Tian Huidi, Wang Lei, Aiken Elizabeth, Ortega Robert Jervine V, Hardy Rachel, Placek Lindsey, Kozhaya Lina, Unutmaz Derya, Oh Julia, Yao Xudong
Department of Chemistry, University of Connecticut, Storrs, Connecticut 06269, United States.
The Jackson Laboratory, 10 Discovery Drive, Farmington, Connecticut 06032, United States.
bioRxiv. 2024 Jul 29:2024.07.29.605643. doi: 10.1101/2024.07.29.605643.
Disruptions in microbial metabolite interactions due to gut microbiome dysbiosis and metabolomic shifts may contribute to Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) and other immune-related conditions. The aryl hydrocarbon receptor (AhR), activated upon binding various tryptophan metabolites, modulates host immune responses. This study investigates whether the metabolic diversity-the concentration distribution-of bacterial indole pathway metabolites can differentiate bacterial strains and classify ME/CFS samples. A fast targeted liquid chromatography-parallel reaction monitoring method at a rate of 4 minutes per sample was developed for large-scale analysis. This method revealed significant metabolic differences in indole derivatives among strains cultured from human isolates. Principal component analysis identified two major components (PC1, 68.9%; PC2, 18.7%), accounting for 87.6% of the variance and distinguishing two distinct clusters. The metabolic difference between clusters was particularly evident in the relative contributions of indole-3-acrylate and indole-3-aldehyde. We further measured concentration distributions of indole derivatives in ME/CFS by analyzing fecal samples from 10 patients and 10 healthy controls using the fast targeted metabolomics method. An AdaBoost-LOOCV model achieved moderate classification success with a mean LOOCV accuracy of 0.65 (Control: precision of 0.67, recall of 0.60, F1-score of 0.63; ME/CFS: precision of 0.64, recall of 0.7000, F1-score of 0.67). These results suggest that the metabolic diversity of indole derivatives from tryptophan degradation, facilitated by the fast targeted metabolomics and machine learning, is a potential biomarker for differentiating bacterial strains and classifying ME/CFS samples. Mass spectrometry datasets are accessible at the National Metabolomics Data Repository (ST002308, DOI: 10.21228/M8G13Q; ST003344, DOI: 10.21228/M8RJ9N; ST003346, DOI: 10.21228/M8RJ9N).
由于肠道微生物群失调和代谢组学变化导致的微生物代谢物相互作用紊乱，可能会导致肌痛性脑脊髓炎/慢性疲劳综合征（ME/CFS）和其他免疫相关疾病。芳烃受体（AhR）在与各种色氨酸代谢物结合后被激活，可调节宿主免疫反应。本研究调查了细菌吲哚途径代谢物的代谢多样性——浓度分布——是否能够区分细菌菌株并对ME/CFS样本进行分类。开发了一种快速靶向液相色谱-平行反应监测方法，每个样本分析速度为4分钟，用于大规模分析。该方法揭示了从人类分离株培养的菌株之间吲哚衍生物存在显著的代谢差异。主成分分析确定了两个主要成分（PC1，68.9%；PC2，18.7%），占方差的87.6%，并区分出两个不同的簇。簇之间的代谢差异在吲哚-3-丙烯酸酯和吲哚-3-醛的相对贡献中尤为明显。我们进一步使用快速靶向代谢组学方法分析10名患者和10名健康对照的粪便样本，测量了ME/CFS中吲哚衍生物的浓度分布。一个AdaBoost-LOOCV模型取得了中等程度的分类成功，平均LOOCV准确率为0.65（对照组：精确率0.67，召回率0.60，F1分数0.63；ME/CFS组：精确率0.64，召回率0.7000，F1分数0.67）。这些结果表明，在快速靶向代谢组学和机器学习的助力下，色氨酸降解产生的吲哚衍生物的代谢多样性是区分细菌菌株和对ME/CFS样本进行分类的潜在生物标志物。质谱数据集可在国家代谢组学数据存储库获取（ST002308，DOI: 10.21228/M8G13Q；ST003344，DOI: 10.21228/M8RJ9N；ST003346，DOI: 10.21228/M8RJ9N）。
