P20-19. High-throughput transcriptomics identified synergism between different signaling pathways in HIV-1 Elite Controllers

Wang Zhang [1,2], Anoop T Ambikan [1], Maike Sperk [1], Robert Van Domselaar [1], Piotr Nowak [1,3], Kajsa Noyan [1], Aman Russom [2], Anders Sonnerborg [1,2,3], Ujjwal Neogi [1,2]
Affiliates: [1] Division of Clinical Microbiology, Department of Laboratory Medicine, Karolinska Institutet, Huddinge, Stockholm, Sweden. [2] Science for Life Laboratory, Division of Proteomics and Nanobiotechnology, KTH Royal Institute of Technology, Solna, Stockholm, Sweden. [3] Department of Medicine Huddinge, Unit of Infectious Diseases, Karolinska Institutet, Karolinska University Hospital, Stockholm, Sweden.

A small subset of HIV-1 positive individuals, the “Elite Controllers” (EC), is able to control viral replication and to restrain progression of immunodeficiency without antiretroviral therapy (ART). Gene expression data from EC are limited and mainly focusing on the CD4+ T-cells. In one study, elevated levels of Schlafen-11 were identified as a signature in T-cells of the EC, but in another study using high throughput RNA sequencing on CD4+ T-cells from EC did not reveal a fully distinctive mRNA expression pattern. In this study, we aimed to explore a comprehensive analysis of host transcriptomics coupled to clinical phenotypes in a well-defined Swedish cohort of HIV-1 positive ECs with up to 20 years of clinical follow-up data.

The patient populations includes untreated HIV-1-positive EC (n=19), treatment naïve patients with viremia (VP, n=8) and HIV-1-negative persons (HC, n=14). Total RNA was extracted from isolated PBMCs using RNeasy Mini (Qiagen, Hilden, Germany). The libraries were prepared using Illumina TruSeq® Stranded mRNA Library Prep Kit with poly-A selection, pooled and sequenced on HiSeq2500 with a 2×126 setup using ‘HiSeq SBS Kit v4’ chemistry. The reads were then aligned to the human reference genome GRCh37 Ensembl release 75 using Tophat v2.0.4. Gene level count data was generated using Htseq v2.0.4 to summarize read counts for each gene. Counts per million (CPM) for protein coding genes and non-coding transcripts was generated by using edgeR, the CPM were log transformed to reduce the sequencing depth differences among the libraries. The voom transformation data was used for differential expression (DE) analysis using Limma R package. The STRING (http://string.embl.de/) was used to identify known and predicted interactions among the differential expressed genes and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses. Gene ontology (GO) term biological process was only considered with FDR less than 0.05.

DE analysis gave rise to 11 differentially expressed transcripts in EC compared to HC, 421 in VP compared to EC, and 1048 in VP compared to HC. This data indicates that EC are more similar to HC in terms of the host gene expression profile. The functional enrichments identified by GO revealed that 37 biological processes were significantly associated with the gene sets different between EC and VP. Based on the biological significance, cell surface receptor signalling pathway had the maximum number of genes (n=44; FDR=0.046). There were several genes identified in this process that were also present in the pathways related to programmed cell death (FDR=0.029), Response to cytokine (FDR=0.046), or Cytokine-mediated signalling (FDR=0.017).

The transcriptomic analyses revealed that the DGE profile of EC closely resembles that of HC. Further, combining the gene expression analysis strongly indicated cell surface receptor signaling pathway, programmed cell death, response to cytokine and cytokine-mediated signaling may synergistically play important role in virus replication control in EC.