.
  • ABR genes identified than the rate of resistance from P-AST results. Resistance was detected in only 6.2% of cases, while 35.7% of cases showed ABR resistance genes.
  • Cabolt et al. showed that resistance to piperacillin/tazobactam requires the overexpression of AmpC along with the assistance from two additional ABR genes, mexB and mexY, both of which were not targeted for in the study.
  • the increased discordance may be due to a need to overexpress AmpC, as discussed earlier, and perhaps overexpression of AmpC is reduced with the introduction of additional species to the mix. Regardless, these three antibiotics are relatively strong and often reserved for highly resistant bacterial infections.
  • P-AST could not generate susceptibility results on 372 samples that PCR identified as containing exclusively fastidious bacteria because they do not grow in culture. Therefore, the study could not create a concordance rate analysis for these patients. However, the presence or absence of ABR genes in these samples was analyzed by PCR, which revealed 15 ABR genes in these samples. Due to the same reason of fastidious growth, traditional urine culture, and isolates-based antimicrobial susceptibility tests would also not be clinically feasible. Therefore, ABR gene results provide clinically valuable information for patients with an exclusive fastidious bacterial infection.
  • descriptions of the inventions described herein using the phrase “comprising” includes embodiments that could be described as “consisting essentially of’ or “consisting of’, and as such the written description requirement for claiming one or more embodiments of the present invention using the phrase “consisting essentially of’ or “consisting of’ is met.

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