1
|
Ye Z, Xiong H, Huang L, Zhao Q, Xiong Z, Zhang H, Zhang W. Mechanisms underlying the combination effect of arsenite and high-fat diet on aggravating liver injury in mice. ENVIRONMENTAL TOXICOLOGY 2024; 39:1323-1334. [PMID: 37955338 DOI: 10.1002/tox.24037] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/13/2023] [Revised: 09/23/2023] [Accepted: 10/31/2023] [Indexed: 11/14/2023]
Abstract
Arsenic (As) is a highly toxic metalloid that can be found in insufficiently purified drinking water and exerts adverse effects on the physiology of living organisms that can negatively affect human health after subchronic exposure, causing several diseases, such as liver damage. A high-fat diet, which is increasing in frequency worldwide, can aggravate hepatic pathology. However, the mechanisms behind liver injury caused by the combinatory effects of As exposure and a high-fat diet remain unclear. In this study, we investigated such underlying mechanisms by focusing on three different aspects: As biotransformation, pathological liver damage, and differential expression of signaling pathway components. We employed mice that were fed a regular diet or a high-fat diet and exposed them to a range of arsenite concentrations (As(III), 0.05-50 mg/L) for 12 weeks. Our results showed that a high-fat diet increased the absorption of As into the liver and enhanced liver toxicity, which became progressively more severe as the As concentration increased. Co-exposure to a high-fat diet and As(III) activated PI3K/AKT and PPAR signaling as well as fatty acid metabolism pathways. In addition, the expression of proteins related to lipid cell function, lipid metabolism, and the regulation of body weight was also affected. Our study provides insights into the mechanisms that contribute to liver injury from subchronic combinatory exposure to As and a high-fat diet and showcases the importance of a healthy lifestyle, which may be of particular benefit to people living in areas with high As(III) concentrations, as a means to reduce or prevent aggravated liver damage.
Collapse
Affiliation(s)
- Zijun Ye
- School of Environmental Science and Engineering, Guangzhou University, Guangzhou, China
| | - Haiyan Xiong
- Key Laboratory of the Coastal and Wetland Ecosystems, Ministry of Education, College of Environment and Ecology, Xiamen University, Xiamen, China
| | - Liping Huang
- School of Environmental Science and Engineering, Guangzhou University, Guangzhou, China
| | - Qianyu Zhao
- School of Environmental Science and Engineering, Guangzhou University, Guangzhou, China
| | - Zhu Xiong
- School of Environmental Science and Engineering, Guangzhou University, Guangzhou, China
| | - Hongguo Zhang
- School of Environmental Science and Engineering, Guangzhou University, Guangzhou, China
| | - Wei Zhang
- School of Environmental Science and Engineering, Guangzhou University, Guangzhou, China
| |
Collapse
|
2
|
Yang R, Lu Y, Yin N, Faiola F. Transcriptomic Integration Analyses Uncover Common Bisphenol A Effects Across Species and Tissues Primarily Mediated by Disruption of JUN/FOS, EGFR, ER, PPARG, and P53 Pathways. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2023; 57:19156-19168. [PMID: 37978927 DOI: 10.1021/acs.est.3c02016] [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: 11/19/2023]
Abstract
Bisphenol A (BPA) is a common endocrine disruptor widely used in the production of electronic, sports, and medical equipment, as well as consumer products like milk bottles, dental sealants, and thermal paper. Despite its widespread use, current assessments of BPA exposure risks remain limited due to the lack of comprehensive cross-species comparative analyses. To address this gap, we conducted a study aimed at identifying genes and fundamental molecular processes consistently affected by BPA in various species and tissues, employing an effective data integration method and bioinformatic analyses. Our findings revealed that exposure to BPA led to significant changes in processes like lipid metabolism, proliferation, and apoptosis in the tissues/cells of mammals, fish, and nematodes. These processes were found to be commonly affected in adipose, liver, mammary, uterus, testes, and ovary tissues. Additionally, through an in-depth analysis of signaling pathways influenced by BPA in different species and tissues, we observed that the JUN/FOS, EGFR, ER, PPARG, and P53 pathways, along with their downstream key transcription factors and kinases, were all impacted by BPA. Our study provides compelling evidence that BPA indeed induces similar toxic effects across different species and tissues. Furthermore, our investigation sheds light on the underlying molecular mechanisms responsible for these toxic effects. By uncovering these mechanisms, we gain valuable insights into the potential health implications associated with BPA exposure, highlighting the importance of comprehensive assessments and awareness of this widespread endocrine disruptor.
