RELATED APPLICATIONS

This application claims the benefit of United States Provisional Patent Application Serial

No. 62/187,683, filed July 1, 2015, and United States Provisional Patent Application Serial No. 62/257,406, filed November 19, 2015, the disclosure of each of which is incorporated by reference herein in its entirety. FEDERAL FUNDING LEGEND

This invention was made with Government Support under Federal Grant Nos.

U01AI066569, P20RR016480 and HHSN266200400064C awarded by the National Institutes of Health (NIH) and Federal Grant Nos. N66001-07-C-2024 and N66001-09-C-2082 awarded by the Defense Advanced Research Projects Agency (DARPA). The U.S. Government has certain rights to this invention.

BACKGROUND

Acute respiratory infection is common in acute care environments and results in significant mortality, morbidity, and economic losses worldwide. Respiratory tract infections, or acute respiratory infections (ARI) caused 3.2 million deaths around the world and 164 million disability-adjusted life years lost in 2011 , more than any other cause (World Health

Organization., 2013a, 2013b). In 2012, the fourth leading cause of death worldwide was lower respiratory tract infections, and in low and middle income countries, where less supportive care is available, lower respiratory tract infections are the leading cause of death (WHO factsheet, accessed August 22, 2014). These illnesses are also problematic in developed countries. In the United States in 2010, the Centers for Disease Control (CDC) determined that pneumonia and influenza alone caused 15.1 deaths for every 100,000 people in the US population. The aged and children under the age of 5 years are particularly vulnerable to poor outcomes due to ARIs. For example, in 2010, pneumonia accounted for 18.3% of all deaths, or almost 1.4 million deaths, worldwide in children aged 5 years or younger.

Pneumonia and other lower respiratory tract infections can be due to many different pathogens that are primarily viral, bacterial, or less frequently fungal. Among viral pathogens, influenza is among the most notorious based on numbers of affected individuals, variable severity from season to season, and the ever-present worry about new strains causing much higher morbidity and mortality (e.g., Avian flu). However, among viral pathogens, influenza is only one of many that cause significant human disease. Respiratory Syncytial Virus (RSV) is the leading cause of hospitalization of children in developed countries during the winter months. Worldwide, about 33 million new cases of RSV infections were reported in 2005 in children under 5, with 3.4 million severe enough for hospitalization. It is estimated that this viral infection alone kills between 66,000 and 199,000 children each year. And, in the United States alone, about 10,000 deaths annually are associated with RSV infections in the over-65 population. In addition to known viral pathogens, history has shown that new and emerging infections can manifest at any time, spreading globally within days or weeks. Recent examples include SARS- coronavirus, which had a 10% mortality rate when it appeared in 2003-2004. More recently, Middle East respiratory syndrome (MERS) coronavirus continues to simmer in the Middle East and has been associated with a 30% mortality rate. Both of these infections present with respiratory symptoms and may at first be indistinguishable from any other ARI.

Although viral infections cause the majority of ARI, bacterial etiologies are also prominent especially in the context of lower respiratory tract infections. Specific causes of bacterial ARI vary geographically and by clinical context but include Streptococcus pneumoniae, Staphylococcus aureus, Haemophilus influenzae, Chlamydia pneumoniae, Mycoplasma pneumoniae, Klebsiella pneumoniae, Escherichia coli, and Pseudomonas aeruginosa. The identification of these pathogens relies on their growth in culture, which typically requires days and has limited sensitivity for detection of the infectious agent. Obtaining an adequate sample to test is problematic: In a study of 1669 patients with community-acquired pneumonia, only 14% of patients could provide a "good-quality" sputum sample that resulted in a positive culture (Garcia- Vazquez et al., 2004). Clinicians are aware of the limitations of these tests, which drives uncertainty and, consequently, antibacterial therapies are frequently prescribed without any confirmation of a bacterial infection.

The ability to rapidly diagnose the etiology of ARIs is an urgent global problem with far- reaching consequences at multiple levels: optimizing treatment for individual patients;

epidemiological surveillance to identify and track outbreaks; and guiding appropriate use of antimicrobials to stem the rising tide of antimicrobial resistance. It has been well established that early and appropriate antimicrobial therapy improves outcomes in patients with severe infection. This in part drives the over-utilization of antimicrobial therapies. Up to 73% of ambulatory care patients with acute respiratory illness are prescribed an antibiotic, accounting for approximately 40% of all antibiotics prescribed to adults in this setting. It has, however, been estimated that only a small fraction of these patients require anti -bacterial treatment (Cantrell et al. 2003, Clin. Ther. Jan;24(l):170-82). A similar trend is observed in emergency departments. Even if the presence of a viral pathogen has been microbiologically confirmed, it does not preclude the possibility of a concurrent bacterial infection. As a result, antibacterials are often prescribed "just in case." This spiraling empiricism contributes to the rising tide of antimicrobial resistance (Gould, 2009; Kim & Gallis, 1989), which is itself associated with higher mortality, length of hospitalization, and costs of health care (Cosgrove 2006, Clin. Infect. Dis., Jan 15;42 Suppl 2:S82-9). In addition, the inappropriate use of antibiotics may lead to drug-related adverse effects and other complications, e.g., Clostridium difficile-associated diarrhea (Zaas et al., 2014).

Acute respiratory infections are frequently characterized by non-specific symptoms (such as fever or cough) that are common to many different illnesses, including illnesses that are not caused by an infection. Existing diagnostics for ARI fall short in a number of ways.

Conventional microbiological testing is limited by poor sensitivity and specificity, slow turnaround times, or by the complexity of the test (Zaas et al. 2014, Trends Mol Med 20(10):579-88). One limitation of current tests that detect specific viral pathogens, for example the multiplex PCR-based assays, is the inability to detect emergent or pandemic viral strains. Influenza pandemics arise when new viruses circulate against which populations have no natural resistance. Influenza pandemics are frequently devastating. For example, in 1918-1919 the Spanish flu affected about 20% to 40% of the world's population and killed about 50 million people; in 1957-1958, Asian flu killed about 2 million people; in 1968-1969 the Hong Kong flu killed about 1 million people; and in 2009-2010, the Centers for Disease Control estimates that approximately 43 million to 89 million people contracted swine flu resulting in 8,870 to 18,300 related deaths. The emergence of these new strains challenges existing diagnostics which are not designed to detect them. This was particularly evident during the 2009 influenza pandemic where confirmation of infection required days and only occurred at specialized testing centers such as state health departments or the CDC (Kumar & Henrickson 2012, Clin Microbiol Rev 25(2):344- 61). The Ebola virus disease outbreak in West Africa poses similar challenges at the present time. Moreover, there is every expectation we will continue to face this issue as future outbreaks of infectious diseases are inevitable.

A further limitation of diagnostics that use the paradigm of testing for specific viruses or bacteria is that even though a pathogenic microbe may be detected, this is not proof that the patient's symptoms are due to the detected pathogen. A microorganism may be present as part of the individual's normal flora, known as colonization, or it may be detected due to contamination of the tested sample (e.g., a nasal swab or wash). Although recently-approved multiplex PCR assays, including those that detect viruses and bacteria, offer high sensitivity, these tests do not differentiate between asymptomatic carriage of a virus and true infection. For example, there is a high rate of asymptomatic viral shedding in ARI, particularly in children (Jansen et al. 2011, J Clin Microbiol 49(7):2631 -2636). Similarly, even though one pathogen is detected, illness may be due to a second pathogen for which there was no test available or performed,

Reports have described host gene expression profiles differentiating viral ARI from healthy controls (Huang et al. 2011 PLoS Genetics 7(8): el002234; Mejias et al., 2013; Thach et al. 2005 Genes and Immunity 6:588-595; Woods et al., 2013; A. K. Zaas et al., 2013; A. K. Zaas et al., 2009). However, few among these differentiate viral from bacterial ARI, which is a more clinically meaningful distinction than is detection of viral infection versus healthy or bacterial infection versus healthy (Hu, Yu, Crosby, & Storch, 2013; Parnell et al, 2012; Ramilo et al., 2007).

Current diagnostics methods are thus limited in their ability to differentiate between a bacterial and viral infection, and symptoms arising from non-infectious causes, or to identify co- infections with bacteria and virus.

SUMMARY

The present disclosure provides, in part, a molecular diagnostic test that overcomes many of the limitations of current methods for the determination of the etiology of respiratory symptoms. The test detects the host's response to an infectious agent or agents by measuring and analyzing the patterns of co-expressed genes, or signatures. These gene expression signatures may be measured in a blood sample in a human or animal presenting with symptoms that are consistent with an acute respiratory infection or in a human or animal that is at risk of developing (e.g. , presymptomatic) an acute respiratory infection (e.g., during an epidemic or local disease outbreak). Measurement of the host response as taught herein differentiates between bacterial ARI, viral ARI, and a non-infectious cause of illness, and may also detect ARI resulting from co- infection with bacteria and virus.

This multi-component test performs with unprecedented accuracy and clinical applicability, allowing health care providers to use the response of the host (the subject or patient) to reliably determine the nature of the infectious agent, to the level of pathogen class, or to exclude an infectious cause of symptoms in an individual patient presenting with symptoms that, by themselves, are not specific, In some embodiments, the results are agnostic to the species of respiratory virus or bacteria (i.e., while differentiating between virus or bacteria, it does not differentiate between particular genus or species of virus or bacteria). This offers an advantage over current tests that include probes or reagents directed to specific pathogens and thus are limited to detecting only those specific pathogens.

One aspect of the present disclosure provides a method for determining whether acute respiratory symptoms in a subject are bacterial in origin, viral in origin, or non-infectious in origin comprising, consisting of, or consisting essentially of: (a) obtaining a biological sample from the subject; (b) determining the gene expression profile of the subject from the biological sample by evaluating the expression levels of pre-defined sets of genes, termed signatures; (c) normalizing gene expression levels for the technology (i.e., platform) used to make said measurement to generate a normalized value; (d) entering the normalized values into a bacterial classifier, a viral classifier and/or a non-infectious illness classifier that have pre-defined weighting values (coefficients) for each of the genes in each signature; (e) comparing the output of the classifiers to pre-defined thresholds, cut-off values, or ranges of values that indicate likelihood of infection; and (f) using the output to determine whether the patient providing the sample has an infection of bacterial origin, viral origin, or has a non-infectious illness, or some combination of these conditions.

Another aspect of the present disclosure provides a method for determining whether an acute respiratory infection (ARI) in a subject is bacterial in origin, viral in origin, or noninfectious in origin comprising, consisting of, or consisting essentially of: (a) obtaining a biological sample from the subject; (b) determining the gene expression profile of the subject from the biological sample by evaluating the expression levels of pre-defined sets of genes; (c) normalizing gene expression levels for the technology (i.e., platform) used to make said measurement to generate a normalized value; (d) entering the normalized value into classifiers that have pre-defined weighting values for each of the genes in each signature; e) comparing the output of the classifiers to pre-defined thresholds, cut-off values, or ranges of values that indicate likelihood of infection; (f) if the sample is negative for bacteria, repeating step (d) using only the viral classifier and non-infectious classifier; and (g) classifying the sample as being of viral etiology or noninfectious illness.

Another aspect of the present disclosure provides a method for determining whether an acute respiratory infection (ARI) in a subject is bacterial in origin, viral in origin, or noninfectious in origin comprising, consisting of, or consisting essentially of: (a) obtaining a biological sample from the subject; (b) determining the gene expression profile of the subject from the biological sample by evaluating the expression levels of pre-defined sets of genes; (c) normalizing gene expression levels for the technology (i.e., platform) used to make said measurement to generate a normalized value; (d) entering the normalized values into classifiers that have pre-defined weighting values for each of the genes in each signature; (e) comparing the output of the classifiers to pre-defined thresholds, cut-off values, or ranges of values that indicate likelihood of infection; (f) if the sample is negative for virus, repeating step (d) using only the bacteria classifier and non-infectious classifier; and (g) classifying the sample as being of bacterial etiology or noninfectious illness. Another aspect of the present disclosure provides a method for determining whether an acute respiratory infection (ARI) in a subject is bacterial in origin, viral in origin, or noninfectious in origin comprising, consisting of, or consisting essentially of: (a) obtaining a biological sample from the subject; (b) determining the gene expression profile of the subject from the biological sample by evaluating the expression levels of pre-defined sets of genes; (c) normalizing gene expression levels for the technology (i.e., platform) used to make said measurement to generate a normalized value; (d) entering the normalized values into classifiers that have pre-defined weighting values for each of the genes in each signature; (e) comparing the output of the classifiers to pre-defined thresholds, cut-off values, or ranges of values that indicate likelihood of infection; (f) if the sample is negative for non-infectious illness, repeating step (d) using only the viral classifier and bacterial classifier; and (g) classifying the sample as being of viral etiology or bacterial etiology.

Yet another aspect of the present disclosure provides a method of treating an acute respiratory infection (ARI) whose etiology is unknown in a subject, said method comprising, consisting of, or consisting essentially of: (a) obtaining a biological sample from the subject; (b) determining the gene expression profile of the subject from the biological sample by evaluating the expression levels of pre-defined sets of genes (e.g., one, two or three or more signatures); (c) normalizing gene expression levels for the technology (i. e., platform) used to make said measurement to generate a normalized value; (d) entering the normalized values into a bacterial classifier, a viral classifier and non-infectious illness classifier that have pre-defined weighting values for each of the genes in each signature; (e) comparing the output of the classifiers to predefined thresholds, cut-off values, or ranges of values that indicate likelihood of infection; (f) classifying the sample as being of bacterial etiology, viral etiology, or noninfectious illness; and (g) administering to the subject an appropriate treatment regimen as identified by step (e). In some embodiments, step (g) comprises administering an antibacterial therapy when the etiology of the ARI is determined to be bacterial. In other embodiments, step (g) comprises administering an antiviral therapy when the etiology of the ARI is determined to be viral.

Another aspect is a method of monitoring response to a vaccine or a drug in a subject suffering from or at risk of an acute respiratory illness selected from bacterial, viral and/or non- infectious, comprising determining a host response of said subject, said determining carried out by a method as taught herein. In some embodiments, the drug is an antibacterial drug or an antiviral drug.

In some embodiments of the aspects, the methods further comprise generating a report assigning the subject a score indicating the probability of the etiology of the ARI. Further provided is a system for determining an etiology of an acute respiratory illness in a subject selected from bacterial, viral and/or non-infectious, comprising one or more of (inclusive of combinations thereof): at least one processor; a sample input circuit configured to receive a biological sample from the subject; a sample analysis circuit coupled to the at least one processor and configured to determine gene expression levels of the biological sample; an input/output circuit coupled to the at least one processor; a storage circuit coupled to the at least one processor and configured to store data, parameters, and/or classifiers; and a memory coupled to the processor and comprising computer readable program code embodied in the memory that when executed by the at least one processor causes the at least one processor to perform operations comprising: controlling/performing measurement via the sample analysis circuit of gene expression levels of a pre-defined set of genes (i.e. , signature) in said biological sample; normalizing the gene expression levels to generate normalized gene expression values; retrieving from the storage circuit a bacterial acute respiratory infection (ARI) classifier, a viral ARI classifier and a non-infectious illness classifier, said classifier(s) comprising pre-defined weighting values (i.e., coefficients) for each of the genes of the pre-defined set of genes; entering the normalized gene expression values into one or more acute respiratory illness classifiers selected from the bacterial acute respiratory infection (ARI) classifier, the viral ARI classifier and the non-infectious illness classifier; calculating an etiology probability for one or more of a bacterial ARI, viral ARI and non-infectious illness based upon said classifier(s); and controlling output via the input/output circuit of a determination whether the acute respiratory illness in the subject is bacterial in origin, viral in origin, non-infectious in origin, or some combination thereof.

In some embodiments, the system comprises computer readable code to transform quantitative, or semi-quantitative, detection of gene expression to a cumulative score or probability of the etiology of the ARI.

In some embodiments, the system comprises an array platform, a thermal cycler platform (e.g. , multiplexed and/or real-time PCR platform), a hybridization and multi-signal coded (e.g. , fluorescence) detector platform, a nucleic acid mass spectrometry platform, a nucleic acid sequencing platform, or a combination thereof.

In some embodiments of the aspects, the pre-defined sets of genes comprise at least three genetic signatures.

In some embodiments of the aspects, the biological sample comprises a sample selected from the group consisting of peripheral blood, sputum, nasopharyngeal swab, nasopharyngeal wash, bronchoalveolar lavage, endotracheal aspirate, and combinations thereof. In some embodiments of the aspects, the bacterial classifier comprises expression levels of 5, 10, 20, 30 or 50, to 80, 100, 150 or 200 of the genes (measurable, e.g. , with oligonucleotide probes homologous to said genes or gene transcripts) listed as part of a bacterial classifier in Table 1 , Table 2, Table 9, Table 10 and/or Table 12. In some embodiments, the viral classifier comprises expression levels of 5, 10, 20, 30 or 50, to 80, 100, 150 or 200 of the genes

(measurable, e.g., with oligonucleotide probes homologous to said genes or gene transcripts) listed as part of a viral classifier in Table 1, Table 2, Table 9, Table 10 and/or Table 12. In some embodiments, the non-infectious illness classifier comprises expression levels of 5, 10, 20, 30 or 50, to 80, 100, 150 or 200 of the genes (measurable, e.g., with oligonucleotide probes homologous to said genes or gene transcripts) listed as part of a non-infectious illness classifier in Table 1, Table 2, Table 9, Table 10 and/or Table 12.

A kit for determining the etiology of an acute respiratory infection (ARI) in a subject is also provided, comprising, consisting of, or consisting essentially of (a) a means for extracting mRNA from a biological sample; (b) a means for generating one or more arrays consisting of a plurality of synthetic oligonucleotides with regions homologous to transcripts from of 5, 10, 20, 30 or 50, to 80, 100, 150 or 200 of the genes from Table 1 , Table 2, Table 9, Table 10 and/or Table 12; and (c) instructions for use.

Another aspect of the present disclosure provides a method of using a kit for assessing the acute respiratory infection (ARI) classifier comprising, consisting of, or consisting essentially of: (a) generating one or more arrays consisting of a plurality of synthetic oligonucleotides with regions homologous to of 5, 10, 20, 30 or 50, to 80, 100, 150 or 200 of the genes from Table 1, Table 2, Table 9, Table 10 and/or Table 12; (b) adding to said array oligonucleotides with regions homologous to normalizing genes; (c) obtaining a biological sample from a subject suffering from an acute respiratory infection (ARI); (d) isolating RNA from said sample to create a transcriptome; (e) measuring said transcriptome on said array (e.g. , by measuring fluorescence or electric current proportional to the level of gene expression, etc.); (f) normalizing the measurements of said transcriptome to the normalizing genes, electronically transferring normalized measurements to a computer to implement the classifier(s), (g) generating a report; and optionally (h) administering an appropriate treatment based on the results.

