Review

The current epidemiology and clinical decisions surrounding acute respiratory infections Aimee K. Zaas1,2, Bronwen H. Garner1, Ephraim L. Tsalik1,2,3, Thomas Burke1, Christopher W. Woods1,2,3, and Geoffrey S. Ginsburg1 1

Center for Applied Genomics and Precision Medicine, Duke University School of Medicine, Durham, NC, USA Department of Medicine, Duke University School of Medicine, Durham, NC, USA 3 Durham Veterans Affairs Medical Center, Durham, NC, USA 2

Acute respiratory infection (ARI) is a common diagnosis in outpatient and emergent care settings. Currently available diagnostics are limited, creating uncertainty in the use of antibacterial, antiviral, or supportive care. Up to 72% of ambulatory care patients with ARI are treated with an antibacterial, despite only a small fraction actually needing one. Antibiotic overuse is not restricted to ambulatory care: ARI accounts for approximately 5 million emergency department (ED) visits annually in the USA, where 52–61% of such patients receive antibiotics. Thus, an accurate test for the presence or absence of viral or bacterial infection is needed. In this review, we focus on recent research showing that the host-response (genomic, proteomic, or miRNA) can accomplish this task. Current epidemiology and clinical decisions ARI is a common diagnosis in outpatient and emergent care settings [1,2]. Although identifying an ARI is relatively easy, defining the causative pathogen is deceptively difficult. Currently available diagnostics are limited, whether in availability (e.g., multiplex respiratory viral PCR), turn-around time (e.g., virus-specific PCR), or performance characteristics, creating uncertainty in management. Consequently, up to 72% of ambulatory care patients with ARI are treated with an antibacterial, despite only a small fraction needing one [3]. Therefore, ARI accounts for 41% of all antibiotics prescribed in the adult outpatient setting [4]. Antibiotic overuse is not restricted to ambulatory care: ARI accounts for approximately 5 million ED visits annually in the USA [5], where 52–61% of such patients receive antibiotics. This overuse of antibacterials feeds the growing epidemic of resistant bacteria and is associated with complications such as Clostridium difficileassociated diarrhea [6–10]. The challenge for practitioners is to identify patients who would benefit from antibacterial treatment. This task is made difficult by the realities of clinical practice, where clinicians must reconcile patients’ expectations for antibiotics with evidence that they confer Corresponding author: Zaas, A.K. ([email protected]). Keywords: genomics; viral respiratory infection; host–pathogen interaction. 1471-4914/ ß 2014 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.molmed.2014.08.001

little, if any, benefit in ARI [11,12]. Therefore, an accurate test for the presence or absence of viral or bacterial infection would fill this diagnostic void, offering a means to direct appropriate antimicrobial use. In this review, we focus on recent research showing that the host response can accomplish this task, the implications of which extend far beyond the doctor’s office (Box 1). Current bacterial diagnostic tests The current diagnostic armamentarium for bacterial infection relies almost entirely on pathogen identification. The gold standard to identify respiratory bacterial pathogens is culture, which requires days to achieve a result, often with low yield [13,14]. Additional modalities include antigen testing (e.g., for Streptococcus pneumoniae and Legionella), and pathogen-specific PCR. Despite developments in molecular diagnostic techniques, pathogens such as Chlamydophila pneumoniae and Mycoplasma pneumoniae still have no US Food and Drug Administration (FDA)-approved molecular detection tests, and detection does not always indicate disease causation [15]. Thus, despite these tools, the pathogen remains unknown in most cases of respiratory tract infection. Without an identified pathogen, antibacterials are often prescribed ‘just in case’, feeding the epidemic of antibacterial overutilization [4]. Current viral diagnostic tests In contrast to the diagnosis of bacterial pathogens, molecular detection of respiratory viruses is more feasible (Table 1). The first generation of molecular-based tests to enter routine clinical practice focused on antigen detection, as typified by influenza rapid antigen assays. The proliferation of such assays was rooted in their point-of-care availability, requiring approximately 15 min to determine a result. Unfortunately, these tests have poor sensitivity (40–59%), which became highly apparent and problematic during the 2009 H1N1 pandemic [16]. Immunofluorescence testing for viral pathogens is fast, sensitive, and specific when performed on good-quality samples by trained personnel. However, the necessary laboratory support makes these tests unavailable in most point-of-care settings and, therefore, less broadly applicable. The most recent addition is multiplex PCR, a Trends in Molecular Medicine, October 2014, Vol. 20, No. 10