Collapse
Affiliation(s)
- Renjun Yang
- State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China
- College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yuanping Lu
- State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China
- College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Nuoya Yin
- State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China
- College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Francesco Faiola
- State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China
- College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
| |
Collapse
|
3
|
Oku Y, Madia F, Lau P, Paparella M, McGovern T, Luijten M, Jacobs MN. Analyses of Transcriptomics Cell Signalling for Pre-Screening Applications in the Integrated Approach for Testing and Assessment of Non-Genotoxic Carcinogens. Int J Mol Sci 2022; 23:ijms232112718. [PMID: 36361516 PMCID: PMC9659232 DOI: 10.3390/ijms232112718] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Revised: 10/18/2022] [Accepted: 10/18/2022] [Indexed: 12/03/2022] Open
Abstract
With recent rapid advancement of methodological tools, mechanistic understanding of biological processes leading to carcinogenesis is expanding. New approach methodologies such as transcriptomics can inform on non-genotoxic mechanisms of chemical carcinogens and can be developed for regulatory applications. The Organisation for the Economic Cooperation and Development (OECD) expert group developing an Integrated Approach to the Testing and Assessment (IATA) of Non-Genotoxic Carcinogens (NGTxC) is reviewing the possible assays to be integrated therein. In this context, we review the application of transcriptomics approaches suitable for pre-screening gene expression changes associated with phenotypic alterations that underlie the carcinogenic processes for subsequent prioritisation of downstream test methods appropriate to specific key events of non-genotoxic carcinogenesis. Using case studies, we evaluate the potential of gene expression analyses especially in relation to breast cancer, to identify the most relevant approaches that could be utilised as (pre-) screening tools, for example Gene Set Enrichment Analysis (GSEA). We also consider how to address the challenges to integrate gene panels and transcriptomic assays into the IATA, highlighting the pivotal omics markers identified for assay measurement in the IATA key events of inflammation, immune response, mitogenic signalling and cell injury.
Collapse
Affiliation(s)
- Yusuke Oku
- The Organisation for Economic Cooperation and Development (OECD), 2 Rue Andre Pascal, 75016 Paris, France
- Correspondence: (Y.O.); (M.N.J.)
| | - Federica Madia
- European Commission, Joint Research Centre (JRC), Via Enrico Fermi, 2749, 21027 Ispra, Italy
| | - Pierre Lau
- Istituto Italiano di Tecnologia, 16163 Genova, Italy
| | - Martin Paparella
- Institute of Medical Biochemistry, Biocenter, Medical University of Innsbruck, Innrain 80, 6020 Innbruck, Austria
| | - Timothy McGovern
- US Food and Drug Administration (FDA), 10903 New Hampshire Avenue, Silver Spring, MD 20901, USA
| | - Mirjam Luijten
- Centre for Health Protection, National Institute for Public Health and the Environment (RIVM), Antonie van Leeuwenhoeklaan 9, Bilthoven, 3721 MA Utrecht, The Netherlands
| | - Miriam N. Jacobs
- Centre for Radiation, Chemical and Environmental Hazard (CRCE), Public Health England (PHE), Chilton OX11 0RQ, Oxfordshire, UK
- Correspondence: (Y.O.); (M.N.J.)
| |
Collapse
|
4
|
Lee F, Shah I, Soong YT, Xing J, Ng IC, Tasnim F, Yu H. Reproducibility and robustness of high-throughput S1500+ transcriptomics on primary rat hepatocytes for chemical-induced hepatotoxicity assessment. Curr Res Toxicol 2021; 2:282-295. [PMID: 34467220 PMCID: PMC8384775 DOI: 10.1016/j.crtox.2021.07.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2021] [Revised: 07/15/2021] [Accepted: 07/31/2021] [Indexed: 11/06/2022] Open
Abstract
TempO-Seq assays of rat hepatocytes in collagen sandwich are highly reproducible. Gene expression analysis shows S1500+ is representative of the whole transcriptome. Connectivity mapping shows consistency between TempO-Seq and Affymetrix data. Gene set enrichment shows consistency between S1500+ and the whole transcriptome. Gene set enrichment using hallmark gene sets informs hepatotoxicity.
Cell-based in vitro models coupled with high-throughput transcriptomics (HTTr) are increasingly utilized as alternative methods to animal-based toxicity testing. Here, using a panel of 14 chemicals with different risks of human drug-induced liver injury (DILI) and two dosing concentrations, we evaluated an HTTr platform comprised of collagen sandwich primary rat hepatocyte culture and the TempO-Seq surrogate S1500+ (ST) assay. First, the HTTr platform was found to exhibit high reproducibility between technical and biological replicates (r greater than 0.85). Connectivity mapping analysis further demonstrated a high level of inter-platform reproducibility between TempO-Seq data and Affymetrix GeneChip data from the Open TG-GATES project. Second, the TempO-Seq ST assay was shown to be a robust surrogate to the whole transcriptome (WT) assay in capturing chemical-induced changes in gene expression, as evident from correlation analysis, PCA and unsupervised hierarchical clustering. Gene set enrichment analysis (GSEA) using the Hallmark gene set collection also demonstrated consistency in enrichment scores between ST and WT assays. Lastly, unsupervised hierarchical clustering of hallmark enrichment scores broadly divided the samples into hepatotoxic, intermediate, and non-hepatotoxic groups. Xenobiotic metabolism, bile acid metabolism, apoptosis, p53 pathway, and coagulation were found to be the key hallmarks driving the clustering. Taken together, our results established the reproducibility and performance of collagen sandwich culture in combination with TempO-Seq S1500+ assay, and demonstrated the utility of GSEA using the hallmark gene set collection to identify potential hepatotoxicants for further validation.
