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HIV-1 Infection Transcriptomics: Meta-Analysis of CD4+ T Cells Gene Expression Profiles.

ABSTRACT: HIV-1 infection elicits a complex dynamic of the expression various host genes. High throughput sequencing added an expressive amount of information regarding HIV-1 infections and pathogenesis. RNA sequencing (RNA-Seq) is currently the tool of choice to investigate gene expression in a several range of experimental setting. This study aims at performing a meta-analysis of RNA-Seq expression profiles in samples of HIV-1 infected CD4+ T cells compared to uninfected cells to assess consistently differentially expressed genes in the context of HIV-1 infection. We selected two studies (22 samples: 15 experimentally infected and 7 mock-infected). We found 208 differentially expressed genes in infected cells when compared to uninfected/mock-infected cells. This result had moderate overlap when compared to previous studies of HIV-1 infection transcriptomics, but we identified 64 genes already known to interact with HIV-1 according to the HIV-1 Human Interaction Database. A gene ontology (GO) analysis revealed enrichment of several pathways involved in immune response, cell adhesion, cell migration, inflammation, apoptosis, Wnt, Notch and ERK/MAPK signaling.

SUBMITTER: Coelho AVC

PROVIDER: S-EPMC7913929 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature

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HIV-1 Infection Transcriptomics: Meta-Analysis of CD4+ T Cells Gene Expression Profiles.

Coelho Antonio Victor Campos AVC Gratton Rossella R Melo João Paulo Britto de JPB Andrade-Santos José Leandro JL Guimarães Rafael Lima RL Crovella Sergio S Tricarico Paola Maura PM Brandão Lucas André Cavalcanti LAC

Viruses 20210204 2

HIV-1 infection elicits a complex dynamic of the expression various host genes. High throughput sequencing added an expressive amount of information regarding HIV-1 infections and pathogenesis. RNA sequencing (RNA-Seq) is currently the tool of choice to investigate gene expression in a several range of experimental setting. This study aims at performing a meta-analysis of RNA-Seq expression profiles in samples of HIV-1 infected CD4+ T cells compared to uninfected cells to assess consistently dif ...[more]

PMID: 33557210

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Project description:BACKGROUND:In the absence of antiretroviral treatments (ARTs), a small group of individuals infected with HIV, including long-term non-progressors (LTNPs) who maintain high levels of CD4+ T cells for more than 7-10?years in the absence of ART and in particular a subgroup of LTNPs, elite controllers (ECs), who have low levels of viremia, remain clinically and/or immunologically stable for years. However, the mechanism of stable disease progression in LTNPs and ECs needs to be elucidated to help those infected with HIV-1 remain healthy. In this study, to identify the characteristics of gene expression profiles and biomarkers in LTNPs, we performed a meta-analysis using multiple gene expression profiles among LTNPs, individuals infected with HIV-1 without ART, individuals infected with HIV-1 with ART, and healthy controls. METHODS:The gene expression profiles obtained from the Gene Expression Omnibus (GEO) microarray data repositories were classified into three groups: LTNPs versus healthy controls (first group, 3 studies), LTNPs versus patients infected with HIV-1 without ART (second group, 3 studies), and LTNPs versus patients infected with HIV-1 with ART (third group, 3 studies). In addition, we considered a fourth group, patients infected with HIV-1 without ART versus healthy controls (3 studies), to exclude genes associated with HIV-1 infection in the three groups. For each group, we performed a meta-analysis using the RankProd method to identify and compare the differentially expressed genes (DEGs) in the three groups. RESULTS:We identified the 14 common DEGs in the three groups when comparing them with each other. Most belonged to immune responses, antigen processing and presentation, the interferon-gamma-mediated signaling pathway, and T cell co-stimulation. Of these DEGs, PHLDA1 was up-regulated and ACTB and ACTG1 were down-regulated in all three groups. However, the rest of the up- or down-regulated genes were discordant in the three groups. Additionally, ACTB and ACTG1 are known to inhibit viral assembly and production, and THBS1 is known to inhibit HIV-1 infection. CONCLUSIONS:These results suggest that significant genes identified in a meta-analysis provide clues to the cause of delayed disease progression and give a deeper understanding of HIV pathogenesis in LTNPs.