In some embodiments, the method further comprises externally validating an ARI classifier against a known dataset comprising at least two relevant clinical attributes. In some embodiments, the dataset is selected from the group consisting of GSE6269, GSE42026, GSE40396, GSE20346, GSE42834 and combinations thereof.

Yet another aspect of the present disclosure provides all that is disclosed and illustrated herein. Also provided is the use of an ARI classifier as taught herein in a method of treatment for acute respiratory infection (ARI) in a subject of unknown etiology.

BRIEF DESCRIPTION OF THE DRAWINGS

The foregoing aspects and other features of the disclosure are explained in the following description, taken in connection with the accompanying drawings, herein:

FIG. 1 is a schematic showing a method of obtaining classifiers (training 10) according to some embodiments of the present disclosure, where each classifier is composed of a weighted sum of all or a subset of normalized gene expression levels. This weighted sum defines a probability that allows for a decision (classification), particularly when compared to a threshold value or a confidence interval. The exact combination of genes, their weights and the threshold for each classifier obtained by the training are particular to a specific platform. The classifier (or more precisely its components, namely weights and threshold or confidence interval (values)) go to a database. Weights with a nonzero value determine the subset of genes used by the classifier. Repeat to obtain all three classifiers (bacterial ARI, viral ARI and non-infectious ARI) within a specified platform matching the gene expression values.

FIG. 2 is a diagram showing an example of generating and/or using classifiers in accordance with some embodiments of the present disclosure.

FIG. 3 is a schematic showing a method of classification 20 of an etiology of acute respiratory symptoms suffered by a subject making use of classifiers according to some embodiments of the present disclosure.

FIG. 4 presents schematics showing the decision pattern for using secondary

classification to determine the etiology of an ARI in a subject in accordance with some embodiments of the present disclosure.

FIG. 5 is a diagram of an example training method presented in Example 1. A cohort of patients encompassing bacterial ARI, viral ARI, or non-infectious illness was used to develop classifiers of each condition. This combined ARI classifier was validated using leave one out cross-validation and compared to three published classifiers of bacterial vs. viral infection. The combined ARI classifier was also externally validated in six publically available datasets. In one experiment, healthy volunteers were included in the training set to determine their suitability as "no-infection" controls. All subsequent experiments were performed without the use of this healthy subject cohort.

FIG. 6 presents graphs showing the results of leave-one-out cross-validation of three classifiers (bacterial ARI, viral ARI and noninfectious illness) according an example training method presented in Example 1. Each patient is assigned probabilities of having bacterial ARI (triangle), viral ARI (circle), and non-infectious illness (square). Patients clinically adjudicated as having bacterial ARI, viral ARI, or non-infectious illness, are presented in the top, center, and bottom panels, respectively. Overall classification accuracy was 87%.

FIG. 7 is a graph showing the evaluation of healthy adults as a no-infection control, rather than an ill-but-uninfected control. This figure demonstrates the unexpected superiority of the use of ill-but-not infected subjects as the control.

FIG. 8 shows the positive and negative predictive values for A) Bacterial and B) Viral ARI classification as a function of prevalence.

FIG. 9 is a Venn diagram representing overlap in the Bacterial ARI, Viral ARI, and Non- infectious Illness Classifiers. There are 71 genes in the Bacterial ARI Classifier, 33 in the Viral ARI Classifier, and 26 in the Non-infectious Illness Classifier. One gene overlaps between the Bacterial and Viral ARI Classifiers. Five genes overlap between the Bacterial ARI and Noninfectious Illness Classifiers. Four genes overlap between the Viral ARI and Non-infectious Illness Classifiers.

FIG. 10 is a graph showing Classifier performance in patients with co-infection by the identification of bacterial and viral pathogens. Bacterial and Viral ARI classifiers were trained on subjects with bacterial (n=22) or viral (n=71) infection (GSE60244). This same dataset also included 25 subjects with bacterial/viral co-infection. Bacterial and viral classifier predictions were normalized to the same scale, as shown in the figure. Each subject receives two

probabilities: that of a bacterial ARI host response and a viral ARI host response. A probability score of 0.5 or greater was considered positive. Subjects 1-6 have a bacterial host response. Subjects 7-9 have both bacterial and viral host responses which may indicate true co-infection. Subjects 10-23 have a viral host response. Subjects 24-25 do not have bacterial or viral host responses.

FIG. 11 is a block diagram of a classification system and/or computer program product that may be used in a platform. A classification system and/or computer program product 1100 may include a processor subsystem 1140, including one or more Central Processing Units (CPU) on which one or more operating systems and/or one or more applications run. While one processor 1 140 is shown, it will be understood that multiple processors 1140 may be present, which may be either electrically interconnected or separate. Processor(s) 1140 are configured to execute computer program code from memory devices, such as memory 1150, to perform at least some of the operations and methods described herein. The storage circuit 1170 may store databases which provide access to the data/parameters/classifiers used by the classification system 1110 such as the signatures, weights, thresholds, etc. An input/output circuit 1160 may include displays and/or user input devices, such as keyboards, touch screens and/or pointing devices. Devices attached to the input/output circuit 1160 may be used to provide information to the processor 1140 by a user of the classification system 1100. Devices attached to the input/output circuit 1160 may include networking or communication controllers, input devices (keyboard, a mouse, touch screen, etc.) and output devices (printer or display). An optional update circuit 1180 may be included as an interface for providing updates to the classification system 1 100 such as updates to the code executed by the processor 1140 that are stored in the memory 1150 and/or the storage circuit 1170. Updates provided via the update circuit 1180 may also include updates to portions of the storage circuit 1 170 related to a database and/or other data storage format which maintains information for the classification system 1100, such as the signatures, weights, thresholds, etc. The sample input circuit 1110 provides an interface for the classification system 1 100 to receive biological samples to be analyzed. The sample processing circuit 1 120 may further process the biological sample within the classification system 1100 so as to prepare the biological sample for automated analysis.

DETAILED DESCRIPTION

For the purposes of promoting an understanding of the principles of the present disclosure, reference will now be made to preferred embodiments and specific language will be used to describe the same. It will nevertheless be understood that no limitation of the scope of the disclosure is thereby intended, such alteration and further modifications of the disclosure as illustrated herein, being contemplated as would normally occur to one skilled in the art to which the disclosure relates.

Articles "a" and "an" are used herein to refer to one or to more than one {i.e., at least one) of the grammatical object of the article. By way of example, "an element" means at least one element and can include more than one element.

Unless otherwise defined, all technical terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs.

The present disclosure provides that alterations in gene, protein and metabolite expression in blood in response to pathogen exposure that causes acute respiratory infections can be used to identify and characterize the etiology of the ARI in a subject with a high degree of accuracy.

Definitions

As used herein, the term "acute respiratory infection" or "ARI" refers to an infection, or an illness showing symptoms and/or physical findings consistent with an infection (e.g., symptoms such as coughing, wheezing, fever, sore throat, congestion; physical findings such as elevated heart rate, elevated breath rate, abnormal white blood cell count, low arterial carbon dioxide tension (PaC0 ₂ ), etc.), of the upper or lower respiratory tract, often due to a bacterial or viral pathogen, and characterized by rapid progression of symptoms over hours to days. ARIs may primarily be of the upper respiratory tract (URIs), the lower respiratory tract (LRIs), or a combination of the two. ARIs may have systemic effects due to spread of the infection beyond the respiratory tract or due to collateral damage induced by the immune response. An example of the former includes Staphylococcus aureus pneumonia that has spread to the blood stream and can result in secondary sites of infection, including endocarditis (infection of the heart valves), septic arthritis (joint infection), or osteomyelitis (bone infection). An example of the latter includes influenza pneumonia leading to acute respiratory distress syndrome and respiratory failure.

The term "signature" as used herein refers to a set of biological analytes and the measurable quantities of said analytes whose particular combination signifies the presence or absence of the specified biological state. These signatures are discovered in a plurality of subjects with known status (e.g. , with a confirmed respiratory bacterial infection, respiratory viral infection, or suffering from non- infectious illness), and are discriminative (individually or jointly) of one or more categories or outcomes of interest. These measurable analytes, also known as biological markers, can be (but are not limited to) gene expression levels, protein or peptide levels, or metabolite levels. See also US 2015/0227681 to Courchesne et al; US 2016/0153993 to Eden et al.

In some embodiments as disclosed herein, the "signature" is a particular combination of genes whose expression levels, when incorporated into a classifier as taught herein, discriminate a condition such as a bacterial ARI, viral ARI or non-infectious illness. See, for example, Table 1, Table 2, Table 9, Table 10 and Table 12 hereinbelow. In some embodiments, the signature is agnostic to the species of respiratory virus or bacteria (i.e., while differentiating between virus or bacteria, it does not differentiate between particular genus or species of virus or bacteria) and/or agnostic to the particular cause of the non-infectious illness.

As used herein, the terms "classifier" and "predictor" are used interchangeably and refer to a mathematical function that uses the values of the signature (e.g. , gene expression levels for a defined set of genes) and a pre-determined coefficient (or weight) for each signature component to generate scores for a given observation or individual patient for the purpose of assignment to a category. The classifier may be linear and/or probabilistic. A classifier is linear if scores are a function of summed signature values weighted by a set of coefficients. Furthermore, a classifier is probabilistic if the function of signature values generates a probability, a value between 0 and 1.0 (or 0 and 100%) quantifying the likelihood that a subject or observation belongs to a particular category or will have a particular outcome, respectively. Probit regression and logistic regression are examples of probabilistic linear classifiers that use probit and logistic link functions, respectively, to generate a probability.

A classifier as taught herein may be obtained by a procedure known as "training," which makes use of a set of data containing observations with known category membership {e.g., bacterial ARI, viral ARI, and/or non-infection illness). See FIG. 1. Specifically, training seeks to find the optimal coefficient (i.e., weight) for each component of a given signature {e.g., gene expression level components), as well as an optimal signature, where the optimal result is determined by the highest achievable classification accuracy.

"Classification" refers to a method of assigning a subject suffering from or at risk for acute respiratory symptoms to one or more categories or outcomes {e.g., a patient is infected with a pathogen or is not infected, another categorization may be that a patient is infected with a virus and/or infected with a bacterium). See FIG. 3. In some cases, a subject may be classified to more than one category, e.g., in case of bacterial and viral co-infection. The outcome, or category, is determined by the value of the scores provided by the classifier, which may be compared to a cut-off or threshold value, confidence level, or limit. In other scenarios, the probability of belonging to a particular category may be given {e.g., if the classifier reports probabilities).

As used herein, the term "indicative" when used with gene expression levels, means that the gene expression levels are up-regulated or down-regulated, altered, or changed compared to the expression levels in alternative biological states (e.g., bacterial ARI or viral ARI) or control. The term "indicative" when used with protein levels means that the protein levels are higher or lower, increased or decreased, altered, or changed compared to the standard protein levels or levels in alternative biological states.

The term "subject" and "patient" are used interchangeably and refer to any animal being examined, studied or treated. It is not intended that the present disclosure be limited to any particular type of subject. In some embodiments of the present invention, humans are the preferred subject, while in other embodiments non-human animals are the preferred subject, including, but not limited to, mice, monkeys, ferrets, cattle, sheep, goats, pigs, chicken, turkeys, dogs, cats, horses and reptiles. In certain embodiments, the subject is suffering from an ARI or is displaying ARI-like symptoms.

"Platform" or "technology" as used herein refers to an apparatus {e.g., instrument and associated parts, computer, computer-readable media comprising one or more databases as taught herein, reagents, etc.) that may be used to measure a signature, e.g., gene expression levels, in accordance with the present disclosure. Examples of platforms include, but are not limited to, an array platform, a thermal cycler platform {e.g., multiplexed and/or real-time PCR platform), a nucleic acid sequencing platform, a hybridization and multi-signal coded {e.g., fluorescence) detector platform, etc., a nucleic acid mass spectrometry platform, a magnetic resonance platform, and combinations thereof.

In some embodiments, the platform is configured to measure gene expression levels semi-quantitatively, that is, rather than measuring in discrete or absolute expression, the expression levels are measured as an estimate and/or relative to each other or a specified marker or markers (e.g. , expression of another, "standard" or "reference," gene).

In some embodiments, semi-quantitative measuring includes "real-time PCR" by performing PCR cycles until a signal indicating the specified mRNA is detected, and using the number of PCR cycles needed until detection to provide the estimated or relative expression levels of the genes within the signature.

A real-time PCR platform includes, for example, a TaqMan® Low Density Array (TLDA), in which samples undergo multiplexed reverse transcription, followed by real-time PCR on an array card with a collection of wells in which real-time PCR is performed. See Kodani et al. 2011 , J. Clin. Microbiol. 49(6):2175-2182. A real-time PCR platform also includes, for example, a Biocartis Idylla™ sample-to-result technology, in which cells are lysed,

DNA/RNA extracted and real-time PCR is performed and results detected.

A magnetic resonance platform includes, for example, T2 Biosystems® T2 Magnetic Resonance (T2MR®) technology, in which molecular targets may be identified in biological samples without the need for purification.

The terms "array," "microarray" and "micro array" are interchangeable and refer to an arrangement of a collection of nucleotide sequences presented on a substrate. Any type of array can be utilized in the methods provided herein. For example, arrays can be on a solid substrate (a solid phase array), such as a glass slide, or on a semi-solid substrate, such as nitrocellulose membrane. Arrays can also be presented on beads, i.e., a bead array. These beads are typically microscopic and may be made of, e.g. , polystyrene. The array can also be presented on nanoparticles, which may be made of, e.g., particularly gold, but also silver, palladium, or platinum. See, e.g. , Nano sphere Verigene® System, which uses gold nanoparticle probe technology. Magnetic nanoparticles may also be used. Other examples include nuclear magnetic resonance microcoils. The nucleotide sequences can be DNA, RNA, or any permutations thereof (e g-, nucleotide analogues, such as locked nucleic acids (LNAs), and the like). In some embodiments, the nucleotide sequences span exon/intron boundaries to detect gene expression of spliced or mature RNA species rather than genomic DNA. The nucleotide sequences can also be partial sequences from a gene, primers, whole gene sequences, non-coding sequences, coding sequences, published sequences, known sequences, or novel sequences. The arrays may additionally comprise other compounds, such as antibodies, peptides, proteins, tissues, cells, chemicals, carbohydrates, and the like that specifically bind proteins or metabolites.

An array platform includes, for example, the TaqMan® Low Density Array (TLDA) mentioned above, and an Affymetrix® microarray platform.

A hybridization and multi-signal coded detector platform includes, for example,

NanoString nCounter® technology, in which hybridization of a color-coded barcode attached to a target-specific probe (e.g., corresponding to a gene expression transcript of interest) is detected; and Luminex® xMAP® technology, in which microsphere beads are color coded and coated with a target-specific (e.g., gene expression transcript) probe for detection; and Illumina® BeadArray, in which microbeads are assembled onto fiber optic bundles or planar silica slides and coated with a target-specific (e.g., gene expression transcript) probe for detection.

A nucleic acid mass spectrometry platform includes, for example, the Ibis Biosciences Plex-ID® Detector, in which DNA mass spectrometry is used to detect amplified DNA using mass profiles.

A thermal cycler platform includes, for example, the FilmArray® multiplex PCR system, which extract and purifies nucleic acids from an unprocessed sample and performs nested multiplex PCR; and the RainDrop Digital PCR System, which is a droplet-based PCR platform using micro fluidic chips.

The term "computer readable medium" refers to any device or system for storing and providing information (e.g., data and instructions) to a computer processor. Examples of computer readable media include, but are not limited to, DVDs, CDs hard disk drives, magnetic tape and servers for streaming media over networks, and applications, such as those found on smart phones and tablets. In various embodiments, aspects of the present invention including data structures and methods may be stored on a computer readable medium. Processing and data may also be performed on numerous device types, including but not limited to, desk top and lap top computers, tablets, smart phones, and the like.

As used herein, the term "biological sample" comprises any sample that may be taken from a subject that contains genetic material that can be used in the methods provided herein. For example, a biological sample may comprise a peripheral blood sample. The term "peripheral blood sample" refers to a sample of blood circulating in the circulatory system or body taken from the system of body. Other samples may comprise those taken from the upper respiratory tract, including but not limited to, sputum, nasopharyngeal swab and nasopharyngeal wash. A biological sample may also comprise those samples taken from the lower respiratory tract, including but not limited to, bronchoalveolar lavage and endotracheal aspirate. A biological sample may also comprise any combinations thereof. The term "genetic material" refers to a material used to store genetic information in the nuclei or mitochondria of an organism's cells. Examples of genetic material include, but are not limited to, double-stranded and single-stranded DNA, cDNA, RNA, and mRNA.

The term "plurality of nucleic acid oligomers" refers to two or more nucleic acid oligomers, which can be DNA or RNA.

As used herein, the terms "treat", "treatment" and "treating" refer to the reduction or amelioration of the severity, duration and/or progression of a disease or disorder or one or more symptoms thereof resulting from the administration of one or more therapies. Such terms refer to a reduction in the replication of a virus or bacteria, or a reduction in the spread of a virus or bacteria to other organs or tissues in a subject or to other subjects. Treatment may also include therapies for ARIs resulting from non-infectious illness, such as allergy treatment, asthma treatments, and the like.

The term "effective amount" refers to an amount of a therapeutic agent that is sufficient to exert a physiological effect in the subject. The term "responsivity" refers to a change in gene expression levels of genes in a subject in response to the subject being infected with a virus or bacteria or suffering from a non-infectious illness compared to the gene expression levels of the genes in a subject that is not infected with a virus, bacteria or suffering from a non-infectious illness or a control subject.

The term "appropriate treatment regimen" refers to the standard of care needed to treat a specific disease or disorder. Often such regimens require the act of administering to a subject a therapeutic agent(s) capable of producing a curative effect in a disease state. For example, a therapeutic agent for treating a subject having bacteremia is an antibiotic which include, but are not limited to, penicillins, cephalosporins, fluroquinolones, tetracyclines, macrolides, and aminoglycosides. A therapeutic agent for treating a subject having a viral respiratory infection includes, but is not limited to, oseltamivir, RNAi antivirals, inhaled ribavirin, monoclonal antibody respigam, zanamivir, and neuraminidase blocking agents. The invention contemplates the use of the methods of the invention to determine treatments with antivirals or antibiotics that are not yet available. Appropriate treatment regimes also include treatments for ARIs resulting from non-infectious illness, such as allergy treatments, including but not limited to,

administration of antihistamines, decongestants, anticholinergic nasal sprays, leukotriene inhibitors, mast cell inhibitors, steroid nasal sprays, etc.; and asthma treatments, including, but not limited to, inhaled corticosteroids, leukotriene modifiers, long-acting beta agonists, combinations inhalers (e.g., fluticasone-salmeterol; budesonide-formoterol; mometasone- formoterol, etc.), theophylline, short-acting beta agonists, ipratropium, oral and intravenous corticosteroids, omalizumab, and the like. Often such regimens require the act of administering to a subject a therapeutic agent(s) capable of producing reduction of symptoms associated with a disease state. Examples such therapeutic agents include, but are not limited to, NSAIDS, acetaminophen, anti-histamines, beta-agonists, anti-tussives or other medicaments that reduce the symptoms associated with the disease process.

