# Patent

## Title
Rapid phenotypic diagnosis of pathogens and drug resistance using transcriptional expression signatures

## 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.

## Background
CROSS REFERENCE TO RELATED APPLICATIONS
This application is the U.S. national stage under 35 USC § 371 of International Application Number PCT/US2011/026092, filed on Feb. 24, 2011 which claims priority to U.S. Application No. 61/307,669, filed on Feb. 24, 2010, and U.S. Application No. 61/323,252, filed on Apr. 12, 2010, the entire contents of which applications is hereby incorporated by reference.


FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT
This invention was made with Government support under Grant Number A1057159 and Grant Number A108002 awarded by the National Institutes of Health. The Government has certain rights in the invention.


TECHNICAL FIELD
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
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.
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
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.
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.
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.
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.
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.
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:

    
    
        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, 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
        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.
    
    


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.
In some embodiments of the invention, the one or more nucleic acid probes are selected from Table 2.
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.
In some embodiments, the methods comprise lysing the cells enzymatically, chemically, and/or mechanically.
In some embodiments, the methods comprise use of a microfluidic device.
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.
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.
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.
In another general aspect, the invention features methods of selecting a treatment for a subject. The methods include:

    
    
        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;
        determining the drug sensitivity of the pathogen using the methods described herein; and
        selecting a drug to which the pathogen is sensitive for use in treating the subject.
    
    


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

    
    
        obtaining a first sample comprising the pathogen at a first time;
        determining the drug sensitivity of the pathogen in the first sample using the method described herein;
        optionally selecting a treatment to which the pathogen is sensitive and administering the selected treatment to the subject;
        obtaining a second sample comprising the pathogen at a second time;
        determining the drug sensitivity of the pathogen in the second sample using the method described herein; and
        comparing the drug sensitivity of the pathogen in the first sample and the second sample, thereby monitoring the infection in the subject.
    
    


In some embodiments of the methods described herein, the subject is immune compromised.
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.
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.
In yet another aspect, the invention features methods of monitoring an infection with a pathogen in a population of subjects. The methods include:

    
    
        obtaining a first plurality of samples from subjects in the population at a first time;
        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;
        optionally administering a treatment to the subjects;
        obtaining a second plurality of samples from subjects in the population at a second time;
        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;
        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.
    
    


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



DESCRIPTION OF DRAWINGS
FIGS. 1A to 1D are a flowchart illustrating an exemplary method to quantify mRNA molecules in a sample using NanoStringTM (direct multiplexed measurement of gene expression with color-coded probe pairs) technology. 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. 1B. Excess reporter is removed by bead purification that hybridizes to a handle on each oligomer, leaving only the hybridized mRNA complexes. 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. 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).
FIGS. 2A to 2F are a panel of figures showing diagnosis of a gene expression signature of drug resistance. 2A. Sample from a patient, e.g., sputum. 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. 2C. Bar-coded probes hybridize to mRNA molecules. Cells are lysed and probes added to the crude sample. 2D. mRNA complexes are captured and aligned. 2E. Complexes are imaged and counted. 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.
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.
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. 
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.
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.
FIG. 7 is a panel of three bar graphs showing pathogen identification using pathogen specific probes.
FIG. 8 is a panel of three bar graphs showing pathogen identification sensitivity.
FIGS. 9A and 9B are panels of three bar graphs showing pathogen identification from simulated clinical samples.
FIG. 10 is a panel of two bar graphs showing identification of two clinical isolates of P. aeruginosa. 
FIG. 11 is a bar graph showing the identification of fluoroquinolone resistance in E. coli. 
FIG. 12 is a bar graph showing the identification of aminoglycoside resistance in E. coli. 
FIG. 13 is a bar graph showing the identification of methicillin resistance in S. aureus. 
FIG. 14 is a bar graph showing the identification of vancomycin resistance in Enterococcus. 
FIG. 15 is a panel of four bar graphs showing drug-specific gene induction in drug-sensitive M. tuberculosis. 
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 (NanoStringTM). 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.
FIG. 17 is a panel of four bar graphs comparing the transcriptional responses of drug-sensitive and drug-resistant M. tuberculosis using NanoStringTM. (A) Strain A50 (INH-R) was treated with INH (0.4 μg/ml) as described herein. (B) The SM-R clone S10 was treated with 2 μg/ml streptomycin.
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.
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 μg/ml INH (5×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.
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 t1 with exposure to rich media to stimulate resuscitation from a dormant state and to active transcription. The optimal t1 is determined experimentally. The bacilli are then pulsed for some time t2 with exposure to drug to elicit a transcriptional response. The optimal t2 is determined experimentally. Finally, all samples are processed and analyzed by expression profiling and confirmed by quantitative RT-PCR.
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.
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 NanoStringTM 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.
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 NanoStringTM 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.
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 μg/mL, the low-level INH-resistant inhA displays a susceptible signature, but at 0.2 μg/mL, it shows a resistant signature.
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 NanoStringTM 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. 
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 NanoStringTM 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.
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.
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.
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.
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.
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
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).
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.
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.
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 (>103) 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®. 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
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.
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.
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.
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
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.
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.
The reporter molecules can be counted to provide a quantitative measure of mRNA molecules. A commercially available nCounterTM 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): e17 (2005).
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.
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
This technology can be applied to obtain a rapid determination of identity or drug resistance of a pathogen.
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.
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.
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.
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).
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.
An exemplary set of genes and probes useful in the methods described herein is provided in Table 2 submitted herewith.
Methods of Treatment
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.
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
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 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.
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
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 32P and 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.
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.
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.
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.
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., 33P-ATP).
In some embodiments, the kits can include a software package for analyzing the results of, e.g., a microarray analysis or expression profile.
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
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° 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 nCounterTM 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).
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).
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_2121c.1, MAV_3252.1, MAV_3239.1, and MAV_1600.1, enable the distinction between M. tuberculosis infection and M. avium or M. avium subsp. paratuberculosis infection.
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).
Staphylococcus aureus and Stenotrophomonas maltophilia 
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) (Table2; and FIG. 31).
Example 2
Sensitivity of the Methods
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.
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.
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.
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° 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).
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).
Identification of Escherichia coli directly in urine samples. E. coli strain K12 was grown in LB media at 37° 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 OD600). Urine samples were then left at room temperature for 0 hours, 4 hours, 24 hours, or 48 hours or placed at 4° C. for 24 hours. 1 ml of spiked urine was centrifuged at 13,000×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 nCounterTM System assays per manufacturer's protocol.
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
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° C. A 2 ml aliquot of each culture was taken, and antibiotics were added to those aliquots at a final concentration of 8 μg/ml ciprofloxacin or 128 μg/ml gentamicin. Cultures were incubated at 37° 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 nCounterTM 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)
Identification of methicillin resistance in Staphylococcus. Six S. aureus clinical isolates were grown to log phase at 37° C. in LB media. A 2 ml aliquot of each culture was then taken; cloxacillin was added to a final concentration of 25 μg/ml. Cultures were incubated at 37° 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 nCounterTM 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).
Identification of vancomycin resistance in Enterococcus. Four Enterococcus clinical isolates were grown in LB media to log phase at 37° C. A 2 ml aliquots were taken; vancomycin was added to a final concentration of 128 μg/ml. Cultures were incubated at 37° 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 nCounterTM 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.
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. 
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.
Log phase M. tuberculosis cells at A600 0.3 were grown in inkwell bottles (10 ml volume, parallel cultures) in the presence of one of four different drugs (isoniazid, 0.4 μg/ml; streptomycin, 2 μg/ml; ofloxacin, 5 μg/ml; rifampicin 0.5 μg/ml). At the indicated time after the initiation of drug treatment (FIG. 15), cultures were harvested by centrifugation (3000×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×g, the aqueous phase was collected for analysis.
Samples were diluted 1:10 and analyzed using NanoStringTM 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.)
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 (NanoStringTM). 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.
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 μg/ml) as described herein. (B) The SM-R clone S10 was treated with 2 μg/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).
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.
Isoniazid: For drug-dependent induction: kasA, fadD32, accD6, efpA, and Rv3675.1.
Rifampin: For drug-dependent induction: bioD, hisI, era, and Rv2296.
Fluoroquinolones: For drug-dependent induction: rpsR, alkA, recA, ltp1, and lhr; for drug-dependent repression: kasA and accD6.
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
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.
A. Define Signature for Identification of TB
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.
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.
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.
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.
B. Define Signature to Distinguish Sensitive and Resistant TB
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.
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.
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.
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.
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 (t1) 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 (t2) 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.
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 (t1) from 0, 1, and 2 hours; for each t1, and the time of exposure to each antibiotic (t2). For each set of t1 and t2, the antibiotic concentration is varied from 1×, 3×, and 5× 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.
Based on the optimized conditions (t1 and t2), 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.
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.
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.
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.
C. Optimization of Sample Processing for Digital Gene Expression with Molecular Bar Codes.
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.
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, M D, 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.
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.
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.
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
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 OD600 of  ̃1 in Luria-Bertani medium (LB). For mixing experiments, equal numbers of bacteria as determined by OD600 were combined prior to lysis for NanoStringTM analysis. Mycobacterium isolates were grown in Middlebrook 7H9 medium to mid-log phase prior to harvest or antibiotic exposure as described below.
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.
Viral and plasmodium infections: HeLa cells (1×106), 293T cells (2×105), and human peripheral blood monocytes (5×105), were infected with HSV-1 strain KOS and HSV-1 strain 186 Syn+, influenza A P R8, or HIV-1 NL-ADA, respectively, at the noted MOIs. Primary red blood cells (5×109) 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.
Antibiotic exposure: Cultures of E. coli or P. aeruginosa were grown to an OD600 of  ̃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 μg/ml or 300 ng/ml, gentamicin 64 or 128 μg/ml, or ampicillin 500 μg/ml; P. aeruginosa for 30 minutes: ciprofloxacin 16 μg/ml). Both treated and untreated portions were maintained at 37° C. with shaking at 200 rpm. Cultures of S. aureus or E. faecium were grown to an OD600 of  ̃1 in LB. Cultures were then exposed to cloxacillin (25 μg/mL) or vancomycin (128 μg/mL), respectively, for 30 minutes.
Cultures of M. tuberculosis were grown to mid-log phase then normalized to OD600 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 μg/ml; streptomycin 5 μg/ml, rifampicin 0.5 μg/ml, or ciprofloxacin 5 μg/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.
Sample processing: For Gram negative isolates, 5-10 μl of culture was added directly to 100 μl RLT buffer and vortexed. For clinical specimens, 20 μl of urine from patients determined by a clinical laboratory to have E. coli urinary tract infection was added directly to 100 μl 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 μl were used directly in hybridizations according to standard NanoStringTM 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.
Selection of organism identification probes: To select NanoStringTM 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.
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.
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).
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 1 hr 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.
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).
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).
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 μg/mL), while the inhA promoter mutation confers only an 8-fold increase in the MIC to 0.4 μg/mL. Exposure to low isoniazid concentrations (0.2 μg/mL) failed to elicit a transcriptional response in either resistant strain, but at higher isoniazid concentrations (1 μg/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.
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).
NanoStringTM data analysis and calculation of distance metric mean squared distance for drug-sensitivity: For all drug-treated samples, raw NanoStringTM 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.
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:
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.
2. A panel of NanoStringTM probes, which we denote Pj, is selected. The subscript j runs from 1 to Nprobes, the total number of selected probes. The analysis is restricted to probes that changed differentially between drug-sensitive and drug-resistant isolates.
3. Replicates of the drug-sensitive strain are defined as Nsamp. For each replicate, normalized counts for each probe Pj before or after drug treatment were denoted Ci,Pjbefore or Ci,Pjafter, with i signifying the sample index.
4. “Log induction ratio” is next computed:

Si,Pj≡ln [Ci,Pjbefore/Ci,Pjafter]

Log transforming the ratio in this way prevents any single probe from dominating the calculated MSD.
5. The average induction ratio of the drug sensitive samples, Sj, is calculated by summing over the different biological replicates and normalizing by the number of samples:




  
    
      S
      j
    
    _
  
  =
  
    
      
        ∑
        
          i
          =
          1
        
        
          N
          samp
        
      
      ⁢
      
        S
        
          i
          ,
          
            P
            j
          
        
      
    
    
      N
      samp
    
  




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
    i
    R
  
  =
  
    
      
        ∑
        
          
            P
            j
          
          =
          1
        
        
          N
          probes
        
      
      ⁢
      
        
      
      ⁢
      
        
          (
          
            
              S
              
                i
                ,
                
                  P
                  j
                
              
            
            -
            
              
                S
                j
              
              _
            
          
          )
        
        2
      
    
    
      N
      probes
    
  




7. Induction ratios for resistant strains, Ri,Pj, are calculated similarly to those of sensitive strains:

Ri,Pj≡ln [Ci,Pjbefore/Ci,Pjafter]

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




  
    MSD
    i
    R
  
  =
  
    
      
        ∑
        
          
            P
            j
          
          =
          1
        
        
          N
          probes
        
      
      ⁢
      
        
          (
          
            
              R
              
                i
                ,
                
                  P
                  j
                
              
            
            -
            
              
                S
                j
              
              _
            
          
          )
        
        2
      
    
    
      N
      probes
    
  




Because most sensitive strains behave similarly to the average sensitive strain the typical value for MSDiS is small compared to the typical value for a resistant strain, MSDiS.
Finally, statistical significance of the measured MSD values were assigned. Because the MSDiS values 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. 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
      _
    
    i
  
  ≡
  
    
      
        MSD
        i
      
      -
      
        
          MSD
          S
        
        _
      
    
    
      σ
      
        MSD
        s
      
    
  




where the standard deviations and means are defined as:



  
    
      σ
      
        MSD
        s
      
    
    ≡
    
      
        
          1
          
            N
            samp
          
        
        ⁢
        
          
            ∑
            
              i
              =
              1
            
            
              N
              samp
            
          
          ⁢
          
            
              (
              
                
                  MSD
                  i
                  S
                
                -
                
                  
                    MSD
                    S
                  
                  _
                
              
              )
            
            2
          
        
      
    
  




  
    and
    ⁢
    
      :
    
  




  
    
      
        MSD
        S
      
      _
    
    =
    
      
        ∑
        
          i
          =
          1
        
        
          N
          samp
        
      
      ⁢
      
        MSD
        i
        S
      
    
  



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.
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.
A panel of NanoStringTM probes, which are denoted Pj, is selected as described. The subscript j runs from 1 to Nprobes, the total number of selected probes.

