Machine learning approaches identify immunologic signatures of total and intact HIV DNA during long-term antiretroviral therapy.
Machine learning approaches identify immunologic signatures of total and intact HIV DNA during long-term antiretroviral therapy.
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机器学习方法可识别长期抗逆转录病毒治疗期间完整且完整的 HIV DNA 的免疫学特征。
DOI：
10.1101/2023.11.16.567386
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发表时间：
2023
期刊：
影响因子：
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通讯作者：
Browne,EdwardP
中科院分区：
文献类型：
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作者：
Semenova,Lesia;Wang,Yingfan;Falcinelli,Shane;Archin,Nancie;Cooper-Volkheimer,AliciaD;Margolis,DavidM;Goonetilleke,Nilu;Murdoch,DavidM;Rudin,CynthiaD;Browne,EdwardP
了解HIV宿主和宿主免疫系统之间的相互作用可能有助于了解抗逆转录病毒治疗（ART）期间HIV的持续性，并为治愈策略提供信息。在这里，我们将机器学习（ML）方法应用于横截面高参数HIV储存库和免疫学数据，以表征宿主-储存库关联并生成有关HIV储存库生物学的新假设。高维免疫表型，定量的HIV特异性T细胞反应，和测量的遗传完整的和总的HIV前病毒DNA的频率进行外周血样本从115人HIV（PWH）长期ART。分析表明，完整的和总的前病毒DNA的频率与T细胞活化和衰竭呈正相关。抗逆转录病毒治疗的年限和选择的双功能HIV特异性CD 4 T细胞应答与完整前病毒的百分比呈负相关。留一协变量推断方法确定了特定的艾滋病毒库和临床人口统计学参数，如年龄和生物性别，这是特别重要的预测免疫表型。总的来说，免疫参数与总的HIV前病毒频率比完整的前病毒频率更密切相关。然而，独特的是，CD 4 T细胞上IL-7受体α链（CD 127）的表达与完整的储库更强相关。无监督降维分析确定了两个主要的集群PWH具有不同的免疫和水库的特点。使用这些初步分析中确定的储库相关性，采用决策树方法来可视化多个免疫和临床人口统计学参数与HIV储库之间的关系。最后，使用我们的数据作为训练测试集的随机分割，ML算法以大约70%的准确率预测给定参与者是否具有定性的高或低水平的总或完整的HIV DNA。这里描述的技术可能是有用的，用于评估全球模式内越来越高的维度数据用于艾滋病毒水库和其他复杂的生物学研究。
Understanding the interplay between the HIV reservoir and the host immune system may yield insights into HIV persistence during antiretroviral therapy (ART) and inform strategies for a cure. Here, we applied machine learning (ML) approaches to cross-sectional high-parameter HIV reservoir and immunology data in order to characterize host–reservoir associations and generate new hypotheses about HIV reservoir biology. High-dimensional immunophenotyping, quantification of HIV-specific T cell responses, and measurement of genetically intact and total HIV proviral DNA frequencies were performed on peripheral blood samples from 115 people with HIV (PWH) on long-term ART. Analysis demonstrated that both intact and total proviral DNA frequencies were positively correlated with T cell activation and exhaustion. Years of ART and select bifunctional HIV-specific CD4 T cell responses were negatively correlated with the percentage of intact proviruses. A leave-one-covariate-out inference approach identified specific HIV reservoir and clinical–demographic parameters, such as age and biological sex, that were particularly important in predicting immunophenotypes. Overall, immune parameters were more strongly associated with total HIV proviral frequencies than intact proviral frequencies. Uniquely, however, expression of the IL-7 receptor alpha chain (CD127) on CD4 T cells was more strongly correlated with the intact reservoir. Unsupervised dimension reduction analysis identified two main clusters of PWH with distinct immune and reservoir characteristics. Using reservoir correlates identified in these initial analyses, decision tree methods were employed to visualize relationships among multiple immune and clinical–demographic parameters and the HIV reservoir. Finally, using random splits of our data as training-test sets, ML algorithms predicted with approximately 70% accuracy whether a given participant had qualitatively high or low levels of total or intact HIV DNA. The techniques described here may be useful for assessing global patterns within the increasingly high-dimensional data used in HIV reservoir and other studies of complex biology.