Although several dozen studies of gene expression in sepsis have been published, distinguishing sepsis from a sterile systemic inflammatory response syndrome (SIRS) is still largely up to clinical ...suspicion. We hypothesized that a multicohort analysis of the publicly available sepsis gene expression data sets would yield a robust set of genes for distinguishing patients with sepsis from patients with sterile inflammation. A comprehensive search for gene expression data sets in sepsis identified 27 data sets matching our inclusion criteria. Five data sets (n = 663 samples) compared patients with sterile inflammation (SIRS/trauma) to time-matched patients with infections. We applied our multicohort analysis framework that uses both effect sizes and P values in a leave-one-data set-out fashion to these data sets. We identified 11 genes that were differentially expressed (false discovery rate ≤1%, inter-data set heterogeneity P > 0.01, summary effect size >1.5-fold) across all discovery cohorts with excellent diagnostic power mean area under the receiver operating characteristic curve (AUC), 0.87; range, 0.7 to 0.98. We then validated these 11 genes in 15 independent cohorts comparing (i) time-matched infected versus noninfected trauma patients (4 cohorts), (ii) ICU/trauma patients with infections over the clinical time course (3 cohorts), and (iii) healthy subjects versus sepsis patients (8 cohorts). In the discovery Glue Grant cohort, SIRS plus the 11-gene set improved prediction of infection (compared to SIRS alone) with a continuous net reclassification index of 0.90. Overall, multicohort analysis of time-matched cohorts yielded 11 genes that robustly distinguish sterile inflammation from infectious inflammation.
Improved diagnostics for acute infections could decrease morbidity and mortality by increasing early antibiotics for patients with bacterial infections and reducing unnecessary antibiotics for ...patients without bacterial infections. Several groups have used gene expression microarrays to build classifiers for acute infections, but these have been hampered by the size of the gene sets, use of overfit models, or lack of independent validation. We used multicohort analysis to derive a set of seven genes for robust discrimination of bacterial and viral infections, which we then validated in 30 independent cohorts. We next used our previously published 11-gene Sepsis MetaScore together with the new bacterial/viral classifier to build an integrated antibiotics decision model. In a pooled analysis of 1057 samples from 20 cohorts (excluding infants), the integrated antibiotics decision model had a sensitivity and specificity for bacterial infections of 94.0 and 59.8%, respectively (negative likelihood ratio, 0.10). Prospective clinical validation will be needed before these findings are implemented for patient care.
OBJECTIVE:In response to a need for better sepsis diagnostics, several new gene expression classifiers have been recently published, including the 11-gene “Sepsis MetaScore,” the “FAIM3-to-PLAC8” ...ratio, and the Septicyte Lab. We performed a systematic search for publicly available gene expression data in sepsis and tested each gene expression classifier in all included datasets. We also created a public repository of sepsis gene expression data to encourage their future reuse.
DATA SOURCES:We searched National Institutes of Health Gene Expression Omnibus and EBI ArrayExpress for human gene expression microarray datasets. We also included the Glue Grant trauma gene expression cohorts.
STUDY SELECTION:We selected clinical, time-matched, whole blood studies of sepsis and acute infections as compared to healthy and/or noninfectious inflammation patients. We identified 39 datasets composed of 3,241 samples from 2,604 patients.
DATA EXTRACTION:All data were renormalized from raw data, when available, using consistent methods.
DATA SYNTHESIS:Mean validation areas under the receiver operating characteristic curve for discriminating septic patients from patients with noninfectious inflammation for the Sepsis MetaScore, the FAIM3-to-PLAC8 ratio, and the Septicyte Lab were 0.82 (range, 0.73–0.89), 0.78 (range, 0.49–0.96), and 0.73 (range, 0.44–0.90), respectively. Paired-sample t tests of validation datasets showed no significant differences in area under the receiver operating characteristic curves. Mean validation area under the receiver operating characteristic curves for discriminating infected patients from healthy controls for the Sepsis MetaScore, FAIM3-to-PLAC8 ratio, and Septicyte Lab were 0.97 (range, 0.85–1.0), 0.94 (range, 0.65–1.0), and 0.71 (range, 0.24–1.0), respectively. There were few significant differences in any diagnostics due to pathogen type.
CONCLUSIONS:The three diagnostics do not show significant differences in overall ability to distinguish noninfectious systemic inflammatory response syndrome from sepsis, though the performance in some datasets was low (area under the receiver operating characteristic curve, < 0.7) for the FAIM3-to-PLAC8 ratio and Septicyte Lab. The Septicyte Lab also demonstrated significantly worse performance in discriminating infections as compared to healthy controls. Overall, public gene expression data are a useful tool for benchmarking gene expression diagnostics.
