Matrix Softness-Mediated 3D Zebrafish Hepatocyte Modulates Response to Endocrine Disrupting Chemicals.
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ABSTRACT:
SUBMITTER: Sullivan KM
PROVIDER: S-EPMC8202163 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:The extent of our knowledge on the number of chemical compounds related to anthropogenic activities that can cause damage to the environment and to organisms is increasing. Endocrine disrupting chemicals (EDCs) are one group of potentially hazardous substances that include natural and synthetic chemicals and have the ability to mimic endogenous hormones, interfering with their biosynthesis, metabolism, and normal functions. Adverse effects associated with EDC exposure have been documented in aquatic biota and there is widespread interest in the characterization and understanding of their modes of action. Fish are considered one of the primary risk organisms for EDCs. Zebrafish (Danio rerio) are increasingly used as an animal model to study the effects of endocrine disruptors, due to their advantages compared to other model organisms. One approach to assess the toxicity of a compound is to identify those patterns of gene expression found in a tissue or organ exposed to particular classes of chemicals, through new technologies in genomics (toxicogenomics), such as microarrays or whole-genome sequencing. Application of these technologies permit the quantitative analysis of thousands of gene expression changes simultaneously in a single experiment and offer the opportunity to use transcript profiling as a tool to predict toxic outcomes of exposure to particular compounds. The application of toxicogenomic tools for identification of chemicals with endocrine disrupting capacity using the zebrafish model system is reviewed.
| S-EPMC5282603 | biostudies-literature
Project description:BackgroundDevelopment and application of transcriptomics-based gene classifiers for ecotoxicological applications lag far behind those of biomedical sciences. Many such classifiers discovered thus far lack vigorous statistical and experimental validations. A combination of genetic algorithm/support vector machines and genetic algorithm/K nearest neighbors was used in this study to search for classifiers of endocrine-disrupting chemicals (EDCs) in zebrafish. Searches were conducted on both tissue-specific and tissue-combined datasets, either across the entire transcriptome or within individual transcription factor (TF) networks previously linked to EDC effects. Candidate classifiers were evaluated by gene set enrichment analysis (GSEA) on both the original training data and a dedicated validation dataset.ResultsMulti-tissue dataset yielded no classifiers. Among the 19 chemical-tissue conditions evaluated, the transcriptome-wide searches yielded classifiers for six of them, each having approximately 20 to 30 gene features unique to a condition. Searches within individual TF networks produced classifiers for 15 chemical-tissue conditions, each containing 100 or fewer top-ranked gene features pooled from those of multiple TF networks and also unique to each condition. For the training dataset, 10 out of 11 classifiers successfully identified the gene expression profiles (GEPs) of their targeted chemical-tissue conditions by GSEA. For the validation dataset, classifiers for prochloraz-ovary and flutamide-ovary also correctly identified the GEPs of corresponding conditions while no classifier could predict the GEP from prochloraz-brain.ConclusionsThe discrepancies in the performance of these classifiers were attributed in part to varying data complexity among the conditions, as measured to some degree by Fisher's discriminant ratio statistic. This variation in data complexity could likely be compensated by adjusting sample size for individual chemical-tissue conditions, thus suggesting a need for a preliminary survey of transcriptomic responses before launching a full scale classifier discovery effort. Classifier discovery based on individual TF networks could yield more mechanistically-oriented biomarkers. GSEA proved to be a flexible and effective tool for application of gene classifiers but a similar and more refined algorithm, connectivity mapping, should also be explored. The distribution characteristics of classifiers across tissues, chemicals, and TF networks suggested a differential biological impact among the EDCs on zebrafish transcriptome involving some basic cellular functions.
| S-EPMC3469349 | biostudies-literature
Project description:A growing epidemic of nonalcoholic fatty liver disease (NAFLD) is paralleling the increase in the incidence of obesity and diabetes mellitus in countries that consume a Western diet. As NAFLD can lead to life-threatening conditions such as cirrhosis and hepatocellular carcinoma, an understanding of the factors that trigger its development and pathological progression is needed. Although by definition this disease is not associated with alcohol consumption, exposure to environmental agents that have been linked to other diseases might have a role in the development of NAFLD. Here, we focus on one class of these agents, endocrine-disrupting chemicals (EDCs), and their potential to influence the initiation and progression of a cascade of pathological conditions associated with hepatic steatosis (fatty liver). Experimental studies have revealed several potential mechanisms by which EDC exposure might contribute to disease pathogenesis, including the modulation of nuclear hormone receptor function and the alteration of the epigenome. However, many questions remain to be addressed about the causal link between acute and chronic EDC exposure and the development of NAFLD in humans. Future studies that address these questions hold promise not only for understanding the linkage between EDC exposure and liver disease but also for elucidating the molecular mechanisms that underpin NAFLD, which in turn could facilitate the development of new prevention and treatment opportunities.