Collapse
Affiliation(s)
- Fan Lee
- Innovations in Food & Chemical Safety Program (IFCS), Institute of Bioengineering and Bioimaging (IBB), Agency for Science Technology and Research, Singapore
| | - Imran Shah
- Center for Computational Toxicology & Exposure, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC, United States
| | - Yun Ting Soong
- Innovations in Food & Chemical Safety Program (IFCS), Institute of Bioengineering and Bioimaging (IBB), Agency for Science Technology and Research, Singapore
| | - Jiangwa Xing
- Innovations in Food & Chemical Safety Program (IFCS), Institute of Bioengineering and Bioimaging (IBB), Agency for Science Technology and Research, Singapore
| | - Inn Chuan Ng
- Department of Physiology and Mechanobiology Institute, National University of Singapore, Singapore
| | - Farah Tasnim
- Innovations in Food & Chemical Safety Program (IFCS), Institute of Bioengineering and Bioimaging (IBB), Agency for Science Technology and Research, Singapore
| | - Hanry Yu
- Innovations in Food & Chemical Safety Program (IFCS), Institute of Bioengineering and Bioimaging (IBB), Agency for Science Technology and Research, Singapore.,Department of Physiology and Mechanobiology Institute, National University of Singapore, Singapore.,Critical Analytics for Manufacturing Personalized-Medicine, Singapore-MIT Alliance for Research and Technology, Singapore
| |
Collapse
|
5
|
Luijten M, Wackers PFK, Rorije E, Pennings JLA, Heusinkveld HJ. Relevance of In Vitro Transcriptomics for In Vivo Mode of Action Assessment. Chem Res Toxicol 2020; 34:452-459. [PMID: 33378166 DOI: 10.1021/acs.chemrestox.0c00313] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
Recently, we reported an in vitro toxicogenomics comparison approach to categorize chemical substances according to similarities in their proposed toxicological modes of action. Use of such an approach for regulatory purposes requires, among others, insight into the extent of biological concordance between in vitro and in vivo findings. To that end, we applied the comparison approach to transcriptomics data from the Open TG-GATEs database for 137 substances with diverging modes of action and evaluated the outcomes obtained for rat primary hepatocytes and for rat liver. The results showed that a relatively small number of matches observed in vitro were also observed in vivo, whereas quite a large number of matches between substances were found to be relevant solely in vivo or in vitro. The latter could not be explained by physicochemical properties, leading to insufficient bioavailability or poor water solubility. Nevertheless, pathway analyses indicated that for relevant matches the mechanisms perturbed in vitro are consistent with those perturbed in vivo. These findings support the utility of the comparison approach as tool in mechanism-based risk assessment.
Collapse
Affiliation(s)
- Mirjam Luijten
- Centre for Health Protection, National Institute for Public Health and the Environment (RIVM), 3720 BA Bilthoven, The Netherlands
| | - Paul F K Wackers
- Centre for Health Protection, National Institute for Public Health and the Environment (RIVM), 3720 BA Bilthoven, The Netherlands
| | - Emiel Rorije
- Centre for Safety of Substances and Products, National Institute for Public Health and the Environment (RIVM), 3720 BA Bilthoven, The Netherlands
| | - Jeroen L A Pennings
- Centre for Health Protection, National Institute for Public Health and the Environment (RIVM), 3720 BA Bilthoven, The Netherlands
| | - Harm J Heusinkveld
- Centre for Health Protection, National Institute for Public Health and the Environment (RIVM), 3720 BA Bilthoven, The Netherlands
| |
Collapse
|
6
|
Nair SK, Eeles C, Ho C, Beri G, Yoo E, Tkachuk D, Tang A, Nijrabi P, Smirnov P, Seo H, Jennen D, Haibe-Kains B. ToxicoDB: an integrated database to mine and visualize large-scale toxicogenomic datasets. Nucleic Acids Res 2020; 48:W455-W462. [PMID: 32421831 PMCID: PMC7319553 DOI: 10.1093/nar/gkaa390] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2020] [Revised: 04/27/2020] [Accepted: 05/04/2020] [Indexed: 11/12/2022] Open
Abstract
In the past few decades, major initiatives have been launched around the world to address chemical safety testing. These efforts aim to innovate and improve the efficacy of existing methods with the long-term goal of developing new risk assessment paradigms. The transcriptomic and toxicological profiling of mammalian cells has resulted in the creation of multiple toxicogenomic datasets and corresponding tools for analysis. To enable easy access and analysis of these valuable toxicogenomic data, we have developed ToxicoDB (toxicodb.ca), a free and open cloud-based platform integrating data from large in vitro toxicogenomic studies, including gene expression profiles of primary human and rat hepatocytes treated with 231 potential toxicants. To efficiently mine these complex toxicogenomic data, ToxicoDB provides users with harmonized chemical annotations, time- and dose-dependent plots of compounds across datasets, as well as the toxicity-related pathway analysis. The data in ToxicoDB have been generated using our open-source R package, ToxicoGx (github.com/bhklab/ToxicoGx). Altogether, ToxicoDB provides a streamlined process for mining highly organized, curated, and accessible toxicogenomic data that can be ultimately applied to preclinical toxicity studies and further our understanding of adverse outcomes.
