Methods of Diagnosing Infectious Disease Pathogens and Their Drug Sensitivity

Abstract

The specification relates generally to methods of detecting, diagnosing, and/or identifying pathogens, e.g., infectious disease pathogens and determining their drug sensitivity and appropriate methods of treatment. This invention also relates generally to methods of monitoring pathogen infection in individual subjects as well as larger populations of subjects.

Description

CROSS REFERENCE TO RELATED APPLICATIONS

[0001] This application claims the benefit of U.S. Provisional Patent Application Nos. 61/307,669, filed on Feb. 24, 2010, and 61/323,252, filed on Apr. 12, 2010, the entire contents of which are hereby incorporated by reference in their entireties.

TECHNICAL FIELD

[0003] The invention relates, inter alia, to methods of detecting, diagnosing, and/or identifying pathogens, e.g., infectious disease pathogens, and determining their sensitivity to known or potential treatments.

BACKGROUND

[0004] The development of molecular diagnostics has revolutionized care in most medical disciplines except infectious disease, where they have failed to play a widespread, transforming role. The reliance on slow culture methods is particularly frustrating in the current crisis of antibiotic resistance as the development of molecular tools to rapidly diagnose the inciting pathogen and its drug resistance profile would transform the management of bacterial, fungal, viral, and parasitic infections, guiding rapid, informed drug treatment in an effort to decrease mortality, control health care costs, and improve public health control of escalating resistance among pathogens. In U.S. hospitals alone, 1.7 million people acquire nosocomial bacterial infection and 99,000 die every year, with 70% of these infections due to bacteria resistant to at least one drug and an estimated annual cost of $45 billion (Klevens et al., 2002. Public Health Rep. 2007;122(2):160-6; Klevens et al., Clin Infect Dis. 2008;47(7):927-30; Scott, The Direct Medical Costs of Healthcare-Associated Infection in U.S. Hospitals and the Benefits of Prevention. In: Division of Healthcare Quality Promotion NCfP, Detection and Control of Infectious Diseases, editor. Atlanta: CDC, 2009). However, the problem is not limited to the U.S. and microbial resistance now impacts the majority of common bacterial infections globally. Global spread of methicillin-resistant S. aureus (MRSA), multi-drug resistant tuberculosis (MDR-TB), and increasingly drug resistant Gram-negative organisms prompted the formulation of an action plan focusing on surveillance, prevention and control, research and product development (US action plan to combat antimicrobial resistance. Infect Control Hosp Epidemiol. 2001;22(3):183-4). However, minimal progress has been made on any of these fronts.

[0005] Prompt administration of the appropriate antibiotic has repeatedly been shown to minimize mortality in patients with severe bacterial infections, whether within the hospital setting with nosocomial pathogens such as E. faecium, S. aureus, K. pneumoniae, A. baumanii, P. aeruginosa, and Enterobacter species, or in resource-poor settings with pathogens such as tuberculosis (TB) (Harbarth et al., Am J Med. 2003;115(7):529-35; Harries et al., Lancet. 2001;357(9267):1519-23; Lawn et al., Int J Tuberc Lung Dis. 1997;1(5):485-6). However, because current diagnostic methods involving culture and sub-culture of organisms can take several days or more to correctly identify both the organism and its drug susceptibility pattern, physicians have resorted to increasing use of empiric broad-spectrum antibiotics, adding to the selective pressure for resistance and increasing the associated health-care costs. A point of care diagnostic to rapidly (e.g., less than 1 hour) detect pathogens and their resistance profiles is urgently needed and could dramatically change the practice of medicine. Some effort into designing DNA- or PCR-based tests has resulted in tools that are able to identify pathogens rapidly with low detection limits. However, global use of these tools is currently limited due to cost and demand for laboratory infrastructure and to the inherent insensitivity of PCR-based methods in the setting of crude samples that are not easily amenable to the required enzymology. Molecular approaches to determining drug resistance have been even more limited, available for some organisms (e.g., MRSA, TB) in very limited ways, based on defining the genotype of the infecting bacteria relative to known resistance conferring mutations. This method however, requires fairly comprehensive identification of all resistance conferring single nucleotide polymorphisms (SNPs) for the test to have high sensitivity (Carroll et al., Mol Diagn Ther. 2008;12(1):15-24).

SUMMARY

[0006] The present invention is based, at least in part, on the discovery of new methods of diagnosing disease, identifying pathogens, and optimizing treatment based on detection of mRNA, e.g., in crude, non-purified samples. The methods described herein provide rapid and accurate identification of pathogens in samples, e.g., clinical samples, and allow for the selection of optimal treatments based on drug sensitivity determinations.

[0007] In one aspect, the invention features methods of determining the drug sensitivity of a pathogen, e.g., a disease-causing organism such as a bacterium, fungus, virus, or parasite. The methods include providing a sample comprising a pathogen and contacting the sample with one or more test compounds, e.g., for less than four hours, to provide a test sample. The test sample can be treated under conditions that release mRNA from the pathogen into the test sample and the test sample is exposed to a plurality of nucleic acid probes, comprising a plurality of subsets of probes, wherein each subset comprises one or more probes that bind specifically to a target mRNA that is differentially expressed in organisms that are sensitive to a test compound as compared to organisms that are resistant, wherein the exposure occurs for a time and under conditions in which binding between the probe and target mRNA can occur. The method comprises determining a level of binding between the probe and target mRNA, thereby determining a level of the target mRNA; and comparing the level of the target mRNA in the presence of the test compound to a reference level, e.g., the level of the target mRNA in the absence of the test compound, wherein a difference in the level of target mRNA relative to the reference level of target mRNA indicates whether the pathogen is sensitive or resistant to the test compound.

[0008] In one embodiment, the pathogen is known, e.g., an identified pathogen. In some embodiments, the methods determine the drug sensitivity of an unknown pathogen, e.g., a yet to be identified pathogen.

[0009] In some embodiments, the sample comprising the pathogen is contacted with two or more test compounds, e.g., simultaneously or in the same sample, e.g., contacted with known or potential treatment compounds, e.g., antibiotics, antifungals, antivirals, and antiparasitics. A number of these compounds are known in the art, e.g., isoniazid, rifampicin, pyrazinamide, ethambutol streptomycin, amikacin, kanamycin, capreomycin, viomycin, enviomycin, ciprofloxacin, levofloxacin, moxifloxacin, ethionamide, prothionamide, cycloserine, p-aminosalicylic acid, rifabutin, clarithromycin, linezolid, thioacetazone, thioridazine, arginine, vitamin D, R207910, ofloxacin, novobiocin, tetracycline, merepenem, gentamicin, neomycin, netilmicin, streptomycin, tobramycin, paromomycin, geldanamycin, herbimycin, loracarbef, ertapenem, doripenem, imipenem/cilastatin, meropenem, cefadroxil, cefazolin, cefalotin, cefalexin, cefaclor, cefamandole, cefoxitin, cefprozil, cefuroxime, cefixime, cefdinir, cefditoren, cefoperazone, cefotaxime, cefpodoxime, ceftazidime, ceftibuten, ceftizoxime, ceftriaxone, cefepime, ceftobiprole, teicoplanin, vancomycin, azithromycin, dirithromycin, erythromycin, roxithromycin, troleandomycin, telithromycin, spectinomycin, aztreonam, amoxicillin, ampicillin, azlocillin, carbenicillin, cloxacillin, dicloxacillin, flucloxacillin, mezlocillin, methicillin, nafcillin, oxacillin, penicillin, piperacillin, ticarcillin, bacitracin, colistin, polymyxin B, enoxacin, gatifloxacin, lomefloxacin, norfloxacin, trovafloxacin, grepafloxacin, sparfloxacin, mafenide, prontosil, sulfacetamide, sulfamethizole, sulfanilimide, sulfasalazine, sulfisoxazole, trimethoprim, trimethoprim-sulfamethoxazole (co-trimoxazole), demeclocycline, doxycycline, minocycline, oxytetracycline, arsphenamine, chloramphenicol, clindamycin, lincomycin, ethambutol, fosfomycin, fusidic acid, furazolidone, metronidazole, mupirocin, nitrofurantoin, platensimycin, quinupristin/dalfopristin, rifampin, thiamphenicol, tinidazole, cephalosporin, teicoplatin, augmentin, cephalexin, rifamycin, rifaximin, cephamandole, ketoconazole, latamoxef, or cefmenoxime.

[0010] In some embodiments, the sample is contacted with the compound for less than four hours, e.g., less than three hours, less than two hours, less than one hour, less than 30 minutes, less than 20 minutes, less than 10 minutes, less than five minutes, less than two minutes, less than one minute.

[0011] In another aspect, the invention features methods of identifying an infectious disease pathogen, e.g., a bacterium, fungus, virus, or parasite, e.g., Mycobacterium tuberculosis, e.g., detecting the presence of the pathogen in a sample, e.g., a clinical sample. The methods include:

[0012] providing a test sample from a subject suspected of being infected with a pathogen;

[0013] treating the test sample under conditions that release messenger ribonucleic acid (mRNA);

[0014] exposing the test sample to a plurality of nucleic acid probes, comprising a plurality of subsets of probes, wherein each subset comprises one or more probes that bind specifically to a target mRNA that uniquely identifies a pathogen, wherein the exposure occurs for a time and under conditions in which binding between the probe and the target mRNA can occur; and

[0015] determining a level of binding between the probe and target mRNA, thereby determining a level of target mRNA. An increase in the target mRNA of the test sample, relative to a reference sample, indicates the identity of the pathogen in the test sample.

[0016] In some embodiments, the methods identify an infectious disease pathogen in or from a sample that is or comprises sputum, blood, urine, stool, joint fluid, cerebrospinal fluid, and cervical/vaginal swab. Such samples may include a plurality of other organisms (e.g., one or more non-disease causing bacteria, fungi, viruses, or parasites) or pathogens. In some embodiments, the sample is a clinical sample, e.g., a sample from a patient or person who is or may be undergoing a medical treatment by a health care provider.

[0017] In some embodiments of the invention, the one or more nucleic acid probes are selected from Table 2.

[0018] In some embodiments, the mRNA is crude, e.g., not purified, before contact with the probes and/or does not include amplifying the mRNA, e.g., to produce cDNA.

[0019] In some embodiments, the methods comprise lysing the cells enzymatically, chemically, and/or mechanically.

[0020] In some embodiments, the methods comprise use of a microfluidic device.

[0021] In some embodiments, the methods are used to monitor pathogen infection, e.g., incidence, prevalence, for public health surveillance of an outbreak of a pathogen, e.g., a sudden rise in numbers of a pathogen within a particular area.

[0022] The methods described herein are effective wherein the pathogen is in a sample from a subject, including humans and animals, such as laboratory animals, e.g., mice, rats, rabbits, or monkeys, or domesticated and farm animals, e.g., cats, dogs, goats, sheep, pigs, cows, horses, and birds, e.g., chickens.

[0023] In some embodiments, the methods further feature determining and/or selecting a treatment for the subject and optionally administering the treatment to the subject, based on the outcome of an assay as described herein.

[0024] In another general aspect, the invention features methods of selecting a treatment for a subject. The methods include:

[0025] optionally identifying an infectious disease pathogen (e.g., detecting the presence and/or identity of a specific pathogen in a sample), e.g., using a method described herein;

[0026] determining the drug sensitivity of the pathogen using the methods described herein; and

[0027] selecting a drug to which the pathogen is sensitive for use in treating the subject.

[0028] In yet another aspect, the invention provides methods for monitoring an infection with a pathogen in a subject. The methods include:

[0029] obtaining a first sample comprising the pathogen at a first time;

[0030] determining the drug sensitivity of the pathogen in the first sample using the method described herein;

[0031] optionally selecting a treatment to which the pathogen is sensitive and administering the selected treatment to the subject;

[0032] obtaining a second sample comprising the pathogen at a second time;

[0033] determining the drug sensitivity of the pathogen in the second sample using the method described herein; and

[0034] comparing the drug sensitivity of the pathogen in the first sample and the second sample, thereby monitoring the infection in the subject.

[0035] In some embodiments of the methods described herein, the subject is immune compromised.

[0036] In some embodiments of the methods described herein, the methods include selecting a treatment to which the pathogen is sensitive and administering the selected treatment to the subject, and a change in the drug sensitivity of the pathogen indicates that the pathogen is or is becoming resistant to the treatment, e.g., the methods include determining the drug sensitivity of the pathogen to the treatment being administered.

[0037] In some embodiments, a change in the drug sensitivity of the pathogen indicates that the pathogen is or is becoming resistant to the treatment, and the method further comprises administering a different treatment to the subject.

[0038] In yet another aspect, the invention features methods of monitoring an infection with a pathogen in a population of subjects. The methods include:

[0039] obtaining a first plurality of samples from subjects in the population at a first time;

[0040] determining the drug sensitivity of pathogens in the first plurality of samples using the method described herein, and optionally identifying an infectious disease pathogen in the first plurality of samples using the method described herein;

[0041] optionally administering a treatment to the subjects;

[0042] obtaining a second plurality of samples from subjects in the population at a second time;

[0043] determining the drug sensitivity of pathogens in the second plurality of samples using the method described herein, and optionally identifying an infectious disease pathogen in the first plurality of samples using the method described herein;

[0044] comparing the drug sensitivity of the pathogens, and optionally the identity of the pathogens, in the first plurality of samples and the second plurality of samples, thereby monitoring the infection in the population of subject.

[0045] In yet another aspect, a plurality of polynucleotides bound to a solid support are provided. Each polynucleotide of the plurality selectively hybridizes to one or more genes from Table 2. In some embodiments, the plurality of polynucleotides comprise SEQ ID NOs:1-227, and any combination thereof,

[0046] "Infectious diseases" also known as communicable diseases or transmissible diseases, comprise clinically evident illness (i.e., characteristic medical signs and/or symptoms of disease) resulting from the infection, presence, and growth of pathogenic biological agents in a subject (Ryan and Ray (eds.) (2004). Sherris Medical Microbiology (4th ed.). McGraw Hill). A diagnosis of an infectious disease can confirmed by a physician through, e.g., diagnostic tests (e.g., blood tests), chart review, and a review of clinical history. In certain cases, infectious diseases may be asymptomatic for some or all of their course. Infectious pathogens can include viruses, bacteria, fungi, protozoa, multicellular parasites, and prions. One of skill in the art would recognize that transmission of a pathogen can occur through different routes, including without exception physical contact, contaminated food, body fluids, objects, airborne inhalation, and through vector organisms. Infectious diseases that are especially infective are sometimes referred to as contagious and can be transmitted by contact with an ill person or their secretions.

[0047] As used herein, the term "gene" refers to a DNA sequence in a chromosome that codes for a product (either RNA or its translation product, a polypeptide). A gene contains a coding region and includes regions preceding and following the coding region (termed respectively "leader" and "trailer"). The coding region is comprised of a plurality of coding segments ("exons") and intervening sequences ("introns") between individual coding segments.

[0048] The term "probe" as used herein refers to an oligonucleotide that binds specifically to a target mRNA. A probe can be single stranded at the time of hybridization to a target.

[0049] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. Methods and materials are described herein for use in the present invention; other, suitable methods and materials known in the art can also be used. The materials, methods, and examples are illustrative only and not intended to be limiting. All publications, patent applications, patents, sequences, database entries, and other references mentioned herein are incorporated by reference in their entirety. In case of conflict, the present specification, including definitions, will control.

[0050] Other features and advantages of the invention will be apparent from the following detailed description and figures, and from the claims.

DESCRIPTION OF DRAWINGS

[0051] FIGS. 1A to 1D are a flowchart illustrating an exemplary method to quantify mRNA molecules in a sample using NanoString.TM. (direct multiplexed measurement of gene expression with color-coded probe pairs) technology. FIG. 1A. Two molecular probes corresponding to each mRNA of interest are added to crude sample lysate. The capture probe consists of a 50 bp oligomer complementary to a given mRNA molecule, conjugated to biotin. The reporter probe consists of a different 50 bp oligomer complementary to a different part of the same mRNA molecule, conjugated to a fluorescent tag. Each tag uniquely identifies a given mRNA molecule. The capture and reporter probes hybridize to their corresponding mRNA molecules within the lysate. FIG. 1B. Excess reporter is removed by bead purification that hybridizes to a handle on each oligomer, leaving only the hybridized mRNA complexes. FIG. 1C. The mRNA complexes are immobilized and aligned on a surface. The mRNA complexes are captured by the biotin-conjugated captures probes onto a strepavidin-coated surface. An electric field is applied to align the complexes all in the same direction on the surface. FIG. 1D. Surface is imaged and codes counted. The mRNA complexes are microscopically imaged and the aligned reporter tags can be counted, thus providing a quantitative measure of mRNA molecules. (Images obtained from nanostring.com).

[0052] FIGS. 2A to 2F are a panel of figures showing diagnosis of a gene expression signature of drug resistance. FIG. 2A. Sample from a patient, e.g., sputum. FIG. 2B. Induction of expression program to distinguish drug sensitive and resistant strains. Sample is partitioned and exposed to different drugs to induce an expression program depending on whether the strain is drug resistant or sensitive. FIG. 2C. Bar-coded probes hybridize to mRNA molecules. Cells are lysed and probes added to the crude sample. FIG. 2D. mRNA complexes are captured and aligned. FIG. 2E. Complexes are imaged and counted. FIG. 2F. Analysis of signatures. The measured mRNA levels will be normalized and compared to the no drug control and drug sensitive and resistant standards to define a resistance profile across all drugs.

[0053] FIG. 3 is a bar graph showing positive identification of E. coli clinical isolates. Using probes designed to six E. coli genes (ftsQ, murC, opgG, putP, secA, and uup), four clinical isolates were positively identified as E. coli. Each value represents average and standard deviation of 4 to 6 replicates.

[0054] FIG. 4 is a bar graph showing positive identification of Pseudomonas aeruginosa clinical isolates. Using probes designed to five P. aeruginosa genes (proA, sltB1, nadD, dacC, and lipB), two clinical isolates were positively identified as P. aeruginosa.

[0055] FIG. 5 is a bar graph showing positive identification of a Klebsiella pneumoniae clinical isolate. Using probes designed to five K. pneumoniae genes (lrp, ycbK, clpS, ihfB, mraW) a clinical isolate was positively identified.

[0056] FIG. 6 is a bar graph showing positive identification of S. aureus clinical isolates. Using probes designed to three S. aureus genes (proC, rpoB, and fabD), four clinical isolates were positively identified.

[0057] FIG. 7 is a panel of three bar graphs showing pathogen identification using pathogen specific probes.

[0058] FIG. 8 is a panel of three bar graphs showing pathogen identification sensitivity.

[0059] FIGS. 9A and 9B are panels of three bar graphs showing pathogen identification from simulated clinical samples.

[0060] FIG. 10 is a panel of two bar graphs showing identification of two clinical isolates of P. aeruginosa.

[0061] FIG. 11 is a bar graph showing the identification of fluoroquinolone resistance in E. coli.

[0062] FIG. 12 is a bar graph showing the identification of aminoglycoside resistance in E. coli.

[0063] FIG. 13 is a bar graph showing the identification of methicillin resistance in S. aureus.

[0064] FIG. 14 is a bar graph showing the identification of vancomycin resistance in Enterococcus.

[0065] FIG. 15 is a panel of four bar graphs showing drug-specific gene induction in drug-sensitive M. tuberculosis.

[0066] FIG. 16 is panel of three scatter plots comparing isoniazid sensitive and resistant TB strains. Each dot represents one of the 24 gene probes. The axes report number of transcripts as measured by digital gene expression technology (NanoString.TM.). Left--Comparison of expression in isoniazid resistant and isoniazid sensitive strains in the absence of drug treatment. Middle--Comparison of expression in drug treated vs. drug untreated isoniazid sensitive strain. Right--Comparison of expression in drug treated vs. drug untreated isoniazid resistant strain.

[0067] FIG. 17 is a panel of four bar graphs comparing the transcriptional responses of drug-sensitive and drug-resistant M. tuberculosis using NanoString.TM.. (A) Strain A50 (INH-R) was treated with INH (0.4 .mu.g/ml) as described herein. (B) The SM-R clone S10 was treated with 2 .mu.g/ml streptomycin.