Si,Pj≡ln [Ci,Pj]

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




  
    
      S
      j
    
    _
  
  =
  
    
      
        ∑
        
          i
          =
          1
        
        
          N
          samp
        
      
      ⁢
      
        S
        
          i
          ,
          
            P
            j
          
        
      
    
    
      N
      samp
    
  




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
    i
    R
  
  =
  
    
      
        ∑
        
          
            P
            j
          
          =
          1
        
        
          N
          probes
        
      
      ⁢
      
        
          (
          
            
              S
              
                i
                ,
                
                  P
                  j
                
              
            
            -
            
              
                S
                j
              
              _
            
          
          )
        
        2
      
    
    
      N
      probes
    
  




Finally, statistical significance was assigned to the measured MSD values. Because the MSDiS values 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
    i
  
  ≡
  
    
      
        MSD
        i
      
      -
      
        
          MSD
          S
        
        _
      
    
    
      σ
      
        MSD
        s
      
    
  




where the standard deviations and means are defined as:



  
    
      σ
      
        MSD
        s
      
    
    ≡
    
      
        
          1
          
            N
            samp
          
        
        ⁢
        
          
            ∑
            
              i
              =
              1
            
            
              N
              samp
            
          
          ⁢
          
            ⁢
            
              
                (
                
                  
                    MSD
                    i
                    S
                  
                  -
                  
                    
                      MSD
                      S
                    
                    _
                  
                
                )
              
              2
            
          
        
      
    
  




  
    and
    ⁢
    
      :
    
  




  
    
      
        MSD
        S
      
      _
    
    =
    
      
        ∑
        
          i
          =
          1
        
        
          N
          samp
        
      
      ⁢
      
        MSD
        i
        S
      
    
  



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 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 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 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
μg/ml




S
CICr08
<.1
μg/ml




R
CIEC1686
50
μg/ml




R
CIEC9779
50
μg/ml




R
CIEC0838
50
μg/ml




R
CIqnrS
6.25
μg/ml




R
CIaac6-Ib
>100
μg/ml




R
CIqnrA
12.5
μg/ml




R
CIqnrB
6.25
μg/ml


E. coli
Gentamicin
S
K12
8
μg/ml




S
CIEC1676
8
μg/ml




S
CIEC9955
16
μg/ml




S
CIEC1801
8
μg/ml




R
CIEC4940
>250
μg/ml




R
CIEC9181
>250
μg/ml




R
CIEC2219
125
μg/ml


E. coli
Ampicillin
S
K12
4
μg/ml





J53
4
μg/ml





DH5α
8
μg/ml




R
CIEC9955
>250
μg/ml





CIEC2219
>250
μg/ml





CIEC0838
>250
μg/ml





CIEC9181
>250
μg/ml


P. aeruginosa
Cipro-
S
PAO-1
1
μg/ml



floxacin
S
CIPA2085
0.4
μg/ml




S
CIPA1189
0.4
μg/ml




S
CIPA9879
0.4
μg/ml




R
CIPA2233
50
μg/ml




R
CIPA1839
25
μg/ml




R
CIPA1489
25
μg/ml


M. tuberculosis
Isoniazid
S
H37Rv
0.05
μg/ml




S
AS1 (CI)
<0.2
μg/ml




S
AS2 (CI)
<0.2
μg/ml




S
AS3 (CI)
<0.2
μg/ml




S
AS4 (CI)
<0.2
μg/ml




S
AS5 (CI)
<0.2
μg/ml




S
AS10 (CI)
<0.2
μg/ml




R
A50
>6.25
μg/ml




R
BAA-812
0.4
μg/ml


M. tuberculosis
Cipro-
S
mc26020
0.5
μg/ml



floxacin
S
AS1 (CI)
<1
μg/ml




S
AS2 (CI)
<1
μg/ml




S
AS3 (CI)
<1
μg/ml




S
AS4 (CI)
<1
μg/ml




S
AS5 (CI)
<1
μg/ml




S
AS10 (CI)
<1
μg/ml




R
C5A15
16
μg/ml


M. tuberculosis
Streptomycin
S
H37Rv
1
μg/ml




S
AS1 (CI)
<2
μg/ml




S
AS2 (CI)
<2
μg/ml




S
AS3 (CI)
<2
μg/ml




S
AS4 (CI)
<2
μg/ml




S
AS5 (CI)
<2
μg/ml




R
CSA1
>32
μg/ml
















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









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
1. Global tuberculosis control—surveillance, planning, financing. WHO Report 2008. WHO/HTM/TB/2008.393.
2. Moore D A, Mendoza D, Gilman R H, Evans C A, Hollm Delgado M G, Guerra J, Caviedes L, Vargas D, Ticona E, Ortiz J, Soto G, Serpa J; Tuberculosis Working Group in Peru. 2004. Microscopic observation drug susceptibility assay, a rapid, reliable diagnostic test for multidrug-resistant tuberculosis suitable for use in resource-poor settings. J Clin Microbiol. 42(10):4432-7.
3. Jacobs W R Jr, Barletta R G, Udani R, Chan J, Kalkut G, Sosne G, Kieser T, Sarkis G J, Hatfull G F, Bloom B R. 1993. Rapid assessment of drug susceptibilities of Mycobacterium tuberculosis by means of luciferase reporter phages. Science. 260(5109):819-22.
4. Piuri M, Jacobs W R Jr, Hatfull G F. 2009. Fluoromycobacteriophages for rapid, specific, and sensitive antibiotic susceptibility testing of Mycobacterium tuberculosis. PLoS ONE. 4(3):e4870.
5. Angeby K A, Klintz L, Hoffner S E. 2002. Rapid and inexpensive drug susceptibility testing of Mycobacterium tuberculosis with a nitrate reductase assay. J Clin Microbiol. 40(2):553-5.
6. García de Viedma D. 2003. Rapid detection of resistance in Mycobacterium tuberculosis: a review discussing molecular approaches. Clin Microbiol Infect. 9(5):349-59.
7. Boshoff H I, Myers T G, Copp B R, McNeil M R, Wilson M A, Barry C E 3rd. 2004. The transcriptional responses of Mycobacterium tuberculosis to inhibitors of metabolism: novel insights into drug mechanisms of action. J Biol. Chem. 279(38):40174-84.
8. Fu L M, Shinnick T M. 2007. Understanding the action of INH on a highly INH-resistant Mycobacterium tuberculosis strain using Genechips. Tuberculosis (Edinb) 87(1):63-70.
9. Geiss G K, Bumgarner R E, Birditt B, Dahl T, Dowidar N, Dunaway D L, Fell H P, Ferree S, George R D, Grogan T, James J J, Maysuria M, Mitton J D, Oliveri P, Osborn J L, Peng T, Ratcliffe A L, Webster P J, Davidson E H, Hood L, Dimitrov K. 2008. Direct multiplexed measurement of gene expression with color-coded probe pairs. Nat. Biotechnol. 26(3):317-25.
10. Mdluli K, Slayden R A, Zhu Y, Ramaswamy S, Pan X, Mead D, Crane D D, Musser J M, Barry C E 3rd. 1998. Inhibition of a Mycobacterium tuberculosis beta-ketoacyl ACP synthase by isoniazid. Science. 280(5369):1607-10.
11. Schnappinger D, Ehrt S, Voskuil M I, Liu Y, Mangan J A, Monahan I M, Dolganov G, Efron B, Butcher P D, Nathan C, Schoolnik G K. 2003. Transcriptional Adaptation of Mycobacterium tuberculosis within Macrophages: Insights into the Phagosomal Environment. J Exp Med. 198(5):693-704.
12. Schwab U, Rohde K H, Wang Z, Chess P R, Notter R H, Russell D G. 2009. Transcriptional responses of Mycobacterium tuberculosis to lung surfactant. Microb Pathog. 46(4):185-93
13. Wayne L G, Hayes L G. 1996. An in vitro model for sequential study of shiftdown of Mycobacterium tuberculosis through two stages of nonreplicating persistence. Infect Immun 64:2062-2069.
14. Sambandamurthy V K, Derrick S C, Jalapathy K V, Chen B, Russell R G, Morris S L, Jacobs W R Jr. 2005. Long-term protection against tuberculosis following vaccination with a severely attenuated double lysine and pantothenate auxotroph of Mycobacterium tuberculosis. Infect Immun. 73(2):1196-203.
15. Garton N J, Waddell S J, Sherratt A L, Lee S M, Smith R J, Senner C, Hinds J, Rajakumar K, Adegbola R A, Besra G S, Butcher P D, Barer M R. 2008. Cytological and transcript analyses reveal fat and lazy persister-like bacilli in tuberculous sputum. PLoS Med. 5(4):e75.
16. Schuch R, Nelson D, Fischetti V A. 2002. A bacteriolytic agent that detects and kills Bacillus anthracis. Nature 418(6900):884-9.
17. Loeffler J M, Nelson D, Fischetti V A. 2001. Rapid killing of Streptococcus pneumoniae with a bacteriophage cell wall hydrolase. Science 294(5549):2170-2.
18. Fischetti V A. 2005. Bacteriophage lytic enzymes: novel anti-infectives. Trends Microbiol. 13(10):491-6
19. Piuri M, Hatfull G F. 2006. A peptidoglycan hydrolase motif within the mycobacteriophage TM4 tape measure protein promotes efficient infection of stationary phase cells. Mol. Microbiol. 62(6):1569-85.
20. Anderson G G, Moreau-Marquis S, Stanton B A, O'Toole G A. In vitro analysis of tobramycin-treated Pseudomonas aeruginosa biofilms on cystic fibrosis-derived airway epithelial cells. Infect Immun 2008 April; 76(4):1423-33.
21. Brazas M. D. and Hancock R. E. W. Ciprofloxacin induction of a susceptibility determinant in Pseudomonas aeruginosa. AAC 2005 August; 49(8):3222-3227.
22. 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.
23. Gmuender, H, Kuratli, K, DiPadova K, Gray, G P, Keck W, Evers S. Gene expression changes triggered by Exposure of Haemophilus Influenzae to Novobiocin or Ciprofloxacin: Combined Transcription and Translation Analysis. Genome Res. 2001; 11(1):28-42.
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.
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.
26. Scott R D. 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.
27. US action plan to combat antimicrobial resistance. Infect Control Hosp Epidemiol. 2001; 22(3):183-4.
28. Harbarth S, Garbino J, Pugin J, Romand J A, Lew D, Pittet D. Inappropriate initial antimicrobial therapy and its effect on survival in a clinical trial of immunomodulating therapy for severe sepsis. Am J. Med. 2003; 115(7):529-35.
29. Harries A D, Hargreaves N J, Kemp J, Jindani A, Enarson D A, Maher D, Salaniponi F M. Deaths from tuberculosis in sub-Saharan African countries with a high prevalence of HIV-1. Lancet. 2001; 357(9267):1519-23.
30. Lawn S D, Shattock R J, Griffin G E. Delays in the diagnosis of tuberculosis: a great new cost. Int J Tuberc Lung Dis. 1997; 1(5):485-6.
31. Carroll K C. Rapid diagnostics for methicillin-resistant Staphylococcus aureus: current status. Mol Diagn Ther. 2008; 12(1):15-24.
32. Fischer H P, Brunner N A, Wieland B, Paquette J, Macko L, Ziegelbauer K, Freiberg C. Identification of antibiotic stress-inducible promoters: a systematic approach to novel pathway-specific reporter assays for antibacterial drug discovery. Genome Res. 2004; 14(1):90-8. PMCID: 314284.
33. Bianchi A A, Baneyx F. Stress responses as a tool To detect and characterize the mode of action of antibacterial agents. Appl Environ Microbiol. 1999; 65(11):5023-7. PMCID: 91676.
34. Shapiro E, Baneyx F. Stress-based identification and classification of antibacterial agents: second-generation Escherichia coli reporter strains and optimization of detection. Antimicrob Agents Chemother. 2002; 46(8):2490-7. PMCID: 127359.
35. Freiberg C, Fischer H P, Brunner N A. Discovering the mechanism of action of novel antibacterial agents through transcriptional profiling of conditional mutants. Antimicrob Agents Chemother. 2005; 49(2):749-59. PMCID: 547252.
36. Hutter B, Schaab C, Albrecht S, Borgmann M, Brunner N A, Freiberg C, Ziegelbauer K, Rock C O, Ivanov I, Loferer H. Prediction of mechanisms of action of antibacterial compounds by gene expression profiling. Antimicrob Agents Chemother. 2004; 48(8):2838-44. PMCID: 478524.
37. Utaida S, Dunman P M, Macapagal D, Murphy E, Projan S J, Singh V K, Jayaswal R K, Wilkinson B J. Genome-wide transcriptional profiling of the response of Staphylococcus aureus to cell-wall-active antibiotics reveals a cell-wall-stress stimulon. Microbiology. 2003; 149(Pt 10):2719-32.
Other Embodiments
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.







TABLE 1





Specimens, clinical syndromes, and pathogens


that can be tested with the present methods
















 1.
Sputum










a.
Pneumonia










i.
Bacterial:










 1.
Streptococcus pneumoniae



 2.
Haemophilus influenza



 3.
Moraxella catarrhalis



 4.
Chlamydia pneumoniae



 5.
Staphylococcus aureus



 6.
Pseudomonas aeruginosa



 7.
Stenotrophomonas maltophila



 8.
Burkholdaria cepaciae



 9.
Mycobacterium tuberculosis



10.
Mycobacterium kansasii



11.
Mycobacterium abscessus



12.
Mycobacterium avium complex



13.
Mycoplasma pneumoniae



14.
Legionella species



15.
Acinetobacter baumannii



16.
Enterobacter



17.
Serratia



18.
Klebsiella










ii.
Viral:










 1.
Influenza



 2.
Respiratory syncytial virus



 3.
Parainfluenza



 4.
Adenovirus



 5.
Rhinovirus



 6.
Coronavirus



 7.
Metapneumovirus



 8.
Coxsackievirus



 9.
Echovirus



10.
Hantavirus



11.
Varicella Zoster



12.
CMV










iii.
Fungal:










 1.
Cryptococcus neoformans



 2.
Blastomycosis



 3.
Histoplasma capsulatum



 4.
Coccidiodes immitis



 5.
Aspergillus species



 6.
Rhizopus species



 7.
Mucor species



 8.
Pneumocystis jirovecii



 9.
Pseudoaleshceria boydii



10.
Scedosproium spp










iv.
Aspiration pneumonia










 1.
Bacteroides



 2.
Fusobacterium



 3.
Peptosteptococcus



 4.
Peptrococcus



 5.
prevotella










b.
Bronchitis and bronchiolitis










 1.
Viral:










a.
Influenze



b.
Adenovirus



c.
Rhinovirus



d.
Coronavirus



e.
Parainfluenza virus



f.
Metapneumovirus



g
Respiratory syncitial virus



h.
Coxsackievirus










 2.
Bacterial










a.
Bordatella pertussis



b.
Mycoplasma pneumoniae



c.
Chlamydia pneumonia








 2.
Urine










a.
Cystitis










i.
Bacterial:










 1.
Escherechia coli



 2.
Klebsiella oxytoca



 3.
Proteus



 4.
pseudomonas



 5.
Enterobacter



 6.
Citrobacter



 7.
Enterococcus



 8.
Staphylococcus saprophyticus










ii.
Fungal:










 1.
Candida species










b.
Pyelonephritis










i.
Bacterial:










 1.
Escherechia coli



 2.
Klebsiella oxytoca



 3.
Enterobacter



 4.
Citrobacter



 5.
Enterococcus



 6.
Candida albicans










ii.
Viral:










 1.
BK virus



 2.
adenovirus










c.
prostatitis










i.
Neisseria gonorrhea



ii.
E. coli



iii.
Klebsiella



iv.
Enterobacter



v.
Citrobacter



vi.
Proteus mirabilis



vii.
Chlamydia trachomatis



viii.
Ureaplasma








 3.
Blood










a.
Endocarditis (native valve and prosthetic valve endocarditis)










i.
Bacterial










 1.
Staphylococcus aureus



 2.
Streptococcus viridans



 3.
Enterococcus faecalis



 4.
Enterococcus faecium



 5.
Coagulase negative Staphylococcus



 6.
Streptococcus bovis



 7.
Pseudomonas aeruginosa



 8.
Haemophilus parainfluenzae



 9.
Haemophilus aphrophilus



10.
Actinobacillus



11.
Cardiobacterium hominis



12.
Eikenella corrodens



13.
Kingella kingii



14.
Bartonella species



15.
Coxiella burnetii



16.
Chlamydia psittaci



17.
Mycoplasma



18.
Legionella pneumophila



19.
Brucella species



20.
Tropheryma whipplei



21.
Propionobacterium acnes










ii.
Fungal










 1.
Candida albicans



 2.
Candida krusei



 3.
Candida tropicalis



 4.
Candida glabrata



 5.
Candida parapsilosis



 6.
Candida guillermondii










b.
Bacteremia










 1.
Staphylococcus aureus



 2.
Streptococcus viridans



 3.
Enterococcus faecalis



 4.
Enterococcus faecium



 5.
Coagulase negative staphylococcus



 6.
Streptococcus bovis



 7.
Pseudomonas aeruginosa



 8.
Stenotrophomonas



 9.
Burkholdaria cepacia



10.
Acinetobacter species



11.
E. coli



12.
Salmonella



13.
Streptococcus pneumoniae



14.
Enterobacter



15.
Seratia marcesens



16.
Klebsiella sp



17.
Proteus



18.
Citrobacter



19.
Propionobacterium acnes










c.
Fungemia:










i.
Candida species



ii.
Fusarium species



iii.
Aspergillus species



iv.










d.
Lemierre's disease










i.
fusobacterium










e.
Fever of unknown origin










i.
Malaria










 1.
Plasmodium falciparum



 2.
Plasmodium malariae



 3.
Plasmodium ovale



 4.
Plasmodium vivax










ii.
Hepatitis:










 1.
Hepatitis A



 2.
Hepatitis E










iii.
Dengue fever



iv.
Typhoid fever



v.
Tick typhus










 1.
Ricketsia ricketsii










vi.
Scrub typhus










 1.
Orientia tsutsufamushi










vii.
Ehrlichia










 1.
Ehrlichia species



 2.
Anaplasma species










viii.
babesia



ix.
Rocky mountain spotted fever










 1.
Rickettsia rickettsii










x.
Lyme disease










 1.
Borrelia bergdorferi










xi.
Syphilis










 1.
Treponema pallidum










xii.
Bartonellosis- bartonella species



xiii.
Leprospirosis- leptospira interrogans



xiv.
Relapsing fever



xv.
HIV








 4.
joint fluid










a.
prosthetic joint infection










i.
staphylococcus aureus



ii.
coagulase negative staphylococcus



iii.
streptococcus viridans



iv.
pseudomonas aeruginosa



v.
enterococcus faecium



vi.
enterococcus faecalis



vii.
E. coli



viii.
Klebsiella pneumoniae



ix.
Klebsiella oxytoca



x.
Candida albicans



xi.
Candida krusei



xii.
Candida glabrata



xiii.
Propionobacteriuma acnes



xiv.
peptostreptococcus










b.
septic arthritis










i.
staphylococcus aureus



ii.
streptococcus pyogenes



iii.
streptococcus agalactiae



iv.
neisseria gonorrhea



v.
e. coli



vi.
pseudomonas aeruginosa










c.
Lyme arthritis










i.
Lyme disease −> borrelia bergdorferi








 5.
CSF










a.
Meningitis










i.
Bacterial










 1.
Streptococcus pneumoniae



 2.
Neissseria meningitides



 3.
Haemophilus influenzae



 4.
Listeria monocytogenes



 5.
E. coli



 6.
Streptococcus agalactiae



 7.
Propionobacterium acnes



 8.
Staphylococcus aureus



 9.
Coagulase negative staphylococcus



10.
Enterococcus



11.
Klebsiella pneumoniae



12.
Pseudomonas aeruginosa



13.
Salmonella species



14.
Acinetobacter species



15.
Streptococcus viridans



16.
Streptococcus bovis



17.
Fusobacterium species



18.
Nocardia species



19.
Mycobacterium tuberculosis



20.
Streptococcus pyogenes










ii.
Viral:










 1.
Herpes viruses



 2.
Enteroviruses










iii.
Spirochetal










 1.
Lyme disease










a.
Borrelia bergdorferi










 2.
Syphilis










a.
Treponema pallidum










iv.
Parasites:










 1.
Naegleria fowleri



 2.
Angiostrongylus cantonensis



 3.
Bayliascaris procyonis



 4.
Strongyloides stercoralis










v.
Fungal










 1.
Croptococcus species



 2.
Coccidiodes immitis



 3.
histoplasma










b.
Encephalitis










i.
Viral:










 1.
herpes simplex I



 2.
herpes simplex II



 3.
human herpes virus 6



 4.
varicella zoster virus



 5.
Lymphocytic choriomeningitis



 6.
Enterovirus



 7.
Eastern equine encephalitis virus



 8.
Western equine encephalitis virus



 9.
Venezuelan equine encephalitis virus



10.
West nile virus



11.
St louis encephalitis virus



12.
Murray valley encephalitis virus



13.
Japanese encephalitis virus



14.
Dengue virus



15.
La crosse virus



16.
Rift valley fever virus



17.
Nipah virus



18.
Lassa fever virus



19.
Rabies virus



20.
Adenovirus



21.
Epstein barr virus








 6.
Cervical/vaginal swab:










a.
Cervicitis










i.
Neisseria gonorrhea



ii.
Chlamydia trachomatis



iii.
trichomoniasis










b.
Colonization (risk for neonatal sepsis/meningitis)










i.
Streptococcus agalactiae








 7.
Stool:










a.
Antibiotic associated diarrhea:










i.
Clostridium difficile associated diarrhea










 1.
Clostridium difficile










ii.
Klebsiella oxytoca associated diarrhea










 1.
Klebsiella oxytoca










b.
Dysentery










i.
Bacterial:










 1.
Salmonella typhimurium



 2.
Shigella species



 3.
Campylobacter jejuni










ii.
Parasitic:










 1.
Entamoeba hystolytica










c.
Diarrhea:










i.
Bacterial:










 1.
E. coli



 2.
Bacillus cereus



 3.
Vibrio cholera



 4.
Vibrio parahaemolyticus



 5.
Clostridium perfringens










ii.
Parasitic:










 1.
Giardia lamblia



 2.
Cryptosporidia



 3.
Microsporidia



 4.
Isospora belli



 5.
cyclospora



 6.
Entamoeba histolytica










iii.
Viral:










 1.
Enteroviruses



 2.
Noroviruses



 3.
Rota viruses



 4.
Astroviruses



 5.
calciviruses



 6.
Adenovirus








 8.
Biliary fluid










a.
Cholangitis










i.
Bacterial:










 1.
Klebsiella oxytoca



 2.
Escherechia coli



 3.
Enterococcus species



 4.
enterobacter










ii.
Parasitic:










 1.
Clonorchis sinensis



 2.
Opisthorchis species



 3.
Fasciola hepatica



 4.
Ascaris lumbrigoides








 9.
Pleural fluid










a.
Empyema








10.
Bone biopsy:










a.
Osteomyelitis










i.
Bacterial:










 1.
Staphylococcus auerus



 2.
Coagulase negative Staphylococcus



 3.
Enterococcus species



 4.
Streptococcus species



 5.
Pseudomonas aeruginosa



 6.
Enterobacter species



 7.
Proteus species



 8.
E. coli



 9.
Serratia species



10.
Peptostreptococcus species



11.
Clostridium species



12.
Bacteroides species



13.
Mycobacterium tuberculosis



14.
Mycobacterium avium complex



15.
Salmonella species



16.
Actinomyces species










ii.
Fungal:










 1.
Candida species



 2.
Blastomyces



 3.
coccidioides








11.
Peritoneal fluid










a.
Bacterial peritonitis (primary and peritoneal dialysis associated]










i.
E. coli



ii.
Klebsiella pneumoniae



iii.
Streptococcus pneumoniae



iv.
Staphylococcus aureus



v.
Coagulase negative staphylococcus



vi.
Bacteroides species



vii.
Clostridium perfringens



viii.
Acinetobacter



ix.
Enterobacter



x.
Proteus mirabilisa



xi.
Pseudomonas aeruginosa










b.
Fungal peritonitis










i.
Candida species








12.
Pericardial fluid:










a.
Infectious pericarditis










i.
Bacteria:










 1.
Staphylococcus aureus



 2.
Streptococcus pneumoniae



 3.
Neisseria meningitides



 4.
Haemophilus influenzea



 5.
Salmonella species



 6.
Pseudomonas aeruginosa



 7.
Mycobacterium tuberculosis










ii.
Fungal:










 1.
Histoplasma capsulatum



 2.
Candida species



 3.
Aspergillus species



 4.
Cryptococcus neoformans










iii.
Viral:










 1.
HIV



 2.
Influenza



 3.
Mumps



 4.
Varicella zoster virus



 5.
Epstein barr virus








13.
Liver biopsy tissue:










a.
Hepatitis:










i.
Viral:










 1.
Hepatitis A



 2.
Hepatitis B



 3.
Hepatitis C



 4.
Hepatitis D



 5.
Hepatitis E



 6.
Hepatitis G



 7.
Herpes simplex virus



 8.
Cytomegalovirus








14.
Lung biopsy:










a.
Pulmonary nodule










i.
Bacterial:










 1.
Nocardia species



 2.
Mycobacterium tuberculosis



 3.
Mycobacterium avium complex



 4.
Mycobacterium abscessus



 5.
Actinomyces species










ii.
Fungal:










 1.
Cryptococcus neoformans



 2.
Blastomyces



 3.
Histoplasma capsulatum



 4.
Coccidiodes immitis



 5.
Aspergillus species



 6.
Rhizopus species



 7.
Mucor species








15.
Brain biopsy: mass lesion










a.
Bacterial abscess/lesion:










i.
Streptococcus anginosis



ii.
Bacteroides species



iii.
Prevotella species



iv.
E. coli



v.
Klebsiella species



vi.
Enterobacter species



vii.
Acinetobacter species



viii.
Citrobacter species



ix.
Staphylococcus aureus



x.
Haemophilus influenzae



xi.
Fusobacterium species



xii.
Streptococcus pneumoniae



xiii.
Actinomyces species



xiv.
Nocardia species



xv.
Propionobacterium acnes










b.
Fungi:










i.
Aspergillus species



ii.
Mucor species



iii.
Blastomyces



iv.
Candida species










c.
Parasites:










i.
Schistosomiasis



ii.
Toxoplasma gondii








16.
Lymph node biopsy:










a.
Lymphadenitis:










i.
Bacterial:










 1.
Mycobacterium tuberculosis



 2.
Mycobacterium avium complex



 3.
Mycobacteriums scrofulaceum



 4.
Brucella spcesis



 5.
Treponema pallidum



 6.
Yersinia pestis



 7.
Francisella tularensis



 8.
Bartonella henslae



 9.
Lymphogranuloma venereum










a.
Chlamydia trachomatis








17.
Esophageal biopsy










a.
Esophageal biopsy










i.
Esophagitis










 1.
Fungal:










a.
Candida species










 2.
Viral










a.
Herpes simplex virus



b.
Cytomegalovirus








18.
Colonic biopsy:










a.
Diarrhea:










i.
Cytomegalovirus








19.
Gastric biopsy:










a.
Peptic ulcer disease and gastritis










i.
Helicobacter pylori








20.
small bowel biopsy:










a.
whipple's disease: tropheryma whipplei








21.
myocardial biopsy:










a.
myocarditis:










i.
viral:










 1.
coxsackie viruses



 2.
echoviruses



 3.
adenovirus



 4.
Epstein barr virus



 5.
Cytomegalovirus



 6.
HIV










ii.
Parasites:










 1.
Toxoplasma gondii



 2.
Trypanosoma cruzi



 3.
Trichinella spiralis



 4.
Toxocara canis








22.
Skin biopsy:










a.
disseminated fungal infection:










i.
coccidioides



ii.
fusarium species



iii.
blastomyces



iv.
histoplasma



v.
aspergillus species



vi.
Cryptococcus



vii.
Penicillium marneffei



viii.
Mucor species








23.
Sinus biopsy:










a.
Mucormycosis:










i.
Mucor species






















TABLE 2









alternate



Organism
RefSeq
nt
Gene
name
GeneID




















E. coli organism ID
NC_000913.2
103155 to 103985
ftsQ

944823



NC_0009132
100765 to 102240
murC

946153



NC_000913.2
1108558 to 1110093
opgG

945005



NC_000913.2
1078528 to 1080036
putP

945602



NC_000913.2
108279 to 110984
secA

944821



NC_000913.2
1009187 to 1011094
uup

945566


Staphylococcus aureus
NC_007793.1
1229595 to 1230521
fabD

3914203


organism ID
NC_007793.1
1604865 to 1605650
proC

3914537



NC_007793.1
586097 to 589648
rpoB

5776819



NC_007793.1
215727 to 216911
rocD

3914607



NC_007793.1
986970 to 987365
spxA

3913388



NC_007793.1
994970 to 995779
ppnK

3914724



NC_007793.1
1010345 to 1011907
prfC

3914032



NC_007793.1
1142688 to 1144469
uvrC

3913389



NC_007793.1
1187077 to 1189830
ileS

3914067



NC_007793.1
1195628 to 1196509
pyrB

3913022



NC_007793.1
1245088 to 1245972
rbgA

3914854


Klebsiella pneumoniae
NC_011283.1
3688745 to 3689062
clpS

6935035


organism ID genes
NC_011283.1
3652997 to 3653284
ihfB

6936866



NC_011283.1
3679404 to 3679898
lrp

6938832



NC_011283.1
4693235 to 4694176
mraW

6936882


Pseudomonas aeruginosa
NC_002516.2
4478979 to 4480139
dacC

878956


organism ID
NC_002516.2
4477974 to 4478627
lipB

878952



NC_002516.2
4498488 to 4499843
mpl

879000



NC_002516.2
4486847 to 4488112
proA

878970



NC_002516.2
4481230 to 4482252
sltB1

878946


Enterococcus faecalis
NC_004668.1
2760409 to 2761338
fabD

1201730


organism ID
NC_004668.1
923358 to 925273
pyrroline reductase

1199849



NC_004668.1
3108782 to 3112405
rpoB

1202073


Streptococcus pyogenes
NC_002737.1
1129645 to 1130586
birA

901436


organism ID
NC_002737.1
1161365 to 1162393
queA

901464



NC_002737.1
10951 to 12237
hpt



NC_002737.1
1340476 to 1341417
cysM

901866



NC_002737.1
1509476 to 1510441
scrR

902038



NC_002737.1
139268 to 141043
ntpA

900480



NC_002737.1
1528370 to 1530823
recD



NC_002737.1
163947 to 164651
araD

900502


Streptococcus pneumoniae
NC_011900.1
2048720 to 2049427
phoP

7328038


organism ID
NC_011900.1
2125295 to 2126311
arcB

7328652



NC_011900.1
291547 to 291864
ftsL

7328131



NC_011900.1
634520 to 635149
thiE

7329171



NC_011900.1
822368 to 823843
speA

7328880



NC_011900.1
878953 to 879894
prsA

7328909



NC_011900.1
967841 to 969100
murZ

7328985



NC_011900.1
1095074 to 1096423
vicK

7329431


Haemophilus infuenzae
NC_000907.1
1033019 to 1034473
panF

950256


organism ID
NC_000907.1
1013845 to 1014501
slmA

949937



NC_000907.1
1027535 to 1027984
aroQ

949969



NC_000907.1
1026452 to 1027477
menC

949968



NC_000907.1
1063986 to 1064345
rnpA

950612



NC_000907.1
1086136 to 1087137
bioB

950009



NC_000907.1
1098172 to 1099116
serB

949401



NC_000907.1
104277 to 105274
hitA

950998


M. tuberculosis species-
NC_000962.2
2223343 to 2224029,
Rv1980c

885925


specific organism ID

complement



NC_000962.2
1574510 to 1574767,
rv1398c

886759




complement



NC_000962.2
2278498 to 2278932,
rv2031c

887579




complement


M. avium/paratuberculosis
AE016958
2349935 to 2350858
MAP_2121c


organism ID
NC_008595.1
3393700 to 3394494
MAV_3252 (MAP_1263)



