Staphylococcus aureus causes a spectrum of human infection. Diagnostic delays and uncertainty lead to treatment delays and inappropriate antibiotic use. A growing literature suggests the host’s inflammatory response to the pathogen represents a potential tool to improve upon current diagnostics. The hypothesis of this study is that the host responds differently to S. aureus than to E. coli infection in a quantifiable way, providing a new diagnostic avenue. This study uses Bayesian sparse factor modeling and penalized binary regression to define peripheral blood gene-expression classifiers of murine and human S. aureus infection. The murine-derived classifier distinguished S. aureus infection from healthy controls and Escherichia coli-infected mice across a range of conditions (mouse and bacterial strain, time post infection) and was validated in outbred mice (AUC>0.97). A S. aureus classifier derived from a cohort of 94 human subjects distinguished S. aureus blood stream infection (BSI) from healthy subjects (AUC 0.99) and E. coli BSI (AUC 0.84). Murine and human responses to S. aureus infection share common biological pathways, allowing the murine model to classify S. aureus BSI in humans (AUC 0.84). Both murine and human S. aureus classifiers were validated in an independent human cohort (AUC 0.95 and 0.92, respectively). The approach described here lends insight into the conserved and disparate pathways utilized by mice and humans in response to these infections. Furthermore, this study advances our understanding of S. aureus infection; the host response to it; and identifies new diagnostic and therapeutic avenues.
Citation: Ahn SH, Tsalik EL, Cyr DD, Zhang Y, van Velkinburgh JC, et al. (2013) Gene Expression-Based Classifiers Identify Staphylococcus aureus Infection in Mice and Humans. PLoS ONE 8(1): e48979. doi:10.1371/journal.pone.0048979
Editor: Michael Otto, National Institutes of Health, United States of America
Received: August 20, 2012; Accepted: September 27, 2012; Published: January 9, 2013
This is an open-access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.
Funding: This work was supported by R01-AI068804 and K24-AI093969 from the National Institute of Allergy and Infectious Diseases (VGF), 5U01AI066569-05 and 3U01AI066569-05S1 from the National Institute of Allergy and Infectious Diseases (SFK), and the Wallace H. Coulter Foundation (AKZ and GSG). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have read the journal’s policy and have the following conflicts: CBC and CWW have served as consultants for bioMerieux Inc. ELT, CWW, and GSG received research funding from Novartis Vaccines and Diagnostics, Inc. In the past 12 months, CWW has served as a consultant to Becton, Dickinson and Company, and Gilead Sciences; and has received research or grant support from Cubist Pharmaceuticals, Novartis Pharmaceuticals, and Roche Molecular Diagnostics. ELT is supported by a Veterans Affairs Career Development Award. In the past 12 months, VGF has received grant or research support from Astellas Pharma US, Merck, Theravance, Cerexa, Pfizer, Novartis, MedImmune, Advance Liquid Logic, and the National Institutes of Health; has served as a consultant for Achaogen, Merck, Novartis, Pfizer, NovaDigm Therapeutics, The Medicines Company, Durata Therapeutics, Galderma, and Biosynexus; and is on the Advisory Committee for Cubist Pharmaceuticals. AKZ is a consultant for Covance. EPR receives research support from Agennix AG, Alere Corporation, and National Institutes of Health; and has previously served as a consultant for Agennix AG, Eisai Pharmaceuticals, Idaho Technology, AstraZeneca, Masimo, and Sangard. This does not alter the authors’ adherence to all the PLOS ONE policies on sharing data and materials.
Septicemia causes substantial morbidity and mortality among patients in the United States, with a rising burden of Staphylococcus aureus infection , . Although blood cultures are the diagnostic gold standard for blood stream infection (BSI), sensitivity is limited and results are not rapidly available . Such diagnostic delays can extend the time to administration of effective antibiotics, which is an independent risk factor for mortality , . Conversely, diagnostic uncertainty also leads to high rates of empiric overtreatment, fueling the burden of antimicrobial resistance , . Thus, novel approaches that are faster and more accurate are needed to differentiate between the major pathogens causing sepsis and BSI.
Whereas conventional diagnostic approaches have focused on identifying the infecting pathogen, a growing body of evidence suggests that the host’s inflammatory response to the pathogen also represents a potential diagnostic tool. In vitro and in vivo experiments have revealed fundamental differences in host response to Gram-positive and Gram-negative bacterial infection –, including significant differences in Toll-like receptor (TLR) signaling ,  and cytokine production , . Distinctive gene expression profiles exist for viral , , bacterial , , and fungal infections ,  in both animal model systems and ex vivo stimulation of human peripheral blood leukocytes. Peripheral blood mononuclear cell (PBMC) gene expression signatures have also been evaluated in humans for a variety of conditions including severe infection , bacterial vs. viral illness , systemic lupus erythematosus , atherosclerosis , and radiation exposure . Taken together, these studies provide strong evidence that global changes in host blood gene expression patterns can be used to differentiate disease states.
The current study used S. aureus and Escherichia coli as prototypical Gram-positive and Gram-negative bacteria due to their prevalence and clinical relevance. Host gene expression was measured in mice with bacterial infection across multiple conditions. From these data, we derived a molecular classifier for S. aureus infection in inbred mice and validated it in a cohort of outbred mice. Next, we used host gene expression data from a well-characterized cohort of septic human subjects to identify a molecular classifier that accurately distinguished S. aureus BSI from E. coli BSI or uninfected controls. Murine and human S. aureus classifiers exhibited significant similarity particularly in comparing S. aureus infection to the healthy state. Furthermore, both murine and human classifiers were validated in an independent human cohort. This study is the first to demonstrate that the in vivo host response to Gram-positive infections is conserved from mouse to human and can be harnessed as a novel diagnostic strategy in patients with bacterial sepsis.
Materials and Methods
All animal experiments were carried out in strict accordance with the recommendations of NIH guidelines, the Animal Welfare Act, and US federal law. All animal procedures were approved by the Institutional Animal Care and Use Committee (IACUC) of Duke University (IACUC number: #1310905) which has been accredited by the Association for Assessment and Accreditation of Laboratory Animal Care (AAALAC) International. All animals were housed in a centralized and AAALAC accredited research animal facility that is fully staffed with trained husbandry, technical, and veterinary personnel. The Institutional Review Boards at Duke University Medical Center, the Durham VA Medical Center, and Henry Ford Hospital approved the human studies referenced in this work. Written informed consent was obtained for all subjects after the nature and possible consequences of the studies were explained.
Preparation of Bacterial Cells
One methicillin-susceptible S. aureus (Sanger 476) and three methicillin-resistant S. aureus genetic backgrounds (USA100, USA300, and MW2) were used. Overnight S. aureus cultures were inoculated into fresh tryptic soy broth and incubated aerobically at 30°C to log-phase growth (optical density 600 nm of ~1.0) . Cells were harvested by centrifugation, rinsed, and resuspended in phosphate-buffered saline (PBS). E. coli O18:K1:H7 was grown at 30°C overnight in Luria-Bertani broth . Cultures were then diluted with fresh medium and grown for an additional 1 to 2 hours. Upon reaching log phase, cells were treated as described for S. aureus.
Subjects were enrolled at Duke University Medical Center (DUMC; Durham, NC), Durham VAMC (Durham, NC), UNC Hospitals (Chapel Hill, NC), and Henry Ford Hospital (Detroit, Michigan) as part of a prospective, NIH-sponsored study to develop novel diagnostic tests for severe sepsis and community-acquired pneumonia (ClinicalTrials.gov NCT00258869) , . Enrolled patients had a known or suspected infection and exhibited two or more Systemic Inflammatory Response Syndrome criteria . Patients were excluded if they had an imminently terminal co-morbid condition, advanced AIDS (CD4 count <50), were being appropriately treated with an antibiotic pre-enrollment, or were enrolled in another clinical trial. Blood was drawn for microarray analysis on the day of hospital presentation with the exception of two subjects (S19 and S29). In these latter two cases, blood was not available for microarray preparation from that time point. However, blood drawn 24 hours into the hospitalization was available and so was used. Subjects in the current report had culture-confirmed monomicrobial BSI due to S. aureus (n = 32; median age 58 years; range 24–91) or E. coli (n = 19; median age 58; range 25–91). Uninfected controls (n = 43; median age 30 years; range 23–59) were enrolled at DUMC as part of a study on the effect of aspirin on platelet function among healthy volunteers . Subjects were recruited through advertisements posted on the Duke campus. Blood used to derive gene expression data in these healthy controls was drawn prior to aspirin challenge.
