Pathway-based assessment of single chemicals and mixtures by a high-throughput transcriptomics approach

Pu Xia; Hanxin Zhang; Ying Peng; Wei Shi; Xiaowei Zhang

doi:10.1016/j.envint.2019.105455

Pathway-based assessment of single chemicals and mixtures by a high-throughput transcriptomics approach

Pu Xia, Hanxin Zhang, Ying Peng, Wei Shi, Xiaowei Zhang^*

^*Corresponding author for this work

Biosciences

Research output: Contribution to journal › Article › peer-review

Abstract

The ever-increasing number of chemicals and complex mixtures demands a high-throughput and cost-effective approach for chemical safety assessment. High-throughput transcriptomics (HTT) is promising in investigating genome-scale perturbation of chemical exposure in concentration-dependent manner. However, the application of HTT has been limited due to lack of methodology for single chemicals and mixture assessment. This study aimed to evaluate the ability of a newly-developed human reduced transcriptomics (RHT) approach to assess pathway-based profiles of single chemicals, and to develop a biological pathway-based approach for benchmarking mixture potency using single chemical-based prediction model. First, concentration-dependent RHT were used to qualitatively and quantitatively differentiate pathway-based patterns of different chemicals, using three model toxicants, 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD), triclosan (TCS) and 5-Chloro-6-hydroxy-2,2′,4,4′-tetrabromodiphenyl ether (5-Cl-6-OH-BDE-47). AHR-regulated genes and pathways were most sensitively induced by TCDD, while TCS and 5-Cl-6-OH-BDE-47 were much less potent in AHR-associated activation, which was concordant with known MoA of each single chemical. Second, two artificial mixtures and their components of twelve individual chemicals were performed with concentration-dependent RHT. Concentration addition (CA) and independent action (IA) models were used to predict transcriptional potency of mixtures from transcriptomics of individual chemicals. For overall bioactivity, CA and IA models can both predict potency of observed responses within 95% confidence interval. For specific biological processes, multiple biological processes such as hormone signaling and DNA damage can be predicted using CA models for mixtures. The concentration-dependent RHT can provide a powerful approach for qualitative and quantitative assessment of biological pathway perturbated by environment chemical and mixtures.

Original language	English
Article number	105455
Journal	Environment international
Volume	136
DOIs	https://doi.org/10.1016/j.envint.2019.105455
Publication status	Published - Mar 2020

Bibliographical note

Funding Information:
For support, we thank National Natural Science Foundation of China (grant no. 21677072 ), Major Science and Technology Program for Water Pollution Control and Treatment ( 2017ZX07602002 , & 2018ZX07208-002 ), and European Union FP7 the SOLUTIONS project ( 603437 ). P.X. was supported by Program B for Outstanding Ph.D. Candidates of Nanjing University (No. 201701B018 ), and Shanghai Tongji GaoTingyao Environmental Science and Technology Development Foundation . The research is also supported by the Fundamental Research Funds for the Central Universities . Appendix A

Funding Information:
For support, we thank National Natural Science Foundation of China (grant no. 21677072), Major Science and Technology Program for Water Pollution Control and Treatment (2017ZX07602002, & 2018ZX07208-002), and European Union FP7 the SOLUTIONS project (603437). P.X. was supported by Program B for Outstanding Ph.D. Candidates of Nanjing University (No. 201701B018), and Shanghai Tongji GaoTingyao Environmental Science and Technology Development Foundation. The research is also supported by the Fundamental Research Funds for the Central Universities.

Publisher Copyright:
© 2020 The Authors

Keywords

Biological potency
Concentration-dependent
Mixture assessment
Transcriptomics

ASJC Scopus subject areas

General Environmental Science

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10.1016/j.envint.2019.105455

Cite this

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title = "Pathway-based assessment of single chemicals and mixtures by a high-throughput transcriptomics approach",

abstract = "The ever-increasing number of chemicals and complex mixtures demands a high-throughput and cost-effective approach for chemical safety assessment. High-throughput transcriptomics (HTT) is promising in investigating genome-scale perturbation of chemical exposure in concentration-dependent manner. However, the application of HTT has been limited due to lack of methodology for single chemicals and mixture assessment. This study aimed to evaluate the ability of a newly-developed human reduced transcriptomics (RHT) approach to assess pathway-based profiles of single chemicals, and to develop a biological pathway-based approach for benchmarking mixture potency using single chemical-based prediction model. First, concentration-dependent RHT were used to qualitatively and quantitatively differentiate pathway-based patterns of different chemicals, using three model toxicants, 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD), triclosan (TCS) and 5-Chloro-6-hydroxy-2,2′,4,4′-tetrabromodiphenyl ether (5-Cl-6-OH-BDE-47). AHR-regulated genes and pathways were most sensitively induced by TCDD, while TCS and 5-Cl-6-OH-BDE-47 were much less potent in AHR-associated activation, which was concordant with known MoA of each single chemical. Second, two artificial mixtures and their components of twelve individual chemicals were performed with concentration-dependent RHT. Concentration addition (CA) and independent action (IA) models were used to predict transcriptional potency of mixtures from transcriptomics of individual chemicals. For overall bioactivity, CA and IA models can both predict potency of observed responses within 95% confidence interval. For specific biological processes, multiple biological processes such as hormone signaling and DNA damage can be predicted using CA models for mixtures. The concentration-dependent RHT can provide a powerful approach for qualitative and quantitative assessment of biological pathway perturbated by environment chemical and mixtures.",