NC_008595.1
3340393 to 3359031
MAV_3239 (MAP1242)



NC_008595.1
1561314 to 1562966
MAV_1600 (MAP2380)


M. tuberculosis, M. avium,
NC_000962.2
1852273 to 1852878
rv1641 (MAP1352, MAV_3127)

885478


M. kansasii organism ID
NC_000962.2
4025056 to 4025544,
Rv3583c (MAV_0570, MAP0475)

887854




complement


malaria organism ID
AF179422.1

Pfg27


(P. falciparum)
AF356146 .1

Pfs48/45



PFI1020c.1

PFI1020c

813484



PFA0660w.1

PFA0660w

813268



PFA0635c.1

PFA0635c

813262



PFA0130c.1

PFA0130c

813163



PF11_0282.1

PF11_0282

810829



PFE0660c.1

PFE0660c

812947



PFC0800w.1

PFC0800w

814495



PFL2520w.1

PFL2520w

811554



XM_001349171.1

PF07_0128

2654998



PF10_0346.1

PF10_0346

810503



PF13_0233.1

PF13_0233

814200



PFA0110w.1

PFA0110w

813159



PFD1170c.1

PFD1170c

812429



PF07_0006.1

PF07_0006

2654973



PF11_0512.1

PF11_0512

811044


influenza organism ID
CY052331

matrix+ A



CY052331

matrix+ B



CY052331

matrix+ C



CY052331

matrix− A



CY052331

matrix− B



CY052331

matrix− C


HIV organism ID
AF033819
336-1838
gag A

155030



AF033819
336-1838
gag B

155030



AF033819
336-1838
gag C

155030



AF033819
336-1838
gag D

155030



AF033819
5516-5591; 7925-8199
rev A

155908



AF033819
5516-5591; 7925-8199
rev B

155908



AF033819
5516-5591; 7925-8199
rev C

155908



AF033819
5516-5591; 7925-8199
rev D

155908


HSV2 organism ID
EU106421

gpG A



EU106421

gpG B



EU106421

gpG C



EU106421

gpG D


E. coli ampicillin
NC_000913.2
1724047 to 1724646
b1649

946166


resistance genes
NC_000913.2
354146 to 355405
codB

944994



NC_000913.2
2873443 to 2874351
cysD

947217



NC_000913.2
3815783 to 3816607
dinD

948153



NC_000913.2
594823 to 596196
ylcB

946288


E. coli aminoglycoside
NC_000913.2
1133025 to 1133780
flgF

945639


resistance genes
NC_000913.2
2873443 to 2874351
cysD

947217



NC_000913.2
4054648 to 4056057
glnA

948370



NC_000913.2
1108558 to 1110093
opgG

945005



NC_000913.2
103155 to 103985
ftsQ

944823



NC_000913.2
1724047 to 1724646
b1649

946166



NC_000913.2
2820730 to 2821791
recA

947170



NC_000913.2
3815783 to 3816607
dinD

948153


E. coli fluoroquinolone
NC_000913.2
29651 to 30799
carA

949025


resistance genes
NC_000913.2
4610434 to 4618849
deoC

948902



NC_000913.2
1133025 to 1133780
flgF

945639



NC_000913.2
3790849 to 3791706
htrL

948137



NC_000913.2
2820730 to 2821791
recA

947170



NC_000913.2
4269072 to 4271894
uvrA

948559



NC_000913.2
2101415 to 2102533
wbbK

946555



NC_000913.2
814962 to 815870
ybhK

945390



NC_000913.2
1009187 to 1011094
uup

945566



NC_000913.2
1148951 to 1149880
fabD

945766


E. coli normalizing genes
NC_000913.2
1148951 to 1149880
fabD

945766



NC_000913.2
404059 to 404868
proC

945034



NC_000913.2
4183296 to 4179268
rpoB

948488


Pseudomonas aeruginosa
NC_002516.2
899830 to 900165
PA 0825

880620


fluoroquinolone resistance
NC_002516.2
106321 to 1067817
PA 0985

877569



NC_002516.2
1385668 to 1384361
cobB

881727



NC_002516.2
4088904 to 4090094
dxr

880464



NC_002516.2
1167488 to 1168237
flgF

878576



NC_002516.2
3367903 to 3368517
lexA

879875



NC_002516.2
4385900 to 4386352
moaE

878985



NC_002516.2
4051564 to 4052604
recA

880173


Pseudomonas aeruginosa
NC_002516.2
317966 to 318148
PA 0284

879699


tobramycin resistance genes
NC_002516.2
674667 to 675026
PA 0613

878382



NC_002516.2
2200685 to 2202652
PA 2012

879012



NC_002516.2
3508717 to 3509166
PA 3126

882640



NC_002516.2
4407760 to 4408734
PA 3932

879072



NC_002516.2
4671319 to 4672707
PA 4175

880208



NC_002516.2
5349201 to 5349761
PA 4762

881766



NC_002516.2
5693138 to 5694481
hslU

881050


Pseudomonas aeruginosa
NC_002516.2
3326145 to 3327083
fabD

880434


normalizing genes
NC_002516.2
434830 to 435651
proC

878413



NC_002516.2
4776544 to 4780627
rpoB

881699


Staphylococcus aureus
NC_007793.1
39127 to 44133
mecA

3913904


methicillin resistance genes
NC_007793.2
39128 to 44133
mecA

3913904


Enterococcus faecalis
NC_008768.1
11489 to 12520
EF vanA


vancomycin resistance genes
NC_008768.1
11489 to 12520
EF vanA



NC_008768.1

EF vanB



NC_008768.1

EF vanB


Mycobacterium tuberculosis
NC_000962.2
907338 to 908018,
Rv0813c.1
Rv0813
885395


streptomycin resistance

complement



NC_000962.2
1807298 to 1807762,
Rv1608c.1
bcpB
885530




complement



NC_000962.2
2075877 to 2078702
Rv1832.1
gcvB
885716



NC_000962.2
3835272 to 3836891,
Rv3417c.1
groEL
887877




complement


Mycobacterium tuberculosis
NC_000962.2
1777859 to 1778539
Rv1570.1
bioD
886338


rifampicin resistance
NC_000962.2
1805653 to 1806000
Rv1606 .1
hisI
886011



NC_000962.2
2567504 to 2568406
Rv2296.1
Rv2296
887796



NC_000962.2
2645771 to 2646673,
Rv2364c.1
era
886027




complement


Mycobacterium tuberculosis
NC_000962.2
2518115 to 2519365
Rv2245.1
kasA
887269


isoniazid resistance
NC_000962.2
2520743 to 2522164
Rv2247 .1
accD6
887671



NC_000962.2
3153039 to 3154631,
Rv2846c.1
efpA
888575




complement



NC_000962.2
4116002 to 4116379
Rv3675 .1
Rv3675
885155



NC_000962.2
4261153 to 4263066,
Rv3801c.1
fadD32
886130




complement


Mycobacterium tuberculosis
NC_000962.2
2518115 to 2519365
Rv2245.1
kasA
887269


fluoroquinlone resistance
NC_000962.2
2520743 to 2522164
Rv2247.1
accD6
887671



NC_000962.2
59122 to 59376
Rv0055.1
rpsR
887022



NC_000962.2
1477628 to 1479118,
Rv1317c.1
alkA
886916




complement



NC_000962.2
3049052 to 3051424,
Rv2737c.1
recA
888371




complement



NC_000962.2
3098964 to 3100169,
Rv2790c.1
ltp1
888585




complement



NC_000962.2
3676775 to 3681316
Rv3296.1
lhr
887503


M. tuberculosis ethambutol
NC_000962.2
2279129 to 2280124
rv2032

887582



NC_000962.2
3134596 to 3135483,
rv2827c

887707




complement



NC_000962.2
4360199 to 4360546,
rv3880c

886205




complement


M. tuberculosis
NC_000962.2
759807 to 763325
Rv0667.1
rpoB
888164


normalization controls
NC_000962.2
2223343 to 2224029,
Rv1980c.1
mpt64
885925




complement



NC_000962.2
3017835 to 3019421
Rv2703.1
sigA
887477


Streptococcus agalactiae
NC_004368.1
113813 to 113862
tig
gbs0104


organism ID
NC_004368.1
116727 to 116776
pyrG
gbs0106



NC_004368.1
1796241 to 1796290
scrR
gbs1736



NC_004368.1
1930084 to 1930133
hslO
gbs1865



NC_004368.1
27753 to 277602
SglyS
gbs0260



NC_004368.1
293911 to 293960
proB
gbs0273



NC_004368.1
296033 to 296082
mraW
gbs0275



NC_004368.1
296975 to 297024
ftsL
gbs0276


Serratia proteamaculans
NC_009832
1003727 to 1003776
dkgB
Spro_0904
5603647


organism ID
NC_009832
1043868 to 1043917
mtnK
Spro_0946
5603764



NC_009832
106923 to 106972
xylF
Spro_0099
5605677



NC_009832
1066040 to 1066089
proA
Spro_0968
5605339



NC_009832
1119783 to 1119832
aroL
Spro_1018
5603038



NC_009832

phosphate ABC





transporter





periplasmic substrate-




11924 to 11973
binding protein PstS
Spro_0012
5603657



NC_009832
1170021 to 1170070
secD
Spro_1063
5606770



NC_009832
1181606 to 1181655
ribD
Spro_1072
5606850


Streptococcus mitis
NC_013853.1
741 to 790
dnaA
smi_0001
8797427


organism ID
NC_013853.1
1121903 to 1121952
mscL
smi_1137
8798101



NC_013853.1
298410 to 298459
purR
smi_0302
8799383



NC_013853.1
317077 to 317126
amiA
smi_0322
8799444



NC_013853.1
539539 to 539588
nanA
smi_0601
8797777



NC_013853.1
637090 to 637139
codY
smi_0687
8799389



NC_013853.1
683274 to 683323
pstS
smi_0732
8797867


Candida albicans
NC_007436.1
102175 to 102224
CaJ7_0076

3704032


organism ID
NC_007436.1
174830 to 174879
CaJ7_0103

3703998



NC_007436.1
376124 to 376173
CaJ7_0197

3704134



NC_007436.1
465177 to 465226
CaJ7_0245

3703842



NC_007436.1
658412 to 658461
CaJ7_0344

3703873



NC_007436.1
771224 to 771273
CaJ7_0399

3704050


Acinetobacter baumanii
NC_009085.1
1008527 to 1008576
fusA
A1S_0868
4918267


organism ID
NC_009085.1
1155941 to 1155990
lysS
A1S_0998
4918633



NC_009085.1
1168069 to 1168118
isocitrate lyase
A1S_1008
4919056



NC_009085.1
1173092 to 1173141
ureC
A1S_1014
4917976



NC_009085.1
1330602 to 1330651
rnhB
A1S_1140
4917576



NC_009085.1
1395212 to 1395261
pyrB
A1S_1190
4919730



NC_009085.1
155583 to 155632
guaA
A1S_0130
4920269



NC_009085.1
1820125 to 1820174
cmk
A1S_1571
4917197


Proteus mirabilis
NC_010554.1
106142 to 106191
secD
PMI0078
6801011


organism ID
NC_010554.1
1061810 to 1061859
map1
PMI0997
6802785



NC_010554.1
1070228 to 1070277
ftnA
PMI1007
6802218



NC_010554.1
1078862 to 1078911
Prc
PMI1016
6802734



NC_010554.1
109131 to 109180
nrdR
PMI0080
6802391























SEQ ID


Gene
Sequence
NO:

















ftsQ
CGACAGTGTTGGTGAGCGGCTGGGTCGTGTTGGGCTGGATGGAAGATGCGCAACGCCTGCCGCTCTCAAAGCTGGT
1



GTTGACCGGTGAACGCCATTACAC



 


murC
GAATTGTTACCGCGAGTGGGGCGTCAGACCACGACTTACGGCTTCAGCGAAGATGCCGACGTGCGTGTAGAAGATT
2



ATCAGCAGATTGGCCCGCAGGGGC



 


opgG
TTGTGGATGTGCAGTCGAAAATCTATCTGCGCGATAAAGTCGGCAAACTGGGGGTTGCACCGTTAACCAGTATGTT
3



CCTGTTTGGGCCGAACCAACCGTC



 


putP
TAGTGTTTAGTTTGCTGGGTAAAGCGCCGTCAGCGGCGATGCAAAAACGCTTTGCCGAGGCCGATGCGCACTATCA
4



TTCGGCTCCGCCGTCACGGTTGCA



 


secA
CTCGGAAATGTATAAACGCGTGAATAAAATTATTCCGCACCTGATCCGTCAGGAAAAAGAAGACTCCGAAACCTTC
5



CAGGGCGAAGGCCACTTCTCGGTG



 


uup
AACGCTATCACGATATTTCGCGCCTGGTGATGAACGACCCGAGCGAGAAAAATCTCAACGAACTGGCGAAGGTTCA
6



GGAACAGCTGGATCACCACAACCT



 