Murine Sepsis Experiments
Except where noted, mice were purchased from The Jackson Laboratory (Bar Harbor, ME) and allowed to acclimate for 7 days. All experiments were performed on 6–8 week old mice. For the murine S. aureus classifier, seven inbred mouse strains (3 mice/strain: 129S1/SvImJ, A/J, AKR/J, BALB/cByJ, C57BL/6J, C3H/HeJ, and NOD/LtJ) were IP inoculated with 107 CFU/g of S. aureus Sanger476, euthanized at 2h after injection, and bled. This was repeated using the four different S. aureus genetic backgrounds (USA100, USA300, MW2, and Sanger476) in A/J mice (n = 3 per S. aureus background). For time series experiments, both A/J and C57BL/6J mouse strains were IP inoculated with S. aureus Sanger476 as above, and sacrificed at 2, 4, 6, and 12 h after injection (n = 5 per mouse strain at each time point). For survival experiments, mice were monitored twice daily after injection and culled upon reaching a moribund state. Animal sacrifice was carried out by carbon dioxide inhalation. Blood was collected by intracardiac puncture and stored in RNAlater at −70°C for microarray experiments.
The murine E. coli infection model was carried out as described above except a smaller inoculum (6×104 CFU/g) was used. Furthermore, the time at which animals were sickest but still alive was 24 hours for E. coli inoculation, which is later than for S. aureus. Consequently, A/J and C57BL/6J mice inoculated with E. coli were sacrificed 24 h after challenge (n = 5 per mouse strain). Control mice were not injected.
Outbred CD-1 mice were purchased from Charles River Laboratories (Wilmington, MA) to validate the murine S. aureus classifier. CD-1 mice were IP inoculated with 107 CFU/g of S. aureus (USA300 and Sanger 476) and 6×104 CFU/g of E. coli. Animals including controls were sacrificed at 2 and 24 h post-infection (n = 10 mice per pathogen at each time point). Blood was collected and stored as described for the derivation cohort.
Microarray Preparation (Additional Details Available in Methods S1)
Total RNA was extracted from mouse blood using the Mouse RiboPure Blood RNA kit (Ambion, Austin, TX) according to the manufacturer’s instructions. Globin mRNA was removed from whole blood RNA using the Globinclear kit (Ambion, Austin, TX). All samples passed the quality criteria of the Agilent Bioanalyzer and were used for microarray analysis. Since the total RNA yield of many samples was low, one round of linear amplification was performed for all samples using the MessageAmp Premier kit (Ambion, Austin, TX). RNA integrity numbers were calculated for all samples and found to be within tolerance limits. Microarrays were normalized using Robust Multichip Average (RMA). Affymetrix GeneChip Mouse Genome 430 2.0 Arrays were used (Santa Clara, CA). Biotin-labeled cDNA was hybridized to the arrays for 16 hours at 45°C according to the manufacturer’s instructions. Arrays were then washed and labeled with streptavidinphycoerythrin (strep-PE), and the signal was amplified using biotinylated antistreptavidin followed by another round of staining with strep-PE. These steps were performed on the Affymetrix fluidics station according to the recommended protocol. Amplification and microarray hybridization were performed at the Duke University Microarray Core. Labeled gene chips were scanned using an Affymetrix Genechip Scanner 7G (Santa Clara, CA). This array contains 45,101 probe sets to analyze the expression level of over 39,000 transcripts and variants from over 34,000 mouse genes.
Human microarrays were prepared by first extracting total RNA from human blood using the PAXgene Blood RNA Kit (Qiagen, Valencia, CA) according to the manufacturer’s recommended protocol including DNase treatment. RNA quantity and quality was assessed using the Agilent 2100 Bioanalyzer (Agilent, Santa Clara, CA). RNA integrity numbers were calculated for all samples and found to be within tolerance limits. Microarrays were normalized using RMA. Hybridization and microarray data collection was then 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”. Fluorescent images were detected in a GeneChip Scanner 3000 and expression data was extracted using the GeneChip Operating System v 1.1 (Affymetrix). All GeneChips were scaled to a median intensity setting of 500. Murine and human microarray data have been deposited in the NCBI GEO (accession # GSE33341).
Deriving the Murine and Human S. aureus Classifiers
Microarray data was analyzed in two steps following the analysis strategy previously outlined and utilized . First, a Bayesian sparse factor model was fit to the expression data without regard to phenotype , . Second, factors were then used as independent variables to build a penalized binary regression with variable selection model  trained to identify S. aureus infection. In order to minimize issues with overfitting, batch was not included in the regression models. We used a Bayesian penalized regression technique for variable selection which allows for weighted model averaging of the resultant models, such that weights are computed from model fit on the training data . The model averaging approach incorporates uncertainty in choice of model as well as regression coefficient. This has been shown to lead to out of sample predictive accuracies that are superior to penalized maximum likelihood approaches . Assumptions for this approach are typical of probit regression including a linear response surface between predictors and the transformed latent probability variable. Genes were filtered for analysis using non-specific filtering for genes with high mean expression and high variance across samples. Samples with a high number of outlying genes were removed during the factor analysis. Mice were batched into discrete experiments with each experiment containing the relevant controls to avoid confounding. The development and application of this methodological approach has been previously described , , , , –. Using the same murine experimental data, another classifier was derived to classify methicillin-resistant vs. methicillin-sensitive S. aureus infection. The methodology was otherwise the same as that described above.
We fit a factor model on the human data independently from the mouse data. The factor model was fit to 9,109 genes after non-specific filtering to remove unexpressed and uniformly expressed genes. Z-scores were computed independently for each gene without regard to experimental design. Subjects with absolute z-scores greater than 3 in more than 5% of the genes on the array were identified as outliers and were not used to fit the factor model. The factor model was trained on the 91 samples (after removal of three outliers) from three batches of expression data, and this resulted in 79 factors. These 79 factors were then projected onto the full data set (including the three subjects removed for validation) with the goal of distinguishing S. aureus BSI from healthy controls or E. coli BSI. Leave-one-out cross-validation was utilized in order to control for overfitting of the penalized binary regression model. In order to minimize issues with overfitting, batch was not included in the regression models. Matlab (Natick, MA, USA) scripts to perform these operations are available. Nonparametric testing was used to evaluate model performance (Wilcoxon rank sum for 2-group comparisons or Kruskal-Wallis for 3 or more-group comparisons) unless otherwise indicated.
One limitation of this approach is that the marginal significance of genes within the factor-based classifier cannot be defined. Instead, gene lists were created to identify genes with differential expression between specified groups with respect to gene-level and factor-level analyses. For 3-group comparisons (S. aureus vs. E. coli vs. Healthy controls) one-way analysis of variance (ANOVA) was used. For pairwise comparisons, Student’s t-test was used. Results were statistically significant at p<0.05 after Bonferroni correction for multiple testing. Spreadsheets of gene/factor lists are provided as supplemental material.
Creating a Human Ortholog of the Murine S. aureus Classifier
We used Chip Comparer (http://chipcomparer.genome.duke.edu/) to identify human orthologs for all possible mouse genes. When there were multiple orthologs, we preferentially used the anti-sense target probes that shared the fewest probes with other genes as identified by the probe label. Chip Comparer identified 17,600 probe sets on the Affymetrix GeneChip Human Genome U133A 2.0 Array that have orthologs in the Affymetrix GeneChip Mouse Genome 430 2.0 Array. Factor scores from the mouse factor model were estimated using this set of 17,600 genes as follows: Given a matrix of expression values, X, and a factor model X = BF+e, we first replaced missing values by mean expression levels for those genes. Step 2: Inverse regression was utilized to compute F*, to estimate the factor scores. Step 3: We estimated X by computing BF* and replaced missing values with the corresponding values from this matrix. Steps 2 and 3 were then repeated until the estimates for the missing values converged.
External Validation in an Independent Cohort
To externally validate the murine and human S. aureus classifiers, we utilized publically available expression data from a pediatric cohort with S. aureus infection and healthy controls . Hospitalized children with invasive S. aureus infection were enrolled with sample collection occurring after microbiological confirmation. Healthy controls included children undergoing elective surgical procedures and at healthy outpatient clinic visits. This dataset includes multiple expression platforms. For the purposes of consistency, we only included subjects with Affymetrix U133A data yielding 46 S. aureus-infected patients and 10 healthy controls. Given the absence of subjects with E. coli infection in the validation cohort, we derived new murine and human S. aureus classifiers that excluded animals or subjects with E. coli infection. These classifiers were derived and then projected onto the 56-sample validation cohort as described heretofore.
