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Dasgupta S, Sharapova T, Mahalingaiah PK, Chorley BN, Shoieb A, Tsuji T, Dos Santos AAC, Chari R, Ebrahimi A, Dalmas Wilk DA, Pettit S, Bawa B, Vaughan E, van Vleet TR, Mitchell CA, Yuen PST. Urinary MicroRNA biomarkers of nephrotoxicity in Macaca fascicularis. Regul Toxicol Pharmacol 2024; 151:105668. [PMID: 38936797 DOI: 10.1016/j.yrtph.2024.105668] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2024] [Revised: 06/10/2024] [Accepted: 06/24/2024] [Indexed: 06/29/2024]
Abstract
Drug-induced kidney injury (DIKI) refers to kidney damage resulting from the administration of medications. The aim of this project was to identify reliable urinary microRNA (miRNAs) biomarkers that can be used as potential predictors of DIKI before disease diagnosis. This study quantified a panel of six miRNAs (miRs-210-3p, 423-5p, 143-3p, 130b-3p, 486-5p, 193a-3p) across multiple time points using urinary samples from a previous investigation evaluating effects of a nephrotoxicant in cynomolgus monkeys. Exosome-associated miRNA exhibited distinctive trends when compared to miRNAs quantified in whole urine, which may reflect a different urinary excretion mechanism of miRNAs than those released passively into the urine. Although further research and mechanistic studies are required to elucidate how these miRNAs regulate signaling in disease pathways, we present, for the first time, data that several miRNAs displayed strong correlations with histopathology scores, thus indicating their potential use as biomarkers to predict the development of DIKI in preclinical studies and clinical trials. Also, these findings can potentially be translated into other non-clinical species or human for the detection of DIKI.
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Affiliation(s)
- Subham Dasgupta
- Department of Biological Sciences, Clemson University, Clemson, SC, USA
| | | | | | - Brian N Chorley
- U.S. Environmental Protection Agency, Research Triangle Park, NC, USA
| | | | - Takayuki Tsuji
- National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, MD, USA
| | - Alef A C Dos Santos
- National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, MD, USA
| | - Rohit Chari
- National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, MD, USA
| | | | | | - Syril Pettit
- Health and Environmental Sciences Institute, Washington, DC, USA
| | | | | | | | | | - Peter S T Yuen
- National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, MD, USA
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2
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Zamora Z, Wang S, Chen YW, Diamante G, Yang X. Systematic transcriptome-wide meta-analysis across endocrine disrupting chemicals reveals shared and unique liver pathways, gene networks, and disease associations. ENVIRONMENT INTERNATIONAL 2024; 183:108339. [PMID: 38043319 PMCID: PMC11216742 DOI: 10.1016/j.envint.2023.108339] [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: 08/07/2023] [Revised: 11/03/2023] [Accepted: 11/19/2023] [Indexed: 12/05/2023]
Abstract
Cardiometabolic disorders (CMD) are a growing public health problem across the world. Among the known cardiometabolic risk factors are compounds that induce endocrine and metabolic dysfunctions, such as endocrine disrupting chemicals (EDCs). To date, how EDCs influence molecular programs and cardiometabolic risks has yet to be fully elucidated, especially considering the complexity contributed by species-, chemical-, and dose-specific effects. Moreover, different experimental and analytical methodologies employed by different studies pose challenges when comparing findings across studies. To explore the molecular mechanisms of EDCs in a systematic manner, we established a data-driven computational approach to meta-analyze 30 human, mouse, and rat liver transcriptomic datasets for 4 EDCs, namely bisphenol A (BPA), bis(2-ethylhexyl) phthalate (DEHP), tributyltin (TBT), and perfluorooctanoic acid (PFOA). Our computational pipeline uniformly re-analyzed pre-processed quality-controlled microarray data and raw RNAseq data, derived differentially expressed genes (DEGs) and biological pathways, modeled gene regulatory networks and regulators, and determined CMD associations based on gene overlap analysis. Our approach revealed that DEHP and PFOA shared stable transcriptomic signatures that are enriched for genes associated with CMDs, suggesting similar mechanisms of action such as perturbations of peroxisome proliferator-activated receptor gamma (PPARγ) signaling and liver gene network regulators VNN1 and ACOT2. In contrast, TBT exhibited highly divergent gene signatures, pathways, network regulators, and disease associations from the other EDCs. In addition, we found that the rat, mouse, and human BPA studies showed highly variable transcriptomic patterns, providing molecular support for the variability in BPA responses. Our work offers insights into the commonality and differences in the molecular mechanisms of various EDCs and establishes a streamlined data-driven workflow to compare molecular mechanisms of environmental substances to elucidate the underlying connections between chemical exposure and disease risks.
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Affiliation(s)
- Zacary Zamora
- Molecular Toxicology Interdepartmental Program, University of California, Los Angeles (UCLA), Los Angeles, CA 90095, USA; Department of Integrative Biology and Physiology, University of California, Los Angeles (UCLA), Los Angeles, CA 90095, USA
| | - Susanna Wang
- Department of Integrative Biology and Physiology, University of California, Los Angeles (UCLA), Los Angeles, CA 90095, USA
| | - Yen-Wei Chen
- Molecular Toxicology Interdepartmental Program, University of California, Los Angeles (UCLA), Los Angeles, CA 90095, USA; Department of Integrative Biology and Physiology, University of California, Los Angeles (UCLA), Los Angeles, CA 90095, USA
| | - Graciel Diamante
- Department of Integrative Biology and Physiology, University of California, Los Angeles (UCLA), Los Angeles, CA 90095, USA.
| | - Xia Yang
- Molecular Toxicology Interdepartmental Program, University of California, Los Angeles (UCLA), Los Angeles, CA 90095, USA; Department of Integrative Biology and Physiology, University of California, Los Angeles (UCLA), Los Angeles, CA 90095, USA; Institute for Quantitative and Computational Biosciences, University of California, Los Angeles (UCLA), Los Angeles, CA 90095, USA.
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3
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Lizarraga LE, Suter GW, Lambert JC, Patlewicz G, Zhao JQ, Dean JL, Kaiser P. Advancing the science of a read-across framework for evaluation of data-poor chemicals incorporating systematic and new approach methods. Regul Toxicol Pharmacol 2022; 137:105293. [PMID: 36414101 DOI: 10.1016/j.yrtph.2022.105293] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2022] [Revised: 10/18/2022] [Accepted: 11/09/2022] [Indexed: 11/21/2022]
Abstract
The assessment of human health hazards posed by chemicals traditionally relies on toxicity studies in experimental animals. However, most chemicals currently in commerce do not meet the minimum data requirements for hazard identification and dose-response analysis in human health risk assessment. Previously, we introduced a read-across framework designed to address data gaps for screening-level assessment of chemicals with insufficient in vivo toxicity information (Wang et al., 2012). It relies on inference by analogy from suitably tested source analogues to a target chemical, based on structural, toxicokinetic, and toxicodynamic similarity. This approach has been used for dose-response assessment of data-poor chemicals relevant to the U.S. EPA's Superfund program. We present herein, case studies of the application of this framework, highlighting specific examples of the use of biological similarity for chemical grouping and quantitative read-across. Based on practical knowledge and technological advances in the fields of read-across and predictive toxicology, we propose a revised framework. It includes important considerations for problem formulation, systematic review, target chemical analysis, analogue identification, analogue evaluation, and incorporation of new approach methods. This work emphasizes the integration of systematic methods and alternative toxicity testing data and tools in chemical risk assessment to inform regulatory decision-making.
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Affiliation(s)
- Lucina E Lizarraga
- Center for Public Health and Environmental Assessment, U.S. Environmental Protection Agency, 26 W. Martin L. King Drive, Cincinnati, OH, 45268, USA.
| | - Glenn W Suter
- Office of Research and Development, Emeritus, U.S. Environmental Protection Agency, 26 W. Martin L. King Drive, Cincinnati, OH, 45268, USA
| | - Jason C Lambert
- Center for Computational Toxicology & Exposure (CCTE), U.S. Environmental Protection Agency, Research Triangle Park, NC, 27709, USA
| | - Grace Patlewicz
- Center for Computational Toxicology & Exposure (CCTE), U.S. Environmental Protection Agency, Research Triangle Park, NC, 27709, USA
| | - Jay Q Zhao
- Center for Public Health and Environmental Assessment, U.S. Environmental Protection Agency, 26 W. Martin L. King Drive, Cincinnati, OH, 45268, USA
| | - Jeffry L Dean
- Center for Public Health and Environmental Assessment, U.S. Environmental Protection Agency, 26 W. Martin L. King Drive, Cincinnati, OH, 45268, USA
| | - Phillip Kaiser
- Center for Public Health and Environmental Assessment, U.S. Environmental Protection Agency, 26 W. Martin L. King Drive, Cincinnati, OH, 45268, USA
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4
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Su R, Yang H, Wei L, Chen S, Zou Q. A multi-label learning model for predicting drug-induced pathology in multi-organ based on toxicogenomics data. PLoS Comput Biol 2022; 18:e1010402. [PMID: 36070305 PMCID: PMC9451100 DOI: 10.1371/journal.pcbi.1010402] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2022] [Accepted: 07/18/2022] [Indexed: 11/18/2022] Open
Abstract
Drug-induced toxicity damages the health and is one of the key factors causing drug withdrawal from the market. It is of great significance to identify drug-induced target-organ toxicity, especially the detailed pathological findings, which are crucial for toxicity assessment, in the early stage of drug development process. A large variety of studies have devoted to identify drug toxicity. However, most of them are limited to single organ or only binary toxicity. Here we proposed a novel multi-label learning model named Att-RethinkNet, for predicting drug-induced pathological findings targeted on liver and kidney based on toxicogenomics data. The Att-RethinkNet is equipped with a memory structure and can effectively use the label association information. Besides, attention mechanism is embedded to focus on the important features and obtain better feature presentation. Our Att-RethinkNet is applicable in multiple organs and takes account the compound type, dose, and administration time, so it is more comprehensive and generalized. And more importantly, it predicts multiple pathological findings at the same time, instead of predicting each pathology separately as the previous model did. To demonstrate the effectiveness of the proposed model, we compared the proposed method with a series of state-of-the-arts methods. Our model shows competitive performance and can predict potential hepatotoxicity and nephrotoxicity in a more accurate and reliable way. The implementation of the proposed method is available at https://github.com/RanSuLab/Drug-Toxicity-Prediction-MultiLabel.
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Affiliation(s)
- Ran Su
- School of Computer Science and Technology, College of Intelligence and Computing, Tianjin University, Tianjin, China
| | - Haitang Yang
- School of Computer Science and Technology, College of Intelligence and Computing, Tianjin University, Tianjin, China
| | - Leyi Wei
- School of Software, Shandong University, Jinan, Shandong, China
| | - Siqi Chen
- School of Computer Science and Technology, College of Intelligence and Computing, Tianjin University, Tianjin, China
| | - Quan Zou
- Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, Zhejiang, China
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5
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Corton JC, Mitchell CA, Auerbach S, Bushel P, Ellinger-Ziegelbauer H, Escobar PA, Froetschl R, Harrill AH, Johnson K, Klaunig JE, Pandiri AR, Podtelezhnikov AA, Rager JE, Tanis KQ, van der Laan JW, Vespa A, Yauk CL, Pettit SD, Sistare FD. A Collaborative Initiative to Establish Genomic Biomarkers for Assessing Tumorigenic Potential to Reduce Reliance on Conventional Rodent Carcinogenicity Studies. Toxicol Sci 2022; 188:4-16. [PMID: 35404422 PMCID: PMC9238304 DOI: 10.1093/toxsci/kfac041] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022] Open
Abstract
There is growing recognition across broad sectors of the scientific community that use of genomic biomarkers has the potential to reduce the need for conventional rodent carcinogenicity studies of industrial chemicals, agrochemicals, and pharmaceuticals through a weight-of-evidence approach. These biomarkers fall into 2 major categories: (1) sets of gene transcripts that can identify distinct tumorigenic mechanisms of action; and (2) cancer driver gene mutations indicative of rapidly expanding growth-advantaged clonal cell populations. This call-to-action article describes a collaborative approach launched to develop and qualify biomarker gene expression panels that measure widely accepted molecular pathways linked to tumorigenesis and their activation levels to predict tumorigenic doses of chemicals from short-term exposures. Growing evidence suggests that application of such biomarker panels in short-term exposure rodent studies can identify both tumorigenic hazard and tumorigenic activation levels for chemical-induced carcinogenicity. In the future, this approach will be expanded to include methodologies examining mutations in key cancer driver gene mutation hotspots as biomarkers of both genotoxic and nongenotoxic chemical tumor risk. Analytical, technical, and biological validation studies of these complementary genomic tools are being undertaken by multisector and multidisciplinary collaborative teams within the Health and Environmental Sciences Institute. Success from these efforts will facilitate the transition from current heavy reliance on conventional 2-year rodent carcinogenicity studies to more rapid animal- and resource-sparing approaches for mechanism-based carcinogenicity evaluation supporting internal and regulatory decision-making.
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Affiliation(s)
- J Christopher Corton
- Center for Computational Toxicology and Exposure, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina, USA
| | - Constance A Mitchell
- Health and Environmental Sciences Institute, Washington, District of Columbia, USA
| | - Scott Auerbach
- Division of the National Toxicology Program, National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina, USA
| | - Pierre Bushel
- Biostatistics and Computational Biology Branch, National Institute of Environmental Health Sciences, Durham, North Carolina, USA
| | | | - Patricia A Escobar
- Safety Assessment and Laboratory Animal Resources, Merck Sharp & Dohme Corp, West Point, Pennsylvania, USA
| | - Roland Froetschl
- BfArM-Bundesinstitut für Arzneimittel und Medizinprodukte, Federal Institute for Drugs and Medical Devices, Bonn, Germany
| | - Alison H Harrill
- Division of the National Toxicology Program, National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina, USA
| | | | - James E Klaunig
- Laboratory of Investigative Toxicology and Pathology, Department of Environmental and Occupational Health, Indiana School of Public Health, Indiana University, Bloomington, Indiana, USA
| | - Arun R Pandiri
- Division of the National Toxicology Program, National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina, USA
| | | | - Julia E Rager
- The Institute for Environmental Health Solutions, Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Keith Q Tanis
- Safety Assessment and Laboratory Animal Resources, Merck Sharp & Dohme Corp, West Point, Pennsylvania, USA
| | - Jan Willem van der Laan
- Section on Pharmacology, Toxicology and Kinetics, Medicines Evaluation Board, Utrecht, The Netherlands
| | - Alisa Vespa
- Therapeutic Products Directorate, Health Canada, Ottawa, Ontario, Canada
| | - Carole L Yauk
- Department of Biology, University of Ottawa, Ottawa, Ontario, Canada
| | - Syril D Pettit
- Health and Environmental Sciences Institute, Washington, District of Columbia, USA
| | - Frank D Sistare
- Division of Pharmacotherapy and Experimental Therapeutics, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, North Carolina, USA
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6
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Suter GW, Lizarraga LE. Clearly weighing the evidence in read-across can improve assessments of data-poor chemicals. Regul Toxicol Pharmacol 2021; 129:105111. [PMID: 34973387 DOI: 10.1016/j.yrtph.2021.105111] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2021] [Revised: 12/22/2021] [Accepted: 12/27/2021] [Indexed: 02/07/2023]
Abstract
This paper provides a systematic weight-of-evidence method for read-across analyses of data-poor chemicals. The read-across technique extrapolates toxicity from analogous chemicals for which suitable test data are available to a target chemical. To determine that a candidate analogue is the 'best' and is sufficiently similar, the evidence for similarity of each candidate analogue to the target is weighed. We present a systematic weight of evidence method that provides transparency and imposes a consistent and rigorous inferential process. The method assembles relevant information concerning structure, physicochemical attributes, toxicokinetics, and toxicodynamics of the target and analogues. The information is then organized by evidence types and subtypes and weighted in terms of properties: relevance, strength, and reliability into weight levels, expressed as symbols. After evidence types are weighted, the bodies of evidence are weighted for collective properties: number, diversity, and coherence. Finally, the weights for the types and bodies of evidence are weighed for each analogue, and, if the overall weight of evidence is sufficient for one or more analogues, the analogue with the greatest weight is used to estimate the endpoint effect. We illustrate this WoE approach with a read-across analysis for screening the organochlorine contaminant, p,p'-dichlorodiphenyldichloroethane (DDD), for noncancer oral toxicity.
