RELATED APPLICATIONS

This application is a continuation of U.S. applicant Ser. No. 15/738,339, filed Dec. 20, 2017, which is a National Phase Entry Application of PCT/US2016/040437, filed Jun. 30, 2016, which claims the benefit of U.S. Provisional Patent Application Ser. No. 62/187,683, filed Jul. 1, 2015, and U.S. Provisional Patent Application Ser. No. 62/257,406, filed Nov. 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 Aug. 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. January; 24(1):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., January 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 turn-around 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): e1002234; 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 methods or systems for sample analysis that overcomes many of the limitations of current methods for detecting pathogens in a sample.

In an aspect, the present disclosure provides a method, comprising: (a) obtaining a first sample from a first subject having a bacterial infection and a second sample from a second subject having a viral infection, wherein the first sample comprises a first plurality of messenger ribonucleic (mRNA) molecules and the second sample comprises a second plurality of mRNA molecules; (b) optically detecting the first plurality of mRNA molecules or derivative thereof in the first sample; and (c) optically detecting the second plurality of mRNA molecules or derivative thereof in the second sample.

In some embodiments, the method in (b) comprises subjecting the first plurality of mRNA molecules to reverse transcription to generate a plurality of complementary deoxyribonucleic acid (cDNA) molecules, and subsequently optically detecting the plurality of cDNA molecules or derivative thereof.

In some embodiments, the method in (c) comprises subjecting the second plurality of mRNA molecules to reverse transcription to generate a plurality of complementary deoxyribonucleic acid (cDNA) molecules, and subsequently optically detecting the plurality of cDNA molecules or derivative thereof.

In some embodiments, the method further comprises, prior to (b), separating the first plurality of mRNA molecules from the first sample.

In some embodiments, the sample, the first sample, or the second sample is a blood sample.

In some embodiments, the method further comprises, prior to optically detecting, subjecting the plurality of cDNA molecules to nucleic acid amplification.

In some embodiments, the nucleic acid amplification comprises polymerase chain reaction (PCR) or isothermal amplification.

In some embodiments, the optically detecting in (b) comprises detecting an optical signal from a probe coupled to a cDNA molecule of the plurality of cDNA molecule or a derivative thereof.

In some embodiments, the optical signal is a fluorescent signal.

In another aspect, the present disclosure provides a system, comprising: one or more computer processors programmed to execute machine executable code that implements a method comprising: (a) obtaining a first sample from a first subject having a bacterial infection and a second sample from a second subject having a viral infection, wherein the first sample comprises a first plurality of messenger ribonucleic (mRNA) molecules and the second sample comprises a second plurality of mRNA molecules; (b) optically detecting the first plurality of mRNA molecules or derivative thereof in the first sample; and (c) optically detecting the second plurality of mRNA molecules or derivative thereof in the second sample.

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 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; (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 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; (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 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; (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 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 (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 Non-infectious 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 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. 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 1100 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 1170 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 1100 to receive biological samples to be analyzed. The sample processing circuit 1120 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 (PaCO₂), 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., Nanosphere 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 microfluidic 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 RNA 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 non-infectious 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 non-infectious 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., cDNA), 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(1):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, BeadArray™ 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 (RIN) 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 non-infectious 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 pre-defined 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:

$\begin{matrix} P (having condition) = Φ (β_{1} X_{1} + β_{2} X_{2} + \dots + β_{d} X_{d)} & (equation 1) \end{matrix}$

where the condition is bacterial ARI, viral ARI, or non-infection illness; Φ(⋅) is the probit (or logistic, etc.) link function; {β₁,β₂, . . . , β_d} are the coefficients obtained during training (e.g., second, third and fourth columns from Table 9) (coefficients may also be denoted {w₁,w₂, . . . , w_d} as “weights” herein); {X₁,X₂, . . . , 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 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 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 ARI 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
NI
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
FAM13A

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
FOLR3

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
MTMR1

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
CAMK1

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

221491_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
SIGLEC1

222059_at
0
0.112261
0
ZNF335

NA
NA
NA
NA
MRC2

209031_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
CEACAM8

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

219777_at
0
0.25509
0
GIMAP6

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_s_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

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
EMR3

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
MRPS31

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
ICAM4

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
TENM3

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

208601_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

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 non-infectious 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 (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 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 non-infectious 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^stlevel 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 non-infectious 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 ARI, 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^ndtier classification occurs that distinguishes between the remaining possibilities. 2L-SO is the 1^sttier 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^rdmodel 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 ‘1’ in these columns.

Table 2 provides a summary of use of members of the gene sets for viral, bacterial, and non-infectious illness classifiers that are constructed according to the required task. A ‘1’ indicates membership of the gene in the classifier.

TABLE 2

Affymetrix

BVS-
BVS-
BVS-

2L-
2L-
2L-
2L-
2L-
2L-

BVSH-
BVSH-
BVSH-

Probe ID
BVS
BO
VO
SO
2L
SO
BV
BO
VS
VO
SB
BVSH
BO
VO
SO

200042_at
1
0
1
0
1
0
0
0
0
1
0
0
0
0
0

200073_s_at
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0

200602_at
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0

200663_at
0
0
0
0
1
0
1
0
0
0
0
0
0
0
0

200709_at
0
0
0
0
1
0
0
0
1
0
0
0
0
0
0

200947_s_at
1
1
0
0
1
0
1
1
0
0
0
0
0
0
0

201055_s_at
1
0
0
1
1
1
0
0
0
0
1
1
0
0
1

201162_at
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0

201166_s_at
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0

201188_s_at
1
1
0
0
1
0
0
1
0
0
0
0
0
0
0

201341_at
1
1
0
0
1
0
0
1
0
0
0
0
0
0
0

201369_s_at
0
0
0
0
1
0
0
0
0
0
1
0
0
0
0

201392_s_at
0
0
0
0
0
0
0
0
0
0
0
1
0
0
1

201454_s_at
0
0
0
0
0
0
0
0
0
0
0
1
0
0
1

201464_x_at
0
0
0
0
0
0
0
0
0
0
0
1
0
1
0

201601_x_at
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0

201651_s_at
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0

201659_s_at
0
0
0
0
1
0
0
0
0
0
1
0
0
0
0

201802_at
0
0
0
0
1
0
1
0
0
0
0
0
0
0
0

201890_at
0
0
0
0
1
0
1
0
0
0
0
0
0
0
0

201949_x_at
0
0
0
0
1
0
0
0
0
0
1
0
0
0
0

201952_at
0
0
0
0
0
0
0
0
0
0
0
1
0
1
0

201972_at
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0

201992_s_at
0
0
0
0
0
0
0
0
0
0
0
1
0
0
1

202005_at
1
1
0
0
1
0
1
1
0
0
0
1
1
0
1

202083_s_at
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0

202090_s_at
0
0
0
0
0
0
0
0
0
0
0
1
0
1
0

202145_at
1
0
1
0
1
0
0
0
1
1
0
1
0
1
0

202160_at
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0

202266_at
0
0
0
0
1
0
1
0
0
0
0
0
0
0
0

202284_s_at
1
1
0
0
1
0
0
1
0
0
0
0
0
0
0

202411_at
1
1
0
0
1
0
1
1
0
0
0
1
0
1
0

202505_at
0
0
0
0
1
0
1
0
0
0
0
0
0
0
0

202509_s_at
1
0
0
1
1
1
0
0
0
0
1
1
0
0
1

202579_x_at
0
0
0
0
0
0
0
0
0
0
0
1
0
1
0

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1
0
0
1
0
0
1
0
0
0
0
0
0
0

216473_x_at
1
0
1
0
1
0
0
0
1
1
0
0
0
0
0

216571_at
1
1
0
0
1
0
1
1
0
0
0
0
0
0
0

216676_x_at
0
0
0
0
0
0
0
0
0
0
0
1
1
0
0

216713_at
1
1
0
0
1
0
0
1
0
0
0
0
0
0
0

216748_at
0
0
0
0
0
0
0
0
0
0
0
1
0
1
0

216867_s_at
1
1
0
0
1
0
0
1
0
0
0
0
0
0
0

216950_s_at
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0

217143_s_at
1
1
0
0
1
0
1
1
0
0
0
1
1
0
0

217408_at
1
0
1
1
1
1
0
0
1
1
0
1
0
1
1

217497_at
0
0
0
0
0
0
0
0
0
0
0
1
0
1
0

217593_at
1
1
0
0
1
0
1
1
0
0
0
1
1
0
0

217717_s_at
1
1
0
0
1
0
1
1
0
0
0
0
0
0
0

218010_x_at
0
0
0
0
1
0
1
0
0
0
0
0
0
0
0

218040_at
0
0
0
0
0
0
0
0
0
0
0
1
0
0
1

218060_s_at
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0

218095_s_at
1
0
1
0
1
0
0
0
1
1
0
0
0
0
0

218135_at
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0

218306_s_at
1
0
0
1
1
1
0
0
0
0
1
0
0
0
0

218510_x_at
0
0
0
0
0
0
0
0
0
0
0
1
1
0
0

218523_at
0
0
0
0
1
0
1
0
0
0
0
1
0
0
1

218595_s_at
1
0
0
1
1
1
0
0
0
0
0
1
0
0
1

218637_at
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0

218700_s_at
0
0
0
0
1
0
0
0
1
0
0
0
0
0
0

218812_s_at
1
1
0
0
1
0
1
1
0
0
0
1
1
0
0

218818_at
0
0
0
0
0
0
0
0
0
0
0
1
0
0
1

218946_at
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0

218999_at
0
0
0
0
0
0
0
0
0
0
0
1
1
0
0

219055_at
1
1
0
0
1
0
0
1
0
0
0
0
0
0
0

219066_at
1
0
1
0
1
0
0
0
0
1
0
1
0
1
0

219124_at
0
0
0
0
0
0
0
0
0
0
0
1
0
0
1

219130_at
1
0
1
0
1
0
0
0
0
1
0
0
0
0
0

219143_s_at
0
0
0
0
1
0
1
0
0
0
0
1
1
1
0

219269_at
0
0
0
0
1
0
1
0
0
0
0
0
0
0
0

219382_at
1
1
0
0
1
0
0
1
0
0
0
0
0
0
0

219437_s_at
1
0
1
1
1
1
0
0
1
1
0
1
0
1
0

219523_s_at
1
0
0
1
1
1
0
0
0
0
1
0
0
0
0

219577_s_at
0
0
0
0
0
0
0
0
0
0
0
1
0
0
1

219599_at
0
0
0
0
0
0
0
0
0
0
0
1
1
0
0

219629_at
0
0
0
0
0
0
0
0
0
0
0
1
0
0
1

219669_at
0
0
0
0
1
0
0
0
0
0
1
0
0
0
0

219693_at
0
0
0
0
0
0
0
0
0
0
0
1
0
1
0

219745_at
0
0
0
0
0
0
0
0
0
0
0
1
1
0
0

219762_s_at
0
0
0
0
1
0
1
0
0
0
0
0
0
0
0

219763_at
0
0
0
0
0
0
0
0
0
0
0
1
0
0
1

219777_at
1
0
1
0
1
0
0
0
0
1
0
0
0
0
0

219872_at
0
0
0
0
1
0
0
0
1
0
0
0
0
0
0

219966_x_at
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0

219999_at
0
0
0
0
0
0
0
0
0
0
0
1
0
0
1

220036_s_at
0
0
0
0
0
0
0
0
0
0
0
1
0
0
1

220059_at
1
1
0
0
1
0
1
1
0
0
0
1
1
0
0

220122_at
1
1
0
0
1
0
1
1
0
0
0
1
1
0
0

220308_at
1
0
1
0
1
0
0
0
1
1
0
0
0
0
0

220319_s_at
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0

220646_s_at
0
0
0
0
1
0
1
0
0
0
0
0
0
0
0

220765_s_at
0
0
0
0
0
0
0
0
0
0
0
1
1
0
0

220935_s_at
0
0
0
0
1
0
0
0
0
0
1
1
1
0
0

221032_s_at
0
0
0
0
1
0
1
0
0
0
0
0
0
0
0

221142_s_at
0
0
0
0
1
0
0
0
1
0
0
0
0
0
0

221211_s_at
0
0
0
0
0
0
0
0
0
0
0
1
1
0
0

221491_x_at
1
1
0
0
1
0
1
1
0
0
0
1
1
0
0

221874_at
1
1
0
1
1
1
1
1
0
0
0
1
1
0
0

221964_at
0
0
0
0
0
0
0
0
0
0
0
1
1
0
0

222059_at
1
0
1
0
1
0
0
0
0
1
0
0
0
0
0

222186_at
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0

222297_x_at
0
0
0
0
0
0
0
0
0
0
0
1
0
0
1

222330_at
0
0
0
0
1
0
0
0
1
0
0
0
0
0
0

320_at
0
0
0
0
1
0
1
0
0
0
0
0
0
0
0

44673_at
1
1
0
0
1
0
0
1
0
0
0
0
0
0
0

49329_at
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0

49452_at
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0

215185_at
0
0
0
0
0
0
0
0
0
0
0
1
0
0
1

AFFX-
0
0
0
0
0
0
0
0
0
0
0
1
1
0
0

HUMGAPDH/

M33197_M_at

206512_at
0
0
0
0
0
0
0
0
0
0
0
1
1
0
0

211781_x_at
0
0
0
0
1
0
1
0
0
0
0
0
0
0
0

216635_at
0
0
0
0
1
0
0
0
1
0
0
0
0
0
0

216943_at
1
1
0
0
1
0
1
1
0
0
0
0
0
0
0

217079_at
0
0
0
0
0
0
0
0
0
0
0
1
0
0
1

220352_x_at
0
0
0
0
0
0
0
0
0
0
0
1
0
0
1

Affymetrix
BVSH-
Gene

Probe ID
HO
Symbol
RefSeq ID
Gene Name

200042_at
0
HSPC117
NM_014306
chromosome 22 open reading frame 28

200073_s_at
1
HNRPD
NM_031369;
heterogeneous nuclear ribonucleoprotein D (AU-rich

NM_001003810;
element RNA binding protein 1, 37 kDa)

NM_031370;

NM_002138

200602_at
1
APP
NM_000484;
amyloid beta (A4) precursor protein

NM_201414;

NM_001136131;

NM_201413;

NM_001136130;

NM_001136016;

NM_001136129

200663_at
0
CD63
NM_001780;
CD63 molecule

NM_001040034

200709_at
0
FKBP1A
NM_000801;
FK506 binding protein 1A, 12 kDa

NM_054014

200947_s_at
0
GLUD1
NM_005271
glutamate dehydrogenase 1

201055_s_at
0
HNRPA0
NM_006805
heterogeneous nuclear ribonucleoprotein A0

201162_at
1
IGFBP7
NM_001553
insulin-like growth factor binding protein 7

201166_s_at
1
PUM1
NM_014676;
pumilio homolog 1 (Drosophila)

NM_001020658

201188_s_at
0
ITPR3
NM_002224
inositol 1,4,5-triphosphate receptor, type 3

201341_at
0
ENC1
NM_003633
ectodermal-neural cortex (with BTB-like domain)