[0068] FIG. 18 is bar graph showing differential gene induction in sensitive vs. resistant TB strain. The ratio of expression of each gene in INH sensitive (wt) cells treated with INH/untreated cells is divided by the expression of each gene in INH resistant cells treated with INH/untreated cells.

[0069] FIG. 19 is a line graph showing the time course of induction of INH-induced genes in M. tuberculosis. Isoniazid sensitive H37Rv was exposed to 0.4 .mu.g/ml INH (5.times.MIC), and RNA was prepared from 10 ml of culture at 1, 2, and 5 hours. qRT-PCR was then used to quantify the abundance of transcripts to kasA, kasB, and sigA, Levels are normalized to sigA and compared to t=0.

[0070] FIG. 20 is an exemplary work flow for detecting expression signatures. Because the actual physiologic state of bacilli in sputum is unknown, both replicating and non-replicating bacteria are modeled in process development. H37Rv grown in axenic culture (either in rich 7H9/OADC/SDS media or starved in 7H9/tyloxapol) represent bacilli in sputum in these experiments. The bacilli are pulsed for some time t.sub.1 with exposure to rich media to stimulate resuscitation from a dormant state and to active transcription. The optimal t.sub.1 is determined experimentally. The bacilli are then pulsed for some time t.sub.2 with exposure to drug to elicit a transcriptional response. The optimal t.sub.2 is determined experimentally. Finally, all samples are processed and analyzed by expression profiling and confirmed by quantitative RT-PCR.

[0071] FIG. 21 is an exemplary method to compare expression ratios of genes to distinguish drug sensitive and resistant bacilli. Using quantitative RT-PCR, mRNA levels are measured for genes that are candidates for inclusion in an expression signature. The mRNA levels of a gene of interest are measured in a sample designated "experimental (exp)" (i.e., clinical isolate) in the presence of drug (induced-drug) and the absence of drug (uninduced-no drug). The mRNA levels of a standard housekeeping gene are also measured in the presence (housekeeping-drug) and absence (housekeeping-no drug) of drug. The ratio of the levels of the gene of interest and the housekeeping gene allow for normalization of expression in the presence of drug (A) and in the absence of drug (B). It is anticipated that for some drug sensitive strains, A>B and for drug resistant strains, A=B. Finally, the same corresponding ratios are generated for control strains (C and D) that are known to be drug sensitive and drug resistant. These control values act as standards for the comparison of experimental ratios obtained from unknown strains.

[0072] FIG. 22 is a panel of bar and scatter plots showing positive identification of bacterial species directly from culture or patient specimens. Bacterial samples were analyzed with NanoString.TM. probes designed to detect species-specific transcripts. Y-axis: transcript raw counts; X-axis: gene name. Probes specific for E. coli (black), K. pneumoniae (white), P. aeruginosa (grey). Error bars reflect the standard deviation of two biological replicates. (A) Detection from culture of Gram-negative bacteria. (B) Detection within mixed culture (Providencia stuartii, Proteus mirabilis, Serratia marcescens, Enterobacter aerogenes, Enterobacter cloacae, Morganella morganii, Klebsiella oxytoca, Citrobacter freundii). (C) Genus- and species-specific detection of mycobacteria in culture. M. tuberculosis (Mtb), M. avium subsp. intracellulare (MAI), M. paratuberculosis (Mpara), and M. marinum (Mmar). Genus-wide probes (grey), M. tuberculosis-specific probes (black). (D) Detection of E. coli directly from clinical urine specimens. (E) Statistical determination of identity of E. coli samples in comparison with non-E. coli samples. Counts for each probe were averaged, log transformed and summed. (F) Detection of mecA mRNA, which confers resistance to methicillin in Staphylococci, and vanA mRNA, which confers resistance to vancomycin in Enterococci. Each point represents a different clinical isolate.

[0073] FIG. 23 is a panel of seven bar graphs showing RNA expression signatures that distinguish sensitive from resistant bacteria upon antibiotic exposure. Sensitive or resistant bacterial strains were grown to log phase, briefly exposed to antibiotic, lysed, and analyzed using NanoString.TM. probe-sets designed to quantify transcripts that change in response to antibiotic exposure. Raw counts were normalized to the mean of all probes for a sample, and fold induction was determined by comparing drug-exposed to unexposed samples. Y-axis: fold-change; X-axis: gene name. Signatures for susceptible strains (black; top panel) or resistant strains (grey; bottom panel) upon exposure to (A) E. coli: ciprofloxacin (CIP), ampicillin (AMP), or gentamicin (GM), (B) P. aeruginosa: ciprofloxacin, and (C) M. tuberculosis: isoniazid (INH), streptomycin (SM), or ciprofloxacin (CIP). Each strain was tested in duplicate; error bars represent standard deviation of two biological replicates of one representative strain. See Table 6 for a full list of strains tested.

[0074] FIG. 24 is a panel of three scatter plots showing statistical separation of antibiotic-resistant and sensitive bacterial strains using mean squared distance of the induction levels of expression signatures. Mean squared distance (MSD) is represented as Z-scores showing deviation of each tested strain from the mean signal for susceptible strains exposed to antibiotic. Susceptible strains: open diamonds; resistant strains: solid diamonds. Dashed line: Z=3.09 (p=0.001) (A) E. coli clinical isolates. Each point represents 2 to 4 biological replicates of one strain. (B and C) Expression-signature response to antibiotic exposure is independent of resistance mechanism. (B) E. coli. Parent strain J53 and derivatives containing either a chromosomal fluoroquinolone resistance-conferring mutation in gyrA or plasmid-mediated quinolone resistance determinants (aac(6')-Ib, qnrB, or oqxAB) were exposed to ciprofloxacin, then analyzed as above. Error bars represent standard deviation of four biological replicates. (C) M tuberculosis. Isoniazid-sensitive and high- or low-level resistant strains were exposed to isoniazid. At 1 .mu.g/mL, the low-level INH-resistant inhA displays a susceptible signature, but at 0.2 .mu.g/mL, it shows a resistant signature.

[0075] FIG. 25 is a panel of five bar graphs depicting detection of viruses and parasites. Cells were lysed, pooled probe sets added, and samples hybridized according to standard NanoString.TM. protocols. (A) Candida albicans detected from axenic culture. (B) HIV-1. Detection from PBMC lysates with probes designed to HIV-1 gag and rev. (C) Influenza A. Detection of PR8 influenza virus in 293T cell lysates with probes designed to matrix proteins 1 and 2. (D) HSV-1 and HSV-2. Detection of HSV-2 strain 186 Syn+ in HeLa cell lysates with probes designed to HSV-2 glycoprotein G. There was little cross-hybridization of the HSV-2 specific probes with HSV-1 even at high MOI. (E) Plasmodium falciparum. Detection of P. falciparum strain 3D7 from red blood cells harvested at the indicated levels of parasitemia. Probes were designed to the indicated blood stage for P. falciparum.

[0076] FIG. 26 is a panel of three scatter plots showing organism identification of clinical isolates. Bacterial cultures were lysed and probes that were designed to detect species-specific transcripts were added, hybridized, and detected by standard NanoString.TM. protocol. A pooled probe-set containing probes that identify E. coli, K. pneumoniae, or P. aeruginosa were used in A and B. In C, species-specific probes for M. tuberculosis were among a larger set of probes against microbial pathogens. The left Y-axis shows the sum of the log-transformed counts from 1-5 independent transcripts for each organism and X-axis indicates the species tested. The dashed line delineates a p value of 0.001 based on the number of standard deviations that the score of a given sample falls from the mean of the control ("non-organism") samples. "Non-organism" samples indicate samples tested that contained other bacterial organisms but where the defined organism was known to be absent. For (C), non-organism samples were non-tuberculous mycobacteria including M. intracellulare, M. paratuberculosis, M. abscessus, M. marinum, M. gordonae, and M. fortuitum. Numbers of strains and clinical isolates tested are shown in Table 4 and genes used for pathogen identification (for which 50 nt probes were designed) are listed in Table 5.

[0077] FIG. 27 depicts the mean square distance (MSD) comparison of gentamicin (left panel) or ampicillin (left panel) sensitive and resistant E. coli strains. The Y axis shows the Z score of the MSD of each sample relative to the centroid of the response of known sensitive strains. The dotted line delineates Z=3.09, which corresponds to a p value of 0.001.

[0078] FIG. 28 is a scatter plot showing mean square distance comparison of ciprofloxacin sensitive and resistant P. aeruginosa strains. The Y axis shows the Z score of the MSD of each sample relative to the centroid of the response of known sensitive strains.

[0079] FIG. 29 is a panel of two scatter plots showing mean square distance comparison of streptomycin (SM) or ciprofloxacin (CIP) sensitive and resistant M. tuberculosis strains. The Y axis shows the Z score of the MSD of each sample relative to the centroid of the response of known sensitive strains.

[0080] FIG. 30 is a bar graph showing positive identification of S. aureus isolates. Using probes designed to five S. aureus genes (ileS, ppnK, pyrB, rocD, and uvrC), three S. aureus isolates were positively identified.

[0081] FIG. 31 is a bar graph showing positive identification of Stenotrophomonas maltophilia isolates. Using probes designed to six S. maltophilia genes (clpP, dnaK, purC, purF, sdhA, and secD), three isolates were positively identified as S. maltophilia.

DETAILED DESCRIPTION

[0082] Described herein are rapid, highly sensitive, phenotypic-based methods for both identifying a pathogen, e.g., bacterium, fungus, virus, and parasite, and its drug resistance pattern based on transcriptional expression profile signatures. Sensitive and resistant pathogens respond very differently to drug exposure with one of the earliest, most rapid responses reflected in alterations in their respective expression profiles. Digital gene expression with molecular barcodes can be used to detect these early transcriptional responses to drug exposure to distinguish drug sensitive and resistant pathogens in a rapid manner that requires no enzymology or molecular biology. The invention is applicable to a broad range of microbial pathogens in a variety of clinical samples and can be used in conjunction with current diagnostic tools or independently. The methods will be described primarily for use with tuberculosis ("TB;" Mycobacterium tuberculosis), although it will be understood by skilled practitioners that they may be adapted for use with other pathogens and their associated clinical syndromes (e.g., as listed in Table 1).

[0083] The diagnosis and the identification of drug resistance is especially challenging regarding TB due to the extremely slow growth of TB that is required for culture testing even using the more rapid "microscopic-observation drug-susceptibility" (MODS) culture method, phage-delivered reporters, or colorimetric indicators. An alternative approach to determining drug resistance is based on defining the genotype of the infecting pathogen relative to known resistance conferring mutations, however, this approach requires a fairly comprehensive identification of all resistance-conferring single nucleotide polymorphisms (SNPs) in order for the test to have high sensitivity.

[0084] The methods described herein can be used, e.g., for identifying a pathogen in a sample, e.g., a clinical sample, as well as determining the drug sensitivity of a pathogen based on expression profile signatures of the pathogen. One of the earliest, most rapid responses that can be used to distinguish drug sensitive and resistant pathogens is their respective transcriptional profile upon exposure to a drug of interest. Pathogens respond very differently to drug exposure depending on whether they are sensitive or resistant to that particular drug. For example, in some cases drug sensitive or drug resistant bacteria will respond within minutes to hours to drug exposure by up- and down-regulating genes, perhaps attempting to overcome the drug as well as the more non-specific stresses that follow while resistant bacteria have no such response. This rapid response is in contrast to the longer time that is required by a compound to kill or inhibit growth of a pathogen. Detecting death or growth inhibition of a pathogen in an efficient manner from clinical samples represents an even greater challenge. Digital gene expression can be used, e.g., with molecular barcodes, to detect these early transcriptional responses to drug exposure to distinguish drug sensitive and resistant pathogens in a rapid manner that requires no enzymology or molecular biology, and thus can be performed directly on crude clinical samples collected from patients. This readout is phenotypic and thus requires no comprehensive definition of SNPs accounting for, e.g., TB drug resistance. Described herein are a set of genes that will provide high specificity for a pathogen, e.g., TB bacillus, and for distinguishing sensitive and resistant pathogens. Based on the selection of genes that constitute the expression signature distinguishing sensitive and resistant pathogens, the sensitivity of the detection limit is optimized by choosing transcripts that are abundantly induced, and thus not limited solely by the number of pathogens within a clinical sample. The size of this set is determined to minimize the numbers of genes required. Thus, the current invention can be used as a highly sensitive, phenotypic test to diagnose a pathogen with its accompanying resistance pattern that is rapid (e.g., a few hours), sensitive, and specific. This test can transform the care of patients infected with a pathogen and is a cost-effective, point-of-care diagnostic for, e.g., TB endemic regions of the world.

[0085] The present methods allow the detection of nucleic acid signatures, specifically RNA levels, directly from crude cellular samples with a high degree of sensitivity and specificity. This technology can be used to identify TB and determine drug sensitivity patterns through measurement of distinct expression signatures with a high degree of sensitivity and with rapid, simple processing directly from clinical samples, e.g. sputum, urine, blood, or feces; the technology is also applicable in other tissues such as lymph nodes. High sensitivity can be attained by detecting mRNA rather than DNA, since a single cell can carry many more copies of mRNA per cell (>10.sup.3) compared to a single genomic DNA copy (which typically requires amplification for detection), and by the high inherent sensitivity of the technology (detects <2000 copies mRNA). The rapid, simple sample processing is possible due to the lack of enzymology and molecular biology required for detection of mRNA molecules; instead, in some embodiments, the methods make use of hybridization of bar-coded probes to the mRNA molecules in crude lysates followed by direct visualization (e.g., as illustrated in FIG. 1). Because hybridization is used in these embodiments, mRNA can be detected directly without any purification step from crude cell lysates, fixed tissue samples, and samples containing guanidinium isothiocyanate, polyacrylamide, and Trizol.RTM.. Crude mRNA samples can be obtained from biological fluids or solids, e.g., sputum, blood, urine, stool, joint fluid, cerebrospinal fluid, cervical/vaginal swab, biliary fluid, pleural fluid, peritoneal fluid, or pericardial fluid; or tissue biopsy samples, e.g., from bone biopsy, liver biopsy, lung biopsy, brain biopsy, lymph node biopsy, esophageal biopsy, colonic biopsy, gastric biopsy, small bowel biopsy, myocardial biopsy, skin biopsy, and sinus biopsy can also be used.

RNA Extraction

[0086] RNA can be extracted from cells in a sample, e.g., a pathogen cell or clinical sample, by treating the sample enzymatically, chemically, or mechanically to lyse cells in the sample and release mRNA. It will be understood by skilled practitioners that other disruption methods may be used in the process.

[0087] The use of enzymatic methods to remove cell walls is well-established in the art. The enzymes are generally commercially available and, in most cases, were originally isolated from biological sources. Enzymes commonly used include lysozyme, lysostaphin, zymolase, mutanolysin, glycanases, proteases, and mannose.

[0088] Chemicals, e.g., detergents, disrupt the lipid barrier surrounding cells by disrupting lipid-lipid, lipid-protein and protein-protein interactions. The ideal detergent for cell lysis depends on cell type and source. Bacteria and yeast have differing requirements for optimal lysis due to the nature of their cell wall. In general, nonionic and zwitterionic detergents are milder. The Triton X series of nonionic detergents and 3-[(3-Cholamidopropyl)dimethylammonio]-1-propanesulfonate (CHAPS), a zwitterionic detergent, are commonly used for these purposes. In contrast, ionic detergents are strong solubilizing agents and tend to denature proteins, thereby destroying protein activity and function. SDS, an ionic detergent that binds to and denatures proteins, is used extensively in the art to disrupt cells.

[0089] Physical disruption of cells may entail sonication, French press, electroporation, or a microfluidic device comprising fabricated structures can be used to mechanically disrupt a cell. These methods are known in the art.

Digital Gene Expression with Molecular Barcodes

[0090] A flow diagram is shown in FIG. 1 of an exemplary procedure to identify a pathogen based on its gene expression profile. Oligonucleotide probes to identify each pathogen of interest were selected by comparing the coding sequences from the pathogen of interest to all gene sequences in other organisms by BLAST software. Only probes of about 50 nucleotides, e.g., 80 nucleotides, 70 nucleotides, 60 nucleotides, 40 nucleotides, 30 nucleotides, and 20 nucleotides, with a perfect match to the pathogen of interest, but no match of >50% to any other organism were selected. Two probes corresponding to each mRNA of interest and within 100 base pairs of each other are selected.

[0091] Two molecular probes are added to a crude sample lysate containing mRNA molecules. A capture probe comprises 50 nucleotides complementary to a given mRNA molecule, and can be conjugated to biotin. A reporter probe comprises a different 50 nucleotides complementary to a different part of the same mRNA molecule, and can be conjugated to a reporter molecule, e.g., a fluorescent tag or quantum dot. Each reporter molecule uniquely identifies a given mRNA molecule. The capture and reporter probes hybridize to their corresponding mRNA molecules within the lysate. Excess reporter is removed by bead purification that hybridizes to a handle on each oligomer, leaving only the hybridized mRNA complexes. The mRNA complexes can be captured and immobilized on a surface, e.g., a streptavidin-coated surface. An electric field can be applied to align the complexes all in the same direction on the surface before the surface is microscopically imaged.

[0092] The reporter molecules can be counted to provide a quantitative measure of mRNA molecules. A commercially available nCounter.TM. Analysis System (NanoString, Seattle, Wash.) can be used in the procedure. However, it will be understood by skilled practitioners that other systems may be used in the process. For example, rather than bar codes the probes can be labeled with quantum dots; see, e.g., Sapsford et al., "Biosensing with luminescent semiconductor quantum dots." Sensors 6(8): 925-953 (2006); Stavis et al., "Single molecule studies of quantum dot conjugates in a submicrometer fluidic channel." Lab on a Chip 5(3): 337-343 (2005); and Liang et al., "An oligonucleotide microarray for microRNA expression analysis based on labeling RNA with quantum dot and nanogold probe." Nucleic Acids Research 33(2): ell (2005).

[0093] In some embodiments, microfluidic (e.g., "lab-on-a-chip") devices can be used in the present methods for detection and quantification of mRNA in a sample. Such devices have been successfully used for microfluidic flow cytometry, continuous size-based separation, and chromatographic separation. In particular, such devices can be used for the detection of specific target mRNA in crude samples as described herein. A variety of approaches may be used to detect changes in levels of specific mRNAs. Accordingly, such microfluidic chip technology may be used in diagnostic and prognostic devices for use in the methods described herein. For examples, see, e.g., Stavis et al., Lab on a Chip 5(3): 337-343 (2005); Hong et al., Nat. Biotechnol. 22(4):435-439 (2004); Wang et al., Biosensors and Bioelectronics 22(5): 582-588 (2006); Carlo et al., Lab on a Chip 3(4):287-291 (2003); Lion et al., Electrophoresis 24 21 3533-3562 (2003); Fortier et al., Anal. Chem., 77(6):1631-1640 (2005); U.S. Patent Publication No. 2009/0082552; and U.S. Pat. No. 7,611,834. Also included in the present application are microfluidic devices comprising binding moieties, e.g., antibodies or antigen-binding fragments thereof that bind specifically to the pathogens as described herein.

[0094] These microfluidic devices can incorporate laser excitation of labeled quantum dots and other reporter molecules. The devices can also incorporate the detection of the resulting emission through a variety of detection mechanisms including visible light and a variety of digital imaging sensor methods including charge-coupled device based cameras. These devices can also incorporate image processing and analysis capabilities to translate the resulting raw signals and data into diagnostic information.

Rapid, Phenotypic Diagnosis of Pathogen Identity and Pathogen Drug Resistance Using Expression Signatures

[0095] This technology can be applied to obtain a rapid determination of identity or drug resistance of a pathogen.

