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Updated: Dec 14, 2025
Microbiota Analysis Using Two-step PCR and Next-generation 16S rRNA Gene Sequencing
Published on: October 15, 2019
Gut Microbiota in T1DM-Onset Pediatric Patients: Machine-Learning Algorithms to Classify Microorganisms as Disease
Roberto Biassoni1, Eddi Di Marco1, Margherita Squillario2
1Molecular Diagnostics, Analysis Laboratory, IRCCS Istituto Giannina Gaslini, Genoa, Italy.
Insights
Type 1 diabetes in children is linked to distinct gut microbial patterns. Specific bacteria like Bacteroides stercoris are more abundant, while others like Bacteroides vulgatus are less common, impacting metabolism.
Area of Science:
- Microbiome research
- Pediatric endocrinology
- Metabolic disease
Background:
- Type 1 diabetes (T1D) is an autoimmune disease affecting children.
- The gut microbiome plays a role in immune system development and metabolic regulation.
- Understanding T1D-associated gut microbial changes is crucial for potential therapeutic strategies.
Purpose of the Study:
- To identify the unique gut microbial fingerprint in pediatric patients with T1D.
- To compare the gut microbiome composition of children with T1D at onset versus healthy children.
Main Methods:
- 16S ribosomal RNA gene sequencing was used to analyze the microbiome of 31 children with T1D and 25 healthy children.
- Machine-learning and metagenome functional analyses were employed to identify significant microbial taxa and metabolic pathways.
Main Results:
- Children with T1D exhibited higher levels of Bacteroides stercoris, Bacteroides fragilis, Bifidobacterium bifidum, Gammaproteobacteria, Holdemania, and Synergistetes.
- Conversely, Bacteroides vulgatus, Parasutterella, Lactobacillus, and Turicibacter were less abundant in T1D patients.
- Predicted metabolic pathways associated with T1D included carbon metabolism, sugar metabolism, and iron metabolism.
- Clinical factors like BMI, autoantibodies, glycemia, HbA1c, and age at onset correlated with specific microbial clusters.
Conclusions:
- The study confirms the distinct gut microbial composition in pediatric T1D patients, highlighting the importance of Bacteroides stercoris and Synergistetes.
- These findings underscore the value of multi-region sequencing and diverse analytical approaches for microbiome studies.
Aims:
The purpose of this work is to find the gut microbial fingerprinting of pediatric patients with type 1 diabetes.
Methods:
The microbiome of 31 children with type 1 diabetes at onset and of 25 healthy children was determined using multiple polymorphic regions of the 16S ribosomal RNA. We performed machine-learning analyses and metagenome functional analysis to identify significant taxa and their metabolic pathways content.
Results:
Compared with healthy controls, patients showed a significantly higher relative abundance of the following most important taxa: Bacteroides stercoris, Bacteroides fragilis, Bacteroides intestinalis, Bifidobacterium bifidum, Gammaproteobacteria and its descendants, Holdemania, and Synergistetes and its descendants. On the contrary, the relative abundance of Bacteroides vulgatus, Deltaproteobacteria and its descendants, Parasutterella and the Lactobacillus, Turicibacter genera were significantly lower in patients with respect to healthy controls. The predicted metabolic pathway more associated with type 1 diabetes patients concerns "carbon metabolism," sugar and iron metabolisms in particular. Among the clinical variables considered, standardized body mass index, anti-insulin autoantibodies, glycemia, hemoglobin A1c, Tanner stage, and age at onset emerged as most significant positively or negatively correlated with specific clusters of taxa.
Conclusions:
The relative abundance and supervised analyses confirmed the importance of B stercoris in type 1 diabetes patients at onset and showed a relevant role of Synergistetes and its descendants in patients with respect to healthy controls. In general the robustness and coherence of the showed results underline the relevance of studying the microbioma using multiple polymorphic regions, different types of analysis, and different approaches within each analysis.
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