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

Eaves, Lauren A.; Bulka, Catherine M.; Rager, Julia E.; Gardner, Amaree J.; Galusha, Aubrey L.; Parsons, Patrick J.; Fry, Rebecca C.

doi:10.1016/j.chemosphere.2022.137469

Cited by 4 publications

(5 citation statements)

References 55 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Similar to our results, the current literature also shows Pb as having robust effects on transcriptomic changes. Specifically, in a prior study by our research group investigating the effects of metal and metal mixture exposures on differential gene expression, Pb had the greatest genomic response, though the majority of the genes demonstrated decreased expression [48]. In the present study, most of the Pb-associated proteins demonstrated increased expression among females and decreased expression among males.…”

Section: Discussionsupporting

confidence: 53%

“…Many factors influence protein expression, including mRNA abundance, and numerous factors control mRNA abundance, such as epigenetic modification and transcription factor abundance. Previous research performed by this research group examined the associations between placental gene expression networks and metal exposure in this cohort and found several key gene networks enriched for pathways related to Nuclear Factor Kappa-B (a master regulator of the immune system) [48]. Here, we analyzed proteins for their known roles as targets to various transcription factors.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Prenatal Metal Exposure Alters the Placental Proteome in a Sex-Dependent Manner in Extremely Low Gestational Age Newborns: Links to Gestational Age

Freedman,

Roell,

Engwall

et al. 2023

IJMS

Self Cite

View full text Add to dashboard Cite

Prenatal exposure to toxic metals is associated with altered placental function and adverse infant and child health outcomes. Adverse outcomes include those that are observed at the time of birth, such as low birthweight, as well as those that arise later in life, such as neurological impairment. It is often the case that these adverse outcomes show sex-specific responses in relation to toxicant exposures. While the precise molecular mechanisms linking in utero toxic metal exposures with later-in-life health are unknown, placental inflammation is posited to play a critical role. Here, we sought to understand whether in utero metal exposure is associated with alterations in the expression of the placental proteome by identifying metal associated proteins (MAPs). Within the Extremely Low Gestational Age Newborns (ELGAN) cohort (n = 230), placental and umbilical cord tissue samples were collected at birth. Arsenic (As), cadmium (Cd), lead (Pb), selenium (Se), and manganese (Mn) concentrations were measured in umbilical cord tissue samples via ICP-MS/MS. Protein expression was examined in placental samples using an LC-MS/MS-based, global, untargeted proteomics analysis measuring more than 3400 proteins. MAPs were then evaluated for associations with pregnancy and neonatal outcomes, including placental weight and gestational age. We hypothesized that metal levels would be positively associated with the altered expression of inflammation/immune-associated pathways and that sex-specific patterns of metal-associated placental protein expression would be observed. Sex-specific analyses identified 89 unique MAPs expressed in female placentas and 41 unique MAPs expressed in male placentas. Notably, many of the female-associated MAPs are known to be involved in immune-related processes, while the male-associated MAPs are associated with intracellular transport and cell localization. Further, several MAPs were significantly associated with gestational age in males and females and placental weight in males. These data highlight the linkage between prenatal metal exposure and an altered placental proteome, with implications for altering the trajectory of fetal development.

show abstract

Section: Discussionsupporting

confidence: 53%

Section: Discussionmentioning

confidence: 99%

Prenatal Metal Exposure Alters the Placental Proteome in a Sex-Dependent Manner in Extremely Low Gestational Age Newborns: Links to Gestational Age

Freedman,

Roell,

Engwall

et al. 2023

IJMS

Self Cite

View full text Add to dashboard Cite

show abstract

“…Yet human observational studies of gene expression have often failed to report on the treatment of missing data, despite its prevalence. When missing data are explicitly addressed in this context, researchers typically utilize complete case analyses (23)(24)(25), while single imputation is a less common alternative (6). Despite its advantages, we are unaware of any studies with transcriptomic outcomes that have utilized multiple imputation for missing covariate data.…”

Section: Discussionmentioning

confidence: 99%

“…In an experimental setting, studies may employ two-group analyses with no additional variables or utilize covariates for which collecting data is trivial (e.g., sequencing batch and sex). However, the cost of sequencing has decreased over time (4), and transcriptomic data are already becoming more common in large human observational studies where missing data is a prevailing concern (5,6). Therefore, guidelines for handling missing data in this context are critically needed to facilitate the integration of transcriptomic and epidemiologic approaches.…”

