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Chen C, Huang Z, Zou X, Li S, Zhang D, Wang SL. Prediction of molecular-specific mutagenic alerts and related mechanisms of chemicals by a convolutional neural network (CNN) model based on SMILES split. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 917:170435. [PMID: 38286298 DOI: 10.1016/j.scitotenv.2024.170435] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/11/2023] [Revised: 01/20/2024] [Accepted: 01/23/2024] [Indexed: 01/31/2024]
Abstract
Structural alerts (SAs) are essential to identify chemicals for toxicity evaluation and health risk assessment. We constructed a novel SMILES split-based deep learning model (SSDL) that was trained and verified with 5850 chemicals from the ISSSTY database and 384 external test chemicals from published papers. The training accuracy was above 0.90 and the evaluation metrics (precision, recall and F1-score) all reached 0.78 or above on both internal and external test chemicals. In this model, the molecular-specific fragment importance of chemicals was first quantified independently. Then, the SA identification method based on the importance of these fragments was statistically analyzed and verified with the ISSSTY test and external test chemicals containing one of 28 typical SAs, and most of the performances were better than that of expert rules. Furthermore, a mutagenicity mechanism prediction method was developed using 237 chemicals with four known mutagenic mechanisms based on molecular similarity calibrated by the SSDL method and fragment importance, which significantly improved accuracy in three mechanisms and had comparable accuracy in the other one compared to traditional methods. Overall, the SSDL model quantifying fragment toxicity within molecules would be a novel potentially powerful tool in the determination and visualization of molecular-specific SAs and the prediction of mutagenicity mechanisms for environmental or industrial compounds and drugs.
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Affiliation(s)
- Chao Chen
- Key Laboratory of Modern Toxicology of Ministry of Education, Center for Global Health, School of Public Health, Nanjing Medical University, 101 Longmian Avenue, Nanjing 211166, PR China
| | - Zhengliang Huang
- Key Laboratory of Modern Toxicology of Ministry of Education, Center for Global Health, School of Public Health, Nanjing Medical University, 101 Longmian Avenue, Nanjing 211166, PR China; School of Public Health, Hubei University of Medicine, Shiyan 442000, PR China
| | - Xuyan Zou
- Key Laboratory of Modern Toxicology of Ministry of Education, Center for Global Health, School of Public Health, Nanjing Medical University, 101 Longmian Avenue, Nanjing 211166, PR China
| | - Sheng Li
- Key Laboratory of Modern Toxicology of Ministry of Education, Center for Global Health, School of Public Health, Nanjing Medical University, 101 Longmian Avenue, Nanjing 211166, PR China
| | - Di Zhang
- Key Laboratory of Modern Toxicology of Ministry of Education, Center for Global Health, School of Public Health, Nanjing Medical University, 101 Longmian Avenue, Nanjing 211166, PR China
| | - Shou-Lin Wang
- Key Laboratory of Modern Toxicology of Ministry of Education, Center for Global Health, School of Public Health, Nanjing Medical University, 101 Longmian Avenue, Nanjing 211166, PR China; State Key Lab of Reproductive Medicine and Offspring Health, Institute of Toxicology, Nanjing Medical University, 101 Longmian Avenue, Nanjing 211166, PR China.
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2
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Zhang H, Yi H, Hao Y, Zhao L, Pan W, Xue Q, Liu X, Fu J, Zhang A. Deciphering exogenous chemical carcinogenicity through interpretable deep learning: A novel approach for evaluating atmospheric pollutant hazards. JOURNAL OF HAZARDOUS MATERIALS 2024; 465:133092. [PMID: 38039812 DOI: 10.1016/j.jhazmat.2023.133092] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Revised: 11/07/2023] [Accepted: 11/23/2023] [Indexed: 12/03/2023]
Abstract
Cancer remains a significant global health concern, with millions of deaths attributed to it annually. Environmental pollutants play a pivotal role in cancer etiology and contribute to the growing prevalence of this disease. The carcinogenic assessment of these pollutants is crucial for chemical health evaluation and environmental risk assessments. Traditional experimental methods are expensive and time-consuming, prompting the development of alternative approaches such as in silico methods. In this regard, deep learning (DL) has shown potential but lacks optimal performance and interpretability. This study introduces an interpretable DL model called CarcGC for chemical carcinogenicity prediction, utilizing a graph convolutional neural network (GCN) that employs molecular structural graphs as inputs. Compared to existing models, CarcGC demonstrated enhanced performance, with the area under the receiver operating characteristic curve (AUCROC) reaching 0.808 on the test set. Due to air pollution is closely related to the incidence of lung cancers, we applied the CarcGC to predict the potential carcinogenicity of chemicals listed in the United States Environmental Protection Agency's Hazardous Air Pollutants (HAPs) inventory, offering a foundation for environmental carcinogenicity screening. This study highlights the potential of artificially intelligent methods in carcinogenicity prediction and underscores the value of CarcGC interpretability in revealing the structural basis and molecular mechanisms underlying chemical carcinogenicity.
