EcoToxChip: A next‐generation toxicogenomics tool for chemical prioritization and environmental management

Basu, Niladri; Crump, Doug; Head, Jessica; Hickey, Gordon M.; Hogan, Natacha S.; Maguire, Steve; Xia, Jianguo; Hecker, Markus

doi:10.1002/etc.4309

Cited by 58 publications

(65 citation statements)

References 20 publications

(25 reference statements)

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“…Over the last 4 decades, environmental toxicity testing in support of regulatory decision making has crystalized into a set of highly codified practices and standardized protocols. Although conventional risk assessment methods (relying primarily on whole animal in vivo assays) are useful for determining acceptable thresholds for chemicals, they have notable drawbacks, including being very expensive, time consuming, and requiring the use of large numbers of live animals (Basu et al 2019). These limitations have created a situation in which most synthetic chemicals entering the environment are considered “data poor” (Hartung 2011).…”

Section: Introductionmentioning

confidence: 99%

“…Given current legislative mandates in North America (Chemicals Management Plan [Government of Canada 2016] in Canada, and Toxic Substances Control Act [Bergeson et al 1976] in the United States) and Europe (Registration, Evaluation, Authorisation and Restriction of Chemicals) (Smith 2006) and the slow pace at which conventional testing proceeds, there is a need to accelerate the pace of chemical risk assessment (Kavlock et al 2018). The gap between these legislative mandates and the number of chemical substances in need of toxicity data is large and widening (Basu et al 2019). The United States Environmental Protection Agency (USEPA) estimated that using whole animal testing for a single chemical can take 4 y and cost between US$1 million and $20 million (Martin et al 2012).…”

Section: Introductionmentioning

confidence: 99%

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Factors Affecting the Perception of New Approach Methodologies (NAMs) in the Ecotoxicology Community

Mondou

Hickey

Rahman

et al. 2020

Integr Envir Assess & Manag

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Given current legislative mandates to assess the safety of thousands of chemicals and the slow pace at which conventional testing proceeds, there is a need to accelerate chemical risk assessment. Governments and businesses are increasingly interested in new approach methodologies (NAMs) that promise to reduce costs and delays. We explore 5 sociological factors within the ecotoxicology community that can influence the perception of NAMs: 1) professional profile (educational cohort, employer), 2) internal science communication within professional forums, 3) concern for “error cost,” 4) collaboration across stakeholders, and 5) fundamental beliefs regarding toxicology. We conducted an online survey (n = 171; 2018) asking participants about their experiences and perspectives at events of the Society of Environmental Toxicology and Chemistry (SETAC) to assess 1) how NAMs are discussed compared to conventional testing and 2) how respondents perceive their viability. We developed ordered logistic regression (OLR) models to understand the influence of exploratory variables (cohort, core views on toxicology, frequency of collaboration) on respondents' evaluation of the viability of different NAMs. Our results showed that 1) NAMs were more likely than conventional methods to be challenged in forum discussions, which may be fueled by concerns for error costs in regulatory decision making; 2) perceptions of the viability of NAMs tended to follow a “pattern of familiarity,” whereby respondents that were more knowledgeable about a test method tended to find it more viable; 3) respondents who agreed with the Paracelsus maxim had a greater likelihood of finding conventional testing viable; and 4) the more a respondent reported collaborating with industry on alternative testing strategies, the more likely she or he was to report that NAMs were less viable. These results suggest that there are professional and organizational barriers to greater acceptance of NAMs that can be addressed through a social learning process within the professional community. Integr Environ Assess Manag 2020;16:269–281. © 2020 SETAC

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Factors Affecting the Perception of New Approach Methodologies (NAMs) in the Ecotoxicology Community

Mondou

Hickey

Rahman

et al. 2020

Integr Envir Assess & Manag

Self Cite

View full text Add to dashboard Cite

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“…Step 7: Dose-response analysis with an external dataset Overall, the aim of the evaluation was to assess the ability of T1000 gene sets to predict apical outcomes according to previously published methods (Farmahin et al 2017). Additionally, we repeated step 4 of the T1000 approach to select the top 384 (T384; i.e., a number conducive to study in a QCPR microplate format as per the EcoToxChip project; (Basu et al 2019)) and 1,500…”

