Transcriptional network predicts viral set point during acute HIV-1 infection

Abstract

Background HIV-1-infected individuals with higher viral set points progress to AIDS more rapidly than those with lower set points. Predicting viral set point early following infection can contribute to our understanding of early control of HIV-1 replication, to predicting long-term clinical outcomes, and to the choice of optimal therapeutic regimens. Methods In a longitudinal study of 10 untreated HIV-1infected patients, we used gene expression profiling of peripheral blood mononuclear cells to identify transcriptional networks for viral set point prediction. At each sampling time, a statistical analysis inferred the optimal transcriptional network that best predicted viral set point. We then assessed the accuracy of this transcriptional model by predicting viral set point in an independent cohort of 10 untreated HIV-1-infected patients from Malawi. Results The gene network inferred at time of enrollment predicted viral set point 24 weeks later in the independent Malawian cohort with an accuracy of 87.5%. As expected, the predictive accuracy of the networks inferred at later time points was even greater, exceeding 90% after week 4. The composition of the inferred networks was largely conserved between time points. The 12 genes comprising this dynamic signature of viral set point implicated the involvement of two major canonical pathways: interferon signaling (p<0.0003) and membrane fraction (p<0.02). A silico knockout study showed that HLA-DRB1 and C4BPA may contribute to restricting HIV-1 replication. Conclusions Longitudinal gene expression profiling of peripheral blood mononuclear cells from patients with acute HIV-1 infection can be used to create transcriptional network models to early predict viral set point with a high degree of accuracy.

ANIMA: Association network integration for multiscale analysis

“…We analysed three publicly available microarray datasets using the ANIMA pipeline and toolset (ArrayExpress/GEO identifiers: E-GEOD-29429, E-GEOD-34404/GSE34404, E-GEOD-68310/GSE68310). The first compares a cohort of subjects with acute HIV infection to healthy controls 25 , the second compares symptomatic malaria to asymptomatic controls in children from a malaria-endemic region 26 , and the last compares the host response in early symptomatic viral respiratory infections 27 . Where the datasets contained samples from multiple timepoints, we restricted the analysis to healthy controls and the first disease timepoint.…”

ANIMA: Association network integration for multiscale analysis

“…The first compares a cohort of subjects with acute HIV infection to healthy controls 22 , the second compares symptomatic malaria to asymptomatic controls in children from a malaria-endemic region 23 , and the last compares the host response in early symptomatic viral respiratory infections 24 . Where the datasets contained samples from multiple timepoints, we restricted the analysis to healthy controls and the first disease timepoint.…”

“…The user can submit a search string (in the form of a regular expression) containing gene names (HUGO Gene Nomenclature Committee (HGNC) symbols), and box-and whisker plots for the results are returned. In the original paper on acute HIV infection the authors discuss a gene set of six conserved genes that appear at multiple timepoints in an inferred regulatory network of viral set point 22 . We show the normalized expression data stratified by HIV status and sex in the two datasets included in the HIV analysis for these six genes ( Figure 3).…”

ANIMA: Association Network Integration for Multiscale Analysis

“…The user can submit a search string (in the form of a regular expression) containing gene names (HUGO Gene Nomenclature Committee (HGNC) symbols), and box-and whisker plots for the results are returned. In the original paper on acute HIV infection the authors discuss a gene set of six conserved genes that appear at multiple timepoints in an inferred regulatory network of viral set point 6 . We show the normalized expression data stratified by HIV status and sex in the two datasets included in the HIV analysis for these six genes (Figure 3).…”