Collapse
Affiliation(s)
- Sisira Kadambat Nair
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON M5G 0A3, Canada
| | - Christopher Eeles
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON M5G 0A3, Canada
| | - Chantal Ho
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON M5G 0A3, Canada
| | - Gangesh Beri
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON M5G 0A3, Canada
| | - Esther Yoo
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON M5G 0A3, Canada
| | - Denis Tkachuk
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON M5G 0A3, Canada
| | - Amy Tang
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON M5G 0A3, Canada
| | - Parwaiz Nijrabi
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON M5G 0A3, Canada.,Department of Medical Biophysics, University of Toronto, Toronto, ON M5G 1L7, Canada
| | - Petr Smirnov
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON M5G 0A3, Canada.,Department of Medical Biophysics, University of Toronto, Toronto, ON M5G 1L7, Canada
| | - Heewon Seo
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON M5G 0A3, Canada
| | - Danyel Jennen
- Department of Toxicogenomics, GROW School of Oncology and Development Biology, Maastricht University, Maastricht, The Netherlands
| | - Benjamin Haibe-Kains
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON M5G 0A3, Canada.,Department of Medical Biophysics, University of Toronto, Toronto, ON M5G 1L7, Canada.,Department of Computer Science, University of Toronto, Toronto, ON M5T 3A1, Canada.,Ontario Institute for Cancer Research, Toronto, ON M5G 1L7, Canada.,Vector Institute for Artificial Intelligence, Toronto, ON M5G 1L7, Canada
| |
Collapse
|
7
|
Zhou Y, Shen JX, Lauschke VM. Comprehensive Evaluation of Organotypic and Microphysiological Liver Models for Prediction of Drug-Induced Liver Injury. Front Pharmacol 2019; 10:1093. [PMID: 31616302 PMCID: PMC6769037 DOI: 10.3389/fphar.2019.01093] [Citation(s) in RCA: 48] [Impact Index Per Article: 9.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2019] [Accepted: 08/26/2019] [Indexed: 12/21/2022] Open
Abstract
Drug-induced liver injury (DILI) is a major concern for the pharmaceutical industry and constitutes one of the most important reasons for the termination of promising drug development projects. Reliable prediction of DILI liability in preclinical stages is difficult, as current experimental model systems do not accurately reflect the molecular phenotype and functionality of the human liver. As a result, multiple drugs that passed preclinical safety evaluations failed due to liver toxicity in clinical trials or postmarketing stages in recent years. To improve the selection of molecules that are taken forward into the clinics, the development of more predictive in vitro systems that enable high-throughput screening of hepatotoxic liabilities and allow for investigative studies into DILI mechanisms has gained growing interest. Specifically, it became increasingly clear that the choice of cell types and culture method both constitute important parameters that affect the predictive power of test systems. In this review, we present current 3D culture paradigms for hepatotoxicity tests and critically evaluate their utility and performance for DILI prediction. In addition, we highlight possibilities of these emerging platforms for mechanistic evaluations of selected drug candidates and present current research directions towards the further improvement of preclinical liver safety tests. We conclude that organotypic and microphysiological liver systems have provided an important step towards more reliable DILI prediction. Furthermore, we expect that the increasing availability of comprehensive benchmarking studies will facilitate model dissemination that might eventually result in their regulatory acceptance.