201369_s_at
0
ZFP36L2
NM_006887
zinc finger protein 36, C3H type-like 2

201392_s_at
0
IGF2R
NM_000876
insulin-like growth factor 2 receptor

201454_s_at
0
NPEPPS
NM_006310;
hypothetical protein FLJ11822; aminopeptidase

XM_001725441;
puromycin sensitive

XM_001725426

201464_x_at
0
JUN
NM_002228
jun oncogene

201601_x_at
1
IFITM1
NM_003641
interferon induced transmembrane protein 1 (9-27)

201651_s_at
1
PACSIN2
NM_007229
protein kinase C and casein kinase substrate in

neurons 2

201659_s_at
0
ARL1
NM_001177
ADP-ribosylation factor-like 1

201802_at
0
SLC29A1
NM_001078176;
solute carrier family 29 (nucleoside transporters),

NM_001078177;
member 1

NM_001078175;

NM_004955;

NM_001078174

201890_at
0
RRM2
NM_001034;
ribonucleotide reductase M2 polypeptide

NM_001165931

201949_x_at
0
CAPZB
NM_004930
capping protein (actin filament) muscle Z-line, beta

201952_at
0
ALCAM
XM_001720217;
hypothetical protein LOC100133690; activated

NM_001627
leukocyte cell adhesion molecule

201972_at
1
ATP6V1A
NM_001690
ATPase, H+ transporting, lysosomal 70 kDa, V1

subunit A

201992_s_at
0
KIF5B
NM_004521
kinesin family member 5B

202005_at
0
ST14
NM_021978
suppression of tumorigenicity 14 (colon carcinoma)

202083_s_at
1
SEC14L1
NM_001143998;
SEC14-like 1 (S. cerevisiae); SEC14-like 1 pseudogene

NM_001039573;

NM_001144001;

NM_001143999;

NM_003003

202090_s_at
0
UQCR
NM_006830
ubiquinol-cytochrome c reductase, 6.4 kDa subunit

202145_at
0
LY6E
NM_002346;
lymphocyte antigen 6 complex, locus E

NM_001127213

202160_at
1
CREBBP
NM_004380;
CREB binding protein

NM_001079846

202266_at
0
TTRAP
NM_016614
TRAF and TNF receptor associated protein

202284_s_at
0
CDKN1A
NM_078467;
cyclin-dependent kinase inhibitor 1A (p21, Cip1)

NM_000389

202411_at
0
IFI27
NM_005532;
interferon, alpha-inducible protein 27

NM_001130080

202505_at
0
SNRPB2
NM_003092;
small nuclear ribonucleoprotein polypeptide B″

NM_198220

202509_s_at
0
TNFAIP2
NM_006291
tumor necrosis factor, alpha-induced protein 2

202579_x_at
0
HMGN4
NM_006353
high mobility group nucleosomal binding domain 4

202589_at
0
TYMS
NM_001071
thymidylate synthetase

202617_s_at
0
MECP2
NM_001110792;
methyl CpG binding protein 2 (Rett syndrome)

NM_004992

202644_s_at
0
TNFAIP3
NM_006290
tumor necrosis factor, alpha-induced protein 3

202679_at
0
NPC1
NM_000271
Niemann-Pick disease, type C1

202688_at
0
TNFSF10
NM_003810
tumor necrosis factor (ligand) superfamily, member

10

202709_at
0
FMOD
NM_002023
fibromodulin

202720_at
0
TES
NM_152829;
testis derived transcript (3 LIM domains)

NM_015641

202748_at
0
GBP2
NM_004120
guanylate binding protein 2, interferon-inducible

202864_s_at
0
SP100
NM_003113;
SP100 nuclear antigen

NM_001080391

202973_x_at
0
FAM13A1
NM_014883;
family with sequence similarity 13, member A

NM_001015045

203023_at
0
HSPC111
NM_016391
NOP16 nucleolar protein homolog (yeast)

203045_at
0
NINJ1
NM_004148
ninjurin 1

203153_at
0
IFIT1
NM_001548
interferon-induced protein with tetratricopeptide

repeats 1

203275_at
0
IRF2
NM_002199
interferon regulatory factor 2

203290_at
0
HLA-DQA1
NM_002122;
similar to hCG2042724; similar to HLA class II

XM_001719804;
histocompatibility antigen, DQ(1) alpha chain

XM_001129369;
precursor (DC-4 alpha chain); major

XM_001722105
histocompatibility complex, class II, DQ alpha 1

203313_s_at
0
TGIF
NM_173211;
TGFB-induced factor homeobox 1

NM_173210;

NM_003244;

NM_174886;

NM_173209;

NM_173208;

NM_173207;

NM_170695

203392_s_at
0
CTBP1
NM_001328;
C-terminal binding protein 1

NM_001012614

203414_at
1
MMD
NM_012329
monocyte to macrophage differentiation-associated

203455_s_at
0
SAT
NM_002970
spermidine/spermine N1-acetyltransferase 1

203570_at
0
LOXL1
NM_005576
lysyl oxidase-like 1

203615_x_at
0
SULT1A1
NM_177529;
sulfotransferase family, cytosolic, 1A, phenol-

NM_177530;
preferring, member 1

NM_177534;

NM_001055;

NM_177536

203633_at
1
CPT1A
NM_001876;
carnitine palmitoyltransferase 1A (liver)

NM_001031847

203717_at
1
DPP4
NM_001935
dipeptidyl-peptidase 4

203882_at
0
ISGF3G
NM_006084
interferon regulatory factor 9

203940_s_at
0
VASH1
NM_014909
vasohibin 1

203979_at
0
CYP27A1
NM_000784
cytochrome P450, family 27, subfamily A,

polypeptide 1

204069_at
1
MEIS1
NM_002398
Meis homeobox 1

204392_at
0
CAMK1
NM_003656
calcium/calmodulin-dependent protein kinase I

204490_s_at
0
CD44
NM_000610;
CD44 molecule (Indian blood group)

NM_001001389;

NM_001001390;

NM_001001391;

NM_001001392

204545_at
0
PEX6
NM_000287
peroxisomal biogenesis factor 6

204592_at
1
DLG4
NM_001365;
discs, large homolog 4 (Drosophila)

NM_001128827

204647_at
0
HOMER3
NM_001145724;
homer homolog 3 (Drosophila)

NM_004838;

NM_001145722;

NM_001145721

204724_s_at
0
COL9A3
NM_001853
collagen, type IX, alpha 3

204750_s_at
0
DSC2
NM_004949;
desmocollin 2

NM_024422

204853_at
0
ORC2L
NM_006190
origin recognition complex, subunit 2-like (yeast)

204858_s_at
1
ECGF1
NM_001953;
thymidine phosphorylase

NM_001113755;

NM_001113756

204981_at
0
SLC22A18
NM_002555;
solute carrier family 22, member 18

NM_183233

205001_s_at
0
DDX3Y
NM_001122665;
DEAD (Asp-Glu-Ala-Asp) box polypeptide 3, γ-linked

NM_004660

205008_s_at
0
CIB2
NM_006383
calcium and integrin binding family member 2

205033_s_at
0
DEFA1 ///
NM_004084;
defensin, alpha 1

DEFA3
NM_001042500

205048_s_at
0
PSPH
NM_004577
phosphoserine phosphatase-like; phosphoserine

phosphatase

205053_at
0
PRIM1
NM_000946
primase, DNA, polypeptide 1 (49 kDa)

205098_at
0
CCR1
NM_001295
chemokine (C-C motif) receptor 1

205153_s_at
0
CD40
NM_152854;
CD40 molecule, TNF receptor superfamily member 5

NM_001250

205164_at
0
GCAT
NM_014291;
glycine C-acetyltransferase (2-amino-3-ketobutyrate

NM_001171690
coenzyme A ligase)

205200_at
0
CLEC3B
NM_003278
C-type lectin domain family 3, member B

205312_at
0
SPI1
NM_001080547;
spleen focus forming virus (SFFV) proviral

NM_003120
integration oncogene spi1

205376_at
0
INPP4B
NM_003866;
inositol polyphosphate-4-phosphatase, type II,

NM_001101669
105 kDa

205382_s_at
0
DF
NM_001928
complement factor D (adipsin)

205826_at
0
MYOM2
NM_003970
myomesin (M-protein) 2, 165 kDa

206005_s_at
1
C6orf84
NM_014895
KIAA1009

206035_at
0
REL
NM_002908
v-rel reticuloendotheliosis viral oncogene homolog

(avian)

206082_at
0

NM_006674
HLA complex P5

206207_at
0
CLC
NM_001828
Charcot-Leyden crystal protein

206214_at
0
PLA2G7
NM_005084;
phospholipase A2, group VII (platelet-activating

NM_001168357
factor acetylhydrolase, plasma)

206371_at
0
FOLR3
NM_000804
folate receptor 3 (gamma)

206508_at
1
TNFSF7
NM_001252
CD70 molecule

206558_at
0
SIM2
NM_009586;
single-minded homolog 2 (Drosophila)

NM_005069

206647_at
0
HBZ
NM_005332
hemoglobin, zeta

206676_at
0
CEACAM8
NM_001816
carcinoembryonic antigen-related cell adhesion

molecule 8

206734_at
0
JRKL
NM_003772
jerky homolog-like (mouse)

206896_s_at
0
GNG7
NM_052847
guanine nucleotide binding protein (G protein),

gamma 7

206918_s_at
0
CPNE1
NM_152929;
RNA binding motif protein 12; copine I

NM_152928;

NM_152927;

NM_003915;

NM_152931;

NM_152930;

NM_006047;

NM_152925;

NM_152926;

NM_152838

206934_at
0
SIRPB1
NM_001135844;
signal-regulatory protein beta 1

NM_006065;

NM_001083910

207008_at
0
IL8RB
NM_001168298;
interleukin 8 receptor, beta

NM_001557

207075_at
0
CIAS1
NM_004895;
NLR family, pyrin domain containing 3

NM_001079821;

NM_001127462;

NM_001127461;

NM_183395

207194_s_at
0
ICAM4
NM_022377;
intercellular adhesion molecule 4 (Landsteiner-

NM_001544;
Wiener blood group)

NM_001039132

207244_x_at
0
CYP2A6
NM_000762
cytochrome P450, family 2, subfamily A, polypeptide 6

207306_at
0
TCF15
NM_004609
transcription factor 15 (basic helix-loop-helix)

207436_x_at
0
KIAA0894

ambiguous (pending)

207536_s_at
0
TNFRSF9
NM_001561
tumor necrosis factor receptor superfamily, member 9

207606_s_at
0
ARHGAP12
NM_018287
Rho GTPase activating protein 12

207718_x_at
0
CYP2A6 ///
NM_000764;
cytochrome P450, family 2, subfamily A, polypeptide 7

CYP2A7 ///
NM_030589

CYP2A7P1

///

CYP2A13

207721_x_at
0
HINT1
NM_005340
histidine triad nucleotide binding protein 1

207808_s_at
0
PROS1
NM_000313
protein S (alpha)

207840_at
0
CD160
NM_007053
CD160 molecule

207860_at
0
NCR1
NM_001145457;
natural cytotoxicity triggering receptor 1

NM_001145458;

NM_004829

207983_s_at
1
STAG2
NM_006603;
stromal antigen 2

NM_001042749;

NM_001042751;

NM_001042750

208029_s_at
0
LAPTM4B
NM_018407
lysosomal protein transmembrane 4 beta

208241_at
0
NRG1
NM_001160001;
neuregulin 1

NM_001159995;

NM_001160007;

NM_001160008;

NM_001159996;

NM_001159999;

NM_001160002;

NM_001160004;

NM_004495;

NM_001160005;

NM_013964;

NM_013960;

NM_013962;

NM_013961;

NM_013959;

NM_013958;

NM_013957;

NM_013956

208501_at
0
GFI1B
NM_001135031,
growth factor independent 1B transcription

NM_004188
repressor

208545_x_at
0
TAF4
NM_003185
TAF4 RNA polymerase II, TATA box binding protein

(TBP)-associated factor, 135 kDa

208601_s_at
0
TUBB1
NM_030773
tubulin, beta 1

208702_x_at
0
APLP2
NM_001642;
amyloid beta (A4) precursor-like protein 2

NM_001142277;

NM_001142278;

NM_001142276

208710_s_at
0
AP3D1
NM_003938;
adaptor-related protein complex 3, delta 1 subunit

NM_001077523

208736_at
0
ARPC3
NM_005719
similar to actin related protein ⅔ complex subunit

3; hypothetical LOC729841; actin related protein 2/3

complex, subunit 3, 21 kDa

208743_s_at
0
YWHAB
NM_139323;
tyrosine 3-monooxygenase/tryptophan 5-

NM_003404
monooxygenase activation protein, beta

polypeptide

208782_at
0
FSTL1
NM_007085
follistatin-like 1

208886_at
0
H1F0
NM_005318
H1 histone family, member 0

208974_x_at
0
KPNB1
NM_002265
karyopherin (importin) beta 1

209031_at
0
IGSF4
NM_014333;
cell adhesion molecule 1

NM_001098517

209218_at
0
SQLE
NM_003129
squalene epoxidase

209360_s_at
0
RUNX1
NM_001122607;
runt-related transcription factor 1

NM_001001890;

NM_001754

209396_s_at
0
CHI3L1
NM_001276
chitinase 3-like 1 (cartilage glycoprotein-39)

209422_at
1
PHF20
NM_016436
PHD finger protein 20

209511_at
0
POLR2F
NM_021974
polymerase (RNA) II (DNA directed) polypeptide F

209605_at
0
TST
NM_003312
thiosulfate sulfurtransferase (rhodanese)

209691_s_at
0
DOK4
NM_018110
docking protein 4

209906_at
0
C3AR1
NM_004054
complement component 3a receptor 1

209919_x_at
0
GGT1
XM_001129425;
gamma-glutamyltransferase light chain 3; gamma-

NM_013430;
glutamyltransferase 4 pseudogene; gamma-

NM_001032365;
glutamyltransferase 2; gamma-glutamyltransferase

NM_005265;
1; gamma-glutamyltransferase light chain 5

NM_001032364;
pseudogene

XM_001129377

210164_at
0
GZMB
NM_004131
granzyme B (granzyme 2, cytotoxic T-lymphocyte-

associated serine esterase 1)

210172_at
0
SF1
NM_004630;
splicing factor 1

NM_201995;

NM_201997;

NM_201998

210240_s_at
0
CDKN2D
NM_001800;
cyclin-dependent kinase inhibitor 2D (p19, inhibits

NM_079421
CDK4)

210365_at
0
RUNX1
NM_001122607;
runt-related transcription factor 1

NM_001001890;

NM_001754

210499_s_at
0
PQBP1
NM_005710;
polyglutamine binding protein 1

NM_001032384;

NM_001032383;

NM_001167989;

NM_001167990;

NM_144495;

NM_001167992;

NM_001032381;

