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Understanding the complex interplay of genetic and environmental factors in disease etiology and the role of gene-environment interactions (GEIs) across human development stages is important. We review the state of GEI research, including challenges in measuring environmental factors and advantages of GEI analysis in understanding disease mechanisms. We discuss the evolution of GEI studies from candidate gene-environment studies to genome-wide interaction studies (GWISs) and the role of multi-omics in mediating GEI effects. We review advancements in GEI analysis methods and the importance of large-scale datasets. We also address the translation of GEI findings into precision environmental health (PEH), showcasing real-world applications in healthcare and disease prevention. Additionally, we highlight societal considerations in GEI research, including environmental justice, the return of results to participants, and data privacy. Overall, we underscore the significance of GEI for disease prediction and prevention and advocate for integrating the exposome into PEH omics studies.
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Salud Ambiental , Interacción Gen-Ambiente , Medicina de Precisión , Humanos , Medicina de Precisión/métodos , Estudio de Asociación del Genoma Completo , Exposición a Riesgos Ambientales/efectos adversosRESUMEN
The Toxicological Prioritization Index (ToxPi) is a visual analysis and decision support tool for dimension reduction and visualization of high throughput, multi-dimensional feature data. ToxPi was originally developed for assessing the relative toxicity of multiple chemicals or stressors by synthesizing complex toxicological data to provide a single comprehensive view of the potential health effects. It continues to be used for profiling chemicals and has since been applied to other types of "sample" entities, including geospatial (e.g. county-level Covid-19 risk and sites of historical PFAS exposure) and other profiling applications. For any set of features (data collected on a set of sample entities), ToxPi integrates the data into a set of weighted slices that provide a visual profile and a score metric for comparison. This scoring system is highly dependent on user-provided feature weights, yet users often lack knowledge of how to define these feature weights. Common methods for predicting feature weights are generally unusable due to inappropriate statistical assumptions and lack of global distributional expectation. However, users often have an inherent understanding of expected results for a small subset of samples. For example, in chemical toxicity, prior knowledge can often place subsets of chemicals into categories of low, moderate or high toxicity (reference chemicals). Ordinal regression can be used to predict weights based on these response levels that are applicable to the entire feature set, analogous to using positive and negative controls to contextualize an empirical distribution. We propose a semi-supervised method utilizing ordinal regression to predict a set of feature weights that produces the best fit for the known response ("reference") data and subsequently fine-tunes the weights via a customized genetic algorithm. We conduct a simulation study to show when this method can improve the results of ordinal regression, allowing for accurate feature weight prediction and sample ranking in scenarios with minimal response data. To ground-truth the guided weight optimization, we test this method on published data to build a ToxPi model for comparison against expert-knowledge-driven weight assignments.
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Per- and poly-fluoroalkyl substances (PFAS) are synthetic chemicals widely used in commercial products. PFAS are a global concern due to their persistence in the environment and extensive associations with adverse health outcomes. While legacy PFAS have been extensively studied, many non-legacy PFAS lack sufficient toxicity information. In this study, we first analyzed the bioactivity of PFAS using Tox21 screening data surveying more than 75 assay endpoints (e.g., nuclear receptors, stress response, and metabolism) to understand the toxicity of non-legacy PFAS and investigate potential new targets of PFAS. From the Tox21 screening data analysis, we confirmed several known PFAS targets/pathways and identified several potential novel targets/pathways of PFAS. To confirm the effect of PFAS on these novel targets/pathways, we conducted several cell- and enzyme-based assays in the follow-up studies. We found PFAS inhibited cytochromes P450s (CYPs), especially CYP2C9 with IC50 values of < 1 µM. Considering PFAS affected other targets/pathways at > 10 µM, PFAS have a higher affinity to CYP2C9. This PFAS-CYP2C9 interaction was further investigated using molecular docking analysis. The result suggested that PFAS directly bind to the active sites of CYP2C9. These findings have important implications to understand the mechanism of PFAS action and toxicity.