Methods of Generating Classifiers (Training)

The present disclosure provides methods of generating classifiers (also referred to as training 10) for use in the methods of determining the etiology of an acute respiratory illness in a subject. Gene expression-based classifiers are developed that can be used to identify and characterize the etiology of an ARI in a subject with a high degree of accuracy.

Hence, and as shown in FIG. 1, one aspect of the present disclosure provides a method of making an acute respiratory infection (ARI) classifier comprising, consisting of, or consisting essentially of: (i) obtaining a biological sample (e.g. , a peripheral blood sample) from a plurality of subjects suffering from bacterial, viral or non- infectious acute respiratory infection 100; (ii) optionally, isolating RNA from said sample (e.g. , total R A to create a transcriptome) (105, not shown in FIG. 1); (iii) measuring gene expression levels of a plurality of genes 110 (i.e., some or all of the genes expressed in the RNA); (iv) normalizing the gene expression levels 120; and (v) generating a bacterial ARI classifier, a viral ARI classifier or a non-infectious illness classifier 130 based on the results.

In some embodiments, the sample is not purified after collection. In some embodiments, the sample may be purified to remove extraneous material, before or after lysis of cells. In some embodiments, the sample is purified with cell lysis and removal of cellular materials, isolation of nucleic acids, and/or reduction of abundant transcripts such as globin or ribosomal RNAs.

In some embodiments, measuring gene expression levels may include generating one or more microarrays using said transcriptomes; measuring said transcriptomes using a plurality of primers; analyzing and correcting batch differences.

In some embodiments, the method further includes uploading 140 the final gene target list for the generated classifier, the associated weights (w _n ), and threshold values to one or more databases.

An example of generating said classifiers is detailed in FIG. 2. As shown in FIG. 2, biological samples from a cohort of patients encompassing bacterial ARI, viral ARI, or noninfectious illness are used to develop gene expression-based classifiers for each condition (i.e. , bacterial acute respiratory infection, viral acute respiratory infection, or non-infectious cause of illness). Specifically, the bacterial ARI classifier is obtained to positively identifying those with bacterial ARI vs. either viral ARI or non-infectious illnesses. The viral ARI classifier is obtained to positively identifying those with viral ARI vs. bacterial ARI or non-infectious illness (NI). The non-infectious illness classifier is generated to improve bacterial and viral ARI classifier specificity. Next, signatures for bacterial ARI classifiers, viral ARI classifiers, and noninfectious illness classifiers are generated (e.g., by applying a sparse logistic regression model).

These three classifiers may then be combined, if desired, into a single classifier termed

"the ARI classifier" by following a one-versus-all scheme whereby largest membership probability assigns class label. See also FIG. 5. The combined ARI classifier may be validated in some embodiments using leave-one-out cross-validation in the same population from which it was derived and/or may be validated in some embodiments using publically available human gene expression datasets of samples from subjects suffering from illness of known etiology. For example, validation may be performed using publically available human gene expression datasets (e.g., GSE6269, GSE42026, GSE40396, GSE20346, and or GSE42834), the datasets chosen if they included at least two clinical groups (bacterial ARI, viral ARI, or non-infectious illness).

The classifier may be validated in a standard set of samples from subjects suffering from illness of known etiology, i.e. , bacterial ARI, viral ARI, or non-infectious illness.

The methodology for training described herein may be readily translated by one of ordinary skill in the art to different gene expression detection (e.g. , mRNA detection and quantification) platforms.

The methods and assays of the present disclosure may be based upon gene expression, for example, through direct measurement of RNA, measurement of derived materials (e.g., cD A), and measurement of RNA products (e.g., encoded proteins or peptides). Any method of extracting and screening gene expression may be used and is within the scope of the present disclosure.

In some embodiments, the measuring comprises the detection and quantification (e.g., semi-quantification) of mRNA in the sample. In some embodiments, the gene expression levels are adjusted relative to one or more standard gene level(s) ("normalized"). As known in the art, normalizing is done to remove technical variability inherent to a platform to give a quantity or relative quantity (e.g., of expressed genes).

In some embodiments, detection and quantification of mRNA may first involve a reverse transcription and/or amplification step, e.g. , RT-PCR such as quantitative RT-PCR. In some embodiments, detection and quantification may be based upon the unamplified mRNA molecules present in or purified from the biological sample. Direct detection and measurement of RNA molecules typically involves hybridization to complementary primers and/or labeled probes. Such methods include traditional northern blotting and surface-enhanced Raman spectroscopy (SERS), which involves shooting a laser at a sample exposed to surfaces of plasmonic-active metal structures with gene-specific probes, and measuring changes in light frequency as it scatters.

Similarly, detection of RNA derivatives, such as cDNA, typically involves hybridization to complementary primers and/or labeled probes. This may include high-density oligonucleotide probe arrays (e.g., solid state microarrays and bead arrays) or related probe-hybridization methods, and polymerase chain reaction (PCR)-based amplification and detection, including real-time, digital, and end-point PCR methods for relative and absolute quantitation of specific RNA molecules.

Additionally, sequencing-based methods can be used to detect and quantify RNA or

RNA-derived material levels. When applied to RNA, sequencing methods are referred to as RNAseq, and provide both qualitative (sequence, or presence/absence of an RNA, or its cognate cDNA, in a sample) and quantitative (copy number) information on RNA molecules from a sample. See, e.g. , Wang et al. 2009 Nat. Rev. Genet. 10(l):57-63. Another sequence-based method, serial analysis of gene expression (SAGE), uses cDNA "tags" as a proxy to measure expression levels of RNA molecules.

Moreover, use of proprietary platforms for mRNA detection and quantification may also be used to complete the methods of the present disclosure. Examples of these are Pixel™ System, incorporating Molecular Indexing™, developed by CELLULAR RESEARCH, INC., NanoString® Technologies nCounter gene expression system; mRNA-Seq, Tag-Profiling,

BeadArrayTM technology and VeraCode from Illumina, the ICEPlex System from PrimeraDx, and the QuantiGene 2.0 Multiplex Assay from Affymetrix.

As an example, RNA from whole blood from a subject can be collected using RNA preservation reagents such as PAXgene™ RNA tubes (PreAnalytiX, Valencia, Calif). The RNA can be extracted using a standard PAXgene™ or Versagene™ (Gentra Systems, Inc,

Minneapolis, Minn.) RNA extraction protocol. The Versagene™ kit produces greater yields of higher quality RNA from the PAXgene™ RNA tubes. Following RNA extraction, one can use GLOBINCIear™ (Ambion, Austin, Tex.) for whole blood globin reduction. (This method uses a bead-oligonucleotide construct to bind globin mRNA and, in our experience, we are able to remove over 90% of the globin mRNA.) Depending on the technology, removal of abundant and non-interesting transcripts may increase the sensitivity of the assay, such as with a microarray platform.

Quality of the RNA can be assessed by several means. For example, RNA quality can be assessed using an Agilent 2100 Bioanalyzer immediately following extraction. This analysis provides an RNA Integrity Number (RTN) as a quantitative measure of RNA quality. Also, following globin reduction the samples can be compared to the globin-reduced standards. In addition, the scaling factors and background can be assessed following hybridization to microarrays.

Real-time PCR may be used to quickly identify gene expression from a whole blood sample. For example, the isolated RNA can be reverse transcribed and then amplified and detected in real time using non-specific fluorescent dyes that intercalate with the resulting ds- DNA, or sequence-specific DNA probes labeled with a fluorescent reporter which permits detection only after hybridization of the probe with its complementary DNA target.

Hence, it should be understood that there are many methods of mRNA quantification and detection that may be used by a platform in accordance with the methods disclosed herein.

The expression levels are typically normalized following detection and quantification as appropriate for the particular platform using methods routinely practiced by those of ordinary skill in the art.

With mRNA detection and quantification and a matched normalization methodology in place for platform, it is simply a matter of using carefully selected and adjudicated patient samples for the training methods. For example, the cohort described hereinbelow was used to generate the appropriate weighting values (coefficients) to be used in conjunction with the genes in the three signatures in the classifier for a platform. These subject-samples could also be used to generate coefficients and cut-offs for a test implemented using a different mRNA detection and quantification platform.

In some embodiments, the individual categories of classifiers (i.e. , bacterial ARI, viral ARI, non-infectious illness) are formed from a cohort inclusive of a variety of such causes thereof. For instance, the bacterial ARI classifier is obtained from a cohort having bacterial infections from multiple bacterial genera and/or species, the viral ARI classifier is obtained from a cohort having viral infections from multiple viral genera and/or species, and the non-infectious illness classifier is obtained from a cohort having a non-infectious illness due to multiple noninfectious causes. See, e.g. , Table 8. In this way, the respective classifiers obtained are agnostic to the underlying bacteria, virus, and non-infectious cause. In some embodiments, some or all of the subjects with non-infectious causes of illness in the cohort have symptoms consistent with a respiratory infection.

In some embodiments, the signatures may be obtained using a supervised statistical approach known as sparse linear classification in which sets of genes are identified by the model according to their ability to separate phenotypes during a training process that uses the selected set of patient samples. The outcomes of training are gene signatures and classification coefficients for the three comparisons. Together the signatures and coefficients provide a classifier or predictor. Training may also be used to establish threshold or cut-off values.

Threshold or cut-off values can be adjusted to change test performance, e.g., test sensitivity and specificity. For example, the threshold for bacterial ARI may be intentionally lowered to increase the sensitivity of the test for bacterial infection, if desired.

In some embodiments, the classifier generating comprises iteratively: (i) assigning a weight for each normalized gene expression value, entering the weight and expression value for each gene into a classifier (e.g. , a linear regression classifier) equation and determining a score for outcome for each of the plurality of subjects, then (ii) determining the accuracy of classification for each outcome across the plurality of subjects, and then (iii) adjusting the weight until accuracy of classification is optimized. Genes having a non-zero weight are included in the respective classifier.

In some embodiments, the classifier is a linear regression classifier and said generating comprises converting a score of said classifier to a probability using a link function. As known in the art, the link function specifies the link between the target/output of the model (e.g., probability of bacterial infection) and systematic components (in this instance, the combination of explanatory variables that comprise the predictor) of the linear model. It says how the expected value of the response relates to the linear predictor of explanatory variable.

Methods of Classification

The present disclosure further provides methods for determining whether a patient has a respiratory illness due to a bacterial infection, a viral infection, or a non-infectious cause. The method for making this determination relies upon the use of classifiers obtained as taught herein. The methods may include: a) measuring the expression levels of pre-defined sets of genes (i.e. , for one or more of the three signatures); b) normalizing gene expression levels for the technology used to make said measurement; c) taking those values and entering them into a bacterial classifier, a viral classifier and/or non-infectious illness classifier (i.e., predictors) that have predefined weighting values (coefficients) for each of the genes in each signature; d) comparing the output of the classifiers to pre-defined thresholds, cut-off values, confidence intervals or ranges of values that indicate likelihood of infection; and optionally e) jointly reporting the results of the classifiers.

A simple overview of such methods is provided in FIG. 3. In this representation, each of the three gene signatures is informative of the patient's host response to a different ARI etiology (bacterial or viral) or to an ill, but not infected, state (NI). These signatures are groups of gene transcripts which have consistent and coordinated increased or decreased levels of expression in response to one of three clinical states: bacterial ARI, viral ARI, or a non-infected but ill state. These signatures are derived using carefully adjudicated groups of patient samples with the condition(s) of interest (training 10).

With reference to FIG. 3, after obtaining a biological sample from the patient (e.g. , a blood sample), in some embodiments the mRNA is extracted. The mRNA (or a defined region of each mRNA), is quantified for all, or a subset, of the genes in the signatures. Depending upon the apparatus that is used for quantification, the mRNA may have to be first purified from the sample.

The signature is reflective of a clinical state and is defined relative to at least one of the other two possibilities. For example, the bacterial ARI signature is identified as a group of biomarkers (here, represented by gene mRNA transcripts) that distinguish patients with bacterial ARI and those without bacterial ARI (including patients with viral ARI or non-infectious illness as it pertains to this application). The viral ARI signature is defined by a group of biomarkers that distinguish patients with viral ARI from those without viral ARI (including patients with either bacterial ARI or non-infectious illness). The non- infectious illness signature is defined by a group of biomarkers that distinguish patients with non-infectious causes of illness relative to those with either bacterial or viral ARI.

The normalized expression levels of each gene of the signature (e.g., first column Table 9) are the explanatory or independent variables or features used in the classifier. As an example, the classifier may have a general form as a probit regression formulation:

P(having condition) = Φ(βιΧι+ β ₂ Χ ₂ + . ..+P _{d d} ) (equation 1) where the condition is bacterial ARI, viral ARI, or non-infection illness; Φ(.) is the probit (or logistic, etc.) link function; {βι,β _2: ...,β( _ΐ } are the coefficients obtained during training (e.g. , second, third and fourth columns from Table 9) (coefficients may also be denoted {wi,w ₂ ,...,wa} as "weights" herein); {Xi,X ₂ ,... ,X _d } are the normalized gene expression levels of the signature; and d is the size of the signature (i. e. , number of genes).

As would be understood by one skilled in the art, the value of the coefficients for each explanatory variable will change for each technology platform used to measure the expression of the genes or a subset of genes used in the probit regression model. For example, for gene expression measured by Affymetrix U133A 2.0 microarray, the coefficients for each of the features in the classifier algorithm are shown in Table 9.

The sensitivity, specificity, and overall accuracy of each classifier may be optimized by changing the threshold for classification using receiving operating characteristic (ROC) curves.

Another aspect of the present disclosure provides a method for determining whether an acute respiratory infection (ARI) in a subject is bacterial in origin, viral in origin, or non- infectious in origin comprising, consisting of, or consisting essentially of a) obtaining a biological sample from the subject; b) determining the gene expression profile of the subject from the biological sample by evaluating the expression levels of pre-defined sets of genes {i.e., three signatures); c) normalizing gene expression levels for the technology used to make said measurement to generate a normalized value; d) entering the normalized value into a bacterial classifier, a viral classifier and non-infectious illness classifier (i.e., predictors) that have predefined weighting values (coefficients) for each of the genes in each signature; e) comparing the output of the classifiers to pre-defined thresholds, cut-off values, or ranges of values that indicate likelihood of infection; and e) classifying the sample as being of bacterial etiology, viral etiology, or noninfectious illness. In some embodiments, the method further comprises generating a report assigning the patient a score indicating the probability of the etiology of the ARI.

The classifiers that are developed during training and using a training set of samples are applied for prediction purposes to diagnose new individuals ("classification"). For each subject or patient, a biological sample is taken and the normalized levels of expression (i.e. , the relative amount of mRNA expression) in the sample of each of the genes specified by the signatures found during training are the input for the classifiers. The classifiers also use the weighting coefficients discovered during training for each gene. As outputs, the classifiers are used to compute three probability values. Each probability value may be used to determine the likelihood of the three considered clinical states: bacterial ARI, viral ARI, and non-infectious illness.

In some embodiments, the results of each of the classifiers - the probability a new subject or patient has a bacterial ARI, viral ARI, or non-infectious illness - are reported. In final form, the three signatures with their corresponding coefficients are applied to an individual patient to obtain three probability values, namely probability of having a bacterial ARI, viral ARI, and a non-infectious illness. In some embodiments, these values may be reported relative to a reference range that indicates the confidence with which the classification is made. In some embodiments, the output of the classifier may be compared to a threshold value, for example, to report a "positive" in the case that the classifier score or probability exceeds the threshold indicating the presence of one or more of a bacterial ARI, viral ARI, or non-infectious illness. If the classifier score or probability fails to reach the threshold, the result would be reported as "negative" for the respective condition. Optionally, the values for bacterial and viral ARJ alone are reported and the report is silent on the likelihood of ill but not infected.

It should be noted that a classifier obtained with one platform may not show optimal performance on another platform. This could be due to the promiscuity of probes or other technical issues particular to the platform. Accordingly, also described herein are methods to adapt a signature as taught herein from one platform for another.

For example, a signature obtained from an Affymetrix platform may be adapted to a TLDA platform by the use of corresponding TLDA probes for the genes in the signature and/or substitute genes correlated with those in the signature, for the Affymetrix platform. Table 1 shows a list of Affymetrix probes and the genes they measure, plus "replacement genes" that are introduced as resplacements for gene probes that either may not perform well on the TLDA platform for technical reasons or to replace those Affymetrix probes for which there is no cognate TLDA probe. These replacements may indicate highly correlated genes or may be probes that bind to a different location in the same gene transcript. Additional genes may be included, such as pan- viral gene probes. The weights shown in Table 1 are weights calculated for a classifier implemented on the microarray platform. Weights that have not been estimated are indicated by "NA" in the table. (Example 4 below provides the completed translation of these classifiers to the TLDA platform.) Reference probes for TLDA (i.e., normalization genes, e.g., TRAP1 , PPIB, GAPDH and 18S) also have "NA" in the columns for weights and Affymetrix probeset ID (these are not part of the classifier). Additional gene probes that do not necessarily correspond to the Affymetrix probeset also have "NA" in the Affymetrix probeset ID column.