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Review Box 1. Possible applications of a host-based approach to diagnosing and classifying infectious illness Classic clinical situations provide ample opportunity to use hostbased approaches to infectious illness classification. The most direct example is use of these tests to diagnose viral infection to reduce overuse of antibacterial therapies. Additionally, detection of exposed individuals with impending illness in setting of deliberate bioterrorism-related release, or those with impending illness in setting of pandemic influenza, could have important public health implications. Outside of the clinical setting, such tests could be used for screening of livestock or the blood supply for viral infections, assessment of durable vaccine response, and in population screening, such as military deployment or before vacationing on a cruise ship.

technique that simultaneously detects multiple pathogens. The specific pathogens and performance characteristics vary depending on the platform. For example, the FilmArray (Biofire Diagnostics; Salt Lake City, UT, USA), detects 20 pathogens (including three bacterial targets), requires only 2 min of hands-on time, and has results available in 1 h. Performance characteristics for this and related multiplex products are excellent: 84–100% sensitivity and 89–100% specificity [17–20]. However, virus detection does not equate to viral infection. Rates of asymptomatic viral carriage are high, occurring in up to 27% of healthy individuals [21]. Moreover, the presence of virus does not exclude the presence of bacteria. Therefore, a positive result can be helpful but does not exclude a concomitant bacterial infection. Another limitation of these multiplex PCR assays is they are limited to the detection of viruses on the panel. Novel or mutated pathogens could be missed. For example, new PCR assays had to be developed to detect 2009 H1N1 influenza during that pandemic, although these have since been incorporated into most commercially available assays. Host response to infection In light of the limitations mentioned thus far, there has long been a search for host-derived biomarkers indicative of infection. This search goes back nearly a century to the characterization of the erythrocyte sedimentation rate in 1917 [22], followed shortly thereafter by C-reactive protein in 1930 [23]. These tests, which indeed reflect the host response, are highly nonspecific and, therefore, unable to differentiate infection from noninfection, let alone between pathogen types. Moreover, an immense body of research tells of efforts (largely unsuccessful) to identify the ideal biomarker: one that is fast; sensitive and specific for infection; differentiates between pathogen types; reflects response to treatment; and is prognostic of outcome [24]. More recent efforts to identify an infection biomarker stem from the growing knowledge of host immunity, particularly in the context of sepsis. Infection activates several humoral and cellular systems, including the release of immune mediators into the bloodstream. Based on their mechanisms of action and role in the immune response, several bloodstream biomarkers have been investigated for their ability to diagnose sepsis and, more broadly, to identify bacterial infection. The most promising biomarker is procalcitonin, the calcitonin prohormone secreted by multiple cell types as an acute-phase reactant in bacterial sepsis [25–27]. Procalcitonin offers several advantages 580

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over other potential biomarkers because it is easily measured in standard hospital laboratories, is rapidly induced during infection, and has a long half-life [28,29]. Procalcitonin has been investigated as a biomarker in multiple clinical settings where bacterial infection is difficult to distinguish from alternative diagnoses, based on observations that viral infections do not stimulate procalcitonin release. As such, this biomarker is well suited to differentiate bacterial from viral etiologies of ARI. Indeed, procalcitonin-guided treatment algorithms safely reduce antibacterial prescription by approximately 50% in ARI, including both ambulatory care and hospital settings [30]. Despite these encouraging findings, there remain uncertainties regarding procalcitonin use. For example, different cut-offs exist for estimating the likelihood of bacterial infection depending on the clinical situation, few of which have been validated prospectively. Although procalcitonin has greater specificity for bacterial infection than other biomarkers, the possibility of false positives remains. Furthermore, the assay is only FDA approved to identify critically ill patients at risk for progression to severe sepsis and septic shock upon admission to the intensive care unit. Despite these limitations, procalcitonin is a promising adjunct to existing diagnostic modalities. It underscores the potential of the host response as a target to differentiate bacterial from viral etiologies. Several additional candidate biomarkers have emerged from sepsis-related research. However, as biologically plausible as they may be, none of these candidates has thus far shown improved performance over procalcitonin upon independent validation. This suggests an alternative approach may be necessary to advance the field of infectious disease diagnostics, which is in dire need of novel and rapid diagnostic methods. A systems biology approach that focuses on the host response to bacterial or viral infection offers such an opportunity. Recent technical advances that enable the rapid detection of other potential biomarkers, such as mRNA, miRNA, protein, metabolite, or a combination of these, suggest that the means to identify an agnostic biomarker development now exists. A ‘paradigm shift’: host peripheral blood gene expression for diagnosis of infection Advances in gene expression measurement and analysis Several major technological developments have enabled the use of peripheral blood leukocyte gene expression to identify the infectious etiology of ARI. Sequencing of the human genome was a necessary step, followed by the advent of gene expression arrays. Concomitantly, RNA stabilization upon collection (e.g., PAXgene1 blood RNA tubes) enabled specimen banking under various conditions while maintaining a standard methodology, which is crucial to cross-experiment comparisons. ‘Whole transcriptome shotgun sequencing’ has emerged as another means to quantify and compare gene expression changes under different conditions. The continued development of more rapid and affordable sample processing (e.g., bglobin reduction) and sequencing methods can enable the use of such platforms outside specialized settings. Lastly, advanced computational and statistical tools have