NM_001032382

210724_at
0
EMR3
NM_032571
egf-like module containing, mucin-like, hormone

receptor-like 3

210797_s_at
0
OASL
NM_198213;
2′-5′-oligoadenylate synthetase-like

NM_003733

210846_x_at
0
TRIM14
NM_033219;
tripartite motif-containing 14

NM_033220;

NM_014783;

NM_033221

211137_s_at
0
ATP2C1
NM_014382;
ATPase, Ca++ transporting, type 2C, member 1

NM_001001486;

NM_001001487;

NM_001001485

211792_s_at
0
CDKN2C
NM_001262;
cyclin-dependent kinase inhibitor 2C (p18, inhibits

NM_078626
CDK4)

211878_at
1

XM_001718220
immunoglobulin heavy constant gamma 1 (G1m

marker); immunoglobulin heavy constant mu;

immunoglobulin heavy variable 3-7;

immunoglobulin heavy constant gamma 3 (G3m

marker); immunoglobulin heavy variable 3-11

(gene/pseudogene); immunoglobulin heavy variable

4-31; immunoglobulin heavy locus

211966_at
0
COL4A2
NM_001846
collagen, type IV, alpha 2

212035_s_at
0
EXOC7
NM_001145298;
exocyst complex component 7

NM_001145299;

NM_015219;

NM_001145297;

NM_001145296;

NM_001013839

212036_s_at
1
PNN
NM_002687
pinin, desmosome associated protein

212118_at
0
RFP
NM_006510
tripartite motif-containing 27

212162_at
0
KIDINS220
NM_020738
kinase D-interacting substrate, 220 kDa

212574_x_at
0
C19orf6
NM_033420;
chromosome 19 open reading frame 6

NM_001033026

212590_at
0
RRAS2
XM_001726427;
related RAS viral (r-ras) oncogene homolog 2; similar

NM_012250;
to related RAS viral (r-ras) oncogene homolog 2

XM_001726471;

NM_001102669;

XM_001726315

212655_at
1
ZCCHC14
NM_015144
zinc finger, CCHC domain containing 14

212657_s_at
0
IL1RN
NM_000577;
interleukin 1 receptor antagonist

NM_173841;

NM_173842;

NM_173843

212659_s_at
0
IL1RN
NM_000577;
interleukin 1 receptor antagonist

NM_173841;

NM_173842;

NM_173843

212676_at
0
NF1
NM_000267;
neurofibromin 1

NM_001042492;

NM_001128147

212697_at
0
LOC162427
NM_178126
family with sequence similarity 134, member C

212708_at
0
LOC339287
NM_001012241
male-specific lethal 1 homolog (Drosophila)

212810_s_at
0
SLC1A4
NM_003038;
solute carrier family 1 (glutamate/neutral amino

NM_001135581
acid transporter), member 4

212816_s_at
0
CBS
NM_000071
cystathionine-beta-synthase

212914_at
0
CBX7
NM_175709
chromobox homolog 7

212947_at
0
SLC9A8
NM_015266
solute carrier family 9 (sodium/hydrogen

exchanger), member 8

213223_at
0
RPL28
NM_001136134;
ribosomal protein L28

NM_000991;

NM_001136137;

NM_001136135;

NM_001136136

213300_at
0
KIAA0404
NM_015104
ATG2 autophagy related 2 homolog A (S. cerevisiae)

213422_s_at
0
MXRA8
NM_032348
matrix-remodelling associated 8

213573_at
0
KPNB1
NM_002265
karyopherin (importin) beta 1

213633_at
1
SH3BP1
NM_018957
SH3-domain binding protein 1

213700_s_at
1
PKM2
NM_002654;
similar to Pyruvate kinase, isozymes M1/M2

NM_182471;
(Pyruvate kinase muscle isozyme) (Cytosolic thyroid

NM_182470;
hormone-binding protein) (CTHBP) (THBP1);

XM_001719890
pyruvate kinase, muscle

213831_at
0
HLA-DOA1
NM_002122;
similar to hCG2042724; similar to HLA class II

XM_001719804;
histocompatibility antigen, DQ(1) alpha chain

XM_001129369;
precursor (DC-4 alpha chain); major

XM_001722105
histocompatibility complex, class II, DQ alpha 1

213907_at
0
EEF1E1
NM_004280;
eukaryotic translation elongation factor 1 epsilon 1

NM_001135650

214085_x_at
0
GLIPR1
NM_006851
GLI pathogenesis-related 1

214097_at
0
RPS21
NM_001024
ribosomal protein S21

214175_x_at
0
PDLIM4
NM_003687;
PDZ and LIM domain 4

NM_001131027

214321_at
0
NOV
NM_002514
nephroblastoma overexpressed gene

214326_x_at
0
JUND
NM_005354
jun D proto-oncogene

214511_x_at
0
FCGR1A ///
NM_001017986;
Fc fragment of IgG, high affinity Ib, receptor (CD64)

LOC440607
NM_001004340

214582_at
0
PDE3B
NM_000922
phosphodiesterase 3B, cGMP-inhibited

214617_at
0
PRF1
NM_005041;
perforin 1 (pore forming protein)

NM_001083116

214800_x_at
0
BTF3 ///
NM_001037637;
basic transcription factor 3; basic transcription

LOC345829
NM_001207
factor 3, like 1 pseudogene

214955_at
0
TMPRSS6
NM_153609
transmembrane protease, serine 6

215012_at
0
ZNF451
NM_001031623;
zinc finger protein 451

NM_015555

215088_s_at
0
SDHC
NM_003001;
succinate dehydrogenase complex, subunit C,

NM_001035513;
integral membrane protein, 15 kDa

NM_001035511;

NM_001035512

215184_at
0
DAPK2
NM_014326
death-associated protein kinase 2

215268_at
0
KIAA0754
NM_015038
hypothetical LOC643314

215606_s_at
0
RAB6IP2
NM_178040;
ELKS/RAB6-interacting/CAST family member 1

NM_015064;

NM_178037;

NM_178038;

NM_178039

215630_at
0

NM_015150
raftlin, lipid raft linker 1

215696_s_at
0
KIAA0310
NM_014866
SEC16 homolog A (S. cerevisiae)

215804_at
0
EPHA1
NM_005232
EPH receptor A1

215848_at
0
ZNF291
NM_001145923;
S-phase cyclin A-associated protein in the ER

NM_020843

216289_at
0

XM_002347085;
G protein-coupled receptor 144

XM_002342934;

XM_002346195;

NM_001161808

216303_s_at
0
MTMR1
NM_003828
myotubularin related protein 1

216473_x_at
0
DUX4 ///
XM_927996;
double homeobox, 4-like; similar to double

LOC399839
XM_001720078;
homeobox 4c; similar to double homeobox, 4;

///
XM_001722088;
double homeobox, 4

LOC401650
NM_001164467;

///
XM_928023;

LOC440013
XM_495858;

///
XM_941455;

LOC440014
NM_001127386;

///
XM_001720082;

LOC440015
XM_001720798;

///
XM_496731;

LOC440016
NM_001127387;

///
XM_495854;

LOC440017
XM_495855;

///
NM_001127388;

LOC441056
NM_033178;

NM_001127389;

XM_001724713

216571_at
0

NM_000543;
sphingomyelin phosphodiesterase 1, acid lysosomal

NM_001007593

216676_x_at
0
KIR3DL3
NM_153443
killer cell immunoglobulin-like receptor, three

domains, long cytoplasmic tail, 3

216713_at
0
KRIT1
NM_194454;
KRIT1, ankyrin repeat containing

NM_001013406;

NM_004912;

NM_194456;

NM_194455

216748_at
0
PYHIN1
NM_198928;
pyrin and HIN domain family, member 1

NM_152501;

NM_198930;

NM_198929

216867_s_at
0
PDGFA
NM_033023;
platelet-derived growth factor alpha polypeptide

NM_002607

216950_s_at
1
FCGR1A
NM_000566
Fc fragment of IgG, high affinity Ic, receptor (CD64);

Fc fragment of IgG, high affinity Ia, receptor (CD64)

217143_s_at
0
TRA@ ///

ambiguous (pending)

TRD@

217408_at
0
MRPS18B
NM_014046
mitochondrial ribosomal protein S18B

217497_at
0
ECGF1
NM_001953;
thymidine phosphorylase

NM_001113755;

NM_001113756

217593_at
0
ZNF447
NM_001145542;
zinc finger and SCAN domain containing 18

NM_001145543;

NM_001145544;

NM_023926

217717_s_at
0
YWHAB
NM_139323;
tyrosine 3-monooxygenase/tryptophan 5-

NM_003404
monooxygenase activation protein, beta

polypeptide

218010_x_at
0
C20orf149
NM_024299
pancreatic progenitor cell differentiation and

proliferation factor homolog (zebrafish)

218040_at
0
PRPF38B
NM_018061
PRP38 pre-mRNA processing factor 38 (yeast)

domain containing B

218060_s_at
1
FLJ13154
NM_024598
chromosome 16 open reading frame 57

218095_s_at
0
TPARL
NM_018475
transmembrane protein 165

218135_at
1
PTX1
NM_016570
ERGIC and golgi 2

218306_s_at
0
HERC1
NM_003922
hect (homologous to the E6-AP (UBE3A) carboxyl

terminus) domain and RCC1 (CHC1)-like domain

(RLD) 1

218510_x_at
0
FLJ20152
NM_001034850;
family with sequence similarity 134, member B

NM_019000

218523_at
0
LHPP
NM_022126;
phospholysine phosphohistidine inorganic

NM_001167880
pyrophosphate phosphatase

218595_s_at
0
HEATR1
NM_018072
HEAT repeat containing 1

218637_at
1
IMPACT
NM_018439
Impact homolog (mouse)

218700_s_at
0
RAB7L1
NM_001135664;
RAB7, member RAS oncogene family-like 1

NM_001135663;

NM_001135662;

NM_003929

218812_s_at
0
C7orf19
NM_032831;
ORAI calcium release-activated calcium modulator 2

NM_001126340

218818_at
0
FHL3
NM_004468
four and a half LIM domains 3

218946_at
1
HIRIP5
NM_001002755;
NFU1 iron-sulfur cluster scaffold homolog (S. cerevisiae)

NM_001002756;

NM_001002757;

NM_015700

218999_at
0
FLJ11000
NM_018295
transmembrane protein 140

219055_at
0
FLJ10379
NM_018079
S1 RNA binding domain 1

219066_at
0
PPCDC
NM_021823
phosphopantothenoylcysteine decarboxylase

219124_at
0
C8orf41
NM_001102401;
chromosome 8 open reading frame 41

NM_025115

219130_at
0
FLJ10287
NM_019083
coiled-coil domain containing 76

219143_s_at
0
RPP25
NM_017793
ribonuclease P/MRP 25 kDa subunit

219269_at
0
FLJ21616
NM_001135726;
homeobox containing 1

NM_024567

219382_at
0
SERTAD3
NM_013368;
SERTA domain containing 3

NM_203344

219437_s_at
0
ANKRD11
XM_001720760;
ankyrin repeat domain 11; hypothetical protein

NM_013275;
LOC100128265

XM_001721661;

XM_001721649

219523_s_at
0
ODZ3
NM_001080477
odz, odd Oz/ten-m homolog 3 (Drosophila)

219577_s_at
0
ABCA7
NM_019112
ATP-binding cassette, sub-family A (ABC1), member 7

219599_at
0
PRO1843
NM_001417
similar to eukaryotic translation initiation factor 4H;

eukaryotic translation initiation factor 4B

219629_at
0
C22orf8
NM_017911;
family with sequence similarity 118, member A

NM_001104595

219669_at
0
CD177
NM_020406
CD177 molecule

219693_at
0
AGPAT4
NM_020133
1-acylglycerol-3-phosphate O-acyltransferase 4

(lysophosphatidic acid acyltransferase, delta)

219745_at
0
C10orf77
NM_024789
transmembrane protein 180

219762_s_at
0
RPL36
NM_033643;
ribosomal protein L36; ribosomal protein L36

NM_015414
pseudogene 14

219763_at
0
DENND1A
NM_020946;
DENN/MADD domain containing 1A

NM_024820

219777_at
0
GIMAP6
NM_024711
GTPase, IMAP family member 6

219872_at
0
DKFZp434L142
NM_001031700;
chromosome 4 open reading frame 18

NM_016613;

NM_001128424

219966_x_at
1
BANP
NM_017869;
BTG3 associated nuclear protein

NM_079837

219999_at
0
MAN2A2
NM_006122
mannosidase, alpha, class 2A, member 2

220036_s_at
0
LMBR1L
NM_018113
limb region 1 homolog (mouse)-like

220059_at
0
BRDG1
NM_012108
signal transducing adaptor family member 1

220122_at
0
MCTP1
NM_024717;
multiple C2 domains, transmembrane 1

NM_001002796

220308_at
0
CCDC19
NM_012337
coiled-coil domain containing 19

220319_s_at
1
MYLIP
NM_013262
myosin regulatory light chain interacting protein

220646_s_at
0
KLRF1
NM_016523
killer cell lectin-like receptor subfamily F, member 1

220765_s_at
0
LIMS2
NM_017980;
LIM and senescent cell antigen-like domains 2

NM_001161404;

NM_001161403;

NM_001136037

220935_s_at
0
CDK5RAP2
NM_018249;
CDK5 regulatory subunit associated protein 2

NM_001011649

221032_s_at
0
TMPRSS5
NM_030770
transmembrane protease, serine 5

221142_s_at
0
PECR
NM_018441
peroxisomal trans-2-enoyl-CoA reductase

221211_s_at
0
C21orf7
NM_020152
chromosome 21 open reading frame 7

221491_x_at
0
HLA-DRB1
XM_002346768;
major histocompatibility complex, class II, DR beta 3

/// HLA-
NM_022555;

DRB3 ///
XM_002346769

HLA-DRB4

221874_at
0
KIAA1324
NM_020775
KIAA1324

221964_at
0
TULP3
NM_001160408;
tubby like protein 3

NM_003324

222059_at
0
ZNF335
NM_022095
zinc finger protein 335

222186_at
1
ZA20D3
NM_019006
zinc finger, AN1-type domain 6

222297_x_at
0
RPL18

ribosomal protein L18

222330_at
0
PDE3B
NM_000922
phosphodiesterase 3B, cGMP-inhibited

320_at
0
PEX6
NM_000287
peroxisomal biogenesis factor 6

44673_at
0
SN
NM_023068
sialic acid binding Ig-like lectin 1, sialoadhesin

49329_at
1
KLHL22
NM_032775
kelch-like 22 (Drosophila)

49452_at
1
ACACB
NM_001093
acetyl-Coenzyme A carboxylase beta

215185_at
0
LOC441468

AFFX-
0
GAPDH

HUMGAPDH/

M33197_M_at

206512_at
0
U2AF1L1

ambiguous (pending)

211781_x_at
0

216635_at
0

216943_at
0

217079_at
0

220352_x_at
0

Methods of Treating a Subject with an ARI

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. 11, 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 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 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 Read-Only 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 1100 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 1180 may also include updates to portions of the storage circuit 1170 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 of the classification system 1100 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 1100 and transport the biological sample within the classification system 1100 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 1130 may automatically analyze the processed biological sample. The sample analysis circuit 1130 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 1130 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 non-infectious 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 1130 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 1110, the sample processing circuit 1120, the sample analysis circuit 1130, 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 1140 of the classification system 1100. As used herein, executing “under the control” of the processor 1140 means that the operations performed by the sample input circuit 1110, the sample processing circuit 1120, the sample analysis circuit 1130, the input/output circuit 1160, the storage circuit 1170, and/or the update circuit 1180 may be at least partially executed and/or directed by the processor 1140, 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 1100, 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 1100, partly on the classification system 1100, 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 1100 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 non-infectious 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):1-9, 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 (26probes). 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.⁸This strategy has successfully characterized the host response to viral^8-13and 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.¹⁵However, 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).^16-19Additional 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.²⁰ARI 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.¹⁷The 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, HKU1), 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 mini VIDAS 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.²¹Therefore, 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.²²Specifically, 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-versus-all scheme whereby largest membership probability assigns class label.²¹Classification 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.¹⁵The 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=115), 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 μg/L assigned patients as having bacterial ARI, whereas values ≤0.25 μg/L assigned patients as non-bacterial, which could be either viral ARI or non-infectious 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-value<0.001], 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 sepsis¹¹; a 33-probe classifier (Hu) derived from children with febrile viral illness or bacterial infection¹⁴; and a 29-probe classifier (Parnell) derived from adult ICU patients with community-acquired pneumonia or influenza¹². 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).^24,25This 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):1-9, 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 Non-infectious 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.^26.27Its 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.²⁸

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 2011, more than any other cause.^1,2Despite 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.^3,4Even 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.⁷The 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 population^12,30, and tuberculosis^31-33. Typically, these define host response profiles compared to the healthy state, offering valuable insights into host biology.^16,34,35However, 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.¹⁵Healthy 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, RNA 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. non-respiratory 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 ARI 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.