Active pulmonary tuberculosis is difficult to diagnose and treatment response is difficult to effectively monitor. A WHO consensus statement has called for new non-sputum diagnostics. The aim of this ...study was to use an integrated multicohort analysis of samples from publically available datasets to derive a diagnostic gene set in the peripheral blood of patients with active tuberculosis.
We searched two public gene expression microarray repositories and retained datasets that examined clinical cohorts of active pulmonary tuberculosis infection in whole blood. We compared gene expression in patients with either latent tuberculosis or other diseases versus patients with active tuberculosis using our validated multicohort analysis framework. Three datasets were used as discovery datasets and meta-analytical methods were used to assess gene effects in these cohorts. We then validated the diagnostic capacity of the three gene set in the remaining 11 datasets.
A total of 14 datasets containing 2572 samples from 10 countries from both adult and paediatric patients were included in the analysis. Of these, three datasets (N=1023) were used to discover a set of three genes (GBP5, DUSP3, and KLF2) that are highly diagnostic for active tuberculosis. We validated the diagnostic power of the three gene set to separate active tuberculosis from healthy controls (global area under the ROC curve (AUC) 0·90 95% CI 0·85-0·95), latent tuberculosis (0·88 0·84-0·92), and other diseases (0·84 0·80-0·95) in eight independent datasets composed of both children and adults from ten countries. Expression of the three-gene set was not confounded by HIV infection status, bacterial drug resistance, or BCG vaccination. Furthermore, in four additional cohorts, we showed that the tuberculosis score declined during treatment of patients with active tuberculosis.
Overall, our integrated multicohort analysis yielded a three-gene set in whole blood that is robustly diagnostic for active tuberculosis, that was validated in multiple independent cohorts, and that has potential clinical application for diagnosis and monitoring treatment response. Prospective laboratory validation will be required before it can be used in a clinical setting.
National Institute of Allergy and Infectious Diseases, National Library of Medicine, the Stanford Child Health Research Institute, the Society for University Surgeons, and the Bill and Melinda Gates Foundation.
Respiratory viral infections are a significant burden to healthcare worldwide. Many whole genome expression profiles have identified different respiratory viral infection signatures, but these have ...not translated to clinical practice. Here, we performed two integrated, multi-cohort analyses of publicly available transcriptional data of viral infections. First, we identified a common host signature across different respiratory viral infections that could distinguish (1) individuals with viral infections from healthy controls and from those with bacterial infections, and (2) symptomatic from asymptomatic subjects prior to symptom onset in challenge studies. Second, we identified an influenza-specific host response signature that (1) could distinguish influenza-infected samples from those with bacterial and other respiratory viral infections, (2) was a diagnostic and prognostic marker in influenza-pneumonia patients and influenza challenge studies, and (3) was predictive of response to influenza vaccine. Our results have applications in the diagnosis, prognosis, and identification of drug targets in viral infections.
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•MVS is a common transcriptional host response to respiratory viral infection•MVS could be used in clinics as a diagnostic and/or prognostic biomarker•IMS distinguishes influenza from other viral and bacterial infections•IMS correlates with infection symptomatology and vaccine response
Clinically relevant respiratory viral signatures have not been defined. Khatri and colleagues identified host transcriptional responses common to multiple respiratory viruses (MVS) or specific to influenza (IMS) by leveraging heterogeneity present in public datasets. Both signatures distinguish viral from bacterial infections and IMS also distinguishes influenza from other viral infections.
Recent advances in parallel genomic processing and computational mapping have been applied to the native human microbial environment to provide a new understanding of the role of the microbiome in ...health and disease. In particular, studies of the distal gut microbiome have proposed that changes in gut microbiota are related to obesity, the metabolic syndrome, and Western diet. We examined the changes in the distal gut microbiome composition as it relates to the lean and obese phenotypes, particularly after surgical weight loss. A PubMed search of publications from January 1, 2005, through December 31, 2012, used the search terms weight, obesity, microbiome, and bariatric surgery. We included studies that provided information on subjects' weight and/or body mass index and a formal assessment of the microbiome. Certain bacteria, specifically the archaeon Methanobrevibacter smithii, have enhanced ability to metabolize dietary substrate, thereby increasing host energy intake and weight gain. With weight loss, there is a decrease in the ratio of Firmicutes to Bacteroidetes phyla. One major finding from microbial sequencing analyses after Roux-en-Y gastric bypass is the comparative overabundance of Proteobacteria in the distal gut microbiome, which is distinct from the changes seen in weight loss without Roux-en-Y gastric bypass. This review provides the practicing surgeon with (1) an update on the state of a rapidly innovating branch of clinical bioinformatics, specifically, the microbiome; (2) a new understanding of the microbiome changes after Roux-en-Y gastric bypass and weight loss; and (3) a basis for understanding further clinical applications of studies of the distal gut microbiome, such as in Crohn disease, ulcerative colitis, and infectious colitis.
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