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Table 1. Comparison of commercially available pathogenbased influenza and RSV testsa Assay tested

SD Bioline Influenza Ag A/B Influ A/B Respistrip; SD Bioline; Directigen EZ Flu A+B and QuickVue Influenza A+B; QuickVue Influenza A+B ClearView Exact Influenza A+B QuickVue Influenza A+B; Influenzatop SD Bioline Influenza Ag A/B FilmArray Respiratory Panel NucliSENS Analyte Specific Reagent RSV Immunochromatographic Testing RSV

Sensitivity (probability of detection) 0.45 0.32–0.5

Specificity (1–probability of false alarm) 0.99 0.98

0.19 0.64 0.7 0.88–1.0 0.93

0.99 0.99 0.97 1.0 0.97

0.79

0.67

a

Probability of detection is the likelihood of a test reporting that a person with influenza A or RSV has influenza A or RSV (true positive); probability of false alarm indicates that a person who is not sick with influenza A or RSV has influenza A or RSV (false positive). Most rapid tests currently available have a relatively high false negative rate, and perform better in the pediatric setting compared with the adult setting.

been vital, allowing for processing and analysis of tremendous amounts of complex data from a relatively small sample size (the ‘large p, small n’ problem) [31]. Many approaches are now available to reduce data dimensionality, match phenotype to transcriptional changes, compare groups, and make predictions. Specific examples of successfully applied algorithms include sparse factor modeling [32], Bayesian constructions of the elastic net [33], sparse principal component analysis [34], penalized matrix decomposition [35], modular transcriptional analysis [36], and the molecular distance to health [37]. Although we focus here on the human host’s response to infection, these various advances were often pioneered and honed in animal model systems. Fields, such as systems virology in animal models, exemplify how such research can parallel and augment human subject research (Box 2) [38,39]. Disease states characterized by gene expression Oncology was among the first fields to define diseaseassociated changes in gene expression, and these have since been translated into clinically useful assays. For example, the Oncotype DX1 Breast Cancer Assay measures the expression of 21 genes to generate a prognosis, and guide therapeutic decision making in breast cancer. Similar advances have come in cardiology [40], where the Corus1 CAD test measures the expression of 23 genes to identify patients with obstructive coronary artery disease. Environmental exposures, such as ionizing radiation, also induce measureable gene expression changes [41]. These examples highlight the feasibility of gene expression-based classifiers to aid diagnosis, prognosis, and treatment decisions. Pathogens induce stereotyped host responses The specificity of the immune response to various pathogens is a well-characterized phenomenon (Figure 1). Both the innate and adaptive immune responses are mediated through multiple biological outputs, including gene