Table 1: Preliminary Gene List for TLDA platform Columns are as follows:

Column 1 : Affymetrix probeset ID - this was the probeset identified in the Affy discovery analyses (primary probeset)

Columns 2,3,4: estimated coefficients (weights) for contribution of each probates to the 3 classifiers from Affymetrix weights

Column 5: Gene name

AFFXProbeSet Bacterial Viral Nl Gene

216867. _s_at 0.0534745 0 0 PDGFA

203313. _s_at 1.09463 0 0 TGIF1

NA NA NA NA TRAP1

NA NA NA NA PPIB

202720. _at 0 0.0787402 0 TES

210657. s_at NA NA NA SEPT4

NA NA NA NA EPHB3

NA NA NA NA SYDE1

202864. _s_at 0 0.100019 0 SP100

213633. _at 1.01336 0 0 SH3BP1

NA NA NA NA 18S

NA NA NA NA 18S

NA NA NA NA GIT2

205153. _s_at 0.132886 0 0 CD40

202709. .at 0.427849 0 0 FMOD

202973 x at 0.112081 0 0 FA 13A AFFXProbeSet Bacterial Viral Nl Gene

204415_at NA NA NA IFI6

202509_s_at 0 0 0.416714 TNFAIP2

200042_at 0 0.0389975 0 RTCB

206371_at 0.0439022 0 0 FOL 3

212914_at 0 0 0.0099678 CBX7

215804_at 1.94364 0 0 EPHA1

215268_at 0.0381782 0 0 KIAA0754

203153_at NA NA NA IFIT1

217502_at NA NA NA IFIT2

205569_at NA NA NA LAMP3

218943_s_at NA NA NA DDX58

NA NA NA NA GAPDH

213300_at 0.578303 0 0 ATG2A

200663_at 0.176027 0 0 CD63

216303_s_at 0.31126 0 0 MT R1

NA NA NA NA ICAM2

NA NA NA NA EXOSC4

208702_x_at 0 0 0.0426262 APLP2

NA NA NA NA 18S

NA NA NA NA 18S

NA NA NA NA FPGS

217408_at 0 1.089 0.0690681 MRPS18B

206918_s_at 1.00926 0 0 CPNE1

208029_s_at 0.020511 0 0.394049 LAPTM4B

203153_at 0.133743 0 0 IFIT1

NA NA NA NA DECR1

200986_at NA NA NA SERPING1

214097_at 0.211804 0.576801 0 RPS21

204392_at 0 0.129465 0 CA K1

219382_at 0.866643 0 0 SERTAD3

205048_s_at 0.0114514 0 0 PSPH

205552_s_at NA NA NA OAS1

219684_at NA NA NA RTP4

22149 l_x_at 0.651431 0 0 HLA-DRB3

NA NA NA NA TRAP1

NA NA NA NA PPIB

216571_at 0.878426 0 0 SMPD1

215606_s_at 0.479765 0 0 ERC1

44673_at 0.0307987 0 0 SIGLECl

222059_at 0 0.112261 0 ZNF335

NA NA NA NA MRC2

20903 l_at 0 0 0.237916 CADM1

209919_x_at 0.613197 0 0 GGT1

214085_x_at 0.367611 0 0 GLIPR1

NA NA NA NA ELF4

200947_s_at 1.78944 0 0 GLUD1

206676_at 0 0 0.0774651 CEACA 8

NA NA NA NA IFNGR2

207718_x_at 0.0392962 0 0 CYP2A7

220308_at 0 0.0345586 0 CCDC19

205200_at 0.87833 0 0 CLEC3B

202284_S_at 0.356457 0 0 CDKN1A

213223_at 0.686657 0 0 RPL28

205312_at 0 0 0.394304 SPI1

212035_s_at 2.0241 0 1.3618 EXOC7

218306_s_at 0 0 0.784894 HERC1

205008 s at 0 0.223868 0 CIB2 AFFXProbeSet Bacterial Viral Nl Gene

219777_at 0 0.25509 0 GI AP6

218812_s_at 0.967987 0 0 ORAI2

NA NA NA NA GAPDH

208736_at 0 0.582264 0.0862941 ARPC3

203455_s_at 0 0 0.0805395 SAT1

208545_x_at 0.265408 0 0 TAF4

NA NA NA NA TLDC1

202509_s_at NA NA NA TNFAIP2

205098_at 0.116414 0 0 CCR1

222154_s_at NA NA NA SPATS2L

201188_s_at 0.606326 0 0 ITPR3

NA NA NA NA FPGS

205483 _at NA NA NA ISG15

205965_at 0.02668 0 0 BATF

220059_at 0.86817 0 0 STAP1

214955_at 0.100645 0 0 TMPRSS6

NA NA NA NA DECR1

218595_s_at 0 0 0.422722 HEATR1

221874_at 0.40581 0 0.017015 KIAA1324

205001_s_at 0 0.067117 0 DDX3Y

219211_at NA NA NA USP18

209605_at 0.499338 0 0 TST

212708_at 0.0325637 0 0 MSL1

203392_s_at 0 0.0139199 0 CTBP1

202688_at 0 0.0050837 0 TNFSF10

NA NA NA NA TRAP1

NA NA NA NA PPIB

203979_at 0.00999102 0 0.301178 CYP27A1

204490_s_at 0.00732794 0 0 CD44

206207_at 0.0852924 0 0 CLC

216289_at 0 0.00074607 0 GPR144

201949_x_at 0 0 0.034093 CAPZB

NA NA NA NA EXOG

216473_x_at 0 0.0769736 0 DUX4

212900_at 0.0573273 0 0 SEC24A

204439_at NA NA NA IFI44L

212162_at 0 0.0102331 0 KIDINS220

209511_at 0 0.031194 0 POLR2F

214175_x_at 0 0 0.266628 PDLIM4

219863_at NA NA NA HERC5

206896_s_at 0.482822 0 0 GNG7

208886_at 0.149103 0 0 H1FO

212697_at 0 0 1.02451 FAM134C

NA NA NA NA FNBP4

202672_s_at NA NA NA ATF3

201341_at 0.109677 0 0 ENC1

210797_s_at 0 0.188667 0 OASL

206647_at 0.0650386 0 0 HBZ

215848_at 0 0.326241 0 SCAPER

213573_at 0 0 0.50859 KPNB1

NA NA NA NA GAPDH

NA NA NA NA POLR1C

214582_at 0 0 0.0377349 PDE3B

218700_s_at 0 0.00086067 0 RAB7L1

203045_at 0.850903 0 0 NINJ1

NA NA NA NA ZER1

206133_at NA NA NA XAF1 AFFXProbeSet Bacterial Viral Nl Gene

213797_at NA NA NA RSAD2

219437_s_at 0 0.405445 0.217428 ANKRD11

NA NA NA NA FPGS

212947_at 0.286979 0 0 SLC9A8

NA NA NA NA SOX4

202145_at 0 0.166043 0 LY6E

213633_at 1.01336 0 0 SH3BP1

NA NA NA NA DECR1

210724_at 0 0 0.482166 E R3

220122_at 0.399475 0 0 MCTP1

218400_at NA NA NA OAS3

201659_s_at 0.110991 0 0 ARL1

214326_x_at 0.698109 0 0.261075 JUND

NA NA NA NA RPS31

217717_s_at 0.638943 0 0 YWHAB

218095_s_at 0.00541128 0.613773 0 TMEM165

NA NA NA NA TRAP1

NA NA NA NA PPIB

219066_at 0 0.221446 0 PPCDC

214022_s_at 0 0 0.0380438 IFITM1

214453_S_at NA NA NA IFI44

215342_s_at 0.0497241 0 0 RABGAP1L

204545_at 0.342478 0 0 PEX6

220935_s_at 0.170358 0 0 CDK5RAP2

201802_at 0.00859629 0 0 SLC29A1

202086_at NA NA NA MX1

209360_s_at 0.319632 0 0 RUNX1

NA NA NA NA LY75-CD302

203275_at 0 0.118256 0 IRF2

NA NA NA NA MYL10

203882_at 0 0.0776936 0 IRF9

206934_at 0.151959 0 0 SIRPB1

207860_at 0.376517 0 0 NCR1

207194_s_at 0.3162 0 0 ICA 4

209396_s_at 0 0 0.0355749 CHI3L1

204750_s_at 0.537475 0 0 DSC2

207840_at 0 0.118889 0 CD160

202411_at 0.0522361 0 0 IFI27

215184_at 0 0.0650331 0 DAPK2

202005_at 0.680527 0 0 ST14

214800_x_at 0 0.103261 0 BTF3

NA NA NA NA GAPDH

207075_at 0.0627344 0 0 NLRP3

206026_s_at NA NA NA TNFAIP6

219523_s_at 0 0 0.07715 TEN 3

217593_at 0.0747507 0 0 ZSCAN18

204747_at NA NA NA IFIT3

212657_s_at 0 0 0.254507 IL1RN

204972_at NA NA NA OAS2

207606_s_at 0.299775 0 0 ARHGAP12

NA NA NA NA FPGS

205033_s_at 0 0.0878603 0 DEFA3

219143_s_at 0.415444 0 0 RPP25

20860 l_s_at 0.270581 0 0 TUBB1

216713_at 0.510039 0 0 KRIT1

NA NA NA NA DECR1

214617_at 0.261957 0 0 PRF1 AFFXProbeSet Bacterial Viral Nl Gene

201055_s_at 0 0 1.25363 HNRNPAO

219055_at 0.0852367 0 0 SRBD1

219130_at 0 0.150771 0 TRMT13

202644_s_at 0.340624 0 0 TNFAIP3

205164 at 0.46638 0 0 GCAT

Further discussion of this example signature for a TLDA platform is provided below in Examples 3 and 4.

This method of determining the etiology of an ARI may be combined with other tests. For example, if the patient is determined to have a viral ARI, a follow-up test may be to determine if influenza A or B can be directly detected or if a host response indicative of such an infection can be detected. Similarly, a follow-up test to a result of bacterial ARI may be to determine if a Gram positive or a Gram negative bacterium can be directly detected or if a host response indicative of such an infection can be detected. In some embodiments, simultaneous testing may be performed to determine the class of infection using the classifiers, and also to test for specific pathogens using pathogen-specific probes or detection methods. See, e.g. , US 2015/0284780 to Eley et al. (method for detecting active tuberculosis); US 2014/0323391 to Tsalik et al. (method for classification of bacterial infection).

Methods of Determining a Secondary Classification of an ARI in a Subject

The present disclosure also provides methods of classifying a subject using a secondary classification scheme. Accordingly, another aspect of the present invention provides a method for determining whether an acute respiratory infection (ARI) in a subject is bacterial in origin, viral in origin, or non-infectious in origin comprising, consisting of, or consisting essentially of (a) obtaining a biological sample from the subject; (b) determining the gene expression profile of the subject from the biological sample by evaluating the expression levels of pre-defined sets of genes (i.e., three signatures); (c) normalizing gene expression levels as required for the technology used to make said measurement to generate a normalized value; (d) entering the normalized value into classifiers (i.e. , predictors) that have pre-defined weighting values (coefficients) for each of the genes in each signature; (e) comparing the output of the classifiers to pre-defined thresholds, cut-off values, or ranges of values that indicate likelihood of infection; (f) if the sample is negative for bacteria, repeating step (d) using only the viral classifier and noninfectious classifier; and (g) classifying the sample as being of viral etiology or non-infectious illness.

Another aspect of the present provides a method for determining whether an acute respiratory infection (ARI) in a subject is bacterial in origin, viral in origin, or non-infectious in origin comprising, consisting of, or consisting essentially of (a) obtaining a biological sample from the subject; (b) determining the gene expression profile of the subject from the biological sample by evaluating the expression levels of pre-defined sets of genes {i.e., three signatures); (c) normalizing gene expression levels for the technology used to make said measurement to generate a normalized value; (d) entering the normalized value into classifiers (z. e. , predictors) that have pre-defined weighting values (coefficients) for each of the genes in each signature; (e) comparing the output of the classifiers to pre-defined thresholds, cut-off values, or ranges of values that indicate likelihood of infection; (f) if the sample is negative for virus, repeating step (d) using only the bacteria classifier and non-infectious classifier; and (g) classifying the sample as being of bacterial etiology or noninfectious illness.

Yet another aspect of the present provides a method for determining whether an acute respiratory infection (ARI) in a subject is bacterial in origin, viral in origin, or non-infectious in origin comprising, consisting of, or consisting essentially of (a) obtaining a biological sample from the subject; (b) determining the gene expression profile of the subject from the biological sample by evaluating the expression levels of pre-defined sets of genes {i.e. , three signatures); (c) normalizing gene expression levels for the technology used to make said measurement to generate a normalized value; (d) entering the normalized value into classifiers {i.e., predictors) that have pre-defined weighting values (coefficients) for each of the genes in each signature; (e) comparing the output of the classifiers to pre-defined thresholds, cut-off values, or ranges of values that indicate likelihood of infection; (f) if the sample is negative for non-infectious illness, repeating step (d) using only the viral classifier and bacterial classifier; and (g) classifying the sample as being of viral etiology or bacterial etiology.

In some embodiments, the method further comprises generating a report assigning the patient a score indicating the probability of the etiology of the ARI.

Classifying the status of a patient using a secondary classification scheme is shown in FIG. 4. In this example, the bacterial ARI classifier will distinguish between patients with a bacterial ARI from those without a bacterial ARI, which could, instead, be a viral ARI or a noninfectious cause of illness. A secondary classification can then be imposed on those patients with non-bacterial ARI to further discriminate between viral ARI and non-infectious illness. This same process of primary and secondary classification can also be applied to the viral ARI classifier where patients determined not to have a viral infection would then be secondarily classified as having a bacterial ARI or non-infectious cause of illness. Likewise, applying the non-infectious illness classifier as a primary test will determine whether patients have such a non-infectious illness or instead have an infectious cause of symptoms. The secondary classification step would determine if that infectious is due to bacterial or viral pathogens. Results from the three primary and three secondary classifications can be summed through various techniques by those skilled in the art (such as summation, counts, or average) to produce an actionable report for the provider. In some embodiments, the genes used for this secondary level of classification can be some or all of those presented in Table 2.

In such examples, the three classifiers described above (bacteria classifier, virus classifier and non- infectious illness classifier) are used to perform the 1 ^st level classification. Then for those patients with non-bacterial infection, a secondary classifier is defined to distinguish viral ARI from those with non-infectious illness (FIG. 4, left panel). Similarly, for those patients with non-viral infection, a new classifier is used to distinguish viral from non-infectious illness (FIG. 4, middle panel), and for those patients who are not classified as having a non-infectious illness in the first step, a new classifier is used to distinguish between viral and bacterial ARI (FIG. 4, right panel).

In this two-tier method, nine probabilities may be generated, and those probabilities may be combined in a number of ways. Two strategies are described here as a way to reconcile the three sets of predictions, where each has a probability of bacterial ARI, viral ARI, and noninfectious illness. For example: Highest predicted average probability: All predicted probabilities for bacterial ARI are averaged, as are all the predicted probabilities of viral ARI and, similarly, all predicted probabilities of non-infectious illness. The greatest averaged probability denotes the diagnosis.

Greatest number of predictions: Instead of averaging the predicted probabilities of each condition, the number of times a particular diagnosis is predicted for that patient sample (i.e. , bacterial ARJ, viral ARI or non-infectious illness) is counted. The best-case scenario is when the three classification schemes give the same answer (e.g., bacterial ARI for scheme 1 , bacterial ARI for scheme 2, and bacterial ARI for scheme 3). The worst case is that each scheme nominates a different diagnosis, resulting in a 3-way tie.

Using the training set of patient samples previously described, the Result of Tier 1 classification could be, for example (clinical classification presented in rows; diagnostic test prediction presented in columns) similar to that presented in Table 3.

Table 3

bacterial viral ni counts bacterial 82.8 12.8 4.2 58 9 3 viral 3.4 90.4 6.0 4 104 7 ni 9.0 4.5 86.3 8 4 76 Following Tier 2 classification using the highest predicted average probability strategy (clinical classification presented in rows; diagnostic test prediction presented in columns), results may be similar to Table 4. Table 4 - Mean (average predictions than max):

bacterial viral ni counts bacterial 82.8 11.4 5.7 58 8 4 viral 1.7 91.3 6.9 2 105 8 ni 7.9 7.9 84.0 7 7 74

Following Tier 2 classification using the greatest number of predictions strategy (clinical classification presented in rows; diagnostic test prediction presented in columns), results may be similar to Table 5.

Table 5 - Max (max predictions then count votes, 7 ties):

bacterial viral ni counts bacterial 84.2 11.4 4.2 59 8 3 viral 4.3 89.5 6.0 5 103 7 ni 11.3 7.9 80.6 10 7 71

Classification can be achieved, for example, as described above, and/or as summarized in Table 2. Table 2 summarizes the gene membership in three distinct classification strategies that solve different diagnostic questions. There are a total of 270 probes that collectively comprise three complex classifiers. The first is referred to as BVS (Bacterial ARI, Viral ARI, SIRS), which is the same as that presented below in Example 1. These probes are the same as those presented in Table 9, which offers probe/gene weights used in classification. They also correspond to the genes presented in Table 10.

The second is referred to as 2L for 2-layer or 2-tier. This is the hierarchical scheme presented in FIG. 4.

The third is a one-tier classification scheme, BVSH, which is similar to BVS but also includes a population of healthy controls (similarly described in Example 1). This group has been shown to be a poor control for non-infection, but there are use cases in which

discrimination from healthy may be clinically important. For example, this can include the serial measurement of signatures to correlate with convalescence. It may also be used to discriminate patients who have been exposed to an infectious agent and are presymptomatic vs.

asymptomatic. In the BVSH scheme, four groups are represented in the training cohort - those with bacterial ARI, viral ARI, SIRS (non-infectious illness), and Healthy. These four groups are used to generate four distinct signatures that distinguish each class from all other possibilities. Table 2 legend:

Probe = Affymetrix probe ID

BVS = Three-classifier model trained on patients with Bacterial ARI, Viral ARI, and Non- Infectious Illness (with respiratory symptoms). 1 denotes this probe is included in this three- classifier model. 0 denotes the probe is not present in this classification scheme.

BVS-BO = Genes or probes included in the Bacterial ARI classifier as part of the BVS classification scheme. This classifier specifically discriminates patients with bacterial ARI from other etiologies (viral ARI or or 10)

BVS-VO = As for BVS-BO except this column identifies genes included in the Viral ARI classifier. This classifier specifically discriminates patients with viral ARI from other etiologies (bacterial ARI or non-infectious illness)

BVS-SO = As for BVS-BO or BVS-VO, except this column identifies genes included in the non-infectious illness classifier. This classifier specifically discriminates patients with non- infectious illness from other etiologies (bacterial or viral ARI)

2L refers to the two-tier hierarchical classification scheme. A 1 in this column indicates the specified probe or gene was included in the classification task. This 2-tier classification scheme is itself comprised of three separate tiered tasks. The first applies a one vs. others, where one can be Bacterial ARI, Viral ARI, or non-infectious illness. If a given subject falls into the "other" category, a 2 ^nd tier classification occurs that distinguishes between the remaining possibilities. 2L-SO is the 1 ^st tier for a model that determines with a given subject has a non-infectious illness or not, followed by SL-BV which discriminates between bacterial and viral ARI as possibilities. A 1 in these columns indicates that gene or probe are included in that specified classification model. 2L-BO and 2L-VS make another 2-tier classification scheme. 2L-VO and 2L-SB comprise the 3 ^rd model in the 2-tier classification scheme.

Finally, BVSH refers to a one-level classification scheme that includes healthy individuals in the training cohort and therefore includes a classifier for the healthy state as compared to bacterial ARI, viral ARI, or non-infectious illness. The dark grey BVSH column identifies any gene or probe included in this classification scheme. This scheme is itself comprised by BVSH-BO, BVSH-VO, BVSH-SO, and BVSH-HO with their respective probe/gene compositions denoted by ' Γ in these columns.

