Combined transcriptomics classifier analysis reveals a set of adverse effect genes for use as potential endpoint in effect based screening
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ABSTRACT:
ORGANISM(S): Folsomia candida
PROVIDER: GSE70877 | GEO | 2015/11/09
SECONDARY ACCESSION(S): PRJNA289752
REPOSITORIES: GEO
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Project description:Environmental risk assessment relies heavily on the use of bioassays to assess the environmental impact of chemicals. Gene expression is gaining acceptance as a valuable mechanistic endpoint in bioassays and effect-based screening. Data analysis and its results however, are often complex and not directly applicable in risk assessment. Classifier analysis is a promising method to turn complex gene expression analysis results into answers suitable for risk assessment. We have assembled a large gene expression dataset assembled from multiple studies and experiments in the springtail Folsomia candida, with the aim of selecting a set of genes that can be trained to classify general toxic stress. By performing differential expression analysis prior to classification we were able to select a set of 135 genes which was enriched in stress related processes. This set was then used to classify two test sets comprised of chemical spiked soils, polluted soils and clean soils and compared to another, more traditional feature selection for classification. The gene set presented here outperformed the more traditionally selected gene set. This gene set has the potential to be used as a biomarker to test for adverse effects caused by chemicals in springtails to provide endpoints in environmental risk assessment. The data presented in our manuscript is part of a larger experiment which was performed in single, large loop design. Only the samples used in the study presented here are named while the other samples will remain unnamed. All the data can still be used for normalization after which the analysis presented in the manuscript can be replicated. A single channel, interwoven loop design was used to test animals exposed to a control and to 5 concentrations of cadmium and phenanthrene (cadmium concentrations are: cad_1 till cad_5: 5.8, 14.5, 28.9, 57.9 and 115.8 mg/Kg respectively and phenanthrene concentrations are: phe_1 till Phe_5: 0, 4.6, 11.4, 22.9, 45.8 and 91.6 mg/Kg soil respectively). Exposures lasted for 2 days and used 4 biological replicates per condition each containing 30 grams soil and 30 individuals.
2015-11-09 | E-GEOD-70877 | biostudies-arrayexpress
Project description:Many innovative techniques and scientific improvements are available to tackle societal concerns, like public health safety and confining the risk of cancerous exposure to chemicals, but have not been thoroughly tested and implicated yet. We investigated if microRNA and mRNA transcription profiles can be implemented in a short-term carcinogen classifier assay. Our study is additionally focusing on the drawbacks of present-day carcinogen screening strategies and also aims to contribute to a more ethical approach towards animal use and welfare within risk assessment. Since current in vitro and in silico assays are still not able to mimic the in vivo situation accurately we set out to develop an alternative short-term in vivo assay. Five genotoxic, seven non-genotoxic and five non-carcinogen exposure studies were used to investigate if murine hepatic microRNA and mNA profiles after 7-day exposure are suitable tools to classify carcinogens. Classification analyses showed that a small transcript set, consisting of both microRNA and mRNA, is able to classify the genotoxic, non-genotoxic and non-carcinogens tested with 100% accuracy. The results indicate that microRNAs have the potential to be used as transcriptional classifiers and that a short-term transcriptional classifier assay in mice can be a powerful tool in carcinogenicity risk assessment. Since current in vitro and in silico assays are still not able to mimic the in vivo situation accurately we set out to develop an alternative short-term in vivo assay. Five genotoxic, seven non-genotoxic and five non-carcinogen exposure studies were used to investigate if murine hepatic microRNA and mNA profiles after 7-day exposure are suitable tools to classify carcinogens. Classification analyses showed that a small transcript set, consisting of both microRNA and mRNA, is able to classify the genotoxic, non-genotoxic and non-carcinogens tested with 100% accuracy. The results indicate that microRNAs have the potential to be used as transcriptional classifiers and that a short-term transcriptional classifier assay in mice can be a powerful tool in carcinogenicity risk assessment. [mRNA profling] 96 hepatic samples in total, 8 control untreated samples, replicates per treated group n=4-6
2014-04-01 | E-GEOD-43977 | biostudies-arrayexpress
Project description:Many innovative techniques and scientific improvements are available to tackle societal concerns, like public health safety and confining the risk of cancerous exposure to chemicals, but have not been thoroughly tested and implicated yet. We investigated if microRNA and mRNA transcription profiles can be implemented in a short-term carcinogen classifier assay. Our study is additionally focusing on the drawbacks of present-day carcinogen screening strategies and also aims to contribute to a more ethical approach towards animal use and welfare within risk assessment. Since current in vitro and in silico assays are still not able to mimic the in vivo situation accurately we set out to develop an alternative short-term in vivo assay. Five genotoxic, seven non-genotoxic and five non-carcinogen exposure studies were used to investigate if murine hepatic microRNA and mNA profiles after 7-day exposure are suitable tools to classify carcinogens. Classification analyses showed that a small transcript set, consisting of both microRNA and mRNA, is able to classify the genotoxic, non-genotoxic and non-carcinogens tested with 100% accuracy. The results indicate that microRNAs have the potential to be used as transcriptional classifiers and that a short-term transcriptional classifier assay in mice can be a powerful tool in carcinogenicity risk assessment. Since current in vitro and in silico assays are still not able to mimic the in vivo situation accurately we set out to develop an alternative short-term in vivo assay. Five genotoxic, seven non-genotoxic and five non-carcinogen exposure studies were used to investigate if murine hepatic microRNA and mNA profiles after 7-day exposure are suitable tools to classify carcinogens. Classification analyses showed that a small transcript set, consisting of both microRNA and mRNA, is able to classify the genotoxic, non-genotoxic and non-carcinogens tested with 100% accuracy. The results indicate that microRNAs have the potential to be used as transcriptional classifiers and that a short-term transcriptional classifier assay in mice can be a powerful tool in carcinogenicity risk assessment.