Heat Map Generation
In order to generate heat maps of gene expression, we first turned to the factors from the murine and human S. aureus classifiers. Probes from each factor were identified and tested for differential expression in a one-way ANOVA. Probes with significantly different levels of expression after Bonferroni correction were retained. For the murine data, there were thousands of probes (~1000–3000, typically) meeting these criteria. Consequently, the p-values were sorted in ascending order and the 100 most significant probes from each factor were retained. Duplicate probes across the factors were removed. The human expression heat map was created in the same manner except all significant probes are presented considering there were fewer factors and genes in the human S. aureus classifier as compared to the murine classifier. Heat maps were generated using Matlab (Natick, MA, USA).
Pathway analysis for functional annotation of genes was performed with the MetaCore tool of the GeneGO package (GeneGo, Inc., St. Joseph, MI, USA) (http://www.genego.com). P-values were assigned to pathways based on the number of genes mapping to a particular pathway relative to the total number of genes in that pathway. Statistically significant pathways were defined as a p-value <0.05 (False Discovery Rate [FDR]-adjusted) based on hypergeometric distributions .
Murine Sepsis due to S. aureus and E. coli
Clinically relevant S. aureus infections in humans typically arise from a primary focus with secondary dissemination. To mimic this process, mice were inoculated via the intraperitoneal (IP) route . Infection-susceptible and infection-resistant inbred mouse strains (A/J and C57BL/6J, respectively) ,  were inoculated with S. aureus (Sanger476) or E. coli (O18:K1:H7) (n = 5 per mouse strain and bacterial species). A survival analysis was carried out to determine the optimal duration of infection for subsequent experiments (Figure S1A). Based on this data, A/J and C57BL/6J mice were infected with S. aureus (sacrificed at t = 0, 2, 4, 6, and 12 hours post-infection; n = 10 animals/time point) or E. coli (t = 0, 2, 6, 12, and 24 hours post-infection; n = 10 animals/time point). The effect of infection status, bacterial pathogen, and duration of infection on global patterns of gene expression was assessed using principal component analysis (PCA) (Partek Genomics Suite) (Figure S1B-D) . Gene expression patterns clustered by infection status and by pathogen (S. aureus vs. E. coli). Animals infected with S. aureus demonstrated a time-dependent change in gene expression that first manifested at two hours, by which time bacteremia has occurred . This pattern remained stable through 12 hours, when most animals have succumbed to sepsis. E. coli-infected animals did not reveal this time-dependent progression based on the time points sampled, but had a distinctly different pattern of gene expression that was evident at 2 hours and persisted through 24 hours following infection. A heat map depicting the time-dependent nature of these gene expression changes is presented in Figure S2.
Peripheral Blood Gene Expression Signatures Classify S. aureus-infected from Uninfected Mice
To create a host gene expression-based classifier for S. aureus infection, mice from a variety of experimental conditions were utilized (n = 187 total). Seven strains of inbred mice were challenged with 4 S. aureus genetic backgrounds via IP inoculation and sacrificed at various time points as described in Experimental Procedures. The comparator group for model derivation included 50 A/J or C57BL/6J mice inoculated with E. coli (O18:K1:H7) as well as 54 non-inoculated mice. Whole blood mRNA was used to generate microarray expression data. A list of differentially expressed genes is presented in Table S1. Figure S3 presents the number of overlapping genes in each pairwise comparison. Patterns of co-expressing genes were defined using sparse latent factor regression in an unsupervised manner (i.e. without knowledge of the source animal’s infection status) , . Factor models are a well-known technique for describing correlation structure in high dimension, low sample size data sets. Our sparse latent factor model works by collecting genes that are highly correlated into groups. Predictive models are then built from the latent factors – vectors that describe the aggregate behavior of the group. Subsequently, these factors served as independent variables in a variable selection, binary regression model to distinguish animals with and without S. aureus infection. This approach was taken in lieu of using individual gene expression changes for several reasons. A given gene with biological relevance may be differentially expressed in response to S. aureus infection but not to the degree that would meet statistical significance. Considering this altered gene expression exists amid a network of other such changes, the collective perturbations in that particular pathway would be more easily detected using factor analysis. Furthermore, changes across multiple biological pathways will be reflected across multiple factors. These can then be collectively harnessed for their diagnostic potential using a binary regression model.
Thirty factors were identified, of which 16 demonstrated a pattern of expression significantly associated with infection status (mFactors 15, 7, 23, 13, 9, 29, 28, 2, 17, 16, 21, 1, 5, 4, 26, and 19 in order of greatest significance; ANOVA; p<0.0017 for S. aureus vs. control vs. E. coli after Bonferroni correction; Figure S4). These 30 factors were fitted into a penalized binary regression model, termed the “murine S. aureus classifier”. The best performing model, as defined by the model with the largest log-likelihood value, included four factors (mFactors 7, 15, 23, and 26). Other models may be just as adequate, but we are only referring to this “top” model. Leave-one-out cross-validation was used to control overfitting and to estimate the model’s performance in subgroups of experimental conditions as described below (mouse strain, S. aureus genetic background, duration of infection, and bacterial species [S. aureus vs. E. coli]). A schematic of the derivation and validation experiments is depicted in Figure 1.
Figure 1. Schematic of derivation and validation cohorts.
The Murine Derivation Cohort includes S. aureus infection (n = 83), healthy control mice (n = 54), and E. coli infection (n = 50). It served as a validation cohort to assess Mouse Strain Effect, S. aureus Genetic Background Effect, Time Course, and to compare S. aureus vs. E. coli and E. coli vs. Healthy. The murine S. aureus classifier was externally validated in Outbred Mice (n = 30) and the CAPSOD Human Cohort. The CAPSOD Human Cohort includes S. aureus BSI (n = 32), healthy volunteers (n = 43), and E. coli BSI (n = 19). It served as a validation cohort to compare S. aureus vs. Healthy, S. aureus vs. E. coli, and E. coli vs. Healthy. Model derivation and validation using the entire cohort of animals or humans is depicted by the blue outline and arrows. An independent classifier was generated using only subjects with S. aureus or E. coli BSI (green outline). This classifier was validated using leave one out cross validation (green arrow). The Human Pediatric Cohort (n = 46 S. aureus, 10 Healthy) used for external validation does not include patients with E. coli infection. Therefore, S. aureus classifiers were generated from the murine and CAPSOD cohorts that excluded E. coli data (red outline and thick red arrow). The Human Pediatric Cohort was used to derive a Human S. aureus vs. Healthy classifier which was validated in the S. aureus-infected and Healthy populations within the murine and CAPSOD human cohorts (thin red arrow).doi:10.1371/journal.pone.0048979.g001
The ability of the murine-derived host gene expression classifier to identify S. aureus infection was tested in 7 inbred mouse strains of varying infection susceptibilities . In all 7 strains, the murine S. aureus classifier accurately differentiated S. aureus-infected from control mice (p = 4.89×10−16; AUC = 0.9964) (Figure 2A). The ability to characterize S. aureus infection persisted when A/J mice (infection-susceptible) were challenged with four different S. aureus backgrounds: USA100 (the predominant US nosocomial methicillin resistant S. aureus [MRSA] genetic background); USA300 (the predominant US community-acquired MRSA genetic background); USA400 (MW2); and Sanger 476 (a methicillin-susceptible genetic background) (p = 1.92×10−10 vs. control mice; AUC = 1.00) (Figure 2B). Furthermore, the murine S. aureus classifier consistently discriminated S. aureus infected mice from controls at 2, 4, 6, and 12 hours post-inoculation (p = 4.41×10−16 vs. uninfected mice; AUC 1.00) (Figure 2C). This time interval was selected because two hours is the earliest time point at which S. aureus can be cultured from blood; while 12 hours was the point at which animals began to die of S. aureus sepsis (Figure S1A). In summary, a classifier based on murine-derived host gene expression accurately identified the presence of S. aureus infection in mice under a variety of host, pathogen, and temporal conditions.
Figure 2. Murine S. aureus classifier accurately identifies S. aureus infection under a variety of conditions.
