Gastrointestinal, vaginal, nasopharyngeal, and breast milk microbiota profiles and breast milk metabolomic changes in Gambian infants over the first two months of lactation: A prospective cohort study

Abstract

Microbiota composition in breast milk affects intestinal and respiratory microbiota colonization and the mucosal immune system's development in infants. The metabolomic content of breast milk is thought to interact with the microbiota and may influence developing infant immunity. One hundred seven Gambian mothers and their healthy, vaginally delivered, exclusively breastfed infants were included in our study. We analyzed 32 breast milk samples, 51 maternal rectovaginal swabs and 30 infants' rectal swabs at birth. We also analyzed 9 breast milk samples and 18 infants' nasopharyngeal swabs 60 days post-delivery. We used 16S rRNA gene sequencing to determine the microbiota composition. Metabolomic profiling analysis was performed on colostrum and mature breast milk samples using a multiplatform approach combining 1-H Nuclear Magnetic Resonance Spectroscopy and Gas Chromatography-Mass Spectrometry. Bacterial communities were distinct in composition and diversity across different sample types. Breast milk composition changed over the first 60 days of lactation. α-1,4- and α-1,3-fucosylated human milk oligosaccharides, and other 33 key metabolites in breast milk (monosaccharides, sugar alcohols and fatty acids) increased between birth and day 60 of life. This study's results indicate that infant gut and respiratory microbiota are unique bacterial communities, distinct from maternal gut and breast milk, respectively. Breast milk microbiota composition and metabolomic profile change throughout lactation. These changes may contribute to the infant's immunological, metabolic, and neurological development and could consist the basis for future interventions to correct disrupted early life microbial colonization.

Figure 1.

Alpha diversity of maternal and infant microbiomes across different body sites, determined using (A) observed species, (B) Shannon index, or (C) Chao1 index, (D) beta diversity of maternal and infant microbiomes across different body sites, based on NMDS analysis (stress = 0.11): NMDS1 v NMDS2, (E) NMDS2 v NMDS3, (F) maternal rectovaginal swabs versus infant rectal swabs at birth from mother/infant pairs, and (G) maternal rectovaginal swabs versus maternal colostrum at birth from the same participants. Statistics: Shapiro–Wilk normality test was performed. Kruskal-Wallis rank-sum test and pairwise Wilcoxon rank-sum test were performed to assess significance between groups. Multiple testing corrections were performed using the Benjamini-Hochburg procedure. I_NPS_60: Infant nasopharyngeal swabs day 60; I_RS_0: Infant rectal swabs at birth; M_BM_GM_0: Breast milk at birth (colostrum); M_BM_GM_60: Breast milk on D60 (mature breast milk); M_RVS_0: Maternal rectovaginal swabs at birth. NMDS = non-metric multidimensional scaling.

Figure 2.

Beta diversity of maternal and infant microbiomes across different body sites, based on non-metric multidimensional scaling (NMDS) analysis (using the R vegan metaMDS function) with significant species overlaid (using the vegan envfit function, P < .05). The arrows represent species with significant correlation with the community structure and are coloured by direction. Previously described notable taxa are labeled. I_NPS_60: Infant nasopharyngeal swabs day 60; I_RS_0: Infant rectal swabs at birth; M_BM_GM_0: Breast milk at birth (colostrum); M_BM_GM_60: Breast milk on D60 (mature breast milk); M_RVS_0: Maternal rectovaginal swabs at birth.

Figure 3.

(A) Relative abundance of genera, with greater than 10% relative abundance in at least one sample for each group. (B) Relative abundance of Staphylococcus and Streptococcus for each Group. I_NPS_60: Infant nasopharyngeal swabs day 60; I_RS_0: Infant rectal swabs at birth; M_BM_GM_0: Breast milk at birth (colostrum); M_BM_GM_60: Breast milk on day 60 (mature breast milk); M_RVS_0: Maternal rectovaginal swabs at birth.

Figure 4.

1H-NMR RM-MCCV-PLS scores plot model comparing Breast milk samples at D0 (Ciano dots) with D60 of life (red crosses). The top part of the panel gives the Kernel Density Estimate (KDE) of each group’s predicted scores. (A) The bottom part shows the predicted scores (Tpred) from MCCV for each sample. (B) Fragment of the average 600 MHz 1H-NMR breast milk spectrum to visualized some identified labeled metabolites. (C) Manhattan plot showing -log10(q) × sign of regression coefficient (β) of the RM MCCV–PLS model for the 16,000 spectral variables. Red peaks represent the variables that significantly increased over time (D60), and blue peaks represent the variables that significantly decreased over time (D0). (D) GC-MS RM-MCCV-PLS scores plot model comparing Breast milk samples at D0 (Ciano dots) with D60 of life (red crosses). The top part of the panel gives the KDE of each group’s predicted scores. The bottom part shows the predicted scores (Tpred) from MCCV for each sample. (E) Manhattan plot showing -log10(q) × sign of regression coefficient (β) of the RM MCCV–PLS model for the 16,000 spectral variables. Red dots represent the variables (metabolites) that significantly increased over time (D60, and blue dots represent the variables that significantly decreased over time (D0). A P-value was calculated for each variable. P-values were adjusted for multiple testing using the Storey-Tibshirani False Discovery Rate (FDR, q-value). KDE = Kernel Density Estimate, RM MCCV–PLS = repeated measures Monte Carlo cross-validated partial least squares analysis.