[0096] A pathogen can be identified in a sample based on detection of unique genes. Thus, for example, a sputum sample may be obtained from a subject who has symptoms associated with a respiratory disease such as pneumonia or bronchitis, and an assay is performed to determine which disease is present and what pathogen is the cause of that disease (see, e.g., Table 1). Urine samples may be obtained to diagnose cystitis, pyelonephritis, or prostatitis (see, e.g., Table 1). A skilled practitioner will appreciate that a particular type of sample can be obtained and assayed depending on the nature of the symptoms exhibited by the patient and the differential diagnosis thereof. Specific genes for identifying each organism can be identified by methods described herein; exemplary genes for identifying certain pathogens are included in Table 2.

[0097] The principle for greatly accelerated resistance testing is based on detecting the differences in transcriptional response that occur between drug sensitive and resistant strains of a pathogen upon exposure to a particular drug of interest. These transcriptional profiles are the earliest phenotypic response to drug exposure that can be measured and they can be detected long before bacillary death upon drug exposure. This transcription-based approach also carries the distinct advantage over genotype-based approaches in that it measures direct response of the pathogen to drug exposure rather than a surrogate SNP.

[0098] In some embodiments, the test can be performed as described in FIG. 2. A sample, e.g., a sputum sample from a patient with TB, is partitioned into several smaller sub-samples. The different sub-samples are exposed to either no drug or different, known or potential drugs (e.g., in the case of a TB sample, isoniazid, rifampin, ethambutol, moxifloxacin, streptomycin) for a determined period of time (e.g., less than four hours, less than three hours, less than two hours, less than one hour, less than 30 minutes, less than 20 minutes, less than 10 minutes, less than five minutes, less than two minutes, less than one minute), during which an expression profile is induced in drug sensitive strains that distinguishes it from drug resistant strains. The TB bacilli in the sub-samples are then lysed, the bar-coded molecular probes added for hybridization, and the sub-samples analyzed after immobilization and imaging. The set of transcriptional data is then analyzed to determine resistance to a panel of drugs based on expression responses for drug sensitive and drug resistant strains of TB. Thus, an expression signature to uniquely identify TB and its response to individual antibiotics can be determined, a probe set for the application of digital gene expression created, and sample processing and collection methods optimized.

[0099] Two issues that should be taken into account in defining the expression signatures and optimizing the transcriptional signal are: 1. the currently undefined metabolic state of the bacilli in sputum since the cells may be in either a replicating or non-replicating state, and 2. the possibility that the TB bacilli in collected sputum have been pre-exposed to antibiotics (i.e., the patient has already been treated empirically with antibiotics).

[0100] In some embodiments, the methods of identifying a pathogen and the methods of determining drug sensitivity are performed concurrently, e.g., on the same sample, in the same microarray or microfluidic device, or subsequently, e.g., once the identity of the pathogen has been determined, the appropriate assay for drug sensitivity is selected and performed.

[0101] An exemplary set of genes and probes useful in the methods described herein is provided in Table 2 submitted herewith.

Methods of Treatment

[0102] The methods described herein include, without limitation, methods for the treatment of disorders, e.g., disorders listed in Table 1. Generally, the methods include using a method described herein to identify a pathogen in a sample from a subject, or identify a drug (or drugs) to which a pathogen in a subject is sensitive, and administering a therapeutically effective amount of therapeutic compound that neutralizes the pathogen to a subject who is in need of, or who has been determined to be in need of, such treatment. As used in this context, to "treat" means to ameliorate at least one symptom of the disorder associated with one of the disorders listed in Table 1. For example, the methods include the treatment of TB, which often results in a cough, chest pain, fever, fatigue, unintended weight loss, loss of appetite, chills and night sweats, thus, a treatment can result in a reduction of these symptoms. Clinical symptoms of the other diseases are well known in the art.

[0103] An "effective amount" is an amount sufficient to effect beneficial or desired results. For example, a therapeutic amount is one that achieves the desired therapeutic effect. This amount can be the same or different from a prophylactically effective amount, which is an amount necessary to prevent onset of disease or disease symptoms. An effective amount can be administered in one or more administrations, applications or dosages. A therapeutically effective amount of a composition depends on the composition selected. The compositions can be administered from one or more times per day to one or more times per week, including once every other day. The compositions can also be administered from one or more times per month to one or more times per year. The skilled artisan will appreciate that certain factors may influence the dosage and timing required to effectively treat a subject, including but not limited to the severity of the disease or disorder, previous treatments, the general health and/or age of the subject, and other diseases present. Moreover, treatment of a subject with a therapeutically effective amount of the compositions described herein can include a single treatment or a series of treatments.

Methods of Diagnosis

[0104] Included herein are methods for identifying a pathogen and/or determining its drug sensitivity. The methods include obtaining a sample from a subject, and evaluating the presence and/or drug sensitivity of a pathogen in the sample, and comparing the presence and/or drug sensitivity with one or more references, e.g., a level in an unaffected subject or a wild type pathogen. The presence and/or level of a mRNA can be evaluated using methods described herein and are known in the art, e.g., using quantitative immunoassay methods. In some embodiments, high throughput methods, e.g., gene chips as are known in the art (see, e.g., Ch. 12, Genomics, in Griffiths et al., Eds. Modern Genetic Analysis, 1999,W. H. Freeman and Company; Ekins and Chu, Trends in

[0105] Biotechnology, 1999, 17:217-218; MacBeath and Schreiber, Science 2000, 289(5485):1760-1763; Simpson, Proteins and Proteomics: A Laboratory Manual, Cold Spring Harbor Laboratory Press; 2002; Hardiman, Microarrays Methods and Applications: Nuts & Bolts, DNA Press, 2003), can be used to detect the presence and/or level of mRNA.

[0106] In some embodiments, the sample includes biological fluids or solids, e.g., sputum, blood, urine, stool, joint fluid, cerebrospinal fluid, cervical/vaginal swab, biliary fluid, pleural fluid, peritoneal fluid, or pericardial fluid; or tissue biopsy samples, e.g., from a bone biopsy, liver biopsy, lung biopsy, brain biopsy, lymph node biopsy, esophageal biopsy, colonic biopsy, gastric biopsy, small bowel biopsy, myocardial biopsy, skin biopsy, and sinus biopsy. In some embodiments, once it has been determined that a person has a pathogen, e.g., a pathogen listed in Table 1, or has a drug-resistant pathogen, then a treatment, e.g., as known in the art or as described herein, can be administered.

Kits

[0107] Also within the scope of the invention are kits comprising a probe that hybridizes with a region of gene as described herein and can be used to detect a pathogen described herein. The kit can include one or more other elements including: instructions for use; and other reagents, e.g., a label, or an agent useful for attaching a label to the probe. Instructions for use can include instructions for diagnostic applications of the probe for predicting response to treatment in a method described herein. Other instructions can include instructions for attaching a label to the probe, instructions for performing analysis with the probe, and/or instructions for obtaining a sample to be analyzed from a subject. As discussed above, the kit can include a label, e.g., a fluorophore, biotin, digoxygenin, and radioactive isotopes such as .sup.32P and .sup.3H. In some embodiments, the kit includes a labeled probe that hybridizes to a region of gene as described herein, e.g., a labeled probe as described herein.

[0108] The kit can also include one or more additional probes that hybridize to the same gene or another gene or portion thereof that is associated with a pathogen. A kit that includes additional probes can further include labels, e.g., one or more of the same or different labels for the probes. In other embodiments, the additional probe or probes provided with the kit can be a labeled probe or probes. When the kit further includes one or more additional probe or probes, the kit can further provide instructions for the use of the additional probe or probes.

[0109] Kits for use in self-testing can also be provided. For example, such test kits can include devices and instructions that a subject can use to obtain a sample, e.g., of sputum, buccal cells, or blood, without the aid of a health care provider. For example, buccal cells can be obtained using a buccal swab or brush, or using mouthwash.

[0110] Kits as provided herein can also include a mailer, e.g., a postage paid envelope or mailing pack, that can be used to return the sample for analysis, e.g., to a laboratory. The kit can include one or more containers for the sample, or the sample can be in a standard blood collection vial. The kit can also include one or more of an informed consent form, a test requisition form, and instructions on how to use the kit in a method described herein. Methods for using such kits are also included herein. One or more of the forms, e.g., the test requisition form, and the container holding the sample, can be coded, e.g., with a bar code, for identifying the subject who provided the sample.

[0111] In some embodiments, the kits can include one or more reagents for processing a sample. For example, a kit can include reagents for isolating mRNA from a sample. The kits can also, optionally, contain one or more reagents for detectably-labeling an mRNA or mRNA amplicon, which reagents can include, e.g., an enzyme such as a Klenow fragment of DNA polymerase, T4 polynucleotide kinase, one or more detectably-labeled dNTPs, or detectably-labeled gamma phosphate ATP (e.g., .sup.33P-ATP).

[0112] In some embodiments, the kits can include a software package for analyzing the results of, e.g., a microarray analysis or expression profile.

[0113] The invention is further described in the following examples, which do not limit the scope of the invention described in the claims.

EXAMPLES

Example 1

Pathogen Identification

[0114] Escherichia coli, Pseudomonas aeruginosa, Klebsiella pneumoniae, and Staphylococcus aureus. Unique coding sequences in Escherichia coli, Pseudomonas aeruginosa, Klebsiella pneumoniae, Staphylococcus aureus, and Enterococcus faecalis were identified (Table 2) and used to positively identify these organisms (FIGS. 3-6). Clinical isolates were grown in LB media at 37.degree. C. to log phase. Five microliters of each culture were then added to 100 microliters of guanidinium isothiocyanate lysis buffer (RLT buffer, Qiagen) and vortexed for 5 seconds. Four microliters of each lysate preparation were then used in the nCounter.TM. System assay according to the manufacturer's standard protocol for lysates. Criteria for identification were counts for all five (for P. aeruginosa or K. pneumoniae) or six (for E. coli) organism identification probes at least two-fold above the average background (the average of counts for all organism identification probes for the other two organisms). To compare between replicates, counts were normalized to counts of proC. Using the organism identification probes described in Table 2, four E. coli clinical isolates were correctly identified using probes designed to six E. coli genes (ftsQ, murC, opgG, putP, secA, and uup) (FIG. 3). Two clinical isolates were correctly identified as P. aeruginosa using probes designed to five P. aeruginosa genes (proA, sltB1, nadD, dacC, and lipB) as shown in FIG. 4. As shown in FIG. 5, probes designed to five K. pneumoniae genes (lrp, ycbK, clpS, ihfB, and mraW) positively identified a K. pneumoniae clinical isolate. Using probes designed to three S. aureus genes (proC, rpoB, and fabD), four clinical isolates were positively identified (FIG. 6). Cut-off criteria for identification were that counts for rpoB and fabD are at least two-fold above the average background (the average of counts for all organism identification probes for E. coli, P. aeruginosa, and K. pneumoniae).

[0115] On average, 4-5 sequences for each organism were included in the larger pool to obtain desired levels of specificity. Using this technology, each of these three organisms were detected, identified, and distinguished in axenic culture and in a complex mixture including eight additional Gram-negative pathogens by directly probing crude lysates (FIGS. 22A and 22B).

[0116] TB. Probes to Rv1641.1 and Rv3583c.1 detect highly abundant transcripts in M. tuberculosis (reference 8) and will detect orthologous transcripts in M. avium, and M. avium subsp. paratuberculosis, thus can be used for detection of any of these three species. Further, probes to three TB genes (Rv1980c.1, Rv1398c.1, and Rv2031c.1) can be used to differentially identify M. tuberculosis, i.e., they will not detect M. avium or M. avium subsp. paratuberculosis. Probes to MAP 2121c.1, MAV 3252.1, MAV 3239.1, and MAV 1600.1 can be used to detect M. avium or M. avium subsp. paratuberculosis, but will not detect M. tuberculosis. Thus, maximum sensitivity is achieved with the Rv1980c and Rv3853 probes, while the probes to Rv1980c.1, Rv1398c.1, and Rv2031c.1, and MAP.sub.--2121c.1, MAV.sub.--3252.1, MAV.sub.--3239.1, and MAV.sub.--1600.1, enable the distinction between M. tuberculosis infection and M. avium or M. avium subsp. paratuberculosis infection.

[0117] Probes were designed to genes both conserved throughout the mycobacterium genus and specific only to Mycobacterium tuberculosis. The pan-mycobacterial probes recognized multiple species, while the M tuberculosis probes were highly specific (FIG. 22C).

[0118] Staphylococcus aureus and Stenotrophomonas maltophilia

[0119] Using the organism identification probes described Table 2, three S. aureus isolates were correctly identified using probes designed to five S. aureus genes (ileS, ppnK, pyrB, rocD, and uvrC) (FIG. 30). Similarly, three Stenotrophomonas maltophilia isolates were correctly identified using probes designed to six S. maltophilia genes (clpP, dnaK, purC, purF, sdhA, and secD) (Table 2; and FIG. 31).

Example 2

Sensitivity of the Methods

[0120] As shown in FIGS. 7-10, the present methods are specific for each pathogen of interest and sensitive to detect less than 100 cells in clinical samples, e.g., blood and urine.

[0121] RNA isolated from each of the three pathogens (1 ng) was probed with a 24 gene probe set (FIG. 7). E. coli genes, left; K. pneumoniae genes, middle; and P. aeruginosa genes, right. E. coli RNA, top. K. pneumoniae, middle; and P. aeruginosa, bottom. The y-axis shows number of counts for each gene as detected by using digital gene expression technology. RNA from each of the organisms shows distinct expression signatures that allow facile identification of each of the pathogens.

[0122] This 24 gene probe set was used to probe crude E. coli lysates from 10,000 cells, 1000 cells, and 100 cells (FIG. 8). The distinct E. coli expression signature could be distinguished for down to 100 cells.

[0123] Clinical samples were simulated in spiked urine and blood samples. In the spiked urine sample, a urine sample was spiked with 105 E. coli bacteria/mL of urine. The sample was refrigerated overnight at 4.degree. C. and then the crude bacterial sample was lysed and probed with the 24-gene probe set used for the Gram negative bacteria to identify E. coli (FIGS. 9A, top panel, and 9B). Blood was spiked with 1000 cfu/ml and also detected with the 24-gene probe set (FIG. 9A, bottom panel).

[0124] Two clinical isolates of P. aeruginosa (obtained from Brigham and Women's clinical microbiology lab) were probed with the 24-gene probe set used for the Gram negative bacteria to demonstrate that the gene set is able to identify clinical diverse strains of the same bacterial genus (FIG. 10).

[0125] Identification of Escherichia coli directly in urine samples. E. coli strain K12 was grown in LB media at 37.degree. C. to late log phase culture. Bacteria were then added to urine specimens from healthy donors to a final concentration of 100,000 cfu/ml (as estimated by OD.sub.600). Urine samples were then left at room temperature for 0 hours, 4 hours, 24 hours, or 48 hours or placed at 4.degree. C. for 24 hours. 1 ml of spiked urine was centrifuged at 13,000.times.g for 1 minute. The supernatant was removed; pellets were resuspended in 100 microliters of LB media. Bacteria were treated with Bacteria RNase Protect (Qiagen), and then lysed in guianidinium isothiocyanate lysis buffer (RLT buffer, Qiagen). Lysates were used in the nCounter.TM. System assays per manufacturer's protocol.

[0126] Aliquots of patient urine specimens were directly assayed to detect E. coli transcripts in urinary tract infections (FIG. 22D). To condense the signals from multiple transcripts into a single metric that assesses the presence or absence of an organism, the raw counts from each probe were log transformed and summed. When applied to a set of 17 clinical E. coli isolates, every isolate was easily differentiated from a set of 13 non-E. coli samples (Z score>6.5 relative to non-E. coli controls, FIG. 22E).

Example 3

Drug Sensitivity of a Pathogen

[0127] Identification of fluoroquinolone and aminoglycoside resistance in Escherichia coli. Using published expression array data for E. coli upon exposure to fluoroquinolones and aminoglycosides (Sangurdekar D P, Srienc F, Khodursky A B. A classification based framework for quantitative description of large-scale microarray data. Genome Biol 2006;7(4):R32) sets of genes expected to be significantly down- or up-regulated upon exposure to fluoroquinolones and aminoglycosides were chosen. The pan-sensitive lab strain (K12), fluoroquinolone-resistant clinical isolates 1 and 2, and gentamicin-resistant clinical isolates (E2729181 and EB894940) were grown in LB media to log phase at 37.degree. C. A 2 ml aliquot of each culture was taken, and antibiotics were added to those aliquots at a final concentration of 8 .mu.g/ml ciprofloxacin or 128 .mu.g/ml gentamicin. Cultures were incubated at 37.degree. C. for 10 minutes. Five microliters of each culture was added to 100 microliters of guanidinium isothiocyanate lysis buffer and vortexed for 5 seconds. Lysates were used in the nCounter.TM. System assays per manufacturer's protocol. Counts were normalized to counts of proC; again proC appeared to be most comparable between experiments; fold induction for each gene was determined by comparing counts in the presence and absence of antibiotic exposure. There were clear signals from 9 probes (carA, deoC, flgF, htrL, recA, uvrA, ybhK, uup, and fabD) that show induction or repression in the drug sensitive K12 strain that distinguishes it from the two resistant clinical isolates (FIG. 11). A tenth probe, wbbK, was neither induced nor repressed, offering a useful comparison for genes with changes expression. Similarly, probes to eight genes show that these genes are either repressed (flgF, cysD, glnA, opgG) induced (ftsQ, b1649, recA, dinD) in the drug sensitive K12 strain that distinguishes it from the two resistant clinical isolates (FIG. 12)

[0128] Identification of methicillin resistance in Staphylococcus. Six S. aureus clinical isolates were grown to log phase at 37.degree. C. in LB media. A 2 ml aliquot of each culture was then taken; cloxacillin was added to a final concentration of 25 .mu.g/ml. Cultures were incubated at 37.degree. C. for 30 minutes. Five microliters of each culture was added to 100 microliters of guanidinium isothiocyanate lysis buffer and vortexed for 5 seconds. Lysates were used in the nCounter.TM. System assays per manufacturer's protocol. Using two independent probes (Table 2), expression of mecA was identified in the four isolates known to be methicillin-resistant. In contrast, there was no detectable mecA expression in the two isolates known to be methicillin-sensitive and minimal mecA expression in the absence of cloxacillin (FIG. 13).

[0129] Identification of vancomycin resistance in Enterococcus. Four Enterococcus clinical isolates were grown in LB media to log phase at 37.degree. C. A 2 ml aliquots were taken; vancomycin was added to a final concentration of 128 .mu.g/ml. Cultures were incubated at 37.degree. C. for 30 minutes. Five microliters of each culture was added to 100 microliters of guanidinium isothiocyanate lysis buffer and vortexed for 5 seconds. Lysates were used in the nCounter.TM. System assays per manufacturer's protocol. Using two independent probes (Table 2), expression of vanA was identified in the two isolates known to be vancomycin resistant. In contrast, there was no detectable vanA expression in the two isolates known to be vancomycin sensitive and minimal expression of vanA in the absence of vancomycin (FIG. 14). There was no detectable vanB expression in any of the four isolates.

[0130] Beyond the detection of transcripts for organism identification, detection of genes encoded on mobile genetic elements can provide greater genomic detail about a particular isolate. For example, bacterial isolates were probed for mecA mRNA, which confers resistance to methicillin in Staphylococci, and vanA mRNA, which confers resistance to vancomycin in Enterococci. In both cases, relevant transcripts were detected that allowed for rapid identification of MRSA and vancomycin-resistant Enterococcus (VRE) (FIG. 22F). Thus, direct detection of RNA is able to detect known resistance elements. In addition, this approach is able to discriminate isolates by other genetic factors, such as virulence factors acquired through horizontal genetic exchange in food-borne pathogens, i.e., Shiga toxin in Enterohemorrhagic or Shigatoxigenic E. coli.

[0131] Identification of drug resistance in TB. A 24 gene probe set was identified from published gene expression data to identify an expression signature that would allow identification of expression changes of drug sensitive TB upon exposure to different antibiotics, including isoniaid, rifampin, streptomycin, and fluoroquinolones (FIGS. 15-18). The magnitude of induction or repression after drug exposure is shown in Table 3.