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Sistema Enzimático del Citocromo P-450 , Fluorocarburos , Receptores Citoplasmáticos y Nucleares , Fluorocarburos/toxicidad , Sistema Enzimático del Citocromo P-450/metabolismo , Humanos , Receptores Citoplasmáticos y Nucleares/metabolismo , Estrés Fisiológico/efectos de los fármacos , Contaminantes Ambientales/toxicidad , Simulación del Acoplamiento MolecularRESUMEN
Per- and polyfluoroalkyl substances (PFAS) are environmental pollutants that have been associated with adverse health effects including liver damage, decreased vaccine responses, cancer, developmental toxicity, thyroid dysfunction, and elevated cholesterol. The specific molecular mechanisms impacted by PFAS exposure to cause these health effects remain poorly understood, however there is some evidence of lipid dysregulation. Thus, lipidomic studies that go beyond clinical triglyceride and cholesterol tests are greatly needed to investigate these perturbations. Here, we have utilized a platform coupling liquid chromatography, ion mobility spectrometry, and mass spectrometry (LC-IMS-MS) separations to simultaneously evaluate PFAS bioaccumulation and lipid metabolism disruptions. For the study, liver samples collected from C57BL/6 mice exposed to either of the emerging PFAS hexafluoropropylene oxide dimer acid (HFPO-DA or "GenX") or Nafion byproduct 2 (NBP2) were assessed. Sex-specific differences in PFAS accumulation and liver size were observed for both PFAS, in addition to disturbed hepatic liver lipidomic profiles. Interestingly, GenX resulted in less hepatic bioaccumulation than NBP2 yet gave a higher number of significantly altered lipids when compared to the control group, implying that the accumulation of substances in the liver may not be a reliable measure of the substance's capacity to disrupt the liver's natural metabolic processes. Specifically, phosphatidylglycerols, phosphatidylinositols, and various specific fatty acyls were greatly impacted, indicating alteration of inflammation, oxidative stress, and cellular signaling processes due to emerging PFAS exposure. Overall, these results provide valuable insight into the liver bioaccumulation and molecular mechanisms of GenX- and NBP2-induced hepatotoxicity.
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Ácidos Alcanesulfónicos , Polímeros de Fluorocarbono , Fluorocarburos , Propionatos , Masculino , Femenino , Ratones , Animales , Lipidómica , Ratones Endogámicos C57BL , Fluorocarburos/análisis , Hígado/metabolismo , Ácidos Alcanesulfónicos/metabolismoRESUMEN
The goal of lipidomic studies is to provide a broad characterization of cellular lipids present and changing in a sample of interest. Recent lipidomic research has significantly contributed to revealing the multifaceted roles that lipids play in fundamental cellular processes, including signaling, energy storage, and structural support. Furthermore, these findings have shed light on how lipids dynamically respond to various perturbations. Continued advancement in analytical techniques has also led to improved abilities to detect and identify novel lipid species, resulting in increasingly large datasets. Statistical analysis of these datasets can be challenging not only because of their vast size, but also because of the highly correlated data structure that exists due to many lipids belonging to the same metabolic or regulatory pathways. Interpretation of these lipidomic datasets is also hindered by a lack of current biological knowledge for the individual lipids. These limitations can therefore make lipidomic data analysis a daunting task. To address these difficulties and shed light on opportunities and also weaknesses in current tools, we have assembled this review. Here, we illustrate common statistical approaches for finding patterns in lipidomic datasets, including univariate hypothesis testing, unsupervised clustering, supervised classification modeling, and deep learning approaches. We then describe various bioinformatic tools often used to biologically contextualize results of interest. Overall, this review provides a framework for guiding lipidomic data analysis to promote a greater assessment of lipidomic results, while understanding potential advantages and weaknesses along the way.
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Lipidómica , Lípidos , Lípidos/análisis , Macrodatos , Metabolismo de los Lípidos , Biología Computacional/métodosRESUMEN
Although specific environmental chemical exposures, including flame retardants, are known risk factors for neurodevelopmental disorders (NDDs), direct experimental evidence linking specific chemicals to NDDs is limited. Studies focusing on the mechanisms by which the social processing systems are vulnerable to chemical exposure are underrepresented in the literature, even though social impairments are defining characteristics of many NDDs. We have repeatedly demonstrated that exposure to Firemaster 550 (FM 550), a prevalent flame retardant mixture used in foam-based furniture and infant products, can adversely impact a variety of behavioral endpoints. Our recent work in prairie voles (Microtus ochrogaster), a prosocial animal model, demonstrated that perinatal exposure to FM 550 sex specifically impacts socioemotional behavior. Here, we utilized a factor analysis approach on a battery of behavioral data from our prior study to extract underlying factors that potentially explain patterns within the FM 550 behavior data. This approach identified which aspects of the behavioral battery are most robust and informative, an outcome critical for future study designs. Pearson's correlation identified behavioral endpoints associated with distance and stranger interactions that were highly correlated across 5 behavioral tests. Using these behavioral endpoints, exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) extracted 2 factors that could explain the data: Activity (distance traveled endpoints) and Sociability (time spent with a novel conspecific). Exposure to FM 550 significantly decreased Activity and decreased Sociability. This factor analysis approach to behavioral data offers the advantages of modeling numerous measured variables and simplifying the data set by presenting the data in terms of common, overarching factors in terms of behavioral function.
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Retardadores de Llama , Organofosfatos , Animales , Embarazo , Femenino , Humanos , Conducta Animal , Exposición a Riesgos Ambientales/análisis , Conducta Social , Retardadores de Llama/farmacologíaRESUMEN
To estimate half-lives for novel fluoroethers, the GenX Exposure Study obtained two serum measurements for per- and polyfluoroalkyl substances (PFAS) for 44 participants of age 12-86 years from North Carolina, collected 5 and 11 months after fluoroether discharges into the drinking water source were controlled. The estimated half-lives for these compounds were 127 days (95% confidence interval (95% CI) = 86, 243 days) for perfluorotetraoxadecanoic acid (PFO4DA), 296 days for Nafion byproduct 2 (95% CI = 176, 924 days), and 379 days (95% CI = 199, 3870 days) for perfluoro-3,5,7,9,11-pentaoxadodecanoic acid (PFO5DoA). Using these estimates and the literature values, a model was built that predicted PFAS half-lives using structural properties. Three chemical properties predicted 55% of the variance of PFAS half-lives based on 15 PFAS. A model with only molecular weight predicted 69% of the variance. Some properties can predict the half-lives of PFAS, but a deeper understanding is needed. These fluoroethers had biological half-lives longer than published half-lives for PFHxA and PFHpA (30-60 days) but shorter than those for PFOA and PFOS (800-1200 days). These are the first and possibly only estimates of human elimination half-lives of these fluoroethers.
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Ácidos Alcanesulfónicos , Fluorocarburos , Contaminantes Químicos del Agua , Humanos , Niño , Adolescente , Adulto Joven , Adulto , Persona de Mediana Edad , Anciano , Anciano de 80 o más Años , Éteres , Contaminantes Químicos del Agua/análisis , Caprilatos , Fluorocarburos/análisisRESUMEN
Zebrafish have become an essential tool in screening for developmental neurotoxic chemicals and their molecular targets. The success of zebrafish as a screening model is partially due to their physical characteristics including their relatively simple nervous system, rapid development, experimental tractability, and genetic diversity combined with technical advantages that allow for the generation of large amounts of high-dimensional behavioral data. These data are complex and require advanced machine learning and statistical techniques to comprehensively analyze and capture spatiotemporal responses. To accomplish this goal, we have trained semi-supervised deep autoencoders using behavior data from unexposed larval zebrafish to extract quintessential "normal" behavior. Following training, our network was evaluated using data from larvae shown to have significant changes in behavior (using a traditional statistical framework) following exposure to toxicants that include nanomaterials, aromatics, per- and polyfluoroalkyl substances (PFAS), and other environmental contaminants. Further, our model identified new chemicals (Perfluoro-n-octadecanoic acid, 8-Chloroperfluorooctylphosphonic acid, and Nonafluoropentanamide) as capable of inducing abnormal behavior at multiple chemical-concentrations pairs not captured using distance moved alone. Leveraging this deep learning model will allow for better characterization of the different exposure-induced behavioral phenotypes, facilitate improved genetic and neurobehavioral analysis in mechanistic determination studies and provide a robust framework for analyzing complex behaviors found in higher-order model systems.
