Multimodal metagenomic analysis reveals microbial InDels as superior biomarkers for pediatric Crohn’s disease

医学 基因组 克罗恩病 疾病 索引 计算生物学 生物信息学 内科学 遗传学 基因 生物 基因型 单核苷酸多态性
作者
Mengping Shen,Sheng Gao,Ruixin Zhu,Wei Wang,Wenxing Gao,Liwen Tao,Wanning Chen,Xinyue Zhu,Yuwei Yang,Tingjun Xu,Tingting Zhao,Na Jiao,Min Zhi,Lixin Zhu
出处
期刊:Journal of Crohn's and Colitis [Oxford University Press]
卷期号:19 (4) 被引量:1
标识
DOI:10.1093/ecco-jcc/jjaf039
摘要

Abstract Background and aims The gut microbiome is closely associated with pediatric Crohn’s disease (CD), while the multidimensional microbial signature and their capabilities for distinguishing pediatric CD are underexplored. This study aims to characterize the microbial alterations in pediatric CD and develop a robust classification model. Methods A total of 1175 fecal metagenomic sequencing samples, predominantly from 3 cohorts of pediatric CD patients, were re-analyzed from raw sequencing data using uniform process pipelines to obtain multidimensional microbial alterations in pediatric CD, including taxonomic profiles, functional profiles, and multi-type genetic variants. Random forest algorithms were used to construct classification models after comparing multiple machine learning algorithms. Results We found pediatric CD samples exhibited reduced microbial diversity and unique microbial characteristics. Pronounced abundance differences in 45 species and 1357 KEGG orthology genes. Particularly, Enterocloster bolteae emerged as a pivotal pediatric CD-associated species. Additionally, we identified a vast amount of microbial genetic variants linked to pediatric CD, including 192 structural variants, 1256 insertions/deletions (InDels), and 3567 single nucleotide variants, with a considerable portion of these variants located in non-genic regions. The InDel-based model outperformed other predictive models against multidimensional microbial signatures, achieving an area under the ROC curve (AUC) of 0.982. The robustness and disease specificity were further confirmed in an independent CD cohort (AUC = 0.996) and 5 other microbiome-associated pediatric cohorts. Conclusions Our study provided a comprehensive landscape of microbial alterations in pediatric CD and introduced a highly effective diagnostic model rooted in microbial InDels, which contributes to the development of noninvasive diagnostic tools and targeted therapies.
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