[0132] Log phase M. tuberculosis cells at A.sub.600 0.3 were grown in inkwell bottles (10 ml volume, parallel cultures) in the presence of one of four different drugs (isoniazid, 0.4 .mu.g/ml; streptomycin, 2 .mu.g/ml; ofloxacin, 5 .mu.g/ml; rifampicin 0.5 .mu.g/ml). At the indicated time after the initiation of drug treatment (FIG. 15), cultures were harvested by centrifugation (3000.times.g, 5 minutes), resuspended in 1 ml Trizol, and bead beaten (100 nm glass beads, max speed, two one-minute pulses). Chloroform (0.2 ml) was added to the samples, and following a five minute centrifugation at 6000.times.g, the aqueous phase was collected for analysis.

[0133] Samples were diluted 1:10 and analyzed using NanoString.TM. probeset described in Table 2 per the manufacturer's protocol. The relative abundance of each transcript is first calculated by normalizing to the average counts of three housekeeping genes (sigA, rpoB, and mpt64), and then the data is plotted as a fold change relative to samples from untreated controls. The boxes indicate probes that were selected based on previous evidence of drug-specific induction (Boshoff et al., J Biol Chem. 2004, 279(38):40174-84.)

[0134] The drug resistant TB strain shows no expression signature induction upon exposure to isoniazid, in contrast to a drug sensitive strain, which clearly shows induction of an expression signature upon isoniazid exposure (FIG. 16). Three scatter plots comparing isoniazid sensitive and resistant TB strains are shown in FIG. 16, with each dot representing one of the 24 gene probes. The axes report number of transcripts as measured by digital gene expression technology (NanoString.TM.). Left--Comparison of expression in isoniazid resistant and isoniazid sensitive strains in the absence of drug treatment. Middle--Comparison of expression in drug treated vs. drug untreated isoniazid sensitive strain. Right--Comparison of expression in drug treated vs. drug untreated isoniazid resistant strain.

[0135] Different sets of genes are induced in drug-sensitive M. tuberculosis depending on the drug as seen in FIG. 17. The transcriptional responses of drug-sensitive and drug-resistant M. tuberculosis (A) Strain A50 (INH-R) treated with INH (0.4 .mu.g/ml) as described herein. (B) The SM-R clone S10 was treated with 2 .mu.g/ml streptomycin. Differential gene induction can be measured by digital gene expression of the TB 24 gene probe set to reveal a clear signature and allow identification of drug sensitivity (FIG. 18).

[0136] Three housekeeping genes, mpt64, rpoB, and sigA, were used for normalization. For each experimental sample, the raw counts for the experimental genes were normalized to the average of the raw counts of these three housekeeping genes, providing a measure of the abundance of the test genes relative to the control genes. Induction or repression is defined as a change in these normalized counts in drug-exposed samples as compared to non-drug-exposed samples. Using this methodology, the following genes were found to be induced or repressed in drug-sensitive TB after exposure to isoniazid, rifampin, fluoroquinolones, and streptomycin.

[0137] Isoniazid: For drug-dependent induction: kasA, fadD32, accD6, efpA, and Rv3675.1.

[0138] Rifampin: For drug-dependent induction: bioD, hisl, era, and Rv2296.

[0139] Fluoroquinolones: For drug-dependent induction: rpsR, alkA, recA, ltpl, and lhr; for drug-dependent repression: kasA and accD6.

[0140] Streptomycin: For drug-dependent induction: CHP, bcpB, gcvB, and groEL.

Example 4

A Phenotypic Expression Signature-Based Test to Identify Drug Sensitive And Resistant TB Using Digital Gene Expression With Molecular Barcodes

[0141] This example describes a phenotypic expression-signature-based test for the diagnosis of TB in sputum and rapid determination of resistance profile. The method is based on detection of genes whose expression profiles will uniquely detect TB and distinguish drug resistant and sensitive strains, with the creation of a probe set of bar-coded, paired molecular probes. The choice of genes was determined through bioinformatic analysis of expression profile data obtained using microarrays under a variety of growth conditions, including TB in axenic culture (both replicating and non-replicating states), TB in cell cultured macrophages, and TB spiked in sputum.

[0142] A. Define Signature for Identification of TB

[0143] A set of molecular probes have been identified that will specifically hybridize to mRNA from both replicating and non-replicating TB. The probes are specific for mRNA that is highly abundant under all growth conditions and is conserved across all TB strains. While unique DNA sequences have been previously defined to identify TB recognizing 16S rRNA (Amplicor, Roche) or the IS6110 region (Gen-probe), these defined regions do not have the optimal characteristics required for signatures in digital gene expression. The 16S rRNA is not sufficiently divergent among mycobacterial species that could distinguish between the different species using 50-base oligomer gene probes, which can tolerate low levels of genetic variability due to their length. The IS6110 region of the genome is not expressed at high enough levels under all growth conditions that would allow it to be used it as a robust signal to identify TB. Thus, an expression signature that will allow identification of TB from other mycobacterial species is described.

[0144] i. Bioinformatic gene analysis for conserved TB genes. Unique expression signatures for the detection of TB over other mycobacteria species have been defined. In general, the optimal genes for inclusion in a signature will fulfill the criteria of 1. having high expression levels (high mRNA copy number) to increase sensitivity, 2. being highly conserved across all TB strains as well as having highly conserved sequence, and 3. being highly specific for TB genome over all other mycobacteria species. Such genes were identified using a bioinformatic analysis of conserved genes in the available TB genomes that are not present in all other sequenced mycobacteria species (i.e., M. marinum, M. avium-intracellulaire, M. kansaii, M. fortuitum, M. abscessus). Over 40 TB genomes from clinically isolated strains that have been sequenced at the Broad Institute are available for analysis.

[0145] ii. Expression profile analysis of mRNA levels of candidate genes. A second criterion for selection of molecular probes for the detection of TB bacilli in sputum is that they hybridize to highly abundant, stable mRNAs to allow maximum sensitivity. Such mRNAs are anticipated to correspond to essential housekeeping genes. Genes have been selected using a combination of bioinformatic analysis of existing, publicly available expression data in a database created at the Broad Institute and Stanford University (tbdb.org) and experimental expression profiles on TB strain H37Rv using expression profiling to confirm a high level of expression of candidate genes under conditions permissive for replication (logarithmic growth) and non-replication induced by carbon starvation, stationary phase, and hypoxia. Expression profiling experiments on H37Rv are performed using a carbon starvation model of TB that has been established (starvation for 5 weeks in 7H9/tyloxapol), stationary phase growth, and the Wayne model for anaerobic growth (slowly agitated cultures in sealed tubes). Solexa/Illumina sequencing is used to determine expression profiles by converting mRNA to cDNA and using sequencing to count cDNA molecules. This quantitative method for identifying expression levels is more likely to reflect levels obtained using digital gene expression than microarray data and is a method that has been established with the Broad Institute Sequencing Platform. It is possible to multiplex 12 samples per sequencing lane given 75 bp reads and 10 million reads per lane.

[0146] iii. Probe selection of expression signature identifying TB. Because the digital gene expression technology is based on the hybridization of two 50 nucleotide probes to the mRNA of interest, two 50 base pair regions in the genes are identified from (Ai) and (Aii) that are unique within the genome to minimize non-specific hybridization and that contain minimal polymorphisms as evidenced from sequenced TB genomes. The probes are selected bioinformatically to fit within a 5 degree melting temperature window and with minimal mRNA secondary structure. The probes are tested against mRNA isolated from replicating and non-replicating TB (including multiple strains i.e., H37Rv, CDC1551, F11, Erdman), M. marinum, M. avium-intracellulaire, M. kansaii, and M. fortuitum to confirm the specificity of the entire probe set using available technology. Probes may be selected for these other mycobacterial species, which will allow for identification of these pathogens from sputum as well. The ability to identify intracellular bacilli is tested in a macrophage model of infection, to demonstrate the ability to detect TB mRNA in the presence of host mRNA. Finally, the sensitivity of the assay was determined by titrating down the number of TB bacilli (and thus mRNA present in cell lysates) in the sample tested. All experiments using digital gene expression is confirmed using quantitative RT-PCR against the same gene set. Improvement and refinement of the set will occur in an iterative manner.

[0147] B. Define Signature to Distinguish Sensitive and Resistant TB

[0148] A set of molecular probes that hybridizes to mRNAs that are specifically induced upon exposure to each individual TB drug has been identified, allowing a profile to be obtained that distinguishes drug sensitive and resistant strains. Signatures have been determined for exposure to isoniazid, rifampin, ethambutol, streptomycin, and moxifloxacin.

[0149] In addition to the above characteristics for ideal genes to be included in the signature (i.e., conserved across TB strains, specific for TB genome), several other characteristics are prioritized in gene selection for signatures of drug resistance. Because drug resistance will be determined by the difference between transcript induction in drug sensitive and drug resistant strains, ideal gene candidates will be highly induced in drug sensitive strains upon exposure to a given drug. Ideally, these genes are induced early and quickly, as this time period will determine to a large extent, the rapidity of the overall diagnostic test. Based on data using qRT-PCR, a transcriptional response to drug exposure is observed in as little as 1-2 hours (FIG. 19). Given the half-lives of mRNA molecules, exploiting gene induction rather than gene repression provides a more rapid and detectable response.

[0150] For all the described experiments involving isoniazid and streptomycin, TB strain H37Rv was used in a BSL3 setting in which a set of singly resistant strains has been generated to be used to compare to the wild-type, fully drug sensitive H37Rv. To ensure that rifampin remains a treatment option in the unlikely event of a laboratory-acquired infection, rifampin resistant mutants will be generated in an auxotrophic strain of TB (lysA, panC) that requires the addition of lysine and pantothenate for growth.

[0151] Finally, signatures that are unique to each antibiotic have been identified rather than a general stress response to any or all antibiotics. The rationale for this specificity is that in a clinical setting, many patients will have already been empirically treated with different antibiotics and thus some general stress response may have already been activated in the bacilli within a sputum sample. However, drug specific responses are preserved for testing and analysis.

[0152] i. Expression profiling in response to antibiotic exposure. Expression profiling to identify candidate genes that distinguish transcriptional responses in drug sensitive and resistant strains of H37Rv has been performed. Because the replication state or transcriptional activity of the bacilli in sputum is unknown, additional experiments are performed on non-replicating (induced through a 5 week carbon starvation model) bacilli. It will determined if the non-replicating bacilli require a short period (t.sub.1) of "growth stimulation" in rich media (7H9/OADC) in order to stimulate some basal transcription that can then be responsive to drug exposure (FIG. 20). The optimal period of time (t.sub.2) that is required for drug exposure in order to obtain robust signature to distinguish drug sensitive and resistant strains and the optimal drug concentration is also determined to obtain a robust, reproducible response. These experiments will be performed for each of the individual antibiotics.

[0153] A completely non-replicating state is the "worst case scenario" (i.e., the longest period that would be required) if bacilli in sputum is in a non-replicating, dormant state. In fact, based on published work examining expression profiles from bacilli in patient sputum, this period will be extremely short if necessary at all, given that expression profiles were obtained directly from sputum bacilli. (Of note, this published data will also be incorporated into the analysis to provide initial insight into possible gene candidates in bacteria in sputum.) A matrix of profiling experiments are performed, varying the time of exposure to rich 7H9/OADC media (ti) from 0, 1, and 2 hours; for each t.sub.1, and the time of exposure to each antibiotic (t.sub.2). For each set of t.sub.1 and t.sub.2, the antibiotic concentration is varied from lx, 3.times., and 5.times. the minimum inhibitory concentration (MIC) for each antibiotic, for both sensitive and resistant H37Rv strains to determine the optimal parameters. Expression profiling will be used to identify optimal conditions for producing robust, reproducible profiles.

[0154] Based on the optimized conditions (t.sub.1 and t.sub.2), expression profiling is performed on drug sensitive and resistant H37Rv strains under these conditions. Bioinformatic analysis is performed to identify genes for each drug in which the level of induction is high in drug sensitive strains relative to drug resistant strains (with the exception of rifampin in which the level of repression is high in drug sensitive strains relative to drug resistant strains). The levels of expression will be compared between drug sensitive and drug resistant strains and confirmed by quantitative RT-PCR.

[0155] ii. Develop analysis algorithm to identify drug resistance. An optimal algorithm is determined to analyze expression ratios for sets of genes that distinguish sensitive and resistant strains (as defined by standard MIC measurements). One of the strengths of this method is that for the majority of cases (i.e., those cases which have not been pre-exposed to TB antibiotics), a comparison can be done between the gene expression levels of the same strain not exposed and exposed to a given antibiotic. Quantitative RT-PCR is used to measure mRNA levels from H37Rv under conditions that include 1. exposure to no antibiotic, 2. exposure to isoniazid, 3. exposure to rifampin, 4. exposure to ethambutol, 5. exposure to streptomycin, and 6. exposure to moxifloxacin. The level of expression from a given gene after exposure to antibiotic will be normalized to the level of expression from a set of steady-state, housekeeping genes (i.e., sigA, which encodes the principal sigma factor that stimulates the transcription of housekeeping genes, and rpoB, which encodes a synthetic subunit of RNA polymerase) and compared to the same normalized level of expression of the same gene in the absence of antibiotic exposure. Comparisons will also be made to standard sensitive and resistant control strains (FIG. 21). Ideally, exposure to a particular drug will induce gene expression in drug sensitive strains to high levels, A>>B while for drug resistant strains, which are insensitive to the drug exposure, A=B. (The exception will be for rifampin, in which gene repression of the mRNAs with shortest half-life is detected, given the mechanism of rifampin, i.e., A=C<<B=D.) Because of the large dynamic range of transcription levels, genes are selected for which the ratio of C/D is maximal, thus allowing for clear robust differentiation of sensitive and resistant strains. In addition, optimal, unique set of genes have been selected for each individual antibiotic so that there is no overlap in induced responses with other antibiotics.

[0156] iii. Analysis of impact of pre-antibiotic exposure on TB bacilli signatures. To determine the efficacy of these signatures to identify resistance patterns even in the event that a patient has been pre-treated with antibiotics, drug sensitive and resistant TB bacilli (replicating, non-replicating, within macrophages) are pre-expose to amoxicillin, cephalosporins, trimethoprim-sulfamethoxazole, and erythromycin which are common antibiotics to which a patient may be exposed in TB endemic settings, prior to application of this test. Pre-exposure of TB bacilli to different combinations of current TB drugs is also performed to determine if such pre-exposure also interferes with the transcriptional response our ability to detect such a response. Unique signatures should be preserved, thus not impairing our ability to determine resistance. Gene expression levels of a set of genes of interest will be determined using quantitative RT-PCR.

[0157] iv. Probe selection of expression signature to identify resistance profile. Based on the data obtained in Sub-aim Bi, a set of candidate genes have been selected that will create a signature for transcriptional response to antibiotic exposure. Two 50 base-pair regions for each gene are selected within regions that are highly conserved across TB genomes. The probes are selected bioinformatically to fit within a 5 degree melting temperature window and with minimal mRNA secondary structure. These probes will be used to compare drug sensitive and resistant strains using available technology under conditions described above, including bacilli in axenic culture that are initially replicating or nonreplicating, intracellular bacilli in a cell culture macrophage infection model that we have currently in our laboratory, and bacteria pre-exposed to different antibiotic combinations. All results will be compared to data obtained by quantitative RT-PCR. Improvement and refinement of the set will occur in an iterative manner.

[0158] C. Optimization of Sample Processing for Digital Gene Expression with Molecular Bar Codes.

[0159] In addition to defining probe sets for identification of expression signatures, the second major challenge is to optimize processing of samples in order to measure digital gene expression from bacilli present within the sample. Because the majority of TB cases is pulmonary in origin and the majority of samples to be processed is patient sputum, processing of sputum samples to obtain mRNA measurements from infecting TB bacilli is optimized. A spiked sputum model is used in which sputum collected from healthy, uninfected patients (who have not been treated with antibiotics) was spiked with TB bacilli that are either in a replicating or non-replicating (carbon starved) state. Issues that will be addressed include dealing with the variable viscosity of sputum and efficiently lysing the TB bacilli within a sputum sample. One of the major advantages of digital gene expression is the ability to hybridize the mRNAs to their respective probes in extremely crude samples, including crude cell lysates, fixed tissue samples, cells in whole blood and urine, cells from crude lysates of ticks, and samples containing 400 mM guanidinium isothiocyanate (GITC), polyacrylamide, and trizol. Thus, initial indications suggest that no purification step will be required after lysing the bacteria within the sputum, as no purification has been required from whole blood, urine, or fixed tissue samples. The only requirement is sufficient mixing to allow contact between the probes and the mRNAs.

[0160] For these experiments, uninfected sputum is obtained from the Brigham and Women's Hospital (BWH) specimen bank. The specimen bank is an IRB regulated unit directed by Lyn Bry, MD, PhD of the BWH pathology department. Discarded sputum will be obtained after all processing is completed in the laboratory (generally within 12-24 hours of collection). Sputum is only collected from subjects who have not received any antibiotics in the previous 48 hours. All samples will be de-identified and no protected health information is collected. Based on the current load of the specimen lab, the necessary amount of sputum (25-50 mL) is obtained within a matter of weeks.

[0161] i. Sputum processing. Sputum samples vary in bacterial load, consistency, and viscosity. Several approaches are tested to maximize the rapidity with which the bacteria come into contact with bactericidal levels of antibiotic in media conditions and exposure to oligomer probes for hybridization. Several methods of processing sputum, including no processing, passage of sputum through a syringe needle, treatment with lysozyme and/or DNase, Sputalysin (Calbiochem; 0.1% DTT) which is standardly used to treat sputum from cystic fibrosis patients, or simple dilution of the sample into some minimal denaturant (i.e., GITC) are used. Sputum spiked with H37Rv and processed by a variety of methods to alter its viscosity are performed to determine if any of these methods interferes with the technology.

[0162] ii. Bacterial lysis in sputum spiked with TB bacilli. Several approaches to efficiently lyse bacterial cells, arrest transcription and enzyme-based mRNA degradation, and make mRNA accessible to the probes are used in the assay. Previous studies examining the transcriptional responses of bacteria in sputum have first added GTC or similar reagents to the samples to arrest the transcriptional response. Centrifugation can then be used to concentrate bacteria from sputum samples after GTC treatment. Lysis of mycobacteria is generally accomplished through physical means, i.e. homogenization with 0.1 ml glass or zirconium beads. Such physical means are explored to disrupt the bacteria within processed sputum to analyze bacilli that has been spiked into uninfected human sputum using the designed probe set from 1A to detect TB bacilli.

[0163] Alternative methods are used for lysis that may be more amenable to field-based considerations, including phage lysis. Addition of phage, or more optimally, purified phage lysin(s), may provide a low-cost, simple, and non-electrical option for bacterial lysis. The Fischetti lab (Rockefeller University) has recently demonstrated the rapid and thorough lysis of several Gram-positive species using purified bacteriophage lysins, which enzymatically hydrolyse peptidoglycan, leading to osmotic lysis. The Hatfull lab (University of Pittsburgh) is currently working to characterize the activity and optimize the performance of LysA enzymes from several lytic mycobacteriophages. In the absence of purified lysins, investigations are performed to determine whether high MOI-infection of TB with a lytic bacteriophage such as D29 can efficiently lyse TB in sputum. It is currently unclear how this approach will affect the transcriptional profile of the bacteria, since it will likely need to occur in the absence of denaturants that would impair the binding, entry, and subsequent lytic properties of the phage. The mycobacteriophage TM4 also expresses a structural protein, Tmp, with peptidoglycan hydrolase activity, which may allow it to be used as a rapid means of cell lysis at high MOI. Once lysed, the mRNA is stabilized with GITC, RNAlater, or other reagents that will inactivate endogenous RNAse activity.

Example 5

[0164] Bacterial and fungal culture: E. coli, K. pneumoniae, P. aeruginosa, Providencia stuartii, P. mirabilis, S. marcescens, E. aerogenes, E. cloacae, M morganii, K. oxytoca, C. freundii, or C. albicans were grown to an OD.sub.600 of .about.1 in Luria-Bertani medium (LB). For mixing experiments, equal numbers of bacteria as determined by OD.sub.600 were combined prior to lysis for NanoString.TM. analysis. Mycobacterium isolates were grown in Middlebrook 7H9 medium to mid-log phase prior to harvest or antibiotic exposure as described below.