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Affiliation(s)
- Huazhou Zhang
- State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, PR China; College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100190, PR China
| | - Hang Yi
- State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, PR China; College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100190, PR China
| | - Yuxing Hao
- State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, PR China; Sino-Danish College, University of Chinese Academy of Sciences, Beijing 100049, PR China
| | - Lu Zhao
- State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, PR China; Sino-Danish College, University of Chinese Academy of Sciences, Beijing 100049, PR China
| | - Wenxiao Pan
- State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, PR China
| | - Qiao Xue
- State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, PR China
| | - Xian Liu
- State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, PR China; College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100190, PR China.
| | - Jianjie Fu
- State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, PR China; School of Environment, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou 310012, PR China; College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100190, PR China; Institute of Environment and Health, Jianghan University, Wuhan 430056, PR China
| | - Aiqian Zhang
- State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, PR China; School of Environment, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou 310012, PR China; College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100190, PR China; Institute of Environment and Health, Jianghan University, Wuhan 430056, PR China.
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3
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Liu X, Guo Y, Pan W, Xue Q, Fu J, Qu G, Zhang A. Exogenous Chemicals Impact Virus Receptor Gene Transcription: Insights from Deep Learning. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2023; 57:18038-18047. [PMID: 37186679 DOI: 10.1021/acs.est.2c09837] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/17/2023]
Abstract
Despite the fact that coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has been disrupting human life and health worldwide since the outbreak in late 2019, the impact of exogenous substance exposure on the viral infection remains unclear. It is well-known that, during viral infection, organism receptors play a significant role in mediating the entry of viruses to enter host cells. A major receptor of SARS-CoV-2 is the angiotensin-converting enzyme 2 (ACE2). This study proposes a deep learning model based on the graph convolutional network (GCN) that enables, for the first time, the prediction of exogenous substances that affect the transcriptional expression of the ACE2 gene. It outperforms other machine learning models, achieving an area under receiver operating characteristic curve (AUROC) of 0.712 and 0.703 on the validation and internal test set, respectively. In addition, quantitative polymerase chain reaction (qPCR) experiments provided additional supporting evidence for indoor air pollutants identified by the GCN model. More broadly, the proposed methodology can be applied to predict the effect of environmental chemicals on the gene transcription of other virus receptors as well. In contrast to typical deep learning models that are of black box nature, we further highlight the interpretability of the proposed GCN model and how it facilitates deeper understanding of gene change at the structural level.
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Affiliation(s)
- Xian Liu
- State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, P. R. China
| | - Yunhe Guo
- State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, P. R. China
| | - Wenxiao Pan
- State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, P. R. China
| | - Qiao Xue
- State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, P. R. China
| | - Jianjie Fu
- State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, P. R. China
- School of Environment, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou 310012, P. R. China
- College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100190, P. R. China
- Institute of Environment and Health, Jianghan University, Wuhan 430056, P.R. China
| | - Guangbo Qu
- State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, P. R. China
- School of Environment, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou 310012, P. R. China
- College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100190, P. R. China
| | - Aiqian Zhang
- State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, P. R. China
- School of Environment, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou 310012, P. R. China
- College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100190, P. R. China
- Institute of Environment and Health, Jianghan University, Wuhan 430056, P.R. China
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4
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Mittal A, Ahuja G. Advancing chemical carcinogenicity prediction modeling: opportunities and challenges. Trends Pharmacol Sci 2023; 44:400-410. [PMID: 37183054 DOI: 10.1016/j.tips.2023.04.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2023] [Revised: 04/11/2023] [Accepted: 04/18/2023] [Indexed: 05/16/2023]
Abstract
Carcinogenicity assessment of any compound is a laborious and expensive exercise with several associated ethical and practical concerns. While artificial intelligence (AI) offers promising solutions, unfortunately, it is contingent on several challenges concerning the inadequacy of available experimentally validated (non)carcinogen datasets and variabilities within bioassays, which contribute to the compromised model training. Existing AI solutions that leverage classical chemistry-driven descriptors do not provide adequate biological interpretability involved in imparting carcinogenicity. This highlights the urgency to devise alternative AI strategies. We propose multiple strategies, including implementing data-driven (integrated databases) and known carcinogen-characteristic-derived features to overcome these apparent shortcomings. In summary, these next-generation approaches will continue facilitating robust chemical carcinogenicity prediction, concomitant with deeper mechanistic insights.
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Affiliation(s)
- Aayushi Mittal
- Department of Computational Biology, Indraprastha Institute of Information Technology-Delhi (IIIT-Delhi), Okhla, Phase III, New Delhi, 110020, India.
| | - Gaurav Ahuja
- Department of Computational Biology, Indraprastha Institute of Information Technology-Delhi (IIIT-Delhi), Okhla, Phase III, New Delhi, 110020, India.
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5
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Gosline SJC, Kim DN, Pande P, Thomas DG, Truong L, Hoffman P, Barton M, Loftus J, Moran A, Hampton S, Dowson S, Franklin L, Degnan D, Anderson L, Thessen A, Tanguay RL, Anderson KA, Waters KM. The Superfund Research Program Analytics Portal: linking environmental chemical exposure to biological phenotypes. Sci Data 2023; 10:151. [PMID: 36944655 PMCID: PMC10030892 DOI: 10.1038/s41597-023-02021-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Accepted: 02/14/2023] [Indexed: 03/23/2023] Open
Abstract
The OSU/PNNL Superfund Research Program (SRP) represents a longstanding collaboration to quantify Polycyclic Aromatic Hydrocarbons (PAHs) at various superfund sites in the Pacific Northwest and assess their potential impact on human health. To link the chemical measurements to biological activity, we describe the use of the zebrafish as a high-throughput developmental toxicity model that provides quantitative measurements of the exposure to chemicals. Toward this end, we have linked over 150 PAHs found at Superfund sites to the effect of these same chemicals in zebrafish, creating a rich dataset that links environmental exposure to biological response. To quantify this response, we have implemented a dose-response modelling pipeline to calculate benchmark dose parameters which enable potency comparison across over 500 chemicals and 12 of the phenotypes measured in zebrafish. We provide a rich dataset for download and analysis as well as a web portal that provides public access to this dataset via an interactive web site designed to support exploration and re-use of these data by the scientific community at http://srp.pnnl.gov .
