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Wehr MM, Reamon-Buettner SM, Ritter D, Knebel J, Niehof M, Escher SE. A comparison of the TempO-Seq and Affymetrix microarray platform using RTqPCR validation. BMC Genomics 2024; 25:669. [PMID: 38961363 PMCID: PMC11223392 DOI: 10.1186/s12864-024-10586-7] [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: 07/20/2023] [Accepted: 07/01/2024] [Indexed: 07/05/2024] Open
Abstract
Next-generation risk assessment relies on mechanistic data from new approach methods, including transcriptome data. Various technologies, such as high-throughput targeted sequencing methods and microarray technologies based on hybridization with complementary probes, are used to determine differentially expressed genes (DEGs). The integration of data from different technologies requires a good understanding of the differences arising from the use of various technologies.To better understand the differences between the TempO-Seq platform and Affymetrix chip technology, whole-genome data for the volatile compound dimethylamine were compared. Selected DEGs were also confirmed using RTqPCR validation. Although the overlap of DEGs between TempO-Seq and Affymetrix was no higher than 37%, a comparison of the gene regulation in terms of log2fold changes revealed a very high concordance. RTqPCR confirmed the majority of DEGs from either platform in the examined dataset. Only a few conflicts were found (11%), while 22% were not confirmed, and 3% were not detected.Despite the observed differences between the two platforms, both can be validated using RTqPCR. Here we highlight some of the differences between the two platforms and discuss their applications in toxicology.
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Affiliation(s)
- Matthias M Wehr
- Fraunhofer Institute for Toxicology and Experimental Medicine, Nikolai-Fuchs-Str. 1, 30625, Hannover, Germany.
| | | | - Detlef Ritter
- Fraunhofer Institute for Toxicology and Experimental Medicine, Nikolai-Fuchs-Str. 1, 30625, Hannover, Germany
| | - Jan Knebel
- Fraunhofer Institute for Toxicology and Experimental Medicine, Nikolai-Fuchs-Str. 1, 30625, Hannover, Germany
| | - Monika Niehof
- Fraunhofer Institute for Toxicology and Experimental Medicine, Nikolai-Fuchs-Str. 1, 30625, Hannover, Germany
| | - Sylvia E Escher
- Fraunhofer Institute for Toxicology and Experimental Medicine, Nikolai-Fuchs-Str. 1, 30625, Hannover, Germany
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Pang A, Rutter A, Haack E, Zeeb B. Transcriptome analysis of a springtail, Folsomia candida, reveals energy constraint and oxidative stress during petroleum hydrocarbon exposure. CHEMOSPHERE 2023; 342:140185. [PMID: 37716568 DOI: 10.1016/j.chemosphere.2023.140185] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Revised: 09/12/2023] [Accepted: 09/13/2023] [Indexed: 09/18/2023]
Abstract
Petroleum hydrocarbon (PHC) contamination in soil is ubiquitous and poses harmful consequences to many organisms. The toxicity of PHC-impacted soil is difficult to predict due to variations in mixture composition and the impacts of natural weathering processes. Hence, high-throughput methods to assess PHC-impacted soils is required to expedite land management decisions. Next-generation sequencing is a robust tool that allows researchers to investigate the effects of contaminants on the transcriptome of organisms and identify molecular biomarkers. In this study, the effects of PHCs on conventional endpoints (i.e., survival and reproduction) and gene expression rates of a model springtail species, Folsomia candida were investigated. Age-synchronized F. candida were exposed to ecologically-relevant concentrations of soils spiked with fresh crude oil to calculate the reproductive EC25 and EC50 values using conventional toxicity testing. Soils spiked to these concentrations were then used to evaluate effects on the F. candida transcriptome over a 7-day exposure period. RNA-seq analysis found 98 and 132 differentially expressed genes when compared to the control for the EC25 and EC50 treatment groups, respectively. The majority of up-regulated genes were related to xenobiotic biotransformation reactions and oxidative stress response, while down-regulated genes coded for carbohydrate and peptide metabolic processes. Promotion of the pentose phosphate pathway was also found. Results suggest that the decreased reproduction rates of F. candida exposed to PHCs is due to energy constraints caused by inhibition of carbohydrate metabolic processes and allocation of remaining energy to detoxify xenobiotics. These findings provide insights into the molecular effects in F. candida following exposure to crude oil for seven days and highlight their potential to be used as a high-throughput screening test for PHC-contaminated sites. Adverse molecular effects can be measured as early as 24 h following exposure, whereas conventional toxicity tests may require a minimum of four weeks.
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Affiliation(s)
- Adrian Pang
- School of Environmental Studies, Queen's University, Kingston, ON, K7L 3N6, Canada.
| | - Allison Rutter
- School of Environmental Studies, Queen's University, Kingston, ON, K7L 3N6, Canada
| | - Elizabeth Haack
- Ecometrix Incorporated, 6800 Campobello Road, Mississauga, ON, L5N 2L8, Canada
| | - Barbara Zeeb
- Dept. of Chem. & Chem. Eng., Royal Military College of Canada, Kingston, ON, K7K 7B4, Canada
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Gant TW, Auerbach SS, Von Bergen M, Bouhifd M, Botham PA, Caiment F, Currie RA, Harrill J, Johnson K, Li D, Rouquie D, van Ravenzwaay B, Sistