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

IF 11.5 2区生物学 Q1 GENETICS & HEREDITY

Genomics, Proteomics & Bioinformatics Pub Date : 2024-01-11 DOI:10.1093/gpbjnl/qzae003

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

{"title":"Integrated Single-cell Multiomic Analysis of HIV Latency Reversal Reveals Novel Regulators of Viral Reactivation","authors":"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","doi":"10.1093/gpbjnl/qzae003","DOIUrl":null,"url":null,"abstract":"Abstract Despite the success of antiretroviral therapy, human immunodeficiency virus (HIV) cannot be cured because of a reservoir of latently infected cells that evades therapy. To understand the mechanisms of HIV latency, we employed an integrated single-cell RNA sequencing (RNA-seq) and single-cell assay for transposase-accessible chromatin with sequencing (ATAC-seq) approach to simultaneously profile the transcriptomic and epigenomic characteristics of ∼ 125,000 latently infected primary CD4 cells after reactivation using three different latency reversing agents. Differentially expressed genes and differentially accessible motifs were used to examine transcriptional pathways and transcription factor (TF) activities across the cell population. We 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 reactivation. Finally, we validated the role of two candidate HIV-regulating factors, FOXP1 and GATA3, in viral transcription. These data demonstrate the power of integrated multimodal single-cell analysis to uncover novel relationships between host cell factors and HIV latency.","PeriodicalId":12528,"journal":{"name":"Genomics, Proteomics & Bioinformatics","volume":"165 1","pages":""},"PeriodicalIF":11.5000,"publicationDate":"2024-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Genomics, Proteomics & Bioinformatics","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.1093/gpbjnl/qzae003","RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"GENETICS & HEREDITY","Score":null,"Total":0}

引用次数: 0

Abstract Despite the success of antiretroviral therapy, human immunodeficiency virus (HIV) cannot be cured because of a reservoir of latently infected cells that evades therapy. To understand the mechanisms of HIV latency, we employed an integrated single-cell RNA sequencing (RNA-seq) and single-cell assay for transposase-accessible chromatin with sequencing (ATAC-seq) approach to simultaneously profile the transcriptomic and epigenomic characteristics of ∼ 125,000 latently infected primary CD4 cells after reactivation using three different latency reversing agents. Differentially expressed genes and differentially accessible motifs were used to examine transcriptional pathways and transcription factor (TF) activities across the cell population. We 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 reactivation. Finally, we validated the role of two candidate HIV-regulating factors, FOXP1 and GATA3, in viral transcription. These data demonstrate the power of integrated multimodal single-cell analysis to uncover novel relationships between host cell factors and HIV latency.

查看原文本刊更多论文

艾滋病毒潜伏期逆转的单细胞多组学综合分析揭示了病毒再激活的新型调控因子

摘要尽管抗逆转录病毒疗法取得了成功，但人类免疫缺陷病毒（HIV）仍无法治愈，因为潜伏感染的细胞库逃避了治疗。为了了解艾滋病毒潜伏的机制，我们采用了单细胞 RNA 测序（RNA-seq）和单细胞转座酶可访问染色质测序（ATAC-seq）的综合方法，同时分析了使用三种不同的潜伏逆转剂重新激活后 125,000 个潜伏感染的初级 CD4 细胞的转录组和表观组特征。我们利用差异表达基因和差异可及基序来研究整个细胞群的转录途径和转录因子（TF）活性。我们确定了其表达/活性与病毒再活化相关的细胞转录本和转录因子，并证明根据这些数据训练的机器学习模型在预测病毒再活化方面的准确率为 75%-79%。最后，我们验证了两个候选 HIV 调节因子 FOXP1 和 GATA3 在病毒转录中的作用。这些数据证明了综合多模态单细胞分析在揭示宿主细胞因子与 HIV 潜伏期之间的新型关系方面的强大功能。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Genomics, Proteomics & Bioinformatics Biochemistry, Genetics and Molecular Biology-Biochemistry

CiteScore

14.30

自引率

4.20%

发文量

844

审稿时长

61 days

期刊介绍： Genomics, Proteomics and Bioinformatics (GPB) is the official journal of the Beijing Institute of Genomics, Chinese Academy of Sciences / China National Center for Bioinformation and Genetics Society of China. It aims to disseminate new developments in the field of omics and bioinformatics, publish high-quality discoveries quickly, and promote open access and online publication. GPB welcomes submissions in all areas of life science, biology, and biomedicine, with a focus on large data acquisition, analysis, and curation. Manuscripts covering omics and related bioinformatics topics are particularly encouraged. GPB is indexed/abstracted by PubMed/MEDLINE, PubMed Central, Scopus, BIOSIS Previews, Chemical Abstracts, CSCD, among others.