Gut microbiota maturation and early behavioral and cognitive development

Abstract

The presence of gut microbiota-brain-axis has been widely reported. However, few studies have focused on uncovering the potential associations during a time-period that our brain and gut microbiota undergo rapid maturation. We evaluated the potential associations between characteristics of gut microbiota and cognition and temperament using an accelerated longitudinal design in typically developing children over 0-3 years of age. Specifically, we extracted gut microbiota characteristics at three scale levels: diversity measures, microbial networks, and subject-wise longitudinal trajectory features, shedding light on how associations between cognition/temperament and gut microbiota may differ at global (diversity), ecological (microbial networks) and subject-wise levels. Our findings illustrated that associations between gut microbiota and temperament/cognition varied with the analytical approaches and highlighted differential gut microbial features in association with cognition and temperament traits-diversity measures and microbial networks largely with cognition while subject-wise trajectories with temperament. In addition, Ruminococcus bromii exhibited significant associations with cognitions spanning over multiple subdomains. Finally, the associations of gut microbiota with temperament and cognition converge on the potential interplay of language ability and social behaviors and highlight the importance of age-appropriate gut microbiota on early cognition/temperament development.

Fig. 1

The distribution of numbers of fecal samples collected at different ages (A). The temporal behaviors of relative abundance of gut microbiota at phylum level (B). The temporal characteristics of gut microbiota diversity measures, including Shannon (C), Chao 1 (D), evenness (E) and beta diversity (F), respectively. A two-piece linear model with subject-wise random intercept was fitted to the trajectories of Shannon (C), Chao1 (D), and beta diversity (F). Using the smallest Akaike information criterion (AIC) as the selection criterion for the transition ages from “rapid” to “slow,” we found that the age transitions were 16 months old for Shannon and 15 months old for both Chao1 and beta diversity. Therefore, we defined 0–15 months as the rapidly changed phase of the three measures (blue dashed lines). We extracted subject-wise random intercept and slope of age effects for all subjects who had at least 2 visits during 0–15 months old using a linear mixed effect model (478 observations from 143 unique subjects). The random slopes of beta diversity in relation to ELC showing that a faster decreased beta diversity (negative slopes) at 12–15 months of age is associated with a higher ELC (G and H).

Fig. 2

Temporal characteristics of the relative abundance of the eight highest relative abundance species (upper panel) and the corresponding subject-wise trajectories of these eight species (lower panel) where different colors represent different subjects.

Fig. 3

The gut microbial network structure where each circle represents a species, colors represent different modules, and the lines indicate the connections among species (A). The relative abundance of species with age for each module are shown in (B) for modules 1–6, respectively. The mean interaction strength with age for each module are shown in (C) for modules 1–6, respectively. Finally, the heatmap showing the associations between the mean relative abundance and interaction strength with cognition and temperament is provided in (D). The symbols in D indicate adjusted p-values: “!”: p < 0.001; “$”: 0.005 <  = p < 0.01; “*”: 0.01 <  = p < 0.05 and the color bar indicates effect sizes.

Fig. 4

The degrees for all species (A) where the red lines indicate the 25 highest degrees. The effects of simulated targeted and random attacked on the global efficiency of the microbial network (B). The associations between MSEL, IBQ-R, and ECBQ with the Microbiome network features are shown in (C). The symbols in C indicate adjusted p-values: “!”: p < 0.001; “#”: 0.001 <  = p < 0.005; “$”: 0.005 <  = p < 0.01; “*”: 0.01 <  = p < 0.05 and the color bar indicates effective sizes). The x axis in C indicate the 25 degree hubs, 10 strongest co-occurrence pairs, and 10 strongest co-exclusion pairs.

Fig. 5

The subject-wise relative abundance trajectories of Bacteroides_u_s (A) and the three random effects (B) where brown, blue and green lines represent high abundance (HA), low abundance (LA) and probability of transition to high abundance (P_HA), respectively. The clustering results of the Bacteroides_u_s where four clusters (C-F) were obtained representing subjects with different patterns of trajectories. Significant differences of ECBQ motor activation (G) and sadness (H) were observed among subjects in the four clusters. The associations between MSEL, IBQ-R, and ECBQ and the cluster results are shown in (I). Different shapes represent the statistical results for clustering analysis: squares: p < 0.001; circles: 0.001 < p < 0.005; triangles: 0.005 < p < 0.01; diamonds: 0.01 < p < 0.05.

Fig. 6

The associations between MSEL, IBQ-R, and ECBQ and with three subject-wise random effects, including P_HA (A), HA (B) and LA (C), respectively. Different symbols indicate the statistical results for the association analyses with the three subject-wise random effects: “!”: p < 0.001; “#”: 0.001 < p < 0.005; “$”: 0.005 < p < 0.01; “*”: 0.01 < p < 0.05. The color bar indicates log(p-values).