HIV-1 Infection Transcriptomics: Meta-Analysis of CD4+ T Cells Gene Expression Profiles
Antonio Victor Campos Coelho,
Rossella Gratton,
João Paulo Britto de Melo,
José Leandro Andrade-Santos,
Rafael Lima Guimarães,
Sergio Crovella,
Paola Maura Tricarico,
Lucas André Cavalcanti Brandão
Affiliations
Antonio Victor Campos Coelho
Department of Pathology, Federal University of Pernambuco (UFPE), Av. Prof. Moraes Rego, 1235 Cidade Universitária, Recife 50670-901, Brazil
Rossella Gratton
Department of Advanced Translational Microbiology, Institute for Maternal and Child Health IRCCS Burlo Garofolo, Via dell’Istria 65/1, 34137 Trieste, Italy
João Paulo Britto de Melo
Department of Pathology, Federal University of Pernambuco (UFPE), Av. Prof. Moraes Rego, 1235 Cidade Universitária, Recife 50670-901, Brazil
José Leandro Andrade-Santos
Department of Genetics-Federal, University of Pernambuco (UFPE), Av. Prof. Moraes Rego, 1235 Cidade Universitária, Recife 50670-901, Brazil
Rafael Lima Guimarães
Department of Genetics-Federal, University of Pernambuco (UFPE), Av. Prof. Moraes Rego, 1235 Cidade Universitária, Recife 50670-901, Brazil
Sergio Crovella
Department of Biological and Environmental Sciences, College of Arts and Sciences, University of Qatar, Doha P.O. Box 2713, Qatar
Paola Maura Tricarico
Department of Advanced Translational Microbiology, Institute for Maternal and Child Health IRCCS Burlo Garofolo, Via dell’Istria 65/1, 34137 Trieste, Italy
Lucas André Cavalcanti Brandão
Department of Pathology, Federal University of Pernambuco (UFPE), Av. Prof. Moraes Rego, 1235 Cidade Universitária, Recife 50670-901, Brazil
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.