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The exposome, reflecting the range of environmental exposures individuals encounter throughout their life, can influence a variety of health outcomes and can play a role in how the environment impacts our genes. Telomeres, genetic structures regulating cell growth and senescence, are one pathway through which the exposome may impact health. Greenspace exposure, representing the amount of green areas in one's neighborhood, is one component of the exposome and has been associated with multiple health benefits. To investigate the potential link between greenspace exposure and telomere length, we analyzed data from the 1999-2001 National Health and Nutrition Examination Survey (NHANES) sample. Our study examined individual, risk, and contextual factors. We found that greater greenspace exposure in one's neighborhood was associated with longer telomere lengths when considering individual and risk factors, suggesting a positive effect of living in greener neighborhoods. However, this relationship became non-significant when contextual factors, such as air pollution and deprivation, were included in the analysis. These findings highlight a complex relationship between greenspace and telomere length, warranting further research to explore contextual factors in detail.
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Exposición a Riesgos Ambientales , Parques Recreativos , Humanos , Encuestas Nutricionales , Factores de Riesgo , TelómeroRESUMEN
Mass spectrometry imaging (MSI) has gained increasing popularity for tissue-based diagnostics due to its ability to identify and visualize molecular characteristics unique to different phenotypes within heterogeneous samples. Data from MSI experiments are often assessed and visualized using various supervised and unsupervised statistical approaches. However, these approaches tend to fall short in identifying and concisely visualizing subtle, phenotype-relevant molecular changes. To address these shortcomings, we developed aggregated molecular phenotype (AMP) scores. AMP scores are generated using an ensemble machine learning approach to first select features differentiating phenotypes, weight the features using logistic regression, and combine the weights and feature abundances. AMP scores are then scaled between 0 and 1, with lower values generally corresponding to class 1 phenotypes (typically control) and higher scores relating to class 2 phenotypes. AMP scores, therefore, allow the evaluation of multiple features simultaneously and showcase the degree to which these features correlate with various phenotypes. Due to the ensembled approach, AMP scores are able to overcome limitations associated with individual models, leading to high diagnostic accuracy and interpretability. Here, AMP score performance was evaluated using metabolomic data collected from desorption electrospray ionization MSI. Initial comparisons of cancerous human tissues to their normal or benign counterparts illustrated that AMP scores distinguished phenotypes with high accuracy, sensitivity, and specificity. Furthermore, when combined with spatial coordinates, AMP scores allow visualization of tissue sections in one map with distinguished phenotypic borders, highlighting their diagnostic utility.
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Diagnóstico por Imagen , Neoplasias , Humanos , Diagnóstico por Imagen/métodos , Espectrometría de Masa por Ionización de Electrospray/métodos , Neoplasias/diagnóstico por imagen , Metabolómica , Fenotipo , Espectrometría de Masa por Láser de Matriz Asistida de Ionización Desorción/métodos , Imagen Molecular/métodosRESUMEN
Mass spectrometry imaging (MSI) has gained increasing popularity for tissue-based diagnostics due to its ability to identify and visualize molecular characteristics unique to different phenotypes within heterogeneous samples. Data from MSI experiments are often visualized using single ion images and further analyzed using machine learning and multivariate statistics to identify m/z features of interest and create predictive models for phenotypic classification. However, often only a single molecule or m/z feature is visualized per ion image, and mainly categorical classifications are provided from the predictive models. As an alternative approach, we developed an aggregated molecular phenotype (AMP) scoring system. AMP scores are generated using an ensemble machine learning approach to first select features differentiating phenotypes, weight the features using logistic regression, and combine the weights and feature abundances. AMP scores are then scaled between 0 and 1, with lower values generally corresponding to class 1 phenotypes (typically control) and higher scores relating to class 2 phenotypes. AMP scores therefore allow the evaluation of multiple features simultaneously and showcase the degree to which these features correlate with various phenotypes, leading to high diagnostic accuracy and interpretability of predictive models. Here, AMP score performance was evaluated using metabolomic data collected from desorption electrospray ionization (DESI) MSI. Initial comparisons of cancerous human tissues to normal or benign counterparts illustrated that AMP scores distinguished phenotypes with high accuracy, sensitivity, and specificity. Furthermore, when combined with spatial coordinates, AMP scores allow visualization of tissue sections in one map with distinguished phenotypic borders, highlighting their diagnostic utility.