Collapse
Affiliation(s)
| | | | - Volker M. Lauschke
- Department of Physiology and Pharmacology, Karolinska Institutet, Stockholm, Sweden
| |
Collapse
|
8
|
Smirnov P, Kofia V, Maru A, Freeman M, Ho C, El-Hachem N, Adam GA, Ba-Alawi W, Safikhani Z, Haibe-Kains B. PharmacoDB: an integrative database for mining in vitro anticancer drug screening studies. Nucleic Acids Res 2019; 46:D994-D1002. [PMID: 30053271 PMCID: PMC5753377 DOI: 10.1093/nar/gkx911] [Citation(s) in RCA: 105] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2017] [Accepted: 09/27/2017] [Indexed: 12/18/2022] Open
Abstract
Recent cancer pharmacogenomic studies profiled large panels of cell lines against hundreds of approved drugs and experimental chemical compounds. The overarching goal of these screens is to measure sensitivity of cell lines to chemical perturbations, correlate these measures to genomic features, and thereby develop novel predictors of drug response. However, leveraging these valuable data is challenging due to the lack of standards for annotating cell lines and chemical compounds, and quantifying drug response. Moreover, it has been recently shown that the complexity and complementarity of the experimental protocols used in the field result in high levels of technical and biological variation in the in vitro pharmacological profiles. There is therefore a need for new tools to facilitate rigorous comparison and integrative analysis of large-scale drug screening datasets. To address this issue, we have developed PharmacoDB (pharmacodb.pmgenomics.ca), a database integrating the largest cancer pharmacogenomic studies published to date. Here, we describe how the curation of cell line and chemical compound identifiers maximizes the overlap between datasets and how users can leverage such data to compare and extract robust drug phenotypes. PharmacoDB provides a unique resource to mine a compendium of curated cancer pharmacogenomic datasets that are otherwise disparate and difficult to integrate.
Collapse
Affiliation(s)
- Petr Smirnov
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada.,Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada
| | - Victor Kofia
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada
| | - Alexander Maru
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada
| | - Mark Freeman
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada
| | - Chantal Ho
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada
| | - Nehme El-Hachem
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada
| | - George-Alexandru Adam
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada.,Department of Computer Science, University of Toronto, Toronto, Ontario, Canada
| | - Wail Ba-Alawi
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada.,Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada
| | - Zhaleh Safikhani
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada.,Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada
| | - Benjamin Haibe-Kains
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada.,Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada.,Department of Computer Science, University of Toronto, Toronto, Ontario, Canada.,Ontario Institute of Cancer Research, Toronto, Ontario, Canada
| |
Collapse
|
9
|
Taškova K, Fontaine JF, Mrowka R, Andrade-Navarro MA. Literature optimized integration of gene expression for organ-specific evaluation of toxicogenomics datasets. PLoS One 2019; 14:e0210467. [PMID: 30640953 PMCID: PMC6331104 DOI: 10.1371/journal.pone.0210467] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2018] [Accepted: 12/24/2018] [Indexed: 11/30/2022] Open
Abstract
The study of drug toxicity in human organs is complicated by their complex inter-relations and by the obvious difficulty to testing drug effects on biologically relevant material. Animal models and human cell cultures offer alternatives for systematic and large-scale profiling of drug effects on gene expression level, as typically found in the so-called toxicogenomics datasets. However, the complexity of these data, which includes variable drug doses, time points, and experimental setups, makes it difficult to choose and integrate the data, and to evaluate the appropriateness of one or another model system to study drug toxicity (of particular drugs) of particular human organs. Here, we define a protocol to integrate drug-wise rankings of gene expression changes in toxicogenomics data, which we apply to the TG-GATEs dataset, to prioritize genes for association to drug toxicity in liver or kidney. Contrast of the results with sets of known human genes associated to drug toxicity in the literature allows to compare different rank aggregation approaches for the task at hand. Collectively, ranks from multiple models point to genes not previously associated to toxicity, notably, the PCNA clamp associated factor (PCLAF), and genes regulated by the master regulator of the antioxidant response NFE2L2, such as NQO1 and SRXN1. In addition, comparing gene ranks from different models allowed us to evaluate striking differences in terms of toxicity-associated genes between human and rat hepatocytes or between rat liver and rat hepatocytes. We interpret these results to point to the different molecular functions associated to organ toxicity that are best described by each model. We conclude that the expected production of toxicogenomics panels with larger numbers of drugs and models, in combination with the ongoing increase of the experimental literature in organ toxicity, will lead to increasingly better associations of genes for organism toxicity.
