Bio-engineering, Faculty of Engineering, Bar Ilan University, Ramat Gan, Israel.
Bar Ilan Institute of Nanotechnologies and Advanced Materials, Bar Ilan University, Ramat Gan, Israel.
Front Immunol. 2023 Apr 19;14:1031914. doi: 10.3389/fimmu.2023.1031914. eCollection 2023.
The success of the human body in fighting SARS-CoV2 infection relies on lymphocytes and their antigen receptors. Identifying and characterizing clinically relevant receptors is of utmost importance.
We report here the application of a machine learning approach, utilizing B cell receptor repertoire sequencing data from severely and mildly infected individuals with SARS-CoV2 compared with uninfected controls.
In contrast to previous studies, our approach successfully stratifies non-infected from infected individuals, as well as disease level of severity. The features that drive this classification are based on somatic hypermutation patterns, and point to alterations in the somatic hypermutation process in COVID-19 patients.
These features may be used to build and adapt therapeutic strategies to COVID-19, in particular to quantitatively assess potential diagnostic and therapeutic antibodies. These results constitute a proof of concept for future epidemiological challenges.
人体在对抗 SARS-CoV2 感染方面的成功依赖于淋巴细胞及其抗原受体。识别和描述临床相关的受体至关重要。
我们在此报告了一种机器学习方法的应用，该方法利用了 SARS-CoV2 严重和轻度感染个体与未感染对照者的 B 细胞受体库测序数据。
与之前的研究不同，我们的方法成功地区分了未感染者和感染者，以及疾病严重程度。推动这种分类的特征基于体细胞超突变模式，并指向 COVID-19 患者体细胞超突变过程的改变。
这些特征可用于构建和调整针对 COVID-19 的治疗策略，特别是定量评估潜在的诊断和治疗性抗体。这些结果为未来的流行病学挑战提供了一个概念验证。
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