| S-EPMC5657429 | biostudies-other
Project description:Since reports published in 2015 and 2016 identified 15 probable exposure-outcome associations, there has been an increase in studies in humans of exposure to endocrine-disrupting chemicals (EDCs) and a deepened understanding of their effects on human health. In this Series paper, we have reviewed subsequent additions to the literature and identified new exposure-outcome associations with substantial human evidence. Evidence is particularly strong for relations between perfluoroalkyl substances and child and adult obesity, impaired glucose tolerance, gestational diabetes, reduced birthweight, reduced semen quality, polycystic ovarian syndrome, endometriosis, and breast cancer. Evidence also exists for relations between bisphenols and adult diabetes, reduced semen quality, and polycystic ovarian syndrome; phthalates and prematurity, reduced anogenital distance in boys, childhood obesity, and impaired glucose tolerance; organophosphate pesticides and reduced semen quality; and occupational exposure to pesticides and prostate cancer. Greater evidence has accumulated than was previously identified for cognitive deficits and attention-deficit disorder in children following prenatal exposure to bisphenol A, organophosphate pesticides, and polybrominated flame retardants. Although systematic evaluation is needed of the probability and strength of these exposure-outcome relations, the growing evidence supports urgent action to reduce exposure to EDCs.
| S-EPMC7437820 | biostudies-literature
Project description:The emerging field of omics - large-scale data-rich biological measurements of the genome - provides new opportunities to advance and strengthen research into endocrine-disrupting chemicals (EDCs). Although some EDCs have been associated with adverse health effects in humans, our understanding of their impact remains incomplete. Progress in the field has been primarily limited by our inability to adequately estimate and characterize exposure and identify sensitive and measurable outcomes during windows of vulnerability. Evolving omics technologies in genomics, epigenomics and mitochondriomics have the potential to generate data that enhance exposure assessment to include the exposome - the totality of the lifetime exposure burden - and provide biology-based estimates of individual risks. Applying omics technologies to expand our knowledge of individual risk and susceptibility will augment biological data in the prediction of variability and response to disease, thereby further advancing EDC research. Together, refined exposure characterization and enhanced disease-risk prediction will help to bridge crucial gaps in EDC research and create opportunities to move the field towards a new vision - precision public health.
| S-EPMC7141602 | biostudies-literature
Project description:Endocrine-disrupting chemicals (EDCs) can act upon a developing organism to change its endocrine health and behavior in adulthood. Beyond actions on the exposed individuals, transgenerational effects of several EDCs have been reported. This study assessed the combinatorial impact of EDC-altered maternal care and transgenerational inheritance on F3 male and female offspring. Pregnant rats were exposed to EDCs with different modes of action: the weakly estrogenic polychlorinated biphenyl (PCB) mixture Aroclor 1221, the anti-androgenic fungicide vinclozolin (VIN), or the vehicle (6% dimethylsulfoxide in sesame oil; VEH) during embryonic development. The F1 male and female offspring were bred through the paternal- or maternal-lineage with untreated partners to generate F2 offspring. This process was repeated through both maternal and paternal lineages to create the F3 generation. Maternal care of F2 dams towards their F3 offspring was altered in a lineage-dependent manner, particularly in PCB paternal-lineage animals. When F3 pups were recorded for ultrasonic vocalizations (USVs) following separation from the mother, the rate of neonatal USVs in F3 offspring were decreased in PCB paternal-lineage pups. In adulthood, anxiety-like behaviors of the F3 rats were tested, with only small effects of EDCs detected. These interactions of maternal behaviors and EDC effects across generations, especially via the paternal lineage, has implications for health and environmental responses in wildlife and humans.
| S-EPMC6366859 | biostudies-literature
Project description:Endocrine-disrupting chemicals (EDCs) are exogenous chemicals that interfere with endogenous hormonal systems at various levels, resulting in adverse health effects. EDCs belong to diverse chemical families and can accumulate in the environment, diet and body fluids, with different levels of persistence. Their action can be mediated by several receptors, including members of the nuclear receptor family, such as estrogen and androgen receptors. The G protein-coupled estrogen receptor (GPER), a seven-transmembrane domain receptor, has also attracted attention as a potential target of EDCs. This review summarizes our current knowledge concerning GPER as a mediator of EDCs' effects.
| S-EPMC7466721 | biostudies-literature
Project description:Synthetic endocrine disrupting chemicals (EDCs), omnipresent in food, household, and personal care products, have been implicated in adverse trends in human reproduction, including infertility and increasing demand for assisted reproduction. Here, we study the action of 96 ubiquitous EDCs on human sperm. We show that structurally diverse EDCs activate the sperm-specific CatSper channel and, thereby, evoke an intracellular Ca(2+) increase, a motility response, and acrosomal exocytosis. Moreover, EDCs desensitize sperm for physiological CatSper ligands and cooperate in low-dose mixtures to elevate Ca(2+) levels in sperm. We conclude that EDCs interfere with various sperm functions and, thereby, might impair human fertilization.