Table 2 provides a summary of use of members of the gene sets for viral, bacterial, and noninfectious illness classifiers that are constructed according to the required task. A T indicates membership of the gene in the classifier. Table 2

^ O

)

Affymetrix BVS BVS BVS BVS 21 2L- 2L- 2L- 2L- BVSH BVSH BVSH BVSH BVSH Gene RefSeq ID Gene Name

Probe ID -BO - O -SO BV BO S O -BO - O -SO -HO Symbol

214097 at 1 SB! RPS21 NM 001024 ribosomal protein S21

214175 x at ⁰ PDLIM4 NM_003687; PDZ and LIM domain 4

NM 001131027

214321 at 1 NOV NM 002514 nephroblastoma overexpressed gene

214326 x at JUND NM 005354 jun D proto-oncogene

214511 x at FCGR1A /// NM_001017986; Fc fragment of IgG, high affinity lb, receptor (CD64)

LOC440607 NM 001004340

214582 at PDE3B NM 000922 phosphodiesterase 3B, cGMP-inhibited

214617 at PRF1 NM_005041; perforin 1 (pore forming protein)

NM 001083116

214800 x at BTF3 /// NM_001037637; basic transcription factor 3; basic transcription

LOC345829 NM 001207 factor 3, like 1 pseudogene

214955 at TMPRSS6 NM 153609 transmembrane protease, serine 6

215012 at ZNF451 NM_001031623; zinc finger protein 451

NM 015555

215088 s at 111 SDHC NM_003001; succinate dehydrogenase complex, subunit C,

NM_001035513; integral membrane protein, 15kDa

NM_001035511;

NM 001035512

215184 at DAPK2 NM 014326 death-associated protein kinase 2

215268 at , 1 - ' I'O , KIAA0754 NM 015038 hypothetical LOC643314

215606 s at 1 RAB6IP2 NMJL78040 ELKS/RAB6-interacting/CAST family member 1

NM_015064 _;

NM_178037;

NM_178038;

NM 178039

215630 at NM 015150 raftlin, lipid raft linker 1

215696 s at 1 KIAA0310 NM 014866 SEC16 homolog A (S. cerevisiae)

215804 at 0 EPHA1 NM 005232 EPH receptor Al

215848 at 111 ZNF291 NM_001145923; S-phase cyclin A-associated protein in the ER

NM 020843

216289 at XM_002347085 G protein-coupled receptor 144

XM_002342934,

XM_002346195

NM 001161808

216303 s at 0 1 MTMR1 NM 003828 myotubularin related protein 1

O

-J

Methods of Treating a Subject with an AIM

Another aspect of the present disclosure provides a method of treating an acute respiratory infection (ARI) whose etiology is unknown in a subject, said method comprising, consisting of, or consisting essentially of (a) obtaining a biological sample from the subject; (b) determining the gene expression profile of the subject from the biological sample by evaluating the expression levels of pre-defined sets of genes (e.g. , one, two or three or more signatures); (c) normalizing gene expression levels as required for the technology used to make said

measurement to generate a normalized value; (d) entering the normalized value into a bacterial classifier, a viral classifier and non-infectious illness classifier (i.e., predictors) that have pre- defined weighting values (coefficients) for each of the genes in each signature; (e) comparing the output of the classifiers to pre-defined thresholds, cut-off values, or ranges of values that indicate likelihood of infection; (f) classifying the sample as being of bacterial etiology, viral etiology, or noninfectious illness; and (g) administering to the subject an appropriate treatment regimen as identified by step (f).

In some embodiments, step (g) comprises administering an antibacterial therapy when the etiology of the ARI is determined to be bacterial. In other embodiments, step (g) comprises administering an antiviral therapy when the etiology of the ARI is determined to be viral.

After the etiology of the ARI of the subject has been determined, she may undergo treatment, for example anti-viral therapy if the ARI is determined to be viral, and/or she may be quarantined to her home for the course of the infection. Alternatively, bacterial therapy regimens may be administered (e.g., administration of antibiotics) if the ARI is determined to be bacterial. Those subjects classified as non-infectious illness may be sent home or seen for further diagnosis and treatment (e.g. , allergy, asthma, etc.).

The person performing the peripheral blood sample need not perform the comparison, however, as it is contemplated that a laboratory may communicate the gene expression levels of the classifiers to a medical practitioner for the purpose of identifying the etiology of the ARI and for the administration of appropriate treatment. Additionally, it is contemplated that a medical professional, after examining a patient, would order an agent to obtain a peripheral blood sample, have the sample assayed for the classifiers, and have the agent report patient's etiological status to the medical professional. Once the medical professional has obtained the etiology of the ARI, the medical professional could order suitable treatment and/or quarantine.

The methods provided herein can be effectively used to diagnose the etiology of illness in order to correctly treat the patient and reduce inappropriate use of antibiotics. Further, the methods provided herein have a variety of other uses, including but not limited to, (1) a host- based test to detect individuals who have been exposed to a pathogen and have impending, but not symptomatic, illness (e.g., in scenarios of natural spread of diseases through a population but also in the case of bioterrorism); (2) a host-based test for monitoring response to a vaccine or a drug, either in a clinical trial setting or for population monitoring of immunity; (3) a host-based test for screening for impending illness prior to deployment (e.g., a military deployment or on a civilian scenario such as embarkation on a cruise ship); and (4) a host-based test for the screening of livestock for ARIs (e.g., avian flu and other potentially pandemic viruses).

Another aspect of the present disclosure provides a kit for determining the etiology of an acute respiratory infection (ARI) in a subject comprising, consisting of, or consisting essentially of (a) a means for extracting a biological sample; (b) a means for generating one or more arrays consisting of a plurality of synthetic oligonucleotides with regions homologous to a group of gene transcripts as taught herein; and (c) instructions for use.

Yet another aspect of the present disclosure provides a method of using a kit for assessing the acute respiratory infection (ARI) classifier comprising, consisting of, or consisting essentially of: (a) generating one or more arrays consisting of a plurality of synthetic oligonucleotides with regions homologous to a a group of gene transcripts as taught herein; (b) adding to said array oligonucleotides with regions homologous to normalizing genes; (c) obtaining a biological sample from a subject suffering from an acute respiratory infection (ARI); (d) isolating RNA from said sample to create a transcriptome; (e) measuring said transcriptome on said array; (f) normalizing the measurements of said transcriptome to the normalizing genes, electronically transferring normalized measurements to a computer to implement the classifier algorithm(s), (g) generating a report; and optionally (h) administering an appropriate treatment based on the results.

Classification Systems

With reference to FIG. 1 1 , a classification system and/or computer program product 1100 may be used in or by a platform, according to various embodiments described herein. A classification system and/or computer program product 1100 may be embodied as one or more enterprise, application, personal, pervasive and/or embedded computer systems that are operable to receive, transmit, process and store data using any suitable combination of software, firmware and/or hardware and that may be standalone and/or interconnected by any conventional, public and/or private, real and/or virtual, wired and/or wireless network including all or a portion of the global communication network known as the Internet, and may include various types of tangible, non-transitory computer readable medium.

As shown in FIG. 11 , the classification system 1100 may include a processor subsystem 1 140, including one or more Central Processing Units (CPU) on which one or more operating systems and/or one or more applications run. While one processor 1140 is shown, it will be understood that multiple processors 1140 may be present, which may be either electrically interconnected or separate. Processor(s) 1140 are configured to execute computer program code from memory devices, such as memory 1150, to perform at least some of the operations and methods described herein, and may be any conventional or special purpose processor, including, but not limited to, digital signal processor (DSP), field programmable gate array (FPGA), application specific integrated circuit (ASIC), and multi-core processors.

The memory subsystem 1150 may include a hierarchy of memory devices such as Random Access Memory (RAM), Read-Only Memory (ROM), Erasable Programmable Readonly Memory (EPROM) or flash memory, and/or any other solid state memory devices.

A storage circuit 1170 may also be provided, which may include, for example, a portable computer diskette, a hard disk, a portable Compact Disk Read-Only Memory (CDROM), an optical storage device, a magnetic storage device and/or any other kind of disk- or tape-based storage subsystem. The storage circuit 1170 may provide non- volatile storage of

data/parameters/classifiers for the classification system 1100. The storage circuit 1170 may include disk drive and/or network store components. The storage circuit 1170 may be used to store code to be executed and/or data to be accessed by the processor 1140. In some

embodiments, the storage circuit 1170 may store databases which provide access to the data/parameters/classifiers used for the classification system 1110 such as the signatures, weights, thresholds, etc. Any combination of one or more computer readable media may be utilized by the storage circuit 1170. The computer readable media may be a computer readable signal medium or a computer readable storage medium. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. As used herein, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.

An input/output circuit 1160 may include displays and/or user input devices, such as keyboards, touch screens and/or pointing devices. Devices attached to the input/output circuit 1160 may be used to provide information to the processor 1140 by a user of the classification system 1100. Devices attached to the input/output circuit 1160 may include networking or communication controllers, input devices (keyboard, a mouse, touch screen, etc.) and output devices (printer or display). The input/output circuit 1160 may also provide an interface to devices, such as a display and/or printer, to which results of the operations of the classification system 1 100 can be communicated so as to be provided to the user of the classification system 1100.

An optional update circuit 1180 may be included as an interface for providing updates to the classification system 1100, Updates may include updates to the code executed by the processor 1140 that are stored in the memory 1150 and/or the storage circuit 1170. Updates provided via the update circuit 1 180 may also include updates to portions of the storage circuit 1 170 related to a database and/or other data storage format which maintains information for the classification system 1 100, such as the signatures, weights, thresholds, etc.

The sample input circuit 1 110 of the classification system 1 100 may provide an interface for the platform as described hereinabove to receive biological samples to be analyzed. The sample input circuit 1110 may include mechanical elements, as well as electrical elements, which receive a biological sample provided by a user to the classification system 1 100 and transport the biological sample within the classification system 1 100 and/or platform to be processed. The sample input circuit 1110 may include a bar code reader that identifies a bar- coded container for identification of the sample and/or test order form. The sample processing circuit 1120 may further process the biological sample within the classification system 1100 and/or platform so as to prepare the biological sample for automated analysis. The sample analysis circuit 1 130 may automatically analyze the processed biological sample. The sample analysis circuit 1 130 may be used in measuring, e.g., gene expression levels of a pre-defined set of genes with the biological sample provided to the classification system 1100. The sample analysis circuit 1 130 may also generate normalized gene expression values by normalizing the gene expression levels. The sample analysis circuit 1130 may retrieve from the storage circuit 1170 a bacterial acute respiratory infection (ARI) classifier, a viral ARI classifier and a noninfectious illness classifier, these classifier(s) comprising pre-defined weighting values (i.e., coefficients) for each of the genes of the pre-defined set of genes. The sample analysis circuit 1130 may enter the normalized gene expression values into one or more acute respiratory illness classifiers selected from the bacterial acute respiratory infection (ARI) classifier, the viral ARI classifier and the non-infectious illness classifier. The sample analysis circuit 1 130 may calculate an etiology probability for one or more of a bacterial ARI, viral ARI and non-infectious illness based upon said classifier(s) and control output, via the input/output circuit 1160, of a determination whether the acute respiratory illness in the subject is bacterial in origin, viral in origin, non-infectious in origin, or some combination thereof. The sample input circuit 11 10, the sample processing circuit 1 120, the sample analysis circuit 1 130, the input/output circuit 1160, the storage circuit 1170, and/or the update circuit 1180 may execute at least partially under the control of the one or more processors 1 140 of the classification system 1100. As used herein, executing "under the control" of the processor 1 140 means that the operations performed by the sample input circuit 1110, the sample processing circuit 1 120, the sample analysis circuit 1130, the input/output circuit 1 160, the storage circuit 1170, and/or the update circuit 1180 may be at least partially executed and/or directed by the processor 1 140, but does not preclude at least a portion of the operations of those components being separately electrically or mechanically automated. The processor 1140 may control the operations of the classification system 1 100, as described herein, via the execution of computer program code.

Computer program code for carrying out operations for aspects of the present disclosure may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Scala, Smalltalk, Eiffel, JADE, Emerald, C++, C#, VB.NET, Python or the like, conventional procedural programming languages, such as the "C" programming language, Visual Basic, Fortran 2003, Perl, COBOL 2002, PHP, ABAP, dynamic programming languages such as Python, Ruby and Groovy, or other programming languages. The program code may execute entirely on the classification system 1 100, partly on the classification system 1 100, as a stand-alone software package, partly on the classification system 1100 and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the classification system 1 100 through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet Service Provider) or in a cloud computer environment or offered as a service such as a Software as a Service (SaaS).

In some embodiments, the system includes computer readable code that can transform quantitative, or semi -quantitative, detection of gene expression to a cumulative score or probability of the etiology of the ARI.

In some embodiments, the system is a sample-to-result system, with the components integrated such that a user can simply insert a biological sample to be tested, and some time later (preferably a short amount of time, e.g. , 30 or 45 minutes, or 1, 2, or 3 hours, up to 8, 12, 24 or 48 hours) receive a result output from the system.

It is to be understood that the invention is not limited in its application to the details of construction and the arrangement of components set forth in the following description or illustrated in the following drawings. The invention is capable of other embodiments and of being practiced or of being carried out in various ways.

Recitation of ranges of values herein are merely intended to serve as a shorthand method of referring individually to each separate value falling within the range, unless otherwise indicated herein, and each separate value is incorporated into the specification as if it were individually recited herein. All methods described herein can be performed in any suitable order unless otherwise indicated herein or otherwise clearly contradicted by context. The use of any and all examples, or exemplary language (e.g. , "such as") provided herein, is intended merely to better illuminate the invention and does not pose a limitation on the scope of the invention unless otherwise claimed. No language in the specification should be construed as indicating any nonclaimed element as essential to the practice of the invention.

It also is understood that any numerical range recited herein includes all values from the lower value to the upper value. For example, if a concentration range is stated as 1% to 50%, it is intended that values such as 2% to 40%, 10% to 30%, or 1% to 3%, etc., are expressly enumerated in this specification. These are only examples of what is specifically intended, and all possible combinations of numerical values between and including the lowest value and the highest value enumerated are to be considered to be expressly stated in this application.

The following examples are illustrative only and are not intended to be limiting in scope. EXAMPLES

Example 1. Host Gene Expression classifiers Diagnose Acute Respiratory Illness Etiology Acute respiratory infections due to bacterial or viral pathogens are among the most common reasons for seeking medical care. Current pathogen-based diagnostic approaches are not reliable or timely, thus most patients receive inappropriate antibiotics. Host response biomarkers offer an alternative diagnostic approach to direct antimicrobial use.

We asked whether host gene expression patterns discriminate infectious from noninfectious causes of illness in the acute care setting. Among those with acute respiratory infection, we determined whether infectious illness is due to viral or bacterial pathogens.

The samples that formed the basis for discovery were drawn from an observational, cohort study conducted at four tertiary care hospital emergency departments and a student health facility. 44 healthy controls and 273 patients with community-onset acute respiratory infection or non-infectious illness were selected from a larger cohort of patients with suspected sepsis (CAPSOD study). Mean age was 45 years and 45% of participants were male. Further demographic information may be found in Table 1 of Tsalik et al. (2016) Sci Transl Med 9(322): l-9, which is incorporated by reference herein. Clinical phenotypes were adjudicated through manual chart review. Routine

microbiological testing and multiplex PCR for respiratory viral pathogens were performed. Peripheral whole blood gene expression was measured using microarrays. Sparse logistic regression was used to develop classifiers of bacterial vs. viral vs. non-infectious illness. Five independently derived datasets including 328 individuals were used for validation.

Gene expression-based classifiers were developed for bacterial acute respiratory infection (71 probes), viral acute respiratory infection (33 probes), or a non-infectious cause of illness (26 probes). The three classifiers were applied to 273 patients where class assignment was determined by the highest predicted probability. Overall accuracy was 87% (238/273 concordant with clinical adjudication), which was more accurate than procalcitonin (78%, p<0.03) and three published classifiers of bacterial vs. viral infection (78-83%). The classifiers developed here externally validated in five publicly available datasets (AUC 0.90-0.99). We compared the classification accuracy of the host gene expression-based tests to procalcitonin and clinically adjudicated diagnoses, which included bacterial or viral acute respiratory infection or non- infectious illness.

The host's peripheral blood gene expression response to infection offers a diagnostic strategy complementary to those already in use. ⁸ This strategy has successfully characterized the host response to viral ^8"13 and bacterial ARI ¹¹ ' ¹⁴ . Despite these advances, several issues preclude their use as diagnostics in patient care settings. An important consideration in the development of host-based molecular signatures is that they be developed in the intended use population. ¹⁵

However, nearly all published gene expression-based ARI classifiers used healthy individuals as controls and focused on small or homogeneous populations and are thus not optimized for use in acute care settings where patients present with undifferentiated symptoms. Furthermore, the statistical methods used to identify gene-expression classifiers often include redundant genes based on clustering, univariate testing, or pathway association. These strategies identify relevant biology but do not maximize diagnostic performance. An alternative, as exemplified here, is to combine genes from unrelated pathways to generate a more informative classifier.

Methods

Classifier Derivation Cohorts

Studies were approved by relevant Institutional Review Boards, and in accord with the

Declaration of Helsinki. All subjects or their legally authorized representatives provided written informed consent.

Patients with community-onset, suspected infection were enrolled in the Emergency Departments of Duke University Medical Center (DUMC; Durham, NC), the Durham VA Medical Center (DVAMC; Durham, NC), or Henry Ford Hospital (Detroit, MI) as part of the Community Acquired Pneumonia & Sepsis Outcome Diagnostics study (Clinical Trials Identifier No. NCT00258869). ^16"19 Additional patients were enrolled through UNC Health Care

Emergency Department (UNC; Chapel Hill, NC) as part of the Community Acquired Pneumonia and Sepsis Study. Patients were eligible if they had a known or suspected infection and if they exhibited two or more Systemic Inflammatory Response Syndrome (SIRS) criteria. ²⁰ ARI cases included patients with upper or lower respiratory tract symptoms, as adjudicated by emergency medicine (SWG, EBQ) or infectious diseases (ELT) physicians. Adjudications were based on retrospective, manual chart reviews performed at least 28 days after enrollment and prior to any gene expression-based categorization, using previously published criteria. The totality of information used to support these adjudications would not have been available to clinicians at the time of their evaluation. Seventy patients with microbiologically confirmed bacterial ARI were identified including four with pharyngitis and 66 with pneumonia. Microbiological etiologies were determined using conventional culture of blood or respiratory samples, urinary antigen testing (Streptococcus or Legionella), or with serological testing (Mycoplasma). Patients with viral ARI (n= 115) were ascertained based on identification of a viral etiology and compatible symptoms. In addition, 48 students at Duke University as part of the DARPA Predicting Health and Disease study with definitive viral ARI using the same adjudication methods were included. The ResPlex II v2.0 viral PCR multiplex assay (Qiagen; Hilden, Germany) augmented clinical testing for viral etiology identification. This panel detects influenza A and B, adenovirus (B, E), parainfluenza 1-4, respiratory syncytial virus A and B, human metapneumovirus, human rhinovirus, coronavirus (229E, OC43, NL63, HKUl), coxsackie/echo virus, and bocavirus. Upon adjudication, a subset of enrolled patients were determined to have non-infectious illness (n=88) (Table 8). The determination of "non-infectious illness" was made only when an alternative diagnosis was established and results of any routinely ordered microbiological testing failed to support an infectious etiology. Lastly, healthy controls (n=44; median age 30 years; range 23-59) were enrolled as part of a study on the effect of aspirin on platelet function among healthy volunteers without symptoms, where gene expression analyses was performed on pre-aspirin challenge time points. ²¹

Procalcitonin Measurement

Concentrations were measured at different stages during the study and as a result, different platforms were utilized based on availability. Some serum measurements were made on a Roche Elecsys 2010 analyzer (Roche Diagnostics, Laval, Canada) by electrochemiluminescent immunoassay. Additional serum measurements were made using the miniVIDAS immunoassay (bioMerieux, Durham NC, USA). When serum was unavailable, measurements were made by the Phadia Immunology Reference Laboratory in plasma-EDTA by immunofluorescence using the B R A H M-S PCT sensitive KRYPTOR (Thermo Fisher Scientific, Portage MI, USA). Replicates were performed for some paired serum and plasma samples, revealing equivalence in concentrations. Therefore, all procalcitonin measurements were treated equivalently, regardless of testing platform.

Microarray Generation

At initial clinical presentation, patients were enrolled and samples collected for analysis. After adjudications were performed as described above, 317 subjects with clear clinical phenotypes were selected for gene expression analysis. Total RNA was extracted from human blood using the PAXgene Blood RNA Kit (Qiagen, Valencia, CA) according to the

manufacturer's protocol. RNA quantity and quality were assessed using the Nanodrop spectrophotometer (Thermo Scientific, Waltham, MA) and Agilent 2100 Bioanalyzer (Agilent, Santa Clara, CA), respectively. Microarrays were RMA-normalized. Hybridization and data collection were performed at Expression Analysis (Durham, NC) using the GeneChip Human Genome U133A 2.0 Array (Affymetrix, Santa Clara, CA) according to the Affymetrix Technical Manual.

Statistical Analysis

The transcriptomes of 317 subjects (273 ill patients and 44 healthy volunteers) were measured in two microarray batches with seven overlapping samples (GSE63990). Exploratory principal component analysis and hierarchical clustering revealed substantial batch differences. These were corrected by first estimating and removing probe-wise mean batch effects using the Bayesian fixed effects model. Next, we fitted a robust linear regression model with Huber loss function using seven overlapping samples, which was used to adjust the remaining expression values.

Sparse classification methods such as sparse logistic regression perform classification and variable selection simultaneously while reducing over-fitting risk. ²¹ Therefore, separate gene selection strategies such as univariate testing or sparse factor models are unnecessary. Here, a sparse logistic regression model was fitted independently to each of the binary tasks using the 40% of probes with the largest variance after batch correction. ²² Specifically, we used a Lasso regularized generalized linear model with binomial likelihood with nested cross-validation to select for the regularization parameters. Code was written in Matlab using the Glmnet toolbox. This generated Bacterial ARI, Viral ARI, and Non-Infectious Illness classifiers. Provided that each binary classifier estimates class membership probabilities (e.g., probability of bacterial vs. either viral or non-infectious in the case of the Bacterial ARI classifier), we can combine the three classifiers into a single decision model (termed the ARI classifier) by following a one-

21 * * versus-all scheme whereby largest membership probability assigns class label. Classification performance metrics included area-under-the-receiving-operating-characteristic-curve (AUC) for binary outcomes and confusion matrices for ternary outcomes. ²³

Validation

The ARI classifier was validated using leave-one-out cross-validation in the same population from which it was derived. Independent, external validation occurred using publically available human gene expression datasets from 328 individuals (GSE6269, GSE42026,

GSE40396, GSE20346, and GSE42834). Datasets were chosen if they included at least two clinical groups (bacterial ARI, viral ARI, or non-infectious illness). To match probes across different microarray platforms, each ARI classifier probe was converted to gene symbols, which were used to identify corresponding target microarray probes.

Results

Bacterial ARI, Viral ARI, and Non-Infectious Illness classifiers

In generating host gene expression-based classifiers that distinguish between clinical states, all relevant clinical phenotypes should be represented during the model training process. This imparts specificity, allowing the model to be applied to these included clinical groups but not to clinical phenotypes that were absent from model training. ¹⁵ The target population for an ARI diagnostic not only includes patients with viral and bacterial etiologies, but must also distinguish from the alternative - those without bacterial or viral ARI. Historically, healthy individuals have served as the uninfected control group. However, this fails to consider how patients with non-infectious illness, which can present with similar clinical symptoms, would be classified, serving as a potential source of diagnostic error. To our knowledge, no ARI gene- expression based classifier has included ill, uninfected controls in its derivation. We therefore enrolled a large, heterogeneous population of patients at initial clinical presentation with community-onset viral ARI (n=l 15), bacterial ARI (n=70), or non-infectious illness (n=88) (Table 8). We also included a healthy adult control cohort (n=44) to define the most appropriate control population for ARI classifier development.

We first determined whether a gene expression classifier derived with healthy individuals as controls could accurately classify patients with non-infectious illness. Array data from patients with bacterial ARI, viral ARI, and healthy controls were used to generate gene expression classifiers for these conditions. Leave-one-out cross validation revealed highly accurate discrimination between bacterial ARI (AUC 0.96), viral ARI (AUC 0.95), and healthy (AUC 1.0) subjects for a combined accuracy of 90% (FIG. 7). However, when the classifier was applied to ill-uninfected patients, 48/88 were identified as bacterial, 35/88 as viral, and 5/88 as healthy. This highlighted that healthy individuals are a poor substitute for patients with non- infectious illness in the biomarker discovery process. Consequently, we re-derived an ARI classifier using a non-infectious illness control rather than healthy. Specifically, array data from these three groups was used to generate three gene-expression classifiers of host response to bacterial ARI, viral ARI, and non-infectious illness (FIG. 5). Specifically, the Bacterial ARI classifier was tasked with positively identifying those with bacterial ARI vs. either viral ARI or non-infectious illnesses. The Viral ARI classifier was tasked with positively identifying those with viral ARI vs. bacterial ARI or non-infectious illnesses. The Non-Infectious Illness classifier was not generated with the intention of positively identifying all non-infectious illnesses, which would require an adequate representation of all such cases.

Rather, it was generated as an alternative category, so that patients without bacterial or viral ARI could be assigned accordingly. Moreover, we hypothesized that such ill but non- infected patients were more clinically relevant controls because healthy people are unlikely to be the target for such a classification task.

Six statistical strategies were employed to generate these gene-expression classifiers: linear support vector machines, supervised factor models, sparse multinomial logistic regression, elastic nets, K-nearest neighbor, and random forests. All performed similarly although sparse logistic regression required the fewest number of classifier genes and outperformed other strategies by a small margin (data not shown). We also compared a strategy that generated three separate binary classifiers to a single multinomial classifier that would simultaneously assign a given subject to one of the three clinical categories. This latter approach required more genes and achieved an inferior accuracy. Consequently, we applied a sparse logistic regression model to define Bacterial ARI, Viral ARI, and Non-Infectious Illness classifiers containing 71, 33 and 26 probe signatures, respectively. Probe and classifier weights are shown in Table 9.

Clinical decision making is infrequently binary, requiring the simultaneous distinction of multiple diagnostic possibilities. We applied all three classifiers, collectively defined as the ARI classifier, using leave-one-out cross-validation to assign probabilities of bacterial ARI, viral ARI, and non-infectious illness (FIG. 6). These conditions are not mutually exclusive, For example, the presence of a bacterial ARI does not preclude a concurrent viral ARI or non-infectious disease. Moreover, the assigned probability represents the extent to which the patient's gene expression response matches that condition's canonical signature. Since each signature intentionally functions independently of the others, the probabilities are not expected to sum to one, To simplify classification, the highest predicted probability determined class assignment. Overall classification accuracy was 87% (238/273 were concordant with adjudicated phenotype).

Bacterial ARI was identified in 58/70 (83%) patients and excluded 179/191 (94%) without bacterial infection. Viral ARI was identified in 90% (104/115) and excluded in 92% (145/158) of cases. Using the non-infectious illness classifier, infection was excluded in 86% of cases (76/88). Sensitivity analyses was performed for positive and negative predictive values for all three classifiers given that prevalence can vary for numerous reasons including infection type, patient characteristics, or location (FIG. 8). For both bacterial and viral classification, predictive values remained high across a range of prevalence estimates, including those typically found for ARI.

To determine if there was any effect of age, we included it as a variable in the classification scheme. This resulted in two additional correct classifications, likely due to the over-representation of young people in the viral ARI cohort. However, we observed no statistically significant differences between correctly and incorrectly classified subjects due to age (Wilcoxon rank sum p=0.17).

We compared this performance to procalcitonin, a widely used biomarker specific for bacterial infection. Procalcitonin concentrations were determined for the 238 subjects where samples were available and compared to ARI classifier performance for this subgroup.

Procalcitonin concentrations >0.25 g/L assigned patients as having bacterial ARI, whereas values <0.25μg/L assigned patients as non-bacterial, which could be either viral ARI or noninfectious illness. Procalcitonin correctly classified 186 of 238 patients (78%) compared to 204/238 (86%) using the ARI classifier (p=0.03). However, accuracy for the two strategies varied depending on the classification task. For example, performance was similar in

discriminating viral from bacterial ARI. Procalcitonin correctly classified 136/155 (AUC 0.89) compared to 140/155 for the ARI classifier (p-value=0.65 using McNemar's test with Yates correction). However, the ARI classifier was significantly better than procalcitonin in discriminating bacterial ARI from non-infectious illness [105/124 vs. 79/124 (AUC 0.72); p- valueO.001], and discriminating bacterial ARI from all other etiologies including viral and non- infectious etiologies [215/238 vs, 186/238 (AUC 0.82); p-value=0.02].

We next compared the ARI classifier to three published gene expression classifiers of bacterial vs. viral infection, each of which was derived without uninfected ill controls. These included a 35-probe classifier (Ramilo) derived from children with influenza or bacterial sepsis ¹¹ ; a 33-probe classifier (Hu) derived from children with febrile viral illness or bacterial infection ¹⁴ ; and a 29-probe classifier (Parnell) derived from adult ICU patients with community- acquired pneumonia or influenza ¹² . We hypothesized that classifiers generated using only patients with viral or bacterial infection would perform poorly when applied to a clinically relevant population that included ill but uninfected patients. Specifically, when presented with an individual with neither a bacterial nor a viral infection, the previously published classifiers would be unable to accurately assign that individual to a third, alternative category. We therefore applied the derived as well as published classifiers to our 273 -patient cohort. Discrimination between bacterial ARI, viral ARI, and non-infectious illness was better with the derived ARI classifier (McNemar's test with Yates correction, p=0.002 vs. Ramilo; p=0.0001 vs. Parnell; and p=0.08 vs. Hu) (Table 6). ²⁴ ' ²⁵ This underscores the importance of deriving gene-expression classifiers in a cohort representative of the intended use population, which in the case of ARI should include non-infectious illness. ¹⁵

Discordant classifications

To better understand ARI classifier performance, we individually reviewed the 35 discordant cases. Nine adjudicated bacterial infections were classified as viral and three as non- infectious illness. Four viral infections were classified as bacterial and seven as non-infectious. Eight non-infectious cases were classified as bacterial and four as viral. We did not observe a consistent pattern among discordant cases, however, notable examples included atypical bacterial infections. One patient with M. pneumoniae based on serological conversion and one of three patients with Legionella pneumonia were classified as viral ARI. Of six patients with non- infectious illness due to autoimmune or inflammatory diseases, only one adjudicated to have Still's disease was classified as having bacterial infection. See also eTable 3 of Tsalik et al. (2016) Sci Transl Med 9(322): 1-9, which is incorporated by reference herein.

External validation

Generating classifiers from high dimensional, gene expression data can result in over- fitting. We therefore validated the ARI classifier in silico using gene expression data from 328 individuals, represented in five available datasets (GSE6269, GSE42026, GSE40396,

GSE20346, and GSE42834). These were chosen because they included at least two relevant clinical groups, varying in age, geographic distribution, and illness severity (Table 7). Applying the ARI classifier to four datasets with bacterial and viral ARI, AUC ranged from 0.90-0.99. Lastly, GSE42834 included patients with bacterial pneumonia (n=19), lung cancer (n=16), and sarcoidosis (n=68). Overall classification accuracy was 96% (99/103) corresponding to an AUC of 0.99. GSE42834 included five subjects with bacterial pneumonia pre- and post-treatment. All five demonstrated a treatment-dependent resolution of the bacterial infection. See also eFigures 3-8 of Tsalik et al. (2016) Sci Transl Med 9(322):l-9, which is incorporated by reference herein. Biological pathways

The sparse logistic regression model that generated the classifiers penalizes selection of genes from a given pathway if there is no additive diagnostic value. Consequently, conventional gene enrichment pathway analysis is not appropriate to perform. Moreover, such conventional gene enrichment analyses have been described. ⁹ ' ¹² ' ^14,28 ' ²⁹ Instead a literature review was performed for all classifier genes (Table 10). Overlap between Bacterial, Viral, and Noninfectious Illness Classifiers is shown in FIG. 9.

The Viral classifier included known anti-viral response categories such as interferon response, T-cell signaling, and RNA processing. The Viral classifier had the greatest representation of RNA processing pathways such as KPNB1, which is involved in nuclear transport and is co-opted by viruses for transport of viral proteins and genomes. ' Its downregulation suggests it may play an antiviral role in the host response.

The Bacterial classifier encompassed the greatest breadth of cellular processes, notably cell cycle regulation, cell growth, and differentiation. The Bacterial classifier included genes important in T-, B-, and NK-cell signaling. Unique to the Bacterial classifier were genes involved in oxidative stress, and fatty acid and amino acid metabolism, consistent with sepsis- related metabolic perturbations.

Summary of clinical applicability

We determined that host gene expression changes are exquisitely specific to the offending pathogen class and can be used to discriminate common etiologies of respiratory illness. This creates an opportunity to develop and utilize gene expression classifiers as novel diagnostic platforms to combat inappropriate antibiotic use and emerging antibiotic resistance. Using sparse logistic regression, we developed host gene expression profiles that accurately distinguished between bacterial and viral etiologies in patients with acute respiratory symptoms (external validation AUC 0.90-0.99). Deriving the ARI classifier with a non-infectious illness control group imparted a high negative predictive value across a wide range of prevalence estimates.

Respiratory tract infections caused 3.2 million deaths worldwide and 164 million disability-adjusted life years lost in 201 1 , more than any other cause. ¹ ' ² Despite a viral etiology in the majority of cases, 73% of ambulatory care patients in the U.S. with acute respiratory infection (ARI) are prescribed an antibiotic, accounting for 41% of all antibiotics prescribed in this setting. ³ ' ⁴ Even when a viral pathogen is microbiologically confirmed, this does not exclude a possible concurrent bacterial infection leading to antimicrobial prescribing "just in case". This empiricism drives antimicrobial resistance ⁵ ' ⁶ , recognized as a national security priority. ⁷ The encouraging metrics provided in this example provide an opportunity to provide clinically actionable results which will optimize treatment and mitigate emerging antibiotic resistance.

Several studies made notable inroads in developing host-response diagnostics for ARI. This includes response to respiratory viruses ⁸ ' ^10"12 ' ¹⁴ , bacterial etiologies in an ICU

12 30 31 33

population ' , and tuberculosis ^" . Typically, these define host response profiles compared to the healthy state, offering valuable insights into host biology. ¹⁶ ' ³⁴ ' ³⁵ However, these gene lists are suboptimal with respect to a diagnostic application because the gene expression profiles that are a component of the diagnostic is not representative of the population for which the test will be applied. ¹⁵ Healthy individuals do not present with acute respiratory complaints, thus they are excluded from the host-response diagnostic development reported herein.

Including patients with bacterial and viral infections allows for the distinction between these two states but does not address how to classify non-infectious illness. This phenotype is important to include because patients present with infectious and non-infectious etiologies that may share symptoms. That is, symptoms may not provide a clinician with a high degree of diagnostic certainty. The current approach, which uniquely appreciates the necessity of including the three most likely states for ARI symptoms, can be applied to an undifferentiated clinical population where such a test is in greatest need.

The small number of discordant classifications occurred may have arisen either from errors in classification or clinical phenotyping. Errors in clinical phenotyping can arise from a failure to identify causative pathogens due to limitations in current microbiological diagnostics. Alternatively, some non-infectious disease processes may in fact be infection-related through mechanisms that have yet to be discovered. Discordant cases were not clearly explained by a unifying variable such as pathogen type, syndrome, or patient characteristic. As such, the gene expression classifiers presented herein may be impacted by other factors including patient- specific variables (e.g., treatment, comorbidity, duration of illness); test-specific variables (e.g., sample processing, assay conditions, R A quality and yield); or as-of-yet unidentified variables.

Example 2: Classification Performance in Patients with Co-Infection Defined by the

Identification of Bacterial and Viral Pathogens

In addition to determining that age did not significantly impact classification accuracy, we assessed whether severity of illness or etiology of SIRS affected classification. Patients with viral ARI tended to be less ill, as evidenced by lower rate of hospitalization. In the various cohorts, hospitalization was used as a marker of disease severity and its impact on classification performance was assessed. This test revealed no difference (Fisher's exact test p-value of 1). In addition, the SIRS control cohort included subjects with both respiratory and non-respiratory etiologies. We assessed whether classification was different in subjects with respiratory vs. nonrespiratory SIRS and determined it was not (Fisher's exact test p-value of 0.1305).

Some patients with ARI will have both bacterial and viral pathogens identified, often termed co-infection. However, it is unclear how the host responds in such situations. Illness may be driven by the bacteria, the virus, both, or neither at different times in the patient's clinical course. We therefore determined how the bacterial and viral ARI classifiers performed in a population with bacterial and viral co-identification. GSE60244 included bacterial pneumonia (n=22), viral respiratory tract infection (n=71), and bacterial/viral co-identification (n=25). The co-identification group was defined by the presence of both bacterial and viral pathogens without further subcategorization as to the likelihood of bacterial or viral disease. We trained classifiers on subjects in GSE60244 with bacterial or viral infection and then validated in those with co- identification (FIG. 10). A host response was considered positive above a probability threshold of 0.5, We observed all four possible categories. Six of 25 subjects had a positive bacterial signature; 14/25 had a viral response; 3/25 had positive bacterial and viral signatures; and 2/25 had neither.

