Department of Health Data Science, Institute of Population Health, University of Liverpool, Liverpool L69 3GF, UK.
Translational Gastroenterology Unit, Nuffield Department of Medicine, University of Oxford, Oxford OX3 9DU, UK.
Biomolecules. 2023 Nov 25;13(12):1707. doi: 10.3390/biom13121707.
Coeliac disease (CeD) is a T-cell mediated enteropathy triggered by dietary gluten which remains substantially under-diagnosed around the world. The diagnostic gold-standard requires histological assessment of intestinal biopsies taken at endoscopy while consuming a gluten-containing diet. However, there is a lack of concordance between pathologists in histological assessment, and both endoscopy and gluten challenge are burdensome and unpleasant for patients. Identification of gluten-specific T-cell receptors (TCRs) in the TCR repertoire could provide a less subjective diagnostic test, and potentially remove the need to consume gluten. We review published gluten-specific TCR sequences, and develop an interpretable machine learning model to investigate their diagnostic potential. To investigate this, we sequenced the TCR repertoires of mucosal CD4 T cells from 20 patients with and without CeD. These data were used as a training dataset to develop the model, then an independently published dataset of 20 patients was used as the testing dataset. We determined that this model has a training accuracy of 100% and testing accuracy of 80% for the diagnosis of CeD, including in patients on a gluten-free diet (GFD). We identified 20 CD4 TCR sequences with the highest diagnostic potential for CeD. The sequences identified here have the potential to provide an objective diagnostic test for CeD, which does not require the consumption of gluten.
乳糜泻（CeD）是一种由膳食麸质引发的 T 细胞介导的肠病，在全球范围内仍存在大量漏诊。诊断的金标准需要在摄入含麸质饮食的情况下通过内镜检查对肠活检进行组织学评估。然而，病理学家在组织学评估方面缺乏一致性，内镜检查和麸质挑战对患者来说既繁琐又不愉快。在 TCR 谱中鉴定出麸质特异性 T 细胞受体（TCR）可能提供一种不那么主观的诊断测试，并可能消除摄入麸质的需要。我们回顾了已发表的麸质特异性 TCR 序列，并开发了一种可解释的机器学习模型来研究它们的诊断潜力。为了研究这一点，我们对 20 名有和没有 CeD 的患者的黏膜 CD4 T 细胞的 TCR 谱进行了测序。这些数据被用作训练数据集来开发模型，然后使用另一个独立发表的 20 名患者的数据集作为测试数据集。我们确定该模型对 CeD 的诊断具有 100%的训练准确性和 80%的测试准确性，包括在接受无麸质饮食（GFD）的患者中。我们确定了 20 个对 CeD 具有最高诊断潜力的 CD4 TCR 序列。这里鉴定的序列有可能为 CeD 提供一种不需要摄入麸质的客观诊断测试。