Section: Introductionmentioning

confidence: 99%

RNAseqCovarImpute: a multiple imputation procedure that outperforms complete case and single imputation differential expression analysis

Baker

Sathyanarayana

Szpiro

et al. 2023

Preprint

View full text Add to dashboard Cite

Missing covariate data is a common problem that has not been addressed in observational studies of gene expression. Here we present a multiple imputation (MI) method that accommodates high dimensional transcriptomic data by binning genes, creating separate MI datasets and differential expression models within each bin, and pooling results with Rubin's rules. Simulation studies using real and synthetic data show that this method outperforms complete case and single imputation analyses at uncovering true positive differentially expressed genes, limiting false discovery rates, and minimizing bias. This method is easily implemented via an R package, "RNAseqCovarImpute" that integrates with the limma-voom pipeline.

show abstract

“…Preterm birth in dichorionic-diamniotic twins could be attributed to various genetic or/and environmental factors, including genetic dissimilarity and differences in placental mass, placental insu ciency, umbilical venous diameter, and ow (22)(23)(24). On the contrary, monochorionic-diamniotic twins share more genetic resemblance as most of them are monozygosity pairs, and the particular challenges of preterm birth in monochorionic-diamniotic twins arise mainly from the shared placenta and the placental vascular anastomoses, which are almost universal (25).…”

Section: Introductionmentioning

confidence: 99%

Preterm birth affects the gut microbiota, metabolome and health outcomes of twins at 12 months of age: a case control study

Mei,

Hu,

Yang

et al. 2024

Preprint

View full text Add to dashboard Cite

Perinatal factors can influence gut microbiota, adversely impacting infant health outcomes. However, little is known about the combined effect of preterm birth and chorionicity on gut microbiota, metabolism, physical and neurobehavioral development for twin infants. In this study, we profiled and compared the gut microbial colonization of 350 twins aged 12 months. Twins were divided into four groups based on their gestational age at birth and chorionicity as dichorionic-diamniotic full-term birth group, dichorionic-diamniotic preterm-birth group, monochorionic-diamniotic full-term birth group, and monochorionic-diamniotic preterm birth group. Gut microbiota diversity and fecal metabolic alterations at 12 months old were determined by 16S rDNA sequencing and untargeted metabolomics, respectively. Wilcoxon's rank-sum tests were used to compare alpha diversity between the four groups. The general linear models were applied to identify microbiota species that were differentially abundant among the four groups and the health effects of gut microbiota on physical and neurobehavioral development conducted at 12 months of age. In addition, the twin-based ACE model was used to evaluate the contribution of genetic and environmental effects on the composition and function of the gut microbiota. We found that preterm birth and chorionicity dominated genetics in altering the composition of gut microbiota and concentration of metabolites over 12 months of age. The influence of genetic factors differed between preterm and full-term births. There were 16 gestational age and chorionicity specified gut microbiota genera and 285 group-specified metabolites. Association analysis filtered 7 microbiota genera and 19 metabolites associated with twins' physical and neurobehavioral development. Three metabolites, N-Oleoyl dopamine, Ecgonine, and Methyl jasmonate participated in the neuroactive ligand-receptor interaction pathway, tropane, piperidine, and pyridine alkaloid biosynthesis pathway, and alpha-Linolenic acid metabolism and biosynthesis of secondary metabolites, respectively. We concluded that preterm birth is associated with dysbiotic microbiota profiles and significant metabolic alterations, which may eventually influence physical and neurobehavioral development.

show abstract

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

Cited by 4 publications

References 55 publications

Prenatal Metal Exposure Alters the Placental Proteome in a Sex-Dependent Manner in Extremely Low Gestational Age Newborns: Links to Gestational Age

Prenatal Metal Exposure Alters the Placental Proteome in a Sex-Dependent Manner in Extremely Low Gestational Age Newborns: Links to Gestational Age

RNAseqCovarImpute: a multiple imputation procedure that outperforms complete case and single imputation differential expression analysis

Preterm birth affects the gut microbiota, metabolome and health outcomes of twins at 12 months of age: a case control study

Contact Info

Product

Resources

About