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Li N, Shi J, Chen Z, Dong Z, Ma S, Li Y, Huang X, Li X. In silico prediction of drug-induced nephrotoxicity: current progress and pitfalls. Expert Opin Drug Metab Toxicol 2024:1-13. [PMID: 39360665 DOI: 10.1080/17425255.2024.2412629] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2024] [Revised: 09/05/2024] [Accepted: 10/01/2024] [Indexed: 10/04/2024]
Abstract
INTRODUCTION Due to its role in absorption and metabolism, the kidney is an important target for drug toxicity. Drug-induced nephrotoxicity (DIN) presents a significant challenge in clinical practice and drug development. Conventional methods for assessing nephrotoxicity have limitations, highlighting the need for innovative approaches. In recent years, in silico methods have emerged as promising tools for predicting DIN. AREAS COVERED A literature search was performed using PubMed and Web of Science, from 2013 to February 2023 for this review. This review provides an overview of the current progress and pitfalls in the in silico prediction of DIN, which discusses the principles and methodologies of computational models. EXPERT OPINION Despite significant advancements, this review identified issues accentuates the pivotal imperatives of data fidelity, model optimization, interdisciplinary collaboration, and mechanistic comprehension in sculpting the vista of DIN prediction. Integration of multiple data sources and collaboration between disciplines are essential for improving predictive models. Ultimately, a holistic approach combining computational, experimental, and clinical methods will enhance our understanding and management of DIN.
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Affiliation(s)
- Na Li
- Department of Clinical Pharmacy, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Shandong Engineering and Technology Research Center for Pediatric Drug Development, Shandong Medicine and Health Key Laboratory of Clinical Pharmacy, Jinan, China
| | - Juan Shi
- Department of Clinical Pharmacy, The First People's Hospital of Jinan, Jinan, China
| | - Zhaoyang Chen
- Department of Clinical Pharmacy, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Shandong Engineering and Technology Research Center for Pediatric Drug Development, Shandong Medicine and Health Key Laboratory of Clinical Pharmacy, Jinan, China
| | - Zhonghua Dong
- Department of Clinical Pharmacy, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Shandong Engineering and Technology Research Center for Pediatric Drug Development, Shandong Medicine and Health Key Laboratory of Clinical Pharmacy, Jinan, China
| | - Shiyu Ma
- Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yan Li
- Department of Clinical Pharmacy, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Shandong Engineering and Technology Research Center for Pediatric Drug Development, Shandong Medicine and Health Key Laboratory of Clinical Pharmacy, Jinan, China
| | - Xin Huang
- Department of Clinical Pharmacy, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Shandong Engineering and Technology Research Center for Pediatric Drug Development, Shandong Medicine and Health Key Laboratory of Clinical Pharmacy, Jinan, China
| | - Xiao Li
- Department of Clinical Pharmacy, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Shandong Engineering and Technology Research Center for Pediatric Drug Development, Shandong Medicine and Health Key Laboratory of Clinical Pharmacy, Jinan, China
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Corton JC, Ledbetter V, Cohen SM, Atlas E, Yauk CL, Liu J. A transcriptomic biomarker predictive of cell proliferation for use in adverse outcome pathway-informed testing and assessment. Toxicol Sci 2024; 201:174-189. [PMID: 39137154 DOI: 10.1093/toxsci/kfae102] [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] [Indexed: 08/15/2024] Open
Abstract
High-throughput transcriptomics (HTTr) is increasingly being used to identify molecular targets of chemicals that can be linked to adverse outcomes. Cell proliferation (CP) is an important key event in chemical carcinogenesis. Here, we describe the construction and characterization of a gene expression biomarker that is predictive of the CP status in human and rodent tissues. The biomarker was constructed from 30 genes known to be increased in expression in prostate cancers relative to surrounding tissues and in cycling human MCF-7 cells after estrogen receptor (ER) agonist exposure. Using a large compendium of gene expression profiles to test utility, the biomarker could identify increases in CP in (i) 308 out of 367 tumor vs. normal surrounding tissue comparisons from 6 human organs, (ii) MCF-7 cells after activation of ER, (iii) after partial hepatectomy in mice and rats, and (iv) the livers of mice and rats after exposure to nongenotoxic hepatocarcinogens. The biomarker identified suppression of CP (i) under conditions of p53 activation by DNA damaging agents in human cells, (ii) in human A549 lung cells exposed to therapeutic anticancer kinase inhibitors (dasatinib, nilotnib), and (iii) in the mouse liver when comparing high levels of CP at birth to the low background levels in the adult. The responses using the biomarker were similar to those observed using conventional markers of CP including PCNA, Ki67, and BrdU labeling. The CP biomarker will be a useful tool for interpretation of HTTr data streams to identify CP status after exposure to chemicals in human cells or in rodent tissues.
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Affiliation(s)
- J Christopher Corton
- Center for Computational Toxicology and Exposure, US Environmental Protection Agency, Research Triangle Park, NC 27711, United States
| | - Victoria Ledbetter
- Center for Computational Toxicology and Exposure, US Environmental Protection Agency, Research Triangle Park, NC 27711, United States
| | - Samuel M Cohen
- Department of Pathology and Microbiology and Buffett Cancer Center, University of Nebraska Medical Center, Omaha, NE 69198-3135, United States
| | - Ella Atlas
- Environmental Health Science and Research Bureau, Healthy Environments and Consumer Safety Branch (HECSB) Health Canada, Ottawa, ON K2K 0K9, Canada
| | - Carole L Yauk
- Department of Biology, University of Ottawa, Ottawa, ON, Canada
| | - Jie Liu
- Center for Computational Toxicology and Exposure, US Environmental Protection Agency, Research Triangle Park, NC 27711, United States
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3
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Hardy B, Mohoric T, Exner T, Dokler J, Brajnik M, Bachler D, Mbegbu O, Kleisli N, Farcal L, Maciejczuk K, Rašidagić H, Tagorti G, Ankli P, Burgwinkel D, Anand D, Sarkans U, Athar A. Knowledge infrastructure for integrated data management and analysis supporting new approach methods in predictive toxicology and risk assessment. Toxicol In Vitro 2024; 100:105903. [PMID: 39047988 DOI: 10.1016/j.tiv.2024.105903] [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: 11/20/2023] [Revised: 06/25/2024] [Accepted: 07/15/2024] [Indexed: 07/27/2024]
Abstract
The EU-ToxRisk project (2016-2021) was a large European project working towards shifting toxicological testing away from animal tests, towards a toxicological assessment based on comprehensive mechanistic understanding of cause-consequence relationships of chemical adverse effects. More than 40 partners from scientific institutions, industry and regulators coordinated their work towards this goal in a six-year long programme. The breadth and variety of data and knowledge generated, presented a challenging data management landscape. Here, we describe our approach to data management as developed under EU-ToxRisk. The main building blocks of the data infrastructure are: 1) An easy-to-use, extensible data and metadata format; 2) A flexible system with protocols for data capture and sharing from the entire consortium; 3) A methods database for describing and reviewing data generation and processing protocols; 4) Data archiving using a sustainable resource; 5) Data transformation from the archive to the system that provides granular access; 6) Application Programming Interface (API) for access to individual data points; 7) Data exploration and analysis modules, based on a «web notebook» approach to executable data processing documentation; and 8) Knowledge portal that ties together all of the above and provides a collaboration space for information exchange across the consortium. This knowledge infrastructure is being extended and refined for the support of follow-up projects (RISK-HUNT3R, ASPIS cluster, European Open Science Cloud (2021-2026)).
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4
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Jiang P, Zhang Z, Yu Q, Wang Z, Diao L, Li D. ToxDAR: A Workflow Software for Analyzing Toxicologically Relevant Proteomic and Transcriptomic Data, from Data Preparation to Toxicological Mechanism Elucidation. Int J Mol Sci 2024; 25:9544. [PMID: 39273492 PMCID: PMC11394870 DOI: 10.3390/ijms25179544] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2024] [Revised: 08/26/2024] [Accepted: 08/30/2024] [Indexed: 09/15/2024] Open
Abstract
Exploration of toxicological mechanisms is imperative for the assessment of potential adverse reactions to chemicals and pharmaceutical agents, the engineering of safer compounds, and the preservation of public health. It forms the foundation of drug development and disease treatment. High-throughput proteomics and transcriptomics can accurately capture the body's response to toxins and have become key tools for revealing complex toxicological mechanisms. Recently, a vast amount of omics data related to toxicological mechanisms have been accumulated. However, analyzing and utilizing these data remains a major challenge for researchers, especially as there is a lack of a knowledge-based analysis system to identify relevant biological pathways associated with toxicity from the data and to establish connections between omics data and existing toxicological knowledge. To address this, we have developed ToxDAR, a workflow-oriented R package for preprocessing and analyzing toxicological multi-omics data. ToxDAR integrates packages like NormExpression, DESeq2, and igraph, and utilizes R functions such as prcomp and phyper. It supports data preparation, quality control, differential expression analysis, functional analysis, and network analysis. ToxDAR's architecture also includes a knowledge graph with five major categories of mechanism-related biological entities and details fifteen types of interactions among them, providing comprehensive knowledge annotation for omics data analysis results. As a case study, we used ToxDAR to analyze a transcriptomic dataset on the toxicology of triphenyl phosphate (TPP). The results indicate that TPP may impair thyroid function by activating thyroid hormone receptor β (THRB), impacting pathways related to programmed cell death and inflammation. As a workflow-oriented data analysis tool, ToxDAR is expected to be crucial for understanding toxic mechanisms from omics data, discovering new therapeutic targets, and evaluating chemical safety.
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Affiliation(s)
- Peng Jiang
- School of Basic Medical Sciences, Anhui Medical University, Hefei 230032, China
| | - Zuzhen Zhang
- School of Basic Medical Sciences, Anhui Medical University, Hefei 230032, China
| | - Qing Yu
- College of Life Sciences, Hebei University, Baoding 071002, China
| | - Ze Wang
- State Key Laboratory of Medical Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing 102206, China
| | - Lihong Diao
- State Key Laboratory of Medical Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing 102206, China
| | - Dong Li
- School of Basic Medical Sciences, Anhui Medical University, Hefei 230032, China
- College of Life Sciences, Hebei University, Baoding 071002, China
- State Key Laboratory of Medical Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing 102206, China
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5
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Meier MJ, Harrill J, Johnson K, Thomas RS, Tong W, Rager JE, Yauk CL. Progress in toxicogenomics to protect human health. Nat Rev Genet 2024:10.1038/s41576-024-00767-1. [PMID: 39223311 DOI: 10.1038/s41576-024-00767-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/23/2024] [Indexed: 09/04/2024]
Abstract
Toxicogenomics measures molecular features, such as transcripts, proteins, metabolites and epigenomic modifications, to understand and predict the toxicological effects of environmental and pharmaceutical exposures. Transcriptomics has become an integral tool in contemporary toxicology research owing to innovations in gene expression profiling that can provide mechanistic and quantitative information at scale. These data can be used to predict toxicological hazards through the use of transcriptomic biomarkers, network inference analyses, pattern-matching approaches and artificial intelligence. Furthermore, emerging approaches, such as high-throughput dose-response modelling, can leverage toxicogenomic data for human health protection even in the absence of predicting specific hazards. Finally, single-cell transcriptomics and multi-omics provide detailed insights into toxicological mechanisms. Here, we review the progress since the inception of toxicogenomics in applying transcriptomics towards toxicology testing and highlight advances that are transforming risk assessment.
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Affiliation(s)
- Matthew J Meier
- Environmental Health Science and Research Bureau, Health Canada, Ottawa, Ontario, Canada
| | - Joshua Harrill
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, Durham, NC, USA
| | - Kamin Johnson
- Predictive Safety Center, Corteva Agriscience, Indianapolis, IN, USA
| | - Russell S Thomas
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, Durham, NC, USA
| | - Weida Tong
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, United States Food and Drug Administration, Jefferson, AR, USA
- Curriculum in Toxicology & Environmental Medicine, School of Medicine, University of North Carolina, Chapel Hill, NC, USA
| | - Julia E Rager
- Curriculum in Toxicology & Environmental Medicine, School of Medicine, University of North Carolina, Chapel Hill, NC, USA
- The Center for Environmental Medicine, Asthma and Lung Biology, School of Medicine, The University of North Carolina, Chapel Hill, NC, USA
- Department of Environmental Sciences and Engineering, Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- The Institute for Environmental Health Solutions, Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Carole L Yauk
- Department of Biology, University of Ottawa, Ottawa, Ontario, Canada.
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Zhao Y, Park JY, Yang D, Zhang M. A computational framework to in silico screen for drug-induced hepatocellular toxicity. Toxicol Sci 2024; 201:14-25. [PMID: 38902949 PMCID: PMC11347774 DOI: 10.1093/toxsci/kfae078] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/22/2024] Open
Abstract
Drug-induced liver injury (DILI) is the most common trigger for acute liver failure and the leading cause of attrition in drug development. In this study, we developed an in silico framework to screen drug-induced hepatocellular toxicity (INSIGHT) by integrating the post-treatment transcriptomic data from both rodent models and primary human hepatocytes. We first built an early prediction model using logistic regression with elastic net regularization for 123 compounds and established the INSIGHT framework that can screen for drug-induced hepatotoxicity. The 235 signature genes identified by INSIGHT were involved in metabolism, bile acid synthesis, and stress response pathways. Applying the INSIGHT to an independent transcriptomic dataset treated by 185 compounds predicted that 27 compounds show a high DILI risk, including zoxazolamine and emetine. Further integration with cell image data revealed that predicted compounds with high DILI risk can induce abnormal morphological changes in the endoplasmic reticulum and mitochondrion. Clustering analysis of the treatment-induced transcriptomic changes delineated distinct DILI mechanisms induced by these compounds. Our study presents a computational framework for a mechanistic understanding of long-term liver injury and the prospective prediction of DILI risk.
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Affiliation(s)
- Yueshan Zhao
- Department of Pharmaceutical Sciences, Center for Pharmacogenetics, University of Pittsburgh, Pittsburgh, PA 15261, United States
| | - Ji Youn Park
- Department of Pharmaceutical Sciences, Center for Pharmacogenetics, University of Pittsburgh, Pittsburgh, PA 15261, United States
| | - Da Yang
- Department of Pharmaceutical Sciences, Center for Pharmacogenetics, University of Pittsburgh, Pittsburgh, PA 15261, United States
- UPMC Hillman Cancer Institute, University of Pittsburgh, Pittsburgh, PA 15261, United States
- Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA 15261, United States
| | - Min Zhang
- Department of Pharmaceutical Sciences, Center for Pharmacogenetics, University of Pittsburgh, Pittsburgh, PA 15261, United States
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7
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Gi M, Suzuki S, Kanki M, Yokohira M, Tsukamoto T, Fujioka M, Vachiraarunwong A, Qiu G, Guo R, Wanibuchi H. A novel support vector machine-based 1-day, single-dose prediction model of genotoxic hepatocarcinogenicity in rats. Arch Toxicol 2024; 98:2711-2730. [PMID: 38762666 DOI: 10.1007/s00204-024-03755-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2024] [Accepted: 03/27/2024] [Indexed: 05/20/2024]
Abstract
The development of a rapid and accurate model for determining the genotoxicity and carcinogenicity of chemicals is crucial for effective cancer risk assessment. This study aims to develop a 1-day, single-dose model for identifying genotoxic hepatocarcinogens (GHCs) in rats. Microarray gene expression data from the livers of rats administered a single dose of 58 compounds, including 5 GHCs, was obtained from the Open TG-GATEs database and used for the identification of marker genes and the construction of a predictive classifier to identify GHCs in rats. We identified 10 gene markers commonly responsive to all 5 GHCs and used them to construct a support vector machine-based predictive classifier. In the silico validation using the expression data of the Open TG-GATEs database indicates that this classifier distinguishes GHCs from other compounds with high accuracy. To further assess the model's effectiveness and reliability, we conducted multi-institutional 1-day single oral administration studies on rats. These studies examined 64 compounds, including 23 GHCs, with gene expression data of the marker genes obtained via quantitative PCR 24 h after a single oral administration. Our results demonstrate that qPCR analysis is an effective alternative to microarray analysis. The GHC predictive model showed high accuracy and reliability, achieving a sensitivity of 91% (21/23) and a specificity of 93% (38/41) across multiple validation studies in three institutions. In conclusion, the present 1-day single oral administration model proves to be a reliable and highly sensitive tool for identifying GHCs and is anticipated to be a valuable tool in identifying and screening potential GHCs.
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Affiliation(s)
- Min Gi
- Department of Environmental Risk Assessment, Graduate School of Medicine, Osaka Metropolitan University, Osaka, 545-8585, Japan
- Department of Molecular Pathology, Graduate School of Medicine, Osaka Metropolitan University, Osaka, 545-8585, Japan
| | - Shugo Suzuki
- Department of Molecular Pathology, Graduate School of Medicine, Osaka Metropolitan University, Osaka, 545-8585, Japan
| | - Masayuki Kanki
- Department of Molecular Pathology, Graduate School of Medicine, Osaka Metropolitan University, Osaka, 545-8585, Japan
| | - Masanao Yokohira
- Department of Medical Education, Faculty of Medicine, Kagawa University, Miki-cho, Kita-gun, Kagawa, 761-0793, Japan
- Department of Pathology and Host-Defense, Faculty of Medicine, Kagawa University, Miki-cho, Kita-gun, Kagawa, 761-0793, Japan
| | - Tetsuya Tsukamoto
- Department of Diagnostic Pathology, Graduate School of Medicine, Fujita Health University, Toyoake, Aichi, 470-1192, Japan
| | - Masaki Fujioka
- Department of Molecular Pathology, Graduate School of Medicine, Osaka Metropolitan University, Osaka, 545-8585, Japan
| | - Arpamas Vachiraarunwong
- Department of Environmental Risk Assessment, Graduate School of Medicine, Osaka Metropolitan University, Osaka, 545-8585, Japan
| | - Guiyu Qiu
- Department of Molecular Pathology, Graduate School of Medicine, Osaka Metropolitan University, Osaka, 545-8585, Japan
| | - Runjie Guo
- Department of Environmental Risk Assessment, Graduate School of Medicine, Osaka Metropolitan University, Osaka, 545-8585, Japan
| | - Hideki Wanibuchi
- Department of Molecular Pathology, Graduate School of Medicine, Osaka Metropolitan University, Osaka, 545-8585, Japan.
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Bianchi E, Costa E, Harrill J, Deford P, LaRocca J, Chen W, Sutake Z, Lehman A, Pappas-Garton A, Sherer E, Moreillon C, Sriram S, Dhroso A, Johnson K. Discovery Phase Agrochemical Predictive Safety Assessment Using High Content In Vitro Data to Estimate an In Vivo Toxicity Point of Departure. JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY 2024. [PMID: 39033510 DOI: 10.1021/acs.jafc.4c03094] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/23/2024]
Abstract
Utilization of in vitro (cellular) techniques, like Cell Painting and transcriptomics, could provide powerful tools for agrochemical candidate sorting and selection in the discovery process. However, using these models generates challenges translating in vitro concentrations to the corresponding in vivo exposures. Physiologically based pharmacokinetic (PBPK) modeling provides a framework for quantitative in vitro to in vivo extrapolation (IVIVE). We tested whether in vivo (rat liver) transcriptomic and apical points of departure (PODs) could be accurately predicted from in vitro (rat hepatocyte or human HepaRG) transcriptomic PODs or HepaRG Cell Painting PODs using PBPK modeling. We compared two PBPK models, the ADMET predictor and the httk R package, and found httk to predict the in vivo PODs more accurately. Our findings suggest that a rat liver apical and transcriptomic POD can be estimated utilizing a combination of in vitro transcriptome-based PODs coupled with PBPK modeling for IVIVE. Thus, high content in vitro data can be translated with modest accuracy to in vivo models of ultimate regulatory importance to help select agrochemical analogs in early stage discovery program.
