Taxonomic and metabolic development of the human gut microbiome across life stages: a worldwide metagenomic investigation

Abstract

The human gut microbiota is a dynamic community of microorganisms that undergo variable changes over the entire life span. To thoroughly investigate the possible fluctuations of the microbiota throughout human life, we performed a pooled analysis of healthy fecal samples across different age groups covering the entire human life span. Our study integrated data from 79 publicly available studies and new stool samples from an Italian cohort, i.e., the Parma Microbiota project, resulting in 6,653 samples processed through the shotgun metagenomic approach. This approach has allowed species-level taxonomic reconstruction of the gut microbiota and investigation of its metabolic potential across the human life span. From a taxonomic point of view, our findings confirmed and detailed at species-level accuracy that the microbial richness of the gut microbiota gradually increases in the first stage of life, becoming relatively stable during adolescence. Moreover, the analysis identified the potential core microbiota representative of distinct age groups, revealing age-related bacterial patterns and the continuous rearrangement of the microbiota in terms of relative abundances across the life span rather than the acquisition and loss of taxa. Furthermore, the shotgun approach provided insights into the functional contribution of the human gut microbiome. The metagenomic analysis revealed functional age-related differences, particularly in carbohydrate and fiber metabolism, suggesting a co-evolution of the microbiome assembly with diet. Additionally, we identified correlations between vitamin synthesis, such as thiamine and niacin, and early life, suggesting a potential role of the microbiome in human physiology, in particular in the functions of the host's nervous and immune systems.

Importance: In this study, we provided comprehensive insights into the dynamic nature of the human gut microbiota across the human life span. In detail, we analyzed a large data set based on a shotgun metagenomic approach, combining public data sets and new samples from the Parma Microbiota project and obtaining a detailed overview of the possible relationship between gut microbiota development and aging. Our findings confirmed the main stages in microbial richness development and revealed specific core microbiota associated with different age stages. Moreover, the shotgun metagenomic approach allowed the disentangling of the functional changes in the microbiome across the human life span, particularly in diet-related metabolism, which is probably correlated to bacterial co-evolution with dietary habits. Notably, our study also uncovered positive correlations with vitamin synthesis in early life, suggesting a possible impact of the microbiota on human physiology.

Keywords: human gut microbiome; human life span; human microbiota; shotgun metagenomics.

Fig 1

Evaluation of microbial biodiversity. Panel (a) displays the whisker plot representing the species richness identified by subjects of each age group. The x‐axis represents the different age groups, while the y‐axis indicates the number of species. The 25th and 75th percentiles determine the boxes. The whiskers are determined by the 1.5 interquartile range (IQR). The line in the boxes represents the median, while the square represents the average. Different lowercase letters indicate significant differences at P <  0.05 calculated through pairwise Kruskal-Wallis test analyses. In detail, groups with the same letter are not significantly different from each other, while groups with different letters are considered statistically distinct. Panel (b) reports the whisker plot representing the alpha diversity calculated through the Shannon index identified by subjects of each age group. The x‐axis represents the different age groups, while the y‐axis indicates the Shannon index. The 25th and 75th percentiles determine the boxes. The whiskers are determined by the 1.5 IQR. The line in the boxes represents the median, while the square represents the average. Different lowercase letters indicate significant differences at P <  0.05 calculated through pairwise Kruskal-Wallis test analyses. In detail, groups with the same letter are not significantly different from each other, while groups with different letters are considered statistically distinct. Panel (c) shows the pooled analysis of PCoA, subdivided by age groups. The black rows indicate the bacterial species with significant fittings (envit fit P < 0.005).

Fig 2

Correlation analysis between the bacterial species and the age of the individuals included in pooled analysis. In detail, only the bacterial taxa that showed a significant Spearman’s rank correlation coefficient and significantly higher relative abundance in at least one of the age groups calculated through ANOVA test analysis and multiple comparison analyses Tukey’s HSD test were reported.

Fig 3

Correlation analysis between the bacterial species and enzymatic reaction identified in pooled analysis. The red color indicated negative correlations, while the green color represented positive correlations. Only the main key enzymes involved in the human diet resulted in statistical significance; Spearman’s rank correlation coefficient were reported.

Fig 4

Multivariate analysis through MaAsLin2 software based on bacterial species, age groups, and geographical origin. Significant positive correlations are reported in red, while significant negative correlations are reported in blue.