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-1-infected 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.

Figure 1

The transcriptional networks inferred from the mRNA or viral load data for predicting viral set point. An arrow indicates that the source node regulates its target node. The transcriptional networks suggest how gene products and viral replication may interact to modulate viral set point. Each network can further serve as a predictive model for determining viral set point: the viral set point can be determined by substituting the expression levels of the signature genes and viral load into the network model. The panels present the networks at different times: (A) enrollment, (B) week 1, (C) week 2, (D) week 4, (E) week 12, (F) week 24, and (G) using only viral load measurements.

Figure 2

Regulatory relationships among the signature genes. The names of the nine signature genes are shown in bold. The signature genes are compactly related to each other through 4 hubs: hydrogen peroxide, IFNG, TGFB1 and TNF.

Figure 3

In Silico Knockout Study. Each curve depicts the distribution of predicted viral set points when the corresponding gene was eliminated. When the predicted viral set point becomes greater (smaller) than the actual value, it implies that the gene knock-out inhibits (promotes) HIV replication. (A) Training data; (B) Independent test data.