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Affiliation(s)
- Glenn W Suter
- Office of Research and Development, Emeritus, U.S. Environmental Protection Agency, 26 W. Martin L. King Drive, Cincinnati, OH, 45268, USA.
| | - Lucina E Lizarraga
- Center for Public Health and Environmental Assessment, U.S. Environmental Protection Agency, 26 W. Martin L. King Drive, Cincinnati, OH, 45268, USA.
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7
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Antioxidant and Antiproliferative Activity of Finasteride against Glioblastoma Cells. Pharmaceutics 2021; 13:pharmaceutics13091410. [PMID: 34575486 PMCID: PMC8469955 DOI: 10.3390/pharmaceutics13091410] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Revised: 08/25/2021] [Accepted: 09/02/2021] [Indexed: 11/16/2022] Open
Abstract
Glioblastoma is an actively growing and aggressive brain tumor with a high propensity of recurrence. Although the surgical removal of tumor mass is the primary therapeutic option against glioblastoma, supportive pharmacotherapy is highly essential due to incredibly infiltrative characteristic of glioblastoma. Temozolomide, an FDA-approved alkylating agent, has been used as a first-line standard pharmacological approach, but several evident limitations were repeatedly reported. Despite additional therapeutic options suggested, there are no medications that successfully prevent a recurrence of glioblastoma and increase the five-year survival rate. In this study, we tested the possibility that finasteride has the potential to be developed as an anti-glioblastoma drug. Finasteride, an FDA-approved medication for the treatment of benign prostate hyperplasia and androgenic alopecia, is already known to pass through the blood-brain barrier and possess antiproliferative activity of prostate epithelial cells. We showed that finasteride inhibited the maintenance of glioma stem-like cells and repressed the proliferation of glioblastoma. Mechanistically, finasteride lowered intracellular ROS level by upregulating antioxidant genes, which contributed to inefficient β-catenin accumulation. Downregulated β-catenin resulted in the reduction in stemness and cell growth in glioblastoma.
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Zhao Y, Hasse S, Bourgoin SG. Phosphatidylserine-specific phospholipase A1: A friend or the devil in disguise. Prog Lipid Res 2021; 83:101112. [PMID: 34166709 DOI: 10.1016/j.plipres.2021.101112] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2021] [Revised: 04/30/2021] [Accepted: 06/18/2021] [Indexed: 02/06/2023]
Abstract
Various human tissues and cells express phospholipase A1 member A (PLA1A), including the liver, lung, prostate gland, and immune cells. The enzyme belongs to the pancreatic lipase family. PLA1A specifically hydrolyzes sn-1 fatty acid of phosphatidylserine (PS) or 1-acyl-lysophosphatidylserine (1-acyl-lysoPS). PS externalized by activated cells or apoptotic cells or extracellular vesicles is a potential source of substrate for the production of unsaturated lysoPS species by PLA1A. Maturation and functions of many immune cells, such as T cells, dendritic cells, macrophages, and mast cells, can be regulated by PLA1A and lysoPS. Several lysoPS receptors, including GPR34, GPR174 and P2Y10, have been identified. High serum levels and high PLA1A expression are associated with autoimmune disorders such as Graves' disease and systemic lupus erythematosus. Increased expression of PLA1A is associated with metastatic melanomas. PLA1A may contribute to cardiometabolic disorders through mediating cholesterol transportation and producing lysoPS. Furthermore, PLA1A is necessary for hepatitis C virus assembly and can play a role in the antivirus innate immune response. This review summarizes recent findings on PLA1A expression, lysoPS and lysoPS receptors in autoimmune disorders, cancers, cardiometabolic disorders, antivirus immune responses, as well as regulations of immune cells.
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Affiliation(s)
- Yang Zhao
- Centre de recherche du CHU de Québec-Université Laval, Centre ARThrite de l'Université Laval, Département de microbiologie-infectiologie et d'immunologie, Université Laval, Québec, G1V 4G2, Canada
| | - Stephan Hasse
- Centre de recherche du CHU de Québec-Université Laval, Centre ARThrite de l'Université Laval, Département de microbiologie-infectiologie et d'immunologie, Université Laval, Québec, G1V 4G2, Canada
| | - Sylvain G Bourgoin
- Centre de recherche du CHU de Québec-Université Laval, Centre ARThrite de l'Université Laval, Département de microbiologie-infectiologie et d'immunologie, Université Laval, Québec, G1V 4G2, Canada.
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Zhang Y, Zhou J, Liu J, Li S, Zhou S, Zhang C, Wang Y, Shi J, Liu J, Wu Q. RNA-Seq analysis of the protection by Dendrobium nobile alkaloids against carbon tetrachloride hepatotoxicity in mice. Biomed Pharmacother 2021; 137:111307. [PMID: 33561648 DOI: 10.1016/j.biopha.2021.111307] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2020] [Revised: 11/30/2020] [Accepted: 12/26/2020] [Indexed: 01/04/2023] Open
Abstract
OBJECTIVE Dendrobium nobile is a genuine Chinese medicine. Dendrobium nobile Lindl. alkaloids (DNLA) protects against CCl4-induced acute liver injury. This study used RNA-Seq to explore the mechanisms. METHODS Mice were pretreated with DNLA (10 and 20 mg/kg, po) for 7 days, and subsequently intoxicated with CCl4 (20 μL/kg, ip for 24 h). Liver RNA was extracted and subjected to RNA-Seq. The bioinformatics, including PCA, GO, KEGG, two-dimensional clustering, Ingenuity Pathways Analysis (IPA), and Illumina BaseSpace Correlation Engine (BSCE) were used to analyze the data. qPCR was performed on selected genes to verify RNA-Seq results. RESULTS DNLA protection against CCl4 hepatotoxicity was confirmed by histopathology. PCA revealed the distinct gene expression patterns between the different treatment groups. GO showed that CCl4 induced the activation, adhesion and proliferation of immune cells. KEGG showed CCl4 induced oxidative stress, diseases and compromised adaptive responses. CCl4 induced differentially expressed genes (DEGs) were identified by DESeq2 with Padj < 0.05 and 2D-clustered with other groups. DNLA reverted CCl4-induced DEGs in a dose-dependent manner. qPCR analysis of S100 g, Sprr1, CCL3/7, Saa2/3, IL1rn, Cox7a2 and Rad15 confirmed RNA-Seq results. IPA showed that CCl4 treatment altered some signaling and metabolic pathways, which were ameliorated or returned to normal following DNLA treatment. The CCl4-activated mitochondrial oxidative phosphorylation was illustrated as an example. IPA Upstream Regulator Analysis further revealed the activated or inhibited molecules and chemicals that are responsible for CCl4-induced DEGs, and DNLA attenuated these changes. BSCE analysis verified that CCl4-induced DEGs were highly correlated with the GEO database of CCl4 hepatotoxicity in rodents, and DNLA dose-dependently attenuated such correlation. CONCLUSION RNA-Seq revealed CCl4-induced DEGs, disruption of canonical pathways, activation or inhibition of upstream regulators, which are highly correlated with database for CCl4 hepatotoxicity. All these changes were attenuated or returned to normal by DNLA, demonstrating the mechanisms for DNLA to protect against CCl4 hepatotoxicity.
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Affiliation(s)
- Ya Zhang
- Key Laboratory of Basic Pharmacology of Ministry of Education and Joint International Research Laboratory of Ethnocentric of Ministry of Education, Zunyi Medical University, Zunyi, China.
| | - Jinxin Zhou
- Key Laboratory of Basic Pharmacology of Ministry of Education and Joint International Research Laboratory of Ethnocentric of Ministry of Education, Zunyi Medical University, Zunyi, China.
| | - Jiajia Liu
- Key Laboratory of Basic Pharmacology of Ministry of Education and Joint International Research Laboratory of Ethnocentric of Ministry of Education, Zunyi Medical University, Zunyi, China.
| | - Shujun Li
- Key Laboratory of Basic Pharmacology of Ministry of Education and Joint International Research Laboratory of Ethnocentric of Ministry of Education, Zunyi Medical University, Zunyi, China.
| | - Shaoyu Zhou
- Key Laboratory of Basic Pharmacology of Ministry of Education and Joint International Research Laboratory of Ethnocentric of Ministry of Education, Zunyi Medical University, Zunyi, China.
| | - Chengchen Zhang
- Key Laboratory of Basic Pharmacology of Ministry of Education and Joint International Research Laboratory of Ethnocentric of Ministry of Education, Zunyi Medical University, Zunyi, China.
| | - Yan Wang
- Key Laboratory of Basic Pharmacology of Ministry of Education and Joint International Research Laboratory of Ethnocentric of Ministry of Education, Zunyi Medical University, Zunyi, China.
| | - Jingshan Shi
- Key Laboratory of Basic Pharmacology of Ministry of Education and Joint International Research Laboratory of Ethnocentric of Ministry of Education, Zunyi Medical University, Zunyi, China.
| | - Jie Liu
- Key Laboratory of Basic Pharmacology of Ministry of Education and Joint International Research Laboratory of Ethnocentric of Ministry of Education, Zunyi Medical University, Zunyi, China.
| | - Qin Wu
- Key Laboratory of Basic Pharmacology of Ministry of Education and Joint International Research Laboratory of Ethnocentric of Ministry of Education, Zunyi Medical University, Zunyi, China.
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10
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Choudhari JK, Verma MK, Choubey J, Sahariah BP. Investigation of MicroRNA and transcription factor mediated regulatory network for silicosis using systems biology approach. Sci Rep 2021; 11:1265. [PMID: 33446673 PMCID: PMC7809153 DOI: 10.1038/s41598-020-77636-4] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2019] [Accepted: 10/19/2020] [Indexed: 02/08/2023] Open
Abstract
Silicosis is a major health issue among workers exposed to crystalline silica. Genetic susceptibility has been implicated in silicosis. The present research demonstrates key regulatory targets and propagated network of gene/miRNA/transcription factor (TF) with interactions responsible for silicosis by integrating publicly available microarray data using a systems biology approach. Array quality is assessed with the Quality Metrics package of Bioconductor, limma package, and the network is constructed using Cytoscape. We observed and enlist 235 differentially expressed genes (DEGs) having up-regulation expression (85 nos) and down-regulation expression (150 nos.) in silicosis; and 24 TFs for the regulation of these DEGs entangled with thousands of miRNAs. Functional enrichment analysis of the DEGs enlighten that, the maximum number of DEGs are responsible for biological process viz, Rab proteins signal transduction (11 nos.) and Cellular Senescence (20 nos.), whereas IL-17 signaling pathway (16 nos.) and Signalling by Nuclear Receptors (14 nos.) etc. are Biological Pathway involving more DEGs. From the identified 1100 high target microRNA (miRNA)s involved in silicosis, 1055 miRNAs are found to relate with down-regulated genes and 847 miRNAs with up-regulated genes. The CDK19 gene (Up-regulated) is associated with 617 miRNAs whereas down-regulated gene ARID5B is regulated by as high as 747 high target miRNAs. In Prediction of Small-molecule signatures, maximum scoring small-molecule combinations for the DEGs have shown that CGP-60774 (with 20 combinations), alvocidib (with 15 combinations) and with AZD-7762 (24 combinations) with few other drugs having the high probability of success.
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Affiliation(s)
- J K Choudhari
- Chhattisgarh Swami Vivekanand Technical University, Bhilai, C.G, 491107, India
- Raipur Institute of Technology, Raipur, C.G, 492001, India
| | - M K Verma
- Chhattisgarh Swami Vivekanand Technical University, Bhilai, C.G, 491107, India
- National Institute of Technology Raipur, Raipur, C.G, 491020, India
| | - J Choubey
- Raipur Institute of Technology, Raipur, C.G, 492001, India
| | - B P Sahariah
- Chhattisgarh Swami Vivekanand Technical University, Bhilai, C.G, 491107, India.
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11
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Chappell GA, Heintz MM, Haws LC. Transcriptomic analyses of livers from mice exposed to 1,4-dioxane for up to 90 days to assess potential mode(s) of action underlying liver tumor development. Curr Res Toxicol 2021; 2:30-41. [PMID: 34345848 PMCID: PMC8320614 DOI: 10.1016/j.crtox.2021.01.003] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2020] [Revised: 01/06/2021] [Accepted: 01/07/2021] [Indexed: 12/11/2022] Open
Abstract
1,4-Dioxane is a volatile organic compound with industrial and commercial applications as a solvent and in the manufacture of other chemicals. 1,4-Dioxane has been demonstrated to induce liver tumors in chronic rodent bioassays conducted at very high doses. The available evidence for 1,4-dioxane-induced liver tumors in rodents aligns with a threshold-dependent mode of action (MOA), with the underlying mechanism being less clear in the mouse than in rats. To gain a better understanding of the underlying molecular mechanisms related to liver tumor development in mice orally exposed to 1,4-dioxane, transcriptomics analysis was conducted on liver tissue collected from a 90-day drinking water study in female B6D2F1/Crl mice (Lafranconi et al., 2020). Using tissue samples from female mice exposed to 1,4-dioxane in the drinking water at concentrations of 0, 40, 200, 600, 2,000 or 6,000 ppm for 7, 28, and 90 days, transcriptomic analyses demonstrate minimal treatment effects on global gene expression at concentrations below 600 ppm. At higher concentrations, genes involved in phase II metabolism and mitotic cell cycle checkpoints were significantly upregulated. There was an overall lack of enrichment of genes related to DNA damage response. The increase in mitotic signaling is most prevalent in the livers of mice exposed to 1,4-dioxane at the highest concentrations for 90 days. This finding aligns with phenotypic changes reported by Lafranconi et al. (2020) after 90-days of exposure to 6,000 ppm 1,4-dioxane in the same tissues. The transcriptomics analysis further supports overarching study findings demonstrating a non-mutagenic, threshold-based, mitogenic MOA for 1,4-dioxane-induced liver tumors.
