Department of Pediatrics, University of California San Diego, La Jolla, CA, USA.
Center for Microbiome Innovation, Jacobs School of Engineering, University of California San Diego, La Jolla, CA, USA.
ISME J. 2021 Nov;15(11):3399-3411. doi: 10.1038/s41396-021-01016-7. Epub 2021 Jun 2.
Graves' Disease is the most common organ-specific autoimmune disease and has been linked in small pilot studies to taxonomic markers within the gut microbiome. Important limitations of this work include small sample sizes and low-resolution taxonomic markers. Accordingly, we studied 162 gut microbiomes of mild and severe Graves' disease (GD) patients and healthy controls. Taxonomic and functional analyses based on metagenome-assembled genomes (MAGs) and MAG-annotated genes, together with predicted metabolic functions and metabolite profiles, revealed a well-defined network of MAGs, genes and clinical indexes separating healthy from GD subjects. A supervised classification model identified a combination of biomarkers including microbial species, MAGs, genes and SNPs, with predictive power superior to models from any single biomarker type (AUC = 0.98). Global, cross-disease multi-cohort analysis of gut microbiomes revealed high specificity of these GD biomarkers, notably discriminating against Parkinson's Disease, and suggesting that non-invasive stool-based diagnostics will be useful for these diseases.
格雷夫斯病是最常见的器官特异性自身免疫性疾病，在小型试点研究中与肠道微生物组中的分类标记物有关。这项工作的重要局限性包括样本量小和分类标记物分辨率低。因此，我们研究了 162 名轻度和重度格雷夫斯病（GD）患者和健康对照者的肠道微生物组。基于宏基因组组装基因组（MAG）和 MAG 注释基因的分类和功能分析，以及预测的代谢功能和代谢物谱，揭示了一个定义明确的 MAGs、基因和临床指标网络，将健康个体与 GD 患者区分开来。有监督的分类模型确定了一组包括微生物物种、MAGs、基因和 SNPs 的生物标志物，其预测能力优于任何单一生物标志物类型的模型（AUC=0.98）。肠道微生物组的全球、跨疾病多队列分析显示，这些 GD 生物标志物具有很高的特异性，特别能区分帕金森病，并表明基于非侵入性粪便的诊断对这些疾病将是有用的。