fabD
GAGCTAGTAGAAAAAGGTAAATCATTAGGTGCAAAACGTGTCATGCCTTTAGCAGTATCTGGACCATTCCATTCAT
7



CGCTAATGAAAGTGATTGAAGAAG



 


proC
TAACAGCTATCACCGGAAGCGGCCCAGCATTTTTATATCATGTATTCGAGCAATATGTTAAAGCTGGTACGAAACT
8



TGGTCTAGAAAAAGAACAAGTTGA



 


rpoB
GGAGAAATGGCATTAGGTAGAAACGTAGTAGTTGGTTTCATGACTTGGGACGGTTACAACTATGAGGATGCCGTTA
9



TCATGAGTGAAAGACTTGTGAAAG



 


rocD
AAAGATCCTGAAGGCAATAAATATATGGATATGTTATCTGCATATTCCGC
10


 


spxA
AATATTAAAAATGACTGAAGACGGTACTGATGAAATCATTTCTACACGTT
11


 


ppnK
ACAGGTCATTTAGGATTTTATGCGGATTGGTTACCTCATGAAGTTGAAAA
12


 


prfC
AAATTAGACCGAGTAGGTAAAGAACCATTTGAATTATTAGATGAAATCGA
13


 


uvrC
AAATATTTCGGACCGTATCCGAATGCATATTCTGCTCAAGAAACTAAAAA
14


 


ileS
AAATTCAAGAAAAATGGGATGCAGAAGATCAATACCATAAAGCGTTAGAA
15


 


pyrB
AAATATACAAACTTATCCAAAAGGCAAGTCAATTTAAATCTGGTGAACGT
16


 


rbgA
AAACCCTATGATAGATGAAGTTATTAACCAAAAACCACGTGTTGTTATAT
17


 


clpS
CAACTGATGCTCACGGTTCACTATGAAGGTAAGGCGATTTGTGGCGTGTTTACCGCGGAAGTGGCGGAGACCAAAG
18



TCGCTATGGTGAATCAGTACGCGA



 


ihfB
CTCGCACCGGACGTAACCCGAAAACTGGTGATAAAGTCGAACTGGAAGGTAAGTACGTTCCGCACTTTAAGCCCGG
19



GAAAGAATTACGTGACCGCGCCAA



 


lrp
TCATCTGGTTTCCGGTGATTTCGACTATCTGTTGAAAACCCGTGTACCGGATATGTCAGCGTATCGTAAATTACTG
20



GGCGAGACCTTGCTGCGCCTGCCG



 


mraW
TCATTCGCTGGAAGATCGCATTGTGAAGCGCTTTATGCGTGAGCAAAGCCGCGGTCCGCAGGTTCCGGCGGGAATA
21



CCGATGACCGAAGCGCAGCTCAAA



 


dacC
GCGATCTACGCGCAGAAGGAATTCCTCTGGAACAACATCAAGCAGCCGAACCGCAACCTGCTGCTGTGGCGCGACA
22



AGACCGTCGACGGCCTGAAGACCG



 


lipB
CCTGCGGCTACGCCGGGATGCCCATGACCCAACTGCGCGACCTGGTTGGGCCGGTGGATTTTGCCGAGGTGTGTAC
23



CCGATTGCGCGCTGAGCTCGTCTC



 


mpl
ACCGCCCGCGCACGGCGATCCTGAACAACCTGGAATTCGACCACGCGGATATCTTCCCCGACCTCGCGGCCATCGA
24



GCGGCAGTTCCACCATCTGGTGCG



 


proA
AAGTGGATTCCGCTTCGGTGATGGTCAACGCCTCGACCCGCTTCGCCGACGGCTTCGAGTACGGCCTCGGCGCCGA
25



GATCGGGATTTCCACCGACAAGCT



 


sltB1
CGATGCGTTTCGTCGGCGACAAGGGCATCGAGTATTGGGTCGGTTTGCCGAACTTCTACGTGATCACCCGCTATAA
26



TCGCAGCGCCATGTATGCCATGGC



 


fabD
AGCTGGTGTGAAGCGAATGATTCCGTTAAATGTGAGTGGCCCTTTCCATACGGCGCTGTTACAACCAGCATCAAAA
27



AAATTGGCTCAGGATTTAGCAAAA



 


pyrroline
CAAGAAGCACAAATGGCTCTTGGCAATAAAGAAGCCAAAGTTGTTCATGCCATTCCTAATACACCAGTTAGCGTGA
28


reductase
ATCAAGGCGTGATTGGCGTAGCCT



 


rpoB
CACAGTTATCACAGTTCATGGACCAAACAAACCCATTAGGTGAGTTAACCCATAAACGTCGTCTATCAGCCTTAGG
29



GCCTGGTGGTTTGACTCGTGACCG



 


birA
AGCCAAGAAGCTGCCAAAGGACGCCTCGATCGGCAATTTTTTTCAGCTAG
30


 


queA
ATGGTCATGTGGAATTGCTTTTGCTTAAAAATACACAAGGAGATCAATGG
31


 


hpt
ATCAAGAAAAAAATCCGCTTATGATTGGTGTATTAAAAGGATCAGTTCCT
32


 


cysM
AACTTTAGCAATAGAACTAGGTGCTTGGATGCCTATGCAATTTAATAACC
33


 


scrR
ATTGGGCTACAAGCCTAACAATCTCGCTAGAAGTTTGCAAGGTAAATCAA
34


 


ntpA
AGAAATGTTTGACGGTATTCAGCGACCGCTTGATCGTTTTCAAAAAGCAA
35


 


recD
ACCATTGACCATATTTTAGAAGACCCAAGCAAATTAGAAACTATCTCTGG
36


 


araD
AAATTTACGTGGGGCAATGTCTCTGAAGTTTGTCGTGAATTAGGACGTAT
37


 


phoP
ACGACATGGTCGCCAAGCACCGGCAACTGGCCGAGATCATCGCCAGCGAC
38


 


arcB
TCACTTGAAAGATTTGAAAAAACGCAATATTCAACACCACTACCTTGCTG
39


 


ftsL
CGCGTGTGGAAAAAGCTTTTTACTTTTCCATTGCTGTAACCACTCTTATT
40


 


thiE
TGGCGTACATGTAGGTCAAGATGATATTGGTGTTGATGAAATTAGAAAAT
41


 


speA
GATTGGTGGAACAACTTCATCGGTGCAGACTATGATTCTGGCAACCTGCA
42


 


prsA
AGTTTTTGAAAAACAATATGGCTCAGAGCTTGATGATAAAGAGGTTGATG
43


 


murZ
ATTTTTATGGGAGCCTCTTAGGCCGTTTTGGTGAAGCGACAGTTGGTCTA
44


 


vicK
ATTTGATTGCAGGAGATTATTCCAAGGTTCTTGATATGCAAGGTGGGTCT
45


 


panF
AAAGAAGCGGGCAATATGGTGGATCTCGACTCCAACCCGACCAAGTTGAT
46


 


slmA
AGCAAGTGCTTACAGTATTGATACATATGCTTCATTCTGAACGTGGAATG
47


 


aroQ
CTTAAATATGTTAGGGGCTCGCGAGCCAAAACATTATGGCAGTATTTCTC
48


 


menC
ATCCCCGTTGATAGCCAACTTATTCTGCGTGATCGTTTTTTAAAACGCCG
49


 


rnpA
AAATTACAATCCTTGCTAGAAAAAATAATCTTGAACATCCGCGTTTAGGT
50


 


bioB
CATCCAAGTGTAGAATATTGGTCTGTTTGCAAAGTTGAGGCGTTATTTGA
51


 


serB
ATACGGCACAAAGTTGGACATAACTAAGCTAGAAAAATTTCAACAAAAGT
52


 


hitA
AGCCGCTACGGCTGTGGCAAAAGCCTTTGAACAGGAAACAGGCATTAAAG
53


 


Rv1980c
ATCGATAGCGCCGAATGCCGGCTTGGACCCGGTGAATTATCAGAACTTCGCAGTCACGAACGACGGGGTGATTTTC
54



TTCTTCAACCCGGGGGAGTTGCTG



 


rv1398c
GGGACGACCTTGCATCGGACCTGCAGGCTATAAACGATTCGTTCGGCACGCTTCGCCACCTGGATCCGCCGGTGCG
55



TCGCTCCGGTGGTCGTGAACAGCA



 


rv2031c
AGCGCCACCCGCGGTCCCTCTTCCCCGAGTTTTCTGAGCTGTTCGCGGCCTTCCCGTCATTCGCCGGACTCCGGCC
56



CACCTTCGACACCCGGTTGATGCG



 


MAP_2121c
ACCTGGCGCGGTATTCCGCTGATCCCGTCGGACAAGGTGCCGGTGGAGGACGGCAAGACGAAGTTCATCCTGGTCC
57



GCACCGGCGAGGAACGTCAGGGCG



 


MAV_3252
CGGCGCCCAGAGTGTCTACGGCGTGGTCCCCATGTGCGCGGTGATATCGGCGCTCTTCGGCTCCCTCGGCAACTCG
58


(MAP_1263)
GTGGGCATCACCATGGACCGCCAG



 


MAV_3239
ATCGACCCCGGATTGCCCTCGGCGCGAATCGATTTCATGCTCGCCGACGCCGTGCCCGTCGTCACGGTCACCACCG
59


(MAP1242)
CCGAACTGCGCGCTTCGGCCGGCG



 


MAV_1600
GTCGACGCCGGAGAATTGATCGCCCACGCATCGAATTCGCTGGCGCGCTACAAGCTTCCCAAGGCGATCGTGTTCC
60


(MAP2380)
GTCCGGTGATCGAGCGCAGCCCGT



 


rv1641
GTCAAAGAACAAAAGCTGCGACCAAAGATTGACGATCACGATTACGAGACCAAAAAGGGTCACGTCGTCCGCTTCT
61


(MAP1352,
TGGAGGCGGGATCGAAGGTCAAGG



MAV_3127)




 


Rv3583c
GAACAAAAAGAGTATCTCGTCTTGAAAGTTGCGCAGGGCGACCTGACAGTACGAGTTCCCGCTGAAAACGCCGAAT
62


(MAV_0570,
ACGTCGGTGTTCGCGATGTCGTCG



MAP0475)




 


Pfg27
GGTACAAAAGGATAGTGCCAAGCCCTTGGATAAATTTGGAAATATCTATGATTATCACTATGAGCATGAAACACAT
63



GCCCCTCTCTCACCTCGTATTAGA



 


Pfs48/45
AGAGTTGAAACTGATATATCGGAATTAGGTTTAATTGAATATGAAATAGAAGAAAATGATACAAACCCTAATTATA
64



ATGAAAGGACAATAACTATATCTC



 


PFI1020c
AATATGACAGATAACATAACGCTAAAAACACCGGTAATATCATCTCCTATGGATACAGTAACGGGACATAAGATGT
65



CAATAGCTTTAGCTTTGAGCGGTG



 


PFA0660w
ACATATACAAATAGATGAGGTGGTAAAACCTGACACAAAGAAGGTTATAAAAAATGAAGGAATGCCTTACTCAAGA
66



GATCCAAGTATTAGAGGAAATTTG



 


PFA0635c
ACAGGGAAATGATAAACATATAGATAGTGAACATAATGGAATAAATAAAATGTACAAAGAAACAATACATAAAACA
67



CTAACATCTGATGTATCAACAGAA



 


PFA0130c
CCCAATACCTACATGTGGAGCTTCTAGGGTTATGGAGAAATGTCAAAAGATGTATAAGGTGGTTATAAAACCGAAG
68



GAGAAGGACGATAAAGTGGATAAT



 


PF11_0282
CGCTTAGCTAATTCAATTGGACTAATTGATGCAGGTTATAGAGGAGAAATTATTGCCGCCTTGGATAATACTAGTG
69



ACCAAGAGTATCACATTAAAAAAA



 


PFE0660c
GGGACGAAGGGGATTTCGACAACAATTTAGTTCCTCACCAATTAGAAAATATGATTAAAATAGCCTTAGGAGCATG
70



TGCAAAATTAGCAACCAAATATGC



 


PFC0800w
ATTATCTTACCTGTGAATATAAAAAATGCTATGGAAAAACAAGCTGAAGCAGAAAGAAGAAAAAGAGCTGAAATTT
71



TACAAAGTGAAGGAGAAAGAGAAA



 


PFL2520w
TACTTTATCCCGTGATGGTAAGAATGATATTGAAGAAGAAGAAGAAGAAGATGAGGAAGATGAAAAAAATATAAAC
72



AACTCCCAAGATACCACATTAAGT



 


PF07_0128
TGAAGGACCAAAAGGAAATGAACAAAAAAAACGTGATGACGATAGTTTGAGTAAAATAAGTGTATCACCAGAAAAT
73



TCAAGACCTGAAACTGATGCTAAA



 


PF10_0346
GAGGTGCTCCTCAAAATGGAGCTGCAGAAGATAAAAAGACAGAATATTTACTAGAACAAATAAAAATTCCATCATG
74



GGATAGAAATAACATCCCCGATGA



 


PF13_0233
ATACACCACTGCTGTTCCCCTTATTGTTGCAATAAACCCATACAAGGATTTAGGAAACACAACTAATGAATGGATT
75



CGTAGATATCGTGATACAGCTGAT



 


PFA0110w
AGAACCAACTGTTGCTGAAGAACACGTAGAAGAACCAGCTAGTGATGTTCAACAAACTTCAGAAGCAGCTCCAACA
76



ATTGAAATCCCCGATACATTATAT



 


PFD1170c
ACTATTAAAGCTATGGAAATTATATGGGAAGCTACCATGAACAATGAAAGGAGAAAATATGCTGCCACTAAACGTA
77



GCATGCTCAGATATTATGATGATT



 


PF07_0006
ATATACCAGAAAGTAGTAGTACATATACAAATACAAGGTTAGCAGCAAATAACAGTACAACTACAAGCACTACAAA
78



AGTAACAGATAATAATAAAACAAA



 