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Affiliation(s)
- G A Chappell
- ToxStrategies, Inc., Asheville, NC, United States
| | - M M Heintz
- ToxStrategies, Inc., Asheville, NC, United States
| | - L C Haws
- ToxStrategies, Inc., Austin, TX, United States
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12
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Corton JC, Korunes KL, Abedini J, El-Masri H, Brown J, Paul-Friedman K, Liu Y, Martini C, He S, Rooney J. Thresholds Derived From Common Measures in Rat Studies Are Predictive of Liver Tumorigenic Chemicals. Toxicol Pathol 2020; 48:857-874. [DOI: 10.1177/0192623320960412] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
We hypothesized that typical tissue and clinical chemistry (ClinChem) end points measured in rat toxicity studies exhibit chemical-independent biological thresholds beyond which cancer occurs. Using the rat in vivo TG-GATES study, 75 chemicals were examined across chemical-dose-time comparisons that could be linked to liver tumor outcomes. Thresholds for liver weight to body weight (LW/BW) and 21 serum ClinChem end points were defined as the maximum and minimum values for those exposures that did not lead to liver tumors in rats. Upper thresholds were identified for LW/BW (117%), aspartate aminotransferase (195%), alanine aminotransferase (141%), alkaline phosphatase (152%), and total bilirubin (115%), and lower thresholds were identified for phospholipids (82%), relative albumin (93%), total cholesterol (82%), and total protein (94%). Thresholds derived from the TG-GATES data set were consistent across other acute and subchronic rat studies. A training set of ClinChem and LW/BW thresholds derived from a 38 chemical training set from TG-GATES was predictive of liver tumor outcomes for a test set of 37 independent TG-GATES chemicals (91%). The thresholds were most predictive when applied to 7d treatments (98%). These findings provide support that biological thresholds for common end points in rodent studies can be used to predict chemical tumorigenic potential.
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Affiliation(s)
- J. Christopher Corton
- Center for Computational Toxicology and Exposure, U.S. EPA, Research Triangle Park, NC, USA
| | - Katharine L. Korunes
- Center for Computational Toxicology and Exposure, U.S. EPA, Research Triangle Park, NC, USA
- Department of Evolutionary Anthropology, Duke University, Durham, NC, USA
| | - Jaleh Abedini
- Center for Computational Toxicology and Exposure, U.S. EPA, Research Triangle Park, NC, USA
| | - Hisham El-Masri
- Center for Computational Toxicology and Exposure, U.S. EPA, Research Triangle Park, NC, USA
| | - Jason Brown
- Center for Computational Toxicology and Exposure, U.S. EPA, Research Triangle Park, NC, USA
| | - Katie Paul-Friedman
- Center for Computational Toxicology and Exposure, U.S. EPA, Research Triangle Park, NC, USA
| | - Ying Liu
- ASRC Federal, Research Triangle Park, NC, USA
| | | | - Shihan He
- ASRC Federal, Research Triangle Park, NC, USA
| | - John Rooney
- Center for Computational Toxicology and Exposure, U.S. EPA, Research Triangle Park, NC, USA
- Oak Ridge Institute for Science and Education (ORISE), National Health and Environmental Effects Research Laboratory (NHEERL), Office of Research and Development, U.S. Environmental Protection Agency (EPA), Research Triangle Park, NC, USA
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13
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Lewis RW, Hill T, Corton JC. A set of six Gene expression biomarkers and their thresholds identify rat liver tumorigens in short-term assays. Toxicology 2020; 443:152547. [PMID: 32755643 PMCID: PMC10439517 DOI: 10.1016/j.tox.2020.152547] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2020] [Revised: 07/23/2020] [Accepted: 07/28/2020] [Indexed: 02/01/2023]
Abstract
Traditional methods for cancer risk assessment are retrospective, resource-intensive, and not feasible for the vast majority of environmental chemicals. In earlier studies, we used a set of six biomarkers to accurately identify liver tumorigens in transcript profiles derived from chemically-treated rats using either a Toxicological Priority Index (ToxPi) approach or using derived biomarker thresholds for cancer. The biomarkers consisting of 7-113 genes are used to predict the most common liver cancer molecular initiating events: genotoxicity, cytotoxicity and activation of the xenobiotic receptors AhR, CAR, ER, and PPARα. In the present study, we apply and evaluate the performance of these methods for cancer prediction in an independent rat liver study of 44 chemicals (6 h-7d exposures) examined by Affymetrix arrays. In the first approach, ToxPi ranking of biomarker scores consistently gave the highest scores to tumorigenic chemical-dose pairs; balanced accuracies for identification of liver tumorigenic chemicals were up to 89 %. The second approach used tumorigenic thresholds derived in the present study or from our earlier study that were set at the maximum value for chemical-dose exposures without detectable liver tumor outcomes. Using these thresholds, balanced accuracies were up to 90 %. Both approaches identified all tumorigenic chemicals. Almost all of the tumorigenic chemicals activated more than one MIE. We also compared biomarker responses between two types of profiling platforms (Affymetrix full-genome array, TempO-Seq 1500+ array containing ∼2600 genes) and found that the lack of the full set of biomarker genes on the 1500+ array resulted in decreased ability to identify chemicals that activate the MIEs. Overall, these results demonstrate that predictive approaches based on the 6 biomarkers could be used in short-term assays to identify chemicals and their doses that induce liver tumors, the most common endpoint in rodent bioassays.
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Affiliation(s)
- Robert W Lewis
- Center for Computational Toxicology and Exposure, U.S. EPA, Research Triangle Park, NC, United States.
| | - Thomas Hill
- Center for Computational Toxicology and Exposure, U.S. EPA, Research Triangle Park, NC, United States; Oak Ridge Institute for Science and Education (ORISE) fellow Office of Research and Development, U.S. Environmental Protection Agency (EPA), Research Triangle Park, NC, United States.
| | - J Christopher Corton
- Center for Computational Toxicology and Exposure, U.S. EPA, Research Triangle Park, NC, United States.
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14
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Corton JC, Hill T, Sutherland JJ, Stevens JL, Rooney J. A Set of Six Gene Expression Biomarkers Identify Rat Liver Tumorigens in Short-term Assays. Toxicol Sci 2020; 177:11-26. [PMID: 32603430 PMCID: PMC8026143 DOI: 10.1093/toxsci/kfaa101] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022] Open
Abstract
Chemical-induced liver cancer occurs in rodents through well-characterized adverse outcome pathways. We hypothesized that measurement of the 6 most common molecular initiating events (MIEs) in liver cancer adverse outcome pathways in short-term assays using only gene expression will allow early identification of chemicals and their associated doses that are likely to be tumorigenic in the liver in 2-year bioassays. We tested this hypothesis using transcript data from a rat liver microarray compendium consisting of 2013 comparisons of 146 chemicals administered at doses with previously established effects on rat liver tumor induction. Five MIEs were measured using previously characterized gene expression biomarkers composed of gene sets predictive for genotoxicity and activation of 1 or more xenobiotic receptors (aryl hydrocarbon receptor, constitutive activated receptor, estrogen receptor, and peroxisome proliferator-activated receptor α). Because chronic injury can be important in tumorigenesis, we also developed a biomarker for cytotoxicity that had a 96% balanced accuracy. Characterization of the genes in each biomarker set using the unsupervised TXG-MAP network model demonstrated that the genes were associated with distinct functional coexpression modules. Using the Toxicological Priority Index to rank chemicals based on their ability to activate the MIEs showed that chemicals administered at tumorigenic doses clearly gave the highest ranked scores. Balanced accuracies using thresholds derived from either TG-GATES or DrugMatrix data sets to predict tumorigenicity in independent sets of chemicals were up to 93%. These results show that a MIE-directed approach using only gene expression biomarkers could be used in short-term assays to identify chemicals and their doses that cause tumors.
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Affiliation(s)
- J Christopher Corton
- Center for Computational Toxicology and Exposure, U.S. Environmental Protection Agency (EPA), Research Triangle Park, North Carolina
| | - Thomas Hill
- Center for Computational Toxicology and Exposure, U.S. Environmental Protection Agency (EPA), Research Triangle Park, North Carolina
- Oak Ridge Institute for Science and Education (ORISE)
| | | | - James L Stevens
- Indiana Biosciences Research Institute, Indianapolis, Indiana
- Paradox Found LLC, Apex, North Carolina
| | - John Rooney
- Center for Computational Toxicology and Exposure, U.S. Environmental Protection Agency (EPA), Research Triangle Park, North Carolina
- Oak Ridge Institute for Science and Education (ORISE)
- Integrated Lab Services, Research Triangle Park, NC 27560
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15
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Sasaki E, Hamaguchi I, Mizukami T. Pharmacodynamic and safety considerations for influenza vaccine and adjuvant design. Expert Opin Drug Metab Toxicol 2020; 16:1051-1061. [PMID: 32772723 DOI: 10.1080/17425255.2020.1807936] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
Abstract
INTRODUCTION A novel adjuvant evaluation system for safety and immunogenicity is needed. Vaccination is important for infection prevention, for example, from influenza viruses. Adjuvants are considered critical for improving the effectiveness of influenza vaccines. Adjuvant development is an important issue in influenza vaccine design. AREAS COVERED A conventional in vivo evaluation method for vaccine safety has been limited in analyzing phenotypic and pathological changes. Therefore, it is difficult to obtain information on the changes at the molecular level. This review aims to explain the recently developed genomics analysis-based vaccine adjuvant safety evaluation tools verified by AddaVaxTM and polyinosinic-polycytidylic acid (poly I:C) using 18 biomarker genes and whole-virion inactivated influenza vaccine as a toxicity control. Genomics analyzes would help provide safety and efficacy information regarding influenza vaccine design by facilitating appropriate adjuvant selection. EXPERT OPINION The efficacy and safety profiles of influenza vaccines and adjuvants using genomics technologies provide useful information regarding immunogenicity, which is related to safety and efficacy. This approach provides important information to select appropriate inoculation routes, combinations of vaccine antigens and adjuvants, and dosing amounts. The efficacy of vaccine adjuvant evaluation by genomics analysis should be verified by various studies using various vaccines in the future.
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Affiliation(s)
- Eita Sasaki
- Department of Safety Research on Blood and Biological Products, National Institute of Infectious Diseases , Tokyo, Japan
| | - Isao Hamaguchi
- Department of Safety Research on Blood and Biological Products, National Institute of Infectious Diseases , Tokyo, Japan
| | - Takuo Mizukami
- Department of Safety Research on Blood and Biological Products, National Institute of Infectious Diseases , Tokyo, Japan
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16
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Huang SH, Lin YC, Tung CW. Identification of Time-Invariant Biomarkers for Non-Genotoxic Hepatocarcinogen Assessment. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2020; 17:ijerph17124298. [PMID: 32560183 PMCID: PMC7345770 DOI: 10.3390/ijerph17124298] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/26/2020] [Revised: 06/12/2020] [Accepted: 06/14/2020] [Indexed: 12/12/2022]
Abstract
Non-genotoxic hepatocarcinogens (NGHCs) can only be confirmed by 2-year rodent studies. Toxicogenomics (TGx) approaches using gene expression profiles from short-term animal studies could enable early assessment of NGHCs. However, high variance in the modulation of the genes had been noted among exposure styles and datasets. Expanding from our previous strategy in identifying consensus biomarkers in multiple experiments, we aimed to identify time-invariant biomarkers for NGHCs in short-term exposure styles and validate their applicability to long-term exposure styles. In this study, nine time-invariant biomarkers, namely A2m, Akr7a3, Aqp7, Ca3, Cdc2a, Cdkn3, Cyp2c11, Ntf3, and Sds, were identified from four large-scale microarray datasets. Machine learning techniques were subsequently employed to assess the prediction performance of the biomarkers. The biomarker set along with the Random Forest models gave the highest median area under the receiver operating characteristic curve (AUC) of 0.824 and a low interquartile range (IQR) variance of 0.036 based on a leave-one-out cross-validation. The application of the models to the external validation datasets achieved high AUC values of greater than or equal to 0.857. Enrichment analysis of the biomarkers inferred the involvement of chronic inflammatory diseases such as liver cirrhosis, fibrosis, and hepatocellular carcinoma in NGHCs. The time-invariant biomarkers provided a robust alternative for NGHC prediction.
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Affiliation(s)
- Shan-Han Huang
- Ph. D. Program in Toxicology, Kaohsiung Medical University, Kaohsiung 80708, Taiwan; (S.-H.H.); (Y.-C.L.)
| | - Ying-Chi Lin
- Ph. D. Program in Toxicology, Kaohsiung Medical University, Kaohsiung 80708, Taiwan; (S.-H.H.); (Y.-C.L.)
- School of Pharmacy, College of Pharmacy, Kaohsiung Medical University, Kaohsiung 80708, Taiwan
- Research Center for Environmental Medicine, Kaohsiung Medical University, Kaohsiung 80708, Taiwan
| | - Chun-Wei Tung
- Graduate Institute of Data Science, College of Management, Taipei Medical University, Taipei 11031, Taiwan
- National Institute of Environmental Health Sciences, National Health Research Institutes, Miaoli County 35053, Taiwan
- Correspondence:
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17
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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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18
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Jeremias G, Gonçalves FJM, Pereira JL, Asselman J. Prospects for incorporation of epigenetic biomarkers in human health and environmental risk assessment of chemicals. Biol Rev Camb Philos Soc 2020; 95:822-846. [PMID: 32045110 DOI: 10.1111/brv.12589] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2019] [Revised: 01/28/2020] [Accepted: 01/30/2020] [Indexed: 12/18/2022]
Abstract
Epigenetic mechanisms have gained relevance in human health and environmental studies, due to their pivotal role in disease, gene × environment interactions and adaptation to environmental change and/or contamination. Epigenetic mechanisms are highly responsive to external stimuli and a wide range of chemicals has been shown to determine specific epigenetic patterns in several organisms. Furthermore, the mitotic/meiotic inheritance of such epigenetic marks as well as the resulting changes in gene expression and cell/organismal phenotypes has now been demonstrated. Therefore, epigenetic signatures are interesting candidates for linking environmental exposures to disease as well as informing on past exposures to stressors. Accordingly, epigenetic biomarkers could be useful tools in both prospective and retrospective risk assessment but epigenetic endpoints are currently not yet incorporated into risk assessments. Achieving a better understanding on this apparent impasse, as well as identifying routes to promote the application of epigenetic biomarkers within environmental risk assessment frameworks are the objectives of this review. We first compile evidence from human health studies supporting the use of epigenetic exposure-associated changes as reliable biomarkers of exposure. Then, specifically focusing on environmental science, we examine the potential and challenges of developing epigenetic biomarkers for environmental fields, and discuss useful organisms and appropriate sequencing techniques to foster their development in this context. Finally, we discuss the practical incorporation of epigenetic biomarkers in the environmental risk assessment of chemicals, highlighting critical data gaps and making key recommendations for future research within a regulatory context.