Collapse
Affiliation(s)
| | | | - Ralf Mrowka
- Experimentelle Nephrologie, Universitätsklinikum Jena, KIM III, Jena, Germany
| | | |
Collapse
|
10
|
Hosny A, Parmar C, Coroller TP, Grossmann P, Zeleznik R, Kumar A, Bussink J, Gillies RJ, Mak RH, Aerts HJWL. Deep learning for lung cancer prognostication: A retrospective multi-cohort radiomics study. PLoS Med 2018; 15:e1002711. [PMID: 30500819 PMCID: PMC6269088 DOI: 10.1371/journal.pmed.1002711] [Citation(s) in RCA: 310] [Impact Index Per Article: 51.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/29/2018] [Accepted: 11/05/2018] [Indexed: 11/19/2022] Open
Abstract
BACKGROUND Non-small-cell lung cancer (NSCLC) patients often demonstrate varying clinical courses and outcomes, even within the same tumor stage. This study explores deep learning applications in medical imaging allowing for the automated quantification of radiographic characteristics and potentially improving patient stratification. METHODS AND FINDINGS We performed an integrative analysis on 7 independent datasets across 5 institutions totaling 1,194 NSCLC patients (age median = 68.3 years [range 32.5-93.3], survival median = 1.7 years [range 0.0-11.7]). Using external validation in computed tomography (CT) data, we identified prognostic signatures using a 3D convolutional neural network (CNN) for patients treated with radiotherapy (n = 771, age median = 68.0 years [range 32.5-93.3], survival median = 1.3 years [range 0.0-11.7]). We then employed a transfer learning approach to achieve the same for surgery patients (n = 391, age median = 69.1 years [range 37.2-88.0], survival median = 3.1 years [range 0.0-8.8]). We found that the CNN predictions were significantly associated with 2-year overall survival from the start of respective treatment for radiotherapy (area under the receiver operating characteristic curve [AUC] = 0.70 [95% CI 0.63-0.78], p < 0.001) and surgery (AUC = 0.71 [95% CI 0.60-0.82], p < 0.001) patients. The CNN was also able to significantly stratify patients into low and high mortality risk groups in both the radiotherapy (p < 0.001) and surgery (p = 0.03) datasets. Additionally, the CNN was found to significantly outperform random forest models built on clinical parameters-including age, sex, and tumor node metastasis stage-as well as demonstrate high robustness against test-retest (intraclass correlation coefficient = 0.91) and inter-reader (Spearman's rank-order correlation = 0.88) variations. To gain a better understanding of the characteristics captured by the CNN, we identified regions with the most contribution towards predictions and highlighted the importance of tumor-surrounding tissue in patient stratification. We also present preliminary findings on the biological basis of the captured phenotypes as being linked to cell cycle and transcriptional processes. Limitations include the retrospective nature of this study as well as the opaque black box nature of deep learning networks. CONCLUSIONS Our results provide evidence that deep learning networks may be used for mortality risk stratification based on standard-of-care CT images from NSCLC patients. This evidence motivates future research into better deciphering the clinical and biological basis of deep learning networks as well as validation in prospective data.
Collapse
Affiliation(s)
- Ahmed Hosny
- Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Chintan Parmar
- Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Thibaud P. Coroller
- Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Patrick Grossmann
- Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Roman Zeleznik
- Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Avnish Kumar
- Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Johan Bussink
- Department of Radiation Oncology, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Robert J. Gillies
- Department of Cancer Physiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida, United States of America
| | - Raymond H. Mak
- Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Hugo J. W. L. Aerts
- Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, United States of America
- Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, United States of America
| |
Collapse
|
11
|
Piñero J, Furlong LI, Sanz F. In silico models in drug development: where we are. Curr Opin Pharmacol 2018; 42:111-121. [PMID: 30205360 DOI: 10.1016/j.coph.2018.08.007] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2018] [Revised: 07/30/2018] [Accepted: 08/13/2018] [Indexed: 02/07/2023]
Abstract
The use and utility of computational models in drug development has significantly grown in the last decades, fostered by the availability of high throughput datasets and new data analysis strategies. These in silico approaches are demonstrating their ability to generate reliable predictions as well as new knowledge on the mode of action of drugs and the mechanisms underlying their side effects, altogether helping to reduce the costs of drug development. The aim of this review is to provide a panorama of developments in the field in the last two years.
Collapse
Affiliation(s)
- Janet Piñero
- Research Programme on Biomedical Informatics (GRIB), Department of Experimental and Health Sciences (DCEXS), Hospital del Mar Medical Research Institute (IMIM), Universitat Pompeu Fabra (UPF), Carrer Dr. Aiguader 88, 08003 Barcelona, Spain
| | - Laura I Furlong
- Research Programme on Biomedical Informatics (GRIB), Department of Experimental and Health Sciences (DCEXS), Hospital del Mar Medical Research Institute (IMIM), Universitat Pompeu Fabra (UPF), Carrer Dr. Aiguader 88, 08003 Barcelona, Spain
| | - Ferran Sanz
- Research Programme on Biomedical Informatics (GRIB), Department of Experimental and Health Sciences (DCEXS), Hospital del Mar Medical Research Institute (IMIM), Universitat Pompeu Fabra (UPF), Carrer Dr. Aiguader 88, 08003 Barcelona, Spain.