[0165] Derivation of resistant laboratory bacterial strains: E. coli laboratory strain J53 with defined fluoroquinolone-resistant chromosomal mutations in gyrA (gyrA1-G81D; gyrA2 - S83L) were obtained from the Hooper lab, Massachusetts General Hospital, Boston, Mass. Plasma-mediated quinolone resistance determinants (oqxAB, qnrB, aac6-Ib) were purified from clinical isolates previously determined to contain these plasmids. E. coli parent strain J53 was transformed with these plasmids, and their presence was confirmed with PCR.

[0166] Viral and plasmodium infections: HeLa cells (1.times.10.sup.6), 293T cells (2.times.10.sup.5), and human peripheral blood monocytes (5.times.10.sup.5), were infected with HSV-1 strain KOS and HSV-1 strain 186 Syn+, influenza A PR8, or HIV-1 NL-ADA, respectively, at the noted MOIs. Primary red blood cells (5.times.10.sup.9) were infected with P. falciparum strain 3D7 until they reached the noted levels of parasitemia. At the indicated times, the cells were washed once with PBS and harvested.

[0167] Antibiotic exposure: Cultures of E. coli or P. aeruginosa were grown to an OD.sub.600 of .about.1 in LB. Cultures were then divided into two samples, one of which was treated with antibiotic (E. coli for 10 minutes: ciprofloxacin 4-8 .mu.g/ml or 300 ng/ml, gentamicin 64 or 128 .mu.g/ml, or ampicillin 500 .mu.g/ml; P. aeruginosa for 30 minutes: ciprofloxacin 16 .mu.g/ml). Both treated and untreated portions were maintained at 37.degree. C. with shaking at 200 rpm. Cultures of S. aureus or E. faecium were grown to an OD.sub.600 of .about.1 in LB. Cultures were then exposed to cloxacillin (25 .mu.g/mL) or vancomycin (128 .mu.g/mL), respectively, for 30 minutes.

[0168] Cultures of M. tuberculosis were grown to mid-log phase then normalized to OD.sub.600 of 0.2. 2 ml of each culture were treated with either no antibiotic or one of the following (final concentration): isoniazid 0.2 - 1.0 .mu.g/ml; streptomycin 5 .mu.g/ml, rifampicin 0.5 .mu.g/ml, or ciprofloxacin 5 .mu.g/ml. The plates were sealed and incubated without shaking for 3 or 6 hours. Lysates were then made and analyzed as described above, using probes listed in Table 6.

[0169] Sample processing: For Gram negative isolates, 5-10 .mu.l of culture was added directly to 100 .mu.l RLT buffer and vortexed. For clinical specimens, 20 .mu.l of urine from patients determined by a clinical laboratory to have E. coli urinary tract infection was added directly to 100 .mu.l of RLT buffer. For mycobacteria, 1.5 ml of culture was centrifuged, then resuspended in Trizol (Gibco) with or without mechanical disruption by bead beating, and the initial aqueous phase was collected for analysis. Viral and parasite RNA were similarly prepared using Trizol and chloroform. For all lysates, 3-5 .mu.l were used directly in hybridizations according to standard NanoString.TM. protocols. Raw counts were normalized to the mean of all probes for a sample, and fold induction for each gene was determined by comparing antibiotic-treated to untreated samples.

[0170] Selection of organism identification probes: To select NanoString.TM. probes for differential detection of organisms, all publically available sequenced genomes for relevant organisms were compared. Genes conserved within each species were identified by selecting coding sequences (CDS) having at least 50% identity over at least 70% of the CDS length for all sequenced genomes for that species. The CDS was broken into overlapping 50-mers and retained only those 50-mers perfectly conserved within a species and having no greater than 50% identity to a CDS in any other species in the study. Available published expression data in Gene Expression Omnibus was reviewed, and genes with good expression under most conditions were selected. To identify unique M. tuberculosis probes, published microarray data was used to identify highly expressed genes falling into one of two classes: those unique to the M. tuberculosis complex (>70% identity to any other gene in the non-redundant database using BLASTN and conserved across all available M. tuberculosis and M. bovis genomes), as well as those with >85% identity across a set of clinically relevant mycobacteria including M. tuberculosis, M. avium, and M. paratuberculosis. C. albicans probes were designed against 50-mer segments of C. albicans genome unique in comparison with the complete genomes of ten additional pathogenic organisms that were included in its probe set. Viral probes were designed against highly conserved genes within a virus (i.e. all HSV-2 or HIV-1 isolates) that were less conserved among viruses within the same family, (i.e between HSV-1 and HSV-2). Plasmodium falciparum probes were designed against genes expressed abundantly in each of the blood stages of the parasite life cycle. All probes were screened to avoid cross hybridization with human RNA.

[0171] Probe Sets: For Gram-negative organism identification, a pooled probe-set containing probes for E. coli, K. pneumoniae, and P. aeruginosa were used. For mycobacterial organism identification, species-specific probes for M. tuberculosis and broader mycobacterial genus probes were among a larger set of probes against microbial pathogens.

[0172] Probes were designed for genes that are differentially regulated upon exposure to various antimicrobial agents to measure the presence or absence of a response (Sangurdekar et al., Genome Biology 7, R32 (2006); Anderson et al., Infect. Immun. 76, 1423-1433, (2008); Brazas and Hancock, Antimicrob. Agents Chemother. 49, 3222-3227, (2005)). Following 10-30 minute exposures of wild-type E. coli K-12 to ciprofloxacin, gentamicin, or ampicillin, the expected changes in transcript levels that together define the drug-susceptible expression signature for each antibiotic were observed (FIGS. 23A and 23B, Table 7). These signatures were not elicited in the corresponding resistant strains (FIGS. 23A and 23B).

[0173] Rapid phenotypic drug-susceptibility testing would make a particularly profound impact in tuberculosis, as established methods for phenotypic testing take weeks to months (Minion et al., Lancet Infect Dis 10, 688-698, (2010)). Expression signatures in response to anti-tubercular agents isoniazid, ciprofloxacin, and streptomycin were able to distinguish susceptible and resistant isolates after a 3 to 6 hour antibiotic exposure (FIG. 23C). Some genes in the transcriptional profiles are mechanism-specific (i.e., recA, alkA, and lhr for ciprofloxacin; groEL for streptomycin; and kasA and accD6 for isoniazid). Other genes, particularly those involved in mycolic acid synthesis or intermediary metabolism, are down-regulated in response to multiple antibiotics, indicating a shift away from growth towards damage control.

[0174] To condense these complex responses into a single, quantitative metric to distinguish susceptible and resistant strains, the metric of the mean-squared distance (MSD) of the expression response was utilized from each experimental sample from the centroid of control, antibiotic-susceptible samples. Antibiotic-susceptible strains cluster closely, thus possessing small MSDs. Conversely, antibiotic-resistant strains have larger values, the result of numerous genes failing to respond to antibiotic in a manner similar to the average susceptible strain. MSD is reported as dimensionless Z-scores, signifying the number of standard deviations a sample lies from the average of sensitive isolates of E. coli (FIGS. 24A, 24B, 27, and 28) or M. tuberculosis (FIGS. 24C and 29).

[0175] Because expression profiles reflect phenotype rather than genotype, resistance mediated by a variety of mechanisms can be measured using a single, integrated expression signature. The transcriptional responses of ciprofloxacin-susceptible E. coli strain J53 were compared with a series of isogenic mutants with different mechanisms of resistance: two with single mutations in the fluoroquinolone-target gene topoisomerase gyrA (G81D or S83L) and three carrying episomal quinolone resistance genes including aac(6')-Ib (an acetylating, inactivating enzyme), qnrB (which blocks the active site of gyrA), and oqxAB (an efflux pump). In comparison with the parent strain, all J53 derivatives had large Z-scores, reflective of resistance (FIG. 24B).

[0176] Response to isoniazid was compared in a series of sensitive clinical and laboratory isolates and two isoniazid resistant strains, including an H37Rv-derived laboratory strain carrying a mutation in katG (S315T), a catalase necessary for pro-drug activation, and a clinical isolate with a mutation in the promoter of inhA (C-15T), the target of isoniazid. Due to their disparate resistance mechanisms, these two strains have differing levels of resistance to isoniazid, with the katG mutant possessing high level resistance (>100-fold increase in minimal inhibitory concentration (MIC) to >6.4 .mu.g/mL), while the inhA promoter mutation confers only an 8-fold increase in the MIC to 0.4 .mu.g/mL. Exposure to low isoniazid concentrations (0.2 .mu.g/mL) failed to elicit a transcriptional response in either resistant strain, but at higher isoniazid concentrations (1 .mu.g/mL), the inhA mutant responds in a susceptible manner in contrast to the katG mutant (FIG. 24C). Thus, this method is not only mechanism-independent, but can also provide a relative measure to distinguish high and low-level resistance.

[0177] Finally, because RNA is almost universal in pathogens ranging from bacteria, viruses, fungi, to parasites, RNA detection can be integrated into a single diagnostic platform applicable across a broad range of infectious agents. Using a large pool of mixed pathogen probes, we were able to directly and specifically detect signals to identify the fungal pathogen Candida albicans (FIG. 25A); human immunodeficiency virus (HIV), influenza virus, and herpes simplex virus-2 (HSV-2) in cell culture in a dose dependent manner (FIGS. 25B-D); and the different stages of the Plasmodium falciparum life cycle in infected erythrocytes (FIG. 25E).

[0178] NanoString.TM. data analysis and calculation of distance metric mean squared distance for drug-sensitivity: For all drug-treated samples, raw NanoString.TM. counts for each probe were first normalized to the mean of all relevant (i.e., species-appropriate) probes for each sample. Fold-change in transcript levels was determined by comparing the normalized counts for each probe in the antibiotic-treated samples with the corresponding counts in the untreated baseline sample for each test condition.

[0179] To transform qualitative expression signatures into a binary outcome of sensitive or resistant, an algorithm was developed to calculate mean squared distance (MSD) of a sample's transcriptional profile from that of sensitive strains exposed to the same drug. The MSD metric in drug-sensitivity experiments was calculated as follows:

[0180] 1. Variation in sample amount is corrected for by normalizing raw values to the average number of counts for all relevant probes in a sample.

[0181] 2. A panel of NanoString.TM. probes, which we denote is selected. The subscript j runs from 1 to N.sub.probes, the total number of selected probes. The analysis is restricted to probes that changed differentially between drug-sensitive and drug-resistant isolates.

[0182] 3. Replicates of the drug-sensitive strain are defined as N.sub.samp. For each replicate, normalized counts for each probe P.sub.j before or after drug treatment were denoted C.sub.i,p,.sub.j.sup.before or C.sub.i,p,.sub.j.sup.after , with i signifying the sample index.

[0183] 4. "Log induction ratio" is next computed:

S.sub.i,P.sub.j.ident.In[C.sub.l,P.sub.j.sup.before/C.sub.l,P.sub.j.sup.- after]

[0184] Log transforming the ratio in this way prevents any single probe from dominating the calculated MSD.

[0185] 5. The average induction ratio of the drug sensitive samples, S, is calculated by summing over the different biological replicates and normalizing by the number of samples:

S ? = ? = ? ? S ? N ? ##EQU00001## ? indicates text missing or illegible when filed ##EQU00001.2##

[0186] 6. MSD is next calculated for the each of the replicates of the drug sensitive strain (of index i), a number that reflects how different a sample is from the average behavior of all drug sensitive samples:

MSD ? = ? = ? ? ( S ? S _ ? ) ? N ? ##EQU00002## ? indicates text missing or illegible when filed ##EQU00002.2##

[0187] 7. Induction ratios for resistant strains, R.sub.i,P.sub.j, are calculated similarly to those of sensitive strains:

R.sub.i,P.sub.j.ident.IN[C.sub.i,P.sub.j.sup.before/C.sub.i,P.sub.j.sup.- after]

[0188] 8. The MSD for the drug resistant strains is calculated relative to the centroid of the drug-sensitive population:

MSD ? = ? = ? ? ( R ? R _ ? ) ? N ? ##EQU00003## ? indicates text missing or illegible when filed ##EQU00003.2##

[0189] Because most sensitive strains behave similarly to the average sensitive strain the typical value for MSDis small compared to the typical value for a resistant strain, MSD.

[0190] Finally, statistical significance of the measured MSD values were assigned. Because the MSDvalues are the sum of a number of random deviations from a mean, they closely resemble a normal distribution, a consequence of the Central Limit Theorem.

[0191] Therefore, z-scores, which reflect the number of standard deviations away a given sample is relative to the drug sensitive population, were computed for each sample:

Z ? = MSD ? MSD ? _ .sigma. ? ##EQU00004## ? indicates text missing or illegible when filed ##EQU00004.2##

[0192] where the standard deviations and means are defined as:

.sigma. ? = 1 N ? ? = ? ? ( MSD ? MSD ? _ ) ? ##EQU00005## ? indicates text missing or illegible when filed ##EQU00005.2##

[0193] and:

MSD ? _ = ? = ? ? MSD ? ##EQU00006## ? indicates text missing or illegible when filed ##EQU00006.2##

[0194] This metric was applied to the analysis of numerous laboratory and clinical isolates that were tested against different antibiotics and the data are shown in FIGS. 24, 27, 28, and 29.

[0195] Calculation of distance metric for organism identification: To transform the information from multiple probes into a binary outcome, raw counts for each probe were log-transformed. Log transforming the ratio in this way prevents any single probe from dominating the analysis. These log-transformed counts were then averaged between technical replicates.

[0196] A panel of NanoString.TM. probes, which are denoted P.sub.j, is selected as described. The subscript j runs from 1 to N.sub.probes, the total number of selected probes.

S.sub.i,P.sub.J.ident.In[C.sub.i,P.sub.j]

[0197] Because organism identification depends on an ability to detect transcripts relative to mocks or different organisms, background level of NanoString.TM. counts in samples prepared without the organism of interest was thus used to define a control centroid. The centroid of these control samples, S, is calculated by summing over the different biological replicates and normalizing by the number of samples:

S ? = ? = ? ? S ? N ? ##EQU00007## ? indicates text missing or illegible when filed ##EQU00007.2##

[0198] MSD is next calculated for the averaged technical replicates of the experimental samples (of index i), a number that reflects how different a sample is from the average behavior of all control samples:

MSD ? = ? = ? ? ( S ? S _ ? ) ? N ? ##EQU00008## ? indicates text missing or illegible when filed ##EQU00008.2##

[0199] Finally, statistical significance was assigned to the measured MSD values.

[0200] Because the MSDvalues are the sum of a number of random deviations from a mean, they closely resemble a normal distribution, a consequence of the Central Limit Theorem. We therefore computed z-scores for each sample, which reflect the number of standard deviations away a given sample is relative to the control population:

Z ? = MSD ? MSD ? _ .sigma. ? ##EQU00009## ? indicates text missing or illegible when filed ##EQU00009.2##

[0201] where the standard deviations and means are defined as:

.sigma. ? = 1 N ? ? = ? ? ( MSD ? MSD ? _ ) ? ##EQU00010## ? indicates text missing or illegible when filed ##EQU00010.2##

[0202] and:

MSD ? _ = ? = ? ? MSD ? ##EQU00011## ? indicates text missing or illegible when filed ##EQU00011.2##

[0203] This metric was applied to the analysis of numerous laboratory strains and clinical isolates that were tested for the relevant bacterial species as shown in FIG. 26 and Table 4. A strain was identified as a particular organism if the MSD>2 for that organism.

TABLE-US-00001 TABLE 4 Numbers of laboratory and clinical isolates tested with organism identification probes. Organism Laboratory strains tested Clinical isolates tested E. coli 2 17 K. pneumoniae 0 4 P. aeruginosa 1 9 M. tuberculosis 1 10

TABLE-US-00002 TABLE 5 Genes used for bacterial organism identification. Organism Gene Annotated function E. coli ftsQ Divisome assembly murC Peptidoglycan synthesis putP Sodium solute symporter uup Subunit of ABC transporter opgG Glucan biosynthesis K. pneumoniae mraW S-adenosyl-methyltransferase ihfB DNA-binding protein clpS Protease adaptor protein lrp Transcriptional regulator P. aeruginosa mpl Ligase, cell wall synthesis proA Gamma-glutamyl phosphate reductase dacC Carboxypeptidase, cell wall synthesis lipB Lipoate protein ligase sltB1 Transglycosylase Conserved carD Transcription factor Mycobacterium infC Translation initiation factor M. tuberculosis Rv1398c Hypothetical protein mptA Immunogenic protein 64 hspX Heat shock protein

TABLE-US-00003 TABLE 6 Laboratory and clinical isolates tested for susceptibility profiling. Clinical isolates are designated CI. Sensitive (S) Organism Antibiotic or Resistant (R) Strain MIC* E. coli Cipro- S K12 30 ng/ml floxacin S J53 30 ng/ml S CIEC9955 <0.1 .mu.g/ml S CICr08 <.1 .mu.g/ml R CIEC1686 50 .mu.g/ml R CIEC9779 50 .mu.g/ml R CIEC0838 50 .mu.g/ml R CIqnrS 6.25 .mu.g/ml R CIaac6-Ib >100 .mu.g/ml R CIqnrA 12.5 .mu.g/ml R CIqnrB 6.25 .mu.g/ml E. coli Gentamicin S K12 8 .mu.g/ml S CIEC1676 8 .mu.g/ml S CIEC9955 16 .mu.g/ml S CIEC1801 8 .mu.g/ml R CIEC4940 >250 .mu.g/ml R CIEC9181 >250 .mu.g/ml R CIEC2219 125 .mu.g/ml E. coli Ampicillin S K12 4 .mu.g/ml J53 4 .mu.g/ml DH5.alpha. 8 .mu.g/ml R CIEC9955 >250 .mu.g/ml CIEC2219 >250 .mu.g/ml CIEC0838 >250 .mu.g/ml CIEC9181 >250 .mu.g/ml P. aeruginosa Cipro- S PAO-1 1 .mu.g/ml floxacin S CIPA2085 0.4 .mu.g/ml S CIPA1189 0.4 .mu.g/ml S CIPA9879 0.4 .mu.g/ml R CIPA2233 50 .mu.g/ml R CIPA1839 25 .mu.g/ml R CIPA1489 25 .mu.g/ml M. tuberculosis Isoniazid S H37Rv 0.05 .mu.g/ml S AS1 (CI) <0.2 .mu.g/ml S AS2 (CI) <0.2 .mu.g/ml S AS3 (CI) <0.2 .mu.g/ml S AS4 (CI) <0.2 .mu.g/ml S AS5 (CI) <0.2 .mu.g/ml S AS10 (CI) <0.2 .mu.g/ml R A50 >6.25 .mu.g/ml R BAA-812 0.4 .mu.g/ml M. tuberculosis Cipro- S mc.sup.26020 0.5 .mu.g/ml floxacin S AS1 (CI) <1 .mu.g/ml S AS2 (CI) <1 .mu.g/ml S AS3 (CI) <1 .mu.g/ml S AS4 (CI) <1 .mu.g/ml S AS5 (CI) <1 .mu.g/ml S AS10 (CI) <1 .mu.g/ml R C5A15 16 .mu.g/ml M. tuberculosis Streptomycin S H37Rv 1 .mu.g/ml S AS1 (CI) <2 .mu.g/ml S AS2 (CI) <2 .mu.g/ml S AS3 (CI) <2 .mu.g/ml S AS4 (CI) <2 .mu.g/ml S AS5 (CI) <2 .mu.g/ml R CSA1 >32 .mu.g/ml

TABLE-US-00004 TABLE 7 Genes associated with antibiotic sensitivity signatures in E. coli, P. aeruginosa, and M. tuberculosis. Organism Antibiotic Gene Annotated function E. coli Ciprofloxacin dinD DNA-damage inducible protein recA DNA repair, SOS response uvrA ATPase and DNA damage recognition protein uup predicted subunit of ABC transporter Gentamicin pyrB aspartate carbamoyltransferase recA DNA repair, SOS response wbbK lipopolysaccharide biosynthesis Ampicillin hdeA stress response proC pyrroline reductase opgG glucan biosynthesis P. aeruginosa Ciprofloxacin PA_4175 probable endoprotease mpl peptidoglycan biosynthesis proA Glutamate-semialdehyde dehydrogenase M. tuberculosis Ciprofloxacin lhr helicase rpsR ribosomal protein S18-1 ltp1 lipid transfer alkA base excision repair recA recombinase kasA mycolic acid synthesis accD6 mycolic acid synthesis Isoniazid efpA efflux pump kasA mycolic acid synthesis accD6 mycolic acid synthesis Rv3675 Possible membrane protein fadD32 mycolic acid synthesis Streptomycin Rv0813 conserved hypothetical protein groEL Heat shock protein bcpB peroxide detoxification gcvB glycine dehydrogenase accD6 mycolic acid synthesis kasA mycolic acid synthesis

[0204] The direct measurement of RNA expression signatures described herein can provide rapid identification of a range of pathogens in culture and directly from patient specimens. Significantly, phenotypic responses to antibiotic exposure can distinguish susceptible and resistant strains, thus providing an extremely early and rapid determination of susceptibility that integrates varying resistance mechanisms into a common response. This principle represents a paradigm shift in which pathogen RNA forms the basis for a single diagnostic platform that could be applicable in a spectrum of clinical settings and infectious diseases, simultaneously providing pathogen identification and rapid phenotypic antimicrobial susceptibility testing.