expression, miRNA, and protein production (including cytokines). Innate immunity is less specific than adaptive immunity, although there is still a significant amount of pathogen class specificity. Pathogen-associated molecular patterns (PAMPs) bind to Toll-like receptors (TLRs) and other pattern recognition receptors expressed on cells mediating the innate immune response [42]. For example, TLR-4 binds to lipopolysaccharide (LPS) derived from Gram-negative bacteria, whereas TLR-3 binds to doublestranded RNA derived from viruses. Moreover, ex vivo stimulation of human peripheral blood leukocytes with bacteria has shown that active virulence-associated processes manipulate the host immune response and allow discrimination among pathogens [43,44]. These and other mechanisms offer potential targets for biomarker development, which have been described for multiple pathogens and pathogen classes, including viral [42,45,46], bacterial [43,44], and fungal infections [47–49]. Gene expression profiling and infectious disease A gene expression signature obtained from peripheral blood differentiating between bacterial and viral infection in a human cohort was first published by Ramilo et al. [46] from patients infected with influenza A, Staphylococcus aureus, Staphylococcus pneumoniae, or Escherichia coli. Viral and bacterial infections were distinguished by 854 differentially expressed genes. Genes overexpressed in viral infection included biologically plausible candidates related to antiviral immune processes, such as 20 50 oligoadenylate synthase (OAS) proteins and a Type I interferon (IFN) signature. Using a K-Nearest Neighbor strategy, a 35-gene classifier discriminated acute influenza A from acute bacterial infection. This was further validated by projecting the classifier onto an independent cohort of 37 patients (seven influenza A and 30 bacterial infections) with an accuracy of 94.5%. This study represented a major step forward by demonstrating that systems biology strategies can accurately identify biomarkers distinguishing clinically similar patients. Human challenge experiments offer a controlled environment in which the response to a given stimulus can be studied in a coordinated and comprehensive manner. Several such challenge experiments have been performed in healthy adult volunteers inoculated with one of respiratory syncytial virus (RSV), influenza A (H3N2/Wisconsin/67/ 2005), or rhinovirus (HRV) [46]. Importantly, approximately half of the challenged subjects developed symptoms, enabling several relevant comparisons: symptomatic versus asymptomatic, symptomatic versus own baseline, and temporal changes in both groups [50]. Bayesian factor regression modeling (BRFM) reduced data dimensionality, grouping genes with similar expression patterns together into ‘factors’ [51]. Labels (i.e., ‘symptomatic’ or ‘asymptomatic’) were only assigned after factor generation. Several factors differentiated symptomatic from asymptomatic subjects with RSV, influenza, or HRV. The top-performing factor was identified, as well as biologically plausible genes within that factor. They included genes within IFN signaling, OAS and radical S-adenosyl methionine domain (RSAD2) pathways. Twenty-eight genes (represented by 30 probes) were then used to build a sparse probit 581

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Gene expression for bacterial or viral detecon by immune cell

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Figure 1. Schematic of using the host immune response for omics-based diagnosis. Immune effector cells (purple) in the peripheral blood detect pathogens using specific receptors (red, bacterial receptor; yellow, virus receptor). Activation of specific receptors by either bacteria (B, green) or virus (V, blue) leads to specific changes in gene expression (red or yellow §). A simple blood draw into an appropriate RNA preservative solution ‘freezes’ gene expression and allows detection on microarray or PCR-based platforms. The schematic represents differing changes in expression of a subset of genes in response to either bacterial or viral infection.

regression model for leave-one-out cross-validation. The classifier correctly identified 81 of 84 subjects (96.5%) as symptomatic or asymptomatic following viral challenge. These results were robust across three distinct viral challenge experiments, highlighting the conserved nature of the host response to viral ARI [50]. The viral ARI signature was then applied to data from the aforementioned Ramilo study cohort [46], where it discriminated between influenza and pneumococcal infection with 93% accuracy [50], based on making a classification of ‘viral’ or ‘not viral’. Given that the signature classifies viral infection, bacterial infection and asymptomatic controls both fall under the category of ‘nonviral’, establishing the basis for their distinction by the pan-viral gene expression classifier. Moreover, the viral ARI signature performed well despite several important differences between the cohorts: pediatric versus adult, RNA procurement methodology, and original statistical analysis. This highlights the important principal of signature multiplicity, where more than one gene expression model can perform a given classification task [52]. Gene expression signatures can identify viral infection before peak clinical symptoms A molecular classifier that distinguishes symptomatic from asymptomatic viral infection may not seem to be of great clinical relevance. However, the human challenge experiments afford the opportunity to determine whether the detection of biological mediators precedes the clinical phenotypes they evoke. Using the same challenge model described above, the gene expression signature for symptomatic viral infection was detected as early as 29 h post-exposure, and achieves maximal accuracy approximately 40 h before peak clinical symptoms [53]. 582