| |
Collapse
|
12
|
Hou H, Yu Y, Shen Z, Liu S, Wu B. Hepatic transcriptomic responses in mice exposed to arsenic and different fat diet. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2017; 24:10621-10629. [PMID: 28283972 DOI: 10.1007/s11356-017-8743-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/24/2016] [Accepted: 03/01/2017] [Indexed: 06/06/2023]
Abstract
Chronic exposure to inorganic arsenic (iAs) or a high-fat diet (HFD) can produce liver injury. However, effects of HFD on risk assessment of iAs in drinking water are unclear. In this study, we examined how HFD and iAs interact to alter iAs-induced liver injury in C57BL/6 mice. Mice fed low-fat diet (LFD) or HFD were exposed to 3 mg/L iAs or deionized water for 10 weeks. Results showed that HFD changed intake and excretion of iAs by mice. Then, HFD increased the amount of iAs-induced hepatic DNA damage and amplified changes in pathways related to cell death and growth, signal transduction, lipid metabolism, and insulin signaling. Compared to gene expression profiles caused by iAs alone or HFD alone, insulin signaling pathway might play important roles in the interactive effects of iAs and HFD. Our data suggest that HFD increases sensitivity of mice to iAs in drinking water, resulting in increased hepatotoxicity. This study highlight that HFD might enhance the risk of iAs hepatotoxicity in iAs-polluted regions. The diet should be considered during risk assessment of iAs in drinking water.
Collapse
Affiliation(s)
- Hui Hou
- State Key Laboratory of Pollutant Control and Resource Reuse, School of the Environment, Nanjing University, Xianlin Campus, 163 Xianlin Avenue, Nanjing, 210023, People's Republic of China
| | - Yue Yu
- State Key Laboratory of Pollutant Control and Resource Reuse, School of the Environment, Nanjing University, Xianlin Campus, 163 Xianlin Avenue, Nanjing, 210023, People's Republic of China
| | - Zhuoyan Shen
- State Key Laboratory of Pollutant Control and Resource Reuse, School of the Environment, Nanjing University, Xianlin Campus, 163 Xianlin Avenue, Nanjing, 210023, People's Republic of China
| | - Su Liu
- State Key Laboratory of Pollutant Control and Resource Reuse, School of the Environment, Nanjing University, Xianlin Campus, 163 Xianlin Avenue, Nanjing, 210023, People's Republic of China
| | - Bing Wu
- State Key Laboratory of Pollutant Control and Resource Reuse, School of the Environment, Nanjing University, Xianlin Campus, 163 Xianlin Avenue, Nanjing, 210023, People's Republic of China.
| |
Collapse
|
13
|
Sutherland JJ, Jolly RA, Goldstein KM, Stevens JL. Assessing Concordance of Drug-Induced Transcriptional Response in Rodent Liver and Cultured Hepatocytes. PLoS Comput Biol 2016; 12:e1004847. [PMID: 27028627 PMCID: PMC4814051 DOI: 10.1371/journal.pcbi.1004847] [Citation(s) in RCA: 35] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2015] [Accepted: 03/03/2016] [Indexed: 12/13/2022] Open
Abstract
The effect of drugs, disease and other perturbations on mRNA levels are studied using gene expression microarrays or RNA-seq, with the goal of understanding molecular effects arising from the perturbation. Previous comparisons of reproducibility across laboratories have been limited in scale and focused on a single model. The use of model systems, such as cultured primary cells or cancer cell lines, assumes that mechanistic insights derived from the models would have been observed via in vivo studies. We examined the concordance of compound-induced transcriptional changes using data from several sources: rat liver and rat primary hepatocytes (RPH) from Drug Matrix (DM) and open TG-GATEs (TG), human primary hepatocytes (HPH) from TG, and mouse liver / HepG2 results from the Gene Expression Omnibus (GEO) repository. Gene expression changes for treatments were normalized to controls and analyzed with three methods: 1) gene level for 9071 high expression genes in rat liver, 2) gene set analysis (GSA) using canonical pathways and gene ontology sets, 3) weighted gene co-expression network analysis (WGCNA). Co-expression networks performed better than genes or GSA when comparing treatment effects within rat liver and rat vs. mouse liver. Genes and modules performed similarly at Connectivity Map-style analyses, where success at identifying similar treatments among a collection of reference profiles is the goal. Comparisons between rat liver and RPH, and those between RPH, HPH and HepG2 cells reveal lower concordance for all methods. We observe that the baseline state of untreated cultured cells relative to untreated rat liver shows striking similarity with toxicant-exposed cells in vivo, indicating that gross systems level perturbation in the underlying networks in culture may contribute to the low concordance. Gene expression studies in model systems are widely used for understanding the mechanism of drugs and other perturbations in biological systems. Other researchers have examined the reproducibility of microarray studies between laboratories, or comparing microarrays and/or RNA sequencing. However, no large scale studies have compared results from protocols which differ in minor details, or results generated in vivo vs. in vitro culture systems thought to serve as useful models. The rat liver is by far the most extensively studied model evaluating effects of drugs and other perturbations, and existing data allowed us to assess the level of concordance between rat liver and rat primary hepatocytes cultured in collagen-coated plates (i.e. “flat” culture) for hundreds of drugs. We found that the mouse liver serves as a better model of the rat liver than do rat primary hepatocytes, even after allowing for differences due to pharmacokinetics. The low concordance observed between rat liver and rat hepatocytes suggests that validating the utility of ‘omics data generated on emerging cell culture approaches (e.g. “organ-on-a-chip”, 3D-printed tissues) using rat cells and comparison to the rat liver may be necessary in order to gain confidence these approaches substantially improve on traditional culture models of human cells.
