克隆异质性和抗原刺激形成HIV潜伏库的持久性。

IF 3.6 2区 生物学 Q1 BIOCHEMICAL RESEARCH METHODS
PLoS Computational Biology Pub Date : 2025-09-15 eCollection Date: 2025-09-01 DOI:10.1371/journal.pcbi.1013433
Marco Garcia Noceda, Gargi Kher, Shikhar Uttam, John P Barton
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引用次数: 0

摘要

药物治疗可以控制HIV-1的复制,但不能治愈感染。这是因为一种被称为潜伏库(LR)的休眠感染细胞的长寿命群体,即使在数十年成功的药物治疗后,也可以重新开始活跃的复制。LR中的许多细胞属于高度扩增的克隆,但其克隆结构背后的过程尚不清楚。了解LR的动态及其持续存在的关键对于开发HIV-1治疗方法至关重要。在这里,我们开发了一个LR动态的定量模型,该模型适合从几天到几十年的时间尺度上的可用患者数据。我们发现抗原刺激和克隆异质性之间的相互作用决定了LR的动态。特别是,我们发现大型克隆在长期持久性中起着核心作用,即使它们很少重新激活。我们的结果可以为HIV-1治疗策略的发展提供信息。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Clonal heterogeneity and antigenic stimulation shape persistence of the latent reservoir of HIV.

Drug treatment can control HIV-1 replication, but it cannot cure infection. This is because of a long-lived population of quiescent infected cells, known as the latent reservoir (LR), that can restart active replication even after decades of successful drug treatment. Many cells in the LR belong to highly expanded clones, but the processes underlying the clonal structure of the LR are unclear. Understanding the dynamics of the LR and the keys to its persistence is critical for developing an HIV-1 cure. Here we develop a quantitative model of LR dynamics that fits available patient data over time scales spanning from days to decades. We show that the interplay between antigenic stimulation and clonal heterogeneity shapes the dynamics of the LR. In particular, we find that large clones play a central role in long-term persistence, even though they rarely reactivate. Our results could inform the development of HIV-1 cure strategies.

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来源期刊
PLoS Computational Biology
PLoS Computational Biology BIOCHEMICAL RESEARCH METHODS-MATHEMATICAL & COMPUTATIONAL BIOLOGY
CiteScore
7.10
自引率
4.70%
发文量
820
审稿时长
2.5 months
期刊介绍: PLOS Computational Biology features works of exceptional significance that further our understanding of living systems at all scales—from molecules and cells, to patient populations and ecosystems—through the application of computational methods. Readers include life and computational scientists, who can take the important findings presented here to the next level of discovery. Research articles must be declared as belonging to a relevant section. More information about the sections can be found in the submission guidelines. Research articles should model aspects of biological systems, demonstrate both methodological and scientific novelty, and provide profound new biological insights. Generally, reliability and significance of biological discovery through computation should be validated and enriched by experimental studies. Inclusion of experimental validation is not required for publication, but should be referenced where possible. Inclusion of experimental validation of a modest biological discovery through computation does not render a manuscript suitable for PLOS Computational Biology. Research articles specifically designated as Methods papers should describe outstanding methods of exceptional importance that have been shown, or have the promise to provide new biological insights. The method must already be widely adopted, or have the promise of wide adoption by a broad community of users. Enhancements to existing published methods will only be considered if those enhancements bring exceptional new capabilities.
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