Fecal Microbiota Nutrient Utilization Potential Suggests Mucins as Drivers for Initial Gut Colonization of Mother-Child-Shared Bacteria

Abstract

The nutritional drivers for mother-child sharing of bacteria and the corresponding longitudinal trajectory of the infant gut microbiota development are not yet completely settled. We therefore aimed to characterize the mother-child sharing and the inferred nutritional utilization potential for the gut microbiota from a large unselected cohort. We analyzed in depth gut microbiota in 100 mother-child pairs enrolled antenatally from the general population-based Preventing Atopic Dermatitis and Allergies in Children (PreventADALL) cohort. Fecal samples collected at gestational week 18 for mothers and at birth (meconium), 3, 6, and 12 months for infants were analyzed by reduced metagenome sequencing to determine metagenome size and taxonomic composition. The nutrient utilization potential was determined based on the Virtual Metabolic Human (VMH, www.vmh.life) database. The estimated median metagenome size was ∼150 million base pairs (bp) for mothers and ∼20 million bp at birth for the children. Longitudinal analyses revealed mother-child sharing (P < 0.05, chi-square test) from birth up to 6 months for 3 prevalent Bacteroides species (prevalence, >25% for all age groups). In a multivariate analysis of variance (ANOVA), the mother-child-shared Bacteroides were associated with vaginal delivery (1.7% explained variance, P = 0.0001). Both vaginal delivery and mother-child sharing were associated with host-derived mucins as nutrient sources. The age-related increase in metagenome size corresponded to an increased diversity in nutrient utilization, with dietary polysaccharides as the main age-related factor. Our results support host-derived mucins as potential selection means for mother-child sharing of initial colonizers, while the age-related increase in diversity was associated with dietary polysaccharides.IMPORTANCE The initial bacterial colonization of human infants is crucial for lifelong health. Understanding the factors driving this colonization will therefore be of great importance. Here, we used a novel high-taxonomic-resolution approach to deduce the nutrient utilization potential of the infant gut microbiota in a large longitudinal mother-child cohort. We found mucins as potential selection means for the initial colonization of mother-child-shared bacteria, while the transition to a more adult-like microbiota was associated with dietary polysaccharide utilization potential. This knowledge will be important for a future understanding of the importance of diet in shaping the gut microbiota composition and development during infancy.

Keywords: infant gut microbiota.

FIG 1

Diversity analyses based on RMS data. (A) Estimated metagenome size based on the number of unique fragments (assumption of one RMS fragment per 2,000 bp). (B) Fraction of fragments shared with mothers for each infant age group. (C) Portion of fragments assigned to human DNA for each infant age group. Each gray dot represents one sample, while the blue dots represent the mean values, and the error bars represent the 95% confidence interval.

FIG 2

Correlation network across samples based on RMS fragment distribution. Each dot represents one sample, while the lines indicate samples with positive FDR-corrected (P < 0.05) Spearman correlations. Samples represent newborn (meconium) and 3/6/12 months of infant age, while mothers’ samples were from midpregnancy.

FIG 3

Relative gut microbiota composition for the different age groups. The relative abundances are presented with color codes for the different age groups, meconium (newborns), 3 months, 6 months, and 12 months of infant age, and mothers.

FIG 4

Deduced carbon sources for the different age groups. The bar chart represents the relative abundances of potential carbon sources derived from the VMH database, based on the bacterial species. The carbon sources are divided into the five groups simple and complex sugars, putative mucus, amino acids, and miscellaneous (Misc). They are represented with color codes for the different age groups.

FIG 5

Association between age groups and microbiota. The association between age groups and microbiota was determined by ASCA-ANOVA analyses for the first three principal components. (A to C) The first principal component (A) explained 64.8% of the variance, the second component (B) explained 23.8% of the variance, and the third component (C) explained 8.4% of the variance. The top panels represent the scores of the samples, while the bottom panels represent the loading (importance) of the different taxonomic groups.

FIG 6

Association between age groups and carbon sources. The association between age groups and microbiota was determined by ASCA-ANOVA. Results from the two first principal components are shown in panels A and B, respectively. For both components, the top panels represent the score of the samples while the lower panels represent the loading (importance) of the different taxonomic groups for the 10 variables with the highest and the lowest loadings. Abbreviations are defined in Table S2.

FIG 7

Mother-child-associated bacteria. Orange bars represent the prevalence in infants whose mothers have positive samples divided by the total prevalence across all mother-child pairs for each age group. The blue graph represents the overall prevalence for each age group. Asterisks represent significance levels for chi-square tests; *, P < 0.05; **, P < 0.01; ***, P < 0.001; ***, P < 0.0001.

FIG 8

Correlation between nutrients important for mother-child association and delivery mode (x axis). The importance of the nutrients was determined by ASCA-ANOVA analyses. The figure illustrates loadings on the y axis and mother-child associations (MC) and delivery mode (DL) for the different nutrients.

FIG 9

Regression coefficients connected to partial least-squares (PLS) discriminant analysis for breastfeeding at 6 months. (A and B) Coeffects for (A) species composition and (B) carbon sources. The arrows represent the direction of the associations. For the carbon sources, abbreviations are provided in Table S2.