Literature DB >> 32384157

A Rat Liver Transcriptomic Point of Departure Predicts a Prospective Liver or Non-liver Apical Point of Departure.

Kamin J Johnson1, Scott S Auerbach2, Eduardo Costa3.   

Abstract

Identifying a toxicity point of departure (POD) is a required step in human health risk characterization of crop protection molecules, and this POD has historically been derived from apical endpoints across a battery of animal-based toxicology studies. Using rat transcriptome and apical data for 79 molecules obtained from Open TG-GATES (Toxicogenomics Project-Genomics Assisted Toxicity Evaluation System) (632 datasets), the hypothesis was tested that a short-term exposure, transcriptome-based liver biological effect POD (BEPOD) could estimate a longer-term exposure "systemic" apical endpoint POD. Apical endpoints considered were body weight, clinical observation, kidney weight and histopathology and liver weight and histopathology. A BMDExpress algorithm using Gene Ontology Biological Process gene sets was optimized to derive a liver BEPOD most predictive of a systemic apical POD. Liver BEPODs were stable from 3 h to 29 days of exposure; the median fold difference of the 29-day BEPOD to BEPODs from earlier time points was approximately 1 (range: 0.7-1.1). Strong positive correlation (Pearson R = 0.86) and predictive accuracy (root mean square difference = 0.41) were observed between a concurrent (29 days) liver BEPOD and the systemic apical POD. Similar Pearson R and root mean square difference values were observed for comparisons between a 29-day systemic apical POD and liver BEPODs derived from 3 h to 15 days of exposure. These data across 79 molecules suggest that a longer-term exposure study apical POD from liver and non-liver compartments can be estimated using a liver BEPOD derived from an acute or subacute exposure study.
© The Author(s) 2020. Published by Oxford University Press on behalf of the Society of Toxicology. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Entities:  

Keywords:  apical effect; benchmark dose; point of departure; risk assessment; transcriptome

Mesh:

Year:  2020        PMID: 32384157      PMCID: PMC7357187          DOI: 10.1093/toxsci/kfaa062

Source DB:  PubMed          Journal:  Toxicol Sci        ISSN: 1096-0929            Impact factor:   4.849


  32 in total

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3.  BMDExpress 2: enhanced transcriptomic dose-response analysis workflow.

Authors:  Jason R Phillips; Daniel L Svoboda; Arpit Tandon; Shyam Patel; Alex Sedykh; Deepak Mav; Byron Kuo; Carole L Yauk; Longlong Yang; Russell S Thomas; Jeff S Gift; J Allen Davis; Louis Olszyk; B Alex Merrick; Richard S Paules; Fred Parham; Trey Saddler; Ruchir R Shah; Scott S Auerbach
Journal:  Bioinformatics       Date:  2019-05-15       Impact factor: 6.937

Review 4.  Benchmark dose (BMD) modeling: current practice, issues, and challenges.

Authors:  Lynne T Haber; Michael L Dourson; Bruce C Allen; Richard C Hertzberg; Ann Parker; Melissa J Vincent; Andrew Maier; Alan R Boobis
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Journal:  Regul Toxicol Pharmacol       Date:  2017-09-25       Impact factor: 3.271

6.  Temporal concordance between apical and transcriptional points of departure for chemical risk assessment.

Authors:  Russell S Thomas; Scott C Wesselkamper; Nina Ching Y Wang; Q Jay Zhao; Dan D Petersen; Jason C Lambert; Ila Cote; Longlong Yang; Eric Healy; Michael B Black; Harvey J Clewell; Bruce C Allen; Melvin E Andersen
Journal:  Toxicol Sci       Date:  2013-04-17       Impact factor: 4.849

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Journal:  Regul Toxicol Pharmacol       Date:  2018-02-21       Impact factor: 3.271

Review 9.  Adverse outcome pathways: a concise introduction for toxicologists.

Authors:  Mathieu Vinken; Dries Knapen; Lucia Vergauwen; Jan G Hengstler; Michelle Angrish; Maurice Whelan
Journal:  Arch Toxicol       Date:  2017-06-28       Impact factor: 5.153

Review 10.  Organ-Organ Crosstalk and Alcoholic Liver Disease.

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  8 in total

1.  A Rat Liver Transcriptomic Point of Departure Predicts a Prospective Liver or Non-liver Apical Point of Departure.

Authors:  Kamin J Johnson; Scott S Auerbach; Eduardo Costa
Journal:  Toxicol Sci       Date:  2020-07-01       Impact factor: 4.849

Review 2.  Using liver models generated from human-induced pluripotent stem cells (iPSCs) for evaluating chemical-induced modifications and disease across liver developmental stages.

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6.  A microRNA or messenger RNA point of departure estimates an apical endpoint point of departure in a rat developmental toxicity model.

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8.  Transcriptomic analyses of livers from mice exposed to 1,4-dioxane for up to 90 days to assess potential mode(s) of action underlying liver tumor development.

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