PF11_0512
ATTCAAACCACTTATCGTAGATGATGAACTACTTGAATACAACCAAAAGGTTCATAACATAGGAAGAAATGGAGAA
79



GACATTTTAACTGCTATGCAAACA



 


matrix + A
CTCACCGTGCCCAGTGAGCGAGGACTGCAGCGTAGACGCTTTGTCCAAAATGCCCTTAATGGGAATGGGGATCCAA
80



ATAATATGGACAGAGCAGTTAAAC



 


matrix + B
ATCGAAAGCTTAAGAGGGAGATAACATTCCATGGGGCCAAAGAAATAGCACTCAGTTATTCTGCTGGTGCACTTGC
81



CAGTTGTATGGGACTCATATACAA



 


matrix + C
GCAGGCAATGAGAGCCATTGGGACTCATCCTAGCTCTAGCACTGGTCTGAAAAATGATCTCCTTGAAAATTTGCAG
82



GCCTATCAGAAACGAATGGGGGTG



 


matrix − A
GCTGACAAAATGACCATCGTCAGCATCCACAGCATTCTGCTGTTCCTCTCGATATTCTTCCCTCATAGACTCTGGT
83



ACTCCTTCCGTAGAAGGCCCTCTT



 


matrix − B
GGTTGTTGTTACCATTTGCCTATGAGACTTATGCTGGGAGTCGGCAATCTGTTCACAGGTTGCGCATATAAGGCCA
84



AATGCTGATTCGGTGGTCACAGCC



 


matrix − C
ACACAAATCCTAAAATCCCCTTAGTCAGAGGTGACAGGATCGGTCTTGTCTTTAGCCATTCCATGAGAGCCTCAAG
85



ATCGGTATTCTTTCCAGCAAAGAC



 


gag A
AAACATATAGTATGGGCAAGCAGGGAGCTAGAACGATTCGCAGTTAATCCTGGCCTGTTAGAAACATCAGAAGGCT
86



GTAGACAAATACTGGGACAGCTAC



 


gag B
CAGCTGACACAGGACACAGCAATCAGGTCAGCCAAAATTACCCTATAGTGCAGAACATCCAGGGGCAAATGGTACA
87



TCAGGCCATATCACCTAGAACTTT



 


gag C
ATCAGAAGGAGCCACCCCACAAGATTTAAACACCATGCTAAACACAGTGGGGGGACATCAAGCAGCCATGCAAATG
88



TTAAAAGAGACCATCAATGAGGAA



 


gag D
TAGAGACTATGTAGACCGGTTCTATAAAACTCTAAGAGCCGAGCAAGCTTCACAGGAGGTAAAAAATTGGATGACA
89



GAAACCTTGTTGGTCCAAAATGCG



 


rev A
GAGACAGCGACGAAGAGCTCATCAGAACAGTCAGACTCATCAAGCTTCTCTATCAAAGCAACCCACCTCCCAACCC
90



CGAGGGGACCCGACAGGCCCGAAG



 


rev B
CGACAGGCCCGAAGGAATAGAAGAAGAAGGTGGAGAGAGAGACAGAGACAGATCCATTCGATTAGTGAACGGATCC
91



TTGGCACTTATCTGGGACGATCTG



 


rev C
CTTATCTGGGACGATCTGCGGAGCCTGTGCCTCTTCAGCTACCACCGCTTGAGAGACTTACTCTTGATTGTAACGA
92



GGATTGTGGAACTTCTGGGACGCA



 


rev D
GAACTTCTGGGACGCAGGGGGTGGGAAGCCCTCAAATATTGGTGGAATCTCCTACAGTATTGGAGTCAGGAACTAA
93



AGAATAG



 


gpG A
TGGCGCACCCAACGCAACGTATGCGGCCCATGTGACGTACTACCGGCTCACCCGCGCCTGCCGTCAGCCCATCCTC
94



CTTCGGCAGTATGGAGGGTGTCGC



 


gpG B
CTGCTGGTGCCGATCTGGGACCGCGCCGCGGAGACATTCGAGTACCAGATCGAACTCGGCGGCGAGCTGCACGTGG
95



GTCTGTTGTGGGTAGAGGTGGGCG



 


gpG C
CCTACCACGCGTCGCTTTTGCTCCCCAGAGCCTGCTGGTGGGGATTACGGGCCGCACGTTTATTCGGATGGCACGA
96



CCCACGGAAGACGGGGTCCTGCCG



 


gpG D
CCCCTGTTCTGGTTCCTAACGGCCTCCCCTGCTCTAGATATCCTCTTTATCATCAGCACCACCATCCACACGGCGG
97



CGTTCGTTTGTCTGGTCGCCTTGG



 


b1649
GCTAAGCGAATTACTAAAAACCGCTGAAGTGCCGAAAGGGTCCTTCTATCACTACTTTCGCTCTAAAGAAGCGTTT
98



GGCGTTGCCATGCTTGAGCGTCAT



 


codB
GTCGGCTGGTTGACCTTCCTTTCGGCAGCTATTCCTCCAGTGGGTGGCGTGATCATCGCCGACTATCTGATGAACC
99



GTCGCCGCTATGAGCACTTTGCGA



 


cysD
CAAATCCGGTGATGCTCTACTCTATCGGTAAAGATTCCAGCGTCATGCTGCATCTGGCGCGCAAGGCGTTTTATCC
100



AGGTACGCTGCCTTTCCCGTTGCT



 


dinD
TGGATTAGATCAGAAAGCTATTCATCAGCGGAAGGGGCTGAAAAAGAATCAGAAGATCCTGGATCATATGGGTTCA
101



ACAGAACTGGCGGCTAATCTCTTT



 


ylcB
GAATACCGCCACGACACGGGAGTTTTCGACCGGCCTTAACGCCAGCTTTGACCTCGATTTTTTCGGTCGCTTAAAG
102



AACATGAGCGAAGCCGAGCGACAA



 


flgF
GAAGGCAGTAACGTCAATGCCGTTGCGGCAATGAGCGACATGATTGCCAGCGCGCGGCGTTTTGAAATGCAGATGA
103



AGGTGATCAGCAGCGTCGATGATA



 


cysD
CAAATCCGGTGATGCTCTACTCTATCGGTAAAGATTCCAGCGTCATGCTGCATCTGGCGCGCAAGGCGTTTTATCC
104



AGGTACGCTGCCTTTCCCGTTGCT



 


glnA
TTCGGTAAAACCGCGACCTTTATGCCAAAACCGATGTTCGGTGATAACGGCTCCGGTATGCACTGCCACATGTCTC
105



TGTCTAAAAACGGCGTTAACCTGT



 


opgG
TTGTGGATGTGCAGTCGAAAATCTATCTGCGCGATAAAGTCGGCAAACTGGGGGTTGCACCGTTAACCAGTATGTT
106



CCTGTTTGGGCCGAACCAACCGTC



 


ftsQ
CGACAGTGTTGGTGAGCGGCTGGGTCGTGTTGGGCTGGATGGAAGATGCGCAACGCCTGCCGCTCTCAAAGCTGGT
107



GTTGACCGGTGAACGCCATTACAC



 


b1649
GCTAAGCGAATTACTAAAAACCGCTGAAGTGCCGAAAGGGTCCTTCTATCACTACTTTCGCTCTAAAGAAGCGTTT
108



GGCGTTGCCATGCTTGAGCGTCAT



 


recA
AACACGCTGCTGATCTTCATCAACCAGATCCGTATGAAAATTGGTGTGATGTTCGGTAACCCGGAAACCACTACCG
109



GTGGTAACGCGCTGAAATTCTACG



 


dinD
TGGATTAGATCAGAAAGCTATTCATCAGCGGAAGGGGCTGAAAAAGAATCAGAAGATCCTGGATCATATGGGTTCA
110



ACAGAACTGGCGGCTAATCTCTTT



 


carA
GATGAAGAATCTTCTCAGGTACATGCACAAGGTCTGGTGATTCGCGACCTGCCGCTGATTGCCAGCAACTTCCGTA
111



ATACCGAAGACCTCTCTTCTTACC



 


deoC
CAATCGCCTACGGTGCTGATGAAGTTGACGTTGTGTTCCCGTACCGCGCGCTGATGGCGGGTAACGAGCAGGTTGG
112



TTTTGACCTGGTGAAAGCCTGTAA



 


flgF
GAAGGCAGTAACGTCAATGCCGTTGCGGCAATGAGCGACATGATTGCCAGCGCGCGGCGTTTTGAAATGCAGATGA
113



AGGTGATCAGCAGCGTCGATGATA



 


htrL
CCCGATCTCTTCAACCTAAACTATCTGGGGAGAGGAAAATGGTTCGATTTGTTTCGCTGCTTCAGGAGTAACACTT
114



TAGGGGCAAAAATGCAGGCGCTGA



 


recA
AACACGCTGCTGATCTTCATCAACCAGATCCGTATGAAAATTGGTGTGATGTTCGGTAACCCGGAAACCACTACCG
115



GTGGTAACGCGCTGAAATTCTACG



 


uvrA
GCTGCCTGCTATCTCCGACATGAGCATTGGTCATGCGATGGAATTCTTCAACAATCTCAAACTCGCAGGTCAGCGG
116



GCGAAGATTGCAGAAAAAATCCTT



 


wbbK
TTTCCCTCTAGGTTAGAAACATGGGGATTGCCGTTGTCTGAAGCTAAAGAGCGAGGTAAGTGGGTATTAGCATCAG
117



ATTTCCCATTTACTAGAGAAACTC



 


ybhK
AACGGAACCGAGCGTCGCCTCCGCGATGTTTGAATACCGTTTTGGTGGCAATGGCGAACTTTCCGGTCATAATCTC
118



GGAAACTTGATGTTAAAGGCGCTG



 


uup
AACGCTATCACGATATTTCGCGCCTGGTGATGAACGACCCGAGCGAGAAAAATCTCAACGAACTGGCGAAGGTTCA
119



GGAACAGCTGGATCACCACAACCT



 


fabD
TGCCATCCGTGACGCACTGGTACGTCAGTTGTATAACCCGGTTCAGTGGACGAAGTCTGTTGAGTACATGGCAGCG
120



CAAGGCGTAGAACATCTCTATGAA



 


fabD
TGCCATCCGTGACGCACTGGTACGTCAGTTGTATAACCCGGTTCAGTGGACGAAGTCTGTTGAGTACATGGCAGCG
121



CAAGGCGTAGAACATCTCTATGAA



 


proC
AAACCTGGCATCATGATTAAAGTGCTTAGCGAAATCACCTCCAGCCTGAATAAAGACTCTCTGGTCGTTTCTATTG
122



CTGCAGGTGTCACGCTCGACCAGC



 


rpoB
ACTAACGAATACGGCTTCCTTGAGACTCCGTATCGTAAAGTGACCGACGGTGTTGTAACTGACGAAATTCACTACC
123



TGTCTGCTATCGAAGAAGGCAACT



 


PA 0825
TGTGGTTCTTCCTGGGCGGTTTCGGCGCACACCGCTTCTACCTGGGGAAAACCGGCACGGCGGTTACCCAACTGAT
124



CATCACGCTGATCGGTTGTTTCAC



 


PA 0985
CCACAAACAATACTCTTATCAAGAATTCCCCAACCCCTCTAGAAAAGCAGAAAGCCATCTACAATGGTGAGCTACT
125



TGTGGATGAGATAGCCAGTCTACA



 


cobB
GAACACGGCGGAGGCGGTGTTCCGTCTTGGCCGGCTGACGGCTTCCTATATCCATTTCTACCTGCCTTCCAACCCG
126



CAGGCCGCCGCTGCGTTGCTGGCG



 


dxr
CCCGGCCATGCTGAATGCCGCGAACGAGGTGGCCGTGGCCGCATTTCTCGAGCGGCACATCCGCTTCAGCGACATC
127



GCGGTTATCATCGAGGACGTGCTG



 


flgF
TCTCGACCAGCGGCTTTCGTCGCGACTTCGAGCAGGCGCGTTCGATGCAGGTGTTCGGCGACAGCTTCCCGGCGCG
128



GGTATTCGCCATGAGCGAGCGGCC



 


lexA
GCAGCAAGGTCTGGCTGCTGGCGGAAAACCCTGAGTTCGCTCCGATCGAAGTCGATCTGAAGGAGCAGGAACTGAT
129



CATCGAAGGCTTGAGCGTCGGCGT



 


moaE
CGGGGCAGGAGCTTAACGCCCTGCATGCGCAGAACGTCGGCATCGGCGCGGTGGTCGGCTTCGTCGGCTACGTGCG
130



CGACTTCAACGACGGTCGCGAGGT



 


recA
GGTGAAGAACAAGGTTTCCCCGCCGTTCCGCCAGGCCGAGTTCCAGATCCTCTACGGTAAGGGCATCTACCGTACC
131



GGCGAGATCATCGATCTGGGCGTG



 


PA 0284
CTTCGGCGCCGTCGAGATCACCGTGCACAACGGCCAGGTGGTGCAGATCGAGCGCAAGGAAAAATTCCGTCTGCAG
132



CAACCGGCCGTCAAGCAGGCCTGA



 


PA 0613
GAGCACTATCTCAATCGCGACAGCTTCCCCGAGCAGAAGTACCGCCACTGCGGTTGCAGCCGCAACACCTTTTATC
133



TGCGCCTGCATGTGGCGCACCAGG



 


PA 2012
CCTGCTGAAGCCGCGCCACGTGGAGATCCAGGTATTCGCCGACCGCCATGGCCACTGCCTGTACCTCAACGAACGC
134



GACTGTTCGATCCAGCGCCGCCAC



 


PA 3126
TTCAACGATCTGTTCGAGTCGGCCCTGCGTAATGAGGCCGGGAGTACCTACCCGCCCTACAACGTCGAAAAGCACG
135



GTGACGACGAGTATCGCATCGTTA



 


PA 3932
ACGGCCGCCTGAGCCTGCCGCCATTGCGCGAACGTCCGGGAGACATCCTGCCGCTGGCGGAATACTTCATCGGCGT
136



CTATGCCCAGCGCCTGGACCTGCC



 


PA 4175
ACGCTGCAGACCATCTGGTTCTACAACACCACCCAGTGCTACGGCGACGCCTCGACCATCAACCAGAGCGTCACCG
137



TGCTGACCGGCGGGGCGAATATCC



 


PA 4762
CCTGGAGATGTCCGATCCCAACGACGAGGCGATCAAGCCGATGCGCGAAGGGATGGAACTGACCCTGAAGATGTTC
138



GACGACACCCTGCGCCGCTACCAG



 


hs1U
TATGTCGGACGCGACGTCGAATCGATCATCCGCGATCTCGCCGACGCCGCGGTGAAGATGCTCCGCGAACAGGAGA
139



TCCAGAAGGTCAAGTATCGCGCCG



 


fabD
GGTGGCGTTGCCAGTCAGCGTGCCGTCGCATTGCGAACTGATGCGTCCGGCCGCCGAGCAGTTCGCCGCCTCGGTC
140



GAAAGCCTGCAGTGGCAGGCGCCG



 


proC
CCTGTGGCTGGACGACGAAGCGCAGATCGACGCGGTGACCGCAGTGTCGGGCAGCGGCCCGGCGTATTTCTTCCTG
141



CTGATGCAGGCCATGACCGACGCC



 


rpoB
ACCTTCGCCGTACCGCTGCGCGTGAAAGTTCGCCTGATCATCTTCGACCGCGAGTCGTCGAACAAGGCGATCAAGG
142



ACATCAAGGAACAAGAAGTCTACA



 


mecA
AACATGATGATGGCTATTAATGTTAAAGATGTACAAGATAAAGGAATGGCTAGCTACAATGCCAAAATCTCAGGTA
143



AAGTGTATGATGAGCTATATGAGA



 


mecA
AGAATATAAAGGCTATAAAGATGATGCAGTTATTGGTAAAAAGGGACTCGAAAAACTTTACGATAAAAAGCTCCAA
144



CATGAAGATGGCTATCGTGTCACA



 


