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AuthorCoelho, Antonio Victor Campos
AuthorGratton, Rossella
Authorde Melo, João Paulo Britto
AuthorAndrade-Santos, José Leandro
AuthorGuimarães, Rafael Lima
AuthorCrovella, Sergio
AuthorTricarico, Paola Maura
AuthorBrandão, Lucas André Cavalcanti
Available date2022-01-25T05:30:31Z
Publication Date2021-02-01
Publication NameViruses
Identifierhttp://dx.doi.org/10.3390/v13020244
CitationCoelho, 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
URIhttp://hdl.handle.net/10576/25906
AbstractHIV-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.
Languageen
PublisherMDPI
SubjectGene ontology
Genomics
Infection
Latency
Pathway analysis
Transcriptomics
TitleHiv-1 infection transcriptomics: Meta-analysis of cd4+ t cells gene expression profiles
TypeArticle Review
Issue Number2
Volume Number13
ESSN1999-4915
dc.accessType Open Access


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