Literature DB >> 23596260

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

Russell S Thomas1, 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.   

Abstract

The number of legacy chemicals without toxicity reference values combined with the rate of new chemical development is overwhelming the capacity of the traditional risk assessment paradigm. More efficient approaches are needed to quantitatively estimate chemical risks. In this study, rats were dosed orally with multiple doses of six chemicals for 5 days and 2, 4, and 13 weeks. Target organs were analyzed for traditional histological and organ weight changes and transcriptional changes using microarrays. Histological and organ weight changes in this study and the tumor incidences in the original cancer bioassays were analyzed using benchmark dose (BMD) methods to identify noncancer and cancer points of departure. The dose-response changes in gene expression were also analyzed using BMD methods and the responses grouped based on signaling pathways. A comparison of transcriptional BMD values for the most sensitive pathway with BMD values for the noncancer and cancer apical endpoints showed a high degree of correlation at all time points. When the analysis included data from an earlier study with eight additional chemicals, transcriptional BMD values for the most sensitive pathway were significantly correlated with noncancer (r = 0.827, p = 0.0031) and cancer-related (r = 0.940, p = 0.0002) BMD values at 13 weeks. The average ratio of apical-to-transcriptional BMD values was less than two, suggesting that for the current chemicals, transcriptional perturbation did not occur at significantly lower doses than apical responses. Based on our results, we propose a practical framework for application of transcriptomic data to chemical risk assessment.

Entities:  

Keywords:  bioinformatics; dose response; microarray; risk assessment; safety evaluation; toxicogenomics

Mesh:

Substances:

Year:  2013        PMID: 23596260     DOI: 10.1093/toxsci/kft094

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


  48 in total

1.  Embracing Systems Toxicology at Single-Cell Resolution.

Authors:  Qiang Zhang; W Michael Caudle; Jingbo Pi; Sudin Bhattacharya; Melvin E Andersen; Norbert E Kaminski; Rory B Conolly
Journal:  Curr Opin Toxicol       Date:  2019-04-19

2.  FutureTox II: in vitro data and in silico models for predictive toxicology.

Authors:  Thomas B Knudsen; Douglas A Keller; Miriam Sander; Edward W Carney; Nancy G Doerrer; David L Eaton; Suzanne Compton Fitzpatrick; Kenneth L Hastings; Donna L Mendrick; Raymond R Tice; Paul B Watkins; Maurice Whelan
Journal:  Toxicol Sci       Date:  2015-02       Impact factor: 4.849

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

4.  Evaluation of 5-day In Vivo Rat Liver and Kidney With High-throughput Transcriptomics for Estimating Benchmark Doses of Apical Outcomes.

Authors:  William M Gwinn; Scott S Auerbach; Fred Parham; Matthew D Stout; Suramya Waidyanatha; Esra Mutlu; Brad Collins; Richard S Paules; Bruce Alex Merrick; Stephen Ferguson; Sreenivasa Ramaiahgari; John R Bucher; Barney Sparrow; Heather Toy; Jenni Gorospe; Nick Machesky; Ruchir R Shah; Michele R Balik-Meisner; Deepak Mav; Dhiral P Phadke; Georgia Roberts; Michael J DeVito
Journal:  Toxicol Sci       Date:  2020-08-01       Impact factor: 4.849

5.  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

6.  Technical guide for applications of gene expression profiling in human health risk assessment of environmental chemicals.

Authors:  Julie A Bourdon-Lacombe; Ivy D Moffat; Michelle Deveau; Mainul Husain; Scott Auerbach; Daniel Krewski; Russell S Thomas; Pierre R Bushel; Andrew Williams; Carole L Yauk
Journal:  Regul Toxicol Pharmacol       Date:  2015-05-02       Impact factor: 3.271

Review 7.  Adaptive Posttranslational Control in Cellular Stress Response Pathways and Its Relationship to Toxicity Testing and Safety Assessment.

Authors:  Qiang Zhang; Sudin Bhattacharya; Jingbo Pi; Rebecca A Clewell; Paul L Carmichael; Melvin E Andersen
Journal:  Toxicol Sci       Date:  2015-10       Impact factor: 4.849

8.  Population-based dose-response analysis of liver transcriptional response to trichloroethylene in mouse.

Authors:  Abhishek Venkatratnam; John S House; Kranti Konganti; Connor McKenney; David W Threadgill; Weihsueh A Chiu; David L Aylor; Fred A Wright; Ivan Rusyn
Journal:  Mamm Genome       Date:  2018-01-20       Impact factor: 2.957

9.  Comparative toxicity and liver transcriptomics of legacy and emerging brominated flame retardants following 5-day exposure in the rat.

Authors:  Keith R Shockley; Michelle C Cora; David E Malarkey; Daven Jackson-Humbles; Molly Vallant; Brad J Collins; Esra Mutlu; Veronica G Robinson; Surayma Waidyanatha; Amy Zmarowski; Nicholas Machesky; Jamie Richey; Sam Harbo; Emily Cheng; Kristin Patton; Barney Sparrow; June K Dunnick
Journal:  Toxicol Lett       Date:  2020-07-15       Impact factor: 4.372

10.  Differential reconstructed gene interaction networks for deriving toxicity threshold in chemical risk assessment.

Authors:  Yi Yang; Andrew Maxwell; Xiaowei Zhang; Nan Wang; Edward J Perkins; Chaoyang Zhang; Ping Gong
Journal:  BMC Bioinformatics       Date:  2013-10-09       Impact factor: 3.169

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