2014-04-01 | GSE43977 | GEO
Project description:Many innovative techniques and scientific improvements are available to tackle societal concerns, like public health safety and confining the risk of cancerous exposure to chemicals, but have not been thoroughly tested and implicated yet. We investigated if microRNA and mRNA transcription profiles can be implemented in a short-term carcinogen classifier assay. Our study is additionally focusing on the drawbacks of present-day carcinogen screening strategies and also aims to contribute to a more ethical approach towards animal use and welfare within risk assessment. Since current in vitro and in silico assays are still not able to mimic the in vivo situation accurately we set out to develop an alternative short-term in vivo assay. Five genotoxic, seven non-genotoxic and five non-carcinogen exposure studies were used to investigate if murine hepatic microRNA and mRNA profiles after 7-day exposure are suitable tools to classify carcinogens. Classification analyses showed that a small transcript set, consisting of both microRNA and mRNA, is able to classify the genotoxic, non-genotoxic and non-carcinogens tested with 100% accuracy. The results indicate that microRNAs have the potential to be used as transcriptional classifiers and that a short-term transcriptional classifier assay in mice can be a powerful tool in carcinogenicity risk assessment. [microRNA profling] 68 hepatic samples in total, 3 control untreated samples, replicates per treated group n=3-4
2014-04-01 | E-GEOD-43847 | biostudies-arrayexpress
Project description:Structural analogues are assumed to elicit toxicity via similar predominant modes of action (MOAs). Currently, MOA categorization of chemicals in environmental risk assessment is mainly based on the physico-chemical properties of potential toxicants. It is often not known whether such classification schemes are also supported by mechanistic biological data. In this study, the toxic effects of two groups of structural analogues (alcohols and anilines) with predefined MOA (narcotics and polar narcotics) were investigated at different levels of biological organization (gene transcription, energy reserves and growth). Chemical similarity was not indicative of a comparable degree of toxicity and a similar biological response. Categorization of the test chemicals based on the different biological responses (growth, energy use and gene transcription) did not result in a classification of the predefined narcotics versus the predefined polar narcotics. Moreover, gene transcription based clustering profiles were indicative of the observed effects at higher level of biological organization. Furthermore, a small set of classifier genes could be identified that was discriminative for the clustering pattern. These classifier genes co-varied with the organismal and physiological responses. Compared to the physico-chemistry based MOA classification, integrated biological multi-level effect assessment can provide the necessary MOA information that is crucial in high-quality environmental risk assessment. Our findings support the view that transcriptomics tools hold considerable promise to be used in biological response based mechanistic profiling of potential (eco)toxicants.
2012-02-01 | GSE29867 | GEO
Project description:Structural analogues are assumed to elicit toxicity via similar predominant modes of action (MOAs). Currently, MOA categorization of chemicals in environmental risk assessment is mainly based on the physico-chemical properties of potential toxicants. It is often not known whether such classification schemes are also supported by mechanistic biological data. In this study, the toxic effects of two groups of structural analogues (alcohols and anilines) with predefined MOA (narcotics and polar narcotics) were investigated at different levels of biological organization (gene transcription, energy reserves and growth). Chemical similarity was not indicative of a comparable degree of toxicity and a similar biological response. Categorization of the test chemicals based on the different biological responses (growth, energy use and gene transcription) did not result in a classification of the predefined narcotics versus the predefined polar narcotics. Moreover, gene transcription based clustering profiles were indicative of the observed effects at higher level of biological organization. Furthermore, a small set of classifier genes could be identified that was discriminative for the clustering pattern. These classifier genes co-varied with the organismal and physiological responses. Compared to the physico-chemistry based MOA classification, integrated biological multi-level effect assessment can provide the necessary MOA information that is crucial in high-quality environmental risk assessment. Our findings support the view that transcriptomics tools hold considerable promise to be used in biological response based mechanistic profiling of potential (eco)toxicants.