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Affiliation(s)
- Enrica Bianchi
- Corteva Agriscience, Indianapolis ,Indiana 46268, United States
| | | | - Joshua Harrill
- Center for Computational Toxicology and Exposure, United States Environmental Protection Agency, Research Triangle Park ,North Carolina 27709, United States
| | - Paul Deford
- Corteva Agriscience, Indianapolis ,Indiana 46268, United States
| | - Jessica LaRocca
- Corteva Agriscience, Indianapolis ,Indiana 46268, United States
| | - Wei Chen
- Corteva Agriscience, Indianapolis ,Indiana 46268, United States
| | - Zachary Sutake
- Corteva Agriscience, Indianapolis ,Indiana 46268, United States
| | - Audrey Lehman
- Corteva Agriscience, Indianapolis ,Indiana 46268, United States
| | | | - Eric Sherer
- Corteva Agriscience, Indianapolis ,Indiana 46268, United States
| | | | | | - Andi Dhroso
- Corteva Agriscience, Indianapolis ,Indiana 46268, United States
| | - Kamin Johnson
- Corteva Agriscience, Indianapolis ,Indiana 46268, United States
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Fresnais L, Perin O, Riu A, Grall R, Ott A, Fromenty B, Gallardo JC, Stingl M, Frainay C, Jourdan F, Poupin N. A strategy to detect metabolic changes induced by exposure to chemicals from large sets of condition-specific metabolic models computed with enumeration techniques. BMC Bioinformatics 2024; 25:234. [PMID: 38992584 PMCID: PMC11238488 DOI: 10.1186/s12859-024-05845-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2023] [Accepted: 06/14/2024] [Indexed: 07/13/2024] Open
Abstract
BACKGROUND The growing abundance of in vitro omics data, coupled with the necessity to reduce animal testing in the safety assessment of chemical compounds and even eliminate it in the evaluation of cosmetics, highlights the need for adequate computational methodologies. Data from omics technologies allow the exploration of a wide range of biological processes, therefore providing a better understanding of mechanisms of action (MoA) related to chemical exposure in biological systems. However, the analysis of these large datasets remains difficult due to the complexity of modulations spanning multiple biological processes. RESULTS To address this, we propose a strategy to reduce information overload by computing, based on transcriptomics data, a comprehensive metabolic sub-network reflecting the metabolic impact of a chemical. The proposed strategy integrates transcriptomic data to a genome scale metabolic network through enumeration of condition-specific metabolic models hence translating transcriptomics data into reaction activity probabilities. Based on these results, a graph algorithm is applied to retrieve user readable sub-networks reflecting the possible metabolic MoA (mMoA) of chemicals. This strategy has been implemented as a three-step workflow. The first step consists in building cell condition-specific models reflecting the metabolic impact of each exposure condition while taking into account the diversity of possible optimal solutions with a partial enumeration algorithm. In a second step, we address the challenge of analyzing thousands of enumerated condition-specific networks by computing differentially activated reactions (DARs) between the two sets of enumerated possible condition-specific models. Finally, in the third step, DARs are grouped into clusters of functionally interconnected metabolic reactions, representing possible mMoA, using the distance-based clustering and subnetwork extraction method. The first part of the workflow was exemplified on eight molecules selected for their known human hepatotoxic outcomes associated with specific MoAs well described in the literature and for which we retrieved primary human hepatocytes transcriptomic data in Open TG-GATEs. Then, we further applied this strategy to more precisely model and visualize associated mMoA for two of these eight molecules (amiodarone and valproic acid). The approach proved to go beyond gene-based analysis by identifying mMoA when few genes are significantly differentially expressed (2 differentially expressed genes (DEGs) for amiodarone), bringing additional information from the network topology, or when very large number of genes were differentially expressed (5709 DEGs for valproic acid). In both cases, the results of our strategy well fitted evidence from the literature regarding known MoA. Beyond these confirmations, the workflow highlighted potential other unexplored mMoA. CONCLUSION The proposed strategy allows toxicology experts to decipher which part of cellular metabolism is expected to be affected by the exposition to a given chemical. The approach originality resides in the combination of different metabolic modelling approaches (constraint based and graph modelling). The application to two model molecules shows the strong potential of the approach for interpretation and visual mining of complex omics in vitro data. The presented strategy is freely available as a python module ( https://pypi.org/project/manamodeller/ ) and jupyter notebooks ( https://github.com/LouisonF/MANA ).
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Affiliation(s)
- Louison Fresnais
- UMR1331 Toxalim (Research Centre in Food Toxicology), Université de Toulouse, INRAE, ENVT, INP-Purpan, UPS, Toulouse, France.
- L'Oréal Research and Innovation, Aulnay-sous-Bois, France.
| | - Olivier Perin
- L'Oréal Research and Innovation, Aulnay-sous-Bois, France
| | - Anne Riu
- L'Oréal Research and Innovation, Aulnay-sous-Bois, France
| | - Romain Grall
- L'Oréal Research and Innovation, Aulnay-sous-Bois, France
| | - Alban Ott
- L'Oréal Research and Innovation, Aulnay-sous-Bois, France
| | - Bernard Fromenty
- Institut NUMECAN (Nutrition Metabolisms and Cancer) UMR_A 1317, UMR_S 1241, INSERM, Univ Rennes, INRAE, 35000, Rennes, France
| | - Jean-Clément Gallardo
- UMR1331 Toxalim (Research Centre in Food Toxicology), Université de Toulouse, INRAE, ENVT, INP-Purpan, UPS, Toulouse, France
| | - Maximilian Stingl
- UMR1331 Toxalim (Research Centre in Food Toxicology), Université de Toulouse, INRAE, ENVT, INP-Purpan, UPS, Toulouse, France
| | - Clément Frainay
- UMR1331 Toxalim (Research Centre in Food Toxicology), Université de Toulouse, INRAE, ENVT, INP-Purpan, UPS, Toulouse, France
| | - Fabien Jourdan
- UMR1331 Toxalim (Research Centre in Food Toxicology), Université de Toulouse, INRAE, ENVT, INP-Purpan, UPS, Toulouse, France
- MetaboHUB-MetaToul, National Infrastructure of Metabolomics and Fluxomics, Toulouse, France
| | - Nathalie Poupin
- UMR1331 Toxalim (Research Centre in Food Toxicology), Université de Toulouse, INRAE, ENVT, INP-Purpan, UPS, Toulouse, France.
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10
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Ivanov SM, Rudik AV, Lagunin AA, Filimonov DA, Poroikov VV. DIGEP-Pred 2.0: A web application for predicting drug-induced cell signaling and gene expression changes. Mol Inform 2024:e202400032. [PMID: 38979651 DOI: 10.1002/minf.202400032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2024] [Revised: 05/16/2024] [Accepted: 06/14/2024] [Indexed: 07/10/2024]
Abstract
The analysis of drug-induced gene expression profiles (DIGEP) is widely used to estimate the potential therapeutic and adverse drug effects as well as the molecular mechanisms of drug action. However, the corresponding experimental data is absent for many existing drugs and drug-like compounds. To solve this problem, we created the DIGEP-Pred 2.0 web application, which allows predicting DIGEP and potential drug targets by structural formula of drug-like compounds. It is based on the combined use of structure-activity relationships (SARs) and network analysis. SAR models were created using PASS (Prediction of Activity Spectra for Substances) technology for data from the Comparative Toxicogenomics Database (CTD), the Connectivity Map (CMap) for the prediction of DIGEP, and PubChem and ChEMBL for the prediction of molecular mechanisms of action (MoA). Using only the structural formula of a compound, the user can obtain information on potential gene expression changes in several cell lines and drug targets, which are potential master regulators responsible for the observed DIGEP. The mean accuracy of prediction calculated by leave-one-out cross validation was 86.5 % for 13377 genes and 94.8 % for 2932 proteins (CTD data), and it was 97.9 % for 2170 MoAs. SAR models (mean accuracy-87.5 %) were also created for CMap data given on MCF7, PC3, and HL60 cell lines with different threshold values for the logarithm of fold changes: 0.5, 0.7, 1, 1.5, and 2. Additionally, the data on pathways (KEGG, Reactome), biological processes of Gene Ontology, and diseases (DisGeNet) enriched by the predicted genes, together with the estimation of target-master regulators based on OmniPath data, is also provided. DIGEP-Pred 2.0 web application is freely available at https://www.way2drug.com/digep-pred.
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Affiliation(s)
- Sergey M Ivanov
- Department of Bioinformatics, Institute of Biomedical Chemistry, Pogodinskaya Street, 10 bldg. 8, Moscow, 119121, Russia
- Department of Bioinformatics, Pirogov Russian National Research Medical University, Ostrovityanova Street, 1, Moscow, 117997, Russia
| | - Anastasia V Rudik
- Department of Bioinformatics, Institute of Biomedical Chemistry, Pogodinskaya Street, 10 bldg. 8, Moscow, 119121, Russia
| | - Alexey A Lagunin
- Department of Bioinformatics, Institute of Biomedical Chemistry, Pogodinskaya Street, 10 bldg. 8, Moscow, 119121, Russia
- Department of Bioinformatics, Pirogov Russian National Research Medical University, Ostrovityanova Street, 1, Moscow, 117997, Russia
| | - Dmitry A Filimonov
- Department of Bioinformatics, Institute of Biomedical Chemistry, Pogodinskaya Street, 10 bldg. 8, Moscow, 119121, Russia
| | - Vladimir V Poroikov
- Department of Bioinformatics, Institute of Biomedical Chemistry, Pogodinskaya Street, 10 bldg. 8, Moscow, 119121, Russia
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11
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Sun H, Li H, Guan Y, Yuan Y, Xu C, Fu D, Xie P, Li J, Zhao T, Wang X, Feng Y, Wang H, Gao S, Yang S, Shi Y, Liu J, Chang A, Huang C, Hao J. BICC1 drives pancreatic cancer stemness and chemoresistance by facilitating tryptophan metabolism. SCIENCE ADVANCES 2024; 10:eadj8650. [PMID: 38896624 PMCID: PMC11186499 DOI: 10.1126/sciadv.adj8650] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/23/2023] [Accepted: 04/30/2024] [Indexed: 06/21/2024]
Abstract
Pancreatic adenocarcinoma is the fourth leading cause of malignancy-related deaths, with rapid development of drug resistance driven by pancreatic cancer stem cells. However, the mechanisms sustaining stemness and chemotherapy resistance in pancreatic ductal adenocarcinoma (PDAC) remain unclear. Here, we demonstrate that Bicaudal C homolog 1 (BICC1), an RNA binding protein regulating numerous cytoplasmic mRNAs, facilitates chemoresistance and stemness in PDAC. Mechanistically, BICC1 activated tryptophan catabolism in PDAC by up-regulating indoleamine 2,3-dioxygenase-1 (IDO1) expression, a tryptophan-catabolizing enzyme. Increased levels of tryptophan metabolites contribute to NAD+ synthesis and oxidative phosphorylation, leading to a stem cell-like phenotype. Blocking BICC1/IDO1/tryptophan metabolism signaling greatly improves the gemcitabine (GEM) efficacy in several PDAC models with high BICC1 level. These findings indicate that BICC1 is a critical tryptophan metabolism regulator that drives the stemness and chemoresistance of PDAC and thus a potential target for combinatorial therapeutic strategy against chemoresistance.
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MESH Headings
- Tryptophan/metabolism
- Humans
- Drug Resistance, Neoplasm/genetics
- Neoplastic Stem Cells/metabolism
- Neoplastic Stem Cells/pathology
- Pancreatic Neoplasms/metabolism
- Pancreatic Neoplasms/pathology
- Pancreatic Neoplasms/genetics
- Pancreatic Neoplasms/drug therapy
- Cell Line, Tumor
- Animals
- Mice
- Gene Expression Regulation, Neoplastic
- Carcinoma, Pancreatic Ductal/metabolism
- Carcinoma, Pancreatic Ductal/pathology
- Carcinoma, Pancreatic Ductal/drug therapy
- Carcinoma, Pancreatic Ductal/genetics
- Gemcitabine
- Deoxycytidine/analogs & derivatives
- Deoxycytidine/pharmacology
- RNA-Binding Proteins/metabolism
- RNA-Binding Proteins/genetics
- Indoleamine-Pyrrole 2,3,-Dioxygenase/metabolism
- Indoleamine-Pyrrole 2,3,-Dioxygenase/genetics
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Affiliation(s)
- Huizhi Sun
- Pancreas Center, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin Key Laboratory of Digestive Cancer, Tianjin, P. R. China
| | - Hui Li
- Pancreas Center, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin Key Laboratory of Digestive Cancer, Tianjin, P. R. China
| | - Yuqi Guan
- Pancreas Center, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin Key Laboratory of Digestive Cancer, Tianjin, P. R. China
| | - Yudong Yuan
- Pancreas Center, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin Key Laboratory of Digestive Cancer, Tianjin, P. R. China
| | - Chao Xu
- Pancreas Center, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin Key Laboratory of Digestive Cancer, Tianjin, P. R. China
| | - Danqi Fu
- Pancreas Center, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin Key Laboratory of Digestive Cancer, Tianjin, P. R. China
| | - Peng Xie
- Pancreas Center, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin Key Laboratory of Digestive Cancer, Tianjin, P. R. China
| | - Jianming Li
- Pancreas Center, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin Key Laboratory of Digestive Cancer, Tianjin, P. R. China
| | - Tiansuo Zhao
- Pancreas Center, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin Key Laboratory of Digestive Cancer, Tianjin, P. R. China
| | - Xiuchao Wang
- Pancreas Center, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin Key Laboratory of Digestive Cancer, Tianjin, P. R. China
| | - Yukuan Feng
- Pancreas Center, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin Key Laboratory of Digestive Cancer, Tianjin, P. R. China
| | - Hongwei Wang
- Pancreas Center, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin Key Laboratory of Digestive Cancer, Tianjin, P. R. China
| | - Song Gao
- Pancreas Center, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin Key Laboratory of Digestive Cancer, Tianjin, P. R. China
| | - Shengyu Yang
- Department of Cellular and Molecular Physiology, Pennsylvania State University College of Medicine, Hershey, PA, USA
| | - Yi Shi
- Tianjin Key Laboratory of Tumor Microenvironment and Neurovascular Regulation, School of Medicine, Nankai University, Tianjin, P. R. China
| | - Jing Liu
- Pancreas Center, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin Key Laboratory of Digestive Cancer, Tianjin, P. R. China
- Department of Breast Surgery, Fudan University Shanghai Cancer Center, Shanghai, P. R. China
| | - Antao Chang
- Pancreas Center, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin Key Laboratory of Digestive Cancer, Tianjin, P. R. China
| | - Chongbiao Huang
- Pancreas Center, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin Key Laboratory of Digestive Cancer, Tianjin, P. R. China
| | - Jihui Hao
- Pancreas Center, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin Key Laboratory of Digestive Cancer, Tianjin, P. R. China
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12
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Hari A, AbdulHameed MDM, Balik-Meisner MR, Mav D, Phadke DP, Scholl EH, Shah RR, Casey W, Auerbach SS, Wallqvist A, Pannala VR. Exposure to PFAS chemicals induces sex-dependent alterations in key rate-limiting steps of lipid metabolism in liver steatosis. FRONTIERS IN TOXICOLOGY 2024; 6:1390196. [PMID: 38903859 PMCID: PMC11188372 DOI: 10.3389/ftox.2024.1390196] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2024] [Accepted: 05/10/2024] [Indexed: 06/22/2024] Open
Abstract
Toxicants with the potential to bioaccumulate in humans and animals have long been a cause for concern, particularly due to their association with multiple diseases and organ injuries. Per- and polyfluoro alkyl substances (PFAS) and polycyclic aromatic hydrocarbons (PAH) are two such classes of chemicals that bioaccumulate and have been associated with steatosis in the liver. Although PFAS and PAH are classified as chemicals of concern, their molecular mechanisms of toxicity remain to be explored in detail. In this study, we aimed to identify potential mechanisms by which an acute exposure to PFAS and PAH chemicals can induce lipid accumulation and whether the responses depend on chemical class, dose, and sex. To this end, we analyzed mechanisms beginning with the binding of the chemical to a molecular initiating event (MIE) and the consequent transcriptomic alterations. We collated potential MIEs using predictions from our previously developed ToxProfiler tool and from published steatosis adverse outcome pathways. Most of the MIEs are transcription factors, and we collected their target genes by mining the TRRUST database. To analyze the effects of PFAS and PAH on the steatosis mechanisms, we performed a computational MIE-target gene analysis on high-throughput transcriptomic measurements of liver tissue from male and female rats exposed to either a PFAS or PAH. The results showed peroxisome proliferator-activated receptor (PPAR)-α targets to be the most dysregulated, with most of the genes being upregulated. Furthermore, PFAS exposure disrupted several lipid metabolism genes, including upregulation of fatty acid oxidation genes (Acadm, Acox1, Cpt2, Cyp4a1-3) and downregulation of lipid transport genes (Apoa1, Apoa5, Pltp). We also identified multiple genes with sex-specific behavior. Notably, the rate-limiting genes of gluconeogenesis (Pck1) and bile acid synthesis (Cyp7a1) were specifically downregulated in male rats compared to female rats, while the rate-limiting gene of lipid synthesis (Scd) showed a PFAS-specific upregulation. The results suggest that the PPAR signaling pathway plays a major role in PFAS-induced lipid accumulation in rats. Together, these results show that PFAS exposure induces a sex-specific multi-factorial mechanism involving rate-limiting genes of gluconeogenesis and bile acid synthesis that could lead to activation of an adverse outcome pathway for steatosis.
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Affiliation(s)
- Archana Hari
- Department of Defense Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Development Command, Fort Detrick, MD, United States
- The Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc., Bethesda, MD, United States
| | - Mohamed Diwan M. AbdulHameed
- Department of Defense Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Development Command, Fort Detrick, MD, United States
- The Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc., Bethesda, MD, United States
| | | | - Deepak Mav
- Sciome LLC, Research Triangle Park, NC, United States
| | | | | | | | - Warren Casey
- Division of Translational Toxicology, National Institute of Environmental Health Sciences, Research Triangle Park, NC, United States
| | - Scott S. Auerbach
- Division of Translational Toxicology, National Institute of Environmental Health Sciences, Research Triangle Park, NC, United States
| | - Anders Wallqvist
- Department of Defense Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Development Command, Fort Detrick, MD, United States
| | - Venkat R. Pannala
- Department of Defense Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Development Command, Fort Detrick, MD, United States
- The Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc., Bethesda, MD, United States
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13
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Liu H, Wang Y, Huang S, Tai J, Wang X, Dai X, Qiu C, Gu D, Yuan W, Ho HP, Chen J, Shao Y. Advancing MicroRNA Detection: Enhanced Biotin-Streptavidin Dual-Mode Phase Imaging Surface Plasmon Resonance Aptasensor. Anal Chem 2024; 96:8791-8799. [PMID: 38742926 DOI: 10.1021/acs.analchem.4c01234] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/16/2024]
Abstract
MicroRNAs (miRNAs) are novel tumor biomarkers owing to their important physiological functions in cell communication and the progression of multiple diseases. Due to the small molecular weight, short sequence length, and low concentration levels of miRNA, miRNA detection presents substantial challenges, requiring the advancement of more refined and sensitive techniques. There is an urgent demand for the development of a rapid, user-friendly, and sensitive miRNA analysis method. Here, we developed an enhanced biotin-streptavidin dual-mode phase imaging surface plasmon resonance (PI-SPR) aptasensor for sensitive and rapid detection of miRNA. Initially, we evaluated the linear sensing range for miRNA detection across two distinct sensing modalities and investigated the physical factors that influence the sensing signal in the aptamer-miRNA interaction within the PI-SPR aptasensor. Then, an enhanced biotin-streptavidin amplification strategy was introduced in the PI-SPR aptasensor, which effectively reduced the nonspecific adsorption by 20% and improved the limit of detection by 548 times. Furthermore, we have produced three types of tumor marker chips, which utilize the rapid sensing mode (less than 2 min) of PI-SPR aptasensor to achieve simultaneous detection of multiple miRNA markers in the serum from clinical cancer patients. This work not only developed a new approach to detect miRNA in different application scenarios but also provided a new reference for the application of the biotin-streptavidin amplification system in the detection of other small biomolecules.
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Affiliation(s)
- Haoyu Liu
- State Key Laboratory of Radio Frequency Heterogeneous Integration, Key Laboratory of Optoelectronic Devices and Systems of Ministry of Education and Guangdong Province, College of Physics and Optoelectronics Engineering, Shenzhen University, Shenzhen 518060, China
| | - Yuye Wang
- State Key Laboratory of Radio Frequency Heterogeneous Integration, Key Laboratory of Optoelectronic Devices and Systems of Ministry of Education and Guangdong Province, College of Physics and Optoelectronics Engineering, Shenzhen University, Shenzhen 518060, China
| | - Songfeng Huang
- State Key Laboratory of Radio Frequency Heterogeneous Integration, Key Laboratory of Optoelectronic Devices and Systems of Ministry of Education and Guangdong Province, College of Physics and Optoelectronics Engineering, Shenzhen University, Shenzhen 518060, China
| | - Jiali Tai
- State Key Laboratory of Radio Frequency Heterogeneous Integration, Key Laboratory of Optoelectronic Devices and Systems of Ministry of Education and Guangdong Province, College of Physics and Optoelectronics Engineering, Shenzhen University, Shenzhen 518060, China
| | - Xueliang Wang
- State Key Laboratory of Radio Frequency Heterogeneous Integration, Key Laboratory of Optoelectronic Devices and Systems of Ministry of Education and Guangdong Province, College of Physics and Optoelectronics Engineering, Shenzhen University, Shenzhen 518060, China
| | - Xiaoqi Dai
- State Key Laboratory of Radio Frequency Heterogeneous Integration, Key Laboratory of Optoelectronic Devices and Systems of Ministry of Education and Guangdong Province, College of Physics and Optoelectronics Engineering, Shenzhen University, Shenzhen 518060, China
| | - Chuanghua Qiu
- Department of Laboratory Medicine, Shenzhen Second People's Hospital, the First Affiliated Hospital of Shenzhen University, Shenzhen 518035, China
| | - Dayong Gu
- Department of Laboratory Medicine, Shenzhen Second People's Hospital, the First Affiliated Hospital of Shenzhen University, Shenzhen 518035, China
| | - Wu Yuan
- Department of Biomedical Engineering, The Chinese University of Hong Kong, Shatin, Hong Kong 852, China
| | - Ho-Pui Ho
- Department of Biomedical Engineering, The Chinese University of Hong Kong, Shatin, Hong Kong 852, China
| | - Jiajie Chen
- State Key Laboratory of Radio Frequency Heterogeneous Integration, Key Laboratory of Optoelectronic Devices and Systems of Ministry of Education and Guangdong Province, College of Physics and Optoelectronics Engineering, Shenzhen University, Shenzhen 518060, China
| | - Yonghong Shao
- State Key Laboratory of Radio Frequency Heterogeneous Integration, Key Laboratory of Optoelectronic Devices and Systems of Ministry of Education and Guangdong Province, College of Physics and Optoelectronics Engineering, Shenzhen University, Shenzhen 518060, China
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14
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Azuma I, Mizuno T, Kusuhara H. GLDADec: marker-gene guided LDA modeling for bulk gene expression deconvolution. Brief Bioinform 2024; 25:bbae315. [PMID: 38982642 PMCID: PMC11233176 DOI: 10.1093/bib/bbae315] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2024] [Revised: 05/21/2024] [Accepted: 06/14/2024] [Indexed: 07/11/2024] Open
Abstract
Inferring cell type proportions from bulk transcriptome data is crucial in immunology and oncology. Here, we introduce guided LDA deconvolution (GLDADec), a bulk deconvolution method that guides topics using cell type-specific marker gene names to estimate topic distributions for each sample. Through benchmarking using blood-derived datasets, we demonstrate its high estimation performance and robustness. Moreover, we apply GLDADec to heterogeneous tissue bulk data and perform comprehensive cell type analysis in a data-driven manner. We show that GLDADec outperforms existing methods in estimation performance and evaluate its biological interpretability by examining enrichment of biological processes for topics. Finally, we apply GLDADec to The Cancer Genome Atlas tumor samples, enabling subtype stratification and survival analysis based on estimated cell type proportions, thus proving its practical utility in clinical settings. This approach, utilizing marker gene names as partial prior information, can be applied to various scenarios for bulk data deconvolution. GLDADec is available as an open-source Python package at https://github.com/mizuno-group/GLDADec.
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Affiliation(s)
- Iori Azuma
- Graduate School of Pharmaceutical Sciences, The University of Tokyo, 7-3-1, Bunkyo-ku 113-0033, Japan
| | - Tadahaya Mizuno
- Graduate School of Pharmaceutical Sciences, The University of Tokyo, 7-3-1, Bunkyo-ku 113-0033, Japan
| | - Hiroyuki Kusuhara
- Graduate School of Pharmaceutical Sciences, The University of Tokyo, 7-3-1, Bunkyo-ku 113-0033, Japan
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15
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Costa E, Johnson KJ, Walker CA, O’Brien JM. Transcriptomic point of departure determination: a comparison of distribution-based and gene set-based approaches. Front Genet 2024; 15:1374791. [PMID: 38784034 PMCID: PMC11112360 DOI: 10.3389/fgene.2024.1374791] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2024] [Accepted: 04/02/2024] [Indexed: 05/25/2024] Open
Abstract
A key step in assessing the potential human and environmental health risks of industrial and agricultural chemicals is to determine the toxicity point of departure (POD), which is the highest dose level that causes no adverse effect. Transcriptomic POD (tPOD) values have been suggested to accurately estimate toxicity POD values. One step in the most common approach for tPOD determination involves mapping genes to annotated gene sets, a process that might lead to substantial information loss particularly in species with poor gene annotation. Alternatively, methods that calculate tPOD values directly from the distribution of individual gene POD values omit this mapping step. Using rat transcriptome data for 79 molecules obtained from Open TG-GATEs (Toxicogenomics Project Genomics Assisted Toxicity Evaluation System), the hypothesis was tested that methods based on the distribution of all individual gene POD values will give a similar tPOD value to that obtained via the gene set-based method. Gene set-based tPOD values using four different gene set structures were compared to tPOD values from five different individual gene distribution methods. Results revealed a high tPOD concordance for all methods tested, especially for molecules with at least 300 dose-responsive probesets: for 90% of those molecules, the tPOD values from all methods were within 4-fold of each other. In addition, random gene sets based upon the structure of biological knowledge-derived gene sets produced tPOD values with a median absolute fold change of 1.3-1.4 when compared to the original biological knowledge-derived gene set counterparts, suggesting that little biological information is used in the gene set-based tPOD generation approach. These findings indicate using individual gene distributions to calculate a tPOD is a viable and parsimonious alternative to using gene sets. Importantly, individual gene distribution-based tPOD methods do not require knowledge of biological organization and can be applied to any species including those with poorly annotated gene sets.
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Affiliation(s)
| | | | | | - Jason M. O’Brien
- Ecotoxicology and Wildlife Health Division, Environment and Climate Change Canada, Ottawa, ON, Canada
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16
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Wijaya LS, Gabor A, Pot IE, van de Have L, Saez-Rodriguez J, Stevens JL, Le Dévédec SE, Callegaro G, van de Water B. A network-based transcriptomic landscape of HepG2 cells uncovering causal gene-cytotoxicity interactions underlying drug-induced liver injury. Toxicol Sci 2024; 198:14-30. [PMID: 38015832 PMCID: PMC10901150 DOI: 10.1093/toxsci/kfad121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2023] Open
Abstract
Drug-induced liver injury (DILI) remains the main reason for drug development attritions largely due to poor mechanistic understanding. Toxicogenomic to interrogate the mechanism of DILI has been broadly performed. Gene coregulation network-based transcriptome analysis is a bioinformatics approach that potentially contributes to improve mechanistic interpretation of toxicogenomic data. Here we performed an extensive concentration time course response-toxicogenomic study in the HepG2 cell line exposed to 20 DILI compounds, 7 reference compounds for stress response pathways, and 10 agonists for cytokines and growth factor receptors. We performed whole transcriptome targeted RNA sequencing to more than 500 conditions and applied weighted gene coregulated network analysis to the transcriptomics data followed by the identification of gene coregulated networks (modules) that were strongly modulated upon the exposure of DILI compounds. Preservation analysis on the module responses of HepG2 and PHH demonstrated highly preserved adaptive stress response gene coregulated networks. We correlated gene coregulated networks with cell death onset and causal relationships of 67 critical target genes of these modules with the onset of cell death was evaluated using RNA interference screening. We identified GTPBP2, HSPA1B, IRF1, SIRT1, and TSC22D3 as essential modulators of DILI compound-induced cell death. These genes were also induced by DILI compounds in PHH. Altogether, we demonstrate the application of large transcriptome datasets combined with network-based analysis and biological validation to uncover the candidate determinants of DILI.
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Affiliation(s)
- Lukas S Wijaya
- Leiden Academic Centre for Drug Research (LACDR), Faculty of Science, Leiden University, 2333 Leiden, The Netherlands
| | - Attila Gabor
- Institute for Computational Biomedicine, Faculty of Medicine, Heidelberg University, 69120 Heidelberg, Germany
- Heidelberg University Hospital, Molecular Medicine Partnership Unit, 69120 Heidelberg, Germany
| | - Iris E Pot
- Leiden Academic Centre for Drug Research (LACDR), Faculty of Science, Leiden University, 2333 Leiden, The Netherlands
| | - Luca van de Have
- Leiden Academic Centre for Drug Research (LACDR), Faculty of Science, Leiden University, 2333 Leiden, The Netherlands
| | - Julio Saez-Rodriguez
- Institute for Computational Biomedicine, Faculty of Medicine, Heidelberg University, 69120 Heidelberg, Germany
- Heidelberg University Hospital, Molecular Medicine Partnership Unit, 69120 Heidelberg, Germany
| | - James L Stevens
- Leiden Academic Centre for Drug Research (LACDR), Faculty of Science, Leiden University, 2333 Leiden, The Netherlands
| | - Sylvia E Le Dévédec
- Leiden Academic Centre for Drug Research (LACDR), Faculty of Science, Leiden University, 2333 Leiden, The Netherlands
| | - Giulia Callegaro
- Leiden Academic Centre for Drug Research (LACDR), Faculty of Science, Leiden University, 2333 Leiden, The Netherlands
| | - Bob van de Water
- Leiden Academic Centre for Drug Research (LACDR), Faculty of Science, Leiden University, 2333 Leiden, The Netherlands
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17
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Hase T, Ghosh S, Aisaki KI, Kitajima S, Kanno J, Kitano H, Yachie A. DTox: A deep neural network-based in visio lens for large scale toxicogenomics data. J Toxicol Sci 2024; 49:105-115. [PMID: 38432953 DOI: 10.2131/jts.49.105] [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] [Indexed: 03/05/2024]
Abstract
With the advancement of large-scale omics technologies, particularly transcriptomics data sets on drug and treatment response repositories available in public domain, toxicogenomics has emerged as a key field in safety pharmacology and chemical risk assessment. Traditional statistics-based bioinformatics analysis poses challenges in its application across multidimensional toxicogenomic data, including administration time, dosage, and gene expression levels. Motivated by the visual inspection workflow of field experts to augment their efficiency of screening significant genes to derive meaningful insights, together with the ability of deep neural architectures to learn the image signals, we developed DTox, a deep neural network-based in visio approach. Using the Percellome toxicogenomics database, instead of utilizing the numerical gene expression values of the transcripts (gene probes of the microarray) for dose-time combinations, DTox learned the image representation of 3D surface plots of distinct time and dosage data points to train the classifier on the experts' labels of gene probe significance. DTox outperformed statistical threshold-based bioinformatics and machine learning approaches based on numerical expression values. This result shows the ability of image-driven neural networks to overcome the limitations of classical numeric value-based approaches. Further, by augmenting the model with explainability modules, our study showed the potential to reveal the visual analysis process of human experts in toxicogenomics through the model weights. While the current work demonstrates the application of the DTox model in toxicogenomic studies, it can be further generalized as an in visio approach for multi-dimensional numeric data with applications in various fields in medical data sciences.
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Affiliation(s)
- Takeshi Hase
- The Systems Biology Institute, Saisei Ikedayama Bldg
- SBX BioSciences, Inc, Canada
- Institute of Education, Tokyo Medical and Dental University
- Faculty of Pharmacy, Keio University
- Center for Mathematical Modelling and Data Science, Osaka University
| | - Samik Ghosh
- The Systems Biology Institute, Saisei Ikedayama Bldg
| | - Ken-Ichi Aisaki
- Division of Cellular and Molecular Toxicology, Center for Biological Safety and Research (CBSR), National Institute of Health Sciences (NIHS)
| | - Satoshi Kitajima
- Division of Cellular and Molecular Toxicology, Center for Biological Safety and Research (CBSR), National Institute of Health Sciences (NIHS)
| | - Jun Kanno
- The Systems Biology Institute, Saisei Ikedayama Bldg
- Division of Cellular and Molecular Toxicology, Center for Biological Safety and Research (CBSR), National Institute of Health Sciences (NIHS)
- Faculty of Medicine, University of Tsukuba
| | - Hiroaki Kitano
- The Systems Biology Institute, Saisei Ikedayama Bldg
- Integrated Open Systems Unit, Okinawa Institute of Science and Technology (OIST)
| | - Ayako Yachie
- The Systems Biology Institute, Saisei Ikedayama Bldg
- SBX BioSciences, Inc, Canada
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18
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Maedera S, Mizuno T, Kusuhara H. Investigation of latent representation of toxicopathological images extracted by CNN model for understanding compound properties in vivo. Comput Biol Med 2024; 168:107748. [PMID: 38016375 DOI: 10.1016/j.compbiomed.2023.107748] [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: 08/06/2023] [Revised: 10/25/2023] [Accepted: 11/20/2023] [Indexed: 11/30/2023]
Abstract
Toxicopathological images acquired during safety assessment elucidate an individual's biological responses to a given compound, and their numerization can yield valuable insights contributing to the assessment of compound properties. Currently, toxicopathological images are mainly encoded as pathological findings, evaluated by pathologists, which introduces challenges when used as input for modeling, specifically in terms of representation capability and comparability. In this study, we assessed the usefulness of latent representations extracted from toxicopathological images using Convolutional Neural Network (CNN) in estimating compound properties in vivo. Special emphasis was placed on examining the impact of learning pathological findings, the depth of frozen layers during learning, and the selection of the layer for latent representation. Our findings demonstrate that a machine learning model fed with the latent representation as input surpassed the performance of a model directly employing pathological findings as input, particularly in the classification of a compound's Mechanism of Action and in predicting late-phase findings from early-phase images in repeated-dose tests. While learning pathological findings did improve accuracy, the magnitude of improvement was relatively modest. Similarly, the effect of freezing layers during learning was also limited. Notably, the selection of the layer for latent representation had a substantial impact on the accurate estimation of compound properties in vivo.
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Affiliation(s)
- Shotaro Maedera
- Laboratory of Molecular Pharmacokinetics, Graduate School of Pharmaceutical Sciences, The University of Tokyo, 7-3-1 Hongo, Bunkyo, Tokyo, Japan
| | - Tadahaya Mizuno
- Laboratory of Molecular Pharmacokinetics, Graduate School of Pharmaceutical Sciences, The University of Tokyo, 7-3-1 Hongo, Bunkyo, Tokyo, Japan.
| | - Hiroyuki Kusuhara
- Laboratory of Molecular Pharmacokinetics, Graduate School of Pharmaceutical Sciences, The University of Tokyo, 7-3-1 Hongo, Bunkyo, Tokyo, Japan
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19
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Mizuno T, Kusuhara H. Investigation of normalization procedures for transcriptome profiles of compounds oriented toward practical study design. J Toxicol Sci 2024; 49:249-259. [PMID: 38825484 DOI: 10.2131/jts.49.249] [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] [Indexed: 06/04/2024]
Abstract
The transcriptome profile is a representative phenotype-based descriptor of compounds, widely acknowledged for its ability to effectively capture compound effects. However, the presence of batch differences is inevitable. Despite the existence of sophisticated statistical methods, many of them presume a substantial sample size. How should we design a transcriptome analysis to obtain robust compound profiles, particularly in the context of small datasets frequently encountered in practical scenarios? This study addresses this question by investigating the normalization procedures for transcriptome profiles, focusing on the baseline distribution employed in deriving biological responses as profiles. Firstly, we investigated two large GeneChip datasets, comparing the impact of different normalization procedures. Through an evaluation of the similarity between response profiles of biological replicates within each dataset and the similarity between response profiles of the same compound across datasets, we revealed that the baseline distribution defined by all samples within each batch under batch-corrected condition is a good choice for large datasets. Subsequently, we conducted a simulation to explore the influence of the number of control samples on the robustness of response profiles across datasets. The results offer insights into determining the suitable quantity of control samples for diminutive datasets. It is crucial to acknowledge that these conclusions stem from constrained datasets. Nevertheless, we believe that this study enhances our understanding of how to effectively leverage transcriptome profiles of compounds and promotes the accumulation of essential knowledge for the practical application of such profiles.
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Affiliation(s)
- Tadahaya Mizuno
- Laboratory of Molecular Pharmacokinetics, Graduate School of Pharmaceutical Sciences, The University of Tokyo
| | - Hiroyuki Kusuhara
- Laboratory of Molecular Pharmacokinetics, Graduate School of Pharmaceutical Sciences, The University of Tokyo
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20
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O’Donovan SD, Cavill R, Wimmenauer F, Lukas A, Stumm T, Smirnov E, Lenz M, Ertaylan G, Jennen DGJ, van Riel NAW, Driessens K, Peeters RLM, de Kok TMCM. Application of transfer learning to predict drug-induced human in vivo gene expression changes using rat in vitro and in vivo data. PLoS One 2023; 18:e0292030. [PMID: 38032940 PMCID: PMC10688741 DOI: 10.1371/journal.pone.0292030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2023] [Accepted: 09/11/2023] [Indexed: 12/02/2023] Open
Abstract
The liver is the primary site for the metabolism and detoxification of many compounds, including pharmaceuticals. Consequently, it is also the primary location for many adverse reactions. As the liver is not readily accessible for sampling in humans; rodent or cell line models are often used to evaluate potential toxic effects of a novel compound or candidate drug. However, relating the results of animal and in vitro studies to relevant clinical outcomes for the human in vivo situation still proves challenging. In this study, we incorporate principles of transfer learning within a deep artificial neural network allowing us to leverage the relative abundance of rat in vitro and in vivo exposure data from the Open TG-GATEs data set to train a model to predict the expected pattern of human in vivo gene expression following an exposure given measured human in vitro gene expression. We show that domain adaptation has been successfully achieved, with the rat and human in vitro data no longer being separable in the common latent space generated by the network. The network produces physiologically plausible predictions of human in vivo gene expression pattern following an exposure to a previously unseen compound. Moreover, we show the integration of the human in vitro data in the training of the domain adaptation network significantly improves the temporal accuracy of the predicted rat in vivo gene expression pattern following an exposure to a previously unseen compound. In this way, we demonstrate the improvements in prediction accuracy that can be achieved by combining data from distinct domains.