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Affiliation(s)
- Guilherme Jeremias
- Department of Biology, University of Aveiro, 3810-193, Aveiro, Portugal.,CESAM - Centre for Environmental and Marine Studies, University of Aveiro, 3810-193, Aveiro, Portugal
| | - Fernando J M Gonçalves
- Department of Biology, University of Aveiro, 3810-193, Aveiro, Portugal.,CESAM - Centre for Environmental and Marine Studies, University of Aveiro, 3810-193, Aveiro, Portugal
| | - Joana L Pereira
- Department of Biology, University of Aveiro, 3810-193, Aveiro, Portugal.,CESAM - Centre for Environmental and Marine Studies, University of Aveiro, 3810-193, Aveiro, Portugal
| | - Jana Asselman
- Laboratory of Environmental Toxicology and Aquatic Ecology, Environmental Toxicology Unit - GhEnToxLab, Ghent University, 9000, Gent, Belgium
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19
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Krewski D, Andersen ME, Tyshenko MG, Krishnan K, Hartung T, Boekelheide K, Wambaugh JF, Jones D, Whelan M, Thomas R, Yauk C, Barton-Maclaren T, Cote I. Toxicity testing in the 21st century: progress in the past decade and future perspectives. Arch Toxicol 2019; 94:1-58. [DOI: 10.1007/s00204-019-02613-4] [Citation(s) in RCA: 124] [Impact Index Per Article: 24.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2019] [Accepted: 11/05/2019] [Indexed: 12/19/2022]
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20
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Li A, Lu X, Natoli T, Bittker J, Sipes NS, Subramanian A, Auerbach S, Sherr DH, Monti S. The Carcinogenome Project: In Vitro Gene Expression Profiling of Chemical Perturbations to Predict Long-Term Carcinogenicity. ENVIRONMENTAL HEALTH PERSPECTIVES 2019; 127:47002. [PMID: 30964323 PMCID: PMC6785232 DOI: 10.1289/ehp3986] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/16/2023]
Abstract
BACKGROUND Most chemicals in commerce have not been evaluated for their carcinogenic potential. The de facto gold-standard approach to carcinogen testing adopts the 2-y rodent bioassay, a time-consuming and costly procedure. High-throughput in vitro assays are a promising alternative for addressing the limitations in carcinogen screening. OBJECTIVES We developed a screening process for predicting chemical carcinogenicity and genotoxicity and characterizing modes of actions (MoAs) using in vitro gene expression assays. METHODS We generated a large toxicogenomics resource comprising [Formula: see text] expression profiles corresponding to 330 chemicals profiled in HepG2 (human hepatocellular carcinoma cell line) at multiple doses and replicates. Predictive models of carcinogenicity and genotoxicity were built using a random forest classifier. Differential pathway enrichment analysis was performed to identify pathways associated with carcinogen exposure. Signatures of carcinogenicity and genotoxicity were compared with external sources, including Drugmatrix and the Connectivity Map. RESULTS Among profiles with sufficient bioactivity, our classifiers achieved 72.2% Area Under the ROC Curve (AUC) for predicting carcinogenicity and 82.3% AUC for predicting genotoxicity. Chemical bioactivity, as measured by the strength and reproducibility of the transcriptional response, was not significantly associated with long-term carcinogenicity in doses up to [Formula: see text]. However, sufficient bioactivity was necessary for a chemical to be used for prediction of carcinogenicity. Pathway enrichment analysis revealed pathways consistent with known pathways that drive cancer, including DNA damage and repair. The data is available at https://clue.io/CRCGN_ABC , and a portal for query and visualization of the results is accessible at https://carcinogenome.org . DISCUSSION We demonstrated an in vitro screening approach using gene expression profiling to predict carcinogenicity and infer MoAs of chemical perturbations. https://doi.org/10.1289/EHP3986.
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Affiliation(s)
- Amy Li
- Computational Biomedicine, Boston University School of Medicine, Boston, Massachusetts, USA
- Bioinformatics Program, Boston University, Boston, Massachusetts, USA
| | - Xiaodong Lu
- Cancer Program, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
| | - Ted Natoli
- Cancer Program, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
| | - Joshua Bittker
- Cancer Program, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
| | - Nisha S. Sipes
- Toxicoinformatics Group, National Institute of Environmental Health Sciences, Durham, North Carolina, USA
| | - Aravind Subramanian
- Cancer Program, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
| | - Scott Auerbach
- Toxicoinformatics Group, National Institute of Environmental Health Sciences, Durham, North Carolina, USA
| | - David H. Sherr
- Department of Environmental Health, Boston University School of Public Health, Boston, Massachusetts, USA
| | - Stefano Monti
- Computational Biomedicine, Boston University School of Medicine, Boston, Massachusetts, USA
- Bioinformatics Program, Boston University, Boston, Massachusetts, USA
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21
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Jin X, Zimmers TA, Zhang Z, Koniaris LG. Resveratrol Improves Recovery and Survival of Diet-Induced Obese Mice Undergoing Extended Major (80%) Hepatectomy. Dig Dis Sci 2019; 64:93-101. [PMID: 30284135 DOI: 10.1007/s10620-018-5312-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/03/2018] [Accepted: 09/28/2018] [Indexed: 12/24/2022]
Abstract
INTRODUCTION Loss of hepatic epidermal growth factor receptor (EGFR) expression is a cause for the increased perioperative risk for complications and death in patients with obesity and fatty liver undergoing liver resection. Herein, we set out to identify agents that might increase EGFR expression and improve recovery for patients with fatty liver undergoing resection. Using the diet-induced obese (DIO) mouse model of fatty liver, we examined resveratrol as a therapy to induce EGFR expression and improve outcomes following 80% partial hepatectomy (PH) in a murine model. METHODS DIO mice were fed resveratrol or carrier control by gavage. EGFR expression and the response to major (80%) PH were examined. RESULTS Based on an Illumina analysis, resveratrol was identified as increasing EGFR gene expression in A549 cells. Resveratrol was observed to also increase EGFR protein expression in A549 cells. DIO mice fed resveratrol by gavage (75 mg/kg) demonstrated an increased EGFR expression without the identified hepatic toxicity. Resveratrol and control mice subjected to 80% PH, a model of high mortality hepatectomy in DIO mice, demonstrated macroscopically decreased fatty liver and fewer liver hemorrhagic petechiae. Resveratrol pretreatment ameliorated liver injury and accelerated regeneration of the hepatic remnant after 80% PH including decreasing serum ALT and bilirubin, while increasing hepatic PCNA expression. Resveratrol increased induction of p-STAT3 and p-AKT after 80% hepatectomy. Resveratrol pretreatment significantly improved survival rates in DIO mice undergoing extended 80% PH. CONCLUSIONS Oral resveratrol restores EGFR expression in fatty liver. Resveratrol may be a promising protective agent in instances where extensive hepatic resection of fatty liver is required.
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Affiliation(s)
- Xiaoling Jin
- Department of Surgery, Thomas Jefferson University School of Medicine, Philadelphia, PA, USA
| | - Teresa A Zimmers
- Department of Surgery, Indiana University School of Medicine, EH 511 SGEN, Indianapolis, IN, 46202, USA
| | - Zongxiu Zhang
- Department of Surgery, Thomas Jefferson University School of Medicine, Philadelphia, PA, USA
| | - Leonidas G Koniaris
- Department of Surgery, Indiana University School of Medicine, EH 511 SGEN, Indianapolis, IN, 46202, USA.
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22
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Hasan MN, Rana MM, Begum AA, Rahman M, Mollah MNH. Robust Co-clustering to Discover Toxicogenomic Biomarkers and Their Regulatory Doses of Chemical Compounds Using Logistic Probabilistic Hidden Variable Model. Front Genet 2018; 9:516. [PMID: 30450112 PMCID: PMC6225736 DOI: 10.3389/fgene.2018.00516] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2018] [Accepted: 10/12/2018] [Indexed: 11/13/2022] Open
Abstract
Detection of biomarker genes and their regulatory doses of chemical compounds (DCCs) is one of the most important tasks in toxicogenomic studies as well as in drug design and development. There is an online computational platform "Toxygates" to identify biomarker genes and their regulatory DCCs by co-clustering approach. Nevertheless, the algorithm of that platform based on hierarchical clustering (HC) does not share gene-DCC two-way information simultaneously during co-clustering between genes and DCCs. Also it is sensitive to outlying observations. Thus, this platform may produce misleading results in some cases. The probabilistic hidden variable model (PHVM) is a more effective co-clustering approach that share two-way information simultaneously, but it is also sensitive to outlying observations. Therefore, in this paper we have proposed logistic probabilistic hidden variable model (LPHVM) for robust co-clustering between genes and DCCs, since gene expression data are often contaminated by outlying observations. We have investigated the performance of the proposed LPHVM co-clustering approach in a comparison with the conventional PHVM and Toxygates co-clustering approaches using simulated and real life TGP gene expression datasets, respectively. Simulation results show that the proposed method improved the performance over the conventional PHVM in presence of outliers; otherwise, it keeps equal performance. In the case of real life TGP data analysis, three DCCs (glibenclamide-low, perhexilline-low, and hexachlorobenzene-medium) for glutathione metabolism pathway dataset as well as two DCCs (acetaminophen-medium and methapyrilene-low) for PPAR signaling pathway dataset were incorrectly co-clustered by the Toxygates online platform, while only one DCC (hexachlorobenzene-low) for glutathione metabolism pathway was incorrectly co-clustered by the proposed LPHVM approach. Our findings from the real data analysis are also supported by the other findings in the literature.
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Affiliation(s)
- Mohammad Nazmol Hasan
- Bioinformatics Laboratory, Department of Statistics, University of Rajshahi, Rajshahi, Bangladesh.,Department of Statistics, Bangabandhu Sheikh Mujibur Rahman Agricultural University, Gazipur, Bangladesh
| | - Md Masud Rana
- Bioinformatics Laboratory, Department of Statistics, University of Rajshahi, Rajshahi, Bangladesh
| | - Anjuman Ara Begum
- Bioinformatics Laboratory, Department of Statistics, University of Rajshahi, Rajshahi, Bangladesh
| | - Moizur Rahman
- Department of Veterinary and Animal Sciences, University of Rajshahi, Rajshahi, Bangladesh
| | - Md Nurul Haque Mollah
- Bioinformatics Laboratory, Department of Statistics, University of Rajshahi, Rajshahi, Bangladesh
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23
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Sasaki E, Momose H, Hiradate Y, Mizukami T, Hamaguchi I. Establishment of a novel safety assessment method for vaccine adjuvant development. Vaccine 2018; 36:7112-7118. [PMID: 30318166 DOI: 10.1016/j.vaccine.2018.10.009] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2018] [Revised: 09/09/2018] [Accepted: 09/29/2018] [Indexed: 12/27/2022]
Abstract
Vaccines effectively prevent infectious diseases. Many types of vaccines against various pathogens that threaten humans are currently in widespread use. Recently, adjuvant adaptation has been attempted to activate innate immunity to enhance the effectiveness of vaccines. The effectiveness of adjuvants for vaccinations has been demonstrated in many animal models and clinical trials. Although a highly potent adjuvant tends to have high effectiveness, it also has the potential to increase the risk of side effects such as pain, edema, and fever. Indeed, highly effective adjuvants, such as poly(I:C), have not been clinically applied due to their high risks of toxicity in humans. Therefore, the task in the field of adjuvant development is to clinically apply highly effective and non- or low-toxic adjuvant-containing vaccines. To resolve this issue, it is essential to ensure a low risk of side effects and the high efficacy of an adjuvant in the early developmental phases. This review summarizes the theory and history of the current safety assessment methods for adjuvants, using the inactivated influenza vaccine as a model. Our novel method was developed as a system to judge the safety of a candidate compound using biomarkers identified by genomic technology and statistical tools. A systematic safety assessment tool for adjuvants would be of great use for predicting toxicity during novel adjuvant development, screening, and quality control.
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Affiliation(s)
- Eita Sasaki
- Department of Safety Research on Blood and Biological Products, National Institute of Infectious Diseases, 4-7-1 Gakuen, Musashi-Murayama, Tokyo 208-0011, Japan
| | - Haruka Momose
- Department of Safety Research on Blood and Biological Products, National Institute of Infectious Diseases, 4-7-1 Gakuen, Musashi-Murayama, Tokyo 208-0011, Japan
| | - Yuki Hiradate
- Department of Safety Research on Blood and Biological Products, National Institute of Infectious Diseases, 4-7-1 Gakuen, Musashi-Murayama, Tokyo 208-0011, Japan
| | - Takuo Mizukami
- Department of Safety Research on Blood and Biological Products, National Institute of Infectious Diseases, 4-7-1 Gakuen, Musashi-Murayama, Tokyo 208-0011, Japan
| | - Isao Hamaguchi
- Department of Safety Research on Blood and Biological Products, National Institute of Infectious Diseases, 4-7-1 Gakuen, Musashi-Murayama, Tokyo 208-0011, Japan.