Another feature of the human challenge model is that approximately 50% of challenged subjects remained asymptomatic [50]. Therefore, one might assume the asymptomatic host’s biology remained unchanged from baseline. This is not what was observed. A self-organizing map algorithm was used to group gene expression changes into eight clusters, and highlighted differences over time in the symptomatic and asymptomatic groups [54]. Some gene clusters showed robust changes over time only in symptomatic subjects, some were dynamic only in asymptomatic subjects, and yet other clusters showed reciprocal changes in the two clinical groups. Reciprocal changes are of particular interest because they highlight mechanisms that lead to clinical illness or clinical stability. Pathways highlighted by these reciprocal changes include nucleotidebinding domain and leucine-rich repeat containing gene family (NLR)-related genes, which function in pathogen pattern recognition and innate immunity. In particular, nucleotide binding oligomerization domain 2 (NOD2) was highly expressed in the symptomatic group, differentiating them from asymptomatic individuals. NOD2 recognizes single-stranded RNA (ssRNA) of influenza and RSV, leading to activation of serine-threonine kinase 2 (RIPK2) and nuclear factor kappa-light-chain-enhancer of activated B cells (NFkB), both of which were increased in the symptomatic group. However, the asymptomatic group did reveal significant and sustained increases in expression of superoxide dismutase (SOD1) and serine/threonine kinase 25 (STOK25), which have been linked to an antioxidant and stress response. Another pathway highlighting different responses in symptomatic and asymptomatic subjects was that of suppressor of cytokine signaling (SOCS). SOCS genes generally act to regulate negatively the immune response to cytokine and growth factor signaling. In this study, some SOCS genes were upregulated in symptomatic subjects (SOCS1 and SOCS3), whereas others were upregulated in asymptomatic hosts (SOCS2 and SOCS5), suggesting differential roles in modulating the immune response over time. These results highlight the biological pathways involved in divergent responses to viral challenge, informing ongoing basic research and identifying novel areas for clinical research. Virus-specific gene expression patterns The gene expression classifiers described thus far have largely focused on pathogen class discrimination (viral versus bacterial) or symptomatic versus asymptomatic. Attempts to discriminate between pathogens within one class are also valuable diagnostic adjuncts. The viral challenge experiments described above using influenza, HRV, and RSV, did not identify virus-specific signatures [38]. By contrast, a recent prospective observational study of children with severe RSV infection demonstrated distinct gene expression signatures, different to that of influenza or HRV infection [55]. Using a K-Nearest Neighbor strategy, a 70-gene classifier discriminated RSV from HRV and influenza infections. Validation in an independent pediatric population revealed a 91% accuracy. The RSV response included overexpression of neutrophil-related genes, with suppression of lymphoid

Review Box 2. Systems virology Systems biology approaches integrate holistic measurements across multiple systems to gain a fuller understanding of the biology in question. Systems virology is the application of these approaches specifically to the field of virology [100]. Systems virology extends well beyond the human studies referenced in this review. Specifically, there is a significant literature describing host– virus interactions in mice, ferrets, macaques, and other model systems. Despite the uncertainties about applying animal model systems to humans, particularly as they relate to immune function [74], there are questions that cannot be answered any other way. For example, the 1918 influenza pandemic was the most lethal influenza outbreak in modern history. There are several possible reasons for its pathogenicity, including virus-intrinsic factors as well as a high-rate of bacterial co-infection. In lieu of studying this strain in humans today, animal models have provided a means to gain invaluable biological insight. Systems biology approaches have been utilized in mice for a reconstructed version of the 1918 influenza strain, as well as in macaques [101,102]. Another example of a strain too dangerous to test in humans is the highly pathogenic H5N1. Again, mice and macaque models have been described for this strain, revealing that it is the magnitude of the host response, rather than a fundamentally different response, that correlates with illness severity [101–104]. A particularly difficult element of the human host response to quantify is the correlation between easily accessible media, such as blood, and the biology at the site of infection, such as the lung. Animal models allow for such comparisons with relative ease [105,106], and can be performed for one particular system (e.g., transcriptome or proteome) or correlating whole systems with each other (e.g., transcriptome and proteome). Although there are many additional examples of where systems virology in the animal model offers advantages, a particularly notable one is that of bacterial and viral co-infection. Although data have been published pertaining to human bacterial and/or viral coinfection, these patients are highly heterogeneous and the dynamics of host, virus, and bacteria are uncontrolled and unknown. Animal models of co-infection have been described, providing a framework on which to build observational human data [107].

lineages and antimicrobial response genes, compared with HRV or influenza infection. The magnitude of these changes correlated with disease severity. These findings are important because they show that host gene expression changes reflect a response not only to the pathogen class, but also to specific pathogens. Moreover, host gene expression profiles might offer prognostic information related to disease severity that can inform clinical decision-making [55]. Another such example in mycobacterial infection illustrates the broad potential applicability of such findings (Box 3). Each of the studies described show that, by using gene arrays that encompass much of the transcriptome, relatively small sets of genes can be discovered that classify infection with a respiratory virus. The strength of these studies lies in the methodology, where gene expression changes are identified in an unsupervised manner (e.g., phenotypic labels are not assigned to the samples until after analysis has occurred) [50]. Following identification of a characteristic group of genes, investigators can then use various pathway identification software packages (e.g., GATHER [56] or DAVID [57]) to understand the content of the signature. Finding genes that are previously recognized to have a role in host response to viral infection adds biological plausibility to the statistical findings.

