Placental genomic and epigenomic signatures associated with infant birth weight highlight mechanisms involved in collagen and growth factor signaling

Payton, Alexis; Clark, Jeliyah; Eaves, Lauren A.; Santos, Hudson P.; Smeester, Lisa; Bangma, Jacqueline T.; Fry, Rebecca C.; Rager, Julia E.

doi:10.1016/j.reprotox.2020.07.007

Cited by 17 publications

(11 citation statements)

References 57 publications

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“…This objective is met by presenting basic data organization methods using an example environmentally relevant human cohort dataset. This cohort was generated by creating data distributions randomly pulled from our previous publications ( Rager et al, 2014a ; Clark et al, 2019 ; Payton et al, 2020 ; Clark et al, 2021 ), resulting in a bespoke dataset for these training purposes. Data include subject information/demographic data, as well as environmental exposure data, focusing on metals concentrations in drinking water and human urine samples.…”

Section: Resultsmentioning

confidence: 99%

Development of the InTelligence And Machine LEarning (TAME) Toolkit for Introductory Data Science, Chemical-Biological Analyses, Predictive Modeling, and Database Mining for Environmental Health Research

et al. 2022

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Research in environmental health is becoming increasingly reliant upon data science and computational methods that can more efficiently extract information from complex datasets. Data science and computational methods can be leveraged to better identify relationships between exposures to stressors in the environment and human disease outcomes, representing critical information needed to protect and improve global public health. Still, there remains a critical gap surrounding the training of researchers on these in silico methods. We aimed to address this gap by developing the inTelligence And Machine lEarning (TAME) Toolkit, promoting trainee-driven data generation, management, and analysis methods to “TAME” data in environmental health studies. Training modules were developed to provide applications-driven examples of data organization and analysis methods that can be used to address environmental health questions. Target audiences for these modules include students, post-baccalaureate and post-doctorate trainees, and professionals that are interested in expanding their skillset to include recent advances in data analysis methods relevant to environmental health, toxicology, exposure science, epidemiology, and bioinformatics/cheminformatics. Modules were developed by study coauthors using annotated script and were organized into three chapters within a GitHub Bookdown site. The first chapter of modules focuses on introductory data science, which includes the following topics: setting up R/RStudio and coding in the R environment; data organization basics; finding and visualizing data trends; high-dimensional data visualizations; and Findability, Accessibility, Interoperability, and Reusability (FAIR) data management practices. The second chapter of modules incorporates chemical-biological analyses and predictive modeling, spanning the following methods: dose-response modeling; machine learning and predictive modeling; mixtures analyses; -omics analyses; toxicokinetic modeling; and read-across toxicity predictions. The last chapter of modules was organized to provide examples on environmental health database mining and integration, including chemical exposure, health outcome, and environmental justice indicators. Training modules and associated data are publicly available online (https://uncsrp.github.io/Data-Analysis-Training-Modules/). Together, this resource provides unique opportunities to obtain introductory-level training on current data analysis methods applicable to 21st century science and environmental health.

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Section: Resultsmentioning

confidence: 99%

Development of the InTelligence And Machine LEarning (TAME) Toolkit for Introductory Data Science, Chemical-Biological Analyses, Predictive Modeling, and Database Mining for Environmental Health Research

et al. 2022

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“…The miRNAs and mRNAs with BH p < 0.10 were identified as significantly differentially expression in association with an exposure condition, resulting in lists of differentially expressed miRNAs and differentially expressed genes. These statistical filters notably parallel previously implemented criteria when analyzing miRNA and mRNA expression signature alterations ( Clark et al 2021 ; Eaves et al 2020b ; Payton et al 2020 ).…”

Section: Methodsmentioning

confidence: 93%

“…The miRNA and mRNA sequencing data were processed separately through similar pipelines in R Software (v4.0.3). A background filter was applied to the count data by requiring that transcripts be expressed at signals greater than the overall median in at least 20% of the samples, paralleling our previously published methods ( Eaves et al 2020a ; Payton et al 2020 ; Ring et al 2021 ). This filter resulted in a final panel of 155 miRNAs across the plasma EV samples and 17,356 mRNAs across the tissue samples.…”