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- 20. 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.
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- 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; 1(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 beta1 independent of importin alpha. Biochemistry. 2005; 44(38):12887-12895.
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- 31. Anderson et al. Diagnosis of Childhood Tuberculosis and Host RNA Expression in Africa. N Engl J Med. 2014; 370(18):1712-1723.
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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 = 115), or non-

infectious 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

Ramilo et al.
Bacterial
54 (77.1)
4 (3.5)
12 (13.6)
Classifier-

Viral
6 (8.6)
101 (87.8)
12 (13.6)
Predicted

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

Hu et al.
Bacterial
53 (75.7)
4 (3.5)
9 (10.2)

Viral
9 (12.9)
104 (90.4)
9 (10.2)

Non-infectious illness
8 (11.4)
7 (6.1)
70 (79.5)

Parnell et al.
Bacterial
51 (72.8)
8 (7.0)
11 (12.5)

Viral
13 (18.6)
94 (81.7)
10 (11.4)

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

Derived ARI
Bacterial
58 (82.8)
4 (3.4)
8 (9.0)

Classifier
Viral
9 (12.8)
104 (90.4)
4 (4.5)

Non-infectious illness
3 (4.2)
7 (6.0)
76 (86.3)

TABLE 7

External validation of the ARI classifier (combined bacterial ARI, viral ARI, and non-

infectious classifiers). Five Gene Expression Omnibus datasets were identified based on the

inclusion of at least two of the relevant clinical groups: Viral ARI, Bacterial ARI, non-infectious

illness (NI).

Clinical Assignment

Bacterial
Viral
NI
AUC

GSE6269: Hospitalized children with Influenza
Classifier-Predicted
Bacterial
84
1

0.95

A or bacterial infection
Assignment
Viral
2
26

GSE42026: Hospitalized children with

Bacterial
15
3

0.90

Influenza H1N1/09, RSV, or bacterial infection

Viral
6
35

GSE40396: Children with adenovirus, HHV-6,

Bacterial
7
1

0.93

enterovirus, or bacterial infection

Viral
3
32

GSE20346: Hospitalized adults with bacterial

Bacterial
26
0

0.99

pneumonia or Influenza A

Viral
1
18

GSE42834: Adults with bacterial pneumonia,

Bacterial
18

3
0.99

lung cancer, or sarcoidosis

SIRS
1

81

TABLE 8

Etiological causes of illness for subjects with viral ARI,

bacterial ARI, and non-nfectious illness.

Number of

subjects

Total Cohort
273

All Viral ARI
115

Coronavirus
7

Coxsackievirus/Echovirus
3

Cytomegalovirus
1

Enterovirus
20

Human Metapneumovirus
9

Influenza, non-typed
7

Influenza A, non-subtyped
6

Influenza A, 2009 H1N1
37

Parainfluenza
1

Polymicrobial (Coronavirus, Rhinovirus, Coxsackievirus/
1

Echovirus)

Rhinovirus
19

Respiratory Syncitial Virus
6

All Bacterial ARI
70

Bacillus species ^a
1

Bordetella bronchiseptica
1

Enterobacter aerogenes
1

Escherichia coli
1

Haemophilus influenza
3

Legionella sp.
3

Mycoplasma pneumoniae
1

Pasteurella multocida
1

Polymicrobial
11

Pantoea sp.; Coagulase negative Staphylococcus
1

Pseudomonas aeruginosa; Alcaligenes xylosoxidans
1

Pseudomonas aeruginosa; Serratia marcescens
1

Staphylococcus aureus; Haemophilus influenzae
2

Staphylococcus aureus; Proteus mirabilis
1

Staphylococcus aureus; Viridans Group Streptococcus;
1

Escherichia coli

Streptococcus pneumoniae; Haemophilus sp.
1

Streptococcus pneumoniae; Staphylococcus aureus
3

Proteus mirabilis
1

Pseudomonas aeruginosa
4

Staphylococcus aureus
7

Streptococcus pneumoniae
30

Streptococcus pyogenes
4

Viridans Group Streptococcus
1

All Non-Infectious Illness
88

Acute Renal Failure; Hypovolemia
1

Alcohol intoxication; Spinal cord stenosis; Hyperglycemia
1

Arrhythmia
2

Asthma
1

AV Graft Pseudoaneurysm and Thrombus
1

Brain Metastases with Vasogenic Edema
1

Cerebrovascular Accident
1

Chest Pain
2

Cocaine Intoxication
1

Congestive Heart Failure
13

Congestive Heart Failure; Amiodarone Toxicity
1

Congestive Heart Failure; Arrhythmia
1

Chronic Obstructive Pulmonary Disease
5

Cryptogenic Organizing Pneumonia
1

Emphysema
1

Gastrointestinal Hemorrhage
3

Hematoma in Leg
1

Hemochromatosis; Abdominal Pain and Peritoneal Dialysis
1

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

^aThis patient was adjudicated as having a bacterial ARI 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.

Bacterial
Viral
Non-Infectious

Affymetrix
ARI
ARI
Illness

Probe ID
Classifier
Classifier
Classifier
Gene Symbol
RefSeq ID
Gene Name

200042_at
0
0.038998
0
HSPC117
NM_014306
chromosome 22 open reading frame 28

200947_s_at
1.78944
0
0
GLUD1
NM_005271
glutamate dehydrogenase 1

201055_s_at
0
0
1.25363
HNRPA0
NM_006805
heterogeneous nuclear ribonucleoprotein A0

201188_s_at
0.606326
0
0
ITPR3
NM_002224
inositol 1,4,5-triphosphate receptor, type 3

201341_at
0.109677
0
0
ENC1
NM_003633
ectodermal-neural cortex (with BTB-like domain)

202005_at
−0.68053
0
0
ST14
NM_021978
suppression of tumorigenicity 14 (colon carcinoma)

202145_at
0
0.166043
0
LY6E
NM_002346; NM_001127213
lymphocyte antigen 6 complex, locus E

202284_s_at
−0.35646
0
0
CDKN1A
NM_078467; NM_000389
cyclin-dependent kinase inhibitor 1A (p21, Cip1)

202411_at
−0.05224
0
0
IFI27
NM_005532; NM_001130080
interferon, alpha-inducible protein 27

202509_s_at
0
0
0.416714
TNFAIP2
NM_006291
tumor necrosis factor, alpha-induced protein 2

202644_s_at
0.340624
0
0
TNFAIP3
NM_006290
tumor necrosis factor, alpha-induced protein 3

202688_at
0
0.005084
0
TNFSF10
NM_003810
tumor necrosis factor (ligand) superfamily, member 10

202709_at
0.427849
0
0
FMOD
NM_002023
fibromodulin

202720_at
0
0.07874
0
TES
NM_152829; NM_015641
testis derived transcript (3 LIM domains)

202864_s_at
0
0.02937
0
SP100
NM_003113; NM_001080391
SP100 nuclear antigen

202973_x_at
−0.11208
0
0
FAM13A1
NM_014883; NM_001015045
family with sequence similarity 13, member A

203045_at
−0.8509
0
0
NINJ1
NM_004148
ninjurin 1

203153_at
−0.13374
0
0
IFIT1
NM_001548
interferon-induced protein with tetratricopeptide repeats 1

203275_at
0
0.074576
0
IRF2
NM_002199
interferon regulatory factor 2

203313_s_at
−1.09463
0
0
TGIF
NM_173211; NM_173210;
TGFB-induced factor homeobox 1

NM_003244; NM_174886;

NM_173209; NM_173208;

NM_173207; NM_170695

203392_s_at
0
−0.01392
0
CTBP1
NM_001328; NM_001012614
C-terminal binding protein 1

203455_s_at
0
0
−0.0805395
SAT
NM_002970
spermidine/spermine N1-acetyltransferase 1

203882_at
0
0.034534
0
ISGF3G
NM_006084
interferon regulatory factor 9

203979_at
−0.00999
0
0.301178
CYP27A1
NM_000784
cytochrome P450, family 27, subfamily A, polypeptide 1

204392_at
0
0.111394
0
CAMK1
NM_003656
calcium/calmodulin-dependent protein kinase I

204490_s_at
0.007328
0
0
CD44
NM_000610; NM_001001389;
CD44 molecule (Indian blood group)

NM_001001390; NM_001001391;

NM_001001392

204545_at
0.342478
0
0
PEX6
NM_000287
peroxisomal biogenesis factor 6

204750_s_at
0.537475
0
0
DSC2
NM_004949; NM_024422
desmocollin 2

205001_s_at
0
−0.06712
0
DDX3Y
NM_001122665; NM_004660
DEAD (Asp-Glu-Ala-Asp) box polypeptide 3, Y-linked

205008_s_at
0
0.223868
0
CIB2
NM_006383
calcium and integrin binding family member 2

205033_s_at
0
−0.08786
0
DEFA1///DEFA3
NM_004084; NM_001042500
defensin, alpha 1

205048_s_at
−0.01145
0
0
PSPH
NM_004577
phosphoserine phosphatase-like; phosphoserine phosphatase

205098_at
−0.11641
0
0
CCR1
NM_001295
chemokine (C—C motif) receptor 1

205153_s_at
0.132886
0
0
CD40
NM_152854; NM_001250
CD40 molecule, TNF receptor superfamily member 5

205164_at
0.46638
0
0
GCAT
NM_014291; NM_001171690
glycine C-acetyltransferase (2-amino-3-ketobutyrate

coenzyme A ligase)

205200_at
0.87833
0
0
CLEC3B
NM_003278
C-type lectin domain family 3, member B

205312_at
0
0
−0.394304
SPI1
NM_001080547; NM_003120
spleen focus forming virus (SFFV) proviral integration

oncogene spi1

206207_at
−0.08529
0
0
CLC
NM_001828
Charcot-Leyden crystal protein

206371_at
0.043902
0
0
FOLR3
NM_000804
folate receptor 3 (gamma)

206647_at
0.065039
0
0
HBZ
NM_005332
hemoglobin, zeta

206676_at
0
0
0.0774651
CEACAM8
NM_001816
carcinoembryonic antigen-related cell adhesion molecule 8

206896_s_at
0.482822
0
0
GNG7
NM_052847
guanine nucleotide binding protein (G protein), gamma 7

206918_s_at
1.00926
0
0
CPNE1
NM_152929; NM_152928;
RNA binding motif protein 12; copine I

NM_152927; NM_003915;

NM_152931; NM_152930;

NM_006047; NM_152925;

NM_152926; NM_152838

206934_at
0.151959
0
0
SIRPB1
NM_001135844; NM_006065;
signal-regulatory protein beta 1

NM_001083910

207075_at
−0.06273
0
0
CIAS1
NM_004895; NM_001079821;
NLR family, pyrin domain containing 3

NM_001127462; NM_001127461;

NM_183395

207194_s_at
0.3162
0
0
ICAM4
NM_022377; NM_001544;
intercellular adhesion molecule 4 (Landsteiner-Wiener blood

NM_001039132
group)

207244_x_at
1.30636
0
0
CYP2A6
NM_000762
cytochrome P450, family 2, subfamily A, polypeptide 6

207606_s_at
0.299775
0
0
ARHGAP12
NM_018287
Rho GTPase activating protein 12

207718_x_at
0.039296
0
0
CYP2A6///CYP2A7///
NM_000764; NM_030589
cytochrome P450, family 2, subfamily A, polypeptide 7

CYP2A7P1///CYP2A13

207840_at
0
0.118889
0
CD160
NM_007053
CD160 molecule

207860_at
0.376517
0
0
NCR1
NM_001145457; NM_001145458;
natural cytotoxicity triggering receptor 1

NM_004829

208029_s_at
−0.02051
0
0.394049
LAPTM4B
NM_018407
lysosomal protein transmembrane 4 beta

208545_x_at
0.265408
0
0
TAF4
NM_003185
TAF4 RNA polymerase II, TATA box binding protein (TBP)-

associated factor, 135 kDa

208601_s_at
−0.27058
0
0
TUBB1
NM_030773
tubulin, beta 1

208702_x_at
0
0
0.0426262
APLP2
NM_001642; NM_001142277;
amyloid beta (A4) precursor-like protein 2

NM_001142278; NM_001142276

208736_at
0
0.582264
−0.0862941
ARPC3
NM_005719
similar to actin related protein 2/3 complex subunit 3;

hypothetical LOC729841; actin related protein 2/3 complex,

subunit 3, 21 kDa

208886_at
0.149103
0
0
H1F0
NM_005318
H1 histone family, member 0

208974_x_at
0
0.742946
0
KPNB1
NM_002265
karyopherin (importin) beta 1

209031_at
0
0
0.237916
IGSF4
NM_014333; NM_001098517
cell adhesion molecule 1

209360_s_at
0.303561
0
0
RUNX1
NM_001122607; NM_001001890;
runt-related transcription factor 1

NM_001754

209396_s_at
0
0
0.0355749
CHI3L1
NM_001276
chitinase 3-like 1 (cartilage glycoprotein-39)

209511_at
0
−0.03119
0
POLR2F
NM_021974
polymerase (RNA) II (DNA directed) polypeptide F

209605_at
−0.49934
0
0
TST
NM_003312
thiosulfate sulfurtransferase (rhodanese)

