Dataset Information

Differential expression of lncRNAs during the HIV replication cycle

ABSTRACT: Studying the effects of HIV infection on the host transcriptome has typically focused on protein-coding genes. However, recent advances in the field of RNA sequencing revealed that long non-coding RNAs (lncRNAs) add an extensive additional layer to the cell’s molecular network and are highly involved in the regulation of molecular processes, including gene expression. Here, we performed transcriptome profiling throughout a primary HIV infection in vitro to investigate lncRNA expression at the different HIV replication cycle processes (reverse transcription, integration and particle production). Subsequently, guilt-by-association, transcription factor and co-expression analysis were performed to infer biological roles for the lncRNAs identified in the HIV-host interplay. Many lncRNAs were shown to play a role in mechanisms relying on proteasomal and ubiquitinylation pathways, apoptosis, DNA damage responses and cell cycle regulation. Through transcription factor binding analysis, we found that lncRNAs display a distinct transcriptional regulation profile as compared to protein coding mRNAs, suggesting that mRNAs and lncRNAs are independently modulated from each other. In addition, we identified five differentially expressed lncRNA-mRNA pairs with mRNA involvement in HIV pathogenesis (HIV linked co-expression) with possible cis regulatory lncRNAs that control the nearby mRNA expression and function. Altogether, the present study demonstrates that lncRNAs add a new dimension to the HIV-host interplay and should be further investigated as they may represent targets for controlling HIV replication.

ORGANISM(S): Homo sapiens

PROVIDER: GSE74818 | GEO | 2016/10/31

SECONDARY ACCESSION(S): PRJNA301603

REPOSITORIES: GEO

ACCESS DATA

Similar Datasets

Project description:We aimed at systematically inferring the regulatory functions of host lncRNAs in response to influenza A virus (IAV) and severe acute respiratory syndrome coronavirus (SARS-CoV) in the mouse model, using a ‘guilt-by-association’ approach which relies on finding which lncRNAs have similar expression profiles to protein-coding genes of known function. To build a large panel of diverse host responses to viral infection, we took advantage of the genetic diversity present in the 8 founder strains of the Collaborative Cross (CC) mouse resource. Extensive pulmonary host-response profiling was performed on mock and viral-infected lungs at 2 and 4 days post-infection using total RNA-Seq. Overall lncRNAs accounted for about 40% of total genes differentially expressed upon infections (5,329 DE lncRNAs). To predict the functions of these lncRNAs, we constructed a co-expression network using the weighted correlation network analysis (WGCNA) and identified modules of co-expressed genes. Several lncRNAs were identified as belonging to gene modules associated with viral replication or weight loss, and enriched in various infection-related biological processes such as immune response. In addition, each lncRNA was individually annotated using ranked list of DE genes based on signed correlation with each LncRNA. We predicted that a few lncRNAs may be implicated in more than 50 biological processes or pathways. Finally, we identified the lncRNAs that were positively or negatively correlated with all their neighbors and may have cis enhancer or inhibitor effects. We validated our prediction by examining additional RNASeq dataset of mice treated with IFN-alpha. Altogether, these results provide a broad categorization of lncRNA functions and identify subsets of lncRNAs with potential key roles for respiratory virus pathogenesis. These data are fully accessible through The MOuse NOn-Code Lung database (MONOCLdb) http://monocldb.viromics.washington.edu/ At 2 or 4 days post infection, Collaborative Cross founders mice (n=2-3 for infected conditions, n=2 for mocks) were euthanized and lungs were used for total RNA-Seq.

Project description:Background: Many studies have shown that long noncoding RNAs (lncRNAs) derived from the host and human immunodeficiency virus (HIV) itself play important roles in virus-host interactions and viral pathogenesis. It is unclear whether lncRNAs were involved in ART therapy of HIV/AIDS patients. Methods: Six HIV/acquired immunodeficiency syndrome (AIDS) subjects pre-HAART and post-HAART with effective control of plasma viremia (<20 HIV RNA copies/ml) and 6 healthy subjects were recruited in this study to perform RNA sequencing. The expression profiles of lncRNAs and mRNAs were obtained, to exploration of the potential roles of lncRNAs, trans- and cis-acting analysis was used. Then, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed to sort out functional categories of genes. Results: We identified a total of 974 lncRNAs whose expression levels were restored to normal after ART therapy. The results of the cis-acting analysis showed that only six lncRNAs have cis-regulated target genes, among which the target gene RP11-290F5.1, interferon regulatory factors 2 (IRF2), could promote HIV replication. We also identified lncRNA CTB-119C2.1, which regulates most mRNAs with differential expression between pre- and post-HAART, and the differences were significant. We selected lncRNA CTB-119C2.1 for qRT–PCR verification, and the results were consistent with those of RNA-seq. RAB3A and GADD45A, two of the lncRNA CTB-119C2.1-associated genes, have been shown to be associated with HIV infection. KEGG analysis of lncRNA CTB-119C2.1-associated genes revealed that most of the genes are involved in the p53 signaling pathway or pathways related to cell circulation and DNA replication. Conclusion: In this study, we used RNA-seq to systematically compare the expression profiles of lncRNAs in HIV subjects between untreated and treated time points. We successfully identified some lncRNAs with differential expression during certain periods (no HIV infection, HIV infection before treatment, and after treatment). Their expression is associated with HIV viral loads, and some of their regulating genes were found to be involved in HIV pathogenesis through bioinformatic analysis. These findings could help to reveal the underlying molecular mechanism of the progression of HIV infection.

Dataset Information

Differential expression of lncRNAs during the HIV replication cycle

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure