Genome-wide mRNA expression correlates of viral control in CD4+ T-cells from HIV-1-infected individuals

Abstract

There is great interindividual variability in HIV-1 viral setpoint after seroconversion, some of which is known to be due to genetic differences among infected individuals. Here, our focus is on determining, genome-wide, the contribution of variable gene expression to viral control, and to relate it to genomic DNA polymorphism. RNA was extracted from purified CD4+ T-cells from 137 HIV-1 seroconverters, 16 elite controllers, and 3 healthy blood donors. Expression levels of more than 48,000 mRNA transcripts were assessed by the Human-6 v3 Expression BeadChips (Illumina). Genome-wide SNP data was generated from genomic DNA using the HumanHap550 Genotyping BeadChip (Illumina). We observed two distinct profiles with 260 genes differentially expressed depending on HIV-1 viral load. There was significant upregulation of expression of interferon stimulated genes with increasing viral load, including genes of the intrinsic antiretroviral defense. Upon successful antiretroviral treatment, the transcriptome profile of previously viremic individuals reverted to a pattern comparable to that of elite controllers and of uninfected individuals. Genome-wide evaluation of cis-acting SNPs identified genetic variants modulating expression of 190 genes. Those were compared to the genes whose expression was found associated with viral load: expression of one interferon stimulated gene, OAS1, was found to be regulated by a SNP (rs3177979, p = 4.9E-12); however, we could not detect an independent association of the SNP with viral setpoint. Thus, this study represents an attempt to integrate genome-wide SNP signals with genome-wide expression profiles in the search for biological correlates of HIV-1 control. It underscores the paradox of the association between increasing levels of viral load and greater expression of antiviral defense pathways. It also shows that elite controllers do not have a fully distinctive mRNA expression pattern in CD4+ T cells. Overall, changes in global RNA expression reflect responses to viral replication rather than a mechanism that might explain viral control.

Figure 1. Transcriptome analysis in CD4+ T cells from HIV-infected untreated individuals.

Gene clusters are presented on the left. In total, 260 genes are differentially expressed (at adjusted p<0.01) in association with viral load in CD4+ T cells during in vivo HIV-1 infection. Patient clusters are presented at the top for untreated individuals. Clustering was performed on the Spearman correlation coefficient. The phenotype is presented at the bottom, as log10 viral setpoint in gray, and log10 viral load at time of sample collection in red. A smooth of the setpoint viral load values is depicted by the black line. The red rectangle surrounds a cluster of individuals characterized by low viral load (mean Log10 viral setpoint = 2.6), and including several “elite controllers” – individuals that spontaneous control viral replication in the absence of treatment. The blue rectangle identifies a cluster of individuals with high viral setpoint (mean Log10 viral setpoint = 4.4). The remaining clusters illustrate the heterogeneity of transcription profile across the range of viral load values.

Figure 2. Predicted interaction networks of genes differentially expressed during HIV-1 infection.

Differentially expressed genes are depicted: links have been predicted using STRING (http://string.embl.de/). Predicted interactions are depicted according to the type of available evidence. The interactions (see color labels) include direct (physical) and indirect (functional) associations; they are derived from four sources: genomic context, high-throughput experiments, conserved coexpression, and previous knowledge from literature.

Figure 3. Differential expression of genes of the interferon response.

Representative genes of the interferon response pathway are shown in panel A. From grey to red, increasing differential expression with increasing viral setpoint. Selected genes are shown in panel B. While genes associated with interferon receptors, such as TYK2, are not differentially expressed, signaling molecules such STAT1 and interferon-stimulated genes such as MX1 and TAP1 are significantly upregulated with increasing viral load.

Figure 4. Transcriptome analysis in CD4+ T cells from HIV-infected individuals before and after viral suppression.

Analysis was restricted to the 260 genes found to be differently expressed by viral setpoint. Gene clusters are presented on the left. Patient clusters are presented at the top. In red, transcriptome profile before viral suppression, and in yellow, transcriptome profile after viral suppression with effective treatment in 37 individuals with pre- and post-treatment initiation samples. In blue, transcriptome profile of 16 elite controllers. In black, transcriptome profile from 3 HIV-negative healthy controls (8 samples).