EF vanA
TGATAGGCCGGTGGCAGCTACGTTTACCTATCCTGTTTTTGTTAAGCCGGCGCGTTCAGGCTCATCCTTCGGTGTG
145



AAAAAAGTCAATAGCGCGGACGAA



 


EF vanA
GGAGCGAGGACGGATACAGGAAACGGCAAAAAAAATATATAAAGCGCTCGGCTGTAGAGGTCTAGCCCGTGTGGAT
146



ATGTTTTTACAAGATAACGGCCGC



 


EF vanB
GAGGACGCTTACCTACCCTGTCTTTGTGAAGCCGGCACGGTCAGGTTCGTCCTTTGGCGTAACCAAAGTAAACAGT
147



ACGGAAGAACTAAACGCTGCGATA



 


EF vanB
AATCCGGTTGAGCCACGGTATCTTCCGCATCCATCAGGAAAACGAGCCGGAAAAAGGCTCAGAGAATGCGATGATT
227



ATCGTTCCAGCAGACATTCCGGTC



 


Rv0813c.1
ACCAGGCTTACGAGAAGCGGGATTCTGGCGGTTCGTCGCCGACCCGTACGATCCGAGCGAGTCTCAGGCGATCGAG
148



TTGCTATTGGCGCATTCGGCCGGT



 


Rv1608c.1
CCGCCCAATTCGGGGTCAAGCGCGGTCTGTTGGGCAAGTTGATGCCGGTCAAACGCACGACCTTTGTCATCGACAC
149



CGACCGTAAGGTGCTCGACGTGAT



 


Rv1832.1
GTCGATTACCTGGCCTGAATTCGGGCGTCAGCATCCATTTGCCCCGGCATCTGATACCGCTGGGCTGCGTCAACTT
150



GTTGCCGACCTACAGAGTTGGCTG



 


Rv3417c.1
CGTTGATCCTGCTGCACCAAGACAAGATCAGCTCGCTTCCCGATCTGTTGCCATTGCTGGAAAAGGTTGCAGGAAC
151



GGGTAAGCCACTACTGATCGTGGC



 


Rv1570.1
CAGCTGGCCGGCTTGGCGCGATATCCGCAGCCGATGGCCCCGGCCGCCGCCGCCGAACACGCCGGGATGGCGTTGC
152



CCGCCCGCGATCAGATCGTGCGGC



 


Rv1606.1
GGTGCGCCTGGATTGTGACGGCGACGCCGTATTGTTGACGGTTGACCAGGTCGGCGGTGCCTGCCATACCGGCGAT
153



CACAGTTGCTTCGATGCCGCGGTG



 


Rv2296.1
CCGCGCAGGGGCGCACCCCACTCCCCTTCTACGTGTGGCGGGCGTTTGCGCGCTATTCTCCGGTGCTTCCCGCTGG
154



CCGTCTGGTGAACTTCGGCACCGT



 


Rv2364c.1
GGTGGATTGTCGAGCAGCTTCGTTCGACCGGCCCTGCCAATACGACACTGGTGGTCATCGTCACCAAGATTGACAA
155



GGTGCCGAAAGAAAAAGTGGTCGC



 


Rv2245.1
CGGCATCCACGCACTCGAAGACGAGTTCGTCACCAAGTGGGATCTAGCGGTCAAGATCGGCGGTCACCTCAAGGAT
156



CCGGTCGACAGCCACATGGGCCGA



 


Rv2247.1
CGGCGAGTGCACCGTTCCGCGGGTCACGCTGGTCACCCGAAAGACCTACGGCGGGGCATACATTGCGATGAACTCC
157



CGGTCGTTGAACGCGACCAAGGTG



 


Rv2846c.1
GTGTGTCCTCGCAGCTGGTGTCCCGGTTTTCGCCACGGGTGTTGACCATCGGCGGCGGATATCTGCTATTCGGCGC
158



CATGCTGTACGGCTCATTTTTCAT



 


Rv3675.1
GCAGGCCGAGGCCGTGGATGTGCATACGCTCGCTCGGAATGGAATGCCGGAGGCGCTGGATTACCTGCATCGACGT
159



CAAGCCCGGCGAATCACCGATTCA



 


Rv3801c.1
GAGAGAGTGGGATGGCGTACCACAACCCGTTCATCGTGAATGGAAAGATCAGGTTCCCAGCCAACACCAACCTGGT
160



TCGTCACGTCGAAAAGTGGGCGAA



 


Rv2245.1
CGGCATCCACGCACTCGAAGACGAGTTCGTCACCAAGTGGGATCTAGCGGTCAAGATCGGCGGTCACCTCAAGGAT
161



CCGGTCGACAGCCACATGGGCCGA



 


Rv2247.1
CGGCGAGTGCACCGTTCCGCGGGTCACGCTGGTCACCCGAAAGACCTACGGCGGGGCATACATTGCGATGAACTCC
162



CGGTCGTTGAACGCGACCAAGGTG



 


Rv0055.1
CAAGTCCAGCAAGCGGCGCCCGGCTCCGGAAAAGCCGGTCAAGACGCGTAAATGCGTGTTCTGCGCGAAGAAGGAC
163



CAAGCGATCGACTACAAGGACACC



 


Rv1317c.1
CGGCCGCCCGATTCGAGTCTGCCACCGCATCAGCGGGCACGGTGTCGCTGCGGCTACCCGTCCGTGCACCATTCGC
164



CTTCGAGGGTGTTTTCGGCCATCT



 


Rv2737c.1
TCGTCAACGGTCGACGGATCCAGAGCAAACGTCAAGTGTTCGAGGTCCGGATCTCGGGTATGGATAACGTCACGGC
165



ATTCGCGGAGTCAGTTCCCATGTG



 


Rv2790c.1
AACAACCCGTATGCACAGTTTCAGGACGAATACACCCTGGACGACATCTTGGCCTCAAAGATGATTTCCGACCCGC
166



TGACCAAATTGCAGTGCTCTCCCA



 


Rv3296.1
CCGCTACCGCAGTGGTACCCACCGACAGCACATTGTTGGTCGAGCGGTTTCGTGACGAGCTGGGCGATTGGCGGGT
167



GATCTTGCATTCGCCGTATGGGCT



 


rv2032
TCGCTGCGGCTCTACGATTCGTCGTATCATGCCGAACTCTTTTGGTGGACAGGGGCTTTTGAGACTTCTGAGGGCA
168



TACCGCACAGTTCATTGGTATCGG



 


rv2827c
CGGCACACCGAAGTGATGCCGGTGACTCGATTCACCACCGCGCACAGCCGCGACCGTGGCGAGAGTGTCTGGGCTC
169



CCGAGTATCAGCTTGTCGACGAGC



 


rv3880c
CGGTTTCAGTCGGCCCTAGACGGGACGCTCAATCAGATGAACAACGGATCCTTCCGCGCCACCGACGAAGCCGAGA
170



CCGTCGAAGTGACGATCAATGGGC



 


Rv0667.1
AAGAGGTGCTCTACGAGCTGTCTCCGATCGAGGACTTCTCCGGGTCGATGTCGTTGTCGTTCTCTGACCCTCGTTT
171



CGACGATGTCAAGGCACCCGTCGA



 


Rv1980c.1
CGCCGAATGCCGGCTTGGACCCGGTGAATTATCAGAACTTCGCAGTCACGAACGACGGGGTGATTTTCTTCTTCAA
172



CCCGGGGGAGTTGCTGCCCGAAGC



 


Rv2703.1
AGCGACCAAAGCAAGCACGGCGACCGATGAGCCGGTAAAACGCACCGCCACCAAGTCGCCCGCGGCTTCCGCGTCC
173



GGGGCCAAGACCGGCGCCAAGCGA



 


tig
AAGCACTCTACGAAAATGCATTGAATTTAGTGTTGCCAAAGGCTTACGAA
174


 


pyrG
AAATTGTTTGTGATCACTTGAAGCTTGAGACACCTGCTGCTGATATGACA
175


 


scrR
AACTATCTTATGTAATAGTGAAAAGGATCCTATCAAAGAAAAAGAATACC
176


 


hslO
AAATAGTAAAGTAACTGTCAAGGTTATTGGAGATAGCTCTTTTGGTCATA
177


 


SglyS
AAGGGAATTTTTCTAAAGCAGCCCAAGGTTTTGTTCGCGGAAAAGGTTTA
178


 


proB
AAGCGTCCTCAAGAAATATCACAACAACAAGCAGTTTCTAGCGTAGGACA
179


 


mraW
AAGGCTATTGACAATGCTCATATACGTTTAAAGAAATATGTGGATACCGG
180


 


ftsL
AAGTTAAACAAGAAGTAAATCAATTAAATAGTAAAATCAACGATAAACAG
181


 


dkgB
AACCAAATCGAGCTGTCGCCATATCTGCAGAACCGCAAAGTGGTGGAATT
182


 


mtnK
AACCCTGTTGATTCACGGCGGTTTTTGCCCAAGGCATACGGTAAAGGTAC
183


 


xylF
AACCGCTGATTGACGGCGGGAAAATCAAAGTGGTGGGCGATCAGTGGGTC
184


 


proA
AAACTGGAAGCCGAAAGCGAAGTGATTTTACAGGCTAACGAACAGGACAT
185


 


aroL
AAAGCATTGCTCTGCAAACGGTCACTCAACCGTCAACTGTAGTTGCCACT
186


 


PstS
AAAAATGGAACGACCCGGCGATCACCAAGCTCAACCCAGGCGTTAAGCTG
187


 


secD
AACGTGACATGGTGTTCTCTGCCAACGGCACCAACACCCTGAAAGCCAAC
188


 


ribD
AACCTGCGTCAACCGTTGCGCATTATTCTGGATAGCCAAAATCGCGTCAC
189


 


dnaA
AATTGTTCTGACTAGTGATCGTAGTCCTAAACACTTAGAGGGCCTTGAAG
190


 


mscL
AAGGAATTGAAAAAGCTCAAAGCCTTACTAAGAAAGAAGAAGCTGCTGAG
191


 


purR
ACTGGTGCTGGTGGTGGTGTCATTTTCACACCATCAATCTCAAGCCATGA
192


 


amiA
AAGATGGTTTATTCAATTTTCTGGCCATTAAACGAAGAATTTGAAAAATC
193


 


nanA
AGCGATGACCTATACCACCTATGATAGTGGTAATAGTGGTCAACAAACAG
194


 


codY
AACATGATTTACGATACAGAAGCCAATCTGACAGTTGATCATGATTTGAG
195


 


pstS
ACAACTCCGTAGTATCTTCACAGGTCAAGTGACCAACTGGAAAGAAGTCG
196


 


CaJ7_0076
AAAGCTGCTAAATCTGCCAAGACTGCTGCTGCTGGTGGTAAGAAGGAAGC
197


 


CaJ7_0103
AAAATGCTACTACATCTCCATCATACTTTACTACTATTCATCCCGAAACA
198


 


CaJ7_0197
ACATAGTCCAATAACAAATAAACTTGAGGATCATGATGATGAAATTGGAT
199


 


CaJ7_0245
AAAACAAACAATCAACTGGTGATGAAGTCAAGAGCAAGAGAAAATCGGCA
200


 


CaJ7_0344
AATGATTTATGATACGTTTAATAAATTACAAGAATCTAGTGATCAGTCGA
201


 


CaJ7_0399
ACACAAAACTGAAGACAAAGGGACTAGCACTTCATCCAAGGAAGAACCAT
202


 


fusA
ACTGGTGTAGTTGACCTTATCGAAATGAAGGCAATTATCTGGGATGAAGC
203


 


lysS
AATTGTGAATGAAGAAACGCGTAAGACTTTTGAAATTCGTGCCAAAGTCG
204


 


isocitrate
AAAAAGTATTTCGGTACGACAAGCAAACGTTATATCTACCTTTCTGGTTG
205


lyase




 


ureC
ACACAACTAATTGCACCTGAAGCACAAATTTTAACACCTCGTGCTATTGA
206


 


rnhB
AAATTGATGAGCTGAATATCTTGCAGGCAACTTTTTTGGCTATGCAACGT
207


 


pyrB
AAAACTCCACCCGTACTCGTACTACTTTTGAAGCAGCAGCAAAACGTTTG
208


 


guaA
ACCGCAAGTGGTGTTTGAATTAGGCGTTCCAGTATTGGGTATTTGCTATG
209


 


cmk
AGTTGCAGGGTATGGGGCTAGATGCTAAAATAAACGACATTTTAGCTAAT
210


 


secD
AAAATTGAAGCGAAATCAATCGCCCTTGAAAATGGTGCGATTTTGGCTCG
211


 


map1
AAAAAGTAAAGATATTATTAATGTCGATATTACACTAGAAAAAAATGGTT
212


 


ftnA
AAAACTTATGAGCACGAGAAGTTTATTACAGCTGAAATTAATAAACTAGC
213


 


Prc
AAAACAGGTGAATTACCTCCTTTATATGATCTGTTTAACCTTGCGCAAAA
214


 


nrdR
AAAACGCCCTGTGAGTTCTGATGATGTTGAAGCTGCTATTAATCATATCA
215


 


ackA
AAAGGTATTGAGGCGGCTATTCCATTTGCCCCATTACATAACCCAGCTCA
216


 