REFERENCES

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[0222] 18. Fischetti V A. 2005. Bacteriophage lytic enzymes: novel anti-infectives. Trends Microbiol. 13(10):491-6

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[0228] 24. Klevens R M, Edwards J R, Richards C L, Jr., Horan T C, Gaynes R P, Pollock D A, Cardo D M. Estimating health care-associated infections and deaths in U.S. hospitals, 2002. Public Health Rep. 2007;122(2):160-6. PMCID: 1820440.

[0229] 25. Klevens R M, Edwards J R, Gaynes R P. The impact of antimicrobial-resistant, health care-associated infections on mortality in the United States. Clin Infect Dis. 2008;47(7):927-30.

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Other Embodiments

[0242] It is to be understood that while the invention has been described in conjunction with the detailed description thereof, the foregoing description is intended to illustrate and not limit the scope of the invention, which is defined by the scope of the appended claims. Other aspects, advantages, and modifications are within the scope of the following claims.

Sequence CWU 1

1

2511100DNAArtificial Sequencesynthetically generated oligonucleotide 1cgacagtgtt ggtgagcggc tgggtcgtgt tgggctggat ggaagatgcg caacgcctgc 60cgctctcaaa gctggtgttg accggtgaac gccattacac 1002100DNAArtificial Sequencesynthetically generated oligonucleotide 2gaattgttac cgcgagtggg gcgtcagacc acgacttacg gcttcagcga agatgccgac 60gtgcgtgtag aagattatca gcagattggc ccgcaggggc 1003100DNAArtificial Sequencesynthetically generated oligonucleotide 3ttgtggatgt gcagtcgaaa atctatctgc gcgataaagt cggcaaactg ggggttgcac 60cgttaaccag tatgttcctg tttgggccga accaaccgtc 1004100DNAArtificial Sequencesynthetically generated oligonucleotide 4tagtgtttag tttgctgggt aaagcgccgt cagcggcgat gcaaaaacgc tttgccgagg 60ccgatgcgca ctatcattcg gctccgccgt cacggttgca 1005100DNAArtificial Sequencesynthetically generated oligonucleotide 5ctcggaaatg tataaacgcg tgaataaaat tattccgcac ctgatccgtc aggaaaaaga 60agactccgaa accttccagg gcgaaggcca cttctcggtg 1006100DNAArtificial Sequencesynthetically generated oligonucleotide 6aacgctatca cgatatttcg cgcctggtga tgaacgaccc gagcgagaaa aatctcaacg 60aactggcgaa ggttcaggaa cagctggatc accacaacct 1007100DNAArtificial Sequencesynthetically generated oligonucleotide 7gagctagtag aaaaaggtaa atcattaggt gcaaaacgtg tcatgccttt agcagtatct 60ggaccattcc attcatcgct aatgaaagtg attgaagaag 1008100DNAArtificial Sequencesynthetically generated oligonucleotide 8taacagctat caccggaagc ggcccagcat ttttatatca tgtattcgag caatatgtta 60aagctggtac gaaacttggt ctagaaaaag aacaagttga 1009100DNAArtificial Sequencesynthetically generated oligonucleotide 9ggagaaatgg cattaggtag aaacgtagta gttggtttca tgacttggga cggttacaac 60tatgaggatg ccgttatcat gagtgaaaga cttgtgaaag 1001050DNAArtificial Sequencesynthetically generated oligonucleotide 10aaagatcctg aaggcaataa atatatggat atgttatctg catattccgc 501150DNAArtificial Sequencesynthetically generated oligonucleotide 11aatattaaaa atgactgaag acggtactga tgaaatcatt tctacacgtt 501250DNAArtificial Sequencesynthetically generated oligonucleotide 12acaggtcatt taggatttta tgcggattgg ttacctcatg aagttgaaaa 501350DNAArtificial Sequencesynthetically generated oligonucleotide 13aaattagacc gagtaggtaa agaaccattt gaattattag atgaaatcga 501450DNAArtificial Sequencesynthetically generated oligonucleotide 14aaatatttcg gaccgtatcc gaatgcatat tctgctcaag aaactaaaaa 501550DNAArtificial Sequencesynthetically generated oligonucleotide 15aaattcaaga aaaatgggat gcagaagatc aataccataa agcgttagaa 501650DNAArtificial Sequencesynthetically generated oligonucleotide 16aaatatacaa acttatccaa aaggcaagtc aatttaaatc tggtgaacgt 501750DNAArtificial Sequencesynthetically generated oligonucleotide 17aaaccctatg atagatgaag ttattaacca aaaaccacgt gttgttatat 5018100DNAArtificial Sequencesynthetically generated oligonucleotide 18caactgatgc tcacggttca ctatgaaggt aaggcgattt gtggcgtgtt taccgcggaa 60gtggcggaga ccaaagtcgc tatggtgaat cagtacgcga 10019100DNAArtificial Sequencesynthetically generated oligonucleotide 19ctcgcaccgg acgtaacccg aaaactggtg ataaagtcga actggaaggt aagtacgttc 60cgcactttaa gcccgggaaa gaattacgtg accgcgccaa 10020100DNAArtificial Sequencesynthetically generated oligonucleotide 20tcatctggtt tccggtgatt tcgactatct gttgaaaacc cgtgtaccgg atatgtcagc 60gtatcgtaaa ttactgggcg agaccttgct gcgcctgccg 10021100DNAArtificial Sequencesynthetically generated oligonucleotide 21tcattcgctg gaagatcgca ttgtgaagcg ctttatgcgt gagcaaagcc gcggtccgca 60ggttccggcg ggaataccga tgaccgaagc gcagctcaaa 10022100DNAArtificial Sequencesynthetically generated oligonucleotide 22gcgatctacg cgcagaagga attcctctgg aacaacatca agcagccgaa ccgcaacctg 60ctgctgtggc gcgacaagac cgtcgacggc ctgaagaccg 10023100DNAArtificial Sequencesynthetically generated oligonucleotide 23cctgcggcta cgccgggatg cccatgaccc aactgcgcga cctggttggg ccggtggatt 60ttgccgaggt gtgtacccga ttgcgcgctg agctcgtctc 10024100DNAArtificial Sequencesynthetically generated oligonucleotide 24accgcccgcg cacggcgatc ctgaacaacc tggaattcga ccacgcggat atcttccccg 60acctcgcggc catcgagcgg cagttccacc atctggtgcg 10025100DNAArtificial Sequencesynthetically generated oligonucleotide 25aagtggattc cgcttcggtg atggtcaacg cctcgacccg cttcgccgac ggcttcgagt 60acggcctcgg cgccgagatc gggatttcca ccgacaagct 10026100DNAArtificial Sequencesynthetically generated oligonucleotide 26cgatgcgttt cgtcggcgac aagggcatcg agtattgggt cggtttgccg aacttctacg 60tgatcacccg ctataatcgc agcgccatgt atgccatggc 10027100DNAArtificial Sequencesynthetically generated oligonucleotide 27agctggtgtg aagcgaatga ttccgttaaa tgtgagtggc cctttccata cggcgctgtt 60acaaccagca tcaaaaaaat tggctcagga tttagcaaaa 10028100DNAArtificial Sequencesynthetically generated oligonucleotide 28caagaagcac aaatggctct tggcaataaa gaagccaaag ttgttcatgc cattcctaat 60acaccagtta gcgtgaatca aggcgtgatt ggcgtagcct 10029100DNAArtificial Sequencesynthetically generated oligonucleotide 29cacagttatc acagttcatg gaccaaacaa acccattagg tgagttaacc cataaacgtc 60gtctatcagc cttagggcct ggtggtttga ctcgtgaccg 1003050DNAArtificial Sequencesynthetically generated oligonucleotide 30agccaagaag ctgccaaagg acgcctcgat cggcaatttt tttcagctag 503150DNAArtificial Sequencesynthetically generated oligonucleotide 31atggtcatgt ggaattgctt ttgcttaaaa atacacaagg agatcaatgg 503250DNAArtificial Sequencesynthetically generated oligonucleotide 32atcaagaaaa aaatccgctt atgattggtg tattaaaagg atcagttcct 503350DNAArtificial Sequencesynthetically generated oligonucleotide 33aactttagca atagaactag gtgcttggat gcctatgcaa tttaataacc 503450DNAArtificial Sequencesynthetically generated oligonucleotide 34attgggctac aagcctaaca atctcgctag aagtttgcaa ggtaaatcaa 503550DNAArtificial Sequencesynthetically generated oligonucleotide 35agaaatgttt gacggtattc agcgaccgct tgatcgtttt caaaaagcaa 503650DNAArtificial Sequencesynthetically generated oligonucleotide 36accattgacc atattttaga agacccaagc aaattagaaa ctatctctgg 503750DNAArtificial Sequencesynthetically generated oligonucleotide 37aaatttacgt ggggcaatgt ctctgaagtt tgtcgtgaat taggacgtat 503850DNAArtificial Sequencesynthetically generated oligonucleotide 38acgacatggt cgccaagcac cggcaactgg ccgagatcat cgccagcgac 503950DNAArtificial Sequencesynthetically generated oligonucleotide 39tcacttgaaa gatttgaaaa aacgcaatat tcaacaccac taccttgctg 504050DNAArtificial Sequencesynthetically generated oligonucleotide 40cgcgtgtgga aaaagctttt tacttttcca ttgctgtaac cactcttatt 504150DNAArtificial Sequencesynthetically generated oligonucleotide 41tggcgtacat gtaggtcaag atgatattgg tgttgatgaa attagaaaat 504250DNAArtificial Sequencesynthetically generated oligonucleotide 42gattggtgga acaacttcat cggtgcagac tatgattctg gcaacctgca 504350DNAArtificial Sequencesynthetically generated oligonucleotide 43agtttttgaa aaacaatatg gctcagagct tgatgataaa gaggttgatg 504450DNAArtificial Sequencesynthetically generated oligonucleotide 44atttttatgg gagcctctta ggccgttttg gtgaagcgac agttggtcta 504550DNAArtificial Sequencesynthetically generated oligonucleotide 45atttgattgc aggagattat tccaaggttc ttgatatgca aggtgggtct 504650DNAArtificial Sequencesynthetically generated oligonucleotide 46aaagaagcgg gcaatatggt ggatctcgac tccaacccga ccaagttgat 504750DNAArtificial Sequencesynthetically generated oligonucleotide 47agcaagtgct tacagtattg atacatatgc ttcattctga acgtggaatg 504850DNAArtificial Sequencesynthetically generated oligonucleotide 48cttaaatatg ttaggggctc gcgagccaaa acattatggc agtatttctc 504950DNAArtificial Sequencesynthetically generated oligonucleotide 49atccccgttg atagccaact tattctgcgt gatcgttttt taaaacgccg 505050DNAArtificial Sequencesynthetically generated oligonucleotide 50aaattacaat ccttgctaga aaaaataatc ttgaacatcc gcgtttaggt 505150DNAArtificial Sequencesynthetically generated oligonucleotide 51catccaagtg tagaatattg gtctgtttgc aaagttgagg cgttatttga 505250DNAArtificial Sequencesynthetically generated oligonucleotide 52atacggcaca aagttggaca taactaagct agaaaaattt caacaaaagt 505350DNAArtificial Sequencesynthetically generated oligonucleotide 53agccgctacg gctgtggcaa aagcctttga acaggaaaca ggcattaaag 5054100DNAArtificial Sequencesynthetically generated oligonucleotide 54atcgatagcg ccgaatgccg gcttggaccc ggtgaattat cagaacttcg cagtcacgaa 60cgacggggtg attttcttct tcaacccggg ggagttgctg 10055100DNAArtificial Sequencesynthetically generated oligonucleotide 55gggacgacct tgcatcggac ctgcaggcta taaacgattc gttcggcacg cttcgccacc 60tggatccgcc ggtgcgtcgc tccggtggtc gtgaacagca 10056100DNAArtificial Sequencesynthetically generated oligonucleotide 56agcgccaccc gcggtccctc ttccccgagt tttctgagct gttcgcggcc ttcccgtcat 60tcgccggact ccggcccacc ttcgacaccc ggttgatgcg 10057100DNAArtificial Sequencesynthetically generated oligonucleotide 57acctggcgcg gtattccgct gatcccgtcg gacaaggtgc cggtggagga cggcaagacg 60aagttcatcc tggtccgcac cggcgaggaa cgtcagggcg 10058100DNAArtificial Sequencesynthetically generated oligonucleotide 58cggcgcccag agtgtctacg gcgtggtccc catgtgcgcg gtgatatcgg cgctcttcgg 60ctccctcggc aactcggtgg gcatcaccat ggaccgccag 10059100DNAArtificial Sequencesynthetically generated oligonucleotide 59atcgaccccg gattgccctc ggcgcgaatc gatttcatgc tcgccgacgc cgtgcccgtc 60gtcacggtca ccaccgccga actgcgcgct tcggccggcg 10060100DNAArtificial Sequencesynthetically generated oligonucleotide 60gtcgacgccg gagaattgat cgcccacgca tcgaattcgc tggcgcgcta caagcttccc 60aaggcgatcg tgttccgtcc ggtgatcgag cgcagcccgt 10061100DNAArtificial Sequencesynthetically generated oligonucleotide 61gtcaaagaac aaaagctgcg accaaagatt gacgatcacg attacgagac caaaaagggt 60cacgtcgtcc gcttcttgga ggcgggatcg aaggtcaagg 10062100DNAArtificial Sequencesynthetically generated oligonucleotide 62gaacaaaaag agtatctcgt cttgaaagtt gcgcagggcg acctgacagt acgagttccc 60gctgaaaacg ccgaatacgt cggtgttcgc gatgtcgtcg 10063100DNAArtificial Sequencesynthetically generated oligonucleotide 63ggtacaaaag gatagtgcca agcccttgga taaatttgga aatatctatg attatcacta 60tgagcatgaa acacatgccc ctctctcacc tcgtattaga 10064100DNAArtificial Sequencesynthetically generated oligonucleotide 64agagttgaaa ctgatatatc ggaattaggt ttaattgaat atgaaataga agaaaatgat 60acaaacccta attataatga aaggacaata actatatctc 10065100DNAArtificial Sequencesynthetically generated oligonucleotide 65aatatgacag ataacataac gctaaaaaca ccggtaatat catctcctat ggatacagta 60acgggacata agatgtcaat agctttagct ttgagcggtg 10066100DNAArtificial Sequencesynthetically generated oligonucleotide 66acatatacaa atagatgagg tggtaaaacc tgacacaaag aaggttataa aaaatgaagg 60aatgccttac tcaagagatc caagtattag aggaaatttg 10067100DNAArtificial Sequencesynthetically generated oligonucleotide 67acagggaaat gataaacata tagatagtga acataatgga ataaataaaa tgtacaaaga 60aacaatacat aaaacactaa catctgatgt atcaacagaa 10068100DNAArtificial Sequencesynthetically generated oligonucleotide 68cccaatacct acatgtggag cttctagggt tatggagaaa tgtcaaaaga tgtataaggt 60ggttataaaa ccgaaggaga aggacgataa agtggataat 10069100DNAArtificial Sequencesynthetically generated oligonucleotide 69cgcttagcta attcaattgg actaattgat gcaggttata gaggagaaat tattgccgcc 60ttggataata ctagtgacca agagtatcac attaaaaaaa 10070100DNAArtificial Sequencesynthetically generated oligonucleotide 70gggacgaagg ggatttcgac aacaatttag ttcctcacca attagaaaat atgattaaaa 60tagccttagg agcatgtgca aaattagcaa ccaaatatgc 10071100DNAArtificial Sequencesynthetically generated oligonucleotide 71attatcttac ctgtgaatat aaaaaatgct atggaaaaac aagctgaagc agaaagaaga 60aaaagagctg aaattttaca aagtgaagga gaaagagaaa 10072100DNAArtificial Sequencesynthetically generated oligonucleotide 72tactttatcc cgtgatggta agaatgatat tgaagaagaa gaagaagaag atgaggaaga 60tgaaaaaaat ataaacaact cccaagatac cacattaagt 10073100DNAArtificial Sequencesynthetically generated oligonucleotide 73tgaaggacca aaaggaaatg aacaaaaaaa acgtgatgac gatagtttga gtaaaataag 60tgtatcacca gaaaattcaa gacctgaaac tgatgctaaa 10074100DNAArtificial Sequencesynthetically generated oligonucleotide 74gaggtgctcc tcaaaatgga gctgcagaag ataaaaagac agaatattta ctagaacaaa 60taaaaattcc atcatgggat agaaataaca tccccgatga 10075100DNAArtificial Sequencesynthetically generated oligonucleotide 75atacaccact gctgttcccc ttattgttgc aataaaccca tacaaggatt taggaaacac 60aactaatgaa tggattcgta gatatcgtga tacagctgat 10076100DNAArtificial Sequencesynthetically generated oligonucleotide 76agaaccaact gttgctgaag aacacgtaga agaaccagct agtgatgttc aacaaacttc 60agaagcagct ccaacaattg aaatccccga tacattatat 10077100DNAArtificial Sequencesynthetically generated oligonucleotide 77actattaaag ctatggaaat tatatgggaa gctaccatga acaatgaaag gagaaaatat 60gctgccacta aacgtagcat gctcagatat tatgatgatt 10078100DNAArtificial Sequencesynthetically generated oligonucleotide 78atataccaga aagtagtagt acatatacaa atacaaggtt agcagcaaat aacagtacaa 60ctacaagcac tacaaaagta acagataata ataaaacaaa 10079100DNAArtificial Sequencesynthetically generated oligonucleotide 79attcaaacca cttatcgtag atgatgaact acttgaatac aaccaaaagg ttcataacat 60aggaagaaat ggagaagaca ttttaactgc tatgcaaaca 10080100DNAArtificial Sequencesynthetically generated oligonucleotide 80ctcaccgtgc ccagtgagcg aggactgcag cgtagacgct ttgtccaaaa tgcccttaat 60gggaatgggg atccaaataa tatggacaga gcagttaaac 10081100DNAArtificial Sequencesynthetically generated oligonucleotide 81atcgaaagct taagagggag ataacattcc atggggccaa agaaatagca ctcagttatt 60ctgctggtgc acttgccagt tgtatgggac tcatatacaa 10082100DNAArtificial Sequencesynthetically generated oligonucleotide 82gcaggcaatg agagccattg ggactcatcc tagctctagc actggtctga aaaatgatct 60ccttgaaaat ttgcaggcct atcagaaacg aatgggggtg 10083100DNAArtificial Sequencesynthetically generated oligonucleotide 83gctgacaaaa tgaccatcgt cagcatccac agcattctgc tgttcctctc gatattcttc 60cctcatagac tctggtactc cttccgtaga aggccctctt 10084100DNAArtificial Sequencesynthetically generated oligonucleotide 84ggttgttgtt accatttgcc tatgagactt atgctgggag tcggcaatct gttcacaggt 60tgcgcatata aggccaaatg ctgattcggt ggtcacagcc 10085100DNAArtificial Sequencesynthetically generated oligonucleotide 85acacaaatcc taaaatcccc ttagtcagag gtgacaggat cggtcttgtc tttagccatt 60ccatgagagc ctcaagatcg gtattctttc cagcaaagac 10086100DNAArtificial Sequencesynthetically generated oligonucleotide 86aaacatatag tatgggcaag cagggagcta gaacgattcg cagttaatcc tggcctgtta 60gaaacatcag aaggctgtag acaaatactg ggacagctac 10087100DNAArtificial Sequencesynthetically generated oligonucleotide 87cagctgacac aggacacagc aatcaggtca gccaaaatta ccctatagtg cagaacatcc 60aggggcaaat ggtacatcag gccatatcac ctagaacttt 10088100DNAArtificial Sequencesynthetically generated oligonucleotide 88atcagaagga gccaccccac aagatttaaa caccatgcta aacacagtgg ggggacatca 60agcagccatg caaatgttaa aagagaccat caatgaggaa 10089100DNAArtificial Sequencesynthetically generated oligonucleotide 89tagagactat gtagaccggt tctataaaac tctaagagcc gagcaagctt cacaggaggt 60aaaaaattgg atgacagaaa ccttgttggt ccaaaatgcg 10090100DNAArtificial Sequencesynthetically generated oligonucleotide 90gagacagcga cgaagagctc atcagaacag tcagactcat caagcttctc tatcaaagca 60acccacctcc caaccccgag gggacccgac aggcccgaag 10091100DNAArtificial Sequencesynthetically generated oligonucleotide 91cgacaggccc gaaggaatag aagaagaagg tggagagaga gacagagaca gatccattcg 60attagtgaac ggatccttgg cacttatctg ggacgatctg 10092100DNAArtificial Sequencesynthetically generated oligonucleotide 92cttatctggg acgatctgcg gagcctgtgc ctcttcagct accaccgctt gagagactta 60ctcttgattg taacgaggat tgtggaactt ctgggacgca 1009383DNAArtificial Sequencesynthetically generated oligonucleotide 93gaacttctgg gacgcagggg gtgggaagcc ctcaaatatt ggtggaatct cctacagtat 60tggagtcagg aactaaagaa tag 8394100DNAArtificial Sequencesynthetically generated oligonucleotide 94tggcgcaccc aacgcaacgt atgcggccca tgtgacgtac taccggctca cccgcgcctg 60ccgtcagccc atcctccttc ggcagtatgg agggtgtcgc 10095100DNAArtificial Sequencesynthetically generated oligonucleotide