keywords = "Biological potency, Concentration-dependent, Mixture assessment, Transcriptomics",

author = "Pu Xia and Hanxin Zhang and Ying Peng and Wei Shi and Xiaowei Zhang",

note = "Funding Information: For support, we thank National Natural Science Foundation of China (grant no. 21677072 ), Major Science and Technology Program for Water Pollution Control and Treatment ( 2017ZX07602002 , & 2018ZX07208-002 ), and European Union FP7 the SOLUTIONS project ( 603437 ). P.X. was supported by Program B for Outstanding Ph.D. Candidates of Nanjing University (No. 201701B018 ), and Shanghai Tongji GaoTingyao Environmental Science and Technology Development Foundation . The research is also supported by the Fundamental Research Funds for the Central Universities . Appendix A Funding Information: For support, we thank National Natural Science Foundation of China (grant no. 21677072), Major Science and Technology Program for Water Pollution Control and Treatment (2017ZX07602002, & 2018ZX07208-002), and European Union FP7 the SOLUTIONS project (603437). P.X. was supported by Program B for Outstanding Ph.D. Candidates of Nanjing University (No. 201701B018), and Shanghai Tongji GaoTingyao Environmental Science and Technology Development Foundation. The research is also supported by the Fundamental Research Funds for the Central Universities. Publisher Copyright: {\textcopyright} 2020 The Authors",

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doi = "10.1016/j.envint.2019.105455",

language = "English",

volume = "136",

journal = "Environment international",

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AU - Shi, Wei

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N1 - Funding Information: For support, we thank National Natural Science Foundation of China (grant no. 21677072 ), Major Science and Technology Program for Water Pollution Control and Treatment ( 2017ZX07602002 , & 2018ZX07208-002 ), and European Union FP7 the SOLUTIONS project ( 603437 ). P.X. was supported by Program B for Outstanding Ph.D. Candidates of Nanjing University (No. 201701B018 ), and Shanghai Tongji GaoTingyao Environmental Science and Technology Development Foundation . The research is also supported by the Fundamental Research Funds for the Central Universities . Appendix A Funding Information: For support, we thank National Natural Science Foundation of China (grant no. 21677072), Major Science and Technology Program for Water Pollution Control and Treatment (2017ZX07602002, & 2018ZX07208-002), and European Union FP7 the SOLUTIONS project (603437). P.X. was supported by Program B for Outstanding Ph.D. Candidates of Nanjing University (No. 201701B018), and Shanghai Tongji GaoTingyao Environmental Science and Technology Development Foundation. The research is also supported by the Fundamental Research Funds for the Central Universities. Publisher Copyright: © 2020 The Authors

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AB - The ever-increasing number of chemicals and complex mixtures demands a high-throughput and cost-effective approach for chemical safety assessment. High-throughput transcriptomics (HTT) is promising in investigating genome-scale perturbation of chemical exposure in concentration-dependent manner. However, the application of HTT has been limited due to lack of methodology for single chemicals and mixture assessment. This study aimed to evaluate the ability of a newly-developed human reduced transcriptomics (RHT) approach to assess pathway-based profiles of single chemicals, and to develop a biological pathway-based approach for benchmarking mixture potency using single chemical-based prediction model. First, concentration-dependent RHT were used to qualitatively and quantitatively differentiate pathway-based patterns of different chemicals, using three model toxicants, 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD), triclosan (TCS) and 5-Chloro-6-hydroxy-2,2′,4,4′-tetrabromodiphenyl ether (5-Cl-6-OH-BDE-47). AHR-regulated genes and pathways were most sensitively induced by TCDD, while TCS and 5-Cl-6-OH-BDE-47 were much less potent in AHR-associated activation, which was concordant with known MoA of each single chemical. Second, two artificial mixtures and their components of twelve individual chemicals were performed with concentration-dependent RHT. Concentration addition (CA) and independent action (IA) models were used to predict transcriptional potency of mixtures from transcriptomics of individual chemicals. For overall bioactivity, CA and IA models can both predict potency of observed responses within 95% confidence interval. For specific biological processes, multiple biological processes such as hormone signaling and DNA damage can be predicted using CA models for mixtures. The concentration-dependent RHT can provide a powerful approach for qualitative and quantitative assessment of biological pathway perturbated by environment chemical and mixtures.

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Pathway-based assessment of single chemicals and mixtures by a high-throughput transcriptomics approach

Abstract

Bibliographical note

Keywords

ASJC Scopus subject areas

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