Dataset Information

Data on chemical-gene interactions and biological categories enriched with genes sensitive to chemical exposures.

ABSTRACT: A dataset of chemical-gene interactions was created by extracting data from the Comparative Toxicogenomics Database (CTD) with the following filtering criteria: data was extracted only from experiments that used human, rat, or mouse cells/tissues and used high-throughput approaches for gene expression analysis. Genes not present in genomes of all three species were filtered out. The resulting dataset included 591,084 chemical-gene interaction. All chemical compounds in the database were annotated for their major uses. For every gene in the database number of chemical-gene interactions was calculated and used as a metric of gene sensitivity to a variety of chemical exposures. The lists of genes with corresponding numbers of chemical-gene interactions were used in gene-set enrichment analysis (GSEA) to identify potential sensitivity to chemical exposures of molecular pathways in Hallmark, KEGG and Reactome collections. Thus, data presented here represent unbiased and searchable datasets of sensitivity of genes and molecular pathways to a broad range of chemical exposures. As such the data can be used for a diverse range of toxicological and regulatory applications. Approach for the identification of molecular mechanisms sensitive to chemical exposures may inform regulatory toxicology about best toxicity testing strategies. Analysis of sensitivity of genes and molecular pathways to chemical exposures based on these datasets was published in Chemosphere (Suvorov et al., 2021) [1].

SUBMITTER: Suvorov A

PROVIDER: S-EPMC7578666 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature

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Data on chemical-gene interactions and biological categories enriched with genes sensitive to chemical exposures.

Suvorov Alexander A Salemme Victoria V McGaunn Joseph J Poluyanoff Anthony A Amir Saira S

Data in brief 20201009

A dataset of chemical-gene interactions was created by extracting data from the Comparative Toxicogenomics Database (CTD) with the following filtering criteria: data was extracted only from experiments that used human, rat, or mouse cells/tissues and used high-throughput approaches for gene expression analysis. Genes not present in genomes of all three species were filtered out. The resulting dataset included 591,084 chemical-gene interaction. All chemical compounds in the database were annotate ...[more]

PMID: 33102660

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Project description:Given a compound, how can we effectively predict its biological function? It is a fundamentally important problem because the information thus obtained may benefit the understanding of many basic biological processes and provide useful clues for drug design. In this study, based on the information of chemical-chemical interactions, a novel method was developed that can be used to identify which of the following eleven metabolic pathway classes a query compound may be involved with: (1) Carbohydrate Metabolism, (2) Energy Metabolism, (3) Lipid Metabolism, (4) Nucleotide Metabolism, (5) Amino Acid Metabolism, (6) Metabolism of Other Amino Acids, (7) Glycan Biosynthesis and Metabolism, (8) Metabolism of Cofactors and Vitamins, (9) Metabolism of Terpenoids and Polyketides, (10) Biosynthesis of Other Secondary Metabolites, (11) Xenobiotics Biodegradation and Metabolism. It was observed that the overall success rate obtained by the method via the 5-fold cross-validation test on a benchmark dataset consisting of 3,137 compounds was 77.97%, which is much higher than 10.45%, the corresponding success rate obtained by the random guesses. Besides, to deal with the situation that some compounds may be involved with more than one metabolic pathway class, the method presented here is featured by the capacity able to provide a series of potential metabolic pathway classes ranked according to the descending order of their likelihood for each of the query compounds concerned. Furthermore, our method was also applied to predict 5,549 compounds whose metabolic pathway classes are unknown. Interestingly, the results thus obtained are quite consistent with the deductions from the reports by other investigators. It is anticipated that, with the continuous increase of the chemical-chemical interaction data, the current method will be further enhanced in its power and accuracy, so as to become a useful complementary vehicle in annotating uncharacterized compounds for their biological functions.