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Rooney J, Hill T, Qin C, Sistare FD, Corton JC. Adverse outcome pathway-driven identification of rat liver tumorigens in short-term assays. Toxicol Appl Pharmacol 2018; 356:99-113. [DOI: 10.1016/j.taap.2018.07.023] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2018] [Revised: 07/12/2018] [Accepted: 07/20/2018] [Indexed: 02/07/2023]
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25
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Auerbach SS, Paules RS. Genomic dose response: Successes, challenges, and next steps. CURRENT OPINION IN TOXICOLOGY 2018. [DOI: 10.1016/j.cotox.2019.04.002] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
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Api AM, Belsito D, Botelho D, Bruze M, Burton GA, Buschmann J, Dagli ML, Date M, Dekant W, Deodhar C, Francis M, Fryer AD, Jones L, Joshi K, La Cava S, Lapczynski A, Liebler DC, O'Brien D, Patel A, Penning TM, Ritacco G, Romine J, Sadekar N, Salvito D, Schultz TW, Sipes IG, Sullivan G, Thakkar Y, Tokura Y, Tsang S. RIFM fragrance ingredient safety assessment, trans-2-Hexenol, CAS Registry Number 928-95-0. Food Chem Toxicol 2018; 118 Suppl 1:S49-S58. [PMID: 29932994 DOI: 10.1016/j.fct.2018.06.033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2018] [Accepted: 06/17/2018] [Indexed: 10/28/2022]
Affiliation(s)
- A M Api
- Research Institute for Fragrance Materials, Inc., 50 Tice Boulevard, Woodcliff Lake, NJ, 07677, USA
| | - D Belsito
- Member RIFM Expert Panel, Columbia University Medical Center, Department of Dermatology, 161 Fort Washington Ave., New York, NY, 10032, USA
| | - D Botelho
- Research Institute for Fragrance Materials, Inc., 50 Tice Boulevard, Woodcliff Lake, NJ, 07677, USA
| | - M Bruze
- Member RIFM Expert Panel, Malmo University Hospital, Department of Occupational & Environmental Dermatology, Sodra Forstadsgatan 101, Entrance 47, Malmo, SE-20502, Sweden
| | - G A Burton
- Member RIFM Expert Panel, School of Natural Resources & Environment, University of Michigan, Dana Building G110, 440 Church St., Ann Arbor, MI, 58109, USA
| | - J Buschmann
- Member RIFM Expert Panel, Fraunhofer Institute for Toxicology and Experimental Medicine, Nikolai-Fuchs-Strasse 1, 30625, Hannover, Germany
| | - M L Dagli
- Member RIFM Expert Panel, University of Sao Paulo, School of Veterinary Medicine and Animal Science, Department of Pathology, Av. Prof. dr. Orlando Marques de Paiva, 87, Sao Paulo, CEP, 05508-900, Brazil
| | - M Date
- Research Institute for Fragrance Materials, Inc., 50 Tice Boulevard, Woodcliff Lake, NJ, 07677, USA
| | - W Dekant
- Member RIFM Expert Panel, University of Wuerzburg, Department of Toxicology, Versbacher Str. 9, 97078, Würzburg, Germany
| | - C Deodhar
- Research Institute for Fragrance Materials, Inc., 50 Tice Boulevard, Woodcliff Lake, NJ, 07677, USA
| | - M Francis
- Research Institute for Fragrance Materials, Inc., 50 Tice Boulevard, Woodcliff Lake, NJ, 07677, USA
| | - A D Fryer
- Member RIFM Expert Panel, Oregon Health Science University, 3181 SW Sam Jackson Park Rd., Portland, OR, 97239, USA
| | - L Jones
- Research Institute for Fragrance Materials, Inc., 50 Tice Boulevard, Woodcliff Lake, NJ, 07677, USA
| | - K Joshi
- Research Institute for Fragrance Materials, Inc., 50 Tice Boulevard, Woodcliff Lake, NJ, 07677, USA
| | - S La Cava
- Research Institute for Fragrance Materials, Inc., 50 Tice Boulevard, Woodcliff Lake, NJ, 07677, USA
| | - A Lapczynski
- Research Institute for Fragrance Materials, Inc., 50 Tice Boulevard, Woodcliff Lake, NJ, 07677, USA
| | - D C Liebler
- Member RIFM Expert Panel, Vanderbilt University School of Medicine, Department of Biochemistry, Center in Molecular Toxicology, 638 Robinson Research Building, 2200 Pierce Avenue, Nashville, TN, 37232-0146, USA
| | - D O'Brien
- Research Institute for Fragrance Materials, Inc., 50 Tice Boulevard, Woodcliff Lake, NJ, 07677, USA
| | - A Patel
- Research Institute for Fragrance Materials, Inc., 50 Tice Boulevard, Woodcliff Lake, NJ, 07677, USA
| | - T M Penning
- Member RIFM Expert Panel, University of Pennsylvania, Perelman School of Medicine, Center of Excellence in Environmental Toxicology, 1316 Biomedical Research Building (BRB) II/III, 421 Curie Boulevard, Philadelphia, PA, 19104-3083, USA
| | - G Ritacco
- Research Institute for Fragrance Materials, Inc., 50 Tice Boulevard, Woodcliff Lake, NJ, 07677, USA
| | - J Romine
- Research Institute for Fragrance Materials, Inc., 50 Tice Boulevard, Woodcliff Lake, NJ, 07677, USA
| | - N Sadekar
- Research Institute for Fragrance Materials, Inc., 50 Tice Boulevard, Woodcliff Lake, NJ, 07677, USA
| | - D Salvito
- Research Institute for Fragrance Materials, Inc., 50 Tice Boulevard, Woodcliff Lake, NJ, 07677, USA
| | - T W Schultz
- Member RIFM Expert Panel, The University of Tennessee, College of Veterinary Medicine, Department of Comparative Medicine, 2407 River Dr., Knoxville, TN, 37996- 4500, USA
| | - I G Sipes
- Member RIFM Expert Panel, Department of Pharmacology, University of Arizona, College of Medicine, 1501 North Campbell Avenue, P.O. Box 245050, Tucson, AZ, 85724-5050, USA
| | - G Sullivan
- Research Institute for Fragrance Materials, Inc., 50 Tice Boulevard, Woodcliff Lake, NJ, 07677, USA.
| | - Y Thakkar
- Research Institute for Fragrance Materials, Inc., 50 Tice Boulevard, Woodcliff Lake, NJ, 07677, USA
| | - Y Tokura
- Member RIFM Expert Panel, The Journal of Dermatological Science (JDS), Editor-in-Chief, Professor and Chairman, Department of Dermatology, Hamamatsu University School of Medicine, 1-20-1 Handayama, Higashi-ku, Hamamatsu, 431-3192, Japan
| | - S Tsang
- Research Institute for Fragrance Materials, Inc., 50 Tice Boulevard, Woodcliff Lake, NJ, 07677, USA
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Liu Z, Delavan B, Roberts R, Tong W. Transcriptional Responses Reveal Similarities Between Preclinical Rat Liver Testing Systems. Front Genet 2018; 9:74. [PMID: 29616076 PMCID: PMC5870427 DOI: 10.3389/fgene.2018.00074] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2017] [Accepted: 02/19/2018] [Indexed: 01/03/2023] Open
Abstract
Toxicogenomics (TGx) is an important tool to gain an enhanced understanding of toxicity at the molecular level. Previously, we developed a pair ranking (PRank) method to assess in vitro to in vivo extrapolation (IVIVE) using toxicogenomic datasets from the Open Toxicogenomics Project-Genomics Assisted Toxicity Evaluation System (TG-GATEs) database. With this method, we investiagted three important questions that were not addressed in our previous study: (1) is a 1-day in vivo short-term assay able to replace the 28-day standard and expensive toxicological assay? (2) are some biological processes more conservative across different preclinical testing systems than others? and (3) do these preclinical testing systems have the similar resolution in differentiating drugs by their therapeutic uses? For question 1, a high similarity was noted (PRank score = 0.90), indicating the potential utility of shorter term in vivo studies to predict outcome in longer term and more expensive in vivo model systems. There was a moderate similarity between rat primary hepatocytes and in vivo repeat-dose studies (PRank score = 0.71) but a low similarity (PRank score = 0.56) between rat primary hepatocytes and in vivo single dose studies. To address question 2, we limited the analysis to gene sets relevant to specific toxicogenomic pathways and we found that pathways such as lipid metabolism were consistently over-represented in all three assay systems. For question 3, all three preclinical assay systems could distinguish compounds from different therapeutic categories. This suggests that any noted differences in assay systems was biological process-dependent and furthermore that all three systems have utility in assessing drug responses within a certain drug class. In conclusion, this comparison of three commonly used rat TGx systems provides useful information in utility and application of TGx assays.
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Affiliation(s)
- Zhichao Liu
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR, United States
| | - Brian Delavan
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR, United States.,Department of Biosciences, University of Arkansas at Little Rock, Little Rock, AR, United States
| | - Ruth Roberts
- ApconiX, Alderley Edge, United Kingdom.,Department of Biosciences, University of Birmingham, Birmingham, United Kingdom
| | - Weida Tong
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR, United States
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Jin X, Zimmers TA, Jiang Y, Milgrom DP, Zhang Z, Koniaris LG. Meloxicam increases epidermal growth factor receptor expression improving survival after hepatic resection in diet-induced obese mice. Surgery 2018; 163:1264-1271. [PMID: 29361369 DOI: 10.1016/j.surg.2017.11.029] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2017] [Revised: 11/08/2017] [Accepted: 11/28/2017] [Indexed: 02/08/2023]
Abstract
BACKGROUND Patients with fatty liver have delayed regenerative responses, increased hepatocellular injury, and increased risk for perioperative mortality. Currently, no clinical therapy exists to prevent liver failure or improve regeneration in patients with fatty liver. Previously we demonstrated that obese mice have markedly reduced levels of epidermal growth factor receptor in liver. We sought to identify pharmacologic agents to increase epidermal growth factor receptor expression to improve hepatic regeneration in the setting of fatty liver resection. METHODS Lean (20% calories from fat) and diet-induced obese mice (60% calories from fat) were subjected to 70% or 80% hepatectomy. RESULTS Using the BaseSpace Correlation Engine of deposited gene arrays we identified agents that increased hepatic epidermal growth factor receptor. Meloxicam was identified as inducing epidermal growth factor receptor expression across species. Meloxicam improved hepatic steatosis in diet-induced obese mice both grossly and histologically. Immunohistochemistry and Western blot analysis demonstrated that meloxicam pretreatment of diet-induced obese mice dramatically increased epidermal growth factor receptor protein expression in hepatocytes. After 70% hepatectomy, meloxicam pretreatment ameliorated liver injury and significantly accelerated mitotic rates of hepatocytes in obese mice. Recovery of liver mass was accelerated in obese mice pretreated with meloxicam (by 26% at 24 hours and 38% at 48 hours, respectively). After 80% hepatectomy, survival was dramatically increased with meloxicam treatment. CONCLUSION Low epidermal growth factor receptor expression is a common feature of fatty liver disease. Meloxicam restores epidermal growth factor receptor expression in steatotic hepatocytes. Meloxicam pretreatment may be applied to improve outcome after fatty liver resection or transplantation with steatotic graft.
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Affiliation(s)
- Xiaoling Jin
- Department of Surgery, Thomas Jefferson University School of Medicine, Philadelphia, PA, USA
| | - Teresa A Zimmers
- Department of Surgery, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Yanlin Jiang
- Department of Surgery, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Daniel P Milgrom
- Department of Surgery, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Zongxiu Zhang
- Department of Surgery, Thomas Jefferson University School of Medicine, Philadelphia, PA, USA
| | - Leonidas G Koniaris
- Department of Surgery, Indiana University School of Medicine, Indianapolis, IN, USA.
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Identification of consensus biomarkers for predicting non-genotoxic hepatocarcinogens. Sci Rep 2017; 7:41176. [PMID: 28117354 PMCID: PMC5259716 DOI: 10.1038/srep41176] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2016] [Accepted: 12/16/2016] [Indexed: 12/31/2022] Open
Abstract
The assessment of non-genotoxic hepatocarcinogens (NGHCs) is currently relying on two-year rodent bioassays. Toxicogenomics biomarkers provide a potential alternative method for the prioritization of NGHCs that could be useful for risk assessment. However, previous studies using inconsistently classified chemicals as the training set and a single microarray dataset concluded no consensus biomarkers. In this study, 4 consensus biomarkers of A2m, Ca3, Cxcl1, and Cyp8b1 were identified from four large-scale microarray datasets of the one-day single maximum tolerated dose and a large set of chemicals without inconsistent classifications. Machine learning techniques were subsequently applied to develop prediction models for NGHCs. The final bagging decision tree models were constructed with an average AUC performance of 0.803 for an independent test. A set of 16 chemicals with controversial classifications were reclassified according to the consensus biomarkers. The developed prediction models and identified consensus biomarkers are expected to be potential alternative methods for prioritization of NGHCs for further experimental validation.
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Kanki M, Gi M, Fujioka M, Wanibuchi H. Detection of non-genotoxic hepatocarcinogens and prediction of their mechanism of action in rats using gene marker sets. J Toxicol Sci 2016; 41:281-92. [PMID: 26961613 DOI: 10.2131/jts.41.281] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
Abstract
Several studies have successfully detected hepatocarcinogenicity in rats based on gene expression data. However, prediction of hepatocarcinogens with certain mechanisms of action (MOAs), such as enzyme inducers and peroxisome proliferator-activated receptor α (PPARα) agonists, can prove difficult using a single model and requires a highly toxic dose. Here, we constructed a model for detecting non-genotoxic (NGTX) hepatocarcinogens and predicted their MOAs in rats. Gene expression data deposited in the Open Toxicogenomics Project-Genomics Assisted Toxicity Evaluation System (TG-GATEs) was used to investigate gene marker sets. Principal component analysis (PCA) was applied to discriminate different MOAs, and a support vector machine algorithm was applied to construct the prediction model. This approach identified 106 probe sets as gene marker sets for PCA and enabled the prediction model to be constructed. In PCA, NGTX hepatocarcinogens were classified as follows based on their MOAs: cytotoxicants, PPARα agonists, or enzyme inducers. The prediction model detected hepatocarcinogenicity with an accuracy of more than 90% in 14- and 28-day repeated-dose studies. In addition, the doses capable of predicting NGTX hepatocarcinogenicity were close to those required in rat carcinogenicity assays. In conclusion, our PCA and prediction model using gene marker sets will help assess the risk of hepatocarcinogenicity in humans based on MOAs and reduce the number of two-year rodent bioassays.
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Affiliation(s)
- Masayuki Kanki
- Department of Molecular Pathology, Osaka City University Graduate School of Medicine
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Cohen SM, Arnold LL. Critical role of toxicologic pathology in a short-term screen for carcinogenicity. J Toxicol Pathol 2016; 29:215-227. [PMID: 27821906 PMCID: PMC5097964 DOI: 10.1293/tox.2016-0036] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2016] [Accepted: 05/09/2016] [Indexed: 12/28/2022] Open
Abstract
Carcinogenic potential of chemicals is currently evaluated using a two year bioassay in rodents. Numerous difficulties are known for this assay, most notably, the lack of information regarding detailed dose response and human relevance of any positive findings. A screen for carcinogenic activity has been proposed based on a 90 day screening assay. Chemicals are first evaluated for proliferative activity in various tissues. If negative, lack of carcinogenic activity can be concluded. If positive, additional evaluation for DNA reactivity, immunosuppression, and estrogenic activity are evaluated. If these are negative, additional efforts are made to determine specific modes of action in the animal model, with a detailed evaluation of the potential relevance to humans. Applications of this approach are presented for liver and urinary bladder. Toxicologic pathology is critical for all of these evaluations, including a detailed histopathologic evaluation of the 90 day assay, immunohistochemical analyses for labeling index, and involvement in a detailed mode of action analysis. Additionally, the toxicologic pathologist needs to be involved with molecular evaluations and evaluations of new molecularly developed animal models. The toxicologic pathologist is uniquely qualified to provide the expertise needed for these evaluations.