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Affiliation(s)
| | - Doo Nam Kim
- Pacific Northwest National Laboratory, Richland, WA, USA
| | - Paritosh Pande
- Pacific Northwest National Laboratory, Richland, WA, USA
| | | | | | | | | | - Joseph Loftus
- Pacific Northwest National Laboratory, Richland, WA, USA
| | - Addy Moran
- Pacific Northwest National Laboratory, Richland, WA, USA
| | - Shawn Hampton
- Pacific Northwest National Laboratory, Richland, WA, USA
| | - Scott Dowson
- Pacific Northwest National Laboratory, Richland, WA, USA
| | | | - David Degnan
- Pacific Northwest National Laboratory, Richland, WA, USA
| | | | - Anne Thessen
- University of Colorado Anschutz Medical School, Denver, CO, USA
| | | | | | - Katrina M Waters
- Pacific Northwest National Laboratory, Richland, WA, USA.
- Oregon State University, Corvallis, WA, USA.
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Lu H, Yang D, Shi Y, Chen K, Li P, Huang S, Cui D, Feng Y, Wang T, Yang J, Zhu X, Xia D, Wu Y. Toxicogenomics scoring system: TGSS, a novel integrated risk assessment model for chemical carcinogenicity prediction. ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY 2023; 250:114466. [PMID: 36587411 DOI: 10.1016/j.ecoenv.2022.114466] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/18/2022] [Revised: 12/05/2022] [Accepted: 12/22/2022] [Indexed: 06/17/2023]
Abstract
BACKGROUND Given the increasing exposure of humans to environmental chemicals and the limitations of conventional toxicity test, there is an urgent need to develop next-generation risk assessment methods. OBJECTIVES This study aims to establish a novel computational system named Toxicogenomics Scoring System (TGSS) to predict the carcinogenicity of chemicals coupling chemical-gene interactions with multiple cancer transcriptomic datasets. METHODS Chemical-related gene signatures were derived from chemical-gene interaction data from the Comparative Toxicogenomics Database (CTD). For each cancer type in TCGA, genes were ranked by their effects on tumorigenesis, which is based on the differential expression between tumor and normal samples. Next, we developed carcinogenicity scores (C-scores) using pre-ranked GSEA to quantify the correlation between chemical-related gene signatures and ranked gene lists. Then we established TGSS by systematically evaluating the C-scores in multiple chemical-tumor pairs. Furthermore, we examined the performance of our approach by ROC curves or prognostic analyses in TCGA and multiple independent cancer cohorts. RESULTS Forty-six environmental chemicals were finally included in the study. C-score was calculated for each chemical-tumor pair. The C-scores of IARC Group 3 chemicals were significantly lower than those of chemicals in Group 1 (P-value = 0.02) and Group 2 (P-values = 7.49 ×10-5). ROC curves analysis indicated that C-score could distinguish "high-risk chemicals" from the other compounds (AUC = 0.67) with a specificity and sensitivity of 0.86 and 0.57. The results of survival analysis were also in line with the assessed carcinogenicity in TGSS for the chemicals in Group 1. Finally, consistent results were further validated in independent cancer cohorts. CONCLUSION TGSS highlighted the great potential of integrating chemical-gene interactions with gene-cancer relationships to predict the carcinogenic risk of chemicals, which would be valuable for systems toxicology.
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Affiliation(s)
- Haohua Lu
- Department of Toxicology of School of Public Health and Department of Gynecologic Oncology of Women's Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China
| | - Dexin Yang
- Department of Toxicology of School of Public Health and Department of Gynecologic Oncology of Women's Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China
| | - Yu Shi
- The State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China
| | - Kelie Chen
- Department of Toxicology of School of Public Health and Department of Gynecologic Oncology of Women's Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China
| | - Peiwei Li
- Department of Gastroenterology, The Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China
| | - Sisi Huang
- Department of Toxicology of School of Public Health and Department of Gynecologic Oncology of Women's Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China
| | - Dongyu Cui
- Department of Toxicology of School of Public Health and Department of Gynecologic Oncology of Women's Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China
| | - Yuqin Feng
- Department of Toxicology of School of Public Health and Department of Gynecologic Oncology of Women's Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China
| | - Tianru Wang
- Epidemiology Stream, Dalla Lana School of Public Health, University of Toronto, M5T 3M7 ON, Canada
| | - Jun Yang
- Department of Public Health, School of Medicine, Hangzhou Normal University, Hangzhou, Zhejiang, China; Zhejiang Provincial Center for Uterine Cancer Diagnosis and Therapy Research of Women's Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China
| | - Xinqiang Zhu
- Central Laboratory of the Fourth Affiliated Hospital, School of Medicine, Zhejiang University, Yiwu, Zhejiang, China
| | - Dajing Xia
- Department of Toxicology of School of Public Health and Department of Gynecologic Oncology of Women's Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China.
| | - Yihua Wu
- Department of Toxicology of School of Public Health and Department of Gynecologic Oncology of Women's Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China; Research Unit of Intelligence Classification of Tumor Pathology and Precision Therapy, Chinese Academy of Medical Sciences (2019RU042), Hangzhou, Zhejiang, China.