2012-02-01 | GSE29856 | GEO
Project description:Structural analogues are assumed to elicit toxicity via similar predominant modes of action (MOAs). Currently, MOA categorization of chemicals in environmental risk assessment is mainly based on the physico-chemical properties of potential toxicants. It is often not known whether such classification schemes are also supported by mechanistic biological data. In this study, the toxic effects of two groups of structural analogues (alcohols and anilines) with predefined MOA (narcotics and polar narcotics) were investigated at different levels of biological organization (gene transcription, energy reserves and growth). Chemical similarity was not indicative of a comparable degree of toxicity and a similar biological response. Categorization of the test chemicals based on the different biological responses (growth, energy use and gene transcription) did not result in a classification of the predefined narcotics versus the predefined polar narcotics. Moreover, gene transcription based clustering profiles were indicative of the observed effects at higher level of biological organization. Furthermore, a small set of classifier genes could be identified that was discriminative for the clustering pattern. These classifier genes co-varied with the organismal and physiological responses. Compared to the physico-chemistry based MOA classification, integrated biological multi-level effect assessment can provide the necessary MOA information that is crucial in high-quality environmental risk assessment. Our findings support the view that transcriptomics tools hold considerable promise to be used in biological response based mechanistic profiling of potential (eco)toxicants.
2012-02-01 | GSE29858 | GEO
Project description:Structural analogues are assumed to elicit toxicity via similar predominant modes of action (MOAs). Currently, MOA categorization of chemicals in environmental risk assessment is mainly based on the physico-chemical properties of potential toxicants. It is often not known whether such classification schemes are also supported by mechanistic biological data. In this study, the toxic effects of two groups of structural analogues (alcohols and anilines) with predefined MOA (narcotics and polar narcotics) were investigated at different levels of biological organization (gene transcription, energy reserves and growth). Chemical similarity was not indicative of a comparable degree of toxicity and a similar biological response. Categorization of the test chemicals based on the different biological responses (growth, energy use and gene transcription) did not result in a classification of the predefined narcotics versus the predefined polar narcotics. Moreover, gene transcription based clustering profiles were indicative of the observed effects at higher level of biological organization. Furthermore, a small set of classifier genes could be identified that was discriminative for the clustering pattern. These classifier genes co-varied with the organismal and physiological responses. Compared to the physico-chemistry based MOA classification, integrated biological multi-level effect assessment can provide the necessary MOA information that is crucial in high-quality environmental risk assessment. Our findings support the view that transcriptomics tools hold considerable promise to be used in biological response based mechanistic profiling of potential (eco)toxicants.
2012-02-01 | GSE29857 | GEO
Project description:Structural analogues are assumed to elicit toxicity via similar predominant modes of action (MOAs). Currently, MOA categorization of chemicals in environmental risk assessment is mainly based on the physico-chemical properties of potential toxicants. It is often not known whether such classification schemes are also supported by mechanistic biological data. In this study, the toxic effects of two groups of structural analogues (alcohols and anilines) with predefined MOA (narcotics and polar narcotics) were investigated at different levels of biological organization (gene transcription, energy reserves and growth). Chemical similarity was not indicative of a comparable degree of toxicity and a similar biological response. Categorization of the test chemicals based on the different biological responses (growth, energy use and gene transcription) did not result in a classification of the predefined narcotics versus the predefined polar narcotics. Moreover, gene transcription based clustering profiles were indicative of the observed effects at higher level of biological organization. Furthermore, a small set of classifier genes could be identified that was discriminative for the clustering pattern. These classifier genes co-varied with the organismal and physiological responses. Compared to the physico-chemistry based MOA classification, integrated biological multi-level effect assessment can provide the necessary MOA information that is crucial in high-quality environmental risk assessment. Our findings support the view that transcriptomics tools hold considerable promise to be used in biological response based mechanistic profiling of potential (eco)toxicants.
2012-02-01 | GSE29862 | GEO
Project description:Structural analogues are assumed to elicit toxicity via similar predominant modes of action (MOAs). Currently, MOA categorization of chemicals in environmental risk assessment is mainly based on the physico-chemical properties of potential toxicants. It is often not known whether such classification schemes are also supported by mechanistic biological data. In this study, the toxic effects of two groups of structural analogues (alcohols and anilines) with predefined MOA (narcotics and polar narcotics) were investigated at different levels of biological organization (gene transcription, energy reserves and growth). Chemical similarity was not indicative of a comparable degree of toxicity and a similar biological response. Categorization of the test chemicals based on the different biological responses (growth, energy use and gene transcription) did not result in a classification of the predefined narcotics versus the predefined polar narcotics. Moreover, gene transcription based clustering profiles were indicative of the observed effects at higher level of biological organization. Furthermore, a small set of classifier genes could be identified that was discriminative for the clustering pattern. These classifier genes co-varied with the organismal and physiological responses. Compared to the physico-chemistry based MOA classification, integrated biological multi-level effect assessment can provide the necessary MOA information that is crucial in high-quality environmental risk assessment. Our findings support the view that transcriptomics tools hold considerable promise to be used in biological response based mechanistic profiling of potential (eco)toxicants.
2012-02-01 | GSE29854 | GEO