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Affiliation(s)
- Samuel M. Cohen
- Department of Pathology and Microbiology, University of Nebraska Medical Center, 983135 Omaha, NE 68198-3135, USA
| | - Lora L. Arnold
- Department of Pathology and Microbiology, University of Nebraska Medical Center, 983135 Omaha, NE 68198-3135, USA
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Pérez LO, González-José R, García PP. Prediction of Non-Genotoxic Carcinogenicity Based on Genetic Profiles of Short Term Exposure Assays. Toxicol Res 2016; 32:289-300. [PMID: 27818731 PMCID: PMC5080858 DOI: 10.5487/tr.2016.32.4.289] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2016] [Accepted: 06/22/2016] [Indexed: 01/12/2023] Open
Abstract
Non-genotoxic carcinogens are substances that induce tumorigenesis by non-mutagenic mechanisms and long term rodent bioassays are required to identify them. Recent studies have shown that transcription profiling can be applied to develop early identifiers for long term phenotypes. In this study, we used rat liver expression profiles from the NTP (National Toxicology Program, Research Triangle Park, USA) DrugMatrix Database to construct a gene classifier that can distinguish between non-genotoxic carcinogens and other chemicals. The model was based on short term exposure assays (3 days) and the training was limited to oxidative stressors, peroxisome proliferators and hormone modulators. Validation of the predictor was performed on independent toxicogenomic data (TG-GATEs, Toxicogenomics Project-Genomics Assisted Toxicity Evaluation System, Osaka, Japan). To build our model we performed Random Forests together with a recursive elimination algorithm (VarSelRF). Gene set enrichment analysis was employed for functional interpretation. A total of 770 microarrays comprising 96 different compounds were analyzed and a predictor of 54 genes was built. Prediction accuracy was 0.85 in the training set, 0.87 in the test set and increased with increasing concentration in the validation set: 0.6 at low dose, 0.7 at medium doses and 0.81 at high doses. Pathway analysis revealed gene prominence of cellular respiration, energy production and lipoprotein metabolism. The biggest target of toxicogenomics is accurately predict the toxicity of unknown drugs. In this analysis, we presented a classifier that can predict non-genotoxic carcinogenicity by using short term exposure assays. In this approach, dose level is critical when evaluating chemicals at early time points.
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Affiliation(s)
- Luis Orlando Pérez
- Instituto Patagónico de Ciencias Sociales y Humanas (IPCSH), Centro Nacional Patagónico (CENPAT), Boulevard Brown 2915, Puerto Madryn, PC 9120, Provincia de Chubut,
Argentina
| | - Rolando González-José
- Instituto Patagónico de Ciencias Sociales y Humanas (IPCSH), Centro Nacional Patagónico (CENPAT), Boulevard Brown 2915, Puerto Madryn, PC 9120, Provincia de Chubut,
Argentina
| | - Pilar Peral García
- Instituto de Genética Veterinaria “Fernando Noel Dulout”-CONICET, Facultad de Ciencias Veterinarias, Universidad Nacional de La Plata, Calle 60 y 118 S/N, PC 1900, La Plata, Provincia de Buenos Aires,
Argentina
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Luijten M, Olthof ED, Hakkert BC, Rorije E, van der Laan JW, Woutersen RA, van Benthem J. An integrative test strategy for cancer hazard identification. Crit Rev Toxicol 2016; 46:615-39. [PMID: 27142259 DOI: 10.3109/10408444.2016.1171294] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
Assessment of genotoxic and carcinogenic potential is considered one of the basic requirements when evaluating possible human health risks associated with exposure to chemicals. Test strategies currently in place focus primarily on identifying genotoxic potential due to the strong association between the accumulation of genetic damage and cancer. Using genotoxicity assays to predict carcinogenic potential has the significant drawback that risks from non-genotoxic carcinogens remain largely undetected unless carcinogenicity studies are performed. Furthermore, test systems already developed to reduce animal use are not easily accepted and implemented by either industries or regulators. This manuscript reviews the test methods for cancer hazard identification that have been adopted by the regulatory authorities, and discusses the most promising alternative methods that have been developed to date. Based on these findings, a generally applicable tiered test strategy is proposed that can be considered capable of detecting both genotoxic as well as non-genotoxic carcinogens and will improve understanding of the underlying mode of action. Finally, strengths and weaknesses of this new integrative test strategy for cancer hazard identification are presented.
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Affiliation(s)
- Mirjam Luijten
- a Centre for Health Protection, National Institute for Public Health and the Environment (RIVM) , Bilthoven , the Netherlands
| | - Evelyn D Olthof
- a Centre for Health Protection, National Institute for Public Health and the Environment (RIVM) , Bilthoven , the Netherlands
| | - Betty C Hakkert
- b Centre for Safety of Substances and Products, National Institute for Public Health and the Environment (RIVM) , Bilthoven , the Netherlands
| | - Emiel Rorije
- b Centre for Safety of Substances and Products, National Institute for Public Health and the Environment (RIVM) , Bilthoven , the Netherlands
| | | | - Ruud A Woutersen
- d Netherlands Organization for Applied Scientific Research (TNO) , Zeist , the Netherlands
| | - Jan van Benthem
- a Centre for Health Protection, National Institute for Public Health and the Environment (RIVM) , Bilthoven , the Netherlands
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Kim MT, Huang R, Sedykh A, Wang W, Xia M, Zhu H. Mechanism Profiling of Hepatotoxicity Caused by Oxidative Stress Using Antioxidant Response Element Reporter Gene Assay Models and Big Data. ENVIRONMENTAL HEALTH PERSPECTIVES 2016; 124:634-41. [PMID: 26383846 PMCID: PMC4858396 DOI: 10.1289/ehp.1509763] [Citation(s) in RCA: 54] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/27/2015] [Accepted: 09/16/2015] [Indexed: 05/18/2023]
Abstract
BACKGROUND Hepatotoxicity accounts for a substantial number of drugs being withdrawn from the market. Using traditional animal models to detect hepatotoxicity is expensive and time-consuming. Alternative in vitro methods, in particular cell-based high-throughput screening (HTS) studies, have provided the research community with a large amount of data from toxicity assays. Among the various assays used to screen potential toxicants is the antioxidant response element beta lactamase reporter gene assay (ARE-bla), which identifies chemicals that have the potential to induce oxidative stress and was used to test > 10,000 compounds from the Tox21 program. OBJECTIVE The ARE-bla computational model and HTS data from a big data source (PubChem) were used to profile environmental and pharmaceutical compounds with hepatotoxicity data. METHODS Quantitative structure-activity relationship (QSAR) models were developed based on ARE-bla data. The models predicted the potential oxidative stress response for known liver toxicants when no ARE-bla data were available. Liver toxicants were used as probe compounds to search PubChem Bioassay and generate a response profile, which contained thousands of bioassays (> 10 million data points). By ranking the in vitro-in vivo correlations (IVIVCs), the most relevant bioassay(s) related to hepatotoxicity were identified. RESULTS The liver toxicants profile contained the ARE-bla and relevant PubChem assays. Potential toxicophores for well-known toxicants were created by identifying chemical features that existed only in compounds with high IVIVCs. CONCLUSION Profiling chemical IVIVCs created an opportunity to fully explore the source-to-outcome continuum of modern experimental toxicology using cheminformatics approaches and big data sources. CITATION Kim MT, Huang R, Sedykh A, Wang W, Xia M, Zhu H. 2016. Mechanism profiling of hepatotoxicity caused by oxidative stress using antioxidant response element reporter gene assay models and big data. Environ Health Perspect 124:634-641; http://dx.doi.org/10.1289/ehp.1509763.
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Affiliation(s)
- Marlene Thai Kim
- Department of Chemistry, Rutgers University, Camden, New Jersey, USA
- Center for Computational and Integrative Biology, Rutgers University, Camden, New Jersey, USA
| | - Ruili Huang
- National Center for Advancing Translational Sciences, National Institutes of Health, Department of Health and Human Services, Bethesda, Maryland, USA
| | - Alexander Sedykh
- Center for Computational and Integrative Biology, Rutgers University, Camden, New Jersey, USA
- Multicase Inc., Beachwood, Ohio, USA
| | - Wenyi Wang
- Center for Computational and Integrative Biology, Rutgers University, Camden, New Jersey, USA
| | - Menghang Xia
- National Center for Advancing Translational Sciences, National Institutes of Health, Department of Health and Human Services, Bethesda, Maryland, USA
| | - Hao Zhu
- Department of Chemistry, Rutgers University, Camden, New Jersey, USA
- Center for Computational and Integrative Biology, Rutgers University, Camden, New Jersey, USA
- Address correspondence to H. Zhu, 315 Penn St., Rutgers University, Camden, NJ 08102 USA. Telephone: (856) 225-6781. E-mail:
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35
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Risk assessment of Soulatrolide and Mammea (A/BA+A/BB) coumarins from Calophyllum brasiliense by a toxicogenomic and toxicological approach. Food Chem Toxicol 2016; 91:117-29. [PMID: 26995226 DOI: 10.1016/j.fct.2016.03.010] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2015] [Revised: 02/08/2016] [Accepted: 03/12/2016] [Indexed: 12/29/2022]
Abstract
Calophyllum brasiliense (Calophyllaceae) is a tropical rain forest tree distributed in Central and South America. It is an important source of tetracyclic dipyrano coumarins (Soulatrolide) and Mammea type coumarins. Soulatrolide is a potent inhibitor of HIV-1 reverse transcriptase and displays activity against Mycobacterium tuberculosis. Meanwhile, Mammea A/BA and A/BB, pure or as a mixture, are highly active against several human leukemia cell lines, Trypanosoma cruzi and Leishmania amazonensis. Nevertheless, there are few studies evaluating their safety profile. In the present work we performed toxicogenomic and toxicological analysis for both type of compounds. Soulatrolide, and the Mammea A/BA + A/BB mixture (2.1) were slightly toxic accordingly to Lorke assay classification (DL50 > 3000 mg/kg). After a short-term administration (100 mg/kg/daily, orally, 1 week) liver toxicogenomic analysis revealed 46 up and 72 downregulated genes for Mammea coumarins, and 665 up and 1077 downregulated genes for Soulatrolide. Gene enrichment analysis identified transcripts involved in drug metabolism for both compounds. In addition, network analysis through protein-protein interactions, tissue evaluation by TUNEL assay, and histological examination revealed no tissue damage on liver, kidney and spleen after treatments. Our results indicate that both type of coumarins displayed a safety profile, supporting their use in further preclinical studies to determine its therapeutic potential.
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Mesnage R, Defarge N, Spiroux de Vendômois J, Séralini GE. Potential toxic effects of glyphosate and its commercial formulations below regulatory limits. Food Chem Toxicol 2015; 84:133-53. [PMID: 26282372 DOI: 10.1016/j.fct.2015.08.012] [Citation(s) in RCA: 291] [Impact Index Per Article: 32.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2015] [Revised: 08/10/2015] [Accepted: 08/11/2015] [Indexed: 01/05/2023]
Abstract
Glyphosate-based herbicides (GlyBH), including Roundup, are the most widely used pesticides worldwide. Their uses have increased exponentially since their introduction on the market. Residue levels in food or water, as well as human exposures, are escalating. We have reviewed the toxic effects of GlyBH measured below regulatory limits by evaluating the published literature and regulatory reports. We reveal a coherent body of evidence indicating that GlyBH could be toxic below the regulatory lowest observed adverse effect level for chronic toxic effects. It includes teratogenic, tumorigenic and hepatorenal effects. They could be explained by endocrine disruption and oxidative stress, causing metabolic alterations, depending on dose and exposure time. Some effects were detected in the range of the recommended acceptable daily intake. Toxic effects of commercial formulations can also be explained by GlyBH adjuvants, which have their own toxicity, but also enhance glyphosate toxicity. These challenge the assumption of safety of GlyBH at the levels at which they contaminate food and the environment, albeit these levels may fall below regulatory thresholds. Neurodevelopmental, reproductive, and transgenerational effects of GlyBH must be revisited, since a growing body of knowledge suggests the predominance of endocrine disrupting mechanisms caused by environmentally relevant levels of exposure.
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Affiliation(s)
- R Mesnage
- University of Caen, Institute of Biology and Network on Risks, Quality and Sustainable Environment (MRSH), Esplanade de la Paix, 14032 Caen Cedex, France; CRIIGEN, 81 rue de Monceau, 75008 Paris, France
| | - N Defarge
- University of Caen, Institute of Biology and Network on Risks, Quality and Sustainable Environment (MRSH), Esplanade de la Paix, 14032 Caen Cedex, France; CRIIGEN, 81 rue de Monceau, 75008 Paris, France
| | | | - G E Séralini
- University of Caen, Institute of Biology and Network on Risks, Quality and Sustainable Environment (MRSH), Esplanade de la Paix, 14032 Caen Cedex, France; CRIIGEN, 81 rue de Monceau, 75008 Paris, France.
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Jennen DGJ, van Leeuwen DM, Hendrickx DM, Gottschalk RWH, van Delft JHM, Kleinjans JCS. Bayesian Network Inference Enables Unbiased Phenotypic Anchoring of Transcriptomic Responses to Cigarette Smoke in Humans. Chem Res Toxicol 2015; 28:1936-48. [PMID: 26360787 DOI: 10.1021/acs.chemrestox.5b00145] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Microarray-based transcriptomic analysis has been demonstrated to hold the opportunity to study the effects of human exposure to, e.g., chemical carcinogens at the whole genome level, thus yielding broad-ranging molecular information on possible carcinogenic effects. Since genes do not operate individually but rather through concerted interactions, analyzing and visualizing networks of genes should provide important mechanistic information, especially upon connecting them to functional parameters, such as those derived from measurements of biomarkers for exposure and carcinogenic risk. Conventional methods such as hierarchical clustering and correlation analyses are frequently used to address these complex interactions but are limited as they do not provide directional causal dependence relationships. Therefore, our aim was to apply Bayesian network inference with the purpose of phenotypic anchoring of modified gene expressions. We investigated a use case on transcriptomic responses to cigarette smoking in humans, in association with plasma cotinine levels as biomarkers of exposure and aromatic DNA-adducts in blood cells as biomarkers of carcinogenic risk. Many of the genes that appear in the Bayesian networks surrounding plasma cotinine, and to a lesser extent around aromatic DNA-adducts, hold biologically relevant functions in inducing severe adverse effects of smoking. In conclusion, this study shows that Bayesian network inference enables unbiased phenotypic anchoring of transcriptomics responses. Furthermore, in all inferred Bayesian networks several dependencies are found which point to known but also to new relationships between the expression of specific genes, cigarette smoke exposure, DNA damaging-effects, and smoking-related diseases, in particular associated with apoptosis, DNA repair, and tumor suppression, as well as with autoimmunity.