209919_x_at
0.613197
0
0
GGT1
XM_001129425; NM_013430;
gamma-glutamyltransferase light chain 3; gamma-

NM_001032365; NM_005265;
glutamyltransferase 4 pseudogene; gamma-

NM_001032364; XM_001129377
glutamyltransferase 2; gamma-glutamyltransferase 1; gamma-

glutamyltransferase light chain 5 pseudogene

210365_at
0.576935
0
0
RUNX1
NM_001122607; NM_001001890;
runt-related transcription factor 1

NM_001754

210724_at
0
0
0.482166
EMR3
NM_032571
egf-like module containing, mucin-like, hormone receptor-like 3

210797_s_at
0
0.185097
0
OASL
NM_198213; NM_003733
2′-5′-oligoadenylate synthetase-like

212035_s_at
2.0241
0
−1.26034
EXOC7
NM_001145298; NM_001145299;
exocyst complex component 7

NM_015219; NM_001145297;

NM_001145296; NM_001013839

212162_at
0
−0.01023
0
KIDINS220
NM_020738
kinase D-interacting substrate, 220 kDa

212657_s_at
0
0
−0.254507
IL1RN
NM_000577; NM_173841;
interleukin 1 receptor antagonist

NM_173842; NM_173843

212697_at
0
0
−1.02451
LOC162427
NM_178126
family with sequence similarity 134, member C

212708_at
0.032564
0
0
LOC339287
NM_001012241
male-specific lethal 1 homolog (Drosophila)

212914_at
0
0
0.0099678
CBX7
NM_175709
chromobox homolog 7

212947_at
0.286979
0
0
SLC9A8
NM_015266
solute carrier family 9 (sodium/hydrogen exchanger), member 8

213223_at
0.686657
0
0
RPL28
NM_001136134; NM_000991;
ribosomal protein L28

NM_001136137; NM_001136135;

NM_001136136

213300_at
−0.5783
0
0
KIAA0404
NM_015104
ATG2 autophagy related 2 homolog A (S. cerevisiae)

213573_at
0
0
−0.497655
KPNB1
NM_002265
karyopherin (importin) beta 1

213633_at
−1.01336
0
0
SH3BP1
NM_018957
SH3-domain binding protein 1

214085_x_at
−0.36761
0
0
GLIPR1
NM_006851
GLI pathogenesis-related 1

214097_at
0.00915
−0.5768
0
RPS21
NM_001024
ribosomal protein S21

214175_x_at
0
0
−0.266628
PDLIM4
NM_003687; NM_001131027
PDZ and LIM domain 4

214326_x_at
−0.69811
0
0.261075
JUND
NM_005354
jun D proto-oncogene

214582_at
0
0
0.0377349
PDE3B
NM_000922
phosphodiesterase 3B, cGMP-inhibited

214617_at
−0.26196
0
0
PRF1
NM_005041; NM_001083116
perforin 1 (pore forming protein)

214800_x_at
0
0.103261
0
BTF3/// LOC345829
NM_001037637; NM_001207
basic transcription factor 3; basic transcription factor 3, like 1

pseudogene

214955_at
−0.10065
0
0
TMPRSS6
NM_153609
transmembrane protease, serine 6

215184_at
0
−0.06503
0
DAPK2
NM_014326
death-associated protein kinase 2

215268_at
0.038178
0
0
KIAA0754
NM_015038
hypothetical LOC643314

215606_s_at
0.479765
0
0
RAB6IP2
NM_178040; NM_015064;
ELKS/RAB6-interacting/CAST family member 1

NM_178037; NM_178038;

NM_178039

215804_at
1.94364
0
0
EPHA1
NM_005232
EPH receptor A1

215848_at
0
0.326241
0
ZNF291
NM_001145923; NM_020843
S-phase cyclin A-associated protein in the ER

216289_at
0
−0.00075
0

XM_002347085; XM_002342934;
G protein-coupled receptor 144

XM_002346195; NM_001161808

216303_s_at
0.31126
0
0
MTMR1
NM_003828
myotubularin related protein 1

216473_x_at
0
−0.0343
0
DUX4///LOC399839///
XM_927996; XM_001720078;
double homeobox, 4-like; similar to double homeobox 4c;

LOC401650///LOC440013///
XM_001722088; NM_001164467;
similar to double homeobox, 4; double homeobox, 4

LOC440014///LOC440015///
XM_928023; XM_495858;

LOC440016///LOC440017///
XM_941455; NM_001127386;

LOC441056
XM_001720082; XM_001720798;

XM_496731; NM_001127387;

XM_495854; XM_495855;

NM_001127388; NM_033178;

NM_001127389; XM_001724713

216713_at
0.510039
0
0
KRIT1
NM_194454; NM_001013406;
KRIT1, ankyrin repeat containing

NM_004912; NM_194456;

NM_194455

216867_s_at
−0.05347
0
0
PDGFA
NM_033023; NM_002607
platelet-derived growth factor alpha polypeptide

217143_s_at
−0.3891
0
0
TRA@///TRD@

ambiguous (pending)

217408_at
0
1.07798
−0.0690681
MRPS18B
NM_014046
mitochondrial ribosomal protein S18B

217593_at
−0.07475
0
0
ZNF447
NM_001145542; NM_001145543;
zinc finger and SCAN domain containing 18

NM_001145544; NM_023926

217717_s_at
0.638943
0
0
YWHAB
NM_139323; NM_003404
tyrosine 3-monooxygenase/tryptophan 5-monooxygenase

activation protein, beta polypeptide

218095_s_at
0
−0.61377
0
TPARL
NM_018475
transmembrane protein 165

218306_s_at
0
0
0.784894
HERC1
NM_003922
hect (homologous to the E6-AP (UBE3A) carboxyl terminus)

domain and RCC1 (CHC1)-like domain (RLD) 1

218595_s_at
0
0
−0.411708
HEATR1
NM_018072
HEAT repeat containing 1

218812_s_at
−0.96799
0
0
C7orf19
NM_032831; NM_001126340
ORAI calcium release-activated calcium modulator 2

219055_at
−0.08524
0
0
FLJ10379
NM_018079
S1 RNA binding domain 1

219066_at
0
0.221446
0
PPCDC
NM_021823
phosphopantothenoylcysteine decarboxylase

219130_at
0
−0.15077
0
FLJ10287
NM_019083
coiled-coil domain containing 76

219382_at
0.866643
0
0
SERTAD3
NM_013368; NM_203344
SERTA domain containing 3

219437_s_at
0
−0.40545
0.198273
ANKRD11
XM_001720760; NM_013275;
ankyrin repeat domain 11; hypothetical protein

XM_001721661; XM_001721649
LOC100128265

219523_s_at
0
0
−0.0236667
ODZ3
NM_001080477
odz, odd Oz/ten-m homolog 3 (Drosophila)

219777_at
0
0.25509
0
GIMAP6
NM_024711
GTPase, IMAP family member 6

220059_at
−0.86817
0
0
BRDG1
NM_012108
signal transducing adaptor family member 1

220122_at
0.399475
0
0
MCTP1
NM_024717; NM_001002796
multiple C2 domains, transmembrane 1

220308_at
0
−0.03456
0
CCDC19
NM_012337
coiled-coil domain containing 19

221491_x_at
−0.65143
0
0
HLA-DRB1///HLA-DRB3///
XM_002346768; NM_022555;
major histocompatibility complex, class II, DR beta 3

HLA-DRB4
XM_002346769

221874_at
−0.40581
0
0.017015
KIAA1324
NM_020775
KIAA1324

222059_at
0
−0.11226
0
ZNF335
NM_022095
zinc finger protein 335

44673_at
−0.0308
0
0
SN
NM_023068
sialic acid binding Ig-like lectin 1, sialoadhesin

216571_at
0.878426
0
0

NM_000543; NM_001007593
sphingomyelin phosphodiesterase 1, acid lysosomal

216943_at
−0.91643
0
0

207436_x_at
0
0.243737
0
KIAA0894

ambiguous (pending)

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.

Biologic process
Bacterial
Viral
NI

Cell cycle regulation
JUND* (−), NINJ1, IFI27,
ZNF291
JUND* (+)

CDKN1A, C7orf19, SERTAD3

Regulation of cell growth
YWHAB, PDGFA

APLP2

Development/
GLIPR1, RUNX1, ST14, TGIF,
CTBP1
SP1, CEACAM8, ODZ3

Differentiation
EPHA1

RNA transcription,
FLJ10379, RPS21* (+),
DDX3Y, POLR2F, RPS21*
HEATR1, MRPS18B* (−)

processing
RPL28, TAF4, RPP25
(−), BTF3, MRPS18B* (+),

HSPC117, FLJ10287

Role in nuclear transport

KPNB1
KPNB1

Role in cell and membrane
RAB6IP2, SH3BP1, EXOC7*
TPARL
EXOC7* (−), HERC1,

trafficking
(+), LAPTM4B, CPNE1,

LAPTM4B, KIAA1324,

GNG7, TPARL, KIAA1324

APLP2

Cell structure/adhesion
TMPRSS6, TUBB1,
TES, ARPC3* (+),
PDLIM4, IGSF4, PDE3B,

ARHGAP12, ICAM4, DSC2,
KIDINS220
ARPC3* (−), CHI3L1

FMOD

Role in cell stress response
KIAA1324, KRIT1, ENC1

CBX7, APLP2, KIAA1324

Role in autophagy
LAPTM4B* (−), KIAA1324* (−)

KIAA1324* (+),

LAPTM4B* (+)

Role in apoptosis
KRIT1, GLIPR1, CIAS1
DAPK2, TNFSF10

General Inflammatory
TNFA1P3, FMOD, ITPR3,
TNFSF10
HNRPAO, EMR3, IL1RN,

response
CIAS1, GNG7, CLC, IFI27,

TNFAIP2, CHI3L1

CCR1

Interferon response
IFIT1
SP100, IRF2, OASL,

ISGF3G

Cytotoxic response
PRF1
DefA1/3

Toxin response
P450 gene cluster, CYP2A6,

ENC1, GGT1, TST

T-cell signaling
TRA/D@, CD44
Ly6E, CAMK1, CD160

B-cell signaling
BRDG1, HLA-DRB1/3/4,

CD40

NK-cell response
NCR1
CD160

Phospholipid and calcium
MTMR1, CPNE1, PSPH,

signaling
ITPR3, CLC, MCTP1

Fatty acid metabolism
PEX6, GLUD1

Cholesterol metabolism
CYP27A1* (−)

CYP27A1* (+)

Amino acid metabolism
GLUD1, PSPH, GCAT

*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 C_tfor a target RNA is subtracted from the C_tof endogenous normalization RNA (or the geometric mean of multiple normalization RNAs), providing a deltaC_tvalue 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 RNA 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:

$\begin{matrix} P (having condition) = Φ (β_{1} X_{1} + β_{2} X_{2} + \dots + β_{d} X_{d)} & (equation 1) \end{matrix}$

where the condition is bacterial ARI, viral ARI, or non-infection illness; Φ(⋅) is the probit link function; {β₁,β₂, . . . , β_d} are the coefficients obtained during training; {X₁,X₂, . . . , 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 1A. Weights appropriate for the TLDA platform for the respective classifiers were thereafter determined as described below in Example 4.

TABLE 1A

Preliminary list of genes for development of classifiers for TLDA platform.

