The dynamics of the human infant gut microbiome in development and in progression toward type 1 diabetes

Abstract

Colonization of the fetal and infant gut microbiome results in dynamic changes in diversity, which can impact disease susceptibility. To examine the relationship between human gut microbiome dynamics throughout infancy and type 1 diabetes (T1D), we examined a cohort of 33 infants genetically predisposed to T1D. Modeling trajectories of microbial abundances through infancy revealed a subset of microbial relationships shared across most subjects. Although strain composition of a given species was highly variable between individuals, it was stable within individuals throughout infancy. Metabolic composition and metabolic pathway abundance remained constant across time. A marked drop in alpha-diversity was observed in T1D progressors in the time window between seroconversion and T1D diagnosis, accompanied by spikes in inflammation-favoring organisms, gene functions, and serum and stool metabolites. This work identifies trends in the development of the human infant gut microbiome along with specific alterations that precede T1D onset and distinguish T1D progressors from nonprogressors.

Copyright © 2015 Elsevier Inc. All rights reserved.

Figures

Figure 1. A cohort to assess the dynamics of the developing human gut microbiota in infancy

Individuals are represented in rows and each point is a stool sample. The size of the points represents the number of serum autoantibodies (0–5) that were positive at the time of the sample collection. See also Figure S1.

Figure 2. Gut microbial taxonomies shift dramatically whereas microbial metabolites and metabolic pathways remain relatively stable throughout infant development

(A) Principal coordinates analysis on the unweighted UniFrac distances between samples based on 16S sequencing. Samples are colored by age at stool collection. (B) Alpha-diversity using the QIIME ‘observed species’ metric on 16S sequencing. (C) Principal coordinates analysis on stool metabolomics data. (D) Shannon’s diversity measured on stool metabolomics data. (E) Bars indicate relative abundances of KEGG metabolic modules: A Aminoacyl tRNA; B Arginine and proline metabolism; C Aromatic amino acid metabolism; D Branched chain amino acid metabolism; E Carbon fixation; F Central carbohydrate metabolism; G Cofactor and vitamin biosynthesis; H Cysteine and methionine metabolism; I Fatty acid metabolism; J Glycosaminoglycan metabolism; K Histidine metabolism; L Lipid metabolism; M Lipopolysaccharide metabolism; N Lysine metabolism; O Methane metabolism; P Nitrogen metabolism; Q Nucleotide sugar; R Other amino acid metabolism; S Other carbohydrate metabolism; T Polyamine biosynthesis; U Purine metabolism; V Pyrimidine metabolism; W Serine and threonine metabolism; X Sulfur metabolism; Y Terpenoid backbone biosynthesis. (F) A measure of evenness of KEGG metabolic modules.

Figure 3. Temporal dynamics of microbial taxonomies in infant gut development

Family-level network diagram of the correlation between clades in their trajectories across time, excluding individuals with T1D. Positive correlations are in blue, negative correlations are in red, and the line thickness is proportional to the strength of the correlation (cumulative CCREPE Z-statistic). The plots show the abundance of the indicated family as a smoothing spline across all healthy individuals with a 95% confidence interval (shaded region). See also Figure S2.

Figure 4. Bacterial strains are stably maintained in the infant gut throughout development

(A) Distance between samples between subjects (interindividual) and within subjects (intraindividual) based on MetaPhlAn cladespecific strain marker analysis. (B) Shown is the MetaPhlAn clade-specific strain marker profile for a single representative species (Bacteroides ovatus) for three separate individuals. Columns represent the 37 markers for this species, rows represent samples, and arrows indicate discordant markers between individuals 2 and 3, and indicate markers that undergo a change in abundance in individual 1. (C) The Jaccard index (fraction of shared OTUs) between pairs of samples from the same individual within the indicated time window (i.e. 1.5 indicates 0 to 1.5 months, 6 indicates 4.5 to 6 months). The Jaccard index is shown for all pairs of samples across all subjects. A power-law curve was fitted to the medians of the boxplots (line). The box represents the first and third quartiles and error bars indicate 95% confidence of median. See also Table S3.

Figure 5. The gut microbiota distinguishes disease status in T1D prior to diagnosis

(A) Plot of Chao1 alpha-diversity across time, represented as a smoothing spline with a 95% confidence interval (shaded region). The seroconversion window indicates the first and third quartiles for age at seroconversion for all seroconverted and T1D-diagnosed individuals, and the diagnosis window indicates the first quartile of time at T1D diagnosis (third quartile is 1,211 days). (B) Abundances of the significantly differentially abundant taxa between T1D versus non-converter and seroconverted individuals, including only samples between the seroconversion and diagnosis windows. FDR-corrected P-values (Q-values) were calculated using MaAsLin. The box represents the first and third quartiles; error bars indicate 95% confidence of median. (C) Plots of the relative abundance of representative species from shotgun metagenomics data, represented as a smoothing spline with a 95% confidence interval (shaded region). See also Figure S3 and Figure S5.

Figure 6. Gut microbial gene content and serum and gut metabolites are altered prior to T1D onset

(A) Abundances of the significantly differentially abundant KEGG modules between T1D versus seroconverted individuals, including only samples between the seroconversion and diagnosis windows. FDR-corrected P-values (Q-values) were calculated using MaAsLin. The box represents the first and third quartiles; error bars indicate 95% confidence of median. (B) Spearman correlations between serum triglycerides and the five most correlated genera, and (C) between the branched-chain amino acids and OTUs using a cutoff of P < 0.001. +, correlations with P < 0.05; *, correlations with Q < 0.05. “TG*” represents TG(14:0/18:1/18:1)+TG(16:0/16:1/18:1). (D) Spearman correlations between stool metabolites and lipids and the most correlated genera. Coefficients of the canonical variates including Ruminococcus, Veillonella, and correlated metabolites obtained using penalized canonical correlation analysis. See also Figure S4.