95ctgctggtgc cgatctggga ccgcgccgcg gagacattcg agtaccagat cgaactcggc 60ggcgagctgc acgtgggtct gttgtgggta gaggtgggcg 10096100DNAArtificial Sequencesynthetically generated oligonucleotide 96cctaccacgc gtcgcttttg ctccccagag cctgctggtg gggattacgg gccgcacgtt 60tattcggatg gcacgaccca cggaagacgg ggtcctgccg 10097100DNAArtificial Sequencesynthetically generated oligonucleotide 97cccctgttct ggttcctaac ggcctcccct gctctagata tcctctttat catcagcacc 60accatccaca cggcggcgtt cgtttgtctg gtcgccttgg 10098100DNAArtificial Sequencesynthetically generated oligonucleotide 98gctaagcgaa ttactaaaaa ccgctgaagt gccgaaaggg tccttctatc actactttcg 60ctctaaagaa gcgtttggcg ttgccatgct tgagcgtcat 10099100DNAArtificial Sequencesynthetically generated oligonucleotide 99gtcggctggt tgaccttcct ttcggcagct attcctccag tgggtggcgt gatcatcgcc 60gactatctga tgaaccgtcg ccgctatgag cactttgcga 100100100DNAArtificial Sequencesynthetically generated oligonucleotide 100caaatccggt gatgctctac tctatcggta aagattccag cgtcatgctg catctggcgc 60gcaaggcgtt ttatccaggt acgctgcctt tcccgttgct 100101100DNAArtificial Sequencesynthetically generated oligonucleotide 101tggattagat cagaaagcta ttcatcagcg gaaggggctg aaaaagaatc agaagatcct 60ggatcatatg ggttcaacag aactggcggc taatctcttt 100102100DNAArtificial Sequencesynthetically generated oligonucleotide 102gaataccgcc acgacacggg agttttcgac cggccttaac gccagctttg acctcgattt 60tttcggtcgc ttaaagaaca tgagcgaagc cgagcgacaa 100103100DNAArtificial Sequencesynthetically generated oligonucleotide 103gaaggcagta acgtcaatgc cgttgcggca atgagcgaca tgattgccag cgcgcggcgt 60tttgaaatgc agatgaaggt gatcagcagc gtcgatgata 100104100DNAArtificial Sequencesynthetically generated oligonucleotide 104caaatccggt gatgctctac tctatcggta aagattccag cgtcatgctg catctggcgc 60gcaaggcgtt ttatccaggt acgctgcctt tcccgttgct 100105100DNAArtificial Sequencesynthetically generated oligonucleotide 105ttcggtaaaa ccgcgacctt tatgccaaaa ccgatgttcg gtgataacgg ctccggtatg 60cactgccaca tgtctctgtc taaaaacggc gttaacctgt 100106100DNAArtificial Sequencesynthetically generated oligonucleotide 106ttgtggatgt gcagtcgaaa atctatctgc gcgataaagt cggcaaactg ggggttgcac 60cgttaaccag tatgttcctg tttgggccga accaaccgtc 100107100DNAArtificial Sequencesynthetically generated oligonucleotide 107cgacagtgtt ggtgagcggc tgggtcgtgt tgggctggat ggaagatgcg caacgcctgc 60cgctctcaaa gctggtgttg accggtgaac gccattacac 100108100DNAArtificial Sequencesynthetically generated oligonucleotide 108gctaagcgaa ttactaaaaa ccgctgaagt gccgaaaggg tccttctatc actactttcg 60ctctaaagaa gcgtttggcg ttgccatgct tgagcgtcat 100109100DNAArtificial Sequencesynthetically generated oligonucleotide 109aacacgctgc tgatcttcat caaccagatc cgtatgaaaa ttggtgtgat gttcggtaac 60ccggaaacca ctaccggtgg taacgcgctg aaattctacg 100110100DNAArtificial Sequencesynthetically generated oligonucleotide 110tggattagat cagaaagcta ttcatcagcg gaaggggctg aaaaagaatc agaagatcct 60ggatcatatg ggttcaacag aactggcggc taatctcttt 100111100DNAArtificial Sequencesynthetically generated oligonucleotide 111gatgaagaat cttctcaggt acatgcacaa ggtctggtga ttcgcgacct gccgctgatt 60gccagcaact tccgtaatac cgaagacctc tcttcttacc 100112100DNAArtificial Sequencesynthetically generated oligonucleotide 112caatcgccta cggtgctgat gaagttgacg ttgtgttccc gtaccgcgcg ctgatggcgg 60gtaacgagca ggttggtttt gacctggtga aagcctgtaa 100113100DNAArtificial Sequencesynthetically generated oligonucleotide 113gaaggcagta acgtcaatgc cgttgcggca atgagcgaca tgattgccag cgcgcggcgt 60tttgaaatgc agatgaaggt gatcagcagc gtcgatgata 100114100DNAArtificial Sequencesynthetically generated oligonucleotide 114cccgatctct tcaacctaaa ctatctgggg agaggaaaat ggttcgattt gtttcgctgc 60ttcaggagta acactttagg ggcaaaaatg caggcgctga 100115100DNAArtificial Sequencesynthetically generated oligonucleotide 115aacacgctgc tgatcttcat caaccagatc cgtatgaaaa ttggtgtgat gttcggtaac 60ccggaaacca ctaccggtgg taacgcgctg aaattctacg 100116100DNAArtificial Sequencesynthetically generated oligonucleotide 116gctgcctgct atctccgaca tgagcattgg tcatgcgatg gaattcttca acaatctcaa 60actcgcaggt cagcgggcga agattgcaga aaaaatcctt 100117100DNAArtificial Sequencesynthetically generated oligonucleotide 117tttccctcta ggttagaaac atggggattg ccgttgtctg aagctaaaga gcgaggtaag 60tgggtattag catcagattt cccatttact agagaaactc 100118100DNAArtificial Sequencesynthetically generated oligonucleotide 118aacggaaccg agcgtcgcct ccgcgatgtt tgaataccgt tttggtggca atggcgaact 60ttccggtcat aatctcggaa acttgatgtt aaaggcgctg 100119100DNAArtificial Sequencesynthetically generated oligonucleotide 119aacgctatca cgatatttcg cgcctggtga tgaacgaccc gagcgagaaa aatctcaacg 60aactggcgaa ggttcaggaa cagctggatc accacaacct 100120100DNAArtificial Sequencesynthetically generated oligonucleotide 120tgccatccgt gacgcactgg tacgtcagtt gtataacccg gttcagtgga cgaagtctgt 60tgagtacatg gcagcgcaag gcgtagaaca tctctatgaa 100121100DNAArtificial Sequencesynthetically generated oligonucleotide 121tgccatccgt gacgcactgg tacgtcagtt gtataacccg gttcagtgga cgaagtctgt 60tgagtacatg gcagcgcaag gcgtagaaca tctctatgaa 100122100DNAArtificial Sequencesynthetically generated oligonucleotide 122aaacctggca tcatgattaa agtgcttagc gaaatcacct ccagcctgaa taaagactct 60ctggtcgttt ctattgctgc aggtgtcacg ctcgaccagc 100123100DNAArtificial Sequencesynthetically generated oligonucleotide 123actaacgaat acggcttcct tgagactccg tatcgtaaag tgaccgacgg tgttgtaact 60gacgaaattc actacctgtc tgctatcgaa gaaggcaact 100124100DNAArtificial Sequencesynthetically generated oligonucleotide 124tgtggttctt cctgggcggt ttcggcgcac accgcttcta cctggggaaa accggcacgg 60cggttaccca actgatcatc acgctgatcg gttgtttcac 100125100DNAArtificial Sequencesynthetically generated oligonucleotide 125ccacaaacaa tactcttatc aagaattccc caacccctct agaaaagcag aaagccatct 60acaatggtga gctacttgtg gatgagatag ccagtctaca 100126100DNAArtificial Sequencesynthetically generated oligonucleotide 126gaacacggcg gaggcggtgt tccgtcttgg ccggctgacg gcttcctata tccatttcta 60cctgccttcc aacccgcagg ccgccgctgc gttgctggcg 100127100DNAArtificial Sequencesynthetically generated oligonucleotide 127cccggccatg ctgaatgccg cgaacgaggt ggccgtggcc gcatttctcg agcggcacat 60ccgcttcagc gacatcgcgg ttatcatcga ggacgtgctg 100128100DNAArtificial Sequencesynthetically generated oligonucleotide 128tctcgaccag cggctttcgt cgcgacttcg agcaggcgcg ttcgatgcag gtgttcggcg 60acagcttccc ggcgcgggta ttcgccatga gcgagcggcc 100129100DNAArtificial Sequencesynthetically generated oligonucleotide 129gcagcaaggt ctggctgctg gcggaaaacc ctgagttcgc tccgatcgaa gtcgatctga 60aggagcagga actgatcatc gaaggcttga gcgtcggcgt 100130100DNAArtificial Sequencesynthetically generated oligonucleotide 130cggggcagga gcttaacgcc ctgcatgcgc agaacgtcgg catcggcgcg gtggtcggct 60tcgtcggcta cgtgcgcgac ttcaacgacg gtcgcgaggt 100131100DNAArtificial Sequencesynthetically generated oligonucleotide 131ggtgaagaac aaggtttccc cgccgttccg ccaggccgag ttccagatcc tctacggtaa 60gggcatctac cgtaccggcg agatcatcga tctgggcgtg 100132100DNAArtificial Sequencesynthetically generated oligonucleotide 132cttcggcgcc gtcgagatca ccgtgcacaa cggccaggtg gtgcagatcg agcgcaagga 60aaaattccgt ctgcagcaac cggccgtcaa gcaggcctga 100133100DNAArtificial Sequencesynthetically generated oligonucleotide 133gagcactatc tcaatcgcga cagcttcccc gagcagaagt accgccactg cggttgcagc 60cgcaacacct tttatctgcg cctgcatgtg gcgcaccagg 100134100DNAArtificial Sequencesynthetically generated oligonucleotide 134cctgctgaag ccgcgccacg tggagatcca ggtattcgcc gaccgccatg gccactgcct 60gtacctcaac gaacgcgact gttcgatcca gcgccgccac 100135100DNAArtificial Sequencesynthetically generated oligonucleotide 135ttcaacgatc tgttcgagtc ggccctgcgt aatgaggccg ggagtaccta cccgccctac 60aacgtcgaaa agcacggtga cgacgagtat cgcatcgtta 100136100DNAArtificial Sequencesynthetically generated oligonucleotide 136acggccgcct gagcctgccg ccattgcgcg aacgtccggg agacatcctg ccgctggcgg 60aatacttcat cggcgtctat gcccagcgcc tggacctgcc 100137100DNAArtificial Sequencesynthetically generated oligonucleotide 137acgctgcaga ccatctggtt ctacaacacc acccagtgct acggcgacgc ctcgaccatc 60aaccagagcg tcaccgtgct gaccggcggg gcgaatatcc 100138100DNAArtificial Sequencesynthetically generated oligonucleotide 138cctggagatg tccgatccca acgacgaggc gatcaagccg atgcgcgaag ggatggaact 60gaccctgaag atgttcgacg acaccctgcg ccgctaccag 100139100DNAArtificial Sequencesynthetically generated oligonucleotide 139tatgtcggac gcgacgtcga atcgatcatc cgcgatctcg ccgacgccgc ggtgaagatg 60ctccgcgaac aggagatcca gaaggtcaag tatcgcgccg 100140100DNAArtificial Sequencesynthetically generated oligonucleotide 140ggtggcgttg ccagtcagcg tgccgtcgca ttgcgaactg atgcgtccgg ccgccgagca 60gttcgccgcc tcggtcgaaa gcctgcagtg gcaggcgccg 100141100DNAArtificial Sequencesynthetically generated oligonucleotide 141cctgtggctg gacgacgaag cgcagatcga cgcggtgacc gcagtgtcgg gcagcggccc 60ggcgtatttc ttcctgctga tgcaggccat gaccgacgcc 100142100DNAArtificial Sequencesynthetically generated oligonucleotide 142accttcgccg taccgctgcg cgtgaaagtt cgcctgatca tcttcgaccg cgagtcgtcg 60aacaaggcga tcaaggacat caaggaacaa gaagtctaca 100143100DNAArtificial Sequencesynthetically generated oligonucleotide 143aacatgatga tggctattaa tgttaaagat gtacaagata aaggaatggc tagctacaat 60gccaaaatct caggtaaagt gtatgatgag ctatatgaga 100144100DNAArtificial Sequencesynthetically generated oligonucleotide 144agaatataaa ggctataaag atgatgcagt tattggtaaa aagggactcg aaaaacttta 60cgataaaaag ctccaacatg aagatggcta tcgtgtcaca 100145100DNAArtificial Sequencesynthetically generated oligonucleotide 145tgataggccg gtggcagcta cgtttaccta tcctgttttt gttaagccgg cgcgttcagg 60ctcatccttc ggtgtgaaaa aagtcaatag cgcggacgaa 100146100DNAArtificial Sequencesynthetically generated oligonucleotide 146ggagcgagga cggatacagg aaacggcaaa aaaaatatat aaagcgctcg gctgtagagg 60tctagcccgt gtggatatgt ttttacaaga taacggccgc 100147100DNAArtificial Sequencesynthetically generated oligonucleotide 147gaggacgctt acctaccctg tctttgtgaa gccggcacgg tcaggttcgt cctttggcgt 60aaccaaagta aacagtacgg aagaactaaa cgctgcgata 100148100DNAArtificial Sequencesynthetically generated oligonucleotide 148accaggctta cgagaagcgg gattctggcg gttcgtcgcc gacccgtacg atccgagcga 60gtctcaggcg atcgagttgc tattggcgca ttcggccggt 100149100DNAArtificial Sequencesynthetically generated oligonucleotide 149ccgcccaatt cggggtcaag cgcggtctgt tgggcaagtt gatgccggtc aaacgcacga 60cctttgtcat cgacaccgac cgtaaggtgc tcgacgtgat 100150100DNAArtificial Sequencesynthetically generated oligonucleotide 150gtcgattacc tggcctgaat tcgggcgtca gcatccattt gccccggcat ctgataccgc 60tgggctgcgt caacttgttg ccgacctaca gagttggctg 100151100DNAArtificial Sequencesynthetically generated oligonucleotide 151cgttgatcct gctgcaccaa gacaagatca gctcgcttcc cgatctgttg ccattgctgg 60aaaaggttgc aggaacgggt aagccactac tgatcgtggc 100152100DNAArtificial Sequencesynthetically generated oligonucleotide 152cagctggccg gcttggcgcg atatccgcag ccgatggccc cggccgccgc cgccgaacac 60gccgggatgg cgttgcccgc ccgcgatcag atcgtgcggc 100153100DNAArtificial Sequencesynthetically generated oligonucleotide 153ggtgcgcctg gattgtgacg gcgacgccgt attgttgacg gttgaccagg tcggcggtgc 60ctgccatacc ggcgatcaca gttgcttcga tgccgcggtg 100154100DNAArtificial Sequencesynthetically generated oligonucleotide 154ccgcgcaggg gcgcacccca ctccccttct acgtgtggcg ggcgtttgcg cgctattctc 60cggtgcttcc cgctggccgt ctggtgaact tcggcaccgt 100155100DNAArtificial Sequencesynthetically generated oligonucleotide 155ggtggattgt cgagcagctt cgttcgaccg gccctgccaa tacgacactg gtggtcatcg 60tcaccaagat tgacaaggtg ccgaaagaaa aagtggtcgc 100156100DNAArtificial Sequencesynthetically generated oligonucleotide 156cggcatccac gcactcgaag acgagttcgt caccaagtgg gatctagcgg tcaagatcgg 60cggtcacctc aaggatccgg tcgacagcca catgggccga 100157100DNAArtificial Sequencesynthetically generated oligonucleotide 157cggcgagtgc accgttccgc gggtcacgct ggtcacccga aagacctacg gcggggcata 60cattgcgatg aactcccggt cgttgaacgc gaccaaggtg 100158100DNAArtificial Sequencesynthetically generated oligonucleotide 158gtgtgtcctc gcagctggtg tcccggtttt cgccacgggt gttgaccatc ggcggcggat 60atctgctatt cggcgccatg ctgtacggct catttttcat 100159100DNAArtificial Sequencesynthetically generated oligonucleotide 159gcaggccgag gccgtggatg tgcatacgct cgctcggaat ggaatgccgg aggcgctgga 60ttacctgcat cgacgtcaag cccggcgaat caccgattca 100160100DNAArtificial Sequencesynthetically generated oligonucleotide 160gagagagtgg gatggcgtac cacaacccgt tcatcgtgaa tggaaagatc aggttcccag 60ccaacaccaa cctggttcgt cacgtcgaaa agtgggcgaa 100161100DNAArtificial Sequencesynthetically generated oligonucleotide 161cggcatccac gcactcgaag acgagttcgt caccaagtgg gatctagcgg tcaagatcgg 60cggtcacctc aaggatccgg tcgacagcca catgggccga 100162100DNAArtificial Sequencesynthetically generated oligonucleotide 162cggcgagtgc accgttccgc gggtcacgct ggtcacccga aagacctacg gcggggcata 60cattgcgatg aactcccggt cgttgaacgc gaccaaggtg 100163100DNAArtificial Sequencesynthetically generated oligonucleotide 163caagtccagc aagcggcgcc cggctccgga aaagccggtc aagacgcgta aatgcgtgtt 60ctgcgcgaag aaggaccaag cgatcgacta caaggacacc 100164100DNAArtificial Sequencesynthetically generated oligonucleotide 164cggccgcccg attcgagtct gccaccgcat cagcgggcac ggtgtcgctg cggctacccg 60tccgtgcacc attcgccttc gagggtgttt tcggccatct 100165100DNAArtificial Sequencesynthetically generated oligonucleotide 165tcgtcaacgg tcgacggatc cagagcaaac gtcaagtgtt cgaggtccgg atctcgggta 60tggataacgt cacggcattc gcggagtcag ttcccatgtg 100166100DNAArtificial Sequencesynthetically generated oligonucleotide 166aacaacccgt atgcacagtt tcaggacgaa tacaccctgg acgacatctt ggcctcaaag 60atgatttccg acccgctgac caaattgcag tgctctccca 100167100DNAArtificial Sequencesynthetically generated oligonucleotide 167ccgctaccgc agtggtaccc accgacagca cattgttggt cgagcggttt cgtgacgagc 60tgggcgattg gcgggtgatc ttgcattcgc cgtatgggct 100168100DNAArtificial Sequencesynthetically generated oligonucleotide 168tcgctgcggc tctacgattc gtcgtatcat gccgaactct tttggtggac aggggctttt 60gagacttctg agggcatacc gcacagttca ttggtatcgg 100169100DNAArtificial Sequencesynthetically generated oligonucleotide 169cggcacaccg aagtgatgcc ggtgactcga ttcaccaccg cgcacagccg cgaccgtggc 60gagagtgtct gggctcccga gtatcagctt gtcgacgagc 100170100DNAArtificial Sequencesynthetically generated oligonucleotide 170cggtttcagt cggccctaga cgggacgctc aatcagatga acaacggatc cttccgcgcc 60accgacgaag ccgagaccgt cgaagtgacg atcaatgggc 100171100DNAArtificial Sequencesynthetically generated oligonucleotide 171aagaggtgct ctacgagctg tctccgatcg aggacttctc cgggtcgatg tcgttgtcgt 60tctctgaccc tcgtttcgac gatgtcaagg cacccgtcga 100172100DNAArtificial Sequencesynthetically generated oligonucleotide 172cgccgaatgc cggcttggac ccggtgaatt atcagaactt cgcagtcacg aacgacgggg 60tgattttctt cttcaacccg ggggagttgc tgcccgaagc 100173100DNAArtificial Sequencesynthetically generated oligonucleotide 173agcgaccaaa gcaagcacgg cgaccgatga gccggtaaaa cgcaccgcca ccaagtcgcc 60cgcggcttcc gcgtccgggg ccaagaccgg cgccaagcga 10017450DNAArtificial Sequencesynthetically generated oligonucleotide 174aagcactcta cgaaaatgca ttgaatttag tgttgccaaa ggcttacgaa 5017550DNAArtificial Sequencesynthetically generated oligonucleotide 175aaattgtttg tgatcacttg aagcttgaga cacctgctgc tgatatgaca 5017650DNAArtificial Sequencesynthetically generated oligonucleotide 176aactatctta tgtaatagtg aaaaggatcc tatcaaagaa aaagaatacc 5017750DNAArtificial Sequencesynthetically generated oligonucleotide 177aaatagtaaa gtaactgtca aggttattgg agatagctct tttggtcata 5017850DNAArtificial Sequencesynthetically generated oligonucleotide 178aagggaattt ttctaaagca gcccaaggtt ttgttcgcgg aaaaggttta 5017950DNAArtificial Sequencesynthetically generated oligonucleotide 179aagcgtcctc aagaaatatc acaacaacaa gcagtttcta gcgtaggaca 5018050DNAArtificial Sequencesynthetically generated oligonucleotide 180aaggctattg acaatgctca tatacgttta aagaaatatg tggataccgg 5018150DNAArtificial Sequencesynthetically generated oligonucleotide