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Affiliation(s)
- Shauna D. O’Donovan
- Maastricht Centre for Systems Biology (MaCSBio), Maastricht University, Maastricht, The Netherlands
- Dept. of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
- Eindhoven Artificial Intelligence Systems Institute (EAISI), Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Rachel Cavill
- Dept. of Advanced Computing Sciences, Maastricht University, Maastricht, The Netherlands
| | - Florian Wimmenauer
- Dept. of Advanced Computing Sciences, Maastricht University, Maastricht, The Netherlands
| | - Alexander Lukas
- Dept. of Advanced Computing Sciences, Maastricht University, Maastricht, The Netherlands
| | - Tobias Stumm
- Dept. of Advanced Computing Sciences, Maastricht University, Maastricht, The Netherlands
| | - Evgueni Smirnov
- Dept. of Advanced Computing Sciences, Maastricht University, Maastricht, The Netherlands
| | - Michael Lenz
- Maastricht Centre for Systems Biology (MaCSBio), Maastricht University, Maastricht, The Netherlands
- Institute of Organismic and Molecular Evolution, Johannes Gutenberg University Mainz, Mainz, Germany
- Preventive Cardiology and Preventative Medicine – Center for Cardiology, University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany
| | - Gokhan Ertaylan
- Maastricht Centre for Systems Biology (MaCSBio), Maastricht University, Maastricht, The Netherlands
- Sustainable Health, Flemish Institute for Technological Research (VITO), Mol, Belgium
| | - Danyel G. J. Jennen
- Dept. of Toxicogenomics, GROW School for Oncology and Reproduction, Maastricht University, Maastricht, The Netherlands
| | - Natal A. W. van Riel
- Maastricht Centre for Systems Biology (MaCSBio), Maastricht University, Maastricht, The Netherlands
- Dept. of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
- Eindhoven Artificial Intelligence Systems Institute (EAISI), Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Kurt Driessens
- Dept. of Advanced Computing Sciences, Maastricht University, Maastricht, The Netherlands
| | - Ralf L. M. Peeters
- Maastricht Centre for Systems Biology (MaCSBio), Maastricht University, Maastricht, The Netherlands
- Dept. of Advanced Computing Sciences, Maastricht University, Maastricht, The Netherlands
| | - Theo M. C. M. de Kok
- Maastricht Centre for Systems Biology (MaCSBio), Maastricht University, Maastricht, The Netherlands
- Dept. of Toxicogenomics, GROW School for Oncology and Reproduction, Maastricht University, Maastricht, The Netherlands
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21
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Jia X, Wang T, Zhu H. Advancing Computational Toxicology by Interpretable Machine Learning. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2023; 57:17690-17706. [PMID: 37224004 PMCID: PMC10666545 DOI: 10.1021/acs.est.3c00653] [Citation(s) in RCA: 16] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/25/2023] [Revised: 05/05/2023] [Accepted: 05/05/2023] [Indexed: 05/26/2023]
Abstract
Chemical toxicity evaluations for drugs, consumer products, and environmental chemicals have a critical impact on human health. Traditional animal models to evaluate chemical toxicity are expensive, time-consuming, and often fail to detect toxicants in humans. Computational toxicology is a promising alternative approach that utilizes machine learning (ML) and deep learning (DL) techniques to predict the toxicity potentials of chemicals. Although the applications of ML- and DL-based computational models in chemical toxicity predictions are attractive, many toxicity models are "black boxes" in nature and difficult to interpret by toxicologists, which hampers the chemical risk assessments using these models. The recent progress of interpretable ML (IML) in the computer science field meets this urgent need to unveil the underlying toxicity mechanisms and elucidate the domain knowledge of toxicity models. In this review, we focused on the applications of IML in computational toxicology, including toxicity feature data, model interpretation methods, use of knowledge base frameworks in IML development, and recent applications. The challenges and future directions of IML modeling in toxicology are also discussed. We hope this review can encourage efforts in developing interpretable models with new IML algorithms that can assist new chemical assessments by illustrating toxicity mechanisms in humans.
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Affiliation(s)
- Xuelian Jia
- Department
of Chemistry and Biochemistry, Rowan University, Glassboro, New Jersey 08028, United States
| | - Tong Wang
- Department
of Chemistry and Biochemistry, Rowan University, Glassboro, New Jersey 08028, United States
| | - Hao Zhu
- Department
of Chemistry and Biochemistry, Rowan University, Glassboro, New Jersey 08028, United States
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22
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Flynn J, Ahmadi MM, McFarland CT, Kubal MD, Taylor MA, Cheng Z, Torchia EC, Edwards MG. Crowdsourcing temporal transcriptomic coronavirus host infection data: Resources, guide, and novel insights. Biol Methods Protoc 2023; 8:bpad033. [PMID: 38107402 PMCID: PMC10723038 DOI: 10.1093/biomethods/bpad033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2023] [Revised: 10/07/2023] [Accepted: 11/13/2023] [Indexed: 12/19/2023] Open
Abstract
The emergence of severe acute respiratory syndrome-related coronavirus 2 (SARS-CoV-2) reawakened the need to rapidly understand the molecular etiologies, pandemic potential, and prospective treatments of infectious agents. The lack of existing data on SARS-CoV-2 hampered early attempts to treat severe forms of coronavirus disease-2019 (COVID-19) during the pandemic. This study coupled existing transcriptomic data from severe acute respiratory syndrome-related coronavirus 1 (SARS-CoV-1) lung infection animal studies with crowdsourcing statistical approaches to derive temporal meta-signatures of host responses during early viral accumulation and subsequent clearance stages. Unsupervised and supervised machine learning approaches identified top dysregulated genes and potential biomarkers (e.g. CXCL10, BEX2, and ADM). Temporal meta-signatures revealed distinct gene expression programs with biological implications to a series of host responses underlying sustained Cxcl10 expression and Stat signaling. Cell cycle switched from G1/G0 phase genes, early in infection, to a G2/M gene signature during late infection that correlated with the enrichment of DNA damage response and repair genes. The SARS-CoV-1 meta-signatures were shown to closely emulate human SARS-CoV-2 host responses from emerging RNAseq, single cell, and proteomics data with early monocyte-macrophage activation followed by lymphocyte proliferation. The circulatory hormone adrenomedullin was observed as maximally elevated in elderly patients who died from COVID-19. Stage-specific correlations to compounds with potential to treat COVID-19 and future coronavirus infections were in part validated by a subset of twenty-four that are in clinical trials to treat COVID-19. This study represents a roadmap to leverage existing data in the public domain to derive novel molecular and biological insights and potential treatments to emerging human pathogens.
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Affiliation(s)
- James Flynn
- Illumina Corporation, San Diego, CA 92122, United States
| | - Mehdi M Ahmadi
- Gates Center for Regenerative Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, United States
| | | | | | - Mark A Taylor
- Bioinfo Solutions LLC, Parker, CO 80134, United States
| | - Zhang Cheng
- Illumina Corporation, San Diego, CA 92122, United States
| | - Enrique C Torchia
- Gates Center for Regenerative Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, United States
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23
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Morita K, Mizuno T, Azuma I, Suzuki Y, Kusuhara H. Rat Deconvolution as Knowledge Miner for Immune Cell Trafficking from Toxicogenomics Databases. Toxicol Sci 2023; 197:kfad117. [PMID: 37941435 PMCID: PMC10823770 DOI: 10.1093/toxsci/kfad117] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2023] Open
Abstract
Toxicogenomics databases are useful for understanding biological responses in individuals because they include a diverse spectrum of biological responses. Although these databases contain no information regarding immune cells in the liver, which are important in the progression of liver injury, deconvolution that estimates cell-type proportions from bulk transcriptome could extend immune information. However, deconvolution has been mainly applied to humans and mice and less often to rats, which are the main target of toxicogenomics databases. Here, we developed a deconvolution method for rats to retrieve information regarding immune cells from toxicogenomics databases. The rat-specific deconvolution showed high correlations for several types of immune cells between spleen and blood, and between liver treated with toxicants compared with those based on human and mouse data. Additionally, we found 4 clusters of compounds in Open TG-GATEs database based on estimated immune cell trafficking, which are different from those based on transcriptome data itself. The contributions of this work are three-fold. First, we obtained the gene expression profiles of 6 rat immune cells necessary for deconvolution. Second, we clarified the importance of species differences on deconvolution. Third, we retrieved immune cell trafficking from toxicogenomics databases. Accumulated and comparable immune cell profiles of massive data of immune cell trafficking in rats could deepen our understanding of enable us to clarify the relationship between the order and the contribution rate of immune cells, chemokines and cytokines, and pathologies. Ultimately, these findings will lead to the evaluation of organ responses in Adverse Outcome Pathway.
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Affiliation(s)
- Katsuhisa Morita
- Department of Pharmaceutical Sciences, The University of Tokyo, Bunkyo, Tokyo, Japan
| | - Tadahaya Mizuno
- Department of Pharmaceutical Sciences, The University of Tokyo, Bunkyo, Tokyo, Japan
| | - Iori Azuma
- Department of Pharmaceutical Sciences, The University of Tokyo, Bunkyo, Tokyo, Japan
| | - Yutaka Suzuki
- Graduate School of Frontier Sciences, The University of Tokyo, Chiba, Japan
| | - Hiroyuki Kusuhara
- Department of Pharmaceutical Sciences, The University of Tokyo, Bunkyo, Tokyo, Japan
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24
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Chen X, Roberts R, Liu Z, Tong W. A generative adversarial network model alternative to animal studies for clinical pathology assessment. Nat Commun 2023; 14:7141. [PMID: 37932302 PMCID: PMC10628291 DOI: 10.1038/s41467-023-42933-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2023] [Accepted: 10/25/2023] [Indexed: 11/08/2023] Open
Abstract
Animal studies are unavoidable in evaluating chemical and drug safety. Generative Adversarial Networks (GANs) can generate synthetic animal data by learning from the legacy animal study results, thus may serve as an alternative approach to assess untested chemicals. AnimalGAN, a GAN method to simulate 38 rat clinical pathology measures, was developed with significant robustness even for the drugs that vary significantly from these used during training, both in terms of chemical structure, drug class, and the year of FDA approval. AnimalGAN showed comparable results in hepatotoxicity assessment as using the real animal data and outperformed 12 conventional quantitative structure-activity relationship approaches. Using AnimalGAN, a virtual experiment of 100,000 rats ranked hepatotoxicity of three structurally similar drugs in a similar trend that has been observed in human population. AnimalGAN represented a significant step with artificial intelligence towards the global effort in replacement, reduction, and refinement (3Rs) of animal use.
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Affiliation(s)
- Xi Chen
- National Center for Toxicological Research, Food and Drug Administration, Jefferson, AR, 72079, USA
| | - Ruth Roberts
- ApconiX Ltd, Alderley Park, Alderley Edge, SK10 4TG, UK
- University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK
| | - Zhichao Liu
- National Center for Toxicological Research, Food and Drug Administration, Jefferson, AR, 72079, USA.
- Currently working at Integrative Toxicology, Nonclinical Drug Safety, Boehringer Ingelheim Pharmaceuticals, Inc., Ridgefield, CT, 06877, USA.
| | - Weida Tong
- National Center for Toxicological Research, Food and Drug Administration, Jefferson, AR, 72079, USA.
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25
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Jiang J, van Ertvelde J, Ertaylan G, Peeters R, Jennen D, de Kok TM, Vinken M. Unraveling the mechanisms underlying drug-induced cholestatic liver injury: identifying key genes using machine learning techniques on human in vitro data sets. Arch Toxicol 2023; 97:2969-2981. [PMID: 37603094 PMCID: PMC10504391 DOI: 10.1007/s00204-023-03583-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2023] [Accepted: 08/10/2023] [Indexed: 08/22/2023]
Abstract
Drug-induced intrahepatic cholestasis (DIC) is a main type of hepatic toxicity that is challenging to predict in early drug development stages. Preclinical animal studies often fail to detect DIC in humans. In vitro toxicogenomics assays using human liver cells have become a practical approach to predict human-relevant DIC. The present study was set up to identify transcriptomic signatures of DIC by applying machine learning algorithms to the Open TG-GATEs database. A total of nine DIC compounds and nine non-DIC compounds were selected, and supervised classification algorithms were applied to develop prediction models using differentially expressed features. Feature selection techniques identified 13 genes that achieved optimal prediction performance using logistic regression combined with a sequential backward selection method. The internal validation of the best-performing model showed accuracy of 0.958, sensitivity of 0.941, specificity of 0.978, and F1-score of 0.956. Applying the model to an external validation set resulted in an average prediction accuracy of 0.71. The identified genes were mechanistically linked to the adverse outcome pathway network of DIC, providing insights into cellular and molecular processes during response to chemical toxicity. Our findings provide valuable insights into toxicological responses and enhance the predictive accuracy of DIC prediction, thereby advancing the application of transcriptome profiling in designing new approach methodologies for hazard identification.
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Affiliation(s)
- Jian Jiang
- Entity of In Vitro Toxicology and Dermato‑Cosmetology, Department of Pharmaceutical and Pharmacological Sciences, Vrije Universiteit Brussel, Laarbeeklaan 103, 1090, Brussels, Belgium.
| | - Jonas van Ertvelde
- Entity of In Vitro Toxicology and Dermato‑Cosmetology, Department of Pharmaceutical and Pharmacological Sciences, Vrije Universiteit Brussel, Laarbeeklaan 103, 1090, Brussels, Belgium
| | - Gökhan Ertaylan
- Vlaamse Instelling voor Technologisch Onderzoek (VITO) NV, Health, Boeretang 200, 2400, Mol, Belgium
| | - Ralf Peeters
- Maastricht Centre for Systems Biology (MaCSBio), Maastricht University, Maastricht, The Netherlands
- Department of Advanced Computing Sciences, Maastricht University, Maastricht, The Netherlands
| | - Danyel Jennen
- Department of Toxicogenomics, GROW School for Oncology and Reproduction, Maastricht University, Maastricht, The Netherlands
| | - Theo M de Kok
- Maastricht Centre for Systems Biology (MaCSBio), Maastricht University, Maastricht, The Netherlands
- Department of Toxicogenomics, GROW School for Oncology and Reproduction, Maastricht University, Maastricht, The Netherlands
| | - Mathieu Vinken
- Entity of In Vitro Toxicology and Dermato‑Cosmetology, Department of Pharmaceutical and Pharmacological Sciences, Vrije Universiteit Brussel, Laarbeeklaan 103, 1090, Brussels, Belgium.
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26
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Martens M, Stierum R, Schymanski EL, Evelo CT, Aalizadeh R, Aladjov H, Arturi K, Audouze K, Babica P, Berka K, Bessems J, Blaha L, Bolton EE, Cases M, Damalas DΕ, Dave K, Dilger M, Exner T, Geerke DP, Grafström R, Gray A, Hancock JM, Hollert H, Jeliazkova N, Jennen D, Jourdan F, Kahlem P, Klanova J, Kleinjans J, Kondic T, Kone B, Lynch I, Maran U, Martinez Cuesta S, Ménager H, Neumann S, Nymark P, Oberacher H, Ramirez N, Remy S, Rocca-Serra P, Salek RM, Sallach B, Sansone SA, Sanz F, Sarimveis H, Sarntivijai S, Schulze T, Slobodnik J, Spjuth O, Tedds J, Thomaidis N, Weber RJ, van Westen GJ, Wheelock CE, Williams AJ, Witters H, Zdrazil B, Županič A, Willighagen EL. ELIXIR and Toxicology: a community in development. F1000Res 2023; 10:ELIXIR-1129. [PMID: 37842337 PMCID: PMC10568213 DOI: 10.12688/f1000research.74502.2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 09/28/2023] [Indexed: 10/17/2023] Open
Abstract
Toxicology has been an active research field for many decades, with academic, industrial and government involvement. Modern omics and computational approaches are changing the field, from merely disease-specific observational models into target-specific predictive models. Traditionally, toxicology has strong links with other fields such as biology, chemistry, pharmacology and medicine. With the rise of synthetic and new engineered materials, alongside ongoing prioritisation needs in chemical risk assessment for existing chemicals, early predictive evaluations are becoming of utmost importance to both scientific and regulatory purposes. ELIXIR is an intergovernmental organisation that brings together life science resources from across Europe. To coordinate the linkage of various life science efforts around modern predictive toxicology, the establishment of a new ELIXIR Community is seen as instrumental. In the past few years, joint efforts, building on incidental overlap, have been piloted in the context of ELIXIR. For example, the EU-ToxRisk, diXa, HeCaToS, transQST, and the nanotoxicology community have worked with the ELIXIR TeSS, Bioschemas, and Compute Platforms and activities. In 2018, a core group of interested parties wrote a proposal, outlining a sketch of what this new ELIXIR Toxicology Community would look like. A recent workshop (held September 30th to October 1st, 2020) extended this into an ELIXIR Toxicology roadmap and a shortlist of limited investment-high gain collaborations to give body to this new community. This Whitepaper outlines the results of these efforts and defines our vision of the ELIXIR Toxicology Community and how it complements other ELIXIR activities.
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Affiliation(s)
- Marvin Martens
- Department of Bioinformatics - BiGCaT, Maastricht University, Maastricht, 6229 ER, The Netherlands
| | - Rob Stierum
- Risk Analysis for Products In Development (RAPID), Netherlands Organisation for applied scientific research TNO, Utrecht, 3584 CB, The Netherlands
| | - Emma L. Schymanski
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Belvaux, 4367, Luxembourg
| | - Chris T. Evelo
- Department of Bioinformatics - BiGCaT, Maastricht University, Maastricht, 6229 ER, The Netherlands
- Maastricht Centre for Systems Biology (MaCSBio), Maastricht University, Maastricht, 6229 EN, The Netherlands
| | - Reza Aalizadeh
- Laboratory of Analytical Chemistry, Department of Chemistry, National and Kapodistrian University of Athens, Athens, 15771, Greece
| | - Hristo Aladjov
- Institute of Biophysics and Biomedical Engineering, Bulgarian Academy of Sciences, Sofia, 1113, Bulgaria
| | - Kasia Arturi
- Department Environmental Chemistry, Swiss Federal Institute of Aquatic Science and Technology, Dübendorf, 8600, Switzerland
| | | | - Pavel Babica
- RECETOX, Faculty of Science, Masaryk University, Brno, Czech Republic
| | - Karel Berka
- Department of Physical Chemistry, Palacky University Olomouc, Olomouc, 77146, Czech Republic
| | | | - Ludek Blaha
- RECETOX, Faculty of Science, Masaryk University, Brno, Czech Republic
| | - Evan E. Bolton
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, USA
| | | | - Dimitrios Ε. Damalas
- Laboratory of Analytical Chemistry, Department of Chemistry, National and Kapodistrian University of Athens, Athens, 15771, Greece
| | - Kirtan Dave
- School of Science, GSFC University, Gujarat, 391750, India
| | - Marco Dilger
- Forschungs- und Beratungsinstitut Gefahrstoffe (FoBiG) GmbH, Freiburg im Breisgau, 79106, Germany
| | | | - Daan P. Geerke
- AIMMS Division of Molecular Toxicology, Vrije Universiteit, Amsterdam, 1081 HZ, The Netherlands
| | - Roland Grafström
- Department of Toxicology, Misvik Biology, Turku, 20520, Finland
- Institute of Environmental Medicine, Karolinska Institute, Stockholm, 17177, Sweden
| | - Alasdair Gray
- Department of Computer Science, Heriot-Watt University, Edinburgh, UK
| | | | - Henner Hollert
- Department Evolutionary Ecology & Environmental Toxicology (E3T), Goethe-University, Frankfurt, D-60438, Germany
| | | | - Danyel Jennen
- Department of Toxicogenomics, Maastricht University, Maastricht, 6200 MD, The Netherlands
| | - Fabien Jourdan
- MetaboHUB, French metabolomics infrastructure in Metabolomics and Fluxomics, Toulouse, France
- Toxalim (Research Centre in Food Toxicology), Université de Toulouse, Toulouse, France
| | - Pascal Kahlem
- Scientific Network Management SL, Barcelona, 08015, Spain
| | - Jana Klanova
- RECETOX, Faculty of Science, Masaryk University, Brno, Czech Republic
| | - Jos Kleinjans
- Department of Toxicogenomics, Maastricht University, Maastricht, 6200 MD, The Netherlands
| | - Todor Kondic
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Belvaux, 4367, Luxembourg
| | - Boï Kone
- Faculty of Pharmacy, Malaria Research and Training Center, Bamako, BP:1805, Mali
| | - Iseult Lynch
- School of Geography, Earth and Environmental Sciences, University of Birmingham, UK, Birmingham, B15 2TT, UK
| | - Uko Maran
- Institute of Chemistry, University of Tartu, Tartu, 50411, Estonia
| | | | - Hervé Ménager
- Institut Français de Bioinformatique, Evry, F-91000, France
- Bioinformatics and Biostatistics Hub, Institut Pasteur, Paris, F-75015, France
| | - Steffen Neumann
- Research group Bioinformatics and Scientific Data, Leibniz Institute of Plant Biochemistry, Halle, 06120, Germany
| | - Penny Nymark
- Institute of Environmental Medicine, Karolinska Institute, Stockholm, 17177, Sweden
| | - Herbert Oberacher
- Institute of Legal Medicine and Core Facility Metabolomics, Medical University of Innsbruck, Innsbruck, A-6020, Austria
| | - Noelia Ramirez
- Institut d'Investigacio Sanitaria Pere Virgili-Universitat Rovira i Virgili, Tarragona, 43007, Spain
| | | | - Philippe Rocca-Serra
- Data Readiness Group, Department of Engineering Science, University of Oxford, Oxford, UK
| | - Reza M. Salek
- International Agency for Research on Cancer, World Health Organisation, Lyon, 69372, France
| | - Brett Sallach
- Department of Environment and Geography, University of York, UK, York, YO10 5NG, UK
| | | | - Ferran Sanz
- Research Programme on Biomedical Informatics (GRIB), Hospital del Mar Medical Research Institute (IMIM), Department of Experimental and Health Sciences, Pompeu Fabra University, Barcelona, 08003, Spain
| | | | | | - Tobias Schulze
- Helmholtz Centre for Environmental Research - UFZ, Leipzig, 04318, Germany
| | | | - Ola Spjuth
- Department of Pharmaceutical Biosciences and Science for Life Laboratory, Uppsala University, Uppsala, SE-75124, Sweden
| | - Jonathan Tedds
- ELIXIR Hub, Wellcome Genome Campus, Cambridge, CB10 1SD, UK
| | - Nikolaos Thomaidis
- Laboratory of Analytical Chemistry, Department of Chemistry, National and Kapodistrian University of Athens, Athens, 15771, Greece
| | - Ralf J.M. Weber
- School of Biosciences, University of Birmingham, UK, Birmingham, B15 2TT, UK
| | - Gerard J.P. van Westen
- Division of Drug Discovery and Safety, Leiden Academic Center for Drug Research, Leiden, 2333 CC, The Netherlands
| | - Craig E. Wheelock
- Department of Respiratory Medicine and Allergy, Karolinska University Hospital, Stockholm SE-141-86, Sweden
- Department of Medical Biochemistry and Biophysics, Karolinska Institute, Stockholm, 17177, Sweden
| | - Antony J. Williams
- Center for Computational Toxicology and Exposure, United States Environmental Protection Agency, Research Triangle Park, NC 27711, USA
| | | | - Barbara Zdrazil
- Department of Pharmaceutical Sciences, University of Vienna, Vienna, 1090, Austria
| | - Anže Županič
- Department Biotechnology and Systems Biology, National Institute of Biology, Ljubljana, 1000, Slovenia
| | - Egon L. Willighagen
- Department of Bioinformatics - BiGCaT, Maastricht University, Maastricht, 6229 ER, The Netherlands
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27
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Pandiri AR, Auerbach SS, Stevens JL, Blomme EAG. Toxicogenomics Approaches to Address Toxicity and Carcinogenicity in the Liver. Toxicol Pathol 2023; 51:470-481. [PMID: 38288963 PMCID: PMC11014763 DOI: 10.1177/01926233241227942] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/13/2024]
Abstract
Toxicogenomic technologies query the genome, transcriptome, proteome, and the epigenome in a variety of toxicological conditions. Due to practical considerations related to the dynamic range of the assays, sensitivity, cost, and technological limitations, transcriptomic approaches are predominantly used in toxicogenomics. Toxicogenomics is being used to understand the mechanisms of toxicity and carcinogenicity, evaluate the translational relevance of toxicological responses from in vivo and in vitro models, and identify predictive biomarkers of disease and exposure. In this session, a brief overview of various transcriptomic technologies and practical considerations related to experimental design was provided. The advantages of gene network analyses to define mechanisms were also discussed. An assessment of the utility of toxicogenomic technologies in the environmental and pharmaceutical space showed that these technologies are being increasingly used to gain mechanistic insights and determining the translational relevance of adverse findings. Within the environmental toxicology area, there is a broader regulatory consideration of benchmark doses derived from toxicogenomics data. In contrast, these approaches are mainly used for internal decision-making in pharmaceutical development. Finally, the development and application of toxicogenomic signatures for prediction of apical endpoints of regulatory concern continues to be area of intense research.
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Affiliation(s)
- Arun R Pandiri
- National Institute of Environmental Health Sciences, Durham, North Carolina, USA
| | - Scott S Auerbach
- National Institute of Environmental Health Sciences, Durham, North Carolina, USA
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28
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Zou Z, Yoshimura Y, Yamanishi Y, Oki S. Elucidating disease-associated mechanisms triggered by pollutants via the epigenetic landscape using large-scale ChIP-Seq data. Epigenetics Chromatin 2023; 16:34. [PMID: 37743474 PMCID: PMC10518938 DOI: 10.1186/s13072-023-00510-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2023] [Accepted: 09/19/2023] [Indexed: 09/26/2023] Open
Abstract
BACKGROUND Despite well-documented effects on human health, the action modes of environmental pollutants are incompletely understood. Although transcriptome-based approaches are widely used to predict associations between chemicals and disorders, the molecular cues regulating pollutant-derived gene expression changes remain unclear. Therefore, we developed a data-mining approach, termed "DAR-ChIPEA," to identify transcription factors (TFs) playing pivotal roles in the action modes of pollutants. METHODS Large-scale public ChIP-Seq data (human, n = 15,155; mouse, n = 13,156) were used to predict TFs that are enriched in the pollutant-induced differentially accessible genomic regions (DARs) obtained from epigenome analyses (ATAC-Seq). The resultant pollutant-TF matrices were then cross-referenced to a repository of TF-disorder associations to account for pollutant modes of action. We subsequently evaluated the performance of the proposed method using a chemical perturbation data set to compare the outputs of the DAR-ChIPEA and our previously developed differentially expressed gene (DEG)-ChIPEA methods using pollutant-induced DEGs as input. We then adopted the proposed method to predict disease-associated mechanisms triggered by pollutants. RESULTS The proposed approach outperformed other methods using the area under the receiver operating characteristic curve score. The mean score of the proposed DAR-ChIPEA was significantly higher than that of our previously described DEG-ChIPEA (0.7287 vs. 0.7060; Q = 5.278 × 10-42; two-tailed Wilcoxon rank-sum test). The proposed approach further predicted TF-driven modes of action upon pollutant exposure, indicating that (1) TFs regulating Th1/2 cell homeostasis are integral in the pathophysiology of tributyltin-induced allergic disorders; (2) fine particulates (PM2.5) inhibit the binding of C/EBPs, Rela, and Spi1 to the genome, thereby perturbing normal blood cell differentiation and leading to immune dysfunction; and (3) lead induces fatty liver by disrupting the normal regulation of lipid metabolism by altering hepatic circadian rhythms. CONCLUSIONS Highlighting genome-wide chromatin change upon pollutant exposure to elucidate the epigenetic landscape of pollutant responses outperformed our previously described method that focuses on gene-adjacent domains only. Our approach has the potential to reveal pivotal TFs that mediate deleterious effects of pollutants, thereby facilitating the development of strategies to mitigate damage from environmental pollution.
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Affiliation(s)
- Zhaonan Zou
- Department of Drug Discovery Medicine, Kyoto University Graduate School of Medicine, 53 Shogoin Kawahara-Cho, Sakyo-Ku, Kyoto, 606-8507, Japan
| | - Yuka Yoshimura
- Department of Drug Discovery Medicine, Kyoto University Graduate School of Medicine, 53 Shogoin Kawahara-Cho, Sakyo-Ku, Kyoto, 606-8507, Japan
| | - Yoshihiro Yamanishi
- Department of Complex Systems Science, Graduate School of Informatics, Nagoya University, Furo-Cho, Chikusa-Ku, Nagoya, 464-8602, Japan
| | - Shinya Oki
- Department of Drug Discovery Medicine, Kyoto University Graduate School of Medicine, 53 Shogoin Kawahara-Cho, Sakyo-Ku, Kyoto, 606-8507, Japan.
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29
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Béquignon OM, Gómez-Tamayo JC, Lenselink EB, Wink S, Hiemstra S, Lam CC, Gadaleta D, Roncaglioni A, Norinder U, Water BVD, Pastor M, van Westen GJP. Collaborative SAR Modeling and Prospective In Vitro Validation of Oxidative Stress Activation in Human HepG2 Cells. J Chem Inf Model 2023; 63:5433-5445. [PMID: 37616385 PMCID: PMC10498489 DOI: 10.1021/acs.jcim.3c00220] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2023] [Indexed: 08/26/2023]
Abstract
Oxidative stress is the consequence of an abnormal increase of reactive oxygen species (ROS). ROS are generated mainly during the metabolism in both normal and pathological conditions as well as from exposure to xenobiotics. Xenobiotics can, on the one hand, disrupt molecular machinery involved in redox processes and, on the other hand, reduce the effectiveness of the antioxidant activity. Such dysregulation may lead to oxidative damage when combined with oxidative stress overpassing the cell capacity to detoxify ROS. In this work, a green fluorescent protein (GFP)-tagged nuclear factor erythroid 2-related factor 2 (NRF2)-regulated sulfiredoxin reporter (Srxn1-GFP) was used to measure the antioxidant response of HepG2 cells to a large series of drug and drug-like compounds (2230 compounds). These compounds were then classified as positive or negative depending on cellular response and distributed among different modeling groups to establish structure-activity relationship (SAR) models. A selection of models was used to prospectively predict oxidative stress induced by a new set of compounds subsequently experimentally tested to validate the model predictions. Altogether, this exercise exemplifies the different challenges of developing SAR models of a phenotypic cellular readout, model combination, chemical space selection, and results interpretation.
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Affiliation(s)
- Olivier
J. M. Béquignon
- Leiden
Academic Centre for Drug Research, Leiden
University, Wassenaarseweg 76, 2333 AL Leiden, The Netherlands
| | - Jose C. Gómez-Tamayo
- Research
Programme on Biomedical Informatics (GRIB), Department of Medicine
and Life Sciences, Hospital del Mar Medical Research Institute, Universitat Pompeu Fabra, Carrer del Dr. Aiguader 88, 08002 Barcelona, Spain
| | - Eelke B. Lenselink
- Leiden
Academic Centre for Drug Research, Leiden
University, Wassenaarseweg 76, 2333 AL Leiden, The Netherlands
| | - Steven Wink
- Leiden
Academic Centre for Drug Research, Leiden
University, Wassenaarseweg 76, 2333 AL Leiden, The Netherlands
| | - Steven Hiemstra
- Leiden
Academic Centre for Drug Research, Leiden
University, Wassenaarseweg 76, 2333 AL Leiden, The Netherlands
| | - Chi Chung Lam
- Leiden
Academic Centre for Drug Research, Leiden
University, Wassenaarseweg 76, 2333 AL Leiden, The Netherlands
| | - Domenico Gadaleta
- Laboratory
of Environmental Chemistry and Toxicology, Department of Environmental
Health Sciences, IRCCS—Istituto di
Ricerche Farmacologiche Mario Negri, Via la Masa 19, 20156 Milano, Italy
| | - Alessandra Roncaglioni
- Laboratory
of Environmental Chemistry and Toxicology, Department of Environmental
Health Sciences, IRCCS—Istituto di
Ricerche Farmacologiche Mario Negri, Via la Masa 19, 20156 Milano, Italy
| | - Ulf Norinder
- MTM
Research Centre, School of Science and Technology, Örebro University, SE-70182 Örebro, Sweden
| | - Bob van de Water
- Leiden
Academic Centre for Drug Research, Leiden
University, Wassenaarseweg 76, 2333 AL Leiden, The Netherlands
| | - Manuel Pastor
- Research
Programme on Biomedical Informatics (GRIB), Department of Medicine
and Life Sciences, Hospital del Mar Medical Research Institute, Universitat Pompeu Fabra, Carrer del Dr. Aiguader 88, 08002 Barcelona, Spain
| | - Gerard J. P. van Westen
- Leiden
Academic Centre for Drug Research, Leiden
University, Wassenaarseweg 76, 2333 AL Leiden, The Netherlands
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30
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Moore J, Basurto-Lozada D, Besson S, Bogovic J, Bragantini J, Brown EM, Burel JM, Casas Moreno X, de Medeiros G, Diel EE, Gault D, Ghosh SS, Gold I, Halchenko YO, Hartley M, Horsfall D, Keller MS, Kittisopikul M, Kovacs G, Küpcü Yoldaş A, Kyoda K, le Tournoulx de la Villegeorges A, Li T, Liberali P, Lindner D, Linkert M, Lüthi J, Maitin-Shepard J, Manz T, Marconato L, McCormick M, Lange M, Mohamed K, Moore W, Norlin N, Ouyang W, Özdemir B, Palla G, Pape C, Pelkmans L, Pietzsch T, Preibisch S, Prete M, Rzepka N, Samee S, Schaub N, Sidky H, Solak AC, Stirling DR, Striebel J, Tischer C, Toloudis D, Virshup I, Walczysko P, Watson AM, Weisbart E, Wong F, Yamauchi KA, Bayraktar O, Cimini BA, Gehlenborg N, Haniffa M, Hotaling N, Onami S, Royer LA, Saalfeld S, Stegle O, Theis FJ, Swedlow JR. OME-Zarr: a cloud-optimized bioimaging file format with international community support. Histochem Cell Biol 2023; 160:223-251. [PMID: 37428210 PMCID: PMC10492740 DOI: 10.1007/s00418-023-02209-1] [Citation(s) in RCA: 22] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/16/2023] [Indexed: 07/11/2023]
Abstract
A growing community is constructing a next-generation file format (NGFF) for bioimaging to overcome problems of scalability and heterogeneity. Organized by the Open Microscopy Environment (OME), individuals and institutes across diverse modalities facing these problems have designed a format specification process (OME-NGFF) to address these needs. This paper brings together a wide range of those community members to describe the cloud-optimized format itself-OME-Zarr-along with tools and data resources available today to increase FAIR access and remove barriers in the scientific process. The current momentum offers an opportunity to unify a key component of the bioimaging domain-the file format that underlies so many personal, institutional, and global data management and analysis tasks.
