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Updated: May 23, 2025
Microbiota Analysis Using Two-step PCR and Next-generation 16S rRNA Gene Sequencing
Published on: October 15, 2019
Multimodal metagenomic analysis reveals microbial InDels as superior biomarkers for pediatric Crohn's disease
Mengping Shen1, Sheng Gao1, Ruixin Zhu1
1Putuo People's Hospital, School of Life Sciences and Technology, Tongji University, Shanghai, P. R. China.
Insights
Pediatric Crohn's disease (CD) shows unique gut microbiome changes. A new diagnostic model using microbial InDels accurately identifies pediatric CD, aiding noninvasive diagnosis and targeted therapies.
Area of Science:
- Microbiome research
- Pediatric gastroenterology
- Genomics
Background:
- The gut microbiome's role in pediatric Crohn's disease (CD) is recognized, but its full microbial signature and diagnostic potential remain unclear.
- Understanding multidimensional microbial alterations is crucial for pediatric CD diagnosis and management.
Purpose of the Study:
- To characterize comprehensive microbial alterations in pediatric CD patients.
- To develop and validate a robust classification model for diagnosing pediatric CD using microbial data.
Main Methods:
- Re-analysis of 1175 fecal metagenomic samples from pediatric CD cohorts using uniform pipelines.
- Characterization of taxonomic, functional, and genetic variant profiles of the gut microbiome.
- Development of machine learning models, including Random Forest, for classification.
Main Results:
- Pediatric CD samples displayed reduced microbial diversity and distinct microbial compositions, with significant differences in species and KEGG orthology genes.
- Enterocloster bolteae was identified as a key species associated with pediatric CD.
- A microbial insertion/deletion (InDel)-based model achieved high diagnostic accuracy (AUC 0.982–0.996) in distinguishing pediatric CD, outperforming other models.
Conclusions:
- This study presents a detailed microbial landscape of pediatric CD.
- A highly effective diagnostic model based on microbial InDels was developed, offering potential for noninvasive diagnostic tools.
- The findings support the development of novel therapeutic strategies for pediatric CD.
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.