Project description:BACKGROUND:Infections with the West Nile virus (WNV) can attack neurological tissues in the host and alter gene expression levels therein. Several individual studies have analyzed these changes in the transcriptome based on measurements with DNA microarrays. Individual microarray studies produce a high-dimensional data structure with the number of studied genes exceeding the available sample size by far. Therefore, the level of scientific evidence of these studies is rather low and results can remain uncertain. Furthermore, the individual studies concentrate on different types of tissues or different time points after infection. A general statement regarding the transcriptional changes through WNV infection in neurological tissues is therefore hard to make. We screened public databases for transcriptome expression studies related to WNV infections and used different analysis pipelines to perform meta-analyses of these data with the goal of obtaining more stable results and increasing the level of evidence. RESULTS:We generated new lists of genes differentially expressed between WNV infected neurological tissues and control samples. A comparison with these genes to findings of a meta-analysis of immunological tissues is performed to figure out tissue-specific differences. While 5.879 genes were identified exclusively in the neurological tissues, 15 genes were found exclusively in the immunological tissues, and 44 genes were commonly detected in both tissues. Most findings of the original studies could be confirmed by the meta-analysis with a higher statistical power, but some genes and GO terms related to WNV were newly detected, too. In addition, we identified gene ontology terms related to certain infection processes, which are significantly enriched among the differentially expressed genes. In the neurological tissues, 17 gene ontology terms were found significantly different, and 2 terms in the immunological tissues. CONCLUSIONS:A critical discussion of our findings shows benefits but also limitations of the meta-analytic approach. In summary, the produced gene lists, identified gene ontology terms and network reconstructions appear to be more reliable than the results from the individual studies. Our meta-analysis provides a basis for further research on the transcriptional mechanisms by WNV infections in neurological tissues.

Project description:BackgroundThe latent HIV reservoir represents the major barrier to a cure. One curative strategy is targeting diseased cells for elimination based on biomarkers that uniquely define these cells. Single-cell RNA sequencing (scRNA-seq) has enabled the identification of gene expression profiles associated with disease at the single-cell level. Because HIV provirus in many cells during latency is not entirely silent, it became possible to determine gene expression patterns in a subset of cells latently infected with HIV.ObjectiveThe primary objective of this study was the identification of the gene expression profiles of single latently infected CD4+ T cells using scRNA-seq. Different conditions of latency establishment were considered. The identified profiles were then explored to prioritize the identified genes for future experimental validation.MethodsTo facilitate gene prioritization, three approaches were used. First, we characterized and compared the gene expression profiles of HIV latency established in different environments: in cells that encountered an activation stimulus and then returned to quiescence, and in resting cells that were infected directly via cell-to-cell viral transmission from autologous activated, productively infected cells. Second, we characterized and compared the gene expression profiles of HIV latency established with viruses of different tropisms, using an isogenic pair of CXCR4- and CCR5-tropic viruses. Lastly, we used proviral expression patterns in cells from people with HIV to more accurately define the latently infected cells in vitro.ResultsOur analyses demonstrated that a subset of genes is expressed differentially between latently infected and uninfected cells consistently under most conditions tested, including cells from people with HIV. Our second important observation was the presence of latency signatures, associated with variable conditions when latency was established, including cellular exposure and responsiveness to a T cell receptor stimulus and the tropism of the infecting virus.ConclusionCommon signatures, specifically genes that encode proteins localized to the cell surface, should be prioritized for further testing at the protein level as biomarkers for the ability to enrich or target latently infected cells. Cell- and tropism-dependent biomarkers may need to be considered in developing targeting strategies to ensure that all the different reservoir subsets are eliminated.

Dataset Information

HIV-1 Infection Transcriptomics: Meta-Analysis of CD4+ T Cells Gene Expression Profiles.

Publications

HIV-1 Infection Transcriptomics: Meta-Analysis of CD4+ T Cells Gene Expression Profiles.

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