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Affiliation(s)
- Josh Moore
- German BioImaging-Gesellschaft für Mikroskopie und Bildanalyse e.V., Constance, Germany.
| | | | - Sébastien Besson
- Divisions of Molecular Cell and Developmental Biology, and Computational Biology, University of Dundee, Dundee, Scotland, UK
| | - John Bogovic
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | | | - Eva M Brown
- Allen Institute for Cell Science, Seattle, WA, USA
| | - Jean-Marie Burel
- Divisions of Molecular Cell and Developmental Biology, and Computational Biology, University of Dundee, Dundee, Scotland, UK
| | - Xavier Casas Moreno
- Science for Life Laboratory, KTH Royal Institute of Technology, Stockholm, Sweden
| | | | | | - David Gault
- Divisions of Molecular Cell and Developmental Biology, and Computational Biology, University of Dundee, Dundee, Scotland, UK
| | | | - Ilan Gold
- Harvard Medical School, Boston, MA, USA
| | | | - Matthew Hartley
- European Molecular Biology Laboratory, European Bioinformatics Institute, EMBL-EBI, Cambridge, UK
| | - Dave Horsfall
- Biosciences Institute, Newcastle University, Newcastle upon Tyne, UK
| | | | - Mark Kittisopikul
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Gabor Kovacs
- Allen Institute for Neural Dynamics, Seattle, WA, USA
| | - Aybüke Küpcü Yoldaş
- European Molecular Biology Laboratory, European Bioinformatics Institute, EMBL-EBI, Cambridge, UK
| | - Koji Kyoda
- RIKEN Center for Biosystems Dynamics Research, Kobe, Japan
| | | | - Tong Li
- Wellcome Sanger Institute, Hinxton, UK
| | - Prisca Liberali
- Friedrich Miescher Institute for Biomedical Imaging, Basel, Switzerland
| | - Dominik Lindner
- Divisions of Molecular Cell and Developmental Biology, and Computational Biology, University of Dundee, Dundee, Scotland, UK
| | | | - Joel Lüthi
- Friedrich Miescher Institute for Biomedical Imaging, Basel, Switzerland
| | | | | | - Luca Marconato
- Genome Biology Unit, European Molecular Biology Laboratory (EMBL), Heidelberg, Germany
| | | | | | - Khaled Mohamed
- Divisions of Molecular Cell and Developmental Biology, and Computational Biology, University of Dundee, Dundee, Scotland, UK
| | - William Moore
- Divisions of Molecular Cell and Developmental Biology, and Computational Biology, University of Dundee, Dundee, Scotland, UK
| | - Nils Norlin
- Department of Experimental Medical Science & Lund Bioimaging Centre, Lund University, Lund, Sweden
| | - Wei Ouyang
- Science for Life Laboratory, KTH Royal Institute of Technology, Stockholm, Sweden
| | | | - Giovanni Palla
- Institute of Computational Biology, Helmholtz Zentrum München, Neuherberg, Germany
| | | | | | - Tobias Pietzsch
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Stephan Preibisch
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | | | | | | | - Nicholas Schaub
- Information Technology Branch, National Center for Advancing Translational Science, National Institutes of Health, Bethesda, USA
| | | | | | | | | | | | | | - Isaac Virshup
- Institute of Computational Biology, Helmholtz Zentrum München, Neuherberg, Germany
| | - Petr Walczysko
- Divisions of Molecular Cell and Developmental Biology, and Computational Biology, University of Dundee, Dundee, Scotland, UK
| | | | - Erin Weisbart
- Imaging Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Frances Wong
- Divisions of Molecular Cell and Developmental Biology, and Computational Biology, University of Dundee, Dundee, Scotland, UK
| | - Kevin A Yamauchi
- Department of Biosystems Science and Engineering, ETH Zürich, Zürich, Switzerland
| | | | - Beth A Cimini
- Imaging Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | | | | | - Nathan Hotaling
- Information Technology Branch, National Center for Advancing Translational Science, National Institutes of Health, Bethesda, USA
| | - Shuichi Onami
- RIKEN Center for Biosystems Dynamics Research, Kobe, Japan
| | | | - Stephan Saalfeld
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Oliver Stegle
- Genome Biology Unit, European Molecular Biology Laboratory (EMBL), Heidelberg, Germany
| | - Fabian J Theis
- Institute of Computational Biology, Helmholtz Zentrum München, Neuherberg, Germany
| | - Jason R Swedlow
- Divisions of Molecular Cell and Developmental Biology, and Computational Biology, University of Dundee, Dundee, Scotland, UK
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31
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Moein M, Heinonen M, Mesens N, Chamanza R, Amuzie C, Will Y, Ceulemans H, Kaski S, Herman D. Chemistry-Based Modeling on Phenotype-Based Drug-Induced Liver Injury Annotation: From Public to Proprietary Data. Chem Res Toxicol 2023; 36:1238-1247. [PMID: 37556769 PMCID: PMC10445287 DOI: 10.1021/acs.chemrestox.2c00378] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Indexed: 08/11/2023]
Abstract
Drug-induced liver injury (DILI) is an important safety concern and a major reason to remove a drug from the market. Advancements in recent machine learning methods have led to a wide range of in silico models for DILI predictive methods based on molecule chemical structures (fingerprints). Existing publicly available DILI data sets used for model building are based on the interpretation of drug labels or patient case reports, resulting in a typical binary clinical DILI annotation. We developed a novel phenotype-based annotation to process hepatotoxicity information extracted from repeated dose in vivo preclinical toxicology studies using INHAND annotation to provide a more informative and reliable data set for machine learning algorithms. This work resulted in a data set of 430 unique compounds covering diverse liver pathology findings which were utilized to develop multiple DILI prediction models trained on the publicly available data (TG-GATEs) using the compound's fingerprint. We demonstrate that the TG-GATEs compounds DILI labels can be predicted well and how the differences between TG-GATEs and the external test compounds (Johnson & Johnson) impact the model generalization performance.
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Affiliation(s)
- Mohammad Moein
- Department
of Computer Science, Aalto University, Konemiehentie 2, 02150 Espoo, Finland
| | - Markus Heinonen
- Department
of Computer Science, Aalto University, Konemiehentie 2, 02150 Espoo, Finland
| | - Natalie Mesens
- Predictive,
Investigative and Translational Toxicology, PSTS, Janssen Research
& Development, Pharmaceutical Companies
of Johnson & Johnson, 2340 Beerse, Belgium
| | - Ronnie Chamanza
- Pathology,
PSTS, Janssen Research & Development, Pharmaceutical Companies of Johnson & Johnson, 2340 Beerse, Belgium
| | - Chidozie Amuzie
- Johnson
& Johnson Innovation-JLABS, 661 University Avenue, CA014 ON Toronto, Canada
| | - Yvonne Will
- Predictive,
Investigative and Translational Toxicology, PSTS, Janssen Research
& Development, Pharmaceutical Companies
of Johnson & Johnson, 3210 Merryfield Row, San Diego, California 92121, United States
| | - Hugo Ceulemans
- In-Silico
Discovery, Janssen Pharmaceutica, Janssen Research & Development, Pharmaceutical Companies of Johnson & Johnson, 2340 Beerse, Belgium
| | - Samuel Kaski
- Department
of Computer Science, Aalto University, Konemiehentie 2, 02150 Espoo, Finland
| | - Dorota Herman
- In-Silico
Discovery, Janssen Pharmaceutica, Janssen Research & Development, Pharmaceutical Companies of Johnson & Johnson, 2340 Beerse, Belgium
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32
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Bussola N, Xu J, Wu L, Gorini L, Zhang Y, Furlanello C, Tong W. A Weakly Supervised Deep Learning Framework for Whole Slide Classification to Facilitate Digital Pathology in Animal Study. Chem Res Toxicol 2023; 36:1321-1331. [PMID: 37540590 PMCID: PMC10445282 DOI: 10.1021/acs.chemrestox.3c00058] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2023] [Indexed: 08/06/2023]
Abstract
The pathology of animal studies is crucial for toxicity evaluations and regulatory assessments, but the manual examination of slides by pathologists remains time-consuming and requires extensive training. One inherent challenge in this process is the interobserver variability, which can compromise the consistency and accuracy of a study. Artificial intelligence (AI) has demonstrated its ability to automate similar examinations in clinical applications with enhanced efficiency, consistency, and accuracy. However, training AI models typically relies on costly pixel-level annotation of injured regions and is often not available for animal pathology. To address this, we developed the PathologAI system, a "weakly" supervised approach for WSI classification in rat images without explicit lesion annotation at the pixel level. Using rat liver imaging data from the Open TG-GATEs system, PathologAI was applied to predict necrosis of n = 816 WSIs (377 controls). TG-GATEs studied 170 compounds at three dose levels (low, middle, and high) for four time points (3, 7, 14, and 28 days). PathologAI first preprocessed WSIs at the tile level to generate a high-level representation with a Generative Adversarial Network architecture. The prediction of liver necrosis relied on an ensemble model of 5 CNN classifiers trained on 335 WSIs. The ensemble model achieved notable classification accuracy on the holdout test set: 87% among 87 control slides free of findings, 83% among 120 controls with spontaneous necrosis, 67% among 147 treated animals with spontaneous minimal or slight necrosis, and 59% among 127 treated animals with minimal or slight necrosis caused by the treatment. Importantly, PathologAI was able to discriminate WSIs with spontaneous necrosis from those with treatment related necrosis and discriminated mild lesion level findings (slight vs minimal) and between treatment dose levels. PathologAI could provide an inexpensive and rapid screening tool to assist the digital pathology analysis in preclinical applications and general toxicological studies.
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Affiliation(s)
- Nicole Bussola
- Center
for Integrative Biology, University of Trento, Trento 38123, Italy
| | - Joshua Xu
- Division
of Bioinformatics and Biostatistics National
Center for Toxicological Research, Food
and Drug Administration, Jefferson, Arkansas 72079, United States
| | - Leihong Wu
- Division
of Bioinformatics and Biostatistics National
Center for Toxicological Research, Food
and Drug Administration, Jefferson, Arkansas 72079, United States
| | | | - Yifan Zhang
- Division
of Bioinformatics and Biostatistics National
Center for Toxicological Research, Food
and Drug Administration, Jefferson, Arkansas 72079, United States
| | | | - Weida Tong
- Division
of Bioinformatics and Biostatistics National
Center for Toxicological Research, Food
and Drug Administration, Jefferson, Arkansas 72079, United States
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33
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Ji C, Shao K. The Effect of Historical Data-Based Informative Prior on Benchmark Dose Estimation of Toxicogenomics. Chem Res Toxicol 2023; 36:1345-1354. [PMID: 37494567 PMCID: PMC10462436 DOI: 10.1021/acs.chemrestox.3c00088] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/28/2023]
Abstract
High-throughput toxicogenomics as an advanced toolbox of Tox21 plays an increasingly important role in facilitating the toxicity assessment of environmental chemicals. However, toxicogenomic dose-response analyses are typically challenged by limited data, which may result in significant uncertainties in parameter and benchmark dose (BMD) estimation. Integrating historical data via prior distribution using a Bayesian method is a useful but not-well-studied strategy. The objective of this study is to evaluate the effectiveness of informative priors in genomic dose-response modeling and BMD estimation. Specifically, we aim to identify plausible informative priors and evaluate their effects on BMD estimates at both gene and pathway levels. A general informative prior and eight time-specific (from 3 h to 29 d) informative priors for seven commonly used continuous dose-response models were derived. Results suggest that the derived informative priors are sensitive to the specific data sets used for elicitation. Real data-based simulations indicate that BMD estimation with the time-specific informative priors can achieve increased or equivalent accuracy, significantly decreased uncertainty, and a slightly enhanced correlation with the points of departure estimated from apical end points than the counterparts with noninformative priors. Overall, our study systematically examined the effects of historical data-based informative priors on BMD estimates, highlighting the benefits of plausible information priors in advancing the practice of toxicogenomics.
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Affiliation(s)
- Chao Ji
- Department of Environmental and Occupational Health, School of Public Health - Bloomington, Indiana University, Bloomington, Indiana 47405, United States
| | - Kan Shao
- Department of Environmental and Occupational Health, School of Public Health - Bloomington, Indiana University, Bloomington, Indiana 47405, United States
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34
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Sinha K, Ghosh N, Sil PC. A Review on the Recent Applications of Deep Learning in Predictive Drug Toxicological Studies. Chem Res Toxicol 2023; 36:1174-1205. [PMID: 37561655 DOI: 10.1021/acs.chemrestox.2c00375] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/12/2023]
Abstract
Drug toxicity prediction is an important step in ensuring patient safety during drug design studies. While traditional preclinical studies have historically relied on animal models to evaluate toxicity, recent advances in deep-learning approaches have shown great promise in advancing drug safety science and reducing animal use in preclinical studies. However, deep-learning-based approaches also face challenges in handling large biological data sets, model interpretability, and regulatory acceptance. In this review, we provide an overview of recent developments in deep-learning-based approaches for predicting drug toxicity, highlighting their potential advantages over traditional methods and the need to address their limitations. Deep-learning models have demonstrated excellent performance in predicting toxicity outcomes from various data sources such as chemical structures, genomic data, and high-throughput screening assays. The potential of deep learning for automated feature engineering is also discussed. This review emphasizes the need to address ethical concerns related to the use of deep learning in drug toxicity studies, including the reduction of animal use and ensuring regulatory acceptance. Furthermore, emerging applications of deep learning in drug toxicity prediction, such as predicting drug-drug interactions and toxicity in rare subpopulations, are highlighted. The integration of deep-learning-based approaches with traditional methods is discussed as a way to develop more reliable and efficient predictive models for drug safety assessment, paving the way for safer and more effective drug discovery and development. Overall, this review highlights the critical role of deep learning in predictive toxicology and drug safety evaluation, emphasizing the need for continued research and development in this rapidly evolving field. By addressing the limitations of traditional methods, leveraging the potential of deep learning for automated feature engineering, and addressing ethical concerns, deep-learning-based approaches have the potential to revolutionize drug toxicity prediction and improve patient safety in drug discovery and development.
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Affiliation(s)
- Krishnendu Sinha
- Department of Zoology, Jhargram Raj College, Jhargram 721507, West Bengal, India
| | - Nabanita Ghosh
- Department of Zoology, Maulana Azad College, Kolkata 700013, West Bengal, India
| | - Parames C Sil
- Division of Molecular Medicine, Bose Institute, Kolkata 700054, West Bengal, India
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35
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Del Giudice G, Serra A, Saarimäki LA, Kotsis K, Rouse I, Colibaba SA, Jagiello K, Mikolajczyk A, Fratello M, Papadiamantis AG, Sanabria N, Annala ME, Morikka J, Kinaret PAS, Voyiatzis E, Melagraki G, Afantitis A, Tämm K, Puzyn T, Gulumian M, Lobaskin V, Lynch I, Federico A, Greco D. An ancestral molecular response to nanomaterial particulates. NATURE NANOTECHNOLOGY 2023; 18:957-966. [PMID: 37157020 PMCID: PMC10427433 DOI: 10.1038/s41565-023-01393-4] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/11/2022] [Accepted: 03/31/2023] [Indexed: 05/10/2023]
Abstract
The varied transcriptomic response to nanoparticles has hampered the understanding of the mechanism of action. Here, by performing a meta-analysis of a large collection of transcriptomics data from various engineered nanoparticle exposure studies, we identify common patterns of gene regulation that impact the transcriptomic response. Analysis identifies deregulation of immune functions as a prominent response across different exposure studies. Looking at the promoter regions of these genes, a set of binding sites for zinc finger transcription factors C2H2, involved in cell stress responses, protein misfolding and chromatin remodelling and immunomodulation, is identified. The model can be used to explain the outcomes of mechanism of action and is observed across a range of species indicating this is a conserved part of the innate immune system.
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Affiliation(s)
- G Del Giudice
- FHAIVE, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
| | - A Serra
- FHAIVE, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
- Tampere Institute for Advanced Study, Tampere, Finland
| | - L A Saarimäki
- FHAIVE, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
| | - K Kotsis
- School of Physics, University College Dublin, Dublin, Ireland
| | - I Rouse
- School of Physics, University College Dublin, Dublin, Ireland
| | - S A Colibaba
- School of Physics, University College Dublin, Dublin, Ireland
| | - K Jagiello
- Group of Environmental Chemoinformatics, Faculty of Chemistry, University of Gdańsk, Gdańsk, Poland
- QSAR Lab Ltd, Gdańsk, Poland
| | - A Mikolajczyk
- Group of Environmental Chemoinformatics, Faculty of Chemistry, University of Gdańsk, Gdańsk, Poland
- QSAR Lab Ltd, Gdańsk, Poland
| | - M Fratello
- FHAIVE, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
| | - A G Papadiamantis
- School of Geography, Earth and Environmental Sciences, University of Birmingham, Birmingham, UK
- Novamechanics Ltd, Nicosia, Cyprus
| | - N Sanabria
- National Institute for Occupational Health, National Health Laboratory Services, Johannesburg, South Africa
- School of Health Systems and Public Health, University of Pretoria, Pretoria, South Africa
| | - M E Annala
- FHAIVE, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
| | - J Morikka
- FHAIVE, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
| | - P A S Kinaret
- FHAIVE, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
- Institute of Biotechnology, Helsinki Institute of Life Sciences (HiLife), University of Helsinki, Helsinki, Finland
| | | | - G Melagraki
- Division of Physical Sciences and Applications, Hellenic Military Academy, Vari, Greece
| | | | - K Tämm
- Institute of Chemistry, University of Tartu, Tartu, Estonia
| | - T Puzyn
- Group of Environmental Chemoinformatics, Faculty of Chemistry, University of Gdańsk, Gdańsk, Poland
- QSAR Lab Ltd, Gdańsk, Poland
| | - M Gulumian
- National Institute for Occupational Health, National Health Laboratory Services, Johannesburg, South Africa
- Haematology and Molecular Medicine Department, School of Pathology, University of the Witwatersrand, Johannesburg, South Africa
- Water Research Group, Unit for Environmental Sciences and Management, North West University, Potchefstroom, South Africa
| | - V Lobaskin
- School of Physics, University College Dublin, Dublin, Ireland
| | - I Lynch
- School of Geography, Earth and Environmental Sciences, University of Birmingham, Birmingham, UK
| | - A Federico
- FHAIVE, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
- Tampere Institute for Advanced Study, Tampere, Finland
| | - D Greco
- FHAIVE, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland.
- Institute of Biotechnology, Helsinki Institute of Life Sciences (HiLife), University of Helsinki, Helsinki, Finland.
- Division of Pharmaceutical Biosciences, Faculty of Pharmacy, University of Helsinki, Helsinki, Finland.
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36
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Moore J, Basurto-Lozada D, Besson S, Bogovic J, Bragantini J, Brown EM, Burel JM, Moreno XC, de Medeiros G, Diel EE, Gault D, Ghosh SS, Gold I, Halchenko YO, Hartley M, Horsfall D, Keller MS, Kittisopikul M, Kovacs G, Yoldaş AK, Kyoda K, de la Villegeorges ALT, Li T, Liberali P, Lindner D, Linkert M, Lüthi J, Maitin-Shepard J, Manz T, Marconato L, McCormick M, Lange M, Mohamed K, Moore W, Norlin N, Ouyang W, Özdemir B, Palla G, Pape C, Pelkmans L, Pietzsch T, Preibisch S, Prete M, Rzepka N, Samee S, Schaub N, Sidky H, Solak AC, Stirling DR, Striebel J, Tischer C, Toloudis D, Virshup I, Walczysko P, Watson AM, Weisbart E, Wong F, Yamauchi KA, Bayraktar O, Cimini BA, Gehlenborg N, Haniffa M, Hotaling N, Onami S, Royer LA, Saalfeld S, Stegle O, Theis FJ, Swedlow JR. OME-Zarr: a cloud-optimized bioimaging file format with international community support. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.02.17.528834. [PMID: 36865282 PMCID: PMC9980008 DOI: 10.1101/2023.02.17.528834] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/23/2023]
Abstract
A growing community is constructing a next-generation file format (NGFF) for bioimaging to overcome problems of scalability and heterogeneity. Organized by the Open Microscopy Environment (OME), individuals and institutes across diverse modalities facing these problems have designed a format specification process (OME-NGFF) to address these needs. This paper brings together a wide range of those community members to describe the cloud-optimized format itself -- OME-Zarr -- along with tools and data resources available today to increase FAIR access and remove barriers in the scientific process. The current momentum offers an opportunity to unify a key component of the bioimaging domain -- the file format that underlies so many personal, institutional, and global data management and analysis tasks.