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Affiliation(s)
- Danyel G J Jennen
- Department of Toxicogenomics, Maastricht University , Universiteitssingel 40, 6229 ER Maastricht, The Netherlands
| | - Danitsja M van Leeuwen
- Department of Toxicogenomics, Maastricht University , Universiteitssingel 40, 6229 ER Maastricht, The Netherlands
| | - Diana M Hendrickx
- Department of Toxicogenomics, Maastricht University , Universiteitssingel 40, 6229 ER Maastricht, The Netherlands
| | - Ralph W H Gottschalk
- Department of Toxicogenomics, Maastricht University , Universiteitssingel 40, 6229 ER Maastricht, The Netherlands
| | - Joost H M van Delft
- Department of Toxicogenomics, Maastricht University , Universiteitssingel 40, 6229 ER Maastricht, The Netherlands
| | - Jos C S Kleinjans
- Department of Toxicogenomics, Maastricht University , Universiteitssingel 40, 6229 ER Maastricht, The Netherlands
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Api AM, Belsito D, Bhatia S, Bruze M, Calow P, Dagli ML, Dekant W, Fryer AD, Kromidas L, La Cava S, Lalko JF, Lapczynski A, Liebler DC, Miyachi Y, Politano VT, Ritacco G, Salvito D, Shen J, Schultz TW, Sipes IG, Wall B, Wilcox DK. RIFM fragrance ingredient safety assessment, (2E,6Z)-Nona-2,6-dien-1-ol, CAS registry number 28069-72-9. Food Chem Toxicol 2015; 84 Suppl:S57-65. [PMID: 26140952 DOI: 10.1016/j.fct.2015.06.023] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2015] [Accepted: 06/18/2015] [Indexed: 11/26/2022]
Affiliation(s)
- A M Api
- Research Institute for Fragrance Materials, Inc., 50 Tice Boulevard, Woodcliff Lake, NJ 07677, USA.
| | - D Belsito
- Member RIFM Expert Panel, Columbia University Medical Center, Department of Dermatology, 161 Fort Washington Ave., New York, NY 10032, USA
| | - S Bhatia
- Research Institute for Fragrance Materials, Inc., 50 Tice Boulevard, Woodcliff Lake, NJ 07677, USA
| | - M Bruze
- Member RIFM Expert Panel, Malmo University Hospital, Department of Occupational & Environmental Dermatology, Sodra Forstadsgatan 101, Entrance 47, Malmo SE-20502, Sweden
| | - P Calow
- Member RIFM Expert Panel, University of Nebraska Lincoln, 230 Whittier Research Center, Lincoln, NE 68583-0857, USA
| | - M L Dagli
- Member RIFM Expert Panel, University of Sao Paulo, School of Veterinary Medicine and Animal Science, Department of Pathology, Av. Prof. dr. Orlando Marques de Paiva, 87, Sao Paulo CEP 05508-900, Brazil
| | - W Dekant
- Member RIFM Expert Panel, University of Wuerzburg, Department of Toxicology, Versbacher Str. 9, 97078 Würzburg, Germany
| | - A D Fryer
- Member RIFM Expert Panel, Oregon Health Science University, 3181 SW Sam Jackson Park Rd., Portland, OR 97239, USA
| | - L Kromidas
- Research Institute for Fragrance Materials, Inc., 50 Tice Boulevard, Woodcliff Lake, NJ 07677, USA
| | - S La Cava
- Research Institute for Fragrance Materials, Inc., 50 Tice Boulevard, Woodcliff Lake, NJ 07677, USA
| | - J F Lalko
- Research Institute for Fragrance Materials, Inc., 50 Tice Boulevard, Woodcliff Lake, NJ 07677, USA
| | - A Lapczynski
- Research Institute for Fragrance Materials, Inc., 50 Tice Boulevard, Woodcliff Lake, NJ 07677, USA
| | - D C Liebler
- Member RIFM Expert Panel, Vanderbilt University School of Medicine, Department of Biochemistry, Center in Molecular Toxicology, 638 Robinson Research Building, 2200 Pierce Avenue, Nashville, TN 37232-0146, USA
| | - Y Miyachi
- Member RIFM Expert Panel, Department of Dermatology, Kyoto University Graduate School of Medicine, 54 Kawahara-cho, Shogoin, Sakyo-ku, Kyoto 606-8507, Japan
| | - V T Politano
- Research Institute for Fragrance Materials, Inc., 50 Tice Boulevard, Woodcliff Lake, NJ 07677, USA
| | - G Ritacco
- Research Institute for Fragrance Materials, Inc., 50 Tice Boulevard, Woodcliff Lake, NJ 07677, USA
| | - D Salvito
- Research Institute for Fragrance Materials, Inc., 50 Tice Boulevard, Woodcliff Lake, NJ 07677, USA
| | - J Shen
- Research Institute for Fragrance Materials, Inc., 50 Tice Boulevard, Woodcliff Lake, NJ 07677, USA
| | - T W Schultz
- Member RIFM Expert Panel, The University of Tennessee, College of Veterinary Medicine, Department of Comparative Medicine, 2407 River Dr., Knoxville, TN 37996- 4500, USA
| | - I G Sipes
- Member RIFM Expert Panel, Department of Pharmacology, University of Arizona, College of Medicine, 1501 North Campbell Avenue, P.O. Box 245050, Tucson, AZ 85724-5050, USA
| | - B Wall
- Research Institute for Fragrance Materials, Inc., 50 Tice Boulevard, Woodcliff Lake, NJ 07677, USA
| | - D K Wilcox
- Research Institute for Fragrance Materials, Inc., 50 Tice Boulevard, Woodcliff Lake, NJ 07677, USA
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Api AM, Belsito D, Bhatia S, Bruze M, Calow P, Dagli ML, Dekant W, Fryer AD, Kromidas L, La Cava S, Lalko JF, Lapczynski A, Liebler DC, Miyachi Y, Politano VT, Ritacco G, Salvito D, Shen J, Schultz TW, Sipes IG, Wall B, Wilcox DK. RIFM fragrance ingredient safety assessment, (Z)-2-penten-1-ol, CAS Registry Number 1576-95-0. Food Chem Toxicol 2015; 84 Suppl:S66-75. [PMID: 26140953 DOI: 10.1016/j.fct.2015.06.022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2015] [Accepted: 06/18/2015] [Indexed: 12/01/2022]
Affiliation(s)
- A M Api
- Research Institute for Fragrance Materials, Inc., 50 Tice Boulevard, Woodcliff Lake, NJ 07677, USA.
| | - D Belsito
- Member RIFM Expert Panel, Columbia University Medical Center, Department of Dermatology, 161 Fort Washington Ave., New York, NY 10032, USA
| | - S Bhatia
- Research Institute for Fragrance Materials, Inc., 50 Tice Boulevard, Woodcliff Lake, NJ 07677, USA
| | - M Bruze
- Member RIFM Expert Panel, Malmo University Hospital, Department of Occupational & Environmental Dermatology, Sodra Forstadsgatan 101, Entrance 47, Malmo SE-20502, Sweden
| | - P Calow
- Member RIFM Expert Panel, University of Nebraska Lincoln, 230 Whittier Research Center, Lincoln, NE 68583-0857, USA
| | - M L Dagli
- Member RIFM Expert Panel, University of Sao Paulo, School of Veterinary Medicine and Animal Science, Department of Pathology, Av. Prof. dr. Orlando Marques de Paiva, 87, Sao Paulo CEP 05508-900, Brazil
| | - W Dekant
- Member RIFM Expert Panel, University of Wuerzburg, Department of Toxicology, Versbacher Str. 9, 97078 Würzburg, Germany
| | - A D Fryer
- Member RIFM Expert Panel, Oregon Health Science University, 3181 SW Sam Jackson Park Rd., Portland, OR 97239, USA
| | - L Kromidas
- Research Institute for Fragrance Materials, Inc., 50 Tice Boulevard, Woodcliff Lake, NJ 07677, USA
| | - S La Cava
- Research Institute for Fragrance Materials, Inc., 50 Tice Boulevard, Woodcliff Lake, NJ 07677, USA
| | - J F Lalko
- Research Institute for Fragrance Materials, Inc., 50 Tice Boulevard, Woodcliff Lake, NJ 07677, USA
| | - A Lapczynski
- Research Institute for Fragrance Materials, Inc., 50 Tice Boulevard, Woodcliff Lake, NJ 07677, USA
| | - D C Liebler
- Member RIFM Expert Panel, Vanderbilt University School of Medicine, Department of Biochemistry, Center in Molecular Toxicology, 638 Robinson Research Building, 2200 Pierce Avenue, Nashville, TN 37232-0146, USA
| | - Y Miyachi
- Member RIFM Expert Panel, Department of Dermatology, Kyoto University Graduate School of Medicine, 54 Kawahara-cho, Shogoin, Sakyo-ku, Kyoto 606-8507, Japan
| | - V T Politano
- Research Institute for Fragrance Materials, Inc., 50 Tice Boulevard, Woodcliff Lake, NJ 07677, USA
| | - G Ritacco
- Research Institute for Fragrance Materials, Inc., 50 Tice Boulevard, Woodcliff Lake, NJ 07677, USA
| | - D Salvito
- Research Institute for Fragrance Materials, Inc., 50 Tice Boulevard, Woodcliff Lake, NJ 07677, USA
| | - J Shen
- Research Institute for Fragrance Materials, Inc., 50 Tice Boulevard, Woodcliff Lake, NJ 07677, USA
| | - T W Schultz
- Member RIFM Expert Panel, The University of Tennessee, College of Veterinary Medicine, Department of Comparative Medicine, 2407 River Dr., Knoxville, TN 37996-4500, USA
| | - I G Sipes
- Member RIFM Expert Panel, Department of Pharmacology, University of Arizona, College of Medicine, 1501 North Campbell Avenue, P.O. Box 245050, Tucson, AZ 85724-5050, USA
| | - B Wall
- Research Institute for Fragrance Materials, Inc., 50 Tice Boulevard, Woodcliff Lake, NJ 07677, USA
| | - D K Wilcox
- Research Institute for Fragrance Materials, Inc., 50 Tice Boulevard, Woodcliff Lake, NJ 07677, USA
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Becker RA, Patlewicz G, Simon TW, Rowlands JC, Budinsky RA. The adverse outcome pathway for rodent liver tumor promotion by sustained activation of the aryl hydrocarbon receptor. Regul Toxicol Pharmacol 2015; 73:172-90. [PMID: 26145830 DOI: 10.1016/j.yrtph.2015.06.015] [Citation(s) in RCA: 32] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2015] [Revised: 06/19/2015] [Accepted: 06/22/2015] [Indexed: 12/29/2022]
Abstract
An Adverse Outcome Pathway (AOP) represents the existing knowledge of a biological pathway leading from initial molecular interactions of a toxicant and progressing through a series of key events (KEs), culminating with an apical adverse outcome (AO) that has to be of regulatory relevance. An AOP based on the mode of action (MOA) of rodent liver tumor promotion by dioxin-like compounds (DLCs) has been developed and the weight of evidence (WoE) of key event relationships (KERs) evaluated using evolved Bradford Hill considerations. Dioxins and DLCs are potent aryl hydrocarbon receptor (AHR) ligands that cause a range of species-specific adverse outcomes. The occurrence of KEs is necessary for inducing downstream biological responses and KEs may occur at the molecular, cellular, tissue and organ levels. The common convention is that an AOP begins with the toxicant interaction with a biological response element; for this AOP, this initial event is binding of a DLC ligand to the AHR. Data from mechanistic studies, lifetime bioassays and approximately thirty initiation-promotion studies have established dioxin and DLCs as rat liver tumor promoters. Such studies clearly show that sustained AHR activation, weeks or months in duration, is necessary to induce rodent liver tumor promotion--hence, sustained AHR activation is deemed the molecular initiating event (MIE). After this MIE, subsequent KEs are 1) changes in cellular growth homeostasis likely associated with expression changes in a number of genes and observed as development of hepatic foci and decreases in apoptosis within foci; 2) extensive liver toxicity observed as the constellation of effects called toxic hepatopathy; 3) cellular proliferation and hyperplasia in several hepatic cell types. This progression of KEs culminates in the AO, the development of hepatocellular adenomas and carcinomas and cholangiolar carcinomas. A rich data set provides both qualitative and quantitative knowledge of the progression of this AOP through KEs and the KERs. Thus, the WoE for this AOP is judged to be strong. Species-specific effects of dioxins and DLCs are well known--humans are less responsive than rodents and rodent species differ in sensitivity between strains. Consequently, application of this AOP to evaluate potential human health risks must take these differences into account.
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Affiliation(s)
- Richard A Becker
- Regulatory and Technical Affairs Department, American Chemistry Council (ACC), Washington, DC 20002, USA.
| | - Grace Patlewicz
- DuPont Haskell Global Centers for Health and Environmental Sciences, Newark, DE 19711, USA
| | - Ted W Simon
- Ted Simon LLC, 4184 Johnston Road, Winston, GA 30187, USA
| | - J Craig Rowlands
- The Dow Chemical Company, Toxicology & Environmental Research & Consulting, 1803 Building Washington Street, Midland, MI 48674, USA
| | - Robert A Budinsky
- The Dow Chemical Company, Toxicology & Environmental Research & Consulting, 1803 Building Washington Street, Midland, MI 48674, USA
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Sandhu KS, Veeramachaneni V, Yao X, Nie A, Lord P, Amaratunga D, McMillian MK, Verheyen GR. Release of (and lessons learned from mining) a pioneering large toxicogenomics database. Pharmacogenomics 2015; 16:779-801. [PMID: 26067483 DOI: 10.2217/pgs.15.38] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Abstract
AIM We release the Janssen Toxicogenomics database. This rat liver gene-expression database was generated using Codelink microarrays, and has been used over the past years within Janssen to derive signatures for multiple end points and to classify proprietary compounds. MATERIALS & METHODS The release consists of gene-expression responses to 124 compounds, selected to give a broad coverage of liver-active compounds. A selection of the compounds were also analyzed on Affymetrix microarrays. RESULTS The release includes results of an in-house reannotation pipeline to Entrez gene annotations, to classify probes into different confidence classes. High confidence unambiguously annotated probes were used to create gene-level data which served as starting point for cross-platform comparisons. Connectivity map-based similarity methods show excellent agreement between Codelink and Affymetrix runs of the same samples. We also compared our dataset with the Japanese Toxicogenomics Project and observed reasonable agreement, especially for compounds with stronger gene signatures. We describe an R-package containing the gene-level data and show how it can be used for expression-based similarity searches. CONCLUSION Comparing the same biological samples run on the Affymetrix and the Codelink platform, good correspondence is observed using connectivity mapping approaches. As expected, this correspondence is smaller when the data are compared with an independent dataset such as TG-GATE. We hope that this collection of gene-expression profiles will be incorporated in toxicogenomics pipelines of users.
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Affiliation(s)
| | | | - Xiang Yao
- Data Sciences, R&D IT, Janssen Pharmaceutical Research & Development, LLC, 3120 Merryfield Row, San Diego, CA 92121, USA
| | - Alex Nie
- Special Counsel, Patent Atterney, Sheppard, Mullin, Richter & Hampton LLP, 379 Lytton Ave, Palo Alto, CA 94301, USA
| | - Peter Lord
- Discotox Ltd, Hebden Bridge, West Yorkshire, UK
| | | | | | - Geert R Verheyen
- Radius Group, Thomas More University College, Kleinhoefstraat 4, 2440 Geel, Belgium
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Bourdon-Lacombe JA, Moffat ID, Deveau M, Husain M, Auerbach S, Krewski D, Thomas RS, Bushel PR, Williams A, Yauk CL. Technical guide for applications of gene expression profiling in human health risk assessment of environmental chemicals. Regul Toxicol Pharmacol 2015; 72:292-309. [PMID: 25944780 DOI: 10.1016/j.yrtph.2015.04.010] [Citation(s) in RCA: 50] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2015] [Revised: 04/10/2015] [Accepted: 04/13/2015] [Indexed: 01/14/2023]
Abstract
Toxicogenomics promises to be an important part of future human health risk assessment of environmental chemicals. The application of gene expression profiles (e.g., for hazard identification, chemical prioritization, chemical grouping, mode of action discovery, and quantitative analysis of response) is growing in the literature, but their use in formal risk assessment by regulatory agencies is relatively infrequent. Although additional validations for specific applications are required, gene expression data can be of immediate use for increasing confidence in chemical evaluations. We believe that a primary reason for the current lack of integration is the limited practical guidance available for risk assessment specialists with limited experience in genomics. The present manuscript provides basic information on gene expression profiling, along with guidance on evaluating the quality of genomic experiments and data, and interpretation of results presented in the form of heat maps, pathway analyses and other common approaches. Moreover, potential ways to integrate information from gene expression experiments into current risk assessment are presented using published studies as examples. The primary objective of this work is to facilitate integration of gene expression data into human health risk assessments of environmental chemicals.