Project description:The aim of this study was to assess the changes in chemical and microbial properties and enzymatic activity of soil enriched with vermicompost derived from household waste. The vermicompost was tested in the rhizosphere of Larix decidua seedlings cultivated in 10-L pots in: (i) nursery soil (as the control), (ii) soil with 10% v/v vermicompost, and (iii) with 20% v/v vermicompost. The impact of vermicompost was assessed in terms of soil C/N ratio; bacterial, fungal, and nematode counts; and enzymatic activity. It was found that vermicompost increased the C/N ratio from 21 to 32, as well as the content of nitrate from 78 to 134 mg kg-1, of ammonium from 14 to 139 mg kg-1, of phosphorus from 92 to 521 mg kg-1, and of potassium from 142 to 1912 mg kg-1, compared with the control soil. The abundance of beneficial bacteria was increased (from 8.61 × 107 to 37.9 × 107), along with decreases in microbiological ratios of fungi and bacteria (e.g. fungi/Bacillus from 0.18818 to 0.00425). A significant 2- to 4-fold increase was observed compared with the control in the number of beneficial nematodes belonging to bacterivorous, fungivorous, and predatory groups with no change in the abundance of plant-parasitic nematodes. Addition of vermicompost brought about a change in soil enzyme activity. Vermicompost reduced the activity of alkaline phosphatase only. Both doses of vermicompost led to an increase in the activity of acid phosphatase, inorganic pyrophosphatase, dehydrogenases, β-glucosidase, and urease. Only the higher dose had an effect on increasing the activity of o-diphenol oxidase and proteases. No significant change was observed for nitrate reductase. Also, the presence of antibiotics produced by bacteria was detected depending on the dose of vermicompost, e.g. iturin (ituC) and bacillomycin (bmyB) were found in soil with a dose of 20% v/v vermicompost. Overall, vermicompost produced from household waste can be an excellent organic fertilizer for larch forest nurseries.

Project description:BACKGROUND: The National Health and Nutrition Examination Survey (NHANES) is one example of cross-sectional datasets that have been used to draw causal inferences regarding environmental chemical exposures and adverse health outcomes. Our objectives were to analyze four NHANES datasets using consistent a priori selected methods to address the following questions: Is there a consistent association between urinary bisphenol A (BPA) measures and diabetes, coronary heart disease (CHD), and/or heart attack across surveys? Is NHANES an appropriate dataset for investigating associations between chemicals with short physiologic half-lives such as BPA and chronic diseases with multi-factorial etiologies? Data on urinary BPA and health outcomes from 2003-2004, 2005-2006, 2007-2008, and 2009-2010 were available. METHODOLOGY AND FINDINGS: Regression models were adjusted for creatinine, age, gender, race/ethnicity, education, income, smoking, heavy drinking, BMI, waist circumference, calorie intake, family history of heart attack, hypertension, sedentary time, and total cholesterol. Urinary BPA was not significantly associated with adverse health outcomes for any of the NHANES surveys, with ORs (95% CIs) ranging from 0.996 (0.951-1.04) to 1.03 (0.978-1.09) for CHD, 0.987 (0.941-1.04) to 1.04 (0.996-1.09) for heart attack, and 0.957 (0.899-1.02) to 1.01 (0.980-1.05) for diabetes. CONCLUSIONS: Using scientifically and clinically supportable exclusion criteria and outcome definitions, we consistently found no associations between urinary BPA and heart disease or diabetes. These results do not support associations and causal inferences reported in previous studies that used different criteria and definitions. We are not drawing conclusions regarding whether BPA is a risk factor for these diseases. We are stating the opposite--that using cross-sectional datasets like NHANES to draw such conclusions about short-lived environmental chemicals and chronic complex diseases is inappropriate. We need to expend resources on appropriately designed epidemiologic studies and toxicological explorations to understand whether these types of chemicals play a causal role in chronic diseases.

Dataset Information

Data on chemical-gene interactions and biological categories enriched with genes sensitive to chemical exposures.

Publications

Data on chemical-gene interactions and biological categories enriched with genes sensitive to chemical exposures.

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