The major clinical decision faced by clinicians is whether or not to prescribe

antibacterials. A simpler diagnostic strategy might focus only on the probability of bacterial A I according to the result from the Bacterial ARI classifier. However, there is value in providing information about viral or non-infectious alternatives. For example, the confidence to withhold antibacterials in a patient with a low probability of bacterial ARI can be enhanced by a high probability of an alternative diagnosis. Further, a full diagnostic report could identify concurrent illness that a single classifier would miss. We observed this when validating in a population with bacterial and viral co-identification. These patients are more commonly referred to as "co- infected." To have infection, there must be a pathogen, a host, and a maladaptive interaction between the two. Simply identifying bacterial and viral pathogens should not imply co-infection. Although we cannot know the true infection status in the 25 subjects tested, who had evidence of bacterial/viral co-identification, the host response classifiers suggest the existence of multiple host-response states. FIG. 10 is an informative representation of infection status, which could be used by a clinician to diagnose the etiology of ARI. References

1. Organization. WH, Global health estimates summary tables: Deaths by cause, age and sex. 2013. Accessed May 14, 2014.

2. Organization. WH, Global Health Estimates Summary Tables: DALYs by cause, age and sex. 2013. Accessed May 14, 2014.

3. Shapiro et al. Antibiotic prescribing for adults in ambulatory care in the USA, 2007-09. J Antimicrob Chemother. 2014;69(l):234-240.

4. Lee et al. Outpatient antibiotic prescribing in the United States: 2000 to 2010. BMC

Medicine. 2014;12:96.

5. Gould. Antibiotic resistance: the perfect storm. Int J Antimicrob Ag. 2009;34,

Supplement 3(0):S2-S5. Kim and Gallis. Observations on spiraling empiricism: its causes, allure, and perils, with particular reference to antibiotic therapy. Am J Med. 1989;87(2):201-206.

Obama B. Executive Order - Combating Antibiotic-Resistant Bacteria. In: House TW, ed: Office of the Press Secretary; 2014.

Zaas et al. Gene expression signatures diagnose influenza and other symptomatic respiratory viral infections in humans. Cell host & microbe. 2009;6(3):207-217.

Woods et al. A Host Transcriptional Signature for Presymptomatic Detection of Infection in Humans Exposed to Influenza H1N1 or H3N2. PLoS ONE. 2013;8(l):e52198.

Mejias et al. Whole Blood Gene Expression Profiles to Assess Pathogenesis and Disease Severity in Infants with Respiratory Syncytial Virus Infection. PLoS medicine.

2013;10(l l):el001549.

Ramilo et al. Gene expression patterns in blood leukocytes discriminate patients with acute infections. Blood. 2007;109(5):2066-2077.

Parnell et al. A distinct influenza infection signature in the blood transcriptome of patients who presented with severe community acquired pneumonia. Crit Care.

2012;16(4):R157.

Zaas et al. A host-based RT-PCR gene expression signature to identify acute respiratory viral infection. Sci trans med. 2013;5(203):203ral26.

Hu et al. Gene expression profiles in febrile children with defined viral and bacterial infection. Proc Natl Acad Sci U SA. 2013.

Lytkin et al. Expanding the understanding of biases in development of clinical-grade molecular signatures: a case study in acute respiratory viral infections. PLoS One.

2011;6(6):e20662.

Ahn et al. Gene expression-based classifiers identify Staphylococcus aureus infection in mice and humans. PLoS One. 2013;8(l):e48979.

Glickman et al. Disease progression in hemodynamically stable patients presenting to the emergency department with sepsis. Acad Emerg Med. 2010;17(4):383-390.

Tsalik et al. Discriminative value of inflammatory biomarkers for suspected sepsis. J Emerg Med. 2012;43(1):97-106.

Tsalik et al. Multiplex PCR to diagnose bloodstream infections in patients admitted from the emergency department with sepsis. J Clin Microbiol. 2010;48(l):26-33.

Bone et al. Definitions for sepsis and organ failure and guidelines for the use of innovative therapies in sepsis. The ACCP/SCCM Consensus Conference Committee. American College of Chest Physicians/Society of Critical Care Medicine. Chest.

1992;101(6):1644-1655. 21. Bishop. Pattern Recognition and Machine Learning (Information Science and Statistics). Springer-Verlag New York, Inc.; 2006.

22. Friedman et al. Regularization Paths for Generalized Linear Models via Coordinate

Descent. J. Stat. Softw. 2010;33(1): 1-22.

23. Fawcett. An introduction to ROC analysis. Pattern Recognition Letters. 2006;27(8):861- 874.

24. McNemar. Note on the sampling error of the difference between correlated proportions or percentages. Psychometrika. 1947;12(2): 153-157.

25. Yates. Contingency Tables Involving Small Numbers and the χ2 Test. Supplement to the Journal of the Royal Statistical Society. 1934;l(2):217-235.

26. Bukrinsky et al. Active nuclear import of human immunodeficiency virus type 1

preintegration complexes. Proc Natl Acad Sci USA. 1992;89(14):6580-6584.

27. Ghildyalet al. Nuclear import of the respiratory syncytial virus matrix protein is mediated by importin betal independent of importin alpha. Biochemistry. 2005;44(38):12887- 12895.

28. Langley et al. An Integrated Clinico-Metabolomic Model Improves Prediction of Death in Sepsis. Sci Trans Med. 2013;5(195): 195ral95.

29. Schappert and Rechisteiner. Ambulatory medical care utilization estimates for 2007. In:

Stat VH, ed. Vol 132011.

30. Severino et al. Patterns of Gene Expression in Peripheral Blood Mononuclear Cells and

Outcomes from Patients with Sepsis Secondary to Community Acquired Pneumonia.

PLoS ONE. 2014;9(3):e91886.

31. Anderson et al. Diagnosis of Childhood Tuberculosis and Host RNA Expression in

Africa. N Engl J Med. 2014;370(18):1712-1723.

32. Berry et al. An interferon-inducible neutrophil-driven blood transcriptional signature in human tuberculosis. Nature. 2010;466(7309):973-977.

33. Kaforou et al. Detection of tuberculosis in HIV-infected and -uninfected African adults using whole blood RNA expression signatures: a case-control study. PLoS medicine.

2013;10(10):el001538.

34. Banchereau et al. Host immune transcriptional profiles reflect the variability in clinical disease manifestations in patients with Staphylococcus aureus infections. PLoS One. 2012;7(4):e34390.

35. Herberg et al. Transcriptomic Profiling in Childhood H1N1/09 Influenza Reveals

Reduced Expression of Protein Synthesis Genes. J Infect Dis. 2013;208(10): 1664-1668. Bloom et al. Transcriptional blood signatures distinguish pulmonary tuberculosis, pulmonary sarcoidosis, pneumonias and lung cancers. PLoS One. 2013;8(8):e70630.

Table 6: Performance characteristics of the derived ARI classifier. A combination of the Bacterial ARI, Viral ARI, and Non-Infectious Illness classifiers were validated using leave-one- out cross-validation in a population of bacterial ARI (n=70), viral ARI (n=l 15), or noninfectious illness (NI, n=88). Three published bacterial vs. viral classifiers were identified and applied to this same population as comparators. Data are presented as number (%). Shaded cells indicate correct classifications.

Clinical Assignment

Bacterial Viral NI

Bacterial 54 (77.1) 4 (3.5) 12 (13.6)

Ramilo et al. Viral 6 (8.6) 101 (87.8) 12 (13.6)

Non-infectious illness 12 (14.3) 12 (8.7) 64 (72.7)

Bacterial 53 (75.7) 4 (3.5) 9 (10.2)

Hu et al. Viral 9 (12.9) 104 (90.4) 9 (10.2)

Non-infectious illness 8 (11.4) 7 (6.1) 1 70 (79 5)

Bacterial 51 (72.8) 8 (7.0) 11 (12.5)

Parnell et al. Viral 13 (18.6) 94 (81.7) 10 (11.4)

Non-infectious illness 6 (8.6) 13 (11.3) 67 (76.1)

Bacterial 58 (82.8) _T_ 4 (3.4) 8 (9.0)

Derived ARI

Viral 9 (12.8) 104 (90.4) 4 (4 ^r ,) ra Classifier υ

Non-infectious illness 3 (4.2) 7 (6.0) 176 (86 3)

Table 7: External validation of the ARI classifier (combined bacterial ARI, viral ARI, and noninfectious classifiers). Five Gene Expression Omnibus datasets were identified based on the ^dd A ^{i iiPt t} nmeness ^g e ^ci e ^rr - inclusion of at least two of the relevant clinical groups: Viral ARI, Bacterial ARI, non-infectious illness (NI).

Table 8. Etiological causes of illness for subjects with viral ARI, bacterial ARI, and infectious illness.

Number of

subjects

Hemothorax 1

Heroin Overdose 1

Hyperglycemia 2

Hypertensive Emergency 3

Hypertensive Emergency with Pulmonary Edema 1

Hypovolemia 2

Infarcted Uterine Fibroid 1

Lung Cancer; Coronary Artery Disease 1

Lung Cancer; Hemoptysis 1

Mitochondrial Disorder; Acidosis 1

Myocardial Infarction 2

Myocardial Infarction; Hypovolemia 1

Nephrolithiasis 2

Pancreatitis 4

Post-operative Vocal Cord Paralysis 1

Hyperemesis Gravidarum; Allergic Rhinitis 1

Pulmonary Edema 2

Pulmonary Edema; Hypertensive Crisis 1

Pulmonary Embolism 5

Pulmonary Embolism; Myocardial Infarction 1

Pulmonary Embolism; Pulmonary Artery Hypertension 1

Pulmonary Fibrosis 2

Pulmonary Mass 1

Reactive Arthritis 1

Rhabdomyolysis 1

Ruptured Aneurysm; Hypovolemic Shock 1

Severe Aortic Stenosis 1

Small Bowel Obstruction 1

Stills Disease 1

Pulmonary Artery Hypertension; Congestive Heart Failure 1

Systemic Lupus Erythematosis 1

Tracheobronchomalacia 1

Transient Ischemic Attack 1

Ulcerative Colitis 1

Urethral Obstruction 1

a This patient was adjudicated as having a bacterial ARl with Bacillus species identified as the etiologic agent. We later recognized Bacillus species was not the correct microbiological etiology although the clinical history was otherwise consistent with bacterial pneumonia. As this error was identified after model derivation, we included the subject in all subsequent analyses.

Table 9. Probes selected for the Bacterial ARI, Viral ARI, and Non-infectious Illness Classifiers. Probe names are presented as Affymetrix probe IDs. Values for each probe represent the weight of each probe in the specified classifier.

-ο ο

— 1

Table 10. Genes in the Bacterial ARI, Viral ARI, and Non-infectious Illness (NI) Classifiers, grouped by biologic process. Gene accession numbers are provided in Table 9.

* Genes listed in more than one classifier. In cases where such overlapping genes have different directions of expression, increased expression is denoted by (+) and decreased expression is denoted by (-).

Example 3: The Bacterial/Viral/SIRS assay contemplated on a TLDA platform

We will develop a custom multianalyte, quantitative real-time PCR (RT-PCR) assay on the 384-well TaqMan Low Density Array (TLDA, Applied Biosystems) platform. TLDA cards will be manufactured with one or more TaqMan primer/probe sets specific for a gene mRNA transcript in the classifier(s) in each well, along with multiple endogenous control RNA targets (primer/probe sets) for data normalization. For each patient sample, purified total RNA is reverse transcribed into cDNA, loaded into a master well and distributed into each assay well via centrifugation through microfluidic channels. TaqMan hydrolysis probes rely on 5' to 3' exonuclease activity to cleave the dual-labeled probe during hybridization to complementary target sequence with each amplification round, resulting in fluorescent signal production. In this manner, quantitative detection of the accumulated PCR products in "real-time" is possible. During exponential amplification and detection, the number of PCR cycles at which the fluorescent signal exceeds a detection threshold is the threshold cycle (C _t ) or quantification cycle (C _q ) - as determined by commercial software for the RT-PCR instrument. To quantify gene expression, the for a target RNA is subtracted from the C _t of endogenous normalization RNA (or the geometric mean of multiple normalization RNAs), providing a deltaC _t value for each RNA target within a sample which indicates relative expression of a target RNA normalized for variability in amount or quality of input sample &NA or cDNA.

The data for the quantified gene signatures are then processed using a computer and according to the probit classifier described above (equation 1) and reproduce here.

Normalized gene expression levels of each gene of the signature are the explanatory or independent variables or features used in the classifier, in this example the general form of the classifier is a probit regression formulation:

P(having condition) = Φ(βιΧί+ β ₂ Χ ₂ + ...+ dXd) (equation 1)

where the condition is bacterial ARI, viral ARI, or non-infection illness; Φ(.) is the probit link function; {βι,β^. , .,β } are the coefficients obtained during training; {Xi,X ₂ ,...,X _d } are the normalized genes expression values of the signature; and d is the size of the signature (number of genes). The value of the coefficients for each explanatory variable are specific to the technology platform used to measure the expression of the genes or a subset of genes used in the probit regression model. The computer program computes a score, or

probability, and compares the score to a threshold value. The sensitivity, specificity, and overall accuracy of each classifier is optimized by changing the threshold for classification using receiving operating characteristic (ROC) curves.

A preliminary list of genes for the TLDA platform based on the signature from the Affymetrix platform (Affy signature) as well as from other sources is provided below in Table 1 A. Weights appropriate for the TLDA platform for the respective classifiers were thereafter determined as described below in Example 4.

Table 1 A: Preliminary list of genes for development of classifiers for TLDA platform.

Alternate NonTLDA assay

Original Affy ID Affy ID GROUP Bacterial Viral infectious GENE identifier

219437_s_at 212332_at Affy signature ANKRD11 Hs00331872_sl 208702_x_at 201642_at Affy signature APLP2 Hs00155778_ml 207606_s_at 212633_at Affy signature ARHGAP12 Hs00367895_ml 201659_s_at 209444_at Affy signature ARL1 Hs01029870 ml 208736_ at 201132_at Affy signature - ARPC3 Hs00855185_gl

205965_ at 218695_at Affy signature - BATF Hs00232390_ml

21 800_ x_at 209876_at Affy signature - BTF3 Hs00852566_gl

209031 _, at 209340_at Affy signature - CAD Ml Hs00296064_sl

204392_ at 214054_at Affy signature - CAMK1 Hs00269334_ml

201949, _x_at 37012_at Affy signature - CAPZB Hs00191827_ml

207840_ at 213830_at Affy signature - CD160 Hs00199894_ml

200663. at 203234_at Affy signature - CD63 Hs00156390_ml

220935_ s_at 219271_at Affy signature - CDK5RAP2 Hs01001427_ml

206676_ at 207269_at Affy signature - CEACAM8 Hs00266198_ml

209396_ _s_at 209395_at Affy signature - CHI3L1 Hs01072230_gl

205008 _, _at 58900_at Affy signature CIB2 Hs00197280_ml

205200 _, .at 206034_at Affy signature - CLEC3B Hs00162844_ml

203979. .at 49111_at Affy signature - CYP27A1 Hs01017992_gl

207244 _, _x_at 209280_at Affy signature - CYP2A13 Hs00711162_sl

215184 _, .at 217521_at Affy signature - DAPK2 Hs00204888_ml

205001 _, _s_at 214131_at Affy signature - DDX3Y HS00965254 _gH

205033. _s_at 207269_at Affy signature - DEFA3 Hs00414018_ml

204750. _s_at 205418_at Affy signature - DSC2 Hs00951428_ml

216473. x_at 221660_at Affy signature - DUX4 Hs03037970_gl

210724. .at 220246_at Affy signature - EMR3 Hs01128745_ml

215804. .at 206903_at Affy signature - EPHA1 Hs00975876_gl

212035 _, s_at 200935_at Affy signature - EX0C7 Hs01117053_ml

212697. .at 46665_at Affy signature - FAM134C Hs00738661_ml

209919. x_at 218695_at Affy signature - GGT1 Hs00980756_ml

219777. .at 202963_at Affy signature - GI AP6 Hs00226776_ml

200947. _s_at 202126_at Affy signature - GLUD1 Hs03989560_sl

218595. _s_at 217103_at Affy signature - HEATR1 Hs00985319_ml

218306. _s_at 212232_at Affy signature - HERC1 Hs01032528_ml

221491_ _x_at 203290_at Affy signature - HLA-DRB3 Hs00734212_ml

201055. _s_at 37012_at Affy signature - HNRNPAO Hs00246543_sl

203153. .at 219863_at Affy signature - IFIT1 Hs01911452_sl

214022. _s_at 35254_at Affy signature - IFITM1 Hs00705137_sl

212657. _s_at 202837_at Affy signature - IL1RN Hs00893626_ml

203275. .at 213038_at Affy signature - IRF2 Hs01082884_ml

203882. .at 201649_at Affy signature - IRF9 Hs00196051_ml

215268. .at 200837_at Affy signature - KIAA0754 Hs03055204_sl

221874. _at 203063_at Affy signature - KIAA1324 Hs00381767_ml

213573. .at 31845_at Affy signature - KPNB1 Hs00158514_ml

208029. _s_at 212573_at Affy signature - LAPTM4B Hs00363282_ml

202145. .at 204972_at Affy signature - LY6E Hs03045111_gl

220122. .at 218323_at Affy signature - CTP1 Hs01115711_ml

217408. .at 212846_at Affy signature - MRPS18B Hs00204096_ml

207860. .at 212318_at Affy signature - NCR1 Hs00950814_gl

203045. .at 213038_at Affy signature - NINJ1 Hs00982607_ml

210797. _s_at 205660_at Affy signature - OASL Hs00984390_ml

214175 _. x at 204600_at Affy signature - PDGFA Hs00184792_ml

219066. .at 217497_at Affy signature - PPCDC Hs00222418_ml

214617. .at 212070_at Affy signature - PRF1 Hs00169473_ml

218700. _, s_at 203816_at Affy signature RAB7L1 Hs00187510_ml

215342. _s_at 218695_at Affy signature - RABGAP1L Hs02567906_sl

219143. _s_at 204683_at Affy signature - RPP25 Hs00706565_sl

214097. .at 201094_at Affy signature - RPS21 Hs00963477_gl

210365 .at 222307_at Affy signatu e - SAT1 Hs00971739_gl

215848 .at 81811_at Affy signature - SCAPER Hs02569575_sl

212900 .at 204496_at Affy signature - 5EC24A Hs00378456_ml

44673_ at 219211_at Affy signature SIGLECl Hs00988063_ml

201802 .at 206361_at Affy signature - SLC29A1 Hs01085704_gl

202864 _s_at 202863_at Affy signature - 5P100 Hs00162109_ml

205312 .at 205707_at Affy signature - SPI1 Hs00231368_ml

202005 _at 205418_at Affy signature - ST14 Hs04330394_gl

220059 _at 202478_at Affy signature - STAP1 Hs01038134_ml

219523 _s_at 206903_at Affy signature - TENM3 Hs01111787_ml

202720 _at 201344_at Affy signature - TES Hs00210319_ml

203313 _s_at 212232_at Affy signature - TGIF1 Hs00820148_gl 218095_ s_at 219157_at Affy signature - - - TME 165 Hs00218461_ml