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7
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Amolegbe SM, Carlin DJ, Henry HF, Heacock ML, Trottier BA, Suk WA. Understanding exposures and latent disease risk within the National Institute of Environmental Health Sciences Superfund Research Program. Exp Biol Med (Maywood) 2022; 247:529-537. [PMID: 35253496 DOI: 10.1177/15353702221079620] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
Abstract
Understanding the health effects of exposures when there is a lag between exposure and the onset of disease is an important and challenging topic in environmental health research. The National Institute of Environmental Health Sciences (NIEHS) Superfund Basic Research and Training Program (SRP) is a National Institutes of Health (NIH) grant program that uses a multidisciplinary approach to support biomedical and environmental science and engineering research. Because of the multidisciplinary nature of the program, SRP grantees are well-positioned to study exposure and latent disease risk across humans, animal models, and various life stages. SRP-funded scientists are working to address the challenge of connecting exposures that occur early in life and prior to conception with diseases that manifest much later, including developing new tools and approaches to predict how chemicals may affect long-term health. Here, we highlight research from the SRP focused on understanding the health effects of exposures with a lag between exposure and the onset of the disease as well as provide future directions for addressing knowledge gaps for this highly complex and challenging topic. Advancing the knowledge of latency to disease will require a multidisciplinary approach to research, the need for data sharing and integration, and new tools and computation approaches to make better predications about the timing of disease onset. A better understanding of exposures that may contribute to later-life diseases is essential to supporting the implementation of prevention and intervention strategies to reduce or modulate exposures to reduce disease burden.
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Affiliation(s)
- Sara M Amolegbe
- Superfund Research Program, National Institute of Environmental Health Sciences (NIEHS), National Institutes of Health (NIH), Department of Health and Human Services (DHHS), Research Triangle Park, NC 27560, USA
| | - Danielle J Carlin
- Superfund Research Program, National Institute of Environmental Health Sciences (NIEHS), National Institutes of Health (NIH), Department of Health and Human Services (DHHS), Research Triangle Park, NC 27560, USA
| | - Heather F Henry
- Superfund Research Program, National Institute of Environmental Health Sciences (NIEHS), National Institutes of Health (NIH), Department of Health and Human Services (DHHS), Research Triangle Park, NC 27560, USA
| | - Michelle L Heacock
- Superfund Research Program, National Institute of Environmental Health Sciences (NIEHS), National Institutes of Health (NIH), Department of Health and Human Services (DHHS), Research Triangle Park, NC 27560, USA
| | - Brittany A Trottier
- Superfund Research Program, National Institute of Environmental Health Sciences (NIEHS), National Institutes of Health (NIH), Department of Health and Human Services (DHHS), Research Triangle Park, NC 27560, USA
| | - William A Suk
- Superfund Research Program, National Institute of Environmental Health Sciences (NIEHS), National Institutes of Health (NIH), Department of Health and Human Services (DHHS), Research Triangle Park, NC 27560, USA
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Schüttler A, Jakobs G, Fix J, Krauss M, Krüger J, Leuthold D, Altenburger R, Busch W. Transcriptome-Wide Prediction and Measurement of Combined Effects Induced by Chemical Mixture Exposure in Zebrafish Embryos. ENVIRONMENTAL HEALTH PERSPECTIVES 2021; 129:47006. [PMID: 33826412 PMCID: PMC8041271 DOI: 10.1289/ehp7773] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/17/2023]
Abstract
BACKGROUND Humans and environmental organisms are constantly exposed to complex mixtures of chemicals. Extending our knowledge about the combined effects of chemicals is thus essential for assessing the potential consequences of these exposures. In this context, comprehensive molecular readouts as retrieved by omics techniques are advancing our understanding of the diversity of effects upon chemical exposure. This is especially true for effects induced by chemical concentrations that do not instantaneously lead to mortality, as is commonly the case for environmental exposures. However, omics profiles induced by chemical exposures have rarely been systematically considered in mixture contexts. OBJECTIVES In this study, we aimed to investigate the predictability of chemical mixture effects on the whole-transcriptome scale. METHODS We predicted and measured the toxicogenomic effects of a synthetic mixture on zebrafish embryos. The mixture contained the compounds diuron, diclofenac, and naproxen. To predict concentration- and time-resolved whole-transcriptome responses to the mixture exposure, we adopted the mixture concept of concentration addition. Predictions were based on the transcriptome profiles obtained for the individual mixture components in a previous study. Finally, concentration- and time-resolved mixture exposures and subsequent toxicogenomic measurements were performed and the results were compared with the predictions. RESULTS This comparison of the predictions with the observations showed that the concept of concentration addition provided reasonable estimates for the effects induced by the mixture exposure on the whole transcriptome. Although nonadditive effects were observed only occasionally, combined, that is, multicomponent-driven, effects were found for mixture components with anticipated similar, as well as dissimilar, modes of action. DISCUSSION Overall, this study demonstrates that using a concentration- and time-resolved approach, the occurrence and size of combined effects of chemicals may be predicted at the whole-transcriptome scale. This allows improving effect assessment of mixture exposures on the molecular scale that might not only be of relevance in terms of risk assessment but also for pharmacological applications. https://doi.org/10.1289/EHP7773.