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Affiliation(s)
- Josh Moore
- German BioImaging – Gesellschaft für Mikroskopie und Bildanalyse e.V., Konstanz, Germany
| | | | - Sébastien Besson
- Divisions of Molecular Cell and Developmental Biology, and Computational Biology, University of Dundee, Dundee, Scotland, UK
| | - John Bogovic
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | | | - Eva M. Brown
- Allen Institute for Cell Science, Seattle, WA, USA
| | - Jean-Marie Burel
- Divisions of Molecular Cell and Developmental Biology, and Computational Biology, University of Dundee, Dundee, Scotland, UK
| | - Xavier Casas Moreno
- Science for Life Laboratory, KTH Royal Institute of Technology, Stockholm, Sweden
| | | | | | - David Gault
- Divisions of Molecular Cell and Developmental Biology, and Computational Biology, University of Dundee, Dundee, Scotland, UK
| | | | - Ilan Gold
- Harvard Medical School, Boston, MA, USA
| | | | - Matthew Hartley
- European Molecular Biology Laboratory, European Bioinformatics Institute, EMBL-EBI, Cambridge, UK
| | - Dave Horsfall
- Biosciences Institute, Newcastle University, Newcastle upon Tyne, UK
| | | | - Mark Kittisopikul
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Gabor Kovacs
- Allen Institute for Neural Dynamics, Seattle, WA, USA
| | - Aybüke Küpcü Yoldaş
- European Molecular Biology Laboratory, European Bioinformatics Institute, EMBL-EBI, Cambridge, UK
| | - Koji Kyoda
- RIKEN Center for Biosystems Dynamics Research, Kobe, Japan
| | | | - Tong Li
- Wellcome Sanger Institute, Hinxton, UK
| | - Prisca Liberali
- Friedrich Miescher Institute for Biomedical Imaging, Basel, Switzerland
| | - Dominik Lindner
- Divisions of Molecular Cell and Developmental Biology, and Computational Biology, University of Dundee, Dundee, Scotland, UK
| | | | - Joel Lüthi
- Friedrich Miescher Institute for Biomedical Imaging, Basel, Switzerland
| | | | | | - Luca Marconato
- European Molecular Biology Laboratory (EMBL), Genome Biology Unit, Heidelberg, Germany
| | | | - Merlin Lange
- Chan Zuckerberg Biohub, San Francisco, CA, United States
| | - Khaled Mohamed
- Divisions of Molecular Cell and Developmental Biology, and Computational Biology, University of Dundee, Dundee, Scotland, UK
| | - William Moore
- Divisions of Molecular Cell and Developmental Biology, and Computational Biology, University of Dundee, Dundee, Scotland, UK
| | - Nils Norlin
- Department of Experimental Medical Science & Lund Bioimaging Centre, Lund University, Lund, Sweden
| | - Wei Ouyang
- Science for Life Laboratory, KTH Royal Institute of Technology, Stockholm, Sweden
| | | | - Giovanni Palla
- Institute of Computational Biology, Helmholtz Zentrum München, Neuherberg, Germany
| | | | | | - Tobias Pietzsch
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Stephan Preibisch
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | | | | | | | - Nicholas Schaub
- Information Technology Branch, National Center for Advancing Translational Science, National Institutes of Health
| | | | | | | | | | | | | | - Isaac Virshup
- Institute of Computational Biology, Helmholtz Zentrum München, Neuherberg, Germany
| | - Petr Walczysko
- Divisions of Molecular Cell and Developmental Biology, and Computational Biology, University of Dundee, Dundee, Scotland, UK
| | | | - Erin Weisbart
- Imaging Platform, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
| | - Frances Wong
- Divisions of Molecular Cell and Developmental Biology, and Computational Biology, University of Dundee, Dundee, Scotland, UK
| | - Kevin A. Yamauchi
- Department of Biosystems Science and Engineering, ETH Zürich, Switzerland
| | | | - Beth A. Cimini
- Imaging Platform, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
| | | | | | - Nathan Hotaling
- Information Technology Branch, National Center for Advancing Translational Science, National Institutes of Health
| | - Shuichi Onami
- RIKEN Center for Biosystems Dynamics Research, Kobe, Japan
| | - Loic A. Royer
- Chan Zuckerberg Biohub, San Francisco, CA, United States
| | - Stephan Saalfeld
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Oliver Stegle
- European Molecular Biology Laboratory (EMBL), Genome Biology Unit, Heidelberg, Germany
| | - Fabian J. Theis
- Institute of Computational Biology, Helmholtz Zentrum München, Neuherberg, Germany
| | - Jason R. Swedlow
- Divisions of Molecular Cell and Developmental Biology, and Computational Biology, University of Dundee, Dundee, Scotland, UK
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37
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Goel H, Printz RL, Shiota C, Estes SK, Pannala V, AbdulHameed MDM, Shiota M, Wallqvist A. Assessing Kidney Injury Induced by Mercuric Chloride in Guinea Pigs with In Vivo and In Vitro Experiments. Int J Mol Sci 2023; 24:7434. [PMID: 37108594 PMCID: PMC10138559 DOI: 10.3390/ijms24087434] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2023] [Revised: 04/04/2023] [Accepted: 04/07/2023] [Indexed: 04/29/2023] Open
Abstract
Acute kidney injury, which is associated with high levels of morbidity and mortality, affects a significant number of individuals, and can be triggered by multiple factors, such as medications, exposure to toxic chemicals or other substances, disease, and trauma. Because the kidney is a critical organ, understanding and identifying early cellular or gene-level changes can provide a foundation for designing medical interventions. In our earlier work, we identified gene modules anchored to histopathology phenotypes associated with toxicant-induced liver and kidney injuries. Here, using in vivo and in vitro experiments, we assessed and validated these kidney injury-associated modules by analyzing gene expression data from the kidneys of male Hartley guinea pigs exposed to mercuric chloride. Using plasma creatinine levels and cell-viability assays as measures of the extent of renal dysfunction under in vivo and in vitro conditions, we performed an initial range-finding study to identify the appropriate doses and exposure times associated with mild and severe kidney injuries. We then monitored changes in kidney gene expression at the selected doses and time points post-toxicant exposure to characterize the mechanisms of kidney injury. Our injury module-based analysis revealed a dose-dependent activation of several phenotypic cellular processes associated with dilatation, necrosis, and fibrogenesis that were common across the experimental platforms and indicative of processes that initiate kidney damage. Furthermore, a comparison of activated injury modules between guinea pigs and rats indicated a strong correlation between the modules, highlighting their potential for cross-species translational studies.
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Affiliation(s)
- Himanshu Goel
- Department of Defense Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Development Command, Fort Detrick, Frederick, MD 21702, USA
- The Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc., Bethesda, MD 20817, USA
| | - Richard L. Printz
- Department of Molecular Physiology and Biophysics, Vanderbilt University School of Medicine, Nashville, TN 37232, USA
- Division of Diabetes, Endocrinology and Metabolism, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Chiyo Shiota
- Department of Molecular Physiology and Biophysics, Vanderbilt University School of Medicine, Nashville, TN 37232, USA
| | - Shanea K. Estes
- Department of Molecular Physiology and Biophysics, Vanderbilt University School of Medicine, Nashville, TN 37232, USA
| | - Venkat Pannala
- Department of Defense Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Development Command, Fort Detrick, Frederick, MD 21702, USA
- The Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc., Bethesda, MD 20817, USA
| | - Mohamed Diwan M. AbdulHameed
- Department of Defense Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Development Command, Fort Detrick, Frederick, MD 21702, USA
- The Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc., Bethesda, MD 20817, USA
| | - Masakazu Shiota
- Department of Molecular Physiology and Biophysics, Vanderbilt University School of Medicine, Nashville, TN 37232, USA
| | - Anders Wallqvist
- Department of Defense Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Development Command, Fort Detrick, Frederick, MD 21702, USA
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38
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Identifying multiscale translational safety biomarkers using a network-based systems approach. iScience 2023; 26:106094. [PMID: 36895646 PMCID: PMC9988559 DOI: 10.1016/j.isci.2023.106094] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2022] [Revised: 11/30/2022] [Accepted: 01/26/2023] [Indexed: 02/03/2023] Open
Abstract
Animal testing is the current standard for drug and chemicals safety assessment, but hazards translation to human is uncertain. Human in vitro models can address the species translation but might not replicate in vivo complexity. Herein, we propose a network-based method addressing these translational multiscale problems that derives in vivo liver injury biomarkers applicable to in vitro human early safety screening. We applied weighted correlation network analysis (WGCNA) to a large rat liver transcriptomic dataset to obtain co-regulated gene clusters (modules). We identified modules statistically associated with liver pathologies, including a module enriched for ATF4-regulated genes as associated with the occurrence of hepatocellular single-cell necrosis, and as preserved in human liver in vitro models. Within the module, we identified TRIB3 and MTHFD2 as a novel candidate stress biomarkers, and developed and used BAC-eGFPHepG2 reporters in a compound screening, identifying compounds showing ATF4-dependent stress response and potential early safety signals.
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39
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Kuepfer L, Fuellen G, Stahnke T. Quantitative systems pharmacology of the eye: Tools and data for ocular QSP. CPT Pharmacometrics Syst Pharmacol 2023; 12:288-299. [PMID: 36708082 PMCID: PMC10014063 DOI: 10.1002/psp4.12918] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2022] [Revised: 12/21/2022] [Accepted: 01/02/2023] [Indexed: 01/29/2023] Open
Abstract
Good eyesight belongs to the most-valued attributes of health, and diseases of the eye are a significant healthcare burden. Case numbers are expected to further increase in the next decades due to an aging society. The development of drugs in ophthalmology, however, is difficult due to limited accessibility of the eye, in terms of drug administration and in terms of sampling of tissues for drug pharmacokinetics (PKs) and pharmacodynamics (PDs). Ocular quantitative systems pharmacology models provide the opportunity to describe the distribution of drugs in the eye as well as the resulting drug-response in specific segments of the eye. In particular, ocular physiologically-based PK (PBPK) models are necessary to describe drug concentration levels in different regions of the eye. Further, ocular effect models using molecular data from specific cellular systems are needed to develop dose-response correlations. We here describe the current status of PK/PBPK as well as PD models for the eyes and discuss cellular systems, data repositories, as well as animal models in ophthalmology. The application of the various concepts is highlighted for the development of new treatments for postoperative fibrosis after glaucoma surgery.
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Affiliation(s)
- Lars Kuepfer
- Institute for Systems Medicine with Focus on Organ Interaction, University Hospital RWTH Aachen, Aachen, Germany
| | - Georg Fuellen
- Institute for Biostatistics and Informatics in Medicine and Aging Research (IBIMA), Rostock University Medical Center, Rostock, Germany
| | - Thomas Stahnke
- Institute for ImplantTechnology and Biomaterials e.V., Rostock, Germany.,Department of Ophthalmology, Rostock University Medical Center, Rostock, Germany
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40
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He H, Duo H, Hao Y, Zhang X, Zhou X, Zeng Y, Li Y, Li B. Computational drug repurposing by exploiting large-scale gene expression data: Strategy, methods and applications. Comput Biol Med 2023; 155:106671. [PMID: 36805225 DOI: 10.1016/j.compbiomed.2023.106671] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2022] [Revised: 02/05/2023] [Accepted: 02/10/2023] [Indexed: 02/18/2023]
Abstract
De novo drug development is an extremely complex, time-consuming and costly task. Urgent needs for therapies of various diseases have greatly accelerated searches for more effective drug development methods. Luckily, drug repurposing provides a new and effective perspective on disease treatment. Rapidly increased large-scale transcriptome data paints a detailed prospect of gene expression during disease onset and thus has received wide attention in the field of computational drug repurposing. However, how to efficiently mine transcriptome data and identify new indications for old drugs remains a critical challenge. This review discussed the irreplaceable role of transcriptome data in computational drug repurposing and summarized some representative databases, tools and strategies. More importantly, it proposed a practical guideline through establishing the correspondence between three gene expression data types and five strategies, which would facilitate researchers to adopt appropriate strategies to deeply mine large-scale transcriptome data and discover more effective therapies.
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Affiliation(s)
- Hao He
- College of Life Sciences, Chongqing Normal University, Chongqing, 400044, PR China; State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Institutes of Brain Science, Fudan University, Shanghai, 200032, PR China
| | - Hongrui Duo
- College of Life Sciences, Chongqing Normal University, Chongqing, 400044, PR China
| | - Youjin Hao
- College of Life Sciences, Chongqing Normal University, Chongqing, 400044, PR China
| | - Xiaoxi Zhang
- College of Life Sciences, Chongqing Normal University, Chongqing, 400044, PR China
| | - Xinyi Zhou
- College of Life Sciences, Chongqing Normal University, Chongqing, 400044, PR China
| | - Yujie Zeng
- College of Life Sciences, Chongqing Normal University, Chongqing, 400044, PR China
| | - Yinghong Li
- The Key Laboratory on Big Data for Bio Intelligence, Chongqing University of Posts and Telecommunications, Chongqing, 400065, PR China
| | - Bo Li
- College of Life Sciences, Chongqing Normal University, Chongqing, 400044, PR China.
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41
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Cerisier N, Dafniet B, Badel A, Taboureau O. Linking chemicals, genes and morphological perturbations to diseases. Toxicol Appl Pharmacol 2023; 461:116407. [PMID: 36736439 DOI: 10.1016/j.taap.2023.116407] [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: 10/27/2022] [Revised: 01/13/2023] [Accepted: 01/28/2023] [Indexed: 02/05/2023]
Abstract
The progress in image-based high-content screening technology has facilitated high-throughput phenotypic profiling notably the quantification of cell morphology perturbation by chemicals. However, understanding the mechanism of action of a chemical and linking it to cell morphology and phenotypes remains a challenge in drug discovery. In this study, we intended to integrate molecules that induced transcriptomic perturbations and cellular morphological changes into a biological network in order to assess chemical-phenotypic relationships in humans. Such a network was enriched with existing disease information to suggest molecular and cellular profiles leading to phenotypes. Two datasets were used for this study. Firstly, we used the "Cell Painting morphological profiling assay" dataset, composed of 30,000 compounds tested on human osteosarcoma cells (named U2OS). Secondly, we used the "L1000 mRNA profiling assay" dataset, a collection of transcriptional expression data from cultured human cells treated with approximately 20,000 bioactive small molecules from the Library of Integrated Network-based Cellular Signatures (LINCS). Furthermore, pathways, gene ontology terms and disease enrichments were performed on the transcriptomics data. Overall, our study makes it possible to develop a biological network combining chemical-gene-pathway-morphological perturbation and disease relationships. It contains an ensemble of 9989 chemicals, 732 significant morphological features and 12,328 genes. Through diverse examples, we demonstrated that some drugs shared similar genes, pathways and morphological profiles that, taken together, could help in deciphering chemical-phenotype observations.
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Affiliation(s)
- Natacha Cerisier
- Université Paris Cité, INSERM U1133, CNRS UMR 8251, 75006 Paris, France
| | - Bryan Dafniet
- Université Paris Cité, INSERM U1133, CNRS UMR 8251, 75006 Paris, France
| | - Anne Badel
- Université Paris Cité, INSERM U1133, CNRS UMR 8251, 75006 Paris, France
| | - Olivier Taboureau
- Université Paris Cité, INSERM U1133, CNRS UMR 8251, 75006 Paris, France.
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Short-term in vivo testing to discriminate genotoxic carcinogens from non-genotoxic carcinogens and non-carcinogens using next-generation RNA sequencing, DNA microarray, and qPCR. Genes Environ 2023; 45:7. [PMID: 36755350 PMCID: PMC9909887 DOI: 10.1186/s41021-023-00262-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2022] [Accepted: 01/05/2023] [Indexed: 02/10/2023] Open
Abstract
Next-generation RNA sequencing (RNA-Seq) has identified more differentially expressed protein-coding genes (DEGs) and provided a wider quantitative range of expression level changes than conventional DNA microarrays. JEMS·MMS·Toxicogenomics group studied DEGs with targeted RNA-Seq on freshly frozen rat liver tissues and on formalin-fixed paraffin-embedded (FFPE) rat liver tissues after 28 days of treatment with chemicals and quantitative real-time PCR (qPCR) on rat and mouse liver tissues after 4 to 48 h treatment with chemicals and analyzed by principal component analysis (PCA) as statics. Analysis of rat public DNA microarray data (Open TG-GATEs) was also performed. In total, 35 chemicals were analyzed [15 genotoxic hepatocarcinogens (GTHCs), 9 non-genotoxic hepatocarcinogens (NGTHCs), and 11 non-genotoxic non-hepatocarcinogens (NGTNHCs)]. As a result, 12 marker genes (Aen, Bax, Btg2, Ccnf, Ccng1, Cdkn1a, Gdf15, Lrp1, Mbd1, Phlda3, Plk2, and Tubb4b) were proposed to discriminate GTHCs from NGTHCs and NGTNHCs. U.S. Environmental Protection Agency studied DEGs induced by 4 known GTHCs in rat liver using DNA microarray and proposed 7 biomarker genes, Bax, Bcmp1, Btg2, Ccng1, Cdkn1a, Cgr19, and Mgmt for GTHCs. Studies involving the use of whole-transcriptome RNA-Seq upon exposure to chemical carcinogens in vivo have also been performed in rodent liver, kidney, lung, colon, and other organs, although discrimination of GTHCs from NGTHCs was not examined. Candidate genes published using RNA-Seq, qPCR, and DNA microarray will be useful for the future development of short-term in vivo studies of environmental carcinogens using RNA-Seq.
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43
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Lu H, Yang D, Shi Y, Chen K, Li P, Huang S, Cui D, Feng Y, Wang T, Yang J, Zhu X, Xia D, Wu Y. Toxicogenomics scoring system: TGSS, a novel integrated risk assessment model for chemical carcinogenicity prediction. ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY 2023; 250:114466. [PMID: 36587411 DOI: 10.1016/j.ecoenv.2022.114466] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/18/2022] [Revised: 12/05/2022] [Accepted: 12/22/2022] [Indexed: 06/17/2023]
Abstract
BACKGROUND Given the increasing exposure of humans to environmental chemicals and the limitations of conventional toxicity test, there is an urgent need to develop next-generation risk assessment methods. OBJECTIVES This study aims to establish a novel computational system named Toxicogenomics Scoring System (TGSS) to predict the carcinogenicity of chemicals coupling chemical-gene interactions with multiple cancer transcriptomic datasets. METHODS Chemical-related gene signatures were derived from chemical-gene interaction data from the Comparative Toxicogenomics Database (CTD). For each cancer type in TCGA, genes were ranked by their effects on tumorigenesis, which is based on the differential expression between tumor and normal samples. Next, we developed carcinogenicity scores (C-scores) using pre-ranked GSEA to quantify the correlation between chemical-related gene signatures and ranked gene lists. Then we established TGSS by systematically evaluating the C-scores in multiple chemical-tumor pairs. Furthermore, we examined the performance of our approach by ROC curves or prognostic analyses in TCGA and multiple independent cancer cohorts. RESULTS Forty-six environmental chemicals were finally included in the study. C-score was calculated for each chemical-tumor pair. The C-scores of IARC Group 3 chemicals were significantly lower than those of chemicals in Group 1 (P-value = 0.02) and Group 2 (P-values = 7.49 ×10-5). ROC curves analysis indicated that C-score could distinguish "high-risk chemicals" from the other compounds (AUC = 0.67) with a specificity and sensitivity of 0.86 and 0.57. The results of survival analysis were also in line with the assessed carcinogenicity in TGSS for the chemicals in Group 1. Finally, consistent results were further validated in independent cancer cohorts. CONCLUSION TGSS highlighted the great potential of integrating chemical-gene interactions with gene-cancer relationships to predict the carcinogenic risk of chemicals, which would be valuable for systems toxicology.
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Affiliation(s)
- Haohua Lu
- Department of Toxicology of School of Public Health and Department of Gynecologic Oncology of Women's Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China
| | - Dexin Yang
- Department of Toxicology of School of Public Health and Department of Gynecologic Oncology of Women's Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China
| | - Yu Shi
- The State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China
| | - Kelie Chen
- Department of Toxicology of School of Public Health and Department of Gynecologic Oncology of Women's Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China
| | - Peiwei Li
- Department of Gastroenterology, The Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China
| | - Sisi Huang
- Department of Toxicology of School of Public Health and Department of Gynecologic Oncology of Women's Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China
| | - Dongyu Cui
- Department of Toxicology of School of Public Health and Department of Gynecologic Oncology of Women's Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China
| | - Yuqin Feng
- Department of Toxicology of School of Public Health and Department of Gynecologic Oncology of Women's Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China
| | - Tianru Wang
- Epidemiology Stream, Dalla Lana School of Public Health, University of Toronto, M5T 3M7 ON, Canada
| | - Jun Yang
- Department of Public Health, School of Medicine, Hangzhou Normal University, Hangzhou, Zhejiang, China; Zhejiang Provincial Center for Uterine Cancer Diagnosis and Therapy Research of Women's Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China
| | - Xinqiang Zhu
- Central Laboratory of the Fourth Affiliated Hospital, School of Medicine, Zhejiang University, Yiwu, Zhejiang, China
| | - Dajing Xia
- Department of Toxicology of School of Public Health and Department of Gynecologic Oncology of Women's Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China.
| | - Yihua Wu
- Department of Toxicology of School of Public Health and Department of Gynecologic Oncology of Women's Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China; Research Unit of Intelligence Classification of Tumor Pathology and Precision Therapy, Chinese Academy of Medical Sciences (2019RU042), Hangzhou, Zhejiang, China.
