Folsomia candida
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA301049 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:Ecotoxicogenomic assessment of diclofenac toxicity in soil
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Project description:Daphnia magna is a bio-indicator organism accepted by several international water quality regulatory agencies. Current approaches for assessment of water quality rely on acute and chronic toxicity that provide no insight into the cause of toxicity. Recently, molecular approaches, such as genome wide gene expression responses, are enabling an alternative mechanism based approach to toxicity assessment. While these genomic methods are providing important mechanistic insight into toxicity, statistically robust prediction systems that allow the identification of chemical contaminants from the molecular response to exposure are needed. Here we apply advanced machine learning approaches to develop predictive models of contaminant exposure using a D. magna gene expression dataset for 36 chemical exposures. We demonstrate here that we can discriminate between chemicals belonging to different chemical classes including endocrine disruptors, metals and industrial chemicals based on gene expression. We also show that predictive models based on indices of whole pathway transcriptional activity can achieve comparable results while facilitating biological interpretability. D. magna were exposed to 36 Chemicals and 5 control series in quadruplicate.
2013-01-17 | E-GEOD-43564 | biostudies-arrayexpress
Project description:Daphnia magna is a bio-indicator organism accepted by several international water quality regulatory agencies. Current approaches for assessment of water quality rely on acute and chronic toxicity that provide no insight into the cause of toxicity. Recently, molecular approaches, such as genome wide gene expression responses, are enabling an alternative mechanism based approach to toxicity assessment. While these genomic methods are providing important mechanistic insight into toxicity, statistically robust prediction systems that allow the identification of chemical contaminants from the molecular response to exposure are needed. Here we apply advanced machine learning approaches to develop predictive models of contaminant exposure using a D. magna gene expression dataset for 36 chemical exposures. We demonstrate here that we can discriminate between chemicals belonging to different chemical classes including endocrine disruptors, metals and industrial chemicals based on gene expression. We also show that predictive models based on indices of whole pathway transcriptional activity can achieve comparable results while facilitating biological interpretability.
2013-01-17 | GSE43564 | GEO
Project description:Development of a Large-Scale Chemogenomics Database to Improve Drug Candidate Selection and to Understand Mechanisms of Chemical Toxicity and Action These data support the publication titled "Development of a Large-Scale Chemogenomics Database to Improve Drug Candidate Selection and to Understand Mechanisms of Chemical Toxicity and Action" Copyright (c) 2005 by Iconix Pharmaceuticals, Inc. Guidelines for commercial use: http://www.iconixbiosciences.com/guidelineCommUse.pdf Keywords: other
2005-05-31 | E-GEOD-2409 | biostudies-arrayexpress
Project description:Development of a Large-Scale Chemogenomics Database to Improve Drug Candidate Selection and to Understand Mechanisms of Chemical Toxicity and Action These data support the publication titled "Development of a Large-Scale Chemogenomics Database to Improve Drug Candidate Selection and to Understand Mechanisms of Chemical Toxicity and Action" Copyright (c) 2005 by Iconix Pharmaceuticals, Inc. Guidelines for commercial use: http://www.iconixbiosciences.com/guidelineCommUse.pdf Keywords: other
2005-06-01 | GSE2409 | GEO
Project description:INTEGRATING TRANSCRIPTOMICS INTO TRIAD-BASED SOIL QUALITY ASSESSMENT
| PRJNA227772 | ENA
Project description:It is considered that read-across based on only structure similarity has a risk of error on risk assessment. Under this circumstances, considering biological similarity based on adverse outcome pathways (AOPs) using in vitro omics technologies is expected to enhance the accuracy and robustness of conclusions in read-across. However, because of a lack of practical case studies, it is not well discussed key considerations and how to use these technologies for data gap filling. In the present case study, we elucidated and compared the mode of action for structural analogs ; p-dialkoxy chlorobenzenes including 1,4-dichloro-2,5-dimethoxybenzene (DDMB), 2,5-dichloro-1,4-diethoxybenzene (DDEB), 2-chloro-1,4-dimethoxybenzene (CDMB), and 1-chloro-2,5-diethoxybenzene (CDEB) using in vitro omics technologies. To reveal the modes of action, we conducted microarray analysis with rat primary hepatocytes.
2020-04-01 | GSE137379 | GEO
Project description:The eToxPred tool has been developed to predict, on one hand, the synthetic accessibility (SA) score, or how easy it is to make the molecule in the laboratory, and, on the other hand, the toxicity (Tox) score, or the probability of the molecule of being toxic to humans. The authors trained and cross-validated both predictors on a large number of datasets, and demonstrated the method usefulness in building virtual custom libraries.
Model Type: Predicitive machine learning model.
Model Relevance: Predicts Synthetic Accesibility and Toxicity score of a chemical compound
Model Encoded by: Miquel Duran-Frigola (Ersilia)
Metadata Submitted in BioModels by: Zainab Ashimiyu-Abdusalam
Implementation of this model code by Ersilia is available here:
https://github.com/ersilia-os/eos92sw
2024-07-18 | MODEL2406270007 | BioModels
Project description:Narcosis or baseline toxicity is the inert toxicity of hydrophobic compounds, supposed to take place at the level of the cellular membranes. Based on the linear relationship between the toxicity (logEC50) and the hydrophobicity (logKow), class I and II narcotizing compounds are recognized, in which the latter group is assumed to exert additional toxic mechanisms by hydrogen bond donor acidity by their polar groups. Chlorinated anilines, which occur often in the environment as degradation products of certain pesticides , are considered to be narcotizing compounds. In this study the transcriptional responses of the soil arthropod Folsomia candida are investigated upon exposure to a series of anilines with increasing chlorination. A discrimination between class1 and class2 narcotizing compound is being made.
2010-02-03 | GSE19929 | GEO
Project description:Increasing concern about pollution of our environment calls for advanced and rapid methods to estimate ecological toxicity. The use of gene expression microarrays in environmental studies can potentially meet this challenge. We present a novel method to examine soil toxicity. We exposed the collembolan Folsomia candida to soil containing an ecologically relevant cadmium concentration, and found a cumulative total of 1586 differentially expressed transcripts across three exposure durations, including transcripts involved in stress response, detoxification, and hypoxia. Additional enrichment analysis of gene ontology (GO) terms revealed that antibiotic biosynthesis is important at all time points examined. Interestingly, genes involved in the "penicillin and cephalosporin biosynthesis pathway" have never been identified in animals before, but are expressed in F. candida’s tissue. The synthesis of antibiotics can possibly be a response to increased cadmium-induced susceptibility to invading pathogens, which might be caused by repression of genes involved in the immune-system (C-type lectins and Toll receptor). This study presents a first global view on the environmental stress response of an arthropod species exposed to contaminated soil,and provides a mechanistic basis for the development of a gene expression soil quality test. Keywords: cadmium, soil, Collembola, environmental genomics
2008-08-08 | GSE11122 | GEO