Integrated, Multi-cohort Analysis Identifies Conserved Transcriptional Signatures across Multiple Respiratory Viruses

Abstract

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 (a) individuals with viral infections from healthy controls and from those with bacterial infections, and (b) symptomatic from asymptomatic subjects prior to symptom onset in challenge studies. Second, we identified an influenza-specific host response signature that (a) could distinguish influenza-infected samples from those with bacterial and other respiratory viral infections, (b) was a diagnostic and prognostic marker in influenza-pneumonia patients and influenza challenge studies, and (c) was predictive of response to influenza vaccine. Our results have applications in the diagnosis, prognosis, and identification of drug targets in viral infections.

Integrated, multicohort analysis of systemic sclerosis identifies robust transcriptional signature of disease severity

“…PCs are difficult to implement into clinical practice owing to variance introduced by different treatment and technology protocols in individual PCs. Therefore, similar to our previous results (14,17,19,20), we defined SSc skin severity score (4S) for a skin biopsy as the difference between the mean of overexpressed genes and mean of underexpressed genes in the 415-gene set. 4S distinguished SSc skin samples from healthy skin biopsies with very high accuracy in both discovery and validation cohorts (range = 0.88-1 in validation cohorts; Supplemental Figure 5, A and B).…”

“…We have repeatedly demonstrated the utility of this approach in identifying novel drug targets, diagnostic and prognostic biomarkers, and repurposing FDA-approved drugs in a broad spectrum of diseases including organ transplant, cancer, sepsis, and bacterial and viral infections (14)(15)(16)(17)(18)(19)(20)(21). Here, we applied our multicohort analytical method to 2 SSc gene expression datasets, obtained from 158 skin biopsies from SSc patients, referred to as the UCSF1 cohort (GSE9285) (11) and the Boston cohort (GSE32413) (10) to identify a 415-gene signature.…”

Advances in the diagnosis of pneumonia in children

“…A recent analysis of transcriptional data from multiple cohorts identified a transcriptional signature that distinguished influenza virus infection from bacterial and other viral infections. 131 Similarly, a whole blood transcriptional profile that distinguishes RSV infection from other viral infections and predicts the severity of disease has been described. 132 well controlled studies of the cause of pneumonia, and the major limitations of studies of case series in correctly ascribing the cause.…”

Empowering Multi-Cohort Gene Expression Analysis to Increase Reproducibility

“…Many of these results also been further validated in experimental settings. 8,11,16 These results have further demonstrated the ability of our framework to create "Big Data" by combining multiple smaller studies that are collectively representative of the real word patient population heterogeneity.…”

“…We have repeatedly demonstrated the utility of our framework for identifying novel diagnostic and prognostic biomarkers, drug targets, and repurposing FDA-approved drugs in diverse diseases, including organ transplantation, cancer, infection, and neurodegenerative diseases. [8][9][10][11][12][13][14][15][16] In each of these analyses, we analyzed more than a thousand human samples from more than 10 independent cohorts to generate and validate data-driven hypotheses. Many of these results also been further validated in experimental settings.…”