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44
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Using chemical and biological data to predict drug toxicity. SLAS DISCOVERY : ADVANCING LIFE SCIENCES R & D 2023; 28:53-64. [PMID: 36639032 DOI: 10.1016/j.slasd.2022.12.003] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/30/2022] [Revised: 12/19/2022] [Accepted: 12/31/2022] [Indexed: 01/12/2023]
Abstract
Various sources of information can be used to better understand and predict compound activity and safety-related endpoints, including biological data such as gene expression and cell morphology. In this review, we first introduce types of chemical, in vitro and in vivo information that can be used to describe compounds and adverse effects. We then explore how compound descriptors based on chemical structure or biological perturbation response can be used to predict safety-related endpoints, and how especially biological data can help us to better understand adverse effects mechanistically. Overall, the described applications demonstrate how large-scale biological information presents new opportunities to anticipate and understand the biological effects of compounds, and how this can support predictive toxicology and drug discovery projects.
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Wu L, Yan B, Han J, Li R, Xiao J, He S, Bo X. TOXRIC: a comprehensive database of toxicological data and benchmarks. Nucleic Acids Res 2023; 51:D1432-D1445. [PMID: 36400569 PMCID: PMC9825425 DOI: 10.1093/nar/gkac1074] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2022] [Revised: 10/10/2022] [Accepted: 10/26/2022] [Indexed: 11/20/2022] Open
Abstract
The toxic effects of compounds on environment, humans, and other organisms have been a major focus of many research areas, including drug discovery and ecological research. Identifying the potential toxicity in the early stage of compound/drug discovery is critical. The rapid development of computational methods for evaluating various toxicity categories has increased the need for comprehensive and system-level collection of toxicological data, associated attributes, and benchmarks. To contribute toward this goal, we proposed TOXRIC (https://toxric.bioinforai.tech/), a database with comprehensive toxicological data, standardized attribute data, practical benchmarks, informative visualization of molecular representations, and an intuitive function interface. The data stored in TOXRIC contains 113 372 compounds, 13 toxicity categories, 1474 toxicity endpoints covering in vivo/in vitro endpoints and 39 feature types, covering structural, target, transcriptome, metabolic data, and other descriptors. All the curated datasets of endpoints and features can be retrieved, downloaded and directly used as output or input to Machine Learning (ML)-based prediction models. In addition to serving as a data repository, TOXRIC also provides visualization of benchmarks and molecular representations for all endpoint datasets. Based on these results, researchers can better understand and select optimal feature types, molecular representations, and baseline algorithms for each endpoint prediction task. We believe that the rich information on compound toxicology, ML-ready datasets, benchmarks and molecular representation distribution can greatly facilitate toxicological investigations, interpretation of toxicological mechanisms, compound/drug discovery and the development of computational methods.
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Affiliation(s)
- Lianlian Wu
- Department of Bioinformatics, Institute of Health Service and Transfusion Medicine, Beijing 100850, China
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, China
| | - Bowei Yan
- Department of Bioinformatics, Institute of Health Service and Transfusion Medicine, Beijing 100850, China
- State Key Laboratory of Genetic Engineering and Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Institute of Biomedical Sciences, Human Phenome Institute, Fudan University, Shanghai 200433, China
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences, Beijing 102206, China
| | - Junshan Han
- Department of Bioinformatics, Institute of Health Service and Transfusion Medicine, Beijing 100850, China
| | - Ruijiang Li
- Department of Bioinformatics, Institute of Health Service and Transfusion Medicine, Beijing 100850, China
| | - Jian Xiao
- Department of Pharmacy, Xiangya Hospital, Central South University, Changsha 410008, Hunan, China
- Institute for Rational and Safe Medication Practices, National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha 410008, Hunan, China
| | - Song He
- Department of Bioinformatics, Institute of Health Service and Transfusion Medicine, Beijing 100850, China
| | - Xiaochen Bo
- Department of Bioinformatics, Institute of Health Service and Transfusion Medicine, Beijing 100850, China
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, China
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Yamada H. [Computational toxicology in drug safety research]. Nihon Yakurigaku Zasshi 2023; 158:82-88. [PMID: 36596497 DOI: 10.1254/fpj.22098] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
The progress of computational toxicology (CompTox) in drug safety research is highly anticipated. CompTox provides toxicity screening methods for drug discovery in the early stages. CompTox also contributes to fostering the application of the principles of the 3Rs in toxicity testing by expanding non-animal test methods. The mechanism of toxicity is complex and varied, and drug discovery modalities are becoming more diverse. Consequently, the research is considered necessary to predict toxicity using not only chemical structures but experimental data as well. Additionally, various perspectives, such as interpretation of toxicity mechanisms and species differences, must be considered in risk assessment and management in drug safety research. Therefore, it is important to construct a comprehensive CompTox system that not only presents toxicity prediction results but also provides much information related to the relationship between drug candidate substances and living organisms. In this review paper, CompTox is positioned as a discipline of toxicology that applies computer-based technology, including AI (artificial intelligence). I also introduce toxicity prediction systems based on experimental data and an ontology system that supports the interpretation of toxicity prediction results as examples of research on constructing the foundation of a comprehensive CompTox system.
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Affiliation(s)
- Hiroshi Yamada
- Toxicogenomics Informatics Project, National Institutes of Biomedical Innovation, Health and Nutrition
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Tanaka Y, Takahashi K, Hattori N, Yokoyama H, Yamaguchi K, Shibui Y, Kawaguchi S, Shimazaki T, Nakai K, Kusuhara H, Saito Y. The influence of serial 50 μL microsampling on rats administered azathioprine, the immunosuppressive drug. Toxicol Rep 2023; 10:334-340. [PMID: 36923445 PMCID: PMC10008918 DOI: 10.1016/j.toxrep.2023.02.016] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2022] [Revised: 02/20/2023] [Accepted: 02/28/2023] [Indexed: 03/05/2023] Open
Abstract
According to the ICH S3A Q&A, microsampling is applicable to pharmaceutical drugs and toxicological analysis. Few studies have reported the effect of microsampling on the toxicity of immunotoxicological drugs. The aim of this multicenter study was to evaluate the toxicological effects of serial microsampling on rats treated with azathioprine as a model drug with immunotoxic effects. Fifty microliters of blood were collected from the jugular vein of Sprague-Dawley rats at six time points from day 1 to 2 and 7 time points from day 27 to 28. The study was performed at three organizations independently. The microsampling effect on clinical signs, body weights, food consumption, hematological parameters, biochemical parameters, urinary parameters, organ weights, and tissue pathology was evaluated. Azathioprine-induced changes were observed in certain hematological and biochemical parameters and thymus weight and pathology. Microsampling produced minimal or no effects on almost all parameters; however, at 2 organizations, azathioprine-induced changes were apparently masked for two leukocytic, one coagulation, and two biochemical parameters. In conclusion, azathioprine toxicity could be assessed appropriately as overall profiles even with blood microsampling. However, microsampling may influence azathioprine-induced changes in certain parameters, especially leukocytic parameters, and its usage should be carefully considered.
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Key Words
- A/G, albumin/globulin
- ALP, alkaline phosphatase
- ALT, alanine transaminase
- APTT, activated partial thromboplastin time
- AST, aspartate transaminase
- Azathioprine
- BUN, blood urea nitrogen
- CPK, creatine phosphokinase
- Ca, calcium
- Cl, chloride
- Cre, creatinine
- GLDH, glutamate dehydrogenase
- Hematological parameter
- ICH, International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use
- Jugular vein
- K, potassium
- LDH, lactate dehydrogenase
- MCH, mean corpuscular hemoglobin
- MCHC, mean corpuscular hemoglobin concentration
- MCV, mean corpuscular volume
- Microsampling
- Na, sodium
- P, inorganic phosphorus
- PT, prothrombin time
- RBC, red blood cell
- Rat
- TK, toxicokinetics
- Toxicokinetics
- WBC, leukocyte/white blood cell
- γGT, γ-glutamyltranspeptidase
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Affiliation(s)
- Yoichi Tanaka
- Division of Medicinal Safety Science, National Institute of Health Sciences, 3-25-26, Tonomachi, Kawasaki-ku, Kasasaki-shi, Kanagawa, Japan
| | - Kazuaki Takahashi
- LSIM Safety Institute Corporation., 14-1 Sunayama, Kamisu-shi, Ibaraki, Japan
| | - Norimichi Hattori
- Ajinomoto Fine-Techno Co., Inc., 1-1, Suzuki-cho, Kawasaki-ku, Kwasaki-shi, Kanagawa, Japan
| | - Hideaki Yokoyama
- Japan Tobacco Inc., 1-13-2, Fukuura, Kanazawa-ku, Yokohama-shi, Kanagawa, Japan
| | - Koki Yamaguchi
- Japan Tobacco Inc., 1-13-2, Fukuura, Kanazawa-ku, Yokohama-shi, Kanagawa, Japan
| | - Yusuke Shibui
- Ajinomoto Fine-Techno Co., Inc., 1-1, Suzuki-cho, Kawasaki-ku, Kwasaki-shi, Kanagawa, Japan
| | - Sayaka Kawaguchi
- Ajinomoto Fine-Techno Co., Inc., 1-1, Suzuki-cho, Kawasaki-ku, Kwasaki-shi, Kanagawa, Japan
| | - Taishi Shimazaki
- Japan Tobacco Inc., 1-13-2, Fukuura, Kanazawa-ku, Yokohama-shi, Kanagawa, Japan
| | - Keiko Nakai
- LSIM Safety Institute Corporation., 14-1 Sunayama, Kamisu-shi, Ibaraki, Japan
| | - Hiroyuki Kusuhara
- Laboratory of Molecular Pharmacokinetics, Graduate School of Pharmaceutical Sciences, The University of Tokyo, Bunkyo-ku, Tokyo, Japan
| | - Yoshiro Saito
- Division of Medicinal Safety Science, National Institute of Health Sciences, 3-25-26, Tonomachi, Kawasaki-ku, Kasasaki-shi, Kanagawa, Japan
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Naciff JM, Shan YK, Wang X, Daston GP. Article title: Transcriptional profiling efficacy to define biological activity similarity for cosmetic ingredients' safety assessment based on next-generation read-across. FRONTIERS IN TOXICOLOGY 2022; 4:1082222. [PMID: 36618549 PMCID: PMC9811170 DOI: 10.3389/ftox.2022.1082222] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2022] [Accepted: 12/05/2022] [Indexed: 12/24/2022] Open
Abstract
The objective of this work was to use transcriptional profiling to assess the biological activity of structurally related chemicals to define their biological similarity and with that, substantiate the validity of a read-across approach usable in risk assessment. Two case studies are presented, one with 4 short alkyl chain parabens: methyl (MP), ethyl (EP), butyl (BP), and propylparaben (PP), as well as their main metabolite, p-hydroxybenzoic acid (pHBA) with the assumption that propylparaben was the target chemical; and a second one with caffeine and its main metabolites theophylline, theobromine and paraxanthine where CA was the target chemical. The comprehensive transcriptional response of MCF7, HepG2, A549 and ICell cardiomyocytes was evaluated (TempO-Seq) after exposure to vehicle-control, each paraben or pHBA, CA or its metabolites, at 3 non-cytotoxic concentrations, for 6 h. Differentially expressed genes (FDR ≥0.05, and fold change ±1.2≥) were identified for each chemical, at each concentration, and used to determine similarities. Each of the chemicals is able to elicit changes in the expression of a number of genes, as compared to controls. Importantly, the transcriptional profile elicited by each of the parabens shares a high degree of similarity across the group. The highest number of genes commonly affected was between butylparaben and PP. The transcriptional profile of the parabens is similar to the one elicited by estrogen receptor agonists, with BP being the closest structural and biological analogue for PP. In the CA case, the transcriptional profile elicited of all four methylxanthines had a high degree of similarity across the cell types, with CA and theophylline being the most active. The most robust response was obtained in the cardiomyocytes with the highest transcriptional profile similarity between CA and TP. The transcriptional profile of the methylxanthines is similar to the one elicited by inhibitors of phosphatidylinositol 3-kinase as well as other kinase inhibitors. Overall, our results support the approach of incorporating transcriptional profiling in well-designed in vitro tests as one robust stream of data to support biological similarity driven read-across procedures and strengthening the traditional structure-based approaches useful in risk assessment.
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Vidal Yucha SE, Quackenbush D, Chu T, Lo F, Sutherland JJ, Kuzu G, Roberts C, Luna F, Barnes SW, Walker J, Kuss P. "3D, human renal proximal tubule (RPTEC-TERT1) organoids 'tubuloids' for translatable evaluation of nephrotoxins in high-throughput". PLoS One 2022; 17:e0277937. [PMID: 36409750 PMCID: PMC9678317 DOI: 10.1371/journal.pone.0277937] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2022] [Accepted: 11/07/2022] [Indexed: 11/22/2022] Open
Abstract
The importance of human cell-based in vitro tools to drug development that are robust, accurate, and predictive cannot be understated. There has been significant effort in recent years to develop such platforms, with increased interest in 3D models that can recapitulate key aspects of biology that 2D models might not be able to deliver. We describe the development of a 3D human cell-based in vitro assay for the investigation of nephrotoxicity, using RPTEC-TERT1 cells. These RPTEC-TERT1 proximal tubule organoids 'tubuloids' demonstrate marked differences in physiologically relevant morphology compared to 2D monolayer cells, increased sensitivity to nephrotoxins observable via secreted protein, and with a higher degree of similarity to native human kidney tissue. Finally, tubuloids incubated with nephrotoxins demonstrate altered Na+/K+-ATPase signal intensity, a potential avenue for a high-throughput, translatable nephrotoxicity assay.
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Affiliation(s)
- Sarah E. Vidal Yucha
- Novartis Institutes for BioMedical Research-San Diego, La Jolla, CA, United States of America
- * E-mail:
| | - Doug Quackenbush
- Novartis Institutes for BioMedical Research-San Diego, La Jolla, CA, United States of America
| | - Tiffany Chu
- Novartis Institutes for BioMedical Research-San Diego, La Jolla, CA, United States of America
| | - Frederick Lo
- Novartis Institutes for BioMedical Research-San Diego, La Jolla, CA, United States of America
| | - Jeffrey J. Sutherland
- Novartis Institutes for BioMedical Research-Cambridge, Cambridge, MA, United States of America
| | - Guray Kuzu
- Novartis Institutes for BioMedical Research-San Diego, La Jolla, CA, United States of America
| | - Christopher Roberts
- Novartis Institutes for BioMedical Research-San Diego, La Jolla, CA, United States of America
| | - Fabio Luna
- Novartis Institutes for BioMedical Research-San Diego, La Jolla, CA, United States of America
| | - S. Whitney Barnes
- Novartis Institutes for BioMedical Research-San Diego, La Jolla, CA, United States of America
| | - John Walker
- Novartis Institutes for BioMedical Research-San Diego, La Jolla, CA, United States of America
| | - Pia Kuss
- Novartis Institutes for BioMedical Research-San Diego, La Jolla, CA, United States of America
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Shah I, Bundy J, Chambers B, Everett LJ, Haggard D, Harrill J, Judson RS, Nyffeler J, Patlewicz G. Navigating Transcriptomic Connectivity Mapping Workflows to Link Chemicals with Bioactivities. Chem Res Toxicol 2022; 35:1929-1949. [PMID: 36301716 PMCID: PMC10483698 DOI: 10.1021/acs.chemrestox.2c00245] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
Screening new compounds for potential bioactivities against cellular targets is vital for drug discovery and chemical safety. Transcriptomics offers an efficient approach for assessing global gene expression changes, but interpreting chemical mechanisms from these data is often challenging. Connectivity mapping is a potential data-driven avenue for linking chemicals to mechanisms based on the observation that many biological processes are associated with unique gene expression signatures (gene signatures). However, mining the effects of a chemical on gene signatures for biological mechanisms is challenging because transcriptomic data contain thousands of noisy genes. New connectivity mapping approaches seeking to distinguish signal from noise continue to be developed, spurred by the promise of discovering chemical mechanisms, new drugs, and disease targets from burgeoning transcriptomic data. Here, we analyze these approaches in terms of diverse transcriptomic technologies, public databases, gene signatures, pattern-matching algorithms, and statistical evaluation criteria. To navigate the complexity of connectivity mapping, we propose a harmonized scheme to coherently organize and compare published workflows. We first standardize concepts underlying transcriptomic profiles and gene signatures based on various transcriptomic technologies such as microarrays, RNA-Seq, and L1000 and discuss the widely used data sources such as Gene Expression Omnibus, ArrayExpress, and MSigDB. Next, we generalize connectivity mapping as a pattern-matching task for finding similarity between a query (e.g., transcriptomic profile for new chemical) and a reference (e.g., gene signature of known target). Published pattern-matching approaches fall into two main categories: vector-based use metrics like correlation, Jaccard index, etc., and aggregation-based use parametric and nonparametric statistics (e.g., gene set enrichment analysis). The statistical methods for evaluating the performance of different approaches are described, along with comparisons reported in the literature on benchmark transcriptomic data sets. Lastly, we review connectivity mapping applications in toxicology and offer guidance on evaluating chemical-induced toxicity with concentration-response transcriptomic data. In addition to serving as a high-level guide and tutorial for understanding and implementing connectivity mapping workflows, we hope this review will stimulate new algorithms for evaluating chemical safety and drug discovery using transcriptomic data.
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Affiliation(s)
- Imran Shah
- Center for Computational Toxicology and Exposure, Office of Research and Development, US. Environmental Protection Agency, Research Triangle Park, North Carolina 27711, USA
| | - Joseph Bundy
- Center for Computational Toxicology and Exposure, Office of Research and Development, US. Environmental Protection Agency, Research Triangle Park, North Carolina 27711, USA
| | - Bryant Chambers
- Center for Computational Toxicology and Exposure, Office of Research and Development, US. Environmental Protection Agency, Research Triangle Park, North Carolina 27711, USA
| | - Logan J. Everett
- Center for Computational Toxicology and Exposure, Office of Research and Development, US. Environmental Protection Agency, Research Triangle Park, North Carolina 27711, USA
| | - Derik Haggard
- Center for Computational Toxicology and Exposure, Office of Research and Development, US. Environmental Protection Agency, Research Triangle Park, North Carolina 27711, USA
| | - Joshua Harrill
- Center for Computational Toxicology and Exposure, Office of Research and Development, US. Environmental Protection Agency, Research Triangle Park, North Carolina 27711, USA
| | - Richard S. Judson
- Center for Computational Toxicology and Exposure, Office of Research and Development, US. Environmental Protection Agency, Research Triangle Park, North Carolina 27711, USA
| | - Johanna Nyffeler
- Center for Computational Toxicology and Exposure, Office of Research and Development, US. Environmental Protection Agency, Research Triangle Park, North Carolina 27711, USA
- Oak Ridge Institute for Science and Education (ORISE) Postdoctoral Fellow, Oak Ridge, Tennessee, 37831, US
| | - Grace Patlewicz
- Center for Computational Toxicology and Exposure, Office of Research and Development, US. Environmental Protection Agency, Research Triangle Park, North Carolina 27711, USA
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