Probabilities of HIV-1 bNAb development in healthy and chronically infected individuals

Nature Communications(2022)

引用 2|浏览30
暂无评分
摘要
HIV-1 broadly neutralizing antibodies (bNAbs) are able to suppress viremia and prevent infection. Their induction by vaccination is therefore a major goal. However, in contrast to antibodies that neutralize other pathogens, HIV-1-specific bNAbs frequently carry uncommon molecular characteristics that might prevent their induction. Here, we performed unbiased sequence analyses of B cell receptor repertoires from 57 healthy and 46 chronically HIV-1- or HCV-infected individuals and learned probabilistic models to predict the likelihood of bNAb development. We formally show that lower probabilities for bNAbs are predictive of higher HIV-1 neutralization activity. Moreover, ranking of bNAbs by their probabilities allowed to identify highly potent antibodies with superior generation probabilities as preferential targets for vaccination approaches. Importantly, we found equal bNAb probabilities across infected and healthy donors. This implies that chronic infection is not a prerequisite for the generation of bNAbs, fostering the hope that HIV-1 vaccines can induce bNAb development in healthy individuals. Significance Statement While HIV-1 broadly neutralizing antibodies (bNAbs) can develop in chronically HIV-1-infected individuals, they could not yet be elicited by active vaccination. Here, we computationally demonstrate that HIV-1 bNAbs carry distinct sequence features making them unlikely outcomes of the antibody evolution. However, our approach allowed us to identify bNAbs with higher probabilities of being generated. These candidates can now serve as the most promising targets to be induced by vaccination. Moreover, we show that chronic infection has no influence on the probabilities of finding typical bNAb sequence features in the memory B cell compartment. Both findings are critical to design effective vaccination strategies. ### Competing Interest Statement The authors have declared no competing interest.
更多
查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要