Pta
AAAAAAGATGTTCTGATGGAAGAAATTGTTGCTAGATATCACGAAAATAC
217


 


fadL
AAATGGGCAATTGGTGCATCAGGTACTACTAACTTTGGTTTAGCTACCGA
218


 


purF
ATCGTCGGCAACCAGAACGTCGCCGGGCAGTTGTATGACGGCTTGACCGT
219


 


clpP
ATCGACATCCACGCGCGTGAGATCCTGACCCTGCGTTCGCGCCTGAACGA
220


 


sdhA
CAAGATCTACCAGCGTCCGTTCGGTGGCATGACCACCAAGTACGGCGAAG
221


 


dnaK
AAGCGCCTGATCGGCCGCAAGTTCACCGACGCCGAAGTGCAGAAGGACAT
222


 


secD
ATGCTCGAATTTCCACGCTGGAAGTACGTCGTCATCCTGATCGTACTGGC
223


 


cmk
CGAGCGCCGGCATAAGCAGTTGAAAGACAAGGGGGTTTCTGTTAACTTTG
224


 


purC
CCTGCCCGACCCGATCCCGGGCAAGGGCGAGATGCTCTGCCAGGTCTCCA
225


 


pantothenate
CGCCGAAGGCATGGCCGGACAGCCGCCGCACAGCCTGCCCAGCGGCACCA
226


kinase
















TABLE 3







24-gene probeset for pilot experiments









Magnitude of induction/repression



in prior microarray-based assays1













Name
Gene#
Purpose
ETH/INH
SM
RIF
FQ
















Rv0667
rpoB
control
−0.8
1.4
0.1
0.5


Rv1980c
mpt64
control
0.2
0.4
−1.2
0.1


Rv2703
sigA
control
0.0
0.4
−1.4
−0.8


Rv3583c
Rv3583c
control
0.6
1.0
−1.8
1.6


Rv0055
rpsR
FQ
1.4
1.7
−0.9
3.6


Rv1317c
alkA
FQ
1.0
0.4
0.5
5.5


Rv2737c
recA
FQ
−0.2
−0.1
−0.6
4.6


Rv2790c
ltp1
FQ
−0.9
−1.1
−0.7
4.2


Rv3296
lhr
FQ
0.3
0.1
0.2
4.1


Rv2245
kasA
INH
7.4
−1.9
−1.0
−1.1


Rv2247
accD6
INH
5.1
−0.8
−0.8
−1.3


Rv2846c
efpA
INH
6.2
−0.7
−0.9
0.2


RV3675
Rv3675
INH
2.4
0.0
0.0
−0.4


Rv3801c
fadD32
INH
3.3
0.8
−0.6
−0.2


Rv0984
moaB2
RIF
−0.9
−1.1
1.7
0.1


RV1570
bioD
RIF
0.2
−0.3
1.2
0.2


RV1606
hisI
RIF
−0.1
−0.4
1.5
−0.1


Rv2296
haloalkane
RIF
−0.7
0.1
1.2
−0.1


Rv2364c
era
RIF
−1.0
−0.6
1.4
0.0


Rv0813c
CHP
SM
0.3
3.8
0.3
0.1


Rv1511
gmdA
SM
0.4
1.6
−0.3
0.5


Rv1608c
bcpB
SM
0.9
2.2
0.3
0.2


Rv1832
gcvB
SM
−0.4
1.3
−0.4
−0.4


Rv3417c
groEL
SM
−0.3
1.6
−0.9
−0.8





1The transcriptional responses of Mycobacterium tuberculosis to inhibitors of metabolism: novel insights into drug mechanisms of action. Boshoff H I, Myers T G, Copp B R, McNeil M R, Wilson M A, Barry C E 3rd. J BiolChem. 2004 Sep. 17; 279(38): 40174-84


















Gene
Acc. No.
Sequence







lhr
Rv3296.1
CCGCTACCGCAGTGGTACCCACCGACAGCACATTGTTGGTCGAGCGGT




TTCGTGACGAGCTGGGCGATTGGCGGGTGATCTTGCATTCGCCGTATG




GGCT


 


alkA
Rv1317c.1
CGGCCGCCCGATTCGAGTCTGCCACCGCATCAGCGGGCACGGTGTCGC




TGCGGCTACCCGTCCGTGCACCATTCGCCTTCGAGGGTGTTTTCGGCC




ATCT


 


ltp1
Rv2790c.1
AACAACCCGTATGCACAGTTTCAGGACGAATACACCCTGGACGACATC




TTGGCCTCAAAGATGATTTCCGACCCGCTGACCAAATTGCAGTGCTCT




CCCA


 


recombinase_
Rv2737c.1
TCGTCAACGGTCGACGGATCCAGAGCAAACGTCAAGTGTTCGAGGTCC


(contains_

GGATCTCGGGTATGGATAACGTCACGGCATTCGCGGAGTCAGTTCCCA


intein)

TGTG


 


bioD
Rv1570.1
CAGCTGGCCGGCTTGGCGCGATATCCGCAGCCGATGGCCCCGGCCGCC




GCCGCCGAACACGCCGGGATGGCGTTGCCCGCCCGCGATCAGATCGTG




CGGC


 


bex
Rv2364c.1
GGTGGATTGTCGAGCAGCTTCGTTCGACCGGCCCTGCCAATACGACAC




TGGTGGTCATCGTCACCAAGATTGACAAGGTGCCGAAAGAAAAAGTGG




TCGC


 


hisI2
Rv1606.1
GGTGCGCCTGGATTGTGACGGCGACGCCGTATTGTTGACGGTTGACCA




GGTCGGCGGTGCCTGCCATACCGGCGATCACAGTTGCTTCGATGCCGC




GGTG


 


moaB2
Rv0984.1
CCGCGCAGTGTTCAAAGCTCGGATATACGGTGGCACCCATGGAACAGC




GTGCGGAGTTGGTGGTTGGCCGGGCACTTGTCGTCGTCGTTGACGATC




GCAC


 


epfA
Rv2846c.1
GTGTGTCCTCGCAGCTGGTGTCCCGGTTTTCGCCACGGGTGTTGACCA




TCGGCGGCGGATATCTGCTATTCGGCGCCATGCTGTACGGCTCATTTT




TCAT


 


acetyl/
Rv2247.1
CGGCGAGTGCACCGTTCCGCGGGTCACGCTGGTCACCCGAAAGACCTA


propionyl_CoA_

CGGCGGGGCATACATTGCGATGAACTCCCGGTCGTTGAACGCGACCAA


carboxylase_

GGTG


b_subunit




 


Rv3675
Rv3675.1
GCAGGCCGAGGCCGTGGATGTGCATACGCTCGCTCGGAATGGAATGCC




GGAGGCGCTGGATTACCTGCATCGACGTCAAGCCCGGCGAATCACCGA




TTCA


 


gcvB
Rv1832.1
GTCGATTACCTGGCCTGAATTCGGGCGTCAGCATCCATTTGCCCCGGC




ATCTGATACCGCTGGGCTGCGTCAACTTGTTGCCGACCTACAGAGTTG




GCTG


 


bcpB
Rv1608c.1
CCGCCCAATTCGGGGTCAAGCGCGGTCTGTTGGGCAAGTTGATGCCGG




TCAAACGCACGACCTTTGTCATCGACACCGACCGTAAGGTGCTCGACG




TGAT


 


GDP-
Rv1511.1
ACGCCGTTCTACCCGCGGTCACCGTATGGCGCCGCCAAGGTCTATTCG


mannose_4,

TACTGGGCGACCCGCAATTATCGCGAAGCGTACGGATTGTTCGCCGTT


6_

AACG


dehydratase




 


60 kD
Rv3417c.1
CGTTGATCCTGCTGCACCAAGACAAGATCAGCTCGCTTCCCGATCTGT


chaperonin_1

TGCCATTGCTGGAAAAGGTTGCAGGAACGGGTAAGCCACTACTGATCG




TGGC


 


mpt64
Rv1980c.1
CGCCGAATGCCGGCTTGGACCCGGTGAATTATCAGAACTTCGCAGTCA




CGAACGACGGGGTGATTTTCTTCTTCAACCCGGGGGAGTTGCTGCCCG




AAGC


 


rpoB
Rv0667.1
AAGAGGTGCTCTACGAGCTGTCTCCGATCGAGGACTTCTCCGGGTCGA




TGTCGTTGTCGTTCTCTGACCCTCGTTTCGACGATGTCAAGGCACCCG




TCGA


 


Rv3583c
Rv3583c.1
GAACAAAAAGAGTATCTCGTCTTGAAAGTTGCGCAGGGCGACCTGACA




GTACGAGTTCCCGCTGAAAACGCCGAATACGTCGGTGTTCGCGATGTC




GTCG


 


sigA
Rv2703.1
AGCGACCAAAGCAAGCACGGCGACCGATGAGCCGGTAAAACGCACCGC




CACCAAGTCGCCCGCGGCTTCCGCGTCCGGGGCCAAGACCGGCGCCAA




GCGA


 


b-
Rv2245.1
CGGCATCCACGCACTCGAAGACGAGTTCGTCACCAAGTGGGATCTAGC


ketoacyl-

GGTCAAGATCGGCGGTCACCTCAAGGATCCGGTCGACAGCCACATGGG


ACP_

CCGA


synthase_




(meromycolate_




extension)




 


30S_ribosomal_
Rv0055.1
CAAGTCCAGCAAGCGGCGCCCGGCTCCGGAAAAGCCGGTCAAGACGCG


protein_

TAAATGCGTGTTCTGCGCGAAGAAGGACCAAGCGATCGACTACAAGGA


S18

CACC


 


haloalkane
Rv2296.1
CCGCGCAGGGGCGCACCCCACTCCCCTTCTACGTGTGGCGGGCGTTTG


dehalo-

CGCGCTATTCTCCGGTGCTTCCCGCTGGCCGTCTGGTGAACTTCGGCA


genase

CCGT


 


fadD22
Rv3801c.1
GAGAGAGTGGGATGGCGTACCACAACCCGTTCATCGTGAATGGAAAGA




TCAGGTTCCCAGCCAACACCAACCTGGTTCGTCACGTCGAAAAGTGGG




CGAA


 


CHP
Rv0813c.1
ACCAGGCTTACGAGAAGCGGGATTCTGGCGGTTCGTCGCCGACCCGTA




CGATCCGAGCGAGTCTCAGGCGATCGAGTTGCTATTGGCGCATTCGGC




CGGT

## Claims

1. A method comprising:
contacting an uncultured clinical urine or blood sample from a mammalian subject, wherein the sample is known or suspected to comprise pathogenic bacteria comprising Escherichia coli, with one or more test compounds selected from the group consisting of a fluoroquinolone antibiotic, aminoglycoside antibiotic, and ampicillin, to provide a test sample;
treating the test sample under conditions that release messenger ribonucleic acid (mRNA) from any pathogenic bacteria present in the clinical urine sample into the test sample;
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 is differentially expressed in organisms that are sensitive to an antibiotic test compound as compared to organisms that are resistant, wherein the target mRNA is one or more of recA, carA, deoC, figF, htrL, uvrA, ybhK, uup, fabD, cysD, glnA, opgG, ftsQ, b1649, dinD, pyrB, wbbK, or hdeA mRNA, wherein the exposure occurs for a time and under conditions in which binding between the probes and the target mRNA can occur, and wherein the mRNA is crude and unpurified when contacted with the probes and the method does not include amplifying the mRNA; and
determining a level of binding between the probes and target mRNA, thereby determining a level of the target mRNA.

2. The method of claim 1, wherein the method comprises contacting the uncultured clinical urine or blood sample with two or more antibiotic test compounds.

3. The method of claim 1, wherein the uncultured clinical urine or blood sample is contacted with the one or more antibiotic test compounds for less than four hours.

4. The method of claim 1, wherein the uncultured clinical urine or blood sample comprises a plurality of different pathogenic bacteria.

5. The method of claim 1, wherein the plurality of nucleic acid probes comprises probes that bind specifically to recA mRNA and probes that bind specifically to uvrA mRNA.

6. The method of claim 1, comprising partitioning the uncultured clinical urine sample, and contacting the individual partitions with different antibiotic test compounds.

7. The method of claim 1, wherein the test compound is a fluoroquinolone or aminoglycoside antibiotic.

8. A method comprising:
contacting an uncultured clinical blood or urine sample, wherein the sample is known or suspected to comprise pathogenic bacteria comprising Staphylococcus aureus, from a mammalian subject with methicillin to provide a test sample;
treating the test sample under conditions that release messenger ribonucleic acid (mRNA) from any pathogenic bacteria present in the clinical blood sample into the test sample;
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 is differentially expressed in organisms that are sensitive to methicillin as compared to organisms that are resistant, wherein the target mRNA is mecA, wherein the exposure occurs for a time and under conditions in which binding between the probes and the target mRNA can occur, and wherein the mRNA is crude and unpurified when contacted with the probes and the method does not include amplifying the mRNA; and
determining a level of binding between the probes and target mRNA, thereby determining a level of the target mRNA.

9. The method of claim 8, wherein the uncultured clinical blood sample is contacted with the methicillin for less than four hours.

10. The method of claim 8, wherein the uncultured clinical blood or urine sample comprises a plurality of different pathogenic bacteria.

11. A method comprising:
contacting an uncultured clinical sputum, urine, or blood sample from a mammalian subject, wherein the sample is known or suspected to comprise pathogenic bacteria comprising Mycobacterium tuberculosis, with one or more antibiotic test compounds selected from the group consisting of isoniaid, rifampin, streptomycin, and a fluoroquinolone antibiotic, to provide a test sample;
treating the test sample under conditions that release messenger ribonucleic acid (mRNA) from any pathogenic bacteria present in the clinical urine sample into the test sample;
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 is differentially expressed in organisms that are sensitive to an antibiotic test compound as compared to organisms that are resistant, wherein the target mRNA is one or more kasA, fadD32, accD6, efpA, Rv3675.1, bioD, hisl, era, Rv2296, Rv0813, rpsR, alkA, recA, ltp1, lhr, CHP, bcpB, gcvB, or groEL mRNA, wherein the exposure occurs for a time and under conditions in which binding between the probes and the target mRNA can occur, and wherein the mRNA is crude and unpurified when contacted with the probes and the method does not include amplifying the mRNA; and
determining a level of binding between the probes and target mRNA, thereby determining a level of the target mRNA.

12. The method of claim 11, wherein:
the one or more antibiotic test compounds comprise isoniazid, and the target mRNA comprises kasA, fadD32, accD6, efpA, or Rv3675.1;
the one or more antibiotic test compounds comprise Rifampin, and the target mRNA comprises bioD, hisI, era, or Rv2296;
the one or more antibiotic test compounds comprise a fluoroquinolone antibiotic, and the target mRNA comprises rpsR, alkA, recA, ltp1, lhr, kasA, or accD6; or
the one or more antibiotic test compounds comprise streptomycin, and the target mRNA comprises Rv0813, accD6, KasA, CHP, bcpB, gcvB, or groEL.

13. The method of claim 11, wherein the method comprises contacting the uncultured clinical blood or urine sample with two or more antibiotic test compounds.

14. The method of claim 11, wherein the uncultured clinical blood or urine sample is contacted with the one or more antibiotic test compounds for less than four hours.

15. The method of claim 11, wherein the uncultured clinical blood sample further comprises a plurality of different pathogenic bacteria.

16. The method of claim 11, comprising partitioning the uncultured clinical blood or urine sample, and contacting the individual partitions with different antibiotic test compounds.

17. A method comprising:
contacting an uncultured clinical urine or blood sample from a mammalian subject with ciprofloxacin, wherein the sample is known or suspected to comprise pathogenic bacteria comprising Pseudomonas aeruginosa, to provide a test sample;
treating the test sample under conditions that release messenger ribonucleic acid (mRNA) from any pathogenic bacteria present in the clinical urine sample into the test sample;
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 is differentially expressed in organisms that are sensitive to an antibiotic test compound as compared to organisms that are resistant, wherein the target mRNA is one or more of PA 4175, mpl, or proA mRNA, wherein the exposure occurs for a time and under conditions in which binding between the probes and the target mRNA can occur, and wherein the mRNA is crude and unpurified when contacted with the probes and the method does not include amplifying the mRNA; and
determining a level of binding between the probes and target mRNA, thereby determining a level of the target mRNA.

18. The method of claim 17, wherein the uncultured clinical blood or urine sample is contacted with the ciprofloxacin for less than four hours.

19. The method of claim 17, wherein the uncultured clinical blood sample further comprises a plurality of different pathogenic bacteria.