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Monoclonal antibody (mAb) therapy directed against CD20 is an important tool in the treatment of B cell disorders. However, variable patient response and acquired resistance remain important clinical challenges. To identify genetic factors that may influence sensitivity to treatment, the cytotoxic activity of three CD20 mAbs: rituximab; ofatumumab; and obinutuzumab, were screened in high-throughput assays using 680 ethnically diverse lymphoblastoid cell lines (LCLs) followed by a pharmacogenomic assessment. GWAS analysis identified several novel gene candidates. The most significant SNP, rs58600101, in the gene MKL1 displayed ethnic stratification, with the variant being significantly more prevalent in the African cohort and resulting in reduced transcript levels as measured by qPCR. Functional validation of MKL1 by shRNA-mediated knockdown of MKL1 resulted in a more resistant phenotype. Gene expression analysis identified the developmentally associated TGFB1I1 as the most significant gene associated with sensitivity. qPCR among a panel of sensitive and resistant LCLs revealed immunoglobulin class-switching as well as differences in the expression of B cell activation markers. Flow cytometry showed heterogeneity within some cell lines relative to surface Ig isotype with a shift to more IgG+ cells among the resistant lines. Pretreatment with prednisolone could partly reverse the resistant phenotype. Results suggest that the efficacy of anti-CD20 mAb therapy may be influenced by B cell developmental status as well as polymorphism in the MKL1 gene. A clinical benefit may be achieved by pretreatment with corticosteroids such as prednisolone followed by mAb therapy.
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Antineoplásicos , Pruebas de Farmacogenómica , Anticuerpos Monoclonales/farmacología , Anticuerpos Monoclonales/uso terapéutico , Anticuerpos Monoclonales/genética , Antígenos CD20/genética , Prednisolona , HumanosRESUMEN
Temozolomide (TMZ) chemotherapy is an important tool in the treatment of glioma brain tumors. However, variable patient response and chemo-resistance remain exceptionally challenging. Our previous genome-wide association study (GWAS) identified a suggestively significant association of SNP rs4470517 in the RYK (receptor-like kinase) gene with TMZ drug response. Functional validation of RYK using lymphocytes and glioma cell lines resulted in gene expression analysis indicating differences in expression status between genotypes of the cell lines and TMZ dose response. We conducted univariate and multivariate Cox regression analyses using publicly available TCGA and GEO datasets to investigate the impact of RYK gene expression status on glioma patient overall (OS) and progression-free survival (PFS). Our results indicated that in IDH mutant gliomas, RYK expression and tumor grade were significant predictors of survival. In IDH wildtype glioblastomas (GBM), MGMT status was the only significant predictor. Despite this result, we revealed a potential benefit of RYK expression in IDH wildtype GBM patients. We found that a combination of RYK expression and MGMT status could serve as an additional biomarker for improved survival. Overall, our findings suggest that RYK expression may serve as an important prognostic or predictor of TMZ response and survival for glioma patients.
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Oxaliplatin (OXAL) is a commonly used chemotherapy for treating colorectal cancer (CRC). A recent genome wide association study (GWAS) showed that a genetic variant (rs11006706) in the lncRNA gene MKX-AS1 and partnered sense gene MKX could impact the response of genetically varied cell lines to OXAL treatment. This study found that the expression levels of MKX-AS1 and MKX in lymphocytes (LCLs) and CRC cell lines differed between the rs11006706 genotypes, indicating that this gene pair could play a role in OXAL response. Further analysis of patient survival data from the Cancer Genome Atlas (TCGA) and other sources showed that patients with high MKX-AS1 expression status had significantly worse overall survival (HR = 3.2; 95%CI = (1.17-9); p = 0.024) compared to cases with low MKX-AS1 expression status. Alternatively, high MKX expression status had significantly better overall survival (HR = 0.22; 95%CI = (0.07-0.7); p = 0.01) compared to cases with low MKX expression status. These results suggest an association between MKX-AS1 and MKX expression status that could be useful as a prognostic marker of response to OXAL and potential patient outcomes in CRC.