Collapse
Affiliation(s)
- Jeffrey J. Sutherland
- Lilly Research Laboratories, Eli Lilly and Company, Indianapolis, Indiana, United States of America
- * E-mail: (JJS); (JLS)
| | - Robert A. Jolly
- Lilly Research Laboratories, Eli Lilly and Company, Indianapolis, Indiana, United States of America
| | - Keith M. Goldstein
- Lilly Research Laboratories, Eli Lilly and Company, Indianapolis, Indiana, United States of America
| | - James L. Stevens
- Lilly Research Laboratories, Eli Lilly and Company, Indianapolis, Indiana, United States of America
- * E-mail: (JJS); (JLS)
| |
Collapse
|
14
|
Pallocca G, Grinberg M, Henry M, Frickey T, Hengstler JG, Waldmann T, Sachinidis A, Rahnenführer J, Leist M. Identification of transcriptome signatures and biomarkers specific for potential developmental toxicants inhibiting human neural crest cell migration. Arch Toxicol 2015; 90:159-80. [PMID: 26705709 PMCID: PMC4710658 DOI: 10.1007/s00204-015-1658-7] [Citation(s) in RCA: 36] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2015] [Accepted: 12/09/2015] [Indexed: 01/03/2023]
Abstract
The in vitro test battery of the European research consortium ESNATS (‘novel stem cell-based test systems’) has been used to screen for potential human developmental toxicants. As part of this effort, the migration of neural crest (MINC) assay has been used to evaluate chemical effects on neural crest function. It identified some drug-like compounds in addition to known environmental toxicants. The hits included the HSP90 inhibitor geldanamycin, the chemotherapeutic arsenic trioxide, the flame-retardant PBDE-99, the pesticide triadimefon and the histone deacetylase inhibitors valproic acid and trichostatin A. Transcriptome changes triggered by these substances in human neural crest cells were recorded and analysed here to answer three questions: (1) can toxicants be individually identified based on their transcript profile; (2) how can the toxicity pattern reflected by transcript changes be compacted/dimensionality-reduced for practical regulatory use; (3) how can a reduced set of biomarkers be selected for large-scale follow-up? Transcript profiling allowed clear separation of different toxicants and the identification of toxicant types in a blinded test study. We also developed a diagrammatic system to visualize and compare toxicity patterns of a group of chemicals by giving a quantitative overview of altered superordinate biological processes (e.g. activation of KEGG pathways or overrepresentation of gene ontology terms). The transcript data were mined for potential markers of toxicity, and 39 transcripts were selected to either indicate general developmental toxicity or distinguish compounds with different modes-of-action in read-across. In summary, we found inclusion of transcriptome data to largely increase the information from the MINC phenotypic test.
Collapse
Affiliation(s)
- Giorgia Pallocca
- Department of In Vitro Toxicology and Biomedicine, University of Konstanz, Box 657, 78457, Constance, Germany.
| | - Marianna Grinberg
- Department of Statistics, TU Dortmund University, 44139, Dortmund, Germany
| | - Margit Henry
- Center of Physiology and Pathophysiology, Institute of Neurophysiology, University of Cologne, 50931, Cologne, Germany
| | - Tancred Frickey
- Department of Bioinformatics, University of Konstanz, 78457, Constance, Germany
| | - Jan G Hengstler
- Leibniz Research Centre for Working Environment and Human Factors (IfADo), Technical University of Dortmund, 44139, Dortmund, Germany
| | - Tanja Waldmann
- Department of In Vitro Toxicology and Biomedicine, University of Konstanz, Box 657, 78457, Constance, Germany
| | - Agapios Sachinidis
- Department of Bioinformatics, University of Konstanz, 78457, Constance, Germany
| | - Jörg Rahnenführer
- Department of Statistics, TU Dortmund University, 44139, Dortmund, Germany
| | - Marcel Leist
- Department of In Vitro Toxicology and Biomedicine, University of Konstanz, Box 657, 78457, Constance, Germany
| |
Collapse
|