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Proteomics and metabolomics The aim of proteomic and metabolomic analyses is the comprehensive evaluation of protein and/or peptide and chemical-based products of the host genome, including characterization of any modifications or interactions with other biomolecules. Both proteomics and metabolomics primarily utilize mass spectrometry (MS)-based methods for the system-wide interrogation of protein and metabolite analytes. Samples can be separated by different features, such as pK, hydrophobicity, and ion mobility. The resolution of MS measurements of mass-to-charge ratios is now sufficient to differentiate between two identical analytes, except for the inclusion of a single extra neutron [58]. This level of refinement increases the complexity of analysis but provides a means for precise differentiation between varying physiological states. Proteomics and metabolomics for biomarker discovery and clinical application MS technologies and analytic methods continue to evolve, allowing identification and quantitation of larger numbers of proteins and metabolites with greater accuracy [59]. However, limitations in the ability to detect low abundance, hydrophobic, or basic analytes often result in incomplete proteome or metabolome coverage, and a restricted dynamic range [60]. Despite the technological challenges, the potential to develop simple, rapid, and low cost host protein- or metabolite-based assays for infection diagnosis and prognosis is compelling. In contrast to nucleic acid detection, which currently requires amplification, protein and metabolite targets lend themselves to simple and widely accepted clinical applications such as lateral flow immunochromatography (e.g., pregnancy or rapid Group A Streptococcus). There are numerous examples of unbiased discovery proteomics applied to in vitro viral infection models, including adenovirus [61], human RSV [62], and influenza A/H1N1 [63,64]. These primarily used 2D gel electrophoresis or stable isotope labeling by amino acids in cell culture (SILAC) coupled with liquid chromatography (LC)-MS to identify differentially expressed proteins. A reverse phase protein array technology was used with an ex vivo model of Rift Valley fever virus (RVFV) to identify and profile a phosphoprotein signaling pathway modulated during infection [65]. This suggested a viral strategy directed toward transcriptional control of apoptosis through the mitogen activated protein kinase (MAPK) and extracellular signal-regulated kinase (ERK) pathways. Studies such as these have generated comprehensive data sets that may prove useful in understanding the biology of infection and host response, and may lead to clinically useful therapeutic targets or diagnostic tools, but have yet to be validated as diagnostic tools in a clinical setting. Using the same viral challenge experiments described above, LC-MS/MS was used to analyze plasma obtained from influenza H3N2-challenged subjects at baseline and at maximal symptoms (A.K. Zaas et al., unpublished). The resulting MS traces were used to estimate concentrations of approximately 40 000 isotope groups per sample, with around 10% mapping to known proteins. Dimension 583

Review Box 3. A signature for TB A recent example of how gene expression modeling can be applied to nonviral respiratory infection is that of TB. This infection, of great concern particularly in resource-limited settings, poses many diagnostic challenges, especially in HIV-positive individuals, where other opportunistic infections present similarly, and in children. An early example of how gene expression can inform diagnostics involved a comparison of active TB to controls (healthy individuals and those with latent TB), which identified a 393-transcript classifier [108]. A smaller 86-transcript classifier differentiated active TB from other diseases. This work was followed by two more recent reports, one focused on adults [109] and the other on children [110]. These latter two studies reported smaller gene probe classifiers for two diagnostic tasks. The first was to differentiate active TB from latent TB (27 and 42 probes for adult and pediatric signatures, respectively, with one in common). The second was to distinguish active TB from other disease states (44 and 51 gene probes for adult and pediatric signatures, respectively, with six in common). Performance characteristics were good with 78–100% sensitivity and 73–96% specificity, superior to existing diagnostic modalities. One notable finding was the small overlap in gene probes from study to study, highlighting that subtle differences in the analysis population or methodology can produce different classifiers yet equivalent performance.

reduction with Bayesian sparse factor modeling identified 109 protein groups (factors). Highlighting the biological plausibility of this approach, AGL2 was among the most highly correlated with symptomatic influenza infection [58,66]. This protein is associated with lipopolysaccharide binding protein and C-reactive protein, both of which are acute phase reactants. Thus, although not as far in its development as gene expression profiling, proteomic data hold promise to find biomarkers that characterize viral infection and could more readily translate to a clinical platform. Metabolomic profiling in models of influenza virus infection has provided insights into the relations between infection dynamics and host cell metabolism. In cultured cell models of influenza infection, intermediates of glycolysis and tricarboxylic acid (TCA) cycle [67], and fatty acid biosynthesis and cholesterol metabolism [68], were found to be significantly altered upon infection of cultured cells as assayed using LC/MS and gas chromatography (GC)/MS methods. Another study applied LC/MS-based metabolomic methods to provide a comprehensive analysis of host lipid factors regulated during influenza virus infection in murine lung [69]. Lipid metabolites from both 5-lipoxygenase and 12/15-lipoxygenase pathways were differentially regulated by infection as a function of strain virulence and infection phase. Moreover, the ratio of 13- to 9-hydroxyoctadecadienoic acid emerged as a potential biomarker of influenza infection status. Importantly, several of these animal model observations were confirmed in nasopharyngeal lavage from human subjects with influenza. miRNA: a new and promising investigative and diagnostic tool miRNA are highly conserved, small noncoding RNAs involved in post-transcriptional regulation. Intracellularly, miRNAs exert their action in the cytosol as part of the RNA-induced silencing complex (RISC), binding to consensus sequence targets in the 30 untranslated region (UTR) of mature mRNAs to inhibit their translation. miRNAs are 584