181aagttaaaca agaagtaaat caattaaata gtaaaatcaa cgataaacag 5018250DNAArtificial Sequencesynthetically generated oligonucleotide 182aaccaaatcg agctgtcgcc atatctgcag aaccgcaaag tggtggaatt 5018350DNAArtificial Sequencesynthetically generated oligonucleotide 183aaccctgttg attcacggcg gtttttgccc aaggcatacg gtaaaggtac 5018450DNAArtificial Sequencesynthetically generated oligonucleotide 184aaccgctgat tgacggcggg aaaatcaaag tggtgggcga tcagtgggtc 5018550DNAArtificial Sequencesynthetically generated oligonucleotide 185aaactggaag ccgaaagcga agtgatttta caggctaacg aacaggacat 5018650DNAArtificial Sequencesynthetically generated oligonucleotide 186aaagcattgc tctgcaaacg gtcactcaac cgtcaactgt agttgccact 5018750DNAArtificial Sequencesynthetically generated oligonucleotide 187aaaaatggaa cgacccggcg atcaccaagc tcaacccagg cgttaagctg 5018850DNAArtificial Sequencesynthetically generated oligonucleotide 188aacgtgacat ggtgttctct gccaacggca ccaacaccct gaaagccaac 5018950DNAArtificial Sequencesynthetically generated oligonucleotide 189aacctgcgtc aaccgttgcg cattattctg gatagccaaa atcgcgtcac 5019050DNAArtificial Sequencesynthetically generated oligonucleotide 190aattgttctg actagtgatc gtagtcctaa acacttagag ggccttgaag 5019150DNAArtificial Sequencesynthetically generated oligonucleotide 191aaggaattga aaaagctcaa agccttacta agaaagaaga agctgctgag 5019250DNAArtificial Sequencesynthetically generated oligonucleotide 192actggtgctg gtggtggtgt cattttcaca ccatcaatct caagccatga 5019350DNAArtificial Sequencesynthetically generated oligonucleotide 193aagatggttt attcaatttt ctggccatta aacgaagaat ttgaaaaatc 5019450DNAArtificial Sequencesynthetically generated oligonucleotide 194agcgatgacc tataccacct atgatagtgg taatagtggt caacaaacag 5019550DNAArtificial Sequencesynthetically generated oligonucleotide 195aacatgattt acgatacaga agccaatctg acagttgatc atgatttgag 5019650DNAArtificial Sequencesynthetically generated oligonucleotide 196acaactccgt agtatcttca caggtcaagt gaccaactgg aaagaagtcg 5019750DNAArtificial Sequencesynthetically generated oligonucleotide 197aaagctgcta aatctgccaa gactgctgct gctggtggta agaaggaagc 5019850DNAArtificial Sequencesynthetically generated oligonucleotide 198aaaatggtac tacatgtgca tcatacttta ctactattga tccggaaaca 5019950DNAArtificial Sequencesynthetically generated oligonucleotide 199acatagtcca ataacaaata aacttgagga tcatgatgat gaaattggat 5020050DNAArtificial Sequencesynthetically generated oligonucleotide 200aaaacaaaca atcaactggt gatgaagtca agagcaagag aaaatcggca 5020150DNAArtificial Sequencesynthetically generated oligonucleotide 201aatgatttat gatacgttta ataaattaca agaatctagt gatcagtcga 5020250DNAArtificial Sequencesynthetically generated oligonucleotide 202acacaaaact gaagacaaag ggactagcac ttcatccaag gaagaaccat 5020350DNAArtificial Sequencesynthetically generated oligonucleotide 203actggtgtag ttgaccttat cgaaatgaag gcaattatct gggatgaagc 5020450DNAArtificial Sequencesynthetically generated oligonucleotide 204aattgtgaat gaagaaacgc gtaagacttt tgaaattcgt gccaaagtcg 5020550DNAArtificial Sequencesynthetically generated oligonucleotide 205aaaaagtatt tcggtacgac aagcaaacgt tatatctacc tttctggttg 5020650DNAArtificial Sequencesynthetically generated oligonucleotide 206acagaagtaa ttgcaggtga aggacaaatt ttaacagctg gtggtattga 5020750DNAArtificial Sequencesynthetically generated oligonucleotide 207aaattgatga gctgaatatc ttgcaggcaa cttttttggc tatgcaacgt 5020850DNAArtificial Sequencesynthetically generated oligonucleotide 208aaaactccac ccgtactcgt actacttttg aagcagcagc aaaacgtttg 5020950DNAArtificial Sequencesynthetically generated oligonucleotide 209accgcaagtg gtgtttgaat taggcgttcc agtattgggt atttgctatg 5021050DNAArtificial Sequencesynthetically generated oligonucleotide 210agttgcaggg tatggggcta gatgctaaaa taaacgacat tttagctaat 5021150DNAArtificial Sequencesynthetically generated oligonucleotide 211aaaattgaag cgaaatcaat cgcccttgaa aatggtgcga ttttggctcg 5021250DNAArtificial Sequencesynthetically generated oligonucleotide 212aaaaagtaaa gatattatta atgtcgatat tacactagaa aaaaatggtt 5021350DNAArtificial Sequencesynthetically generated oligonucleotide 213aaaacttatg agcacgagaa gtttattaca gctgaaatta ataaactagc 5021450DNAArtificial Sequencesynthetically generated oligonucleotide 214aaaacaggtg aattaggtcc tttatatgat ctgtttaacc ttgcgcaaaa 5021550DNAArtificial Sequencesynthetically generated oligonucleotide 215aaaacgccct gtgagttctg atgatgttga agctgctatt aatcatatca 5021650DNAArtificial Sequencesynthetically generated oligonucleotide 216aaaggtattg aggcggctat tccatttgcc ccattacata acccagctca 5021750DNAArtificial Sequencesynthetically generated oligonucleotide 217aaaaaagatg ttctgatgga agaaattgtt gctagatatc acgaaaatac 5021850DNAArtificial Sequencesynthetically generated oligonucleotide 218aaatgggcaa ttggtgcatc aggtactact aactttggtt tagctaccga 5021950DNAArtificial Sequencesynthetically generated oligonucleotide 219atcgtcggca accagaacgt cgccgggcag ttgtatgacg gcttgaccgt 5022050DNAArtificial Sequencesynthetically generated oligonucleotide 220atcgacatcc acgcgcgtga gatcctgacc ctgcgttcgc gcctgaacga 5022150DNAArtificial Sequencesynthetically generated oligonucleotide 221caagatctac cagcgtccgt tcggtggcat gaccaccaag tacggcgaag 5022250DNAArtificial Sequencesynthetically generated oligonucleotide 222aagcgcctga tcggccgcaa gttcaccgac gccgaagtgc agaaggacat 5022350DNAArtificial Sequencesynthetically generated oligonucleotide 223atgctcgaat ttccacgctg gaagtacgtc gtcatcctga tcgtactggc 5022450DNAArtificial Sequencesynthetically generated oligonucleotide 224cgagcgccgg cataagcagt tgaaagacaa gggggtttct gttaactttg 5022550DNAArtificial Sequencesynthetically generated oligonucleotide 225cctgcccgac ccgatcccgg gcaagggcga gatgctctgc caggtctcca 5022650DNAArtificial Sequencesynthetically generated oligonucleotide 226cgccgaaggc atggccggac agccgccgca cagcctgccc agcggcacca 50227100DNAArtificial Sequencesynthetically generated oligonucleotide 227aatccggttg agccacggta tcttccgcat ccatcaggaa aacgagccgg aaaaaggctc 60agagaatgcg atgattatcg ttccagcaga cattccggtc 100228100DNAArtificial Sequencesynthetically generated oligonucleotide 228ccgctaccgc agtggtaccc accgacagca cattgttggt cgagcggttt cgtgacgagc 60tgggcgattg gcgggtgatc ttgcattcgc cgtatgggct 10022999DNAArtificial Sequencesynthetically generated oligonucleotide 229ggccgcccga ttcgagtctg ccaccgcatc agcgggcacg gtgtcgctgc ggctacccgt 60ccgtgcacca ttcgccttcg agggtgtttt cggccatct 99230100DNAArtificial Sequencesynthetically generated oligonucleotide 230aacaacccgt atgcacagtt tcaggacgaa tacaccctgg acgacatctt ggcctcaaag 60atgatttccg acccgctgac caaattgcag tgctctccca 100231100DNAArtificial Sequencesynthetically generated oligonucleotide 231tcgtcaacgg tcgacggatc cagagcaaac gtcaagtgtt cgaggtccgg atctcgggta 60tggataacgt cacggcattc gcggagtcag ttcccatgtg 100232100DNAArtificial Sequencesynthetically generated oligonucleotide 232cagctggccg gcttggcgcg atatccgcag ccgatggccc cggccgccgc cgccgaacac 60gccgggatgg cgttgcccgc ccgcgatcag atcgtgcggc 100233100DNAArtificial Sequencesynthetically generated oligonucleotide 233ggtggattgt cgagcagctt cgttcgaccg gccctgccaa tacgacactg gtggtcatcg 60tcaccaagat tgacaaggtg ccgaaagaaa aagtggtcgc 100234100DNAArtificial Sequencesynthetically generated oligonucleotide 234ggtgcgcctg gattgtgacg gcgacgccgt attgttgacg gttgaccagg tcggcggtgc 60ctgccatacc ggcgatcaca gttgcttcga tgccgcggtg 100235100DNAArtificial Sequencesynthetically generated oligonucleotide 235ccgcgcagtg ttcaaagctc ggatatacgg tggcacccat ggaacagcgt gcggagttgg 60tggttggccg ggcacttgtc gtcgtcgttg acgatcgcac 100236100DNAArtificial Sequencesynthetically generated oligonucleotide 236gtgtgtcctc gcagctggtg tcccggtttt cgccacgggt gttgaccatc ggcggcggat 60atctgctatt cggcgccatg ctgtacggct catttttcat 100237100DNAArtificial Sequencesynthetically generated oligonucleotide 237cggcgagtgc accgttccgc gggtcacgct ggtcacccga aagacctacg gcggggcata 60cattgcgatg aactcccggt cgttgaacgc gaccaaggtg 100238100DNAArtificial Sequencesynthetically generated oligonucleotide 238gcaggccgag gccgtggatg tgcatacgct cgctcggaat ggaatgccgg aggcgctgga 60ttacctgcat cgacgtcaag cccggcgaat caccgattca 100239100DNAArtificial Sequencesynthetically generated oligonucleotide 239gtcgattacc tggcctgaat tcgggcgtca gcatccattt gccccggcat ctgataccgc 60tgggctgcgt caacttgttg ccgacctaca gagttggctg 100240100DNAArtificial Sequencesynthetically generated oligonucleotide 240ccgcccaatt cggggtcaag cgcggtctgt tgggcaagtt gatgccggtc aaacgcacga 60cctttgtcat cgacaccgac cgtaaggtgc tcgacgtgat 100241100DNAArtificial Sequencesynthetically generated oligonucleotide 241acgccgttct acccgcggtc accgtatggc gccgccaagg tctattcgta ctgggcgacc 60cgcaattatc gcgaagcgta cggattgttc gccgttaacg 100242100DNAArtificial Sequencesynthetically generated oligonucleotide 242cgttgatcct gctgcaccaa gacaagatca gctcgcttcc cgatctgttg ccattgctgg 60aaaaggttgc aggaacgggt aagccactac tgatcgtggc 100243100DNAArtificial Sequencesynthetically generated oligonucleotide 243cgccgaatgc cggcttggac ccggtgaatt atcagaactt cgcagtcacg aacgacgggg 60tgattttctt cttcaacccg ggggagttgc tgcccgaagc 100244100DNAArtificial Sequencesynthetically generated oligonucleotide 244aagaggtgct ctacgagctg tctccgatcg aggacttctc cgggtcgatg tcgttgtcgt 60tctctgaccc tcgtttcgac gatgtcaagg cacccgtcga 100245100DNAArtificial Sequencesynthetically generated oligonucleotide 245gaacaaaaag agtatctcgt cttgaaagtt gcgcagggcg acctgacagt acgagttccc 60gctgaaaacg ccgaatacgt cggtgttcgc gatgtcgtcg 100246100DNAArtificial Sequencesynthetically generated oligonucleotide 246agcgaccaaa gcaagcacgg cgaccgatga gccggtaaaa cgcaccgcca ccaagtcgcc 60cgcggcttcc gcgtccgggg ccaagaccgg cgccaagcga 100247100DNAArtificial Sequencesynthetically generated oligonucleotide 247cggcatccac gcactcgaag acgagttcgt caccaagtgg gatctagcgg tcaagatcgg 60cggtcacctc aaggatccgg tcgacagcca catgggccga 100248100DNAArtificial Sequencesynthetically generated oligonucleotide 248caagtccagc aagcggcgcc cggctccgga aaagccggtc aagacgcgta aatgcgtgtt 60ctgcgcgaag aaggaccaag cgatcgacta caaggacacc 100249100DNAArtificial Sequencesynthetically generated oligonucleotide 249ccgcgcaggg gcgcacccca ctccccttct acgtgtggcg ggcgtttgcg cgctattctc 60cggtgcttcc cgctggccgt ctggtgaact tcggcaccgt 100250100DNAArtificial Sequencesynthetically generated oligonucleotide 250gagagagtgg gatggcgtac cacaacccgt tcatcgtgaa tggaaagatc aggttcccag 60ccaacaccaa cctggttcgt cacgtcgaaa agtgggcgaa 100251100DNAArtificial Sequencesynthetically generated oligonucleotide 251accaggctta cgagaagcgg gattctggcg gttcgtcgcc gacccgtacg atccgagcga 60gtctcaggcg atcgagttgc tattggcgca ttcggccggt 100

Claims

1-4. (canceled)

5. A method of identifying an infectious disease pathogen, the method comprising: providing a test sample from a subject suspected of being infected with a pathogen; treating the test sample under conditions that release messenger ribonucleic acid (mRNA); exposing the test sample to a plurality of nucleic acid probes designed to identify a plurality of pathogens, comprising a plurality of subsets of probes, wherein each subset comprises one or more probes that bind specifically to a target mRNA that uniquely identifies a single pathogen, wherein the exposure occurs for a time and under conditions in which binding between the probe and the target mRNA can occur; and determining a level of binding between the probe and target mRNA, thereby determining a level of target mRNA; wherein an increase in the target mRNA of the test sample, relative to a reference sample, indicates the identity of the pathogen in the test sample.

6. The method of claim 1, wherein the test sample is selected from the group consisting of sputum, blood, urine, stool, joint fluid, cerebrospinal fluid, and cervical/vaginal swab.

7. The method of claim 1, wherein the test sample comprises a plurality of different infectious disease pathogens or non-disease causing organisms.

8. The method of claim 1, wherein the one or more nucleic acid probes are selected from Table 2.

9. The method of claim 1, wherein the pathogen is a bacterium, fungus, virus, or parasite.

10. The method of claim 1, wherein the pathogen is Mycobacterium tuberculosis.

11. The method of claim 1, wherein the mRNA is crude before contact with the probes.

12. The method of claim 1, wherein the method does not include amplifying the mRNA.

13. The method of claim 1, wherein the method comprises lysing the cells enzymatically, chemically, or mechanically.

14. The method of claim 1, wherein the method comprises use of a microfluidic device.

15. The method of claim 1, wherein the method is used to monitor a pathogen infection.

16. (canceled)

17. The method of claim 5, wherein the subject is a human.

18. The method of claim 5, wherein the method further comprises determining or selecting, a treatment for the subject, and optionally administering the treatment to the subject.

19-21. (canceled)

22. The method of claim 18, wherein the method comprises selecting a treatment to which the pathogen is sensitive and administering the selected treatment to the subject, and determining the drug sensitivity of the pathogen in the second sample to the selected treatment using the method of claim 1, wherein a change in the drug sensitivity of the pathogen indicates whether the pathogen is or is becoming resistant to the treatment.

23-24. (canceled)

25. A plurality of polynucleotides bound to a solid support, wherein the plurality comprises at least one polynucleotide, each polynucleotide selectively hybridizing to one or more genes selected from Table 2.

26. The plurality of polynucleotides of claim 25, the plurality comprising SEQ ID NOs:1-227, or any combination thereof.