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Predictions of genotoxic potential, mode of action, molecular targets, and potency via a tiered multiflow® assay data analysis strategy.

ABSTRACT: The in vitro MultiFlow® DNA Damage Assay multiplexes ?H2AX, p53, phospho-histone H3, and polyploidization biomarkers into a single flow cytometric analysis. The current report describes a tiered sequential data analysis strategy based on data generated from exposure of human TK6 cells to a previously described 85 chemical training set and a new pharmaceutical-centric test set (n?=?40). In each case, exposure was continuous over a range of closely spaced concentrations, and cell aliquots were removed for analysis following 4 and 24?hr of treatment. The first data analysis step focused on chemicals' genotoxic potential, and for this purpose, we evaluated the performance of a machine learning (ML) ensemble, a rubric that considered fold increases in biomarkers against global evaluation factors (GEFs), and a hybrid strategy that considered ML and GEFs. This first tier further used ML output and/or GEFs to classify genotoxic activity as clastogenic and/or aneugenic. Test set results demonstrated the generalizability of the first tier, with particularly good performance from the ML ensemble: 35/40 (88%) concordance with a priori genotoxicity expectations and 21/24 (88%) agreement with expected mode of action (MoA). A second tier applied unsupervised hierarchical clustering to the biomarker response data, and these analyses were found to group certain chemicals, especially aneugens, according to their molecular targets. Finally, a third tier utilized benchmark dose analyses and MultiFlow biomarker responses to rank genotoxic potency. The relevance of these rankings is supported by the strong agreement found between benchmark dose values derived from MultiFlow biomarkers compared to those generated from parallel in vitro micronucleus analyses. Collectively, the results suggest that a tiered MultiFlow data analysis pipeline is capable of rapidly and effectively identifying genotoxic hazards while providing additional information that is useful for modern risk assessments-MoA, molecular targets, and potency. Environ. Mol. Mutagen. 60:513-533, 2019. © 2019 Wiley Periodicals, Inc.

SUBMITTER: Dertinger SD

PROVIDER: S-EPMC6880310 | biostudies-literature | 2019 Jul

REPOSITORIES: biostudies-literature

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Predictions of genotoxic potential, mode of action, molecular targets, and potency via a tiered multiflow® assay data analysis strategy.

Dertinger Stephen D SD Kraynak Andrew R AR Wheeldon Ryan P RP Bernacki Derek T DT Bryce Steven M SM Hall Nikki N Bemis Jeffrey C JC Galloway Sheila M SM Escobar Patricia A PA Johnson George E GE

Environmental and molecular mutagenesis 20190227 6

The in vitro MultiFlow® DNA Damage Assay multiplexes γH2AX, p53, phospho-histone H3, and polyploidization biomarkers into a single flow cytometric analysis. The current report describes a tiered sequential data analysis strategy based on data generated from exposure of human TK6 cells to a previously described 85 chemical training set and a new pharmaceutical-centric test set (n = 40). In each case, exposure was continuous over a range of closely spaced concentrations, and cell aliquots were rem ...[more]

PMID: 30702769

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Project description:Several endpoints associated with cellular responses to DNA damage as well as overt cytotoxicity were multiplexed into a miniaturized, "add and read" type flow cytometric assay. Reagents included a detergent to liberate nuclei, RNase and propidium iodide to serve as a pan-DNA dye, fluorescent antibodies against γH2AX, phospho-histone H3, and p53, and fluorescent microspheres for absolute nuclei counts. The assay was applied to TK6 cells and 67 diverse reference chemicals that served as a training set. Exposure was for 24 hrs in 96-well plates, and unless precipitation or foreknowledge about cytotoxicity suggested otherwise, the highest concentration was 1 mM. At 4- and 24-hrs aliquots were removed and added to microtiter plates containing the reagent mix. Following a brief incubation period robotic sampling facilitated walk-away data acquisition. Univariate analyses identified biomarkers and time points that were valuable for classifying agents into one of three groups: clastogenic, aneugenic, or non-genotoxic. These mode of action predictions were optimized with a forward-stepping process that considered Wald test p-values, receiver operator characteristic curves, and pseudo R(2) values, among others. A particularly high performing multinomial logistic regression model was comprised of four factors: 4 hr γH2AX and phospho-histone H3 values, and 24 hr p53 and polyploidy values. For the training set chemicals, the four-factor model resulted in 94% concordance with our a priori classifications. Cross validation occurred via a leave-one-out approach, and in this case 91% concordance was observed. A test set of 17 chemicals that were not used to construct the model were evaluated, some of which utilized a short-term treatment in the presence of a metabolic activation system, and in 16 cases mode of action was correctly predicted. These initial results are encouraging as they suggest a machine learning strategy can be used to rapidly and reliably predict new chemicals' genotoxic mode of action based on data from an efficient and highly scalable multiplexed assay.