Alternate

Non-

TLDA assay

Original Affy ID
Affy ID
GROUP
Bacterial
Viral
infectious
GENE
identifier

219437_s_at
212332_at
Affy signature
—
—
—
ANKRD11
Hs00331872_s1

208702_x_at
201642_at
Affy signature
—
—
—
APLP2
Hs00155778_m1

207606_s_at
212633_at
Affy signature
—
—
—
ARHGAP12
Hs00367895_m1

201659_s_at
209444_at
Affy signature
—
—
—
ARL1
Hs01029870_m1

208736_at
201132_at
Affy signature
—
—
—
ARPC3
Hs00855185_g1

205965_at
218695_at
Affy signature
—
—
—
BATF
Hs00232390_m1

214800_x_at
209876_at
Affy signature
—
—
—
BTF3
Hs00852566_g1

209031_at
209340_at
Affy signature
—
—
—
CADM1
Hs00296064_s1

204392_at
214054_at
Affy signature
—
—
—
CAMK1
Hs00269334_m1

201949_x_at
37012_at
Affy signature
—
—
—
CAPZB
Hs00191827_m1

207840_at
213830_at
Affy signature
—
—
—
CD160
Hs00199894_m1

200663_at
203234_at
Affy signature
—
—
—
CD63
Hs00156390_m1

220935_s_at
219271_at
Affy signature
—
—
—
CDK5RAP2
Hs01001427_m1

206676_at
207269_at
Affy signature
—
—
—
CEACAM8
Hs00266198_m1

209396_s_at
209395_at
Affy signature
—
—
—
CHI3L1
Hs01072230_g1

205008_s_at
58900_at
Affy signature
—
—
—
CIB2
Hs00197280_m1

205200_at
206034_at
Affy signature
—
—
—
CLEC3B
Hs00162844_m1

203979_at
49111_at
Affy signature
—
—
—
CYP27A1
Hs01017992_g1

207244_x_at
209280_at
Affy signature
—
—
—
CYP2A13
Hs00711162_s1

215184_at
217521_at
Affy signature
—
—
—
DAPK2
Hs00204888_m1

205001_s_at
214131_at
Affy signature
—
—
—
DDX3Y
Hs00965254_gH

205033_s_at
207269_at
Affy signature
—
—
—
DEFA3
Hs00414018_m1

204750_s_at
205418_at
Affy signature
—
—
—
DSC2
Hs00951428_m1

216473_x_at
221660_at
Affy signature
—
—
—
DUX4
Hs03037970_g1

210724_at
220246_at
Affy signature
—
—
—
EMR3
Hs01128745_m1

215804_at
206903_at
Affy signature
—
—
—
EPHA1
Hs00975876_g1

212035_s_at
200935_at
Affy signature
—
—
—
EXOC7
Hs01117053_m1

212697_at
46665_at
Affy signature
—
—
—
FAM134C
Hs00738661_m1

209919_x_at
218695_at
Affy signature
—
—
—
GGT1
Hs00980756_m1

219777_at
202963_at
Affy signature
—
—
—
GIMAP6
Hs00226776_m1

200947_s_at
202126_at
Affy signature
—
—
—
GLUD1
Hs03989560_s1

218595_s_at
217103_at
Affy signature
—
—
—
HEATR1
Hs00985319_m1

218306_s_at
212232_at
Affy signature
—
—
—
HERC1
Hs01032528_m1

221491_x_at
203290_at
Affy signature
—
—
—
HLA-DRB3
Hs00734212_m1

201055_s_at
37012_at
Affy signature
—
—
—
HNRNPA0
Hs00246543_s1

203153_at
219863_at
Affy signature
—
—
—
IFIT1
Hs01911452_s1

214022_s_at
35254_at
Affy signature
—
—
—
IFITM1
Hs00705137_s1

212657_s_at
202837_at
Affy signature
—
—
—
IL1RN
Hs00893626_m1

203275_at
213038_at
Affy signature
—
—
—
IRF2
Hs01082884_m1

203882_at
201649_at
Affy signature
—
—
—
IRF9
Hs00196051_m1

215268_at
200837_at
Affy signature
—
—
—
KIAA0754
Hs03055204_s1

221874_at
203063_at
Affy signature
—
—
—
KIAA1324
Hs00381767_m1

213573_at
31845_at
Affy signature
—
—
—
KPNB1
Hs00158514_m1

208029_s_at
212573_at
Affy signature
—
—
—
LAPTM4B
Hs00363282_m1

202145_at
204972_at
Affy signature
—
—
—
LY6E
Hs03045111_g1

220122_at
218323_at
Affy signature
—
—
—
MCTP1
Hs01115711_m1

217408_at
212846_at
Affy signature
—
—
—
MRPS18B
Hs00204096_m1

207860_at
212318_at
Affy signature
—
—
—
NCR1
Hs00950814_g1

203045_at
213038_at
Affy signature
—
—
—
NINJ1
Hs00982607_m1

210797_s_at
205660_at
Affy signature
—
—
—
OASL
Hs00984390_m1

214175_x_at
204600_at
Affy signature
—
—
—
PDGFA
Hs00184792_m1

219066_at
217497_at
Affy signature
—
—
—
PPCDC
Hs00222418_m1

214617_at
212070_at
Affy signature
—
—
—
PRF1
Hs00169473_m1

218700_s_at
203816_at
Affy signature
—
—
—
RAB7L1
Hs00187510_m1

215342_s_at
218695_at
Affy signature
—
—
—
RABGAP1L
Hs02567906_s1

219143_s_at
204683_at
Affy signature
—
—
—
RPP25
Hs00706565_s1

214097_at
201094_at
Affy signature
—
—
—
RPS21
Hs00963477_g1

210365_at
222307_at
Affy signature
—
—
—
SAT1
Hs00971739_g1

215848_at
81811_at
Affy signature
—
—
—
SCAPER
Hs02569575_s1

212900_at
204496_at
Affy signature
—
—
—
SEC24A
Hs00378456_m1

44673_at
219211_at
Affy signature
—
—
—
SIGLEC1
Hs00988063_m1

201802_at
206361_at
Affy signature
—
—
—
SLC29A1
Hs01085704_g1

202864_s_at
202863_at
Affy signature
—
—
—
SP100
Hs00162109_m1

205312_at
205707_at
Affy signature
—
—
—
SPI1
Hs00231368_m1

202005_at
205418_at
Affy signature
—
—
—
ST14
Hs04330394_g1

220059_at
202478_at
Affy signature
—
—
—
STAP1
Hs01038134_m1

219523_s_at
206903_at
Affy signature
—
—
—
TENM3
Hs01111787_m1

202720_at
201344_at
Affy signature
—
—
—
TES
Hs00210319_m1

203313_s_at
212232_at
Affy signature
—
—
—
TGIF1
Hs00820148_g1

218095_s_at
219157_at
Affy signature
—
—
—
TMEM165
Hs00218461_m1

202509_s_at
212603_at
Affy signature
—
—
—
TNFAIP2
Hs00196800_m1

219130_at
200685_at
Affy signature
—
—
—
TRMT13
Hs00219487_m1

208601_s_at
205127_at
Affy signature
—
—
—
TUBB1
Hs00258236_m1

217717_s_at
205037_at
Affy signature
—
—
—
YWHAB
Hs00793604_m1

217593_at
222141_at
Affy signature
—
—
—
ZSCAN18
Hs00225073_m1

213300_at
219014_at
Affy signature
—
—
—
ATG2A
Hs00390076_m1

212914_at
211938_at
Affy signature
—
—
—
CBX7
Hs00545603_m1

220308_at
202452_at
Affy signature
—
—
—
CCDC19
Hs01099244_m1

205098_at
213361_at
Affy signature
—
—
—
CCR1
Hs00928897_s1

205153_s_at
215346_at
Affy signature
—
—
—
CD40
Hs01002913_g1

204490_s_at
205026_at
Affy signature
—
—
—
CD44
Hs00153304_m1

202284_s_at
213324_at
Affy signature
—
—
—
CDKN1A
Hs00355782_m1

206207_at
206361_at
Affy signature
—
—
—
CLC
Hs01055743_m1

206918_s_at
200964_at
Affy signature
—
—
—
CPNE1
Hs00537765_m1

203392_s_at
222265_at
Affy signature
—
—
—
CTBP1
Hs00972289_g1

207718_x_at
44702_at
Affy signature
—
—
—
CYP2A6
Hs00711162_s1

207718_x_at
44702_at
Affy signature
—
—
—
CYP2A7
Hs00711162_s1

201341_at
209717_at
Affy signature
—
—
—
ENC1
Hs00171580_m1

215606_s_at
211999_at
Affy signature
—
—
—
ERC1
Hs00327390_s1

202973_x_at
201417_at
Affy signature
—
—
—
FAM13A
Hs01040170_m1

202709_at
222265_at
Affy signature
—
—
—
FMOD
Hs00157619_m1

206371_at
205844_at
Affy signature
—
—
—
FOLR3
Hs01549264_m1

205164_at
209391_at
Affy signature
—
—
—
GCAT
Hs00606568_gH

214085_x_at
203799_at
Affy signature
—
—
—
GLIPR1
Hs00199268_m1

206896_s_at
206126_at
Affy signature
—
—
—
GNG7
Hs00192999_m1

216289_at
206338_at
Affy signature
—
—
—
GPR144
Hs01369282_m1

208886_at
213096_at
Affy signature
—
—
—
H1F0
Hs00961932_s1

206647_at
40850_at
Affy signature
—
—
—
HBZ
Hs00744391_s1

207194_s_at
218225_at
Affy signature
—
—
—
ICAM4
Hs00169941_m1

202411_at
213797_at
Affy signature
—
—
—
IFI27
Hs01086373_g1

201188_s_at
213958_at
Affy signature
—
—
—
ITPR3
Hs00609948_m1

212162_at
210148_at
Affy signature
—
—
—
KIDINS220
Hs01057000_m1

216713_at
213049_at
Affy signature
—
—
—
KRIT1
Hs01090981_m1

212708_at
202897_at
Affy signature
—
—
—
MSL1
Hs00290567_s1

216303_s_at
222265_at
Affy signature
—
—
—
MTMR1
Hs01021250_m1

207075_at
203906_at
Affy signature
—
—
—
NLRP3
Hs00366465_m1

214582_at
222317_at
Affy signature
—
—
—
ORAI2
Hs01057217_m1

216867_s_at
202909_at
Affy signature
—
—
—
PDE38
Hs00236997_m1

204545_at
320_at
Affy signature
—
—
—
PDLIM4
Hs00165457_m1

209511_at
218333_at
Affy signature
—
—
—
POLR1C
Hs00191646_m1

209511_at
218333_at
Affy signature
—
—
—
POLR2F
Hs00222679_m1

213633_at
204632_at
Affy signature
—
—
—
PSG4
Hs00978711_m1

213633_at
204632_at
Affy signature
—
—
—
PSG4
Hs01652476_m1

205048_s_at
203303_at
Affy signature
—
—
—
PSPH
Hs00190154_m1

213223_at
210607_at
Affy signature
—
—
—
RPL28
Hs00357189_g1

200042_at
212247_at
Affy signature
—
—
—
RTCB
Hs00204783_m1

209360_s_at
203916_at
Affy signature
—
—
—
RUNX1
Hs00231079_m1

219382_at
209575_at
Affy signature
—
—
—
SERTAD3
Hs00705989_s1

213633_at
204632_at
Affy signature
—
—
—
SH3BP1
Hs00978711_m1

213633_at
204632_at
Affy signature
—
—
—
SH3BP1
Hs01652476_m1

206934_at
202545_at
Affy signature
—
—
—
SIRPB1
Hs01092173_m1

212947_at
220404_at
Affy signature
—
—
—
SLC9A8
Hs00905708_m1

216571_at
202396_at
Affy signature
—
—
—
SMPD1
Hs01086851_m1

219055_at
219439_at
Affy signature
—
—
—
SRBD1
Hs01005222_m1

208545_x_at
204600_at
Affy signature
—
—
—
TAF4
Hs01122669_m1

214955_at
217162_at
Affy signature
—
—
—
TMPRSS6
Hs00541789_s1

202644_s_at
55692_at
Affy signature
—
—
—
TNFAIP3
Hs01568119_m1

202688_at
219684_at
Affy signature
—
—
—
TNFSF10
Hs00234356_m1

209605_at
212897_at
Affy signature
—
—
—
TST
Hs04187383_m1

222059_at
216076_at
Affy signature
—
—
—
ZNF335
Hs00223060_m1

202509_s_at
NA
InTxAlternate
—
—
—
TNFAIP2
Hs00969305_m1

202672_s_at
NA
PanViralArray
—
—
—
ATF3
Hs00910173_m1

218943_s_at
NA
PanViralArray
—
—
—
DDX58
Hs01061436_m1

219863_at
NA
PanViralArray
—
—
—
HERC5
Hs01061821_m1

214059_at
NA
PanViralArray
—
—
—
IFI44
Hs00951349_m1

204439_at
NA
PanViralArray
—
—
—
IFI44L
Hs00915294_g1

204415_at
NA
PanViralArray
—
—
—
IFI6
Hs00242571_m1

203153_at
NA
PanViralArray
—
—
—
IFIT1
Hs03027069_s1

217502_at
NA
PanViralArray
—
—
—
IFIT2
Hs01922738_s1

204747_at
NA
PanViralArray
—
—
—
IFIT3
Hs01922752_s1

205483_s_at
NA
PanViralArray
—
—
—
ISG15
Hs01921425_s1

205569_at
NA
PanViralArray
—
—
—
LAMP3
Hs00180880_m1

202145_at
NA
PanViralArray
—
—
—
LY6E
Hs03045111_g1

202086_at
NA
PanViralArray
—
—
—
MX1
Hs00182073_m1

205552_s_at
NA
PanViralArray
—
—
—
OAS1
Hs00973637_m1

202869_at
NA
PanViralArray
—
—
—
OAS2
Hs00973637_m1

218400_at
NA
PanViralArray
—
—
—
OAS3
Hs00934282_g1

205660_at
NA
PanViralArray
—
—
—
OASL
Hs00984390_m1

213797_at
NA
PanViralArray
—
—
—
RSAD2
Hs00369813_m1

219684_at
NA
PanViralArray
—
—
—
RTP4
Hs00223342_m1

210657_s_at
NA
PanViralArray
—
—
—
SEPT4
Hs00910209_g1

200986_at
NA
PanViralArray
—
—
—
SERPING1
Hs00934330_m1

222154_s_at
NA
PanViralArray
—
—
—
SPATS2L
Hs01016364_m1

206026_s_at
NA
PanViralArray
—
—
—
TNFAIP6
Hs01113602_m1

219211_at
NA
PanViralArray
—
—
—
USP18
Hs00276441_m1

206133_at
NA
PanViralArray
—
—
—
XAF1
Hs01550142_m1

NA
NA
Reference
—
—
—
FPGS
Hs00191956_m1

NA
NA
Reference
—
—
—
PPIB
Hs00168719_m1

NA
NA
Reference
—
—
—
TRAP1
Hs00972326_m1

NA
NA
Reference
—
—
—
DECR1
Hs00154728_m1

NA
NA
Reference
—
—
—
GAPDH
Hs99999905_m1

NA
NA
Reference
—
—
—
18S
Hs99999901_s1

NA
203799_at
Replacement
—
—
—
CD302
Hs00208436_m1

NA
31845_at
Replacement
—
—
—
ELF4
Hs01086126_m1

NA
204600_at
Replacement
—
—
—
EPHB3
Hs01082563_g1

NA
206903_at
Replacement
—
—
—
EXOG
Hs01035290_m1

NA
218695_at
Replacement
—
—
—
EXOSC4
Hs00363401_g1

NA
212232_at
Replacement
—
—
—
FNBP4
Hs01553131_m1

NA
209876_at
Replacement
—
—
—
GIT2
Hs00331902_s1

NA
204683_at
Replacement
—
—
—
ICAM2
Hs01015796_m1

NA
201642_at
Replacement
—
—
—
IFNGR2
Hs00985251_m1

NA
203799_at
Replacement
—
—
—
LY75-CD302
Hs00208436_m1

NA
209280_at
Replacement
—
—
—
MRC2
Hs00195862_m1

NA
212603_at
Replacement
—
—
—
MRPS31
Hs00960912_m1

NA
221660_at
Replacement
—
—
—
MYL10
Hs00540809_m1

NA
203290_at
Replacement
—
—
—
PEX6
Hs00165457_m1

NA
201417_at
Replacement
—
—
—
SOX4
Hs00268388_s1

NA
44702_at
Replacement
—
—
—
SYDE1
Hs00973080_m1

NA
222261_at
Replacement
—
—
—
TLDC1
Hs00297285_m1

NA
202452_at
Replacement
—
—
—
ZER1
Hs01115240_m1

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 (TRAP1, 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. Pat. 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 (C_t) 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):1-9, 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 non-infectious illness.

TABLE 11

Demographic information for the enrolled cohort

Number

Mean age,

# Samples (Viral/

of
Gender
years
Ethnicity

Bacterial/Non-

Cohort
subject^a
(M/F)
(Range)^b
(B/W/O)
Admitted
Infectious Illness)

Enrolled
317
122/151
45 (6-88)
135/116/22
61%
115/70/88

Derivation

Cohort

Viral
115
44/71
45 (6-88)
40/59/16
21%

Bacterial
70
35/35
49 (14-88)
46/22/2
94%

Non-infectious
88
43/45
49 (14-88)
49/35/4
88%

Illness^c

Healthy
44
23/21
30 (20-59)
8/27/6^d
0%

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

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

^cNon-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 CO₂<32 mmHg; and white blood cell count <4000 or >12,000 cells/mm³or >10% band form neutrophils.

dThree 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_qvalues 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(w₁x₁+ . . . +w_px_p), where σ is the logistic function, w₁, . . . , w_pare classification weights estimated during the fitting procedure, x₁, . . . , x_prepresent 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 t_bis the threshold for the bacterial ARI classifier. We similarly select thresholds for the viral ARI and non-infectious illness classifiers, t_vand 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 TLDA-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 non-infectious 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 non-infectious 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, w₁, . . . , 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.