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Affiliation(s)
- A. Schüttler
- Department Bioanalytical Ecotoxicology, Helmholtz Centre for Environmental Research (UFZ), Leipzig, Germany
- Institute for Environmental Research, Rheinisch-Westfälische Technische Hochschule Aachen, Aachen, Germany
| | - G. Jakobs
- Department Bioanalytical Ecotoxicology, Helmholtz Centre for Environmental Research (UFZ), Leipzig, Germany
| | - J.M. Fix
- Department Bioanalytical Ecotoxicology, Helmholtz Centre for Environmental Research (UFZ), Leipzig, Germany
| | - M. Krauss
- Department Effect-Directed Analysis, UFZ, Leipzig, Germany
| | - J. Krüger
- Department Bioanalytical Ecotoxicology, Helmholtz Centre for Environmental Research (UFZ), Leipzig, Germany
| | - D. Leuthold
- Department Bioanalytical Ecotoxicology, Helmholtz Centre for Environmental Research (UFZ), Leipzig, Germany
| | - R. Altenburger
- Department Bioanalytical Ecotoxicology, Helmholtz Centre for Environmental Research (UFZ), Leipzig, Germany
- Institute for Environmental Research, Rheinisch-Westfälische Technische Hochschule Aachen, Aachen, Germany
| | - W. Busch
- Department Bioanalytical Ecotoxicology, Helmholtz Centre for Environmental Research (UFZ), Leipzig, Germany
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9
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Kripke M, Brody JG, Hawk E, Hernandez AB, Hoppin PJ, Jacobs MM, Rudel RA, Rebbeck TR. Rethinking Environmental Carcinogenesis. Cancer Epidemiol Biomarkers Prev 2020; 29:1870-1875. [PMID: 33004408 DOI: 10.1158/1055-9965.epi-20-0541] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2020] [Revised: 05/07/2020] [Accepted: 06/11/2020] [Indexed: 11/16/2022] Open
Abstract
The 2010 report of the President's Cancer Panel concluded that the burden of cancer from chemical exposures is substantial, while the programs for testing and regulation of carcinogens remain inadequate. New research on the role of early life exposures and the ability of chemicals to act via multiple biological pathways, including immunosuppression, inflammation, and endocrine disruption as well as mutagenesis, further supports the potential for chemicals and chemical mixtures to influence disease. Epidemiologic observations, such as higher leukemia incidence in children living near roadways and industrial sources of air pollution, and new in vitro technologies that decode carcinogenesis at the molecular level, illustrate the diverse evidence that primary prevention of some cancers may be achieved by reducing harmful chemical exposures. The path forward requires cross-disciplinary approaches, increased environmental research investment, system-wide collaboration to develop safer economic alternatives, and community engagement to support evidence-informed action. Engagement by cancer researchers to integrate environmental risk factors into prevention initiatives holds tremendous promise for reducing the rates of disease.See all articles in this CEBP Focus section, "Environmental Carcinogenesis: Pathways to Prevention."
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Affiliation(s)
- Margaret Kripke
- The University of Texas MD Anderson Cancer Center, Houston, Texas
| | | | - Ernest Hawk
- The University of Texas MD Anderson Cancer Center, Houston, Texas
| | | | - Polly J Hoppin
- University of Massachusetts Lowell and Lowell Center for Sustainable Production, Lowell, Massachusetts
| | - Molly M Jacobs
- University of Massachusetts Lowell and Lowell Center for Sustainable Production, Lowell, Massachusetts
| | | | - Timothy R Rebbeck
- Dana-Farber Cancer Institute and Harvard TH Chan School of Public Health, Boston, Massachusetts.
