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Updated: Jan 15, 2026

Microbiota Analysis Using Two-step PCR and Next-generation 16S rRNA Gene Sequencing
Published on: October 15, 2019
Optimal Algorithm for Gut Microbiota Differentiation: A Pilot Study of Adolescents with Different Body Weights
Natalia Belkova1, Elizaveta Klimenko1, Natalya Semenova2
1Institute of Epidemiology and Microbiology, Federal State Public Scientific Institution "Scientific Center for Family Health and Human Reproduction Problems", 664003 Irkutsk, Russia.
Background:
The enterotype concept allows the differentiation of gut microbiota in relation to individual characteristics and is determined by the genetics and external stressors of the host. It was previously shown that not all clustering methods can accurately identify such enterotypes. Therefore, this pilot study primarily aimed to compare different algorithms for enterotype definition and to estimate the factors that correlate with the differentiation of the gut microbiota in adolescents with different body weights.
Methods:
Adolescents with normal body weight (N) and obesity (O) (aged 11-17 years) were included in this pilot study. Based on the analysis of the V3-V4 variable regions of the 16S ribosomal RNA gene amplicon libraries, the main enterotypes of the gut microbiota of adolescents were characterized using three approaches (E-typing A, B, and C) according to the bacterial taxa that were chosen for differentiation. For sample clustering, we used Bray-Curtis, Jensen-Shannon divergence, and weighted and unweighted UniFrac distance metrics. Clustering was assessed using the silhouette index. Meanwhile, the Kruskal-Wallis test was used to determine the relationship between enterotype and biochemical parameters.
Results:
The O and N groups comprised 18 and 22 adolescents, respectively, and, according to anthropometric data, differed significantly only in weight and body mass index (BMI). The linear discriminant analysis effect size (LEfSe) plot showed that the presence of minor and rare phylotypes in the gut microbiota differed between the two groups of adolescents. The distribution of individual samples based on the principal coordinates analysis (PCoA) showed that the gut microbiomes in the adolescents were not grouped in the N or O groups but were distributed according to the composition of the main bacterial taxa. We assessed the contribution of the Bacteroides, Prevotella, Subdoligranulum, and Ruminococcus phylotypes to the microbiota of the adolescents in the two groups. The Subdoligranulum enterotype was significantly more represented in the N group than in the O group when the E-typing A approach to enterotyping was applied. Pairwise comparisons were performed with corrections for multiple testing between the biochemical parameter levels of the different enterotypes. Bilirubin levels were lower in adolescents with the gut microbiota enterotype Ruminococcus-Subdoligranulum than in those with the enterotype Bacteroides when the E-typing B approach was used for differentiation.
Conclusions:
This pilot study comprised a small group of adolescents with normal body weight and obesity; we identified Bacteroides as the main enterotype, regardless of body weight. A stable microbial community is formed in the gut during adolescence, which determines its stratification by enterotype.