TLDA Assay ID
Bacterial
Viral
Non-infectious
Group
Gene Symbol
RefSeq ID
Gene Name

Hs00153304_m1
0.44206
−0.19499
0

CD44
NM_000610.3; NM_001202555.1;
hCG1811182 Celera Annotation; CD44 molecule (Indian blood

NM_001001392.1; NM_001202556.1;
group)

NM_001001391.1; NM_001001390.1;

NM_001001389.1

Hs00155778_m1
0
0
0

APLP2
NM_001142278.1; NM_001142277.1;
hCG2032871 Celera Annotation; amyloid beta (A4) precursor-like

NM_001142276.1; NR_024515.1;
protein 2

NR_024516.1; NM_001642.2;

NM_001243299.1

Hs00156390_m1
0.07707
−0.15022
0

CD63
NM_001780.5; NM_001267698.1;
CD63 molecule; hCG20743 Celera Annotation

NM_001257389.1; NM_001257390.1;

NM_001257391.1

Hs00158514_m1
0
0
0

KPNB1
NM_002265.5
hCG1773668 Celera Annotation; karyopherin (importin) beta 1

Hs00162109_m1
0
0.012558
0

SP100
NM_003113.3; NM_001080391.1;
SP100 nuclear antigen; hCG34336 Celera Annotation

NM_001206702.1; NM_001206703.1;

NM_001206701.1; NM_001206704.1

Hs00165457_m1
0.14396
−0.00784
0

PEX6
NM_000287.3
peroxisomal biogenesis factor 6; hCG17647 Celera Annotation

Hs00169473_m1
0
−0.04883
0.135154

PRF1
NM_005041.4; NM_001083116.1
hCG22817 Celera Annotation; perforin 1 (pore forming protein)

Hs00169941_m1
0
−0.33225
0

ICAM4
NM_001544.4; NM_022377.3
intercellular adhesion molecule 4 (Landsteiner-Wiener blood

group); hCG28480 Celera Annotation

Hs00171580_m1
0
−0.04133
0

ENC1
NM_001256575.1; NM_001256576.1;
hCG37104 Celera Annotation; ectodermal-neural cortex 1 (with

NM_003633.3; NM_001256574.1
BTB domain)

Hs00187510_m1
0.38204
−0.19399
−0.242396

RAB7L1
NM_001135662.1; NM_003929.2
hCG19156 Celera Annotation; RAB7; member RAS oncogene

family-like 1

Hs00190154_m1
0.0726
0
−0.128456

PSPH
NM_004577.3
phosphoserine phosphatase; hCG1811513 Celera Annotation

Hs00191827_m1
0
0
0

CAPZB
NM_001282162.1; NM_004930.4
capping protein (actin filament) muscle Z-line; beta; hCG41078

Celera Annotation

Hs00192999_m1
0.08266
0
−0.127277

GNG7
NM_052847.2
guanine nucleotide binding protein (G protein); gamma 7;

hCG20107 Celera Annotation

Hs00196051_m1
0.05
−0.4723
0

IRF9
NM_006084.4
interferon regulatory factor 9; hCG40171 Celera Annotation

Hs00196800_m1
0
0
0

TNFAIP2
NM_006291.2
tumor necrosis factor; alpha-induced protein 2; hCG22889 Celera

Annotation

Hs00197280_m1
−0.14204
0.089619
0.147283

CIB2
NM_006383.3; NM_001271888.1
calcium and integrin binding family member 2; hCG38933 Celera

Annotation

Hs00199268_m1
0
−0.10536
0.38895

GLIPR1
NM_006851.2
hCG26513 Celera Annotation; GLI pathogenesis-related 1

Hs00199894_m1
0
−0.10571
0.02064

CD160
NR_103845.1; NM_007053.3
hCG1762288 Celera Annotation; CD160 molecule

Hs00204096_m1
0
0
0

MRPS18B
NM_014046.3
hCG2039591 Celera Annotation; mitochondrial ribosomal protein

S18B

Hs00204783_m1
−0.12369
0.330219
0

RTCB
NM_014306.4
RNA 2′; 3′-cyclic phosphate and 5′-OH ligase; hCG41412 Celera

Annotation

Hs00204888_m1
0
0
0

DAPK2
NM_014326.3
death-associated protein kinase 2; hCG32392 Celera Annotation

Hs00210319_m1
0
0.061489
0

TES
NM_015641.3; NM_152829.2
testis derived transcript (3 LIM domains); hCG39086 Celera

Annotation

Hs00218461_m1
0.18667
0
−0.125865

TMEM165
NR_073070.1; NM_018475.4
hCG20603 Celera Annotation; transmembrane protein 165

Hs00219487_m1
0.32643
0
−0.350154

TRMT13
NM_019083.2
hCG31836 Celera Annotation; tRNA methyltransferase 13 homolog

(S. cerevisiae)

Hs00222418_m1
−0.08795
0.254466
0

PPCDC
NM_021823.3
phosphopantothenoylcysteine decarboxylase; hCG21917 Celera

Annotation

Hs00222679_m1
0
0.072372
0

POLR2F;
NM_021974.3
polymerase (RNA) II (DNA directed) polypeptide F; hCG41858

LOC100131530

Celera Annotation; uncharacterized LOC100131530

Hs00223060_m1
0
−0.12877
0.034889

ZNF335
NM_022095.3
zinc finger protein 335; hCG40026 Celera Annotation

Hs00225073_m1
0
0.661155
−0.183337

ZSCAN18
NM_001145544.1; NM_001145543.1;
hCG201365 Celera Annotation; zinc finger and SCAN domain

NM_023926.4; NM_001145542.1
containing 18

Hs00226776_m1
0
0.198622
−0.254653

GIMAP6
NM_001244072.1; NM_001244071.1;
hCG1655100 Celera Annotation; GTPase; IMAP family member 6

NM_024711.5

Hs00231079_m1
0.0787
0
−0.089259

RUNX1
NM_001001890.2; NM_001754.4
runt-related transcription factor 1; hCG2007747 Celera Annotation

Hs00231368_m1
0.30434
0
−0.130472

SPI1
NM_001080547.1; NM_003120.2
spleen focus forming virus (SFFV) proviral integration oncogene;

hCG25181 Celera Annotation

Hs00232390_m1
0.22771
−0.39445
0

BATF
NM_006399.3
hCG22346 Celera Annotation; basic leucine zipper transcription

factor; ATF-like

Hs00234356_m1
0
0
−0.005804

TNFSF10
NR_033994.1; NM_003810.3
tumor necrosis factor (ligand) superfamily; member 10; hCG20249

Celera Annotation

Hs00246543_s1
0
0.096747
0

HNRNPA0
NM_006805.3
hCG1639951 Celera Annotation; heterogeneous nuclear

ribonucleoprotein A0

Hs00258236_m1
0
0.067758
−0.014686

TUBB1
NM_030773.3
tubulin; beta 1 class VI; hCG28550 Celera Annotation

Hs00259863_m1
−0.03861
0.156335
0

ORAI2
NM_001126340.2; NM_001271818.1;
hCG1736771 Celera Annotation; ORAI calcium release-activated

NM_032831.3
calcium modulator 2

Hs00266198_m1
−0.03709
0.174789
0

CEACAM8
NM_001816.3
carcinoembryonic antigen-related cell adhesion molecule 8;

hCG21882 Celera Annotation

Hs00269334_m1
0
0.11804
−0.054795

CAMK1
NM_003656.4
calcium/calmodulin-dependent protein kinase I; hCG21548 Celera

Annotation

Hs00290567_s1
0.10454
−0.57285
0

MSL1
NM_001012241.1
hCG31740 Celera Annotation; male-specific lethal 1 homolog

(Drosophila)

Hs00296064_s1
−0.11096
0.162636
0

CADM1
NM_014333.3; NM_001098517.1
cell adhesion molecule 1

Hs00327390_s1
−0.27728
0.219012
0.023246

ERC1
NM_178040.2; NR_027949.1;
ELKS/RAB6-interacting/CAST family member 1

NR_027946.1; NR_027948.1;

NM_178039.2

Hs00331872_s1
0
−0.04877
0

ANKRD11
NM_013275.5; NM_001256182.1;
hCG1980824 Celera Annotation; ankyrin repeat domain 11

NM_001256183.1

Hs00355782_m1
0
0
0

CDKN1A
NM_001220778.1; NM_001220777.1;
cyclin-dependent kinase inhibitor 1A (p21; Cip1); hCG15367 Celera

NM_000389.4; NM_078467.2
Annotation

Hs00357189_g1
0
0
0

RPL28
NM_001136137.1; NM_000991.4;
ribosomal protein L28; hCG38234 Celera Annotation

NM_001136134.1; NM_001136135.1;

NM_001136136.1

Hs00363282_m1
0
−0.39826
0.298323

LAPTM4B
NM_018407.4
lysosomal protein transmembrane 4 beta; hCG2008559 Celera

Annotation

Hs00366465_m1
0
0
0

NLRP3
NM_001127461.2; NM_001079821.2;
NLR family; pyrin domain containing 3; hCG1982559 Celera

NM_001243133.1; NM_004895.4;
Annotation

NM_001127462.2; NM_183395.2

Hs00367895_m1
0
0
0

ARHGAP12
NM_001270698.1; NM_001270697.1;
Rho GTPase activating protein 12; hCG2017264 Celera Annotation

NM_018287.6; NM_001270699.1;

NM_001270696.1; NM_001270695.1

Hs00378456_m1
0
0
0

SEC24A
NM_021982.2; NM_001252231.1
SEC24 family; member A (S. cerevisiae); hCG1981418 Celera

Annotation

Hs00381767_m1
−0.08167
−0.02155
0.251085

KIAA1324
NR_049774.1; NM_020775.4;
hCG1997600 Celera Annotation; KIAA1324

NM_001267049.1; NM_001267048.1

Hs00390076_m1
−0.4019
0
0.306895

ATG2A
NM_015104.2
hCG2039982 Celera Annotation; autophagy related 2A

Hs00414018_m1
0
0
0

DEFA3; DEFA1;
NM_004084.3; NM_005217.3;
defensin; alpha 3; neutrophil-specific; defensin; alpha 1; defensin;

DEFA1B
NM_001042500.1
alpha 1B

Hs00537765_m1
0.12016
0
−0.311567

CPNE1
NM_001198863.1; NM_152926.2;
copine I; hCG38213 Celera Annotation

NR_037188.1; NM_152927.2;

NM_152925.2; NM 152928.2;

NM_003915.5

Hs00541789_s1
0
0
0

TMPRS56
NM_153609.2
hCG2011224 Celera Annotation; transmembrane protease; serine 6

Hs00545603_m1
−0.15652
0
0.157219

CBX7
NM_175709.3
chromobox homolog 7; hCG41710 Celera Annotation

Hs00606568_gH
0
0.024977
0

GCAT
NM_014291.3; NM_001171690.1
hCG41842 Celera Annotation; glycine C-acetyltransferase

Hs00609948_m1
−0.1261
0
0.132035

ITPR3
NM_002224.3
hCG40301 Celera Annotation; inositol 1; 4; 5-trisphosphate

receptor; type 3

Hs00705137_s1
0
0.190805
−0.207955

IFITM1
NM_003641.3
interferon induced transmembrane protein 1; hCG1741134 Celera

Annotation

Hs00705989_s1
0
0.264586
−0.237834

SERTAD3
NM_203344.2; NM_013368.3
SERTA domain containing 3; hCG201413 Celera Annotation

Hs00706565_s1
0
0.247956
−0.127891

RPP25
NM_017793.2
ribonuclease P/MRP 25 kDa subunit; hCG1643228 Celera

Annotation

Hs00711162_s1
−0.01602
0.105815
0

CYP2A13;
NM_000764.2; NM_030589.2;
cytochrome P450; family 2; subfamily A; polypeptide 13;

CYP2A7;
NM 000766.4; NM_000762.5
cytochrome P450; family 2; subfamily A; polypeptide 7;

CYP2A6

cytochrome P450; family 2; subfamily A; polypeptide 6;

hCG2039740 Celera Annotation; hCG1780445 Celera Annotation

Hs00734212_m1
0.03633
−0.10881
0

HLA-DRB3;
NM_022555.3
hCG2001518 Celera Annotation; major histocompatibility complex;

HLA-DRB1

class II; DR beta 3; major histocompatibility complex; class II; DR

beta 1

Hs00738661_m1
−0.2813
0
0.255274

FAM134C
NR_026697.1; NM_178126.3
family with sequence similarity 134; member C; hCG2043027

Celera Annotation

Hs00793604_m1
0
0
−0.392469

YWHAB
NM_003404.4; NM_139323.3
hCG38378 Celera Annotation; tyrosine 3-

monooxygenase/tryptophan 5-monooxygenase activation protein;

beta polypeptide

Hs00820148_g1
0
0
0.082524

TGIF1
NM_173207.2; NM_003244.3;
TGFB-induced factor homeobox 1; hCG1994498 Celera Annotation

NM_001278682.1; NM_170695.3;

NM_001278686.1; NM_001278684.1;

NM_173210.2; NM_173209.2;

NM_173208.2; NM_174886.2;

NM_173211.1

Hs00852566_g1
0
0
0.090784

BTF3
NM_001207.4; NM_001037637.1
hCG37844 Celera Annotation; basic transcription factor 3

Hs00855185_g1
0.22884
−0.16129
0

ARPC3
NM_001278556.1; NM_005719.2
hCG1787850 Celera Annotation; hCG1730237 Celera Annotation;

actin related protein 2/3 complex; subunit 3; 21 kDa

Hs00893626_m1
0
0
−0.131321

IL1RN
NM_000577.4; NM_173841.2;
hCG1733963 Celera Annotation; interleukin 1 receptor antagonist

NM_173842.2; NM_173843.2

Hs00905708_m1
0
0
0

SLC9A8
NM_001260491.1; NR_048537.1;
solute carrier family 9; subfamily A (NHE8; cation proton antiporter

NR_048538.1; NR_048539.1;
8); member 8; hCG37890 Celera Annotation

NR_048540.1; NM_015266.2

Hs00928897_s1
0
0
0

CCR1
NM_001295.2
hCG15324 Celera Annotation; chemokine (C—C motif) receptor 1

Hs00950814_g1
0
0
0.035502

NCR1
NM_001145457.2; NM_001242356.2;
hCG19670 Celera Annotation; natural cytotoxicity triggering

NM_004829.6
receptor 1

Hs00951428_m1
0
0.113402
0

DSC2
NM_024422.3; NM_004949.3
hCG24896 Celera Annotation; desmocollin 2

Hs00961932_s1
0
0
0

H1F0
NM_005318.3
hCG1641126 Celera Annotation; H1 histone family; member 0

Hs00963477_g1
0
−0.00884
0

RPS21
NM_001024.3
hCG41768 Celera Annotation; ribosomal protein S21

Hs00971739_g1
0
0.128754
0

SAT1
NR_027783.1; NM_002970.2
hCG17885 Celera Annotation; spermidine/spermine N1-

acetyltransferase 1

Hs00972289_g1
−0.36317
0.301793
0.148178

CTBP1
NM_001012614.1; NM_001328.2
hCG1981976 Celera Annotation; C-terminal binding protein 1

Hs00978711_m1
0
−0.19534
0.079881

SH3BP1
NM_018957.3
hCG41861 Celera Annotation; SH3-domain binding protein 1

Hs00980756_m1
0
−0.27613
0.042497

GGT1
NM_001032364.2; NM_001032365.2;
gamma-glutamyltransferase 1; hCG2010666 Celera Annotation