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We aim to infer bioactivity of each chemical by assay endpoint combination, addressing sparsity of toxicology data. We propose a Bayesian hierarchical framework which borrows information across different chemicals and assay endpoints, facilitates out-of-sample prediction of activity for chemicals not yet assayed, quantifies uncertainty of predicted activity, and adjusts for multiplicity in hypothesis testing. Furthermore, this paper makes a novel attempt in toxicology to simultaneously model heteroscedastic errors and a nonparametric mean function, leading to a broader definition of activity whose need has been suggested by toxicologists. Real application identifies chemicals most likely active for neurodevelopmental disorders and obesity.
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Per- and polyfluoroalkyl substances (PFASs) are used in a multitude of processes and products, including nonstick coatings, food wrappers, and fire-fighting foams. These chemicals are environmentally-persistent, ubiquitous, and can be detected in the serum of 98% of Americans. Despite evidence that PFASs alter adaptive immunity, few studies have investigated their effects on innate immunity. The report here presents results of studies that investigated the impact of nine environmentally-relevant PFASs [e.g. perfluorooctanoic acid (PFOA), perfluorooctane sulfonic acid potassium salt (PFOS-K), perfluorononanoic acid (PFNA), perfluorohexanoic acid (PFHxA), perfluorohexane sulfonic acid (PFHxS), perfluorobutane sulfonic acid (PFBS), ammonium perfluoro(2-methyl-3-oxahexanoate) (GenX), 7H-perfluoro-4-methyl-3,6-dioxa-octane sulfonic acid (Nafion byproduct 2), and perfluoromethoxyacetic acid sodium salt (PFMOAA-Na)] on one component of the innate immune response, the neutrophil respiratory burst. The respiratory burst is a key innate immune process by which microbicidal reactive oxygen species (ROS) are rapidly induced by neutrophils in response to pathogens; defects in the respiratory burst can increase susceptibility to infection. The study here utilized larval zebrafish, a human neutrophil-like cell line, and primary human neutrophils to ascertain whether PFAS exposure inhibits ROS production in the respiratory burst. It was observed that exposure to PFHxA and GenX suppresses the respiratory burst in zebrafish larvae and a human neutrophil-like cell line. GenX also suppressed the respiratory burst in primary human neutrophils. This report is the first to demonstrate that these PFASs suppress neutrophil function and support the utility of employing zebrafish larvae and a human cell line as screening tools to identify chemicals that may suppress human immune function.
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Ácidos Alcanesulfónicos , Contaminantes Ambientales , Fluorocarburos , Animales , Humanos , Pez Cebra , Neutrófilos , Especies Reactivas de Oxígeno , Estallido Respiratorio , Fluorocarburos/toxicidad , Ácidos Alcanesulfónicos/toxicidadRESUMEN
Differentiation of multipotent mesenchymal stem cells (MSCs) into bone-forming osteoblasts requires strict coordination of transcriptional pathways. Aryl hydrocarbon receptor ligands, such as 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD), have been shown to alter osteoblast differentiation in vitro and bone formation in multiple developmental in vivo models. The goal of the present study was to establish a global transcriptomic landscape during early, intermediate, and apical stages of osteogenic differentiation in vitro in response to TCDD exposure. Human bone-derived mesenchymal stem cells (hBMSCs) were cultured in growth media (GM), osteogenic differentiation media (ODM), or ODM containing 10 nM TCDD (ODM + TCDD), thus enabling a comparison of the transcriptomic profiles of undifferentiated, differentiated, and differentiated-TCDD-exposed hBMSCs, respectively. In this test system, exposure to TCDD attenuated the differentiation of hBMSCs into osteoblasts as evidenced by reduced alkaline phosphatase activity and mineralization. At various timepoints, we observed altered expression of genes that play a role in the Wnt, fibroblast growth factor, bone morphogenetic protein/transforming growth factor beta developmental pathways, as well as pathways related to extracellular matrix organization and deposition. Reconstruction of gene regulatory networks with the interactive dynamic regulatory event miner (iDREM) analysis revealed modulation of transcription factors (TFs) including POLR3G, NR4A1, RDBP, GTF2B, POU2F2, and ZEB1, which may putatively influence osteoblast differentiation and the requisite deposition and mineralization of bone extracellular matrix. We demonstrate that the combination of RNA-Seq data in conjunction with the iDREM regulatory model captures the transcriptional dynamics underlying MSC differentiation under different conditions in vitro. Model predictions are consistent with existing knowledge and provide a new tool to identify novel pathways and TFs that may facilitate a better understanding of the osteoblast differentiation process, perturbation by exogenous agents, and potential intervention strategies targeting those specific pathways.