Conditions represented include different murine hosts (A), bacterial genetic backgrounds (B), and time from inoculation (C). Animals with S. aureus infection are depicted by a red “x”. Uninfected control mice are depicted by black circles.doi:10.1371/journal.pone.0048979.g002
Murine S. aureus Classifier Distinguishes S. aureus-infected from E. coli-infected Mice
Next, we determined whether the murine S. aureus classifier could differentiate S. aureus from E. coli infection. Both the infection-susceptible A/J and infection-resistant C57BL/6J strains were infected with either S. aureus (Sanger 476) or E. coli (O18:K1:H7). Animals were sacrificed at 2, 6, and 12 hours after inoculation. The murine S. aureus classifier correctly identified 50 of 53 (94.3%) animals as either infected with S. aureus or not at 2 hours (50/53), 100% of animals at 6 hours (n = 20), and 96.7% of animals at 12 hours (29/30) (Figure 3A). This corresponded to an overall p-value of 7.94×10−26 by Kruskal-Wallis test (comparing S. aureus vs. E. coli vs. Healthy controls) with an AUC of 0.9935 across all time points. Next, the murine S. aureus classifier was independently validated in outbred CD-1 mice with S. aureus infection (Sanger 476 or USA300), E. coli infection (O18:K1:H7), or uninfected controls (10 animals per condition). The murine-derived S. aureus model accurately classified 95% of all animals where the reference standard was the known experimental condition (38/40; p = 1.47×10−6; 90% sensitivity and 100% specificity; AUC = 0.9775) (Figure 3B).
Figure 3. The murine S. aureus classifier differentiates S. aureus from E. coli infection.
(A) Inbred mice were tested under three conditions: uninfected controls (black circles), S. aureus infected (red “x”), and E. coli infected (blue triangles). The y-axis represents the predicted probability that a given animal was infected with S. aureus. (B) The murine S. aureus classifier is validated in outbred CD-1 mice where it differentiates S. aureus infection from E. coli infection and uninfected controls.doi:10.1371/journal.pone.0048979.g003
The murine S. aureus classifier was generated to identify S. aureus infection within a population including both healthy and E. coli-infected animals. However, it is possible this classifier is primarily distinguishing “sick” from “not-sick” phenotypes. In such a case, it would be expected that the classifier would still differentiate animals with E. coli infection from uninfected controls. However, this was not observed (AUC 0.5089; p = 0.8785) demonstrating the specificity of this classifier for S. aureus infection. Thus, a murine-derived host gene expression classifier accurately distinguished S. aureus-infected from E. coli-infected or uninfected mice across multiple host strains, pathogens, post-infection time points, and was validated in outbred mice.
Given this ability to discriminate infection due to different bacterial species, we further explored the potential for a factor-based classifier to distinguish infection due to methicillin-resistant (MRSA) or methicillin-sensitive S. aureus (MSSA), which have been shown to differ in their pathogenicity and virulence. The same 30 factors described above were fitted into a penalized binary regression model with the specific aim of differentiating MRSA from MSSA infection. Leave-one-out cross-validation was used to control overfitting and to estimate the model’s performance in a population of 19 MRSA-infected and 84 MSSA-infected mice (Figure S5). Despite some overlap, this classifier accurately differentiated infection due to MRSA or MSSA (AUC 0.8396; p = 4.14×10−6). Genes discriminating infection due to MRSA or MSSA that remained significant after adjusting for multiple tests are presented in Table S2.
Human S. aureus Classifier
We next determined whether peripheral blood gene expression in humans could yield a classifier for S. aureus BSI. Peripheral whole blood mRNA from 32 patients with S. aureus BSI, 19 patients with E. coli BSI, and 43 healthy control subjects were used to generate microarray data (Table 1). A list of differentially expressed genes is presented in Table S3. Figure S6 presents the number of overlapping genes in each pairwise comparison. Seventy-nine factors were defined and fitted into a linear regression model trained to identify the presence of S. aureus BSI. Although 17 factors were independently associated with S. aureus BSI (Figure S7), only two factors remained in the best-performing model (hFactors 20 and 74). Similar to the murine S. aureus classifier, the human S. aureus classifier was generated blind to microbiological diagnosis in an unsupervised manner. Gender was controlled for in the model’s derivation considering the predilection for female sex in E. coli BSI (Table 2). We then estimated the model’s performance in phenotypic subgroups using leave-one-out cross-validation. The classifier accurately differentiated those with S. aureus BSI from healthy controls (72/75 correctly classified; AUC = 0.9898; p = 5.41×10−13) (Figure 4A). The human S. aureus classifier also correctly distinguished S. aureus from E. coli BSI in 82% (42/51) of cases (AUC = 0.8372; p = 6.77×10−4). When the human S. aureus classifier was applied to subjects with E. coli BSI vs. healthy controls, we observed an intermediate level of discrimination (56/62 correctly classified; AUC 0.9229; p = 1.38×10−7). This suggests that the human classifier is partially pathogen specific since E. coli BSI could also be distinguished from healthy controls but not with the same degree of accuracy as S. aureus BSI. A heat map depicting these gene expression changes is presented in Figure S8.
Figure 4. Performance of the human S. aureus classifier.
(A) The human S. aureus classifier differentiates S. aureus BSI from both uninfected controls and E. coli BSI. (B) A separate classifier was generated using only S. aureus and E. coli-infected human subjects and tested using leave-one-out cross-validation.doi:10.1371/journal.pone.0048979.g004
Table 1. Description of human subjects used to generate a S. aureus classifier.doi:10.1371/journal.pone.0048979.t001
Table 2. Characteristics of human subjects used for S. aureus classifier derivation.doi:10.1371/journal.pone.0048979.t002
In the human S. aureus classifier described above, it is the inclusion of healthy controls that drives the discrimination from S. aureus BSI. Considering the clinical importance of differentiating Gram-positive from Gram-negative infections, rather than sick vs. healthy, we created a penalized binary regression model with the specific aim of differentiating human S. aureus (n = 32) from E. coli (n = 19) BSI. In this cohort, 52 factors were identified (different from the 79 factors identified when Healthy was included) of which only hFactor 40 remained in the top performing model after controlling for gender. Using leave-one-out cross-validation (Figure 4B), this model had a sensitivity of 62.5% (20/32 S. aureus BSIs correctly classified) but a specificity of 94.7% (18/19 E. coli BSIs correctly classified). This corresponds to an AUC of 0.8503 (p = 3.47×10−5).
A Murine S. aureus Classifier Identifies S. aureus Infection in Humans
We then determined whether the murine S. aureus classifier could identify S. aureus BSI in humans. To accomplish this, the murine S. aureus classifier was projected onto human gene expression data. Specifically, Chip Comparer (http://chipcomparer.genome.duke.edu/) provided a modified representation of the Affymetrix Mouse Genome 430 2.0 Array that only included orthologs of transcripts represented on the Affymetrix Human Genome U133A 2.0 Array. This resulted in a murine S. aureus classifier consisting only of genes with human orthologs (68.6% of the total array representation). We then evaluated this classifier in our human cohort. To account for potential species-specific variation in gene expression, predicted probabilities were plotted on a logit rather than a probabilistic scale. Using this murine S. aureus classifier, human patients with S. aureus BSI were distinguished from healthy controls (AUC = 0.9484; p = 4.00×10−11) (Figure 5). Thus, the host response to S. aureus infection was sufficiently conserved that a predictive model generated in one species (Mus musculus) identified S. aureus BSI in another (Homo sapiens). However, the murine-derived S. aureus classifier did not differentiate between S. aureus and E. coli BSI in humans (AUC = 0.5905; p = 0.2883).
Figure 5. Projecting the mouse S. aureus classifier onto human subjects.
The murine S. aureus classifier identifies humans with S. aureus BSI, but does not differentiate S. aureus from E. coli BSI.doi:10.1371/journal.pone.0048979.g005
Validation of Murine and Human Classifiers in an Independent Pediatric Population
We externally validated the murine and human S. aureus classifiers in an independent human cohort . This validation cohort consisted of pediatric patients hospitalized due to invasive S. aureus infection (n = 46) and healthy controls (n = 10) who had gene expression data generated on a compatible platform (U133A array) with that used in this study. This cohort did not enroll children with E. coli infections. For this reason, we excluded E. coli infection from both classifiers. New murine and human S. aureus classifiers were developed in the same manner described above but without E. coli-related expression data. This modified murine S. aureus classifier was comprised of mFactors 7, 15, and 26 but not mFactor23. The modified human S. aureus classifier only contained hFactor4. Both the murine and human S. aureus classifiers differentiated children with S. aureus infection from healthy controls in this validation cohort (murine classifier AUC = 0.9522, p-value = 9.03×10−6 [Figure 6A]; human classifier AUC 0.9217, p-value 3.48×10−5 [Figure 6B]). The converse was also true. A S. aureus classifier trained on this independent pediatric cohort accurately discriminated S. aureus infection from healthy controls in our CAPSOD human cohort (70/75 correctly classified; AUC = 0.9775, p-value = 2.03×10−12) and murine cohort (123/137 correctly classified; AUC = 0.9255; p = 4.56×10−17).
Figure 6. Validation in an independent human cohort .