NM_005265.2; NM_013430.2

Hs00982607_m1
0
0
0

NINJ1
NM_004148.3
ninjurin 1; hCG18015 Celera Annotation

Hs00984390_m1
0
0.074028
−0.022201

OASL
NM_198213.2; NM_003733.3
hCG27362 Celera Annotation; 2′-5′-oligoadenylate synthetase-like

Hs00985319_m1
−0.01147
0.079048
0

HEATR1
NM_018072.5
HEAT repeat containing 1; hCG25461 Celera Annotation

Hs00988063_m1
−0.08452
0.168519
0

SIGLEC1
NM_023068.3
hCG39260 Celera Annotation; sialic acid binding Ig-like lectin 1;

sialoadhesin

Hs01001427_m1
0.04332
−0.60556
0

CDK5RAP2
NR_073558.1; NR_073554.1;
hCG27455 Celera Annotation; CDK5 regulatory subunit associated

NR_073555.1; NR_073556.1;
protein 2

NM_001272039.1; NR_073557.1;

NM_001011649.2; NM_018249.5

Hs01002913_g1
0
0
0

CD40
NM_152854.2; NM_001250.4
hCG40016 Celera Annotation; CD40 molecule; TNF receptor

superfamily member 5

Hs01005222_m1
0
0.326033
0

SRBD1
NM_018079.4
S1 RNA binding domain 1; hCG1987258 Celera Annotation

Hs01017992_g1
0
0
0.179899

CYP27A1
NM_000784.3
hCG15569 Celera Annotation; cytochrome P450; family 27;

subfamily A; polypeptide 1

Hs01021250_m1
0.01799
0.196899
−0.140181

MTMR1
NM_003828.2
hCG1640369 Celera Annotation; myotubularin related protein 1

Hs01029870_m1
0
−0.58215
0.22929

ARL1
NM_001177.4
hCG1782029 Celera Annotation; ADP-ribosylation factor-like 1

Hs01032528_m1
0
−0.36595
0.410577

HERC1
NM_003922.3
hCG1818283 Celera Annotation; HECT and RLD domain containing

E3 ubiquitin protein ligase family member 1

Hs01038134_m1
−0.13717
0.004773
0.049685

STAP1
NM_012108.2
signal transducing adaptor family member 1; hC640344 Celera

Annotation

Hs01040170_m1
0.04344
−0.17845
−0.052769

FAM13A
NM_014883.3; NM_001265578.1;
hCG39059 Celera Annotation; family with sequence similarity 13;

NM_001015045.2; NM_001265580.1;
member A

NM_001265579.1

Hs01055743_m1
−0.30697
0
0.257693

CLC
NM_001828.5
hCG43348 Celera Annotation; Charcot-Leyden crystal galectin

Hs01057000_m1
0
−0.68353
0.082116

KIDINS220
NM_020738.2
hCG23067 Celera Annotation; kinase D-interacting substrate;

220 kDa

Hs01057217_m1
−0.45125
0.327746
0.070281

PDE3B
NM_000922.3
phosphodiesterase 3B; cGMP-inhibited; hCG23682 Celera

Annotation

Hs01072230_g1
0
−0.00364
0.169878

CHI3L1
NM_001276.2
chitinase 3-like 1 (cartilage glycoprotein-39); hCG24326 Celera

Annotation

Hs01082884_m1
0.29147
−0.1223
0

IRF2
NM_002199.3
hCG16244 Celera Annotation; interferon regulatory factor 2

Hs01085704_g1
0
0
0

SLC29A1
NM_001078174.1; NM_004955.2;
hCG19000 Celera Annotation; solute carrier family 29 (equilibrative

NM_001078177.1; NM_001078176.2;
nucleoside transporter); member 1

NM_001078175.2

Hs01086373_g1
−0.11199
0.274551
−0.063877

IFI27
NM_005532.3; NM_001130080.1
interferon; alpha-inducible protein 27; hCG22330 Celera

Annotation

Hs01086851_m1
0.37999
−0.28298
0

SMPD1
NM_001007593.2; NM_000543.4
sphingomyelin phosphodiesterase 1; acid lysosomal; hCG24080

Celera Annotation

Hs01090981_m1
0
0
0

KRIT1
NM_194456.1; NM_194454.1;
hCG1812017 Celera Annotation; KRIT1; ankyrin repeat containing

NM_004912.3; NM_001013406.1;

NM_194455.1

Hs01092173_m1
0.09825
0
0

SIRPB1
NM_001083910.2; NM_006065.3
signal-regulatory protein beta 1; hCG39419 Celera Annotation

Hs01099244_m1
0.01588
−0.22063
0.055484

CCDC19
NM_012337.2
hCG39740 Celera Annotation; coiled-coil domain containing 19

Hs01115711_m1
0.2568
0
−0.127859

MCTP1
NM_001002796.2; NM_024717.4
multiple C2 domains; transmembrane 1; hCG1811111 Celera

Annotation

Hs01117053_m1
0
0
0

EXOC7
NR_028133.1
exocyst complex component 7; hCG40887 Celera Annotation

Hs01122669_m1
0
−0.03893
0.066177

TAF4
NM_003185.3
hCG41771 Celera Annotation; TAF4 RNA polymerase II; TATA box

binding protein (TBP)-associated factor; 135 kDa

Hs01128745_m1
0
0.031228
0

EMR3
NM_032571.3
hCG95683 Celera Annotation; egf-like module containing; mucin-

like; hormone receptor-like 3

Hs01549264_m1
0.02825
−0.12496
0

NM_000804.2
hCG1640300 Celera Annotation; folate receptor 3 (gamma)

Hs01568119_m1
0
0.181259
−0.076525

TNFAIP3
NM_001270508.1; NM_006290.3;
hCG16787 Celera Annotation; tumor necrosis factor; alpha-induced

NM_001270507.1
protein 3

Hs01911452_s1
0
0
0

IFIT1
NM 001548.4; NM_001270928.1;
hCG24571 Celera Annotation; interferon-induced protein with

NM_001270927.1; NM_001270930.1;
tetratricopeptide repeats 1

NM_001270929.1

Hs02567906_s1
−0.22881
0.019641
0

RABGAP1L
NM_001243763.1; NM_014857.4;
hCG2024869 Celera Annotation; RAB GTPase activating protein 1-

NM_001035230.2
like

Hs02569575_s1
0
0
−0.12916

SCAPER
NM_001145923.1; NM_020843.2
hCG40799 Celera Annotation; S-phase cyclin A-associated protein

in the ER

Hs03037970_g1
0
0
0

DUX4L7;
NM_001278056.1; NM_001164467.2;
double homeobox 4 like 7; double homeobox 4 like 5; double

DUX4L5;
NR_038191.1; NM 001177376.2;
homeobox 2; double homeobox 4 like 2; double homeobox 4 like

DUX4L6;
NM_012147.4; NM_001127389.2;
6; double homeobox 4; double homeobox protein 4-like; double

DUX4L2; DUX2;
NM_001127388.2; NM_001127387.2;
homeobox 4-like; double homeobox 4 like 4; double homeobox 4

DUX4;
NM_033178.4; NM_001127386.2
like 3

LOC100653046;

DUX4L;

DUX4L4;

DUX4L3

Hs03045111_g1
−0.02913
0.054676
0

LY6E
NM_002346.2; NM_001127213.1
hCG1765592 Celera Annotation; lymphocyte antigen 6 complex;

locus E

Hs03055204_s1
0
0
0

KIAA0754
NM_015038.1
KIAA0754

Hs03989560_s1
−0.28689
0.169135
0.040358

GLUD1
NM_005271.3
glutamate dehydrogenase 1

Hs04187383_m1
0
0
0

TST
NM 003312.5; NM_001270483.1
thiosulfate sulfurtransferase (rhodanese); hCG41451 Celera

Annotation

Hs00969305_m1
0
−0.50526
0
InTxAlternate
TNFAIP2
NM_006291.2
tumor necrosis factor; alpha-induced protein 2; hCG22889 Celera

Annotation

Hs00180880_m1
0
0
0
PanViral
LAMP3
NM_014398.3
lysosomal-associated membrane protein 3; hCG16067 Celera

Annotation

Hs00182073_m1
0
0.043305
0
PanViral
MX1
NM_002462.3; NM_001144925.1;
myxovirus (influenza virus) resistance 1; interferon-inducible

NM_001178046.1
protein p78 (mouse); hCG401239 Celera Annotation

Hs00213443_m1
0
0.009468
−0.051318
PanViral
OAS2
NM_016817.2
2′-5′-oligoadenylate synthetase 2; 69/71 kDa; hCG38536 Celera

Annotation

Hs00223342_m1
0
0
0
PanViral
RTP4
NM_022147.2
hCG1653633 Celera Annotation; receptor (chemosensory)

transporter protein 4

Hs00242571_m1
0
0
−0.078103
PanViral
IFI6
NM_022873.2; NM_002038.3;
interferon; alpha-inducible protein 6; hCG1727099 Celera

NM_022872.2
Annotation

Hs00276441_m1
0
0.033981
−0.048548
PanViral
USP18
NM_017414.3
ubiquitin specific peptidase 18; hCG21533 Celera Annotation

Hs00369813_m1
−0.02854
0
0
PanViral
RSAD2
NM_080657.4
hCG23898 Celera Annotation; radical S-adenosyl methionine

domain containing 2

Hs00910173_m1
0
0.065635
−0.003951
PanViral
ATF3
NM_001030287.3; NM_001206484.2;
hCG37734 Celera Annotation; activating transcription factor 3

NM_001206488.2; NM_001674.3

Hs00910209_g1
−0.00172
0.07212
0
PanViral
SEP4
NM_080416.2; NM_004574.3;
septin 4; hCG30696 Celera Annotation

NM_001256822.1; NM_080415.2;

NM_001256782.1; NR_037155.1;

NM_001198713.1

Hs00915294_g1
0
0
0
PanViral
IFI44L
NM_006820.2
hCG24062 Celera Annotation; interferon-induced protein 44-like

Hs00934282_g1
0
0
0
PanViral
OAS3
NM_006187.2
2′-5′-oligoadenylate synthetase 3; 100 kDa; hCG40370 Celera

Annotation

Hs00934330_m1
0
0.065027
0
PanViral
SERPING1
NM_000062.2; NM_001032295.1
serpin peptidase inhibitor; clade G (C1 inhibitor); member 1;

hCG39766 Celera Annotation

Hs00951349_m1
0
0
0
PanViral
IFI44
NM_006417.4
interferon-induced protein 44; hCG24065 Celera Annotation

Hs00973637_m1
0
0
−0.060351
PanViral
OAS1
NM_001032409.1; NM_016816.2;
2′-5′-oligoadenylate synthetase 1; 40/46 kDa; hCG40366 Celera

NM_002534.2
Annotation

Hs01016364_m1
0
0
0
PanViral
SPATS2L
NM_001100422.1; NM_015535.2;
spermatogenesis associated; serine-rich 2-like; hCG1811464 Celera

NM_001100424.1; NM_001100423.1
Annotation

Hs01061436_m1
0
0.01828
−0.042268
PanViral
DDX58
NM_014314.3
DEAD (Asp-Glu-Ala-Asp) box polypeptide 58; hCG1811781 Celera

Annotation

Hs01061821_m1
0
0
0
PanViral
HERC5
NM_016323.3
HECT and RLD domain containing E3 ubiquitin protein ligase 5;

hCG1813153 Celera Annotation

Hs01113602_m1
0.05847
0
−0.206842
PanViral
TNFAIP6
NM_007115.3
hCG41965 Celera Annotation; tumor necrosis factor; alpha-induced

protein 6

Hs01550142_m1
0
−0.06086
0
PanViral
XAF1
NR_046398.1; NM_199139.2;
hCG1777063 Celera Annotation; XIAP associated factor 1

NM_017523.3; NR_046396.1;

NR_046397.1

Hs01921425_s1
0
0.018167
−0.032153
PanViral
ISG15
NM_005101.3
ISG15 ubiquitin-like modifier; hCG1771418 Celera Annotation

Hs01922738_s1
−0.0409
0.185197
−0.007029
PanViral
IFIT2
NM_001547.4
interferon-induced protein with tetratricopeptide repeats 2;

hCG1643352 Celera Annotation

Hs01922752_s1
0
0
0
PanViral
IFIT3
NM_001549.4; NM_001031683.2
hCG24570 Celera Annotation; interferon-induced protein with

tetratricopeptide repeats 3

Hs03027069_s1
−0.00733
0
0
PanViral
IFIT1
NM_001548.4; NM_001270928.1;
interferon-induced protein with tetratricopeptide repeats 1;

NM_001270927.1; NM_001270930.1;
hCG24571 Celera Annotation

NM_001270929.1

Hs00191646_m1
0
0
0
Replacement
POLR1C
NM_203290.2
polymerase (RNA) I polypeptide C; 30 kDa; hCG18995 Celera

Annotation

Hs00208436_m1
0
0.013116
0
Replacement
CD302; LY75-
NM_014880.4; NM_001198763.1;
CD302 molecule; hCG40834 Celera Annotation; LY75-CD302

CD302
NM_001198760.1; NM_001198759.1
readthrough

Hs00297285_m1
0
−0.46905
0
Replacement
TLDC1
NM_020947.3
TBC/LysM-associated domain containing 1; hCG39793 Celera

Annotation

Hs00331902_s1
0
−0.45598
0.236611
Replacement
GIT2
NM_057170.3; NM_014776.3;
hCG38510 Celera Annotation; G protein-coupled receptor kinase

NM_001135213.1; NM_001135214.1;
interacting ArfGAP 2

NM_057169.3

Hs00363401_g1
0
0
−0.077823
Replacement
EXOSC4
NM_019037.2
hCG1747868 Celera Annotation; exosome component 4

Hs00960912_m1
0
0.26766
0
Replacement
MRPS31
NM_005830.3
mitochondrial ribosomal protein S31; hCG32763 Celera Annotation

Hs00985251_m1
0.10711
−0.17404
0
Replacement
IFNGR2
NM_005534.3
interferon gamma receptor 2 (interferon gamma transducer 1);

hCG401179 Celera Annotation

Hs01015796_m1
0
0.189857
0
Replacement
ICAM2
NM_001099786.1; NM_001099787.1;
intercellular adhesion molecule 2; hCG41817 Celera Annotation

NM_001099788.1; NM_001099789.1;

NM_000873.3

Hs01035290_m1
−0.05606
0.248968
0
Replacement
EXOG
NM_005107.3; NM_001145464.1
endo/exonuclease (5′-3′); endonuclease G-like; hCG40337 Celera

Annotation

Hs01086126_m1
0
0
0
Replacement
ELF4
NM_001421.3; NM_001127197.1
E74-like factor 4 (ets domain transcription factor); hCG21000

Celera Annotation

Hs01115240_m1
0
−0.79464
0.589673
Replacement
ZER1
NM_006336.3
zyg-11 related; cell cycle regulator; hCG1788209 Celera Annotation

Hs01553131_m1
0
−0.26139
0.697495
Replacement
FNBP4
NM_015308.2
formin binding protein 4; hCG25190 Celera Annotation

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.