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10
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Pain G, Hickey G, Mondou M, Crump D, Hecker M, Basu N, Maguire S. Drivers of and Obstacles to the Adoption of Toxicogenomics for Chemical Risk Assessment: Insights from Social Science Perspectives. ENVIRONMENTAL HEALTH PERSPECTIVES 2020; 128:105002. [PMID: 33112659 PMCID: PMC7592882 DOI: 10.1289/ehp6500] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/05/2023]
Abstract
BACKGROUND Some 20 y ago, scientific and regulatory communities identified the potential of omics sciences (genomics, transcriptomics, proteomics, metabolomics) to improve chemical risk assessment through development of toxicogenomics. Recognizing that regulators adopt new scientific methods cautiously given accountability to diverse stakeholders, the scope and pace of adoption of toxicogenomics tools and data have nonetheless not met the ambitious, early expectations of omics proponents. OBJECTIVE Our objective was, therefore, to inventory, investigate, and derive insights into drivers of and obstacles to adoption of toxicogenomics in chemical risk assessment. By invoking established social science frameworks conceptualizing innovation adoption, we also aimed to develop recommendations for proponents of toxicogenomics and other new approach methodologies (NAMs). METHODS We report findings from an analysis of 56 scientific and regulatory publications from 1998 through 2017 that address the adoption of toxicogenomics for chemical risk assessment. From this purposeful sample of toxicogenomics discourse, we identified major categories of drivers of and obstacles to adoption of toxicogenomics tools and data sets. We then mapped these categories onto social science frameworks for conceptualizing innovation adoption to generate actionable insights for proponents of toxicogenomics. DISCUSSION We identify the most salient drivers and obstacles. From 1998 through 2017, adoption of toxicogenomics was understood to be helped by drivers such as those we labeled Superior scientific understanding, New applications, and Reduced cost & increased efficiency but hindered by obstacles such as those we labeled Insufficient validation, Complexity of interpretation, and Lack of standardization. Leveraging social science frameworks, we find that arguments for adoption that draw on the most salient drivers, which emphasize superior and novel functionality of omics as rationales, overlook potential adopters' key concerns: simplicity of use and compatibility with existing practices. We also identify two perspectives-innovation-centric and adopter-centric-on omics adoption and explain how overreliance on the former may be undermining efforts to promote toxicogenomics. https://doi.org/10.1289/EHP6500.
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Affiliation(s)
- Guillaume Pain
- Faculté des sciences de l’administration, Université Laval, Sainte-Foy, Québec, Canada
| | - Gordon Hickey
- Faculty of Agricultural and Environmental Sciences, McGill University, Sainte Anne de Bellevue, Quebec, Canada
| | - Matthieu Mondou
- Faculty of Agricultural and Environmental Sciences, McGill University, Sainte Anne de Bellevue, Quebec, Canada
| | - Doug Crump
- National Wildlife Research Center, Environment and Climate Change Canada, Ottawa, Ontario, Canada
| | - Markus Hecker
- Toxicology Center and School of the Environment & Sustainability, University of Saskatchewan, Saskatoon, Saskatchewan, Canada
| | - Niladri Basu
- Faculty of Agricultural and Environmental Sciences, McGill University, Sainte Anne de Bellevue, Quebec, Canada
| | - Steven Maguire
- University of Sydney Business School and University of Sydney Nano Institute, Sydney, New South Wales, Australia; Department of Chemistry, Faculty of Science, McGill University, Montreal, Quebec, Canada
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11
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Kang JC, Valerio LG. Investigating DNA adduct formation by flavor chemicals and tobacco byproducts in electronic nicotine delivery system (ENDS) using in silico approaches. Toxicol Appl Pharmacol 2020; 398:115026. [PMID: 32353386 DOI: 10.1016/j.taap.2020.115026] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2020] [Revised: 04/24/2020] [Accepted: 04/26/2020] [Indexed: 01/04/2023]
Abstract
The presence of flavors is one of the commonly cited reasons for use of e-cigarettes by youth; however, the potential harms from inhaling these chemicals and byproducts have not been extensively studied. One mechanism of interest is DNA adduct formation, which may lead to carcinogenesis. We identified two chemical classes of flavors found in tobacco products and byproducts, alkenylbenzenes and aldehydes, documented to form DNA adducts. Using in silico toxicology approaches, we identified structural analogs to these chemicals without DNA adduct information. We conducted a structural similarity analysis and also generated in silico model predictions of these chemicals for genotoxicity, mutagenicity, carcinogenicity, and skin sensitization. The empirical and in silico data were compared, and we identified strengths and limitations of these models. Good concordance (80-100%) was observed between DNA adduct formation and models predicting mammalian mutagenicity (mouse lymphoma sassy L5178Y) and skin sensitization for both chemical classes. On the other hand, different prediction profiles were observed for the two chemical classes for the modeled endpoints, unscheduled DNA synthesis and bacterial mutagenicity. These results are likely due to the different mode of action between the two chemical classes, as aldehydes are direct acting agents, while alkenylbenzenes require bioactivation to form electrophilic intermediates, which form DNA adducts. The results of this study suggest that an in silico prediction for the mouse lymphoma assay L5178Y, may serve as a surrogate endpoint to help predict DNA adduct formation for chemicals found in tobacco products such as flavors and byproducts.
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Affiliation(s)
- Jueichuan Connie Kang
- United States Food and Drug Administration, Center for Tobacco Products, Office of Science, Division of Nonclinical Science, 11785 Beltsville Drive, Calverton, MD 20705, USA; US Public Health Service Commissioned Corps, Rockville, MD, USA.
| | - Luis G Valerio
- United States Food and Drug Administration, Center for Tobacco Products, Office of Science, Division of Nonclinical Science, 11785 Beltsville Drive, Calverton, MD 20705, USA
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12
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Nicolaidou V, Koufaris C. Application of transcriptomic and microRNA profiling in the evaluation of potential liver carcinogens. Toxicol Ind Health 2020; 36:386-397. [PMID: 32419640 DOI: 10.1177/0748233720922710] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Hepatocarcinogens are agents that increase the incidence of liver cancer in exposed animals or humans. It is now established that carcinogenic exposures have a widespread impact on the transcriptome, inducing both adaptive and adverse changes in the activities of genes and pathways. Chemical hepatocarcinogens have also been shown to affect expression of microRNA (miRNA), the evolutionarily conserved noncoding RNA that regulates gene expression posttranscriptionally. Considerable effort has been invested into examining the involvement of mRNA in chemical hepatocarcinogenesis and their potential usage for the classification and prediction of new chemical entities. For miRNA, there has been an increasing number of studies reported over the past decade, although not to the same degree as for transcriptomic studies. Current data suggest that it is unlikely that any gene or miRNA signature associated with short-term carcinogen exposure can replace the rodent bioassay. In this review, we discuss the application of transcriptomic and miRNA profiles to increase mechanistic understanding of chemical carcinogens and to aid in their classification.
