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

IF 8.7
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
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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.

多模态宏基因组分析揭示微生物指数是儿童克罗恩病的优越生物标志物
背景和目的:肠道微生物组与儿童克罗恩病(CD)密切相关,而多维微生物特征及其区分儿童CD的能力尚未得到充分探索。本研究旨在描述儿科乳糜泻的微生物变化,并建立一个可靠的分类模型。方法:使用统一的流程管道,对主要来自3组儿科CD患者的1,175份粪便宏基因组测序样本进行重新分析,以获得儿科CD的多维微生物变化,包括分类谱、功能谱和多类型遗传变异。通过对多种机器学习算法的比较,采用随机森林算法构建分类模型。结果:我们发现儿科乳糜泻样品显示出微生物多样性减少和独特的微生物特征。45个物种和1357个KO基因的丰度差异显著。特别是,bolteae肠闭菌作为一种关键的儿科cd相关物种出现。此外,我们确定了大量与儿科CD相关的微生物遗传变异,包括192个结构变异,1,256个插入/缺失(InDels)和3,567个单核苷酸变异,其中相当一部分变异发生在非基因区域。基于indel的模型在多维微生物特征方面优于其他预测模型,AUC为0.982。在独立的CD队列(AUC=0.996)和其他5个微生物组相关的儿科队列中进一步证实了稳健性和疾病特异性。结论:我们的研究提供了儿科乳糜泻微生物变化的全面图景,并引入了一种基于微生物模型的高效诊断模型,这有助于非侵入性诊断工具和靶向治疗的发展。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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