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在博茨瓦纳的血清转化成年人中预测对广泛中和抗体 (bnAbs) 的耐药性和相关的HIV-1包膜特征

Natasha O Moraka¹, Wonderful T Choga¹, Marea N Pema¹

¹Botswana Harvard AIDS Institute Partnership, Bontleng, Private Bag BO320, Gaborone, Botswana.

Scientific reports

|October 24, 2023

概括

这项研究预测了HIV-1C序列中对广泛中和抗体 (bnAbs) 的抗性. 机器学习确定了影响bnAb有效性的特定HIV Env特征,指导了未来的治疗策略.

科学领域:

病毒学病毒学
免疫学免疫学免疫学
计算生物学计算生物学

背景情况:

广泛中和抗体 (bnAbs) 对于HIV-1治疗和预防至关重要.
了解HIV-1 Env的变异性是预测bnAb疗效的关键.
在 silico 预测工具可以评估不同人群中的 bnAb 抵抗模式.

研究的目的:

使用HIV-1C序列预测33bnAbs的性耐药性.
评估影响bnAb中和的HIV-1 Env特征.
为了识别影响bnAb敏感性的突变特征.

主要方法:

来自博茨瓦纳 (2013-2018) 的140个成年HIV-1血清转换序列的分析.
使用bNAb-ReP工具预测bnAb电阻的情况.
确定N链接的糖化位点 (PNGS) 和Env可变区域特征 (VC).

主要成果:

观察到多种bnAb耐药性;对3BNC117 (72%) 和VRC01 (57%) 预测的高耐药性.
预测对PGDM100,10-1074的耐药性较低;对4E10没有耐药性.
在PNGS频率上没有差异,除了在V3 bnAb耐药菌株中较高的PNGS外. 与V1,V4,V5循环长度和净收费发现的VC关联.

结论:

序列数据和机器学习有效预测bnAb电阻.
艾滋病毒-1 Env特征显著影响bnAb易感性.
在 silico 预测可以指导为特定人群选择有效的 bnAbs.

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