(A) The murine S. aureus classifier differentiates between S. aureus infection and healthy. (B) The human S. aureus classifier differentiates between S. aureus infection and healthy.doi:10.1371/journal.pone.0048979.g006
S. aureus Infection Induces Similar Host Gene-expression Responses in Mouse and Human
Pairwise comparisons were performed to identify genes with significantly different levels of expression (after Bonferroni correction). Comparisons included S. aureus infection vs. Healthy, E. coli infection vs. Healthy, and S. aureus vs. E. coli infection in mice and humans (Tables S1 and S3). Genes from each pairing were entered into the GeneGo pathway map database. The 50 most significant biological pathways arising from the pairwise comparisons are presented in Table S4. The genes represented within common pathways are presented in Table S5. A similar number of pathways overlapped between the murine and human responses to S. aureus (12 of the top 50) and E. coli (14 of the top 50) infection. Most of the overlapping pathways in the murine and human responses to both S. aureus and E. coli belonged to the broad category of immune response including CD28, ICOS, and the MEF2 pathway. Cytoskeletal remodeling (TGF and WNT) and apoptosis were also common to both infection types in mice and humans. Some pathways were highly significant in the S. aureus vs. Healthy comparison but not manifest in E. coli vs. Healthy such as NF-κB-associated pathways; the CD40 immune response pathway; and clathrin-coated vesicle transport. As expected, these pathways were also differentially manifest in the direct comparison of murine S. aureus and E. coli infection. We did not identify any statistically significant probes that distinguished human S. aureus from E. coli BSI. One probe, corresponding to the F2RL3 gene, nearly met this statistical cutoff (p-value 5.94×10−6 with a cutoff of 2.24×10−6). F2RL3 encodes proteinase-activated receptor 4 . This molecule is a G-protein coupled receptor activated by thrombin and trypsin but has not previously been implicated in the sepsis or immune response. It is expressed in multiple tissues with high levels in the lung, pancreas, thyroid, testis, and small intestine but not peripheral blood or lymphoid tissues .
Early diagnostic strategies for S. aureus BSI could improve patient care by reducing the time required to establish the diagnosis and provide appropriate treatment while avoiding unnecessary anti-MRSA antibiotics. The current investigation contributes to this goal through three key findings. First, S. aureus infection induces conserved host gene expression responses in mice that can differentiate from E. coli-infected or uninfected mice. This discovery was consistent and robust across multiple inbred mouse strains, S. aureus genetic backgrounds, time points, and was validated in outbred mice. The validation step strengthens generalizability and is an important improvement over previous murine gene-expression based classifiers that were developed and tested in only a single inbred mouse strain including the fields of infectious diseases , , ; cancer progression , ; and aging , . Furthermore, this murine predictor was specific for S. aureus infection and not simply a marker of illness based on the observation that mice with E. coli sepsis could not be distinguished from healthy, uninfected animals. The murine S. aureus classifier performed equally well at multiple time points despite progression of illness lending additional support to the specificity of this classifier. Second, human-derived host gene expression signatures differentiated S. aureus BSI from E. coli BSI or uninfected controls. In contrast to the murine-based classifier, the human-based model was less pathogen specific but still provided a significant degree of differentiation between S. aureus and E. coli BSI. Finally, the responses to S. aureus infection are highly conserved at the transcriptional and pathway level. This conserved response allowed us to validate the murine- and human-derived S. aureus classifiers in an independent cohort of S. aureus-infected patients.
Previous efforts to identify a discriminatory host gene expression signature for Gram-positive versus Gram-negative infections have yielded inconsistent results. This is likely due to the observation that transcriptional data derived from complex phenotypes such as infection do not produce just one predictive gene set, but rather generate multiple gene sets associated with the phenotype in question . Some studies report a common pattern of host gene expression –, whereas others have identified different expression profiles –, . In the current investigation, we utilized well-established methodologies , , , , – to derive predictors for S. aureus infection in both mice and humans from gene expression data. A key component of this methodology was a dimensional reduction step generating sets of co-expressed genes, termed “factors”. We observed that multiple, individual factors differentiated between various infection states although none performed universally well. For example, mFactor15 was associated with the lowest overall p-value during model generation. The AUC was 0.9587 for S. aureus vs. uninfected control mice but only 0.7942 for S. aureus vs. E. coli. In contrast, mFactor23 had an AUC of 0.9800 for S. aureus vs. E. coli but an AUC of 0.5926 for S. aureus vs. uninfected control mice. In order to generate a more robust classifier, factors were used as independent variables to generate a binary regression model. Factor models are an excellent technique for estimating correlation structure in very high dimensional data sets. This comprised the second step in generating the S. aureus predictors. It was only by including all factors to build the classifier that we could validate the model in the broadest set of conditions including different bacterial pathogens. However, because factors are typically made up of many genes, it is difficult to estimate the marginal effect of removing single genes from predictors. As such, it can be challenging to move from predictors based on factors to predictors based on small gene subsets. Although redundancy among the genes in a molecular classifier is expected and is a potential limitation, such redundancy can also improve robustness for a specific phenotype  as is likely to be the case in discriminating S. aureus from E. coli infection in mice. Comparisons at the individual gene level, as with pairwise comparisons, are likely to reveal differences in relatively simple biological responses. In contrast, dimension reduction with factor modeling as utilized in this study incorporates differences across multiple pathways, allowing for the detection of changes in a more complex pathobiology. Additionally, our factor model construction does not incorporate known biological pathways. This leads to gene groupings that are sometimes difficult to interpret. The advantage of the approach is the extreme dimension reduction which allows for discovery and cross-validation on very small data sets. This is one possible explanation for why the human S. aureus classifier differentiated S. aureus from E. coli whereas no genes met the threshold for differential expression after Bonferroni correction in a pairwise comparison between these two patient populations. The strength of this approach is offset by the possibility that smaller or transient changes in gene expression might be missed. It should be noted that the classifiers described in this study are not intended to be of clinical grade, which would require a more restrictive set of discriminating genes. Furthermore, there are likely many combinations of genes and factors that would perform similarly to that described here. This study presents findings related to the best performing classifier using the described methodologies. Defining the smallest, non-redundant set of genes that retains adequate discriminating power would be a vital next step in generating a clinically-useful diagnostic. In addition, any host response-based diagnostic requires validation across a range of clinical states. Immunocompromise is a particular condition in which it cannot be assumed the host immune response follows the paradigms identified here.
The murine model has been effectively used to gain insights into the pathophysiology of sepsis in general and S. aureus in particular , . Murine-derived gene expression signatures have also been successfully translated to non-infectious human conditions such as radiation exposure and breast cancer , , . Here, we describe the robust performance of a murine-derived S. aureus classifier in both mice and humans and also offer several lines of evidence supporting a partially conserved host response to S. aureus infection in both host species. First, the murine-based predictor could differentiate human S. aureus BSI from uninfected controls. Second, the genetic pathways were highly conserved. For example, most of the relevant murine pathways were also significantly associated with S. aureus BSI in humans. Finally, the murine-based predictor was highly accurate in classifying S. aureus infection in an independent human cohort.
Despite the robust performance of the murine classifier when applied to a human population, the ideal animal model for human sepsis remains elusive –. For example, virtually no murine-based sepsis studies have been replicated in patients , . Other sepsis studies in mice and humans yield discordant results. For example, the impact of TNF-α receptor therapy on septic mice  and humans  yielded contradictory results. In fact, more than 60 incongruities between murine and human immune systems have been recognized many of which involve host-pathogen interactions , . Our results are consistent with these earlier observations. For example, we encountered inconsistences between murine and human responses to S. aureus such that a minority (12) of the top 50 pathways overlapped between the two species. Moreover, the human response to S. aureus when compared to E. coli was differentiated by only one gene, F2RL3, which nearly reached the threshold for statistical significance. This is in contrast to the many genes identified differentiating the murine response to S. aureus and E. coli infection although F2RL3 is notably absent from this list. These host species-specific differences in sepsis, as well as infection-specific characteristics such as anatomic site of infection (e.g. genitourinary tract for E. coli vs. skin/soft tissue for S. aureus) limit our ability to apply knowledge gained from animal sepsis models to humans. It is also worth noting that batch effects and their correction may introduce bias in the form of false positives in the gene selection output . However, this effect would be equally distributed among the S. aureus infected, E. coli infected, and healthy subjects. Finally, the ability to distinguish bacterial sepsis from healthy is expectedly easier than the finer distinction between two offending bacterial pathogens. It is therefore not surprising that the murine S. aureus predictor did not differentiate S. aureus from E. coli infection in the human cohort. Comorbid disease such as diabetes or end-stage renal failure, which we observed in a minority of the infected human cohort, could be confounding the analysis and driving the differentiation between healthy human controls and those with infection (S. aureus or E. coli BSI). Without controlling for comorbid disease, such a confounding effect cannot be excluded. However, the human S. aureus classifier performed exceptionally well in differentiating infected individuals from healthy controls even in those patients without comorbid disease. Furthermore, the murine classifier (derived from mice without comorbid disease) could still differentiate infected human subjects from the healthy human cohort. These factors make it unlikely that comorbid disease is playing a significant role in the analysis although future attempts at deriving a gene-expression-based classifier should make accommodations for the possible confounding effect of comorbid disease.
