Predicting the role of the human gut microbiome in type 1 diabetes using machine-learning methods

生物 肠道菌群 微生物群 小桶 计算生物学 生物标志物 人体微生物群 遗传学 生物信息学 转录组 基因 生物化学 基因表达
作者
Xiaowei Liu,Hanlin Li,Cai-Yi Ma,Tianyu Shi,Tianyu Wang,Dan Yan,Hua Tang,Hao Lin,Kejun Deng
出处
期刊:Briefings in Functional Genomics [Oxford University Press]
卷期号:23 (4): 464-474 被引量:7
标识
DOI:10.1093/bfgp/elae004
摘要

Abstract Gut microbes is a crucial factor in the pathogenesis of type 1 diabetes (T1D). However, it is still unclear which gut microbiota are the key factors affecting T1D and their influence on the development and progression of the disease. To fill these knowledge gaps, we constructed a model to find biomarker from gut microbiota in patients with T1D. We first identified microbial markers using Linear discriminant analysis Effect Size (LEfSe) and random forest (RF) methods. Furthermore, by constructing co-occurrence networks for gut microbes in T1D, we aimed to reveal all gut microbial interactions as well as major beneficial and pathogenic bacteria in healthy populations and type 1 diabetic patients. Finally, PICRUST2 was used to predict Kyoto Encyclopedia of Genes and Genomes (KEGG) functional pathways and KO gene levels of microbial markers to investigate the biological role. Our study revealed that 21 identified microbial genera are important biomarker for T1D. Their AUC values are 0.962 and 0.745 on discovery set and validation set. Functional analysis showed that 10 microbial genera were significantly positively associated with D-arginine and D-ornithine metabolism, spliceosome in transcription, steroid hormone biosynthesis and glycosaminoglycan degradation. These genera were significantly negatively correlated with steroid biosynthesis, cyanoamino acid metabolism and drug metabolism. The other 11 genera displayed an inverse correlation. In summary, our research identified a comprehensive set of T1D gut biomarkers with universal applicability and have revealed the biological consequences of alterations in gut microbiota and their interplay. These findings offer significant prospects for individualized management and treatment of T1D.
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