Metal mixtures modeling identifies birth weight-associated gene networks in the placentas of children born extremely preterm

Eaves LA, Bulka CM, Rager JE, Gardner AJ, Galusha AL, Parsons PJ, O’Shea TM, Fry RC.

Chemosphere. 2023 Feb;313:137469. doi: 10.1016/j.chemosphere.2022.137469. Epub 2022 Dec 6. PMID: 36493891.

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Abstract

Prenatal exposure to toxic metals is linked to numerous adverse birth and later-in-life outcomes. These outcomes are tied to disrupted biological processes in fetal-derived tissues including the placenta and umbilical cord yet the precise pathways are understudied in these target tissues. We set out to examine the relationship between metal concentrations in umbilical cord and altered gene expression networks in placental tissue. These novel relationships were investigated in a subset of the Extremely Low Gestational Age Newborn (ELGAN) cohort (n = 226). Prenatal exposure to 11 metals/metalloids was measured using inductively coupled plasma tandem-mass spectrometry (ICP-MS/MS) in cord tissue, ensuring passage through the placental barrier. RNA-sequencing was used to quantify >37,000 mRNA transcripts. Differentially expressed genes (DEGs) were identified with respect to each metal. Weighted gene co-expression analysis identified gene networks modulated by metals. Two innovative mixtures modeling techniques, namely principal components analysis and quantile-based g-computation, were employed to identify genes/gene networks associated with multi-metal exposure. Individually, lead was associated with the strongest genomic response of 191 DEGs. Joint lead and cadmium exposure was related to 657 DEGs, including DNA Methyl Transferase 1 (DNMT1). These genes were enriched for the Eukaryotic Initiation Factor 2 (EIF2) pathway. Four gene networks, each containing genes within a Nuclear Factor kappa-light-chain-enhancer of Activated B Cells (NF-kB)-mediated network, were significantly increased in average expression level in relation to increases in all metal concentrations. All four of these metal mixture-associated gene networks were negatively correlated with important predictors of neonatal health including birth weight, placenta weight, and fetal growth. Bringing together novel methodologies from epidemiological mixtures analyses and toxicogenomics, applied to a unique cohort of extremely preterm children, the present study highlighted critical genes and pathways in the placenta dysregulated by prenatal metal mixtures. These represent potential mechanisms underlying the developmental origins of metal-induced disease.

ELGAN