Gene expression changes in peripheral blood cells drive the derivation of both the murine and human S. aureus classifiers. It is conceivable these gene expression changes are reflective of transcript abundance driven by myeloid cell lineage expansions and are not pathogen or infection specific. However, previously published data and work presented here suggest this is not the case. For example, Ardura et al. found no differences in the absolute numbers of total B and T cells in patients with S. aureus infection compared to healthy controls . Yet the abundance of lymphocyte-specific transcripts was significantly reduced. In contrast, expansion of the myeloid lineage was associated with high levels of expression among genes associated with neutrophil function. A similar independence between lymphocyte counts and differential gene expression within this lineage was observed in an independent pediatric sepsis cohort . In another example, transcript abundance due to cell lineage expansions was not the primary factor in the development of a tuberculosis-specific gene expression signature . Rather, it is changes in cellular composition and altered gene expression that drive such signatures. The data presented here also indicates that the S. aureus classifiers are not being driven by lineage-specific transcript abundance. Specifically, the total leukocyte count and cell lineage distribution (based on routine automated differential measurements) were not different between patients with S. aureus infection and E. coli infection (15.7×109/L with 86.2% neutrophils vs. 14.1×109/L with 85.8% neutrophils, respectively). However, the human S. aureus classifier was still able to differentiate infection due to the two pathogens. The murine S. aureus classifier was highly successful in differentiating S. aureus infection from healthy and from E. coli infection yet was unable to differentiate E. coli from healthy. This result would not be expected if transcript abundance was driving the derivation of the classifier.
The overlap we observed in the gene expression response to S. aureus infection in mouse and human was also consistent with published studies. NF-κB signaling pathways have been identified as a critical component of the murine response to infection , which was mirrored in the murine and human data presented here. Similar gene expression-based analyses of the human response to bacterial infection have also revealed the importance of other biological pathways including MHC class I and II antigen presentation, immunological synapse formation, TGF-β receptor signaling, TGF and WNT-dependent cytoskeleton remodeling, and T-cell receptor signaling , , , all of which were significantly associated with S. aureus infection in this study. Hence, mice and humans utilize many of the same or overlapping pathways in response to bacterial sepsis supporting the potential utility of murine-based diagnostics for human disease.
S. aureus continues to evolve as a pathogen and leads to a disproportionate burden of sepsis morbidity and mortality. This study takes significant steps forward on multiple levels in the ongoing effort to understand this pathogen; the host response to it; and identify new diagnostic and therapeutic avenues. We describe a potential diagnostic modality capable of differentiating infection from health across species. More importantly, host gene expression classifiers can differentiate infection due to S. aureus from that of E. coli but this effect is less pronounced in the complex human host. The approach described here also affords great insight into the conserved and disparate pathways utilized by mice and humans in response to these infections. Not only have we provided evidence to support the paradigm shift in how we think about diagnostics, but we have also identified new areas for research into the pathways that subserve sepsis pathophysiology.
Bacterial challenge experiments. (A) Survival curves for A/J and C57BL/6J mice following an intra-peritoneal infection with S. aureus (1×107 CFU/g) or E. coli (6×104 CFU/g). Principal Components Analysis plots of the samples in the dataset. Samples are colored by infection status and pathogen. (B) S. aureus infection by time after inoculation (n = 10 animals/time point). (C) E. coli infection by time after inoculation (n = 10 animals/time point). (D) PCA differentiated by pathogen.
Heat maps of genes contributing to the murine S. aureus classifier. (A) Genes within the top five factors contributing to the murine S. aureus classifier were identified and ranked by p-value after Bonferroni correction. A subset of genes (393 after removing duplicates) is depicted here, stratified by pathogen. (B) The same genes depicted in part (A) are categorized first pathogen and then by time since infection.
Venn diagram demonstrating the number of overlapping probes in each murine experimental group pairwise comparison. Probes were included that had significantly different levels of expression after Bonferroni correction.
Sixteen murine factors independently associated with S. aureus infection projected onto healthy controls (left panel, black circles), animals with E. coli infection (middle panel, blue triangles), and animals with S. aureus infection (right panel, red “x”). The y-axis represents the factor score.
A factor-based classifier distinguishes MRSA from MSSA infection in mice. An ROC curve is shown for this classification.
Venn diagram demonstrating the number of overlapping probes in each human experimental group pairwise comparison. Probes were included that had significantly different levels of expression after Bonferroni correction. No probes met this cutoff for the S. aureus vs. E. coli comparison.
Seventeen human factors independently associated with S. aureus BSI projected onto healthy controls (left panel, black circles), subjects with E. coli BSI (middle panel, blue triangles), and subjects with S. aureus BSI (right panel, red “x”). The y-axis represents the factor score.
Heat map of genes contributing to the human S. aureus classifier. Genes within the top two factors contributing to the human S. aureus classifier were identified and ranked by p-value after Bonferroni correction. A subset of genes (86 after removing duplicates) is depicted here, stratified by pathogen.
Probes, ranked by p-value, that were differentially expressed (after Bonferroni correction) in mice with S. aureus infection vs. Healthy controls; S. aureus vs. E. coli infection; and E. coli vs. Healthy controls. Also presented is the average probe expression in each comparator group and the fold-change within the pairwise comparison.
Probes and corresponding genes that were differentially expressed (after Bonferroni correction) in mice with MRSA vs. MSSA infection.
Probes, ranked by p-value, that were differentially expressed (after Bonferroni correction) in humans with S. aureus infection vs. Healthy controls; S. aureus vs. E. coli infection; and E. coli vs. Healthy controls. Also presented is the average probe expression in each comparator group and the fold-change within the pairwise comparison.
Pathway analysis for the genes from pairwise comparisons in the mouse and human study. Top 50 ranked pathways from GeneGo MetaCore pathway analysis based upon p-value. Shaded text corresponds to pathways that are present in both the mouse and human response to the specified pathogen.
Genes in pathways common to murine and human responses to infection. Human genes are in the shaded cells. Murine genes are in the unshaded cells.
A detailed description of microarray preparation.
Conceived and designed the experiments: SHA ELT DC CBC EPR SFK JL CWW GSG VGF. Performed the experiments: SHA YZ. Analyzed the data: SHA ELT DC JCVV RJL SWG JL VGF. Contributed reagents/materials/analysis tools: CBC AKZ EPR RMO TV SFK JL CWW GSG VGF. Wrote the paper: SHA ELT.
- 1. Klein E, Smith DL, Laxminarayan R (2007) Hospitalizations and deaths caused by methicillin-resistant Staphylococcus aureus, United States, 1999–2005. Emerg Infect Dis 13: 1840–1846. doi: 10.3201/eid1312.070629
- 2. Martin GS, Mannino DM, Eaton S, Moss M (2003) The epidemiology of sepsis in the United States from 1979 through 2000. N Engl J Med 348: 1546–1554. doi: 10.1056/nejmoa022139
- 3. Lee A, Mirrett S, Reller LB, Weinstein MP (2007) Detection of bloodstream infections in adults: how many blood cultures are needed? J Clin Microbiol 45: 3546–3548. doi: 10.1128/jcm.01555-07
- 4. Kollef MH, Sherman G, Ward S, Fraser VJ (1999) Inadequate antimicrobial treatment of infections: a risk factor for hospital mortality among critically ill patients. Chest 115: 462–474. doi: 10.1378/chest.115.2.462
- 5. Kumar A, Ellis P, Arabi Y, Roberts D, Light B, et al. (2009) Initiation of inappropriate antimicrobial therapy results in a fivefold reduction of survival in human septic shock. Chest 136: 1237–1248.
