Marín-Benesiu Fernando, Chica-Redecillas Lucia, Cuenca-López Sergio, Entrala-Bernal Carmen, Martín-Esteban Sara, Alvarez-Cubero Maria Jesús, Martínez-González Luis Javier
Department of Biochemistry, Molecular Biology III and Inmunology, Faculty of Medicine, University of Granada, Parque Tecnológico de la Salud, Granada 18016, Spain.
GENYO, Centre for Genomics and Oncological Research: Pfizer, University of Granada, Andalusian Regional Government, Parque Tecnológico de la Salud, Granada 18016, Spain.
Comput Struct Biotechnol J. 2025 May 14;27:2063-2073. doi: 10.1016/j.csbj.2025.05.016. eCollection 2025.
COVID-19 manifests a broad spectrum of clinical outcomes, from asymptomatic cases to severe disease. While several biomarkers have been proposed, comprehensive immunological analyses integrating mass cytometry (CyTOF) and T-cell receptor sequencing (TCRseq) data remain limited. In this study, we applied the Latent Class Model based on the Bayesian Information Criterion (LCM-BIC) algorithm to integrate immunophenotyping, including monocyte-macrophage counts from CyTOF, T-cell receptor repertorie data via TCRseq, SNPs data from (rs2285666), (rs469390), and (rs2070788), and symptomatology data from 61 Spanish COVID-19 patients (33 mild, 28 severe). We identified three novel and distinct patient clusters with significant differences in TCR diversity, monocyte subpopulations, and V allele usage and disease outcome. Cluster 1 was predominantly enriched in severe cases, characterized by unique immunological features. Deep learning analysis of TCR amino acid sequences further distinguished Cluster 1 from the others, identifying SARS-CoV-2-specific TCR sequences associated with disease severity. In addition, analysis of residue sensitivity of cluster 1 SARS-CoV-2-specific TCR sequences further identified conserved aminoacids located in key central positions of the complementarity-determining region 3. This study highlights the value of integrating immunophenotyping and genetic profiling to identify novel immunological markers and patterns, aiding in the stratification and management of COVID-19 patients based on their immune profiles and genetic background.
新冠病毒病（COVID-19）呈现出广泛的临床结果，从无症状病例到重症疾病。虽然已经提出了几种生物标志物，但整合质谱流式细胞术（CyTOF）和T细胞受体测序（TCRseq）数据的全面免疫分析仍然有限。在本研究中，我们应用基于贝叶斯信息准则的潜在类别模型（LCM-BIC）算法来整合免疫表型分析，包括来自CyTOF的单核细胞-巨噬细胞计数、通过TCRseq获得的T细胞受体库数据、来自（rs2285666）、（rs469390）和（rs2070788）的单核苷酸多态性（SNP）数据，以及来自61名西班牙COVID-19患者（33例轻症、28例重症）的症状学数据。我们识别出三个新的、不同的患者聚类，它们在TCR多样性、单核细胞亚群、V等位基因使用和疾病结局方面存在显著差异。聚类1主要富集于重症病例，具有独特的免疫特征。对TCR氨基酸序列的深度学习分析进一步将聚类1与其他聚类区分开来，识别出与疾病严重程度相关的SARS-CoV-2特异性TCR序列。此外，对聚类1的SARS-CoV-2特异性TCR序列的残基敏感性分析进一步确定了位于互补决定区3关键中心位置的保守氨基酸。本研究强调了整合免疫表型分析和基因谱分析以识别新的免疫标志物和模式的价值，有助于根据COVID-19患者的免疫谱和遗传背景对其进行分层和管理。
