Machine learning analysis of the T cell receptor repertoire identifies sequence features of self-reactivity

IF 9 1区 生物学 Q1 BIOCHEMISTRY & MOLECULAR BIOLOGY Cell Systems Pub Date : 2023-12-06 DOI:10.1016/j.cels.2023.11.004
Johannes Textor, Franka Buytenhuijs, Dakota Rogers, Ève Mallet Gauthier, Shabaz Sultan, Inge M.N. Wortel, Kathrin Kalies, Anke Fähnrich, René Pagel, Heather J. Melichar, Jürgen Westermann, Judith N. Mandl
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Abstract

The T cell receptor (TCR) determines specificity and affinity for both foreign and self-peptides presented by the major histocompatibility complex (MHC). Although the strength of TCR interactions with self-pMHC impacts T cell function, it has been challenging to identify TCR sequence features that predict T cell fate. To discern patterns distinguishing TCRs from naive CD4+ T cells with low versus high self-reactivity, we used data from 42 mice to train a machine learning (ML) algorithm that identifies population-level differences between TCRβ sequence sets. This approach revealed that weakly self-reactive T cell populations were enriched for longer CDR3β regions and acidic amino acids. We tested our ML predictions of self-reactivity using retrogenic mice with fixed TCRβ sequences. Extrapolating our analyses to independent datasets, we predicted high self-reactivity for regulatory T cells and slightly reduced self-reactivity for T cells responding to chronic infections. Our analyses suggest a potential trade-off between TCR repertoire diversity and self-reactivity. A record of this paper’s transparent peer review process is included in the supplemental information.

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T细胞受体库的机器学习分析确定了自反应性的序列特征
T细胞受体(TCR)决定了对主要组织相容性复合体(MHC)呈递的外源肽和自身肽的特异性和亲和力。尽管TCR与自身pmhc相互作用的强度影响T细胞功能,但确定预测T细胞命运的TCR序列特征一直具有挑战性。为了区分tcr与初始CD4+ T细胞的模式,我们使用42只小鼠的数据来训练机器学习(ML)算法,该算法可以识别TCRβ序列集之间的群体水平差异。该方法表明,弱自反应性T细胞群富集了较长的CDR3β区域和酸性氨基酸。我们使用具有固定TCRβ序列的逆转录小鼠来测试我们的自我反应性的ML预测。将我们的分析推断到独立的数据集,我们预测调节性T细胞的自我反应性高,而T细胞对慢性感染的自我反应性略有降低。我们的分析表明,TCR曲目多样性和自我反应性之间存在潜在的权衡关系。本文的透明同行评议过程记录包含在补充信息中。
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来源期刊
Cell Systems
Cell Systems Medicine-Pathology and Forensic Medicine
CiteScore
16.50
自引率
1.10%
发文量
84
审稿时长
42 days
期刊介绍: In 2015, Cell Systems was founded as a platform within Cell Press to showcase innovative research in systems biology. Our primary goal is to investigate complex biological phenomena that cannot be simply explained by basic mathematical principles. While the physical sciences have long successfully tackled such challenges, we have discovered that our most impactful publications often employ quantitative, inference-based methodologies borrowed from the fields of physics, engineering, mathematics, and computer science. We are committed to providing a home for elegant research that addresses fundamental questions in systems biology.
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