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Affiliation(s)
| | - Ivy D Moffat
- Water and Air Quality Bureau, Health Canada, Ottawa, ON, Canada.
| | - Michelle Deveau
- Water and Air Quality Bureau, Health Canada, Ottawa, ON, Canada
| | - Mainul Husain
- Environmental Health Science and Research Bureau, Health Canada, Ottawa, ON, Canada
| | - Scott Auerbach
- Biomolecular Screening Branch, Division of the National Toxicology Program, National Institute of Environmental Health Sciences, Research Triangle Park, NC, United States
| | - Daniel Krewski
- McLaughlin Centre for Population Health Risk Assessment, University of Ottawa, Ottawa, ON, Canada
| | - Russell S Thomas
- National Centre for Computational Toxicology, U.S. Environmental Protection Agency, Research Triangle Park, NC, United States
| | - Pierre R Bushel
- Biostatistics and Computational Biology Branch, Division of Intramural Research, National Institute of Environmental Health Sciences, Research Triangle Park, NC, United States
| | - Andrew Williams
- Environmental Health Science and Research Bureau, Health Canada, Ottawa, ON, Canada
| | - Carole L Yauk
- Environmental Health Science and Research Bureau, Health Canada, Ottawa, ON, Canada
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Suksuwan A, Lomlim L, Dickert FL, Suedee R. Tracking the chemical surface properties of racemic thalidomide and its enantiomers using a biomimetic functional surface on a quartz crystal microbalance. J Appl Polym Sci 2015. [DOI: 10.1002/app.42309] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
Affiliation(s)
- Acharee Suksuwan
- Department of Pharmaceutical Chemistry; Faculty of Pharmaceutical Sciences; Molecular Recognition Materials Research Unit, NANOTEC Center of Excellence at PSU/Drug Delivery System Research Center, Prince of Songkla University; Hat Yai Songkhla 90112 Thailand
| | - Luelak Lomlim
- Department of Pharmaceutical Chemistry; Faculty of Pharmaceutical Sciences; Molecular Recognition Materials Research Unit, NANOTEC Center of Excellence at PSU/Drug Delivery System Research Center, Prince of Songkla University; Hat Yai Songkhla 90112 Thailand
| | - Franz L. Dickert
- Department of Analytical Chemistry; University of Vienna; Währingerstrasse 38 A-1090 Vienna Austria
| | - Roongnapa Suedee
- Department of Pharmaceutical Chemistry; Faculty of Pharmaceutical Sciences; Molecular Recognition Materials Research Unit, NANOTEC Center of Excellence at PSU/Drug Delivery System Research Center, Prince of Songkla University; Hat Yai Songkhla 90112 Thailand
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Borlak J, Singh P, Gazzana G. Proteome mapping of epidermal growth factor induced hepatocellular carcinomas identifies novel cell metabolism targets and mitogen activated protein kinase signalling events. BMC Genomics 2015; 16:124. [PMID: 25872475 PMCID: PMC4357185 DOI: 10.1186/s12864-015-1312-z] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2014] [Accepted: 02/03/2015] [Indexed: 02/06/2023] Open
Abstract
Background Hepatocellular carcinoma (HCC) is on the rise and the sixth most common cancer worldwide. To combat HCC effectively research is directed towards its early detection and the development of targeted therapies. Given the fact that epidermal growth factor (EGF) is an important mitogen for hepatocytes we searched for disease regulated proteins to improve an understanding of the molecular pathogenesis of EGF induced HCC. Disease regulated proteins were studied by 2DE MALDI-TOF/TOF and a transcriptomic approach, by immunohistochemistry and advanced bioinformatics. Results Mapping of EGF induced liver cancer in a transgenic mouse model identified n = 96 (p < 0.05) significantly regulated proteins of which n = 54 were tumour-specific. To unravel molecular circuits linked to aberrant EGFR signalling diverse computational approaches were employed and this defined n = 7 key nodes using n = 82 disease regulated proteins for network construction. STRING analysis revealed protein-protein interactions of > 70% disease regulated proteins with individual proteins being validated by immunohistochemistry. The disease regulated network proteins were mapped to distinct pathways and bioinformatics provided novel insight into molecular circuits associated with significant changes in either glycolysis and gluconeogenesis, argine and proline metabolism, protein processing in endoplasmic reticulum, Hif- and MAPK signalling, lipoprotein metabolism, platelet activation and hemostatic control as a result of aberrant EGF signalling. The biological significance of the findings was corroborated with gene expression data derived from tumour tissues to evntually define a rationale by which tumours embark on intriguing changes in metabolism that is of utility for an understanding of tumour growth. Moreover, among the EGF tumour specific proteins n = 11 were likewise uniquely expressed in human HCC and for n = 49 proteins regulation in human HCC was confirmed using the publically available Human Protein Atlas depository, therefore demonstrating clinical significance. Conclusion Novel insight into the molecular pathogenesis of EGF induced liver cancer was obtained and among the 37 newly identified proteins several are likely candidates for the development of molecularly targeted therapies and include the nucleoside diphosphate kinase A, bifunctional ATP-dependent dihydroyacetone kinase and phosphatidylethanolamine-binding protein1, the latter being an inhibitor of the Raf-1 kinase. Electronic supplementary material The online version of this article (doi:10.1186/s12864-015-1312-z) contains supplementary material, which is available to authorized users.
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Talikka M, Boue S, Schlage WK. Causal Biological Network Database: A Comprehensive Platform of Causal Biological Network Models Focused on the Pulmonary and Vascular Systems. METHODS IN PHARMACOLOGY AND TOXICOLOGY 2015. [DOI: 10.1007/978-1-4939-2778-4_3] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
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Stamper BD. Transcriptional profiling of reactive metabolites for elucidating toxicological mechanisms: a case study of quinoneimine-forming agents. Drug Metab Rev 2014; 47:45-55. [DOI: 10.3109/03602532.2014.978081] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
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47
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Vo NS, Phan V. Exploiting dependencies of pairwise comparison outcomes to predict patterns of gene response. BMC Bioinformatics 2014; 15 Suppl 11:S2. [PMID: 25350806 PMCID: PMC4251046 DOI: 10.1186/1471-2105-15-s11-s2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND The analysis of gene expression has played an important role in medical and bioinformatics research. Although it is known that a large number of samples is needed to determine the patterns of gene expression accurately, practical designs of gene expression studies occasionally have insufficient numbers of samples, making it difficult to ascertain true response patterns of variantly expressed genes. RESULTS We describe an approach to cope with the challenge of predicting true orders of gene response to treatments. We show that true patterns of gene response must be orderable sets. In experiments with few samples, we modify the conventional pairwise comparison tests and increase the significance level α intelligently to deduce orderable patterns, which are most likely true orders of gene response. Additionally, motivated by the fact that a gene can be involved in multiple biological functions, our method further resamples experimental replicates and predicts multiple response patterns for each gene. CONCLUSIONS This method can be useful in designing cost-effective experiments with small sample sizes. Patterns of highly-variantly expressed genes can be predicted by varying α intelligently. Furthermore, clusters are labeled meaningfully with patterns that describe precisely how genes in such clusters respond to treatments.
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Gusenleitner D, Auerbach SS, Melia T, Gómez HF, Sherr DH, Monti S. Genomic models of short-term exposure accurately predict long-term chemical carcinogenicity and identify putative mechanisms of action. PLoS One 2014; 9:e102579. [PMID: 25058030 PMCID: PMC4109923 DOI: 10.1371/journal.pone.0102579] [Citation(s) in RCA: 46] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2014] [Accepted: 06/20/2014] [Indexed: 12/19/2022] Open
Abstract
BACKGROUND Despite an overall decrease in incidence of and mortality from cancer, about 40% of Americans will be diagnosed with the disease in their lifetime, and around 20% will die of it. Current approaches to test carcinogenic chemicals adopt the 2-year rodent bioassay, which is costly and time-consuming. As a result, fewer than 2% of the chemicals on the market have actually been tested. However, evidence accumulated to date suggests that gene expression profiles from model organisms exposed to chemical compounds reflect underlying mechanisms of action, and that these toxicogenomic models could be used in the prediction of chemical carcinogenicity. RESULTS In this study, we used a rat-based microarray dataset from the NTP DrugMatrix Database to test the ability of toxicogenomics to model carcinogenicity. We analyzed 1,221 gene-expression profiles obtained from rats treated with 127 well-characterized compounds, including genotoxic and non-genotoxic carcinogens. We built a classifier that predicts a chemical's carcinogenic potential with an AUC of 0.78, and validated it on an independent dataset from the Japanese Toxicogenomics Project consisting of 2,065 profiles from 72 compounds. Finally, we identified differentially expressed genes associated with chemical carcinogenesis, and developed novel data-driven approaches for the molecular characterization of the response to chemical stressors. CONCLUSION Here, we validate a toxicogenomic approach to predict carcinogenicity and provide strong evidence that, with a larger set of compounds, we should be able to improve the sensitivity and specificity of the predictions. We found that the prediction of carcinogenicity is tissue-dependent and that the results also confirm and expand upon previous studies implicating DNA damage, the peroxisome proliferator-activated receptor, the aryl hydrocarbon receptor, and regenerative pathology in the response to carcinogen exposure.
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Affiliation(s)
- Daniel Gusenleitner
- Bioinformatics Program, Boston University, Boston, Massachusetts, United States of America
- Department of Computational Biomedicine, Boston University Medical Campus, Boston, Massachusetts, United States of America
| | - Scott S. Auerbach
- Biomolecular Screening Branch, Division of the National Toxicology Program at the National Institute of Environmental Health Sciences (NIEHS), Research Triangle Park, North Carolina, United States of America
| | - Tisha Melia
- Bioinformatics Program, Boston University, Boston, Massachusetts, United States of America
| | - Harold F. Gómez
- Bioinformatics Program, Boston University, Boston, Massachusetts, United States of America
| | - David H. Sherr
- Department of Environmental Health, Boston University School of Public Health, Boston, Massachusetts, United States of America
| | - Stefano Monti
- Bioinformatics Program, Boston University, Boston, Massachusetts, United States of America
- Department of Computational Biomedicine, Boston University Medical Campus, Boston, Massachusetts, United States of America
- * E-mail:
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Goodsaid FM, Frueh FW, Mattes W. The Predictive Safety Testing Consortium: A synthesis of the goals, challenges and accomplishments of the Critical Path. DRUG DISCOVERY TODAY. TECHNOLOGIES 2014; 4:47-50. [PMID: 24980840 DOI: 10.1016/j.ddtec.2007.10.010] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
The qualification of biomarkers of drug safety requires data on many compounds and nonclinical and clinical studies. The cost and effort associated with these qualifications cannot be easily covered by a single pharmaceutical company. Intellectual property associated with safety biomarkers is also held by many different companies. Consortia between different pharmaceutical companies can overcome cost and intellectual property hurdles to biomarker qualification. The Predictive Safety Testing Consortium (PSTC) is a collaborative effort between 16 different pharmaceutical companies to generate data supporting biomarker qualification. This Consortium is coordinated through the C-Path Institute, and currently has five biomarker qualification working groups engaged in this collaboration: nephrotoxicity, hepatotoxicity, vascular injury, myopathy, and non-genotoxic carcinogenicity. These working groups are aided by a data management team and a translational strategy team. Qualification studies of promising biomarkers are already progressing in several of the working groups, and results in the nephrotoxicity working group warranted a data submission to the FDA and EMEA for regulatory qualification of new nephrotoxicity biomarkers.:
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Affiliation(s)
- Federico M Goodsaid
- Genomics Group, Office of Clinical Pharmacology, Office of Translational Sciences, Center for Drug Evaluation and Research, FDA, Silver Spring, MD, USA.
| | - Felix W Frueh
- Genomics Group, Office of Clinical Pharmacology, Office of Translational Sciences, Center for Drug Evaluation and Research, FDA, Silver Spring, MD, USA
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Römer M, Eichner J, Metzger U, Templin MF, Plummer S, Ellinger-Ziegelbauer H, Zell A. Cross-platform toxicogenomics for the prediction of non-genotoxic hepatocarcinogenesis in rat. PLoS One 2014; 9:e97640. [PMID: 24830643 PMCID: PMC4022579 DOI: 10.1371/journal.pone.0097640] [Citation(s) in RCA: 37] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2014] [Accepted: 04/10/2014] [Indexed: 02/07/2023] Open
Abstract
In the area of omics profiling in toxicology, i.e. toxicogenomics, characteristic molecular profiles have previously been incorporated into prediction models for early assessment of a carcinogenic potential and mechanism-based classification of compounds. Traditionally, the biomarker signatures used for model construction were derived from individual high-throughput techniques, such as microarrays designed for monitoring global mRNA expression. In this study, we built predictive models by integrating omics data across complementary microarray platforms and introduced new concepts for modeling of pathway alterations and molecular interactions between multiple biological layers. We trained and evaluated diverse machine learning-based models, differing in the incorporated features and learning algorithms on a cross-omics dataset encompassing mRNA, miRNA, and protein expression profiles obtained from rat liver samples treated with a heterogeneous set of substances. Most of these compounds could be unambiguously classified as genotoxic carcinogens, non-genotoxic carcinogens, or non-hepatocarcinogens based on evidence from published studies. Since mixed characteristics were reported for the compounds Cyproterone acetate, Thioacetamide, and Wy-14643, we reclassified these compounds as either genotoxic or non-genotoxic carcinogens based on their molecular profiles. Evaluating our toxicogenomics models in a repeated external cross-validation procedure, we demonstrated that the prediction accuracy of our models could be increased by joining the biomarker signatures across multiple biological layers and by adding complex features derived from cross-platform integration of the omics data. Furthermore, we found that adding these features resulted in a better separation of the compound classes and a more confident reclassification of the three undefined compounds as non-genotoxic carcinogens.
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Affiliation(s)
- Michael Römer
- Center of Bioinformatics Tuebingen (ZBIT), University of Tuebingen, Tübingen, Germany
| | - Johannes Eichner
- Center of Bioinformatics Tuebingen (ZBIT), University of Tuebingen, Tübingen, Germany
| | - Ute Metzger
- Natural and Medical Sciences Institute at the University of Tübingen, Reutlingen, Germany
| | - Markus F. Templin
- Natural and Medical Sciences Institute at the University of Tübingen, Reutlingen, Germany
| | - Simon Plummer
- CXR Biosciences, James Lindsay Place, Dundee Technopole, Dundee, Scotland, United Kingdom
| | | | - Andreas Zell
- Center of Bioinformatics Tuebingen (ZBIT), University of Tuebingen, Tübingen, Germany
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