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Open AccessReview

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

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Department of Pathology, Federal University of Pernambuco (UFPE), Av. Prof. Moraes Rego, 1235 Cidade Universitária, Recife 50670-901, Brazil
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Department of Advanced Translational Microbiology, Institute for Maternal and Child Health IRCCS Burlo Garofolo, Via dell’Istria 65/1, 34137 Trieste, Italy
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Department of Genetics-Federal, University of Pernambuco (UFPE), Av. Prof. Moraes Rego, 1235 Cidade Universitária, Recife 50670-901, Brazil
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Laboratory of Immunopathology Keizo Asami (LIKA), Federal University of Pernambuco (UFPE), Av. Prof. Moraes Rego, 1235 Cidade Universitária, Recife 50670-901, Brazil
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Department of Biological and Environmental Sciences, College of Arts and Sciences, University of Qatar, Doha P.O. Box 2713, Qatar
*
Author to whom correspondence should be addressed.
Academic Editor: Julien Van Grevenynghe
Viruses 2021, 13(2), 244; https://doi.org/10.3390/v13020244
Received: 17 December 2020 / Revised: 26 January 2021 / Accepted: 1 February 2021 / Published: 4 February 2021
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. View Full-Text
Keywords: infection; latency; transcriptomics; genomics; gene ontology; pathway analysis infection; latency; transcriptomics; genomics; gene ontology; pathway analysis
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MDPI and ACS Style

Coelho, A.V.C.; Gratton, R.; Melo, J.P.B.d.; Andrade-Santos, J.L.; Guimarães, R.L.; Crovella, S.; Tricarico, P.M.; Brandão, L.A.C. HIV-1 Infection Transcriptomics: Meta-Analysis of CD4+ T Cells Gene Expression Profiles. Viruses 2021, 13, 244. https://doi.org/10.3390/v13020244

AMA Style

Coelho AVC, Gratton R, Melo JPBd, Andrade-Santos JL, Guimarães RL, Crovella S, Tricarico PM, Brandão LAC. HIV-1 Infection Transcriptomics: Meta-Analysis of CD4+ T Cells Gene Expression Profiles. Viruses. 2021; 13(2):244. https://doi.org/10.3390/v13020244

Chicago/Turabian Style

Coelho, Antonio V.C.; Gratton, Rossella; Melo, João P.B.d.; Andrade-Santos, José L.; Guimarães, Rafael L.; Crovella, Sergio; Tricarico, Paola M.; Brandão, Lucas A.C. 2021. "HIV-1 Infection Transcriptomics: Meta-Analysis of CD4+ T Cells Gene Expression Profiles" Viruses 13, no. 2: 244. https://doi.org/10.3390/v13020244

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