Section: Methodsmentioning

confidence: 99%

“…Here, lists of miRNAs were queried against the Ingenuity Pathway Analysis knowledgebase (Ingenuity Systems®, Redwood City, CA) for relationships to disease signatures. The significance of enriched disease signatures was calculated using a modified Fischer’s Exact test, with p-values adjusted for multiple testing (BH procedure), resulting in the identification of miRNA disease signatures that were overrepresented amongst the miRNAs with altered expression associated with exposure (BH p-value < 0.10) ( IPA 2019 ; Payton et al 2020 ). MiRNAs with published roles in cardiovascular disease were identified from these recognized signatures, and all miRNA-disease signature relationships were reported for completeness.…”

Section: Methodsmentioning

confidence: 99%

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Wildfires and extracellular vesicles: Exosomal MicroRNAs as mediators of cross-tissue cardiopulmonary responses to biomass smoke

Carberry

Koval

Payton

et al. 2022

Environment International

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“…A low pre-pregnancy BMI in male placentas has been correlated with a distinct mRNA profile, targeted by miR-4057 and miR-128-1-5p and involving nutrient metabolism and angiogenesis pathways ( 76 ). Another study by Payton et al , identified a set of placental mRNAs and miRNAs associated with the birth weight of extremely preterm infants born to adolescent and adult mothers ( 77 ).…”

Section: Exosomes In Pregnancy Complicationsmentioning

confidence: 99%

Exosomes and exosomal non‑coding RNAs throughout human gestation (Review)

et al. 2022

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In recent years, research on exosomes and their content has been intensive, which has revealed their important role in cell-to-cell communication, and has implicated exosomal biomolecules in a broad spectrum of physiological processes, as well as in the pathogenesis of various diseases. Pregnancy and its normal progression rely highly on the efficient communication between the mother and the fetus, mainly mediated by the placenta. Recent studies have established the placenta as an important source of circulating exosomes and have demonstrated that exosome release into the maternal circulation gradually increases during pregnancy, starting from six weeks of gestation. This orchestrates maternal-fetal crosstalk, including maternal immune tolerance and pregnancy-associated metabolic adaptations. Furthermore, an increased number of secreted exosomes, along with altered patterns of exosomal non-coding RNAs (ncRNAs), especially microRNAs and long non-coding RNAs (lncRNAs), have been observed in a number of pregnancy complications, such as gestational diabetes mellitus and preeclampsia. The early detection of exosomes and specific exosomal ncRNAs in various biological fluids during pregnancy highlights them as promising candidate biomarkers for the diagnosis, prognosis and treatment of numerous pregnancy disorders in adolescents and adults. The present review aimed to provide insight into the current knowledge regarding the potential, only partially elucidated, role of exosomes and exosomal cargo in the regulation and progression of normal pregnancy, as well as their potential dysregulation and contribution to pathological pregnancy situations. Contents1. Introduction 2. Exosomes 3. Exosomes in normal pregnancy 4. Exosomes in pregnancy complications 5. Clinical applications 6. Conclusions and future perspectives

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Placental genomic and epigenomic signatures associated with infant birth weight highlight mechanisms involved in collagen and growth factor signaling

Cited by 17 publications

References 57 publications

Development of the InTelligence And Machine LEarning (TAME) Toolkit for Introductory Data Science, Chemical-Biological Analyses, Predictive Modeling, and Database Mining for Environmental Health Research

Development of the InTelligence And Machine LEarning (TAME) Toolkit for Introductory Data Science, Chemical-Biological Analyses, Predictive Modeling, and Database Mining for Environmental Health Research

Wildfires and extracellular vesicles: Exosomal MicroRNAs as mediators of cross-tissue cardiopulmonary responses to biomass smoke

Exosomes and exosomal non‑coding RNAs throughout human gestation (Review)

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