- 6. Kim JH, Gallis HA (1989) Observations on spiraling empiricism: its causes, allure, and perils, with particular reference to antibiotic therapy. Am J Med 87: 201–206. doi: 10.1016/s0002-9343(89)80697-7
- 7. Boucher HW, Talbot GH, Bradley JS, Edwards JE, Gilbert D, et al. (2009) Bad bugs, no drugs: no ESKAPE! An update from the Infectious Diseases Society of America. Clin Infect Dis 48: 1–12. doi: 10.1086/595011
- 8. Feezor RJ, Oberholzer C, Baker HV, Novick D, Rubinstein M, et al. (2003) Molecular characterization of the acute inflammatory response to infections with gram-negative versus gram-positive bacteria. Infect Immun 71: 5803–5813. doi: 10.1128/iai.71.10.5803-5813.2003
- 9. Yu SL, Chen HW, Yang PC, Peck K, Tsai MH, et al. (2004) Differential gene expression in gram-negative and gram-positive sepsis. Am J Respir Crit Care Med 169: 1135–1143. doi: 10.1164/rccm.200211-1278oc
- 10. Ramilo O, Allman W, Chung W, Mejias A, Ardura M, et al. (2007) Gene expression patterns in blood leukocytes discriminate patients with acute infections. Blood 109: 2066–2077. doi: 10.1182/blood-2006-02-002477
- 11. Takeuchi O, Hoshino K, Kawai T, Sanjo H, Takada H, et al. (1999) Differential roles of TLR2 and TLR4 in recognition of gram-negative and gram-positive bacterial cell wall components. Immunity 11: 443–451. doi: 10.1016/s1074-7613(00)80119-3
- 12. Dziarski R, Wang Q, Miyake K, Kirschning CJ, Gupta D (2001) MD-2 enables Toll-like receptor 2 (TLR2)-mediated responses to lipopolysaccharide and enhances TLR2-mediated responses to Gram-positive and Gram-negative bacteria and their cell wall components. J Immunol 166: 1938–1944.
- 13. Cross ML, Ganner A, Teilab D, Fray LM (2004) Patterns of cytokine induction by gram-positive and gram-negative probiotic bacteria. FEMS Immunol Med Microbiol 42: 173–180. doi: 10.1016/j.femsim.2004.04.001
- 14. Hessle CC, Andersson B, Wold AE (2005) Gram-positive and Gram-negative bacteria elicit different patterns of pro-inflammatory cytokines in human monocytes. Cytokine 30: 311–318. doi: 10.1016/j.cyto.2004.05.008
- 15. Zaas AK, Chen M, Varkey J, Veldman T, Hero AO 3rd, et al (2009) Gene expression signatures diagnose influenza and other symptomatic respiratory viral infections in humans. Cell Host Microbe 6: 207–217. doi: 10.1016/j.chom.2009.07.006
- 16. Kawada J, Kimura H, Kamachi Y, Nishikawa K, Taniguchi M, et al. (2006) Analysis of gene-expression profiles by oligonucleotide microarray in children with influenza. J Gen Virol 87: 1677–1683. doi: 10.1099/vir.0.81670-0
- 17. Ng HH, Frantz CE, Rausch L, Fairchild DC, Shimon J, et al. (2005) Gene expression profiling of mouse host response to Listeria monocytogenes infection. Genomics 86: 657–667. doi: 10.1016/j.ygeno.2005.07.005
- 18. Ardura MI, Banchereau R, Mejias A, Di Pucchio T, Glaser C, et al. (2009) Enhanced monocyte response and decreased central memory T cells in children with invasive Staphylococcus aureus infections. PLoS One 4: e5446. doi: 10.1371/journal.pone.0005446
- 19. Zaas AK, Aziz H, Lucas J, Perfect JR, Ginsburg GS (2010) Blood gene expression signatures predict invasive candidiasis. Sci Transl Med 2: 21ra17. doi: 10.1126/scitranslmed.3000715
- 20. Kim HS, Choi EH, Khan J, Roilides E, Francesconi A, et al. (2005) Expression of genes encoding innate host defense molecules in normal human monocytes in response to Candida albicans. Infect Immun 73: 3714–3724. doi: 10.1128/iai.73.6.3714-3724.2005
- 21. McDunn JE, Husain KD, Polpitiya AD, Burykin A, Ruan J, et al. (2008) Plasticity of the systemic inflammatory response to acute infection during critical illness: development of the riboleukogram. PLoS One 3: e1564. doi: 10.1371/journal.pone.0001564
- 22. Chaussabel D, Quinn C, Shen J, Patel P, Glaser C, et al. (2008) A modular analysis framework for blood genomics studies: application to systemic lupus erythematosus. Immunity 29: 150–164. doi: 10.1016/j.immuni.2008.05.012
- 23. Timofeeva AV, Goriunova LE, Khaspekov GL, Il’inskaia OP, Sirotkin VN, et al. (2009) [Comparative transcriptome analysis of human aorta atherosclerotic lesions and peripheral blood leukocytes from essential hypertension patients]. Kardiologiia 49: 27–38.
- 24. Dressman HK, Muramoto GG, Chao NJ, Meadows S, Marshall D, et al. (2007) Gene expression signatures that predict radiation exposure in mice and humans. PLoS Med 4: e106. doi: 10.1371/journal.pmed.0040106
- 25. Rice KC, Firek BA, Nelson JB, Yang SJ, Patton TG, et al. (2003) The Staphylococcus aureus cidAB operon: evaluation of its role in regulation of murein hydrolase activity and penicillin tolerance. J Bacteriol 185: 2635–2643. doi: 10.1128/jb.185.8.2635-2643.2003
- 26. Miller RL, Ramsey GA, Krenitsky TA, Elion GB (1972) Guanine phosphoribosyltransferase from Escherichia coli, specificity and properties. Biochemistry 11: 4723–4731. doi: 10.1021/bi00775a014
- 27. Glickman SW, Cairns CB, Otero RM, Woods CW, Tsalik EL, et al. (2010) Disease progression in hemodynamically stable patients presenting to the emergency department with sepsis. Acad Emerg Med 17: 383–390. doi: 10.1111/j.1553-2712.2010.00664.x
- 28. Tsalik EL, Jones D, Nicholson B, Waring L, Liesenfeld O, et al. (2010) Multiplex PCR to diagnose bloodstream infections in patients admitted from the emergency department with sepsis. J Clin Microbiol 48: 26–33. doi: 10.1128/jcm.01447-09
- 29. Bone RC, Balk RA, Cerra FB, Dellinger RP, Fein AM, et al. (1992) 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 101: 1644–1655. doi: 10.1378/chest.101.6.1644
- 30. Voora D, Ortel TL, Lucas JE, Chi JT, Becker RC, et al. (2010) Abstract 16293: A Whole Blood RNA Signature Accurately Classifies Multiple Measures of Platelet Function on Aspirin in Healthy Volunteers and Highlights a Common Underlying Pathway. Circulation 122: A16293.
- 31. Carvalho CM, Chang J, Lucas JE, Nevins JR, Wang QL, et al. (2008) High-Dimensional Sparse Factor Modeling: Applications in Gene Expression Genomics. Journal of the American Statistical Association 103: 1438–1456. doi: 10.1198/016214508000000869
- 32. Wang Q, Carvalho C, Lucas JE, West M (2007) BFRM: Bayesian factor regression modeling. Bulletin of the International Society of Bayesian Analysis 14: 4–5.
- 33. Hans C, Dobra A, West M (2007) Shotgun Stochastic search for “Large p” regression. Journal of the American Statistical Association 102: 507–516. doi: 10.1198/016214507000000121
- 34. Raftery AE, Madigan D, Hoeting JA (1997) Bayesian model averaging for linear regression models. Journal of the American Statistical Association 92: 179–191. doi: 10.1080/01621459.1997.10473615
- 35. Chang JT, Carvalho C, Mori S, Bild AH, Gatza ML, et al. (2009) A genomic strategy to elucidate modules of oncogenic pathway signaling networks. Mol Cell 34: 104–114. doi: 10.1016/j.molcel.2009.02.030
- 36. Chen M, Carlson D, Zaas A, Woods CW, Ginsburg GS, et al. (2011) Detection of viruses via statistical gene expression analysis. IEEE Trans Biomed Eng 58: 468–479. doi: 10.1109/tbme.2010.2059702
- 37. Chen M, Zaas A, Woods CW, Ginsburg GS, Lucas JE, et al.. (2011) Predicting Viral Infection from High-Dimensional Biomarker Trajectories. Journal of the American Statistical Association In Press.
- 38. Cyr DD, Lucas JE, Thompson JW, Patel K, Clark PJ, et al. (2011) Characterization of serum proteins associated with IL28B genotype among patients with chronic hepatitis C. PLoS One. 6: e21854. doi: 10.1371/journal.pone.0021854
- 39. Lucas J, Carvalho C, West M (2009) A bayesian analysis strategy for cross-study translation of gene expression biomarkers. Stat Appl Genet Mol Biol 8: Article 11.