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Affiliation(s)
- Vicky Nicolaidou
- Department of Life and Health Sciences, University of Nicosia, Nicosia, Cyprus
| | - Costas Koufaris
- Department of Biological Sciences, University of Cyprus, Nicosia, Cyprus
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13
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Baillif B, Wichard J, Méndez-Lucio O, Rouquié D. Exploring the Use of Compound-Induced Transcriptomic Data Generated From Cell Lines to Predict Compound Activity Toward Molecular Targets. Front Chem 2020; 8:296. [PMID: 32391323 PMCID: PMC7191531 DOI: 10.3389/fchem.2020.00296] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2019] [Accepted: 03/25/2020] [Indexed: 12/17/2022] Open
Abstract
Pharmaceutical or phytopharmaceutical molecules rely on the interaction with one or more specific molecular targets to induce their anticipated biological responses. Nonetheless, these compounds are also prone to interact with many other non-intended biological targets, also known as off-targets. Unfortunately, off-target identification is difficult and expensive. Consequently, QSAR models predicting the activity on a target have gained importance in drug discovery or in the de-risking of chemicals. However, a restricted number of targets are well characterized and hold enough data to build such in silico models. A good alternative to individual target evaluations is to use integrative evaluations such as transcriptomics obtained from compound-induced gene expression measurements derived from cell cultures. The advantage of these particular experiments is to capture the consequences of the interaction of compounds on many possible molecular targets and biological pathways, without having any constraints concerning the chemical space. In this work, we assessed the value of a large public dataset of compound-induced transcriptomic data, to predict compound activity on a selection of 69 molecular targets. We compared such descriptors with other QSAR descriptors, namely the Morgan fingerprints (similar to extended-connectivity fingerprints). Depending on the target, active compounds could show similar signatures in one or multiple cell lines, whether these active compounds shared similar or different chemical structures. Random forest models using gene expression signatures were able to perform similarly or better than counterpart models built with Morgan fingerprints for 25% of the target prediction tasks. These performances occurred mostly using signatures produced in cell lines showing similar signatures for active compounds toward the considered target. We show that compound-induced transcriptomic data could represent a great opportunity for target prediction, allowing to overcome the chemical space limitation of QSAR models.
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Affiliation(s)
| | - Joerg Wichard
- Department of Genetic Toxicology, Bayer AG, Berlin, Germany
| | - Oscar Méndez-Lucio
- Bayer SAS, Bayer CropScience, Sophia Antipolis, France.,Bloomoon, Villeurbanne, France
| | - David Rouquié
- Bayer SAS, Bayer CropScience, Sophia Antipolis, France
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14
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Chung FFL, Herceg Z. The Promises and Challenges of Toxico-Epigenomics: Environmental Chemicals and Their Impacts on the Epigenome. ENVIRONMENTAL HEALTH PERSPECTIVES 2020; 128:15001. [PMID: 31950866 PMCID: PMC7015548 DOI: 10.1289/ehp6104] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/23/2019] [Revised: 12/15/2019] [Accepted: 12/16/2019] [Indexed: 05/02/2023]
Abstract
BACKGROUND It has been estimated that a substantial portion of chronic and noncommunicable diseases can be caused or exacerbated by exposure to environmental chemicals. Multiple lines of evidence indicate that early life exposure to environmental chemicals at relatively low concentrations could have lasting effects on individual and population health. Although the potential adverse effects of environmental chemicals are known to the scientific community, regulatory agencies, and the public, little is known about the mechanistic basis by which these chemicals can induce long-term or transgenerational effects. To address this question, epigenetic mechanisms have emerged as the potential link between genetic and environmental factors of health and disease. OBJECTIVES We present an overview of epigenetic regulation and a summary of reported evidence of environmental toxicants as epigenetic disruptors. We also discuss the advantages and challenges of using epigenetic biomarkers as an indicator of toxicant exposure, using measures that can be taken to improve risk assessment, and our perspectives on the future role of epigenetics in toxicology. DISCUSSION Until recently, efforts to apply epigenomic data in toxicology and risk assessment were restricted by an incomplete understanding of epigenomic variability across tissue types and populations. This is poised to change with the development of new tools and concerted efforts by researchers across disciplines that have led to a better understanding of epigenetic mechanisms and comprehensive maps of epigenomic variation. With the foundations now in place, we foresee that unprecedented advancements will take place in the field in the coming years. https://doi.org/10.1289/EHP6104.