Project description:There is growing interest in quantitative analysis of in vivo genetic toxicity dose-response data, and use of point-of-departure (PoD) metrics such as the benchmark dose (BMD) for human health risk assessment (HHRA). Currently, multiple transgenic rodent (TGR) assay variants, employing different rodent strains and reporter transgenes, are used for the assessment of chemically-induced genotoxic effects in vivo. However, regulatory issues arise when different PoD values (e.g., lower BMD confidence intervals or BMDLs) are obtained for the same compound across different TGR assay variants. This study therefore employed the BMD approach to examine the ability of different TGR variants to yield comparable genotoxic potency estimates. Review of over 2000 dose-response datasets identified suitably-matched dose-response data for three compounds (ethyl methanesulfonate or EMS, N-ethyl-N-nitrosourea or ENU, and dimethylnitrosamine or DMN) across four commonly-used murine TGR variants (Muta™Mouse lacZ, Muta™Mouse cII, gpt delta and BigBlue® lacI). Dose-response analyses provided no conclusive evidence that TGR variant choice significantly influences the derived genotoxic potency estimate. This conclusion was reliant upon taking into account the importance of comparing BMD confidence intervals as opposed to directly comparing PoD values (e.g., comparing BMDLs). Comparisons with earlier works suggested that with respect to potency determination, tissue choice is potentially more important than choice of TGR assay variant. Scoring multiple tissues selected on the basis of supporting toxicokinetic information is therefore recommended. Finally, we used typical within-group variances to estimate preliminary endpoint-specific benchmark response (BMR) values across several TGR variants/tissues. We discuss why such values are required for routine use of genetic toxicity PoDs for HHRA. Environ. Mol. Mutagen. 58:632-643, 2017. © 2017 Her Majesty the Queen in Right of Canada. Environmental and Molecular Mutagenesis Published by Wiley Periodicals, Inc.

Project description:Gene expression analysis has been established as a tool for the characterization of genotoxic mechanisms of chemical mutagens. This approach has been shown to differentiate between DNA reactive genotoxins and non-DNA reactive or indirectly-acting genotoxins. In this context, it has been suggested that expression analysis is capable of distinguishing compounds that cause DNA damage from those that interfere with mitotic spindle function. Formaldehyde (FA) is known to be a DNA-reactive substance which mainly induces chromosomal damage in cultured mammalian cells. However, there has been concern that FA might also act as an aneugen (i.e., induce aneuploidy) but recent cytogenetic studies did not support this assumption. To further characterize FA's genotoxic mode of action, we now used gene expression profiling as a molecular tool to differentiate between clastogenic and aneugenic activity. TK6 cells were exposed to FA for 4 and 24 h and changes in gene expression were analyzed using a whole-genome human microarray. Results were compared to the expression profiles of two DNA-damaging clastogens (methyl methanesulfonate [MMS] and ethyl methanesulfonate [EMS]) and two aneugens (colcemid [COL] and vincristine [VCR]). The gene expression profiles indicated that clastogens and aneugens induce discriminable gene expression patterns. The expression profile of FA showed more similarities to clastogens than to aneugens. Hierarchical clustering analysis as well as several class prediction algorithms revealed a much closer relationship of FA with clastogens than with aneugens. A pathway analysis of differentially regulated genes also demonstrated an overall better agreement of FA with clastogens than with aneugens. Altogether, the results of this study revealed great similarities in gene expression in response to FA and clastogens but did not support an aneugenic activity of FA. Gene expression analysis of untreated TK6 cells or TK6 cells treated with formaldehyde, the DNA-damaging clastogens methyl methanesulfonate or ethyl methanesulfonate, or the aneugens colcemid or vincristine.

Dataset Information

Predictions of genotoxic potential, mode of action, molecular targets, and potency via a tiered multiflow® assay data analysis strategy.

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Predictions of genotoxic potential, mode of action, molecular targets, and potency via a tiered multiflow® assay data analysis strategy.

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