- 40. Lucas JE, Kung HN, Chi JT (2010) Latent factor analysis to discover pathway-associated putative segmental aneuploidies in human cancers. PLoS Comput Biol 6: e1000920. doi: 10.1371/journal.pcbi.1000920
- 41. Meadows SK, Dressman HK, Daher P, Himburg H, Russell JL, et al. (2010) Diagnosis of partial body radiation exposure in mice using peripheral blood gene expression profiles. PLoS One 5: e11535. doi: 10.1371/journal.pone.0011535
- 42. Merl D, Lucas JE, Nevins JR, Shen H, West M (2009) Trans-study Projection of Genomic Biomarkers in Analysis of Oncogene Deregulation and Breast Cancer. In: O’Hagan T, West M, editors. The Oxford Handbook of Applied Bayesian Analysis.
- 43. Ahn SH, Deshmukh H, Johnson N, Cowell LG, Rude TH, et al. (2010) Two genes on A/J chromosome 18 are associated with susceptibility to Staphylococcus aureus infection by combined microarray and QTL analyses. PLoS Pathog 6: e1001088. doi: 10.1371/journal.ppat.1001088
- 44. von Kockritz-Blickwede M, Rohde M, Oehmcke S, Miller LS, Cheung AL, et al. (2008) Immunological mechanisms underlying the genetic predisposition to severe Staphylococcus aureus infection in the mouse model. Am J Pathol 173: 1657–1668. doi: 10.2353/ajpath.2008.080337
- 45. Downey T (2006) Analysis of a multifactor microarray study using Partek genomics solution. Methods Enzymol 411: 256–270. doi: 10.1016/s0076-6879(06)11013-7
- 46. Thakker M, Park JS, Carey V, Lee JC (1998) Staphylococcus aureus serotype 5 capsular polysaccharide is antiphagocytic and enhances bacterial virulence in a murine bacteremia model. Infect Immun 66: 5183–5189.
- 47. Xu W-f, Andersen H, Whitmore TE, Presnell SR, Yee DP, et al. (1998) Cloning and characterization of human protease-activated receptor 4. Proceedings of the National Academy of Sciences 95: 6642–6646. doi: 10.1073/pnas.95.12.6642
- 48. Mueller A, O’Rourke J, Grimm J, Guillemin K, Dixon MF, et al. (2003) Distinct gene expression profiles characterize the histopathological stages of disease in Helicobacter-induced mucosa-associated lymphoid tissue lymphoma. Proc Natl Acad Sci U S A 100: 1292–1297. doi: 10.1073/pnas.242741699
- 49. Zhang H, Su YA, Hu P, Yang J, Zheng B, et al. (2006) Signature patterns revealed by microarray analyses of mice infected with influenza virus A and Streptococcus pneumoniae. Microbes Infect 8: 2172–2185. doi: 10.1016/j.micinf.2006.04.018
- 50. Desai KV, Kavanaugh CJ, Calvo A, Green JE (2002) Chipping away at breast cancer: insights from microarray studies of human and mouse mammary cancer. Endocr Relat Cancer 9: 207–220. doi: 10.1677/erc.0.0090207
- 51. Larsson O, Scheele C, Liang Z, Moll J, Karlsson C, et al. (2004) Kinetics of senescence-associated changes of gene expression in an epithelial, temperature-sensitive SV40 large T antigen model. Cancer Res 64: 482–489. doi: 10.1158/0008-5472.can-03-1872
- 52. Weindruch R, Kayo T, Lee CK, Prolla TA (2001) Microarray profiling of gene expression in aging and its alteration by caloric restriction in mice. J Nutr 131: 918S–923S.
- 53. Wennmalm K, Wahlestedt C, Larsson O (2005) The expression signature of in vitro senescence resembles mouse but not human aging. Genome Biol 6: R109. doi: 10.1186/gb-2005-6-13-r109
- 54. Lytkin NI, McVoy L, Weitkamp JH, Aliferis CF, Statnikov A (2011) Expanding the understanding of biases in development of clinical-grade molecular signatures: a case study in acute respiratory viral infections. PLoS One 6: e20662. doi: 10.1371/journal.pone.0020662
- 55. Nau GJ, Schlesinger A, Richmond JF, Young RA (2003) Cumulative Toll-like receptor activation in human macrophages treated with whole bacteria. J Immunol 170: 5203–5209.
- 56. Boldrick JC, Alizadeh AA, Diehn M, Dudoit S, Liu CL, et al. (2002) Stereotyped and specific gene expression programs in human innate immune responses to bacteria. Proc Natl Acad Sci U S A 99: 972–977. doi: 10.1073/pnas.231625398
- 57. Tang BM, McLean AS, Dawes IW, Huang SJ, Cowley MJ, et al.. (2008) Gene-expression profiling of gram-positive and gram-negative sepsis in critically ill patients. Crit Care Med. 1125–1128.
- 58. Sriskandan S, Cohen J (1999) Gram-positive sepsis. Mechanisms and differences from gram-negative sepsis. Infect Dis Clin North Am 13: 397–412. doi: 10.1016/s0891-5520(05)70082-9
- 59. He M, Mangiameli DP, Kachala S, Hunter K, Gillespie J, et al. (2010) Expression signature developed from a complex series of mouse models accurately predicts human breast cancer survival. Clin Cancer Res 16: 249–259. doi: 10.1158/1078-0432.ccr-09-1602
- 60. Labreche HG, Nevins JR, Huang E (2011) Integrating factor analysis and a transgenic mouse model to reveal a peripheral blood predictor of breast tumors. BMC Med Genomics 4: 61. doi: 10.1186/1755-8794-4-61
- 61. Deitch EA (1998) Animal models of sepsis and shock: a review and lessons learned. Shock 9: 1–11. doi: 10.1097/00024382-199801000-00001
- 62. Dyson A, Singer M (2009) Animal models of sepsis: why does preclinical efficacy fail to translate to the clinical setting? Crit Care Med 37: S30–37. doi: 10.1097/ccm.0b013e3181922bd3
- 63. Esmon CT (2004) Why do animal models (sometimes) fail to mimic human sepsis? Crit Care Med 32: S219–222. doi: 10.1097/01.ccm.0000127036.27343.48
- 64. Unsinger J, McDonough JS, Shultz LD, Ferguson TA, Hotchkiss RS (2009) Sepsis-induced human lymphocyte apoptosis and cytokine production in “humanized” mice. J Leukoc Biol 86: 219–227. doi: 10.1189/jlb.1008615
- 65. Zeni F, Freeman B, Natanson C (1997) Anti-inflammatory therapies to treat sepsis and septic shock: a reassessment. Crit Care Med 25: 1095–1100. doi: 10.1097/00003246-199707000-00001
- 66. Mohler KM, Torrance DS, Smith CA, Goodwin RG, Stremler KE, et al. (1993) Soluble tumor necrosis factor (TNF) receptors are effective therapeutic agents in lethal endotoxemia and function simultaneously as both TNF carriers and TNF antagonists. J Immunol 151: 1548–1561.
- 67. Fisher CJ Jr, Agosti JM, Opal SM, Lowry SF, Balk RA, et al. (1996) Treatment of septic shock with the tumor necrosis factor receptor:Fc fusion protein. The Soluble TNF Receptor Sepsis Study Group. N Engl J Med 334: 1697–1702. doi: 10.1056/nejm199606273342603
- 68. Mestas J, Hughes CC (2004) Of mice and not men: differences between mouse and human immunology. J Immunol 172: 2731–2738.
- 69. von Bernuth H, Picard C, Jin Z, Pankla R, Xiao H, et al. (2008) Pyogenic bacterial infections in humans with MyD88 deficiency. Science 321: 691–696. doi: 10.1126/science.1158298
- 70. Shanley TP, Cvijanovich N, Lin R, Allen GL, Thomas NJ, et al. (2007) Genome-level longitudinal expression of signaling pathways and gene networks in pediatric septic shock. Mol Med 13: 495–508.
- 71. Berry MP, Graham CM, McNab FW, Xu Z, Bloch SA, et al. (2010) An interferon-inducible neutrophil-driven blood transcriptional signature in human tuberculosis. Nature 466: 973–977. doi: 10.1038/nature09247
- 72. Chin CY, Monack DM, Nathan S (2010) Genome wide transcriptome profiling of a murine acute melioidosis model reveals new insights into how Burkholderia pseudomallei overcomes host innate immunity. BMC Genomics 11: 672. doi: 10.1186/1471-2164-11-672
- 73. Pankla R, Buddhisa S, Berry M, Blankenship DM, Bancroft GJ, et al. (2009) Genomic transcriptional profiling identifies a candidate blood biomarker signature for the diagnosis of septicemic melioidosis. Genome Biol 10: R127. doi: 10.1186/gb-2009-10-11-r127