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Affiliation(s)
| | - Zdenko Herceg
- Epigenetics Group, International Agency for Research on Cancer (IARC), Lyon, France
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15
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Liu Z, Zhu L, Thakkar S, Roberts R, Tong W. Can Transcriptomic Profiles from Cancer Cell Lines Be Used for Toxicity Assessment? Chem Res Toxicol 2019; 33:271-280. [DOI: 10.1021/acs.chemrestox.9b00288] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/30/2023]
Affiliation(s)
- Zhichao Liu
- National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, Arkansas 72079, United States
| | - Liyuan Zhu
- National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, Arkansas 72079, United States
| | - Shraddha Thakkar
- National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, Arkansas 72079, United States
| | - Ruth Roberts
- ApconiX, BioHub at Alderley Park, Alderley Edge SK10 4TG, U.K
- University of Birmingham, Edgbaston, Birmingham B15 2TT, U.K
| | - Weida Tong
- National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, Arkansas 72079, United States
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16
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Tryndyak V, Borowa-Mazgaj B, Beland FA, Pogribny IP. Gene expression and cytosine DNA methylation alterations in induced pluripotent stem-cell-derived human hepatocytes treated with low doses of chemical carcinogens. Arch Toxicol 2019; 93:3335-3344. [PMID: 31555880 DOI: 10.1007/s00204-019-02569-5] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2019] [Accepted: 09/04/2019] [Indexed: 12/15/2022]
Abstract
The increasing number of man-made chemicals in the environment that may pose a carcinogenic risk emphasizes the need to develop reliable time- and cost-effective approaches for carcinogen detection. To address this issue, we have investigated the utility of human hepatocytes for the in vitro identification of genotoxic and non-genotoxic carcinogens. Induced pluripotent stem-cell (iPSC)-derived human hepatocytes were treated with the genotoxic carcinogens aflatoxin B1 (AFB1) and benzo[a]pyrene (B[a]P), the non-genotoxic liver carcinogen methapyrilene, and the non-carcinogens aflatoxin B2 (AFB2) and benzo[e]pyrene (B[e]P) at non-cytotoxic concentrations for 7 days, and transcriptomic and DNA methylation profiles were examined. 1569, 1693, and 2061 differentially expressed genes (DEGs) were detected in cells treated with AFB1, B[a]P, and methapyrilene, respectively, whereas no DEGs were found in cells treated with AFB2 or B[e]P. In contrast to the profound cellular transcriptomic responses, exposure of iPSC-derived hepatocytes to the test chemicals resulted in minor random alterations in global DNA methylome, most of which were not associated with changes in gene expression. Overall, our results demonstrate that the major non-genotoxic effect of exposure to carcinogens, regardless of their mode of action, is a profound global transcriptomic response rather than global DNA methylome alterations, indicating the significance of transcriptomic alterations as an informative endpoint in short-term in vitro carcinogen testing.
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Affiliation(s)
- Volodymyr Tryndyak
- Division of Biochemical Toxicology, FDA-National Center for Toxicological Research, Jefferson, AR, USA
| | - Barbara Borowa-Mazgaj
- Division of Biochemical Toxicology, FDA-National Center for Toxicological Research, Jefferson, AR, USA
| | - Frederick A Beland
- Division of Biochemical Toxicology, FDA-National Center for Toxicological Research, Jefferson, AR, USA
| | - Igor P Pogribny
- Division of Biochemical Toxicology, FDA-National Center for Toxicological Research, Jefferson, AR, USA.
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17
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Musa A, Tripathi S, Dehmer M, Emmert-Streib F. L1000 Viewer: A Search Engine and Web Interface for the LINCS Data Repository. Front Genet 2019; 10:557. [PMID: 31258549 PMCID: PMC6588157 DOI: 10.3389/fgene.2019.00557] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2019] [Accepted: 05/28/2019] [Indexed: 12/12/2022] Open
Abstract
The LINCS L1000 data repository contains almost two million gene expression profiles for thousands of small molecules and drugs. However, due to the complexity and the size of the data repository and a lack of an interoperable interface, the creation of pharmacologically meaningful workflows utilizing these data is severely hampered. In order to overcome this limitation, we developed the L1000 Viewer, a search engine and graphical web interface for the LINCS data repository. The web interface serves as an interactive platform allowing the user to select different forms of perturbation profiles, e.g., for specific cell lines, drugs, dosages, time points and combinations thereof. At its core, our method has a database we created from inferring and utilizing the intricate dependency graph structure among the data files. The L1000 Viewer is accessible via http://L1000viewer.bio-complexity.com/.
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Affiliation(s)
- Aliyu Musa
- Predictive Society and Data Analytics Lab, Faculty of Information Technology and Communication Sciences, Tampere University, Tampere, Finland.,Institute of Biosciences and Medical Technology, Tampere, Finland
| | - Shailesh Tripathi
- Predictive Society and Data Analytics Lab, Faculty of Information Technology and Communication Sciences, Tampere University, Tampere, Finland.,Institute for Intelligent Production, Faculty for Management, University of Applied Sciences Upper Austria, Linz, Austria
| | - Matthias Dehmer
- Institute for Intelligent Production, Faculty for Management, University of Applied Sciences Upper Austria, Linz, Austria.,Department of Mechatronics and Biomedical Computer Science, UMIT, Hall in Tyrol, Austria.,College of Computer and Control Engineering, Nankai University, Tianjin, China
| | - Frank Emmert-Streib
- Predictive Society and Data Analytics Lab, Faculty of Information Technology and Communication Sciences, Tampere University, Tampere, Finland.,Institute of Biosciences and Medical Technology, Tampere, Finland
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