Department of Computer Science, University of Tübingen, Tübingen, Germany.
Institute for Biomedical Informatics, University of Tübingen, Tübingen, Germany.
PLoS Comput Biol. 2024 Jun 7;20(6):e1012131. doi: 10.1371/journal.pcbi.1012131. eCollection 2024 Jun.
Immunization through repeated direct venous inoculation of Plasmodium falciparum (Pf) sporozoites (PfSPZ) under chloroquine chemoprophylaxis, using the PfSPZ Chemoprophylaxis Vaccine (PfSPZ-CVac), induces high-level protection against controlled human malaria infection (CHMI). Humoral and cellular immunity contribute to vaccine efficacy but only limited information about the implicated Pf-specific antigens is available. Here, we examined Pf-specific antibody profiles, measured by protein arrays representing the full Pf proteome, of 40 placebo- and PfSPZ-immunized malaria-naïve volunteers from an earlier published PfSPZ-CVac dose-escalation trial. For this purpose, we both utilized and adapted supervised machine learning methods to identify predictive antibody profiles at two different time points: after immunization and before CHMI. We developed an adapted multitask support vector machine (SVM) approach and compared it to standard methods, i.e. single-task SVM, regularized logistic regression and random forests. Our results show, that the multitask SVM approach improved the classification performance to discriminate the protection status based on the underlying antibody-profiles while combining time- and dose-dependent data in the prediction model. Additionally, we developed the new fEature diStance exPlainabilitY (ESPY) method to quantify the impact of single antigens on the non-linear multitask SVM model and make it more interpretable. In conclusion, our multitask SVM model outperforms the studied standard approaches in regard of classification performance. Moreover, with our new explanation method ESPY, we were able to interpret the impact of Pf-specific antigen antibody responses that predict sterile protective immunity against CHMI after immunization. The identified Pf-specific antigens may contribute to a better understanding of immunity against human malaria and may foster vaccine development.
通过在氯喹化学预防下，经反复直接静脉接种疟原虫（Pf）孢子（PfSPZ）进行免疫接种，使用 PfSPZ 化学预防疫苗（PfSPZ-CVac），可诱导针对受控人体疟疾感染（CHMI）的高水平保护。体液和细胞免疫有助于疫苗的功效，但关于涉及的 Pf 特异性抗原的信息有限。在这里，我们检查了 40 名来自先前发表的 PfSPZ-CVac 剂量递增试验的安慰剂和 PfSPZ 免疫的疟疾初免志愿者的 Pf 特异性抗体谱，该谱通过代表 Pf 全蛋白质组的蛋白质阵列进行测量。为此，我们同时利用和改编了监督机器学习方法，以在两个不同时间点识别预测性抗体谱：免疫接种后和 CHMI 之前。我们开发了一种适应的多任务支持向量机（SVM）方法，并将其与标准方法（即单任务 SVM、正则化逻辑回归和随机森林）进行了比较。我们的结果表明，多任务 SVM 方法通过组合预测模型中的时间和剂量依赖性数据，改善了基于基础抗体谱区分保护状态的分类性能。此外，我们开发了新的 fEature diStance exPlainabilitY（ESPY）方法，以量化单个抗原对非线性多任务 SVM 模型的影响，并使其更具可解释性。总之，我们的多任务 SVM 模型在分类性能方面优于所研究的标准方法。此外，通过我们的新解释方法 ESPY，我们能够解释 Pf 特异性抗原抗体反应的影响，这些反应可预测免疫接种后对 CHMI 的无菌保护性免疫。鉴定出的 Pf 特异性抗原可能有助于更好地理解针对人类疟疾的免疫，并可能促进疫苗的开发。