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Células Madre Mesenquimatosas , Dibenzodioxinas Policloradas , Humanos , Osteogénesis/genética , Dibenzodioxinas Policloradas/toxicidad , Diferenciación Celular , Factores de Crecimiento de Fibroblastos/metabolismo , Células Madre Mesenquimatosas/metabolismo , Células Cultivadas , OsteoblastosRESUMEN
Individuals within genetically diverse populations display broad susceptibility differences upon chemical exposures. Understanding the role of gene-environment interactions (GxE) in differential susceptibility to an expanding exposome is key to protecting public health. However, a chemical's potential to elicit GxE is often not considered during risk assessment. Previously, we've leveraged high-throughput zebrafish (Danio rerio) morphology screening data to reveal patterns of potential GxE effects. Here, using a population genetics framework, we apportioned variation in larval behavior and gene expression in three different PFHxA environments via mixed-effect modeling to assess significance of GxE term. We estimated the intraclass correlation (ICC) between full siblings from different families using one-way random-effects model. We found a significant GxE effect upon PFHxA exposure in larval behavior, and the ICC of behavioral responses in the PFHxA exposed population at the lower concentration was 43.7%, while that of the control population was 14.6%. Considering global gene expression data, a total of 3746 genes showed statistically significant GxE. By showing evidence that heritable genetics are directly affecting gene expression and behavioral susceptibility of individuals to PFHxA exposure, we demonstrate how standing genetic variation in a heterogeneous population such as ours can be leveraged to test for potential GxE.
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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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INTRODUCTION: Wildfires are a threat to public health world-wide that are growing in intensity and prevalence. The biological mechanisms that elicit wildfire-associated toxicity remain largely unknown. The potential involvement of cross-tissue communication via extracellular vesicles (EVs) is a new mechanism that has yet to be evaluated. METHODS: Female CD-1 mice were exposed to smoke condensate samples collected from the following biomass burn scenarios: flaming peat; smoldering peat; flaming red oak; and smoldering red oak, representing lab-based simulations of wildfire scenarios. Lung tissue, bronchoalveolar lavage fluid (BALF) samples, peripheral blood, and heart tissues were collected 4 and 24 h post-exposure. Exosome-enriched EVs were isolated from plasma, physically characterized, and profiled for microRNA (miRNA) expression. Pathway-level responses in the lung and heart were evaluated through RNA sequencing and pathway analyses. RESULTS: Markers of cardiopulmonary tissue injury and inflammation from BALF samples were significantly altered in response to exposures, with the greatest changes occurring from flaming biomass conditions. Plasma EV miRNAs relevant to cardiovascular disease showed exposure-induced expression alterations, including miR-150, miR-183, miR-223-3p, miR-30b, and miR-378a. Lung and heart mRNAs were identified with differential expression enriched for hypoxia and cell stress-related pathways. Flaming red oak exposure induced the greatest transcriptional response in the heart, a large portion of which were predicted as regulated by plasma EV miRNAs, including miRNAs known to regulate hypoxia-induced cardiovascular injury. Many of these miRNAs had published evidence supporting their transfer across tissues. A follow-up analysis of miR-30b showed that it was increased in expression in the heart of exposed mice in the absence of changes to its precursor molecular, pri-miR-30b, suggesting potential transfer from external sources (e.g., plasma). DISCUSSION: This study posits a potential mechanism through which wildfire exposures induce cardiopulmonary responses, highlighting the role of circulating plasma EVs in intercellular and systems-level communication between tissues.