202509_ s_at 212603_at Affy signature - - - TNFAIP2 Hs00196800_ml

219130_ at 200685_at Affy signature - - - TR T13 Hs00219487_ml

208601_ .s_at 205127_at Affy signature - - - TUBB1 Hs00258236_ml

217717_ _s_at 205037_at Affy signature - - - YWHAB Hs00793604_ml

217593_ at 222141_at Affy signature - - - ZSCAN18 Hs00225073_ml

213300_ at 219014_at Affy signature - - - ATG2A Hs00390076_ml

212914_ at 211938_at Affy signature - - - CBX7 Hs00545603_ml

220308_ at 202452_at Affy signature - CCDC19 Hs01099244_ml

205098. .at 213361_at Affy signature - - - CCR1 Hs00928897_sl

205153. _s_at 215346_at Affy signature - - - CD40 Hs01002913_gl

204490. _s_at 205026_at Affy signature - - - CD44 Hs00153304_ml

202284_ s at 213324_at Affy signature - - - CDKN1A Hs00355782_ml

206207. .at 206361_at Affy signature - - - CLC Hs01055743_ml

206918. s_at 200964_at Affy signature - - - CPNEl Hs00537765_ml

203392. _s_at 222265_at Affy signature - CTBP1 Hs00972289_gl

207718. _x_at 44702_at Affy signature - - - CYP2A6 Hs00711162_sl

207718. _x_at 44702_at Affy signature - - - CYP2A7 Hs00711162_sl

201341. .at 209717_at Affy signature - - - ENC1 Hs00171580_ml

215606 _. _s_at 211999_at Affy signature - - - ERC1 Hs00327390_sl

202973. _x_at 201417_at Affy signature - - - FAM13A Hs01040170_ml

202709. .at 222265_at Affy signature - - - FMOD Hs00157619_ml

206371. .at 205844_at Affy signature - - - F0LR3 Hs01549264_ml

205164. .at 209391_at Affy signature - - - GCAT Hs00606568_gH

214085. _x_at 203799_at Affy signature - - - GLIPRl Hs00199268_ml

206896. _s_at 206126_at Affy signature - - GNG7 Hs00192999_ml

216289 _. _at 206338_at Affy signature - - - GPR144 Hs01369282_ml

208886. .at 213096_at Affy signature - - - H1F0 Hs00961932_sl

206647. .at 40850_at Affy signature - - - HBZ Hs00744391_sl

207194. _s_at 218225_at Affy signature - 1CAM Hs00169941_rtll

202411. -at 213797_at Affy signature - - - IFI27 Hs01086373 _gl

201188. .s_at 213958_at Affy signature - - - ITPR3 Hs00609948_ml

212162. .at 210148_at Affy signature - - - KIDINS220 Hs01057000_ml

216713. _at 213049_at Affy signature - - - KRIT1 Hs01090981_ml

212708. .at 202897_at Affy signature - - - MSL1 Hs00290567_sl

216303. _s_at 222265_at Affy signature - - - MTMR1 Hs01021250_ml

207075. _at 203906_at Affy signature - NLRP3 Hs00366465_ml

214582. _at 222317_at Affy signature - - - 0RAI2 Hs01057217_ml

216867. _s_at 202909_at Affy signature - - - PDE3B Hs00236997_ml

204545. _at 320_at Affy signature - - - PDLI 4 Hs00165457_ml

209511 _. _at 218333_at Affy signature - - - P0LR1C Hs00191646_ml

209511. .at 218333_at Affy signature - - - P0LR2F Hs00222679_ml

213633. _at 204632_at Affy signature - - - PSG4 Hs00978711_ml

213633. .at 204632_at Affy signature - - - PSG4 Hs01652476_ml

205048. J_at 203303_at Affy signature - - - PSPH Hs00190154_ml

213223. .at 210607_at Affy signature - - - RPL28 Hs00357189_gl

200042. .at 212247_at Affy signature - - RTCB Hs00204783_ml

209360. _s_at 203916_at Affy signature - - - RUNX1 Hs00231079_ml

219382. _at 209575_at Affy signature - - - SERTAD3 Hs00705989_sl

213633 _. _at 204632_at Affy signature - - - SH3BP1 Hs00978711_ml

213633 _. _at 204632_at Affy signature - - - SH3BP1 Hs01652476_ml

206934. _at 202545_at Affy signature - - - SIRPB1 Hs01092173_ml

212947. _at 220404_at Affy signature - - SLC9A8 Hs00905708_ml

216571 _at 202396_at Affy signature - - SMPD1 Hs01086851_ml

219055. _at 219439_at Affy signature - - - SRBD1 Hs01005222_ml

208545 _x_at 204600_at Affy signature - - - TAF4 Hs01122669_ml

214955 _at 217162_at Affy signature - - - TMPRSS6 Hs00541789_sl

202644 _s_at 55692_at Affy signature - - - TNFAIP3 Hs01568119_ml

202688 _at 219684_at Affy signature - - - TNFSF10 Hs00234356_ml

209605 _at 212897_at Affy signature - - - TST Hs04187383_ml

222059 _at 216076_at Affy signature - - - ZNF335 Hs00223060_ml

202509 _s_at NA InTxAlternate - - - TNFAIP2 Hs00969305_ml

202672 _s_at NA PanViralArray - - - ATF3 Hs00910173_ml

218943 _s_at NA PanViralArray - - - DDX58 Hs01061436_ml

219863 _at NA PanViralArray - HERC5 Hs01061821_ml 214059. _at NA PanViralArray IFI44 Hs00951349_ml

204439. .at NA PanViralArray IFI44L Hs00915294_gl

204415. _. at NA PanViralArray IFI6 Hs00242571_ml

203153. .at NA PanViralArray IFIT1 Hs03027069_sl

217502. .at NA PanViralArray IFIT2 Hs01922738_sl

204747. .at NA PanViralArray IFIT3 Hs01922752_sl

205483. _s_at NA PanViralArray ISG15 Hs01921425_sl

205569. .at NA PanViralArray LAMP3 Hs00180880_ml

202145. .at NA PanViralArray LY6E Hs03045111_gl

202086 _. _at NA PanViralArray MX1 Hs00182073_ml

205552. _s_at NA PanViralArray 0AS1 Hs00973637_ml

202869. .at NA PanViralArray 0AS2 Hs00973637_ml

218400 _. .at NA PanViralArray 0AS3 Hs00934282_gl

205660. .at NA PanViralArray OASL Hs00984390_ml

213797. - ^a t NA PanViralArray RSAD2 Hs00369813_ml

219684. .at NA PanViralArray RTP4 Hs00223342_ml

210657 _. _s_at NA PanViralArray SEPT4 Hs00910209_gl

200986. .at NA PanViralArray SERPING1 Hs00934330_ml

222154. _s_at NA PanViralArray SPATS2L Hs01016364_ml

206026. _s_at NA PanViralArray TNFAIP6 Hs01113602_ml

219211. .at NA PanViralArray USP18 Hs00276441_ml

206133. .at NA PanViralArray XAF1 Hs01550142_ml

NA NA Reference FPGS Hs00191956_ml

NA NA Reference PPIB Hs00168719_ml

IMA NA Reference TRAPl Hs00972326_ml

NA NA Reference DECR1 Hs00154728_ml

IMA NA Reference GAPDH Hs99999905_ml

IMA NA Reference 18S Hs99999901_sl

MA 203799_at Replacement CD302 Hs00208436_ml

NA 31845_at Replacement ELF4 Hs01086126_ml

NA 204600_at Replacement EPHB3 Hs01082563_gl

NA 206903_at Replacement EXOG Hs01035290_ml

NA 218695_at Replacement EXOSC4 Hs00363401_gl

NA 212232_at Replacement FNBP4 Hs01553131_ml

NA 209876_at Replacement GIT2 Hs00331902_sl

NA 204683_at Replacement ICAM2 Hs01015796_ml

NA 201642_at Replacement IFNGR2 Hs00985251_ml

NA 203799_at Replacement LY75-CD302 Hs00208436_ml

NA 209280_at Replacement MRC2 Hs00195862_ml

NA 212603_at Replacement MRPS31 Hs00960912_ml

NA 221660_at Replacement MYL10 Hs00540809_ml

NA 203290_at Replacement PEX6 Hs00165457_ml

NA 201417_at Replacement SOX4 Hs00268388_sl

NA 44702_at Replacement SYDE1 Hs00973080_ml

NA 222261_at Replacement TLDC1 Hs00297285_ml

NA 202452_at Replacement ZER1 Hs01115240_ml

Example 4: Bacterial/Viral/SIRS classification using gene expression measured by RT-qPCR implemented on the TLDA platform

The genes of the three signatures that compose the Host Response- ARI (HR-ARI) test were transitioned to a Custom TaqMan® Low Density Array Cards from ThermoFisher Scientific (Waltham, MA). Expression of these gene signatures were measured using custom multianalyte quantitative real time PCR (RT-qPCR) assays on the 384-well TaqMan Low Density Array (TLDA; Thermo-Fisher) platform. TLDA cards were designed and manufactured with one or more TaqMan primer/probe sets per well, each representing a specific RNA transcript in the ARI signatures, along with multiple endogenous control RNA targets (TRAPl, PPIB, GAPDH, FPGS, DECR1 and 18S) that are used to normalize for RNA loading and to control for plate-to-plate variability. In practice, two reference genes (out of five available), which have the smallest coefficient of variation across samples for the normalization procedure, were selected and primer/probe sets with more than 33% missing values (below limits of quantification) were discarded. The remaining missing values (if any), are set to 1 + max(Cq), where Cq is the quantification cycle for RT-qPCR. Normalized expression values were then calculated as the average of the selected references minus the observed Cq values for any given primer/probe set. See Hellemans et al. (2007) Genome Biol 2007;8(2):R19.

A total of 174 unique primer/probe sets were assayed per sample. Of these primer/probes, 144 primer/probe sets measure gene targets representative of the 132 previously described

Affymetrix (microarray) probes of the three ARI gene signatures (i.e., the genes in the bacterial gene expression signature, the viral gene expression signature and the non-infectious gene expression signature); 6 probe sets are for reference genes, and we additionally assayed 24 probe sets from a previously-discovered pan-viral gene signature. See U.S. Patent No. 8,821,876; Zaas et al. Cell Host Microbe (2009) 6(3):207-217. In addition, a number of primer/probe sets for

"replacement" genes were added for training, the expression of these genes being correlated with the expression of some genes from the Affymetrix signature. Some genes are replaced because the RT-qPCR assays for these genes, when performed using TLDA probes, did not perform well.

For each sample, total RNA was purified from PAXgene Blood RNA tubes (PreAnalytix) and reverse transcribed into cDNA using the Superscript VILO cDNA synthesis kit (Thermo- Fisher) according to the manufacturer's recommended protocol. A standard amount of cDNA for each sample was loaded per master well, and distributed into each TaqMan assay well via centrifugation through microfluidic channels. The TaqMan hydrolysis probes rely on 5' to 3' exonuclease activity to cleave the dual-labeled probe during hybridization to complementary target sequence with each amplification round, resulting in fluorescent signal production.

Quantitative detection of the fluorescence indicates accumulated PCR products in "real-time." During exponential amplification and detection, the number of PCR cycles at which the fluorescent signal exceeds a detection threshold is the threshold cycle (Q) or quantification cycle (C _q ) - as determined by commercial software for the RT-qPCR instrument.

Sample/cohort selection:

Under an IRB-approved protocol, we enrolled patients presenting to the emergency department with acute respiratory illness (See Table 11, below). The patients in this cohort are a subset of those reported in Table 1 of Tsalik et al. (2016) Sci Transl Med 9(322):l-9, which is incorporated by reference herein. Retrospective clinical adjudication of the clinical and other test data for these patients leads to one of three assignments: bacterial ARI, viral ARI, or noninfectious illness.

Table 11: Demographic information for the enrolled cohort

a Only subjects with viral, bacterial, or non-infectious illness were included (when available) from each validation cohort.

b When mean age was unavailable or could not be calculated, data is presented as either Adult or Pediatric.

0 Non-infectious illness was defined by the presence of SIRS criteria, which includes at least two of the following four features: Temperature <36° or >38°C; Heart rate >90 beats per minute; Respiratory rate >20 breaths per minute or arterial partial pressure of C0 ₂ <32mmHg; and white blood cell count <4000 or >12,000 cells/mm ³ or >10% band form neutrophils.

d Three subjects did not report ethnicity.

M, Male. F, Female. B, Black. W, White, O, Other Unknown. GSE numbers refer to NCBI Gene Expression Omnibus datasets. N/A, Not available based on published data.

Data analysis methods:

During the data preprocessing stage, we select a subset of at least two reference gene targets (out of five available) with the smallest coefficient of variation across samples and plates. We discard targets with more than 33% missing values (17 targets below the limit of

quantification), only if these values are not over represented in any particular class, e.g. , bacterial ARI. Next we impute the remaining missing values to 1 + max(C _q ), then normalize the expression values for all targets using the reference combination previously selected. In particular, we compute normalized expression values as the mean of the selected references (DECR1 and PPIB) minus the C _q values of any given target.

Once the data has been normalized, we proceed to build the classification model by fitting a sparse logistic regression model to the data (Friedman et al. (2010) J Stat. Softw. 33, 1- 22). This model estimates the probability that a subject belongs to a particular class as a weighted sum of normalized gene targets. Specifically, we write, p(subject is of class) = σ (W]Xi + . . . + WpXp), where σ is the logistic function, w _ls ... , w _p are classification weights estimated during the fitting procedure, xj , ... , x _p represent the p gene targets containing normalized expression values.

Similar to the array-based classifier, we build three binary classifiers: (1) bacterial ARI vs. viral ARI and non-infectious illness; (2) viral ARI vs. bacterial ARI and non-infectious illness; and (3) non-infectious illness vs. bacterial and viral ARI. After having fitted the three classifiers, we have estimates for p(bacterial ARI), p(viral ARI) and p(non-infectious illness). The thresholds for each of the classifiers are selected from Receiving Operating Characteristic (ROC) curves using a symmetric cost function (expected sensitivity and specificity are approximately equal) (Fawcett (2006) Pattern Recogn Lett 27:861-874). As a result, a subject is predicted as bacterial ARI if p(bacterial ARI) > t _b , where ¾ is the threshold for the bacterial ARI classifier. We similarly select thresholds for the viral ARI and non-infectious illness classifiers, t _v and t _n , respectively. If desired, a combined prediction can be made by selecting the most likely condition, i.e., the one with largest probability, specifically we write, argmax{p(bacterial ARI),p(viral ARI),p(non-infectious illness)}.

Results:

During the initial transition of the microarray-discovered genomic classifiers onto the TLDA platform, we assayed 32 samples that also had been assayed by microarray. This group served to confirm that TLDA-based RT-qPCR measurement of the gene transcripts that compose the ARI classifier recapitulates the results obtained for microarray-based measurement of gene transcripts, and is therefore a valid methodology for classifying patients as having bacterial or viral ARI, or having non-infectious illness. We found that from the 32 samples tested both on TLDA and microarray platforms, when assessed using their corresponding classifiers, there is agreement of 84.4%, which means that 27 of 32 subjects had the same combined prediction in both microarray and TLDA-based classification models.

After demonstrating concordance between microarray and TLDA-based classification, we tested an additional 63 samples, using the TLD,A-based classification, from patients with clinical adjudication of ARI status but without previously-characterized gene expression patterns. In total, therefore, 95 samples were assessed using the TLDA-based classification test. This dataset from 95 samples allowed us to evaluate how the TLDA-based RT-qPCR platform classifies new patients, using only the clinical adjudication as the reference standard. In this experiment, we observed an overall accuracy of 81.1%, which corresponds to 77/95 correctly classified samples. More specifically, the model yielded bacterial ARI, viral ARI, and noninfectious illness accuracies of 80% (24 correct of 30), 77.4% (24 correct of 31) and 85.3% (29 correct of 34), respectively. In terms of the performance of the individual classifiers, we observed area under the ROC curves of 0.92, 0.86 and 0.91, for the bacterial ARI, viral ARI and non-infectious illness classifier, respectively. Provided that we do not count with a validation dataset for any of the classifiers, yet we want unbiased estimates of classification performance (accuracies and areas under the ROC curve), we are reporting leave-one-out cross-validated performance metrics. The weights and thresholds for each of the classifiers (bacterial ARI, viral ARI and noninfectious illness) are shown in the Table 12, shown below. Note that this Table lists 151 gene targets instead of 174 gene targets because the reference genes were removed in the

preprocessing stage, as described above, as were 17 targets for which there were missing values. These 17 targets were also removed during the preprocessing stage.

If the panviral signature genes are removed, we see a slight decreased performance, no larger than 5% across AUC, accuracies and percent of agreement values.

Summary:

The composite host-response ARI classifier is composed of gene expression signatures that are diagnostic of bacterial ARI versus viral ARI, versus non-infectious illness and a mathematical classification framework. The mathematical classifiers provide three discrete probabilities: that a subject has a bacterial ARI, viral ARI, or non-infectious illness. In each case, a cutoff or threshold may be specified above which threshold one would determine that a patient has the condition. In addition, one may modify the threshold to alter the sensitive and specificity of the test.

The measurement of these gene expression levels can occur on a variety of technical platforms. Here, we describe the measurement of these signatures using a TLDA-based RT- qPCR platform. Moreover, the mathematical framework that determines ARI etiology probabilities is adapted to the platform by platform-specific training to accommodate transcript measurement methods (i.e., establishing platform-specific weights, w _ls ..., w _P ). Similar, straightforward, methodology could be conducted to translate the gene signatures to other gene expression detection platforms, and then train the associated classifiers. This Example also demonstrates good concordance between TLDA-based and microarray-based classification of etiology of ARI. Finally, we show the use of the TLDA-based RT-qPCR platform and associated mathematical classifier to diagnose new patients with acute respiratory illness.

Table 12: Genes, TLDA probe/primers, and classifier weights for the bacterial, viral and non-infectious illness classifiers.

Any patents or publications mentioned in this specification are indicative of the levels of those skilled in the art to which the invention pertains. These patents and publications are herein incorporated by reference to the same extent as if each individual publication was specifically and individually indicated to be incorporated by reference. In case of conflict, the present specification, including definitions, will control.

One skilled in the art will readily appreciate that the present invention is well adapted to carry out the objects and obtain the ends and advantages mentioned, as well as those inherent therein. The present disclosures described herein are presently representative of preferred embodiments, are exemplary, and are not intended as limitations on the scope of the invention. Changes therein and other uses will occur to those skilled in the art which are encompassed within the spirit of the invention as defined by the scope of the claims.