GSE157198: Next Generation Sequencing Facilitates Quantitative Analysis of HIV Elite controller, HIV ART, HIV ART-naive and healthy control Transcriptomes

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Description

Submission Date: Aug 31, 2020

Summary: A small group of HIV-1-infected individuals, termed elite controllers (ECs), display control of HIV replication in the absence of antiretroviral therapy (ART). However, the mechanisms of resistance to HIV in ECs remains largely unknown. To identify host factors specific to the ECs that might be involved in controlling HIV infection, we performed RNA-seq transcriptome analysis, and a total of 44 samples were included. The monocytes from each sample were enriched, sequenced, and subjected to comparative transcriptome analysis to screen candidate genes that might affect resistance to HIV infection. we found several candidate genes with highly divergent expression that might contribute to the different outcomes of HIV infection in ECs and non-ECs. This finding will help elucidate the mechanisms by which ECs restrict HIV replication and represents a new approach for treatment of HIV.

GEO Accession ID: GSE157198

PMID: 35041523

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GROUP CONDITION SAMPLES
Infection + Treatment
GSM4758788 GSM4758789 GSM4758790 GSM4758791 GSM4758795 GSM4758817 GSM4758818 GSM4758819 GSM4758820 GSM4758830 GSM4758831
GSM4758804 GSM4758806 GSM4758807 GSM4758808 GSM4758809 GSM4758810 GSM4758811 GSM4758824 GSM4758825 GSM4758826
GSM4758797 GSM4758798 GSM4758799 GSM4758800 GSM4758801 GSM4758802 GSM4758803 GSM4758821 GSM4758822 GSM4758823 GSM4758827
GSM4758792 GSM4758793 GSM4758794 GSM4758796 GSM4758805 GSM4758812 GSM4758813 GSM4758814 GSM4758815 GSM4758816 GSM4758828 GSM4758829
Description

Submission Date: Aug 31, 2020

Summary: A small group of HIV-1-infected individuals, termed elite controllers (ECs), display control of HIV replication in the absence of antiretroviral therapy (ART). However, the mechanisms of resistance to HIV in ECs remains largely unknown. To identify host factors specific to the ECs that might be involved in controlling HIV infection, we performed RNA-seq transcriptome analysis, and a total of 44 samples were included. The monocytes from each sample were enriched, sequenced, and subjected to comparative transcriptome analysis to screen candidate genes that might affect resistance to HIV infection. we found several candidate genes with highly divergent expression that might contribute to the different outcomes of HIV infection in ECs and non-ECs. This finding will help elucidate the mechanisms by which ECs restrict HIV replication and represents a new approach for treatment of HIV.

GEO Accession ID: GSE157198

PMID: 35041523

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Differential Gene Expression Analysis
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Bulk RNA-seq Appyter

This pipeline enables you to analyze and visualize your bulk RNA sequencing datasets with an array of downstream analysis and visualization tools. The pipeline includes: PCA analysis, Clustergrammer interactive heatmap, library size analysis, differential gene expression analysis, enrichment analysis, and L1000 small molecule search.