Marine pollutant exposures and human milk extracellular vesicle-microRNAs in a mother-infant cohort from the Faroe Islands

• Published in: Environment International
• Main Authors: Kupsco, Allison, Lee, Jenny Jyoung, Prada, Diddier, Valvi, Damaskini, Hu, Lisa, Petersen, Maria Skaalum, Coull, Brent A., Weihe, Pal, Grandjean, Philippe, Baccarelli, Andrea A.
• Format: Article in Journal/Newspaper
• Language: English
• Published: 2022
• Subjects: Extracellular vesicles; Mercury; microRNA; Perfluoroalkyl substances; Persistent organic pollutants; Faroe Islands
• Online Access: https://portal.findresearcher.sdu.dk/da/publications/f2d0970b-f904-4bc5-bcce-bd3f528c89ca; https://doi.org/10.1016/j.envint.2021.106986; https://findresearcher.sdu.dk/ws/files/196456176/1_s2.0_S0160412021006115_main.pdf

Background/Aims: Early life exposures to marine contaminants can adversely impact child health but modes of action are unclear. Human milk contains extracellular vesicles (EVs) that can transport biologically relevant cargo from mother to infant, including microRNAs (miRNAs), and may partly mediate the effects of pollutants on child health. However, the role of marine pollutants on miRNA expression in milk EVs is unexplored. Methods: We isolated EV RNA from 333 milk samples collected between 2 and 74 days postpartum from a Faroese birth cohort born 1997–2000 and sequenced 2083 miRNAs using a targeted library preparation method. We quantified five perfluoroalkyl substances (PFAS), pesticide metabolite p,p’-dichlorodiphenyldichloroethylene (DDE), and the sum of three major polychlorinated biphenyls (ΣPCBs) in maternal serum at 34 weeks of gestation and maternal hair total mercury (Hg) at birth. We used negative binomial regressions to estimate associations between individual pollutants and 418 reliably expressed EV-miRNAs adjusted for potential confounders. We performed sparse principal components (PCs) analysis to derive the first four components of the EV-miRNA data and examined associations between pollutants and PCs using Bayesian kernel machine regression (BKMR). Results: We observed no associations between pollutants and individual EV-miRNA expression after controlling the false discovery rate at 0.1. However, BKMR suggested that Hg was positively associated with PC1 and negatively associated with PC3, while ΣPCBs was negatively associated with PC3, and two PFAS were associated with PC4. Exploration of PC loadings followed by pathway analyses suggested that miRNAs in PC1 (miR-200b-3p, miR-664a-3p, miR-6738-5p, miR-429, miR-1236-5p, miR-4464, and miR-30b-5p) may be related to Hg neurotoxicity, while remaining PCs require further research. Conclusions: Our findings suggest that groups of milk EV-miRNAs may better serve as environmental biomarkers than individual miRNAs. Future studies are needed to elucidate ...