Dataset Information


Next Generation Sequencing Facilitates Quantitative Analysis of HIV-1 Latency in Central Memory T Cells

ABSTRACT: Purpose: Next-generation sequencing (NGS) has become a powerful microscope to study cell models of HIV. The goals of this study is to analyze latent HIV infection in the TCM model of HIV latency described in Martins et al 2015 and to evaluate potential markers for HIV infection. Methods: Peripheral blood mononuclear cells were collected from 4 healthy donors. Naive CD4 T cells were isolated and utilized to generate a model of latent HIV infection. During model generation, cells were sorted and collected for RNA extraction 10 days post infection along with their uninfected counterparts. 2 days after this, cells were activated with CD3/CD28 stimulation and collected for study for a total of 16 samples from 4 donors. After cell collection, RNA was extracted from infected cells and their uninfected counterparts for deep sequencing by Expression Analysis. Sequence reads that passed quality filters were mapped using Tophat and counted using HTSeq. In addition to human transcripts, we utilized 92 ERCC spikes as negative controls. Any human gene which did not achieve at least 1 count per million reads in at least 4 samples or any ERCC that did not achieve at least 5 reads in at least 4 samples was discarded. Differential expression for activated samples was not performed, but rather used to demonstrate upregulation of T-cell activation markers along with changing to the type and abundance of HIV transcripts produced. Results: Using an custom built data analysis pipeline, 82 million reads per sample to the human genome (build hg38) and identified 13,534 human transcripts 67 ERCC spike in transcripts, and HIV NL43 transcripts were identified with the Tophat/HTSeq workflow. Differential expression analysis was performed between uninfected and latently infected cells. 826 genes were found to be differentially expressed, 275 downregulated and 551 upregulated (FDR < 0.05) with EdgeR. GO and Pathway analysis of differential expressed genes revealed significant dysregulation of genes involved in the p53 signaling pathway. Subsequent studies with pifithrin demonstrated a reduction in latently infected cell during model generation. Conclusions: This study represents the first detailed analysis of HIV latency with this cell model using next generations sequencing. These results demonstrate that the TCM model of HIV latency of Martins et al 2015 is truly reflective of HIV latency. This study also provides a framework for which to analyze future cell models of HIV latency using next generation sequencing. Finally, this work demonstrates that the p53 signaling pathway is a key pathway dysregulated in latency for this cell model with several genes dysregulated in HIV latency. Overall design: TCM model of HIV latency mRNA profiles of 4 donors in 4 conditions were generated by deep sequencing by Expression Analysis.

INSTRUMENT(S): Illumina HiSeq 2000 (Homo sapiens)

SUBMITTER: Cory Haley White 

PROVIDER: GSE81810 | GEO | 2016-11-01



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Transcriptomic Analysis Implicates the p53 Signaling Pathway in the Establishment of HIV-1 Latency in Central Memory CD4 T Cells in an In Vitro Model.

White Cory H CH   Moesker Bastiaan B   Beliakova-Bethell Nadejda N   Martins Laura J LJ   Spina Celsa A CA   Margolis David M DM   Richman Douglas D DD   Planelles Vicente V   Bosque Alberto A   Woelk Christopher H CH  

PLoS pathogens 20161129 11

The search for an HIV-1 cure has been greatly hindered by the presence of a viral reservoir that persists despite antiretroviral therapy (ART). Studies of HIV-1 latency in vivo are also complicated by the low proportion of latently infected cells in HIV-1 infected individuals. A number of models of HIV-1 latency have been developed to examine the signaling pathways and viral determinants of latency and reactivation. A primary cell model of HIV-1 latency, which incorporates the generation of prim  ...[more]

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