| S-EPMC4196979 | biostudies-literature
Project description:BackgroundGlobally, the burden of breast cancer has increased significantly in recent decades. Emerging evidence suggested that endocrine-disrupting chemicals (EDCs), which have the potential to interfere with the function of normal hormones, may play a crucial role in this trend. However, the potential relationships were inconsistent in various studies.Objective and search methodsIn our study, we sought to fully evaluate the currently available epidemiological evidence to ascertain whether certain EDC congeners and their metabolites are related to breast cancer risk. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, we conducted a comprehensive literature search of original peer-reviewed publications in three electronic databases: PubMed, Web of Science, and Embase. Publications that covered xenobiotic EDC exposures and breast cancer-confirmed histological results or antecedent medical records or reporting to health registers were taken into consideration.OutcomesThe final result of the literature search was 6,498 references, out which we found 67 publications that matched the requirements for meta-analysis and eight publications for qualitative trend synthesis. In this meta-analysis, statistically significant associations revealed that (i) 1-chloro-4-[2,2,2-trichloro-1-(4-chlorophenyl)ethyl]benzene (p,p'-DDT) and its major metabolite 2,2-bis(4-chlorophenyl)-1,1-dichloroethylene (p,p'-DDE) were somewhat related to a greater risk of breast cancer. However, this relationship only existed in blood serum but not in adipose tissue. (ii) Breast cancer risk was increased by exposure to chlordane and hexachlorocyclohexane. (iii) Five polychlorinated biphenyls (PCB 99, PCB 105, PCB 118, PCB 138, and PCB 183) can increase the risk of breast cancer. (iv) One phthalate congener (BBP) and one per- and polyfluoroalkyl substance congener (PFDoDA) were negatively associated with breast cancer risk. Unfortunately, heterogeneity was not well explained in our review, and a limited number of available prospective studies investigating the associations between EDC exposure and breast cancer were included in our meta-analysis. To elucidate the overall associations, future large, longitudinal epidemiological investigations are needed.Systematic review registrationhttps://www.crd.york.ac.uk/PROSPERO/, identifier CRD 42023420927.
| S-EPMC10665889 | biostudies-literature
Project description:Background: Development and application of transcriptomics-based gene classifiers for ecotoxicological applications lag far behind those of human biomedical science. Many such classifiers discovered thus far lack vigorous statistical and experimental validations, with their stability and reliability unknown. A combination of genetic algorithm/support vector machines and genetic algorithm/K nearest neighbors were used in this study to search for classifiers of endocrine disrupting chemicals (EDCs) in zebrafish. Searches were conducted on both tissue-specific and all tissue combined datasets, either across the entire transcriptome or within individual transcription factor (TF) networks previously linked to EDC effects. Candidate classifiers were evaluated by gene set enrichment analysis (GSEA) on both the original training data and a dedicated validation dataset. Results: Multi-tissue dataset yielded no classifiers. Among the 19 chemical-tissue conditions evaluated, the transcriptome-wide searches yielded classifiers for six of them, each having approximately 20 to 30 gene features unique to a condition. Searches within individual TF networks produced classifiers for 15 chemical-tissue conditions, each containing 100 or fewer top-ranked gene features pooled from those of multiple TF networks and also unique to each condition. For the training dataset, 10 out of 11 classifiers successfully identified the gene expression profiles (GEPs) of their intended chemical-tissue conditions by GSEA. For the validation dataset, classifiers for prochloraz-ovary and flutamide-ovary also correctly identified the GEPs of corresponding conditions while no classifier could predict the GEP from prochloraz-brain. Conclusions: The discrepancies in the performance of these classifiers were attributed in part to varying data complexity among the conditions, as measured to some degree by Fisher’s discriminant ratio statistic. This variation in data complexity could likely be compensated by adjusting sample size for individual chemical-tissue conditions, thus suggesting a need for a preliminary survey of transcriptomic responses before launching a full scale classifier discovery effort. While GSEA appeared to provide a flexible and effective tool for application of gene classifiers, a similar but more refined algorithm, connectivity mapping, should also be explored for ecotoxicological applications. The distribution characteristics of classifiers across tissues, chemicals, and TF networks suggested a differential biological impact among the EDCs on zebrafish transcriptome involving some basic cellular functions. chemical abbreviations: EE2, 17α-ethynyl estradiol; FAD, fadrozole; TRB, 17 -trenbolone; FIP, fipronil; PRO, prochloraz; FLU, flutamide; MUS, muscimol; KET, ketoconazole; TRI, trilostane; VIN, vinclozolin Since this study was conducted in several phases, three different version of Agilent zebrafish two color microarrays were used based on their availability at the time. These include G2518A (designID 013223) and G2519F (designID 015064, 019161). There were a total of 58 treatment conditions with various combinations of chemical, tissue type, exposure time, and gender. Each condition contained eight to 12 independent samples, half from chemical-treated fish and half from water- control fish.
2012-08-22 | E-GEOD-38070 | biostudies-arrayexpress