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Integrated Single-cell Multiomic Analysis of HIV Latency Reversal Reveals Novel Regulators of Viral Reactivation

Genomics, Proteomics & Bioinformatics 2024年第1期22卷 67-88页

作者： Manickam Ashokkumar Wenwen Mei Jackson J.Peterson Yuriko Harigaya David M.Murdoch David M.Margolis Caleb Kornfein alex oesterling Zhicheng Guo Cynthia D.Rudin Yuchao Jiang Edward P.Browne Department of Medicine University of North Carolina at Chapel HillChapel HillNC 27599USA HIV Cure Center University of North Carolina at Chapel HillChapel HillNC 27599USA Department of Biostatistics University of North Carolina at Chapel HillChapel HillNC 27599USA Department of Microbiology and Immunology University of North Carolina at Chapel HillChapel HillNC 27599USA Department of Genetics University of North Carolina at Chapel HillChapel HillNC 27599USA Department of Medicine Duke UniversityDurhamNC 27708USA Department of Computer Science Duke UniversityDurhamNC 27708USA Department of Statistics Texas A&M UniversityCollege StationTX 77843USA Department of Biology Texas A&M UniversityCollege StationTX 77843USA Department of Biomedical Engineering Texas A&M UniversityCollege StationTX 77843USA

Despite the success of antiretroviral therapy,human immunodeficiency virus(HIV)cannot be cured because of a reservoir of latently infected cells that evades *** understand the mechanisms of HIV latency,we employed an integrated single-cell RNA sequencing(scRNA-seq)and single-cell assay for transposase-accessible chromatin with sequencing(scATAC-seq)approach to simultaneously profile the transcriptomic and epigenomic characteristics of~125,000 latently infected primary CD4^(+)T cells after reactivation using three different latency reversing *** expressed genes and differentially accessible motifs were used to examine transcriptional pathways and transcription factor(TF)activities across the cell *** identified cellular transcripts and TFs whose expression/activity was correlated with viral reactivation and demonstrated that a machine learning model trained on these data was 75%-79%accurate at predicting viral ***,we validated the role of two candidate HIV-regulating factors,FOXP1 and GATA3,in viral *** data demonstrate the power of integrated multimodal single-cell analysis to uncover novel relationships between host cell factors and HIV latency.

关键词： HIV latency reversal Primary CD4þT cell Single-cell multiomics HIV-regulating factor Machine learning

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