Environmental toxicants in breast milk of Norwegian mothers and gut bacteria composition and metabolites in their infants at 1 month

Abstract

Background: Early disruption of the microbial community may influence lifelong health. Environmental toxicants can contaminate breast milk and the developing infant gut microbiome is directly exposed. We investigated whether environmental toxicants in breastmilk affect the composition and function of the infant gut microbiome at 1 month. We measured environmental toxicants in breastmilk, fecal short-chain fatty acids (SCFAs), and gut microbial composition from 16S rRNA gene amplicon sequencing using samples from 267 mother-child pairs in the Norwegian Microbiota Cohort (NoMIC). We tested 28 chemical exposures: polychlorinated biphenyls (PCBs), polybrominated flame retardants (PBDEs), per-and polyfluoroalkyl substances (PFASs), and organochlorine pesticides. We assessed chemical exposure and alpha diversity/SCFAs using elastic net regression modeling and generalized linear models, adjusting for confounders, and variation in beta diversity (UniFrac), taxa abundance (ANCOM), and predicted metagenomes (PiCRUSt) in low, medium, and high exposed groups. Results: PBDE-28 and the surfactant perfluorooctanesulfonic acid (PFOS) were associated with less microbiome diversity. Some sub-OTUs of Lactobacillus, an important genus in early life, were lower in abundance in samples from infants with relative "high" (> 80th percentile) vs. "low" (< 20th percentile) toxicant exposure in this cohort. Moreover, breast milk toxicants were associated with microbiome functionality, explaining up to 34% of variance in acetic and propionic SCFAs, essential signaling molecules. Per one standard deviation of exposure, PBDE-28 was associated with less propionic acid (− 24% [95% CI − 35% to − 14%] relative to the mean), and PCB-209 with less acetic acid (− 15% [95% CI − 29% to − 0.4%]). Conversely, PFOA and dioxin-like PCB-167 were associated with 61% (95% CI 35% to 87%) and 22% (95% CI 8% to 35%) more propionic and acetic acid, respectively. Conclusions: Environmental toxicant exposure may influence infant gut microbial function during a critical developmental window. Future studies are needed to replicate these novel findings and investigate whether this has any impact on child health.

Prenatal exposure to perfluoroalkyl substances modulates neonatal serum phospholipids, increasing risk of type 1 diabetes

“…LCA is also an agonist for membrane receptor TGR5 which mediates the release of glucagon-like peptide 1 (GLP-1) promoting insulin release from pancreatic β-cells 21,23 . Bile acid metabolism is also closely linked with gut microbiotal activity and, indeed, PFAS exposure has been shown to cause reduced microbiome diversity in infants 51 . In the DIABIMMUNE cohort, matching the sample studied here, the children that progressed to multiple islet autoantibodies later in life were found to have decreased alpha-diversity of gut microbiota.…”

Simultaneous determination of perfluoroalkyl substances and bile acids in human serum using ultra-high-performance liquid chromatography–tandem mass spectrometry

“…Additionally, our results appear to suggest increased microbial formation of LCA. Indeed, PFAS exposure has been shown to cause alteration in gut microbiota, with higher exposure to PFAS associated with reduced microbiome diversity [20]. On the other hand, the decreased fecal BA excretion, linked with PFAS exposure [11], may be nonexclusively due to inhibition of the biosynthesis of the BAs [8,21,10] or due to the increased uptake of BAs in the gut.…”

Optimization of Culturomics Strategy in Human Fecal Samples

“…only used for the study of intestinal microorganisms but has also been used to investigate microorganisms in the urinary system, vagina, mouth, and breast milk (Wein et al, 2015;Fenollar and Raoult, 2016;Si et al, 2017;Iszatt et al, 2019). Nevertheless, the optimal culture conditions for many bacteria (such as Proteus, Microaerobacteria, and Halophilicus) are very specific and need to be tested one by one, which multiplies the workload needed Day '0,' '3,' '6,' '15,' '27') 89.89 (Day '0,' '3,' '6,' '18,' '27') 88.33 (Day '0,' '3,' '6,' '9,' '27') 88.15 6 (Day '0,' '3,' '6,' '9,' '15,' '27') 92.63 (Day '0,' '3,' '6,' '15,' '24,' '27') 92.55 (Day '0,' '3,' '6,' '15,' '18,' '27') 92.07 7 (Day '0,' '3,' '6,' '9,' '15,' '24,' '27') 95.29 (Day '0,' '3,' '6,' '9,' '15,' '18,' '27') 94.81 (Day '0,' '3,' '6,…”