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also able to exist stably in the extracellular space (including blood). Resistant to boiling, repeated freeze–thaw cycles, and decay over time, miRNAs offer significant appeal as diagnostic targets [70,71]. Moreover, miRNAs are highly conserved, such that discoveries in animal models may translate more successfully to human disease than historical precedent would suggest [72–74]. The role miRNAs have in various disease processes has entered into clinical diagnostic testing (e.g., for breast [75], prostate [76], and colon [77] cancers). Furthermore, miRNAs have also been successfully used as treatment targets, exemplified by miR-122 in hepatitis C virus (HCV) infection. Administration of miravirsen, a locked nucleic-acid modified DNA antisense to miR-122, led to its sequestration and a log-times reduction in HCV RNA levels [78]. miRNA expression in response to viral infection miRNA expression is a relatively novel addition to the ‘omics suite for evaluating the host response to viral infection (recently reviewed by Zhou et al. [79]). Most of the early work associating miRNAs with respiratory viral infections was done in vitro. For example, a laryngeal epithelial cell model of enterovirus 71 (EV71) infection identified 64 miRNAs with greater than twofold changes in expression [80]. These 64 miRNAs were predicted to target 5765 unique genes based on consensus sequences. Although this is a very large number of genes, the ontologies to which they mapped were highly relevant for enteroviral infection, including neurological processes, immune response, and cell death pathways. Moving beyond cell culture, a miRNA-based diagnostic biomarker was identified in patients with hand-foot-andmouth disease [81]. The authors identified six miRNAs (miR-148a, miR-143, miR-324-3p, miR-628-3p, miR-1405p, and miR-362-3p) that discriminated patients with EV71 infections from healthy controls. The combined six-miRNA classifier had 97.1% sensitivity and 92.7% specificity for EV71 infection. This cohort of human EV71 infection also afforded an opportunity to compare it with the in vitro EV71 model described above [81]. Of the six most relevant miRNAs identified in children, only two were also identified in the 64 miRNA in vitro profile. This underscores the importance of multiple approaches to define the effects of methodology (in vitro versus in vivo), demographics (pediatric versus adult), disease characteristics (mild versus severe), and other parameters that might affect results. miRNA response to influenza A viral exposure in human alveolar and bronchial epithelial cells showed higher levels of miR-7, miR-132, miR-146a, miR-187, miR-200c, and miR-1275 expression in exposed cells compared with unexposed cells [82]. These miRNAs were not induced by IFNs, IL-6, or tumor necrosis factor a (TNFa) exposure, suggesting they act upstream or in parallel to these pathways. Among the downstream mRNA targets and pathways identified were MAPK3 and interleukin-1 receptorassociated kinase 1 (IRAK1), signaling proteins regulating the cellular response to infection. Several recent studies measured miRNAs from patients with infection due to various strains of influenza, including 2009 H1N1 [83], H7N9 [84], and a mix of H1N1 and H3N2

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[85]. Microarrays were used to identify differentially expressed miRNAs in the blood of patients with confirmed influenza, compared with healthy controls. Candidate miRNAs were then verified using RT-PCR and used to build classifiers. Interestingly, not one miRNA overlapped between the three influenza classifiers; neither did any of these in vivo-derived miRNA classifiers overlap with the in vitro results described above. This lack of congruence is a concern and suggests methodological differences are of particular importance in miRNA analysis. The report by Zhu et al. [76] focused only on miRNA-based classification of disease, whereas the other two influenza-related miRNA publications took their work a step further. Specifically, Tambyah et al. [77] and Song et al. [78] identified networks of mRNAs putatively targeted by the identified miRNAs. Subsequently, pathway analysis and protein interaction networks were created that identified multiple relevant pathways, including MAPK signaling, apoptosis, TLR signaling, and B cell receptor signaling. Although these pathways have been implicated in viral pathogenesis and antiviral immune response, they are pleiotropic, making it difficult to interpret these findings. miRNA expression in response to bacterial infection There is less known about miRNAs in bacterial respiratory infections, and most published reports have focused on sepsis [86–91], pertussis [92], or TB [93–96]). miRNAs as diagnostic tools for more common bacterial respiratory pathogens have not yet been reported. Such research is ongoing and will likely follow similar approaches to those taken with respiratory viral infection. Only when relevant

