Callery Emma L, Morais Camilo L M, Taylor Jemma V, Challen Kirsty, Rowbottom Anthony W
Department of Immunology, Lancashire Teaching Hospitals NHS Foundation, Preston PR2 9HT, UK.
Institute of Chemistry, Federal University of Rio Grande do Norte, Natal 59072-970, Brazil.
Diagnostics (Basel). 2024 Oct 19;14(20):2330. doi: 10.3390/diagnostics14202330.
The difference in the immune response to severe acute respiratory syndrome coro-navirus 2 (SARS-CoV-2) in patients with mild versus severe disease remains poorly understood. Recent scientific advances have recognised the vital role of both B cells and T cells; however, many questions remain unanswered, particularly for T cell responses. T cells are essential for helping the generation of SARS-CoV-2 antibody responses but have also been recognised in their own right as a major factor influencing COVID-19 disease outcomes. The examination of T cell receptor (TCR) family differences over a 12-month period in patients with varying COVID-19 disease severity is crucial for understanding T cell responses to SARS-CoV-2.
We applied a machine learning approach to analyse TCR vb family responses in COVID-19 patients ( = 151) across multiple timepoints and disease severities alongside SARS-CoV-2 infection-naïve (healthy control) individ-uals ( = 62).
Blood samples from hospital in-patients with moderate, severe, or critical disease could be classified with an accuracy of 94%. Furthermore, we identified significant variances in TCR vb family specificities between disease and control subgroups.
Our findings suggest advantageous and disadvantageous TCR repertoire patterns in relation to disease severity. Following validation in larger cohorts, our methodology may be useful in detecting protective immunity and the assessment of long-term outcomes, particularly as we begin to unravel the immunological mechanisms leading to post-COVID complications.
对于轻度与重度疾病患者对严重急性呼吸综合征冠状病毒2(SARS-CoV-2)的免疫反应差异,我们仍知之甚少。最近的科学进展已经认识到B细胞和T细胞的重要作用;然而,许多问题仍未得到解答,尤其是关于T细胞反应的问题。T细胞对于帮助产生SARS-CoV-2抗体反应至关重要,但它们本身也被认为是影响COVID-19疾病结局的一个主要因素。在12个月的时间里,对不同COVID-19疾病严重程度患者的T细胞受体(TCR)家族差异进行检测,对于理解T细胞对SARS-CoV-2的反应至关重要。
我们应用机器学习方法,分析了151例COVID-19患者在多个时间点和疾病严重程度下的TCR vb家族反应,并与未感染SARS-CoV-2的(健康对照)个体(62例)进行了比较。
对患有中度、重度或危重症的住院患者的血样进行分类,准确率可达94%。此外,我们还确定了疾病亚组和对照亚组之间TCR vb家族特异性的显著差异。
我们的研究结果表明,与疾病严重程度相关的TCR库模式既有优势也有劣势。在更大规模的队列中进行验证后,我们的方法可能有助于检测保护性免疫和评估长期预后,特别是在我们开始揭示导致COVID-19后并发症的免疫机制时。