Section: Phase Iv: External Testing and Performance Evaluationmentioning

confidence: 99%

“…National Toxicology Program) that were not used for gene selection. The current study is part of the larger EcoToxChip project (Basu et al 2019).…”

Section: Introductionmentioning

confidence: 99%

T1000: A reduced toxicogenomics gene set for improved decision making

Soufan

Ewald

Viau

et al. 2019

Preprint

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There is growing interest within regulatory agencies and toxicological research communities to develop, test, and apply new approaches, such as toxicogenomics, to more efficiently evaluate chemical hazards. Given the complexity of analyzing thousands of genes simultaneously, there is a need to identify reduced gene sets.Though several gene sets have been defined for toxicological applications, few of these were purposefully derived using toxicogenomics data. Here, we developed and applied a systematic approach to identify 1000 genes (called Toxicogenomics-1000 or T1000) highly responsive to chemical exposures. First, a co-expression network of 11,210genes was built by leveraging microarray data from the Open TG-GATEs program. This network was then re-weighted based on prior knowledge of their biological (KEGG, MSigDB) and toxicological (CTD) relevance. Finally, weighted correlation network analysis was applied to identify 258 gene clusters. T1000 was defined by selecting genes from each cluster that were most associated with outcome measures. For model evaluation, we compared the performance of T1000 to that of other gene sets (L1000, S1500, Genes selected by Limma, and random set) using two external datasets. Additionally, a smaller (T384) and a larger version (T1500) of T1000 were used for dose-response modeling to test the effect of gene set size. Our findings demonstrated that the T1000 gene set is predictive of apical outcomes across a range of conditions (e.g.,in vitroand in vivo, dose-response, multiple species, tissues, and chemicals), and generally performs as well, or better than other gene sets available.

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“…National Toxicology Program) that were not used for gene selection. The current study is part of the larger EcoToxChip project (Basu et al 2019). For the processed data, user can download all samples processed from https://zenodo.org/record/3359047#.XUcTwpMzZ24.…”

Section: Manuscript To Be Reviewedmentioning

confidence: 99%

Peer Review #1 of "T1000: a reduced gene set prioritized for toxicogenomic studies (v0.2)"

2019

View full text Add to dashboard Cite

There is growing interest within regulatory agencies and toxicological research communities to develop, test, and apply new approaches, such as toxicogenomics, to more efficiently evaluate chemical hazards. Given the complexity of analyzing thousands of genes simultaneously, there is a need to identify reduced gene sets. Though several gene sets have been defined for toxicological applications, few of these were purposefully derived using toxicogenomics data. Here, we developed and applied a systematic approach to identify 1000 genes (called Toxicogenomics-1000 or T1000) highly responsive to chemical exposures. First, a co-expression network of 11,210 genes was built by leveraging microarray data from the Open TG-GATEs program. This network was then reweighted based on prior knowledge of their biological (KEGG, MSigDB) and toxicological (CTD) relevance. Finally, weighted correlation network analysis was applied to identify 258 gene clusters. T1000 was defined by selecting genes from each cluster that were most associated with outcome measures. For model evaluation, we compared the performance of T1000 to that of other gene sets (L1000, S1500, Genes selected by Limma, and random set) using two external datasets based on the rat model. Additionally, a smaller (T384) and a larger version (T1500) of T1000 were used for dose-response modeling to test the effect of gene set size. Our findings demonstrated that the T1000 gene set is predictive of apical outcomes across a range of conditions (e.g., in vitro and in vivo, dose-response, multiple species, tissues, and chemicals), and generally performs as well, or better than other gene sets available.

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EcoToxChip: A next‐generation toxicogenomics tool for chemical prioritization and environmental management

Abstract: ET&C FOCUSFocus articles are part of a regular series intended to sharpen understanding of current and emerging topics of interest to the scientific community.

Cited by 58 publications

References 20 publications

Factors Affecting the Perception of New Approach Methodologies (NAMs) in the Ecotoxicology Community

Factors Affecting the Perception of New Approach Methodologies (NAMs) in the Ecotoxicology Community

T1000: A reduced toxicogenomics gene set for improved decision making

Peer Review #1 of "T1000: a reduced gene set prioritized for toxicogenomic studies (v0.2)"

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