clinical groups are directly compared will the utility of miRNAs for ARI diagnosis become evident. Concluding remarks and future perspectives Infection requires a pathogen and a clinically relevant host response. That interaction is mediated by a wide array of molecules, pathways, cells, and tissues. Long studied to define the pathophysiology of infection, these processes are now emerging as potential diagnostic, prognostic, and therapeutic targets. This shift away from pathogen-based detection is changing the landscape of infectious diseases. Rather than identifying the specific bacterium, virus, fungus, parasite, or noninfectious process at the root of an illness, host-based approaches assume there are differential responses to each of these conditions. As statistical analysis advances, the ability to build classifiers will advance as well (Figure 2) [97]. Such changes manifest through gene expression, proteomics, metabolomics, and miRNA, although additional targets and platforms are likely to become relevant. The host response will also reveal whether an identified pathogen represents a true infection or asymptomatic carriage. Extrapolating this to the population level, these technologies may become epidemiological tools to identify sentinel events, particularly for novel pathogens. Systems vaccinology is a rapidly emerging field that is already using similar approaches to predict at early time points which individuals will develop a robust immune response upon vaccination [98]. Clinical trials, particularly of new antimicrobials, are plagued by high costs and difficulty identifying the right target population. Host-based diagnostics offer an Funconal interpretaon

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Figure 2. Mapping transcriptional changes at the module-level identifies disease-specific biosignatures in patients with infectious diseases. Reducing the dimensionality of data is critical to developing host signatures of infection. This methodology, first published by Chaussabel and Baldwin [36], was utilized by Ramilo et al. to look for gene expression patterns (‘modules’) that classify varying infectious states compared with controls [46]. To use this information for classification, it is displayed on a grid, with the coordinates corresponding to one of 28 module IDs (e.g., Module M3.1 is at the intersection of the third row and first column, shown in dark green). Each module represents a group of genes with a certain function, such as platelet function, interferon signaling, or neutrophil function. The spots represent the percentage of significantly overexpressed (red) or underexpressed (blue) transcripts within a module (i.e., set of coordinately expressed gene) compared with healthy controls. Blank spots indicate that there are no differences in the genes included in that module between patients and healthy controls. Each pathogen induces an easily identified disease-specific biosignature, with certain modules up- or downregulated. Notably, in the figure shown, influenza induces high expression in Module M3.1 (interferon signaling), whereas Staphylococcus aureus infection induces modest downregulation, and respiratory syncytial virus (RSV) does not induce up- or downregulation of this group of genes. One might envision taking an unknown sample, analyzing it, and comparing the modular components to the known disease states of influenza, RSV, Escherichia coli, and S. aureus to make a classification of infection type. Reproduced, with permission, from [97].

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Review opportunity to screen potential study subjects and include only those with the relevant infection. Outside of human health, these technologies hold great promise in the ability to screen livestock, and could aide in pandemics of zoonotic origin. To realize these possibilities, innovation of molecular techniques is required that can operate at the point of care. The ability to harvest, preserve, and analyze RNA from peripheral blood cells in a meaningful way takes time using existing technologies. Microarrays, with their ability to evaluate multiple RNA sequences simultaneously, were a turning point in transcriptomics research; however, they are not practical to use as a rapid, clinically relevant test. The evolution of next-generation sequencing (NGS) for rapid DNA and RNA analysis circumvents some of these limitations. However, in its current state, NGS creates a massive amount of data to be cataloged and analyzed; thus, it is not fit for rapid laboratory use. New opportunities may be realized from processes such as chemical ligation-dependent probe amplification, which offers a faster and more affordable way to measure multiple RNA targets. Microfluidics offers yet another avenue to achieve these technical aims [99]. In addition, advances in computational biology must occur in step with increases in data generation and requirements for analysis. The synergy of these various biological, technical, computational, and regulatory components will create transformative opportunities for infectious diseases. Acknowledgments E.L.T. was supported by Award Number 1IK2CX000530 from the Clinical Science Research and Development Service of the VA Office of Research and Development. The views expressed in this article are those of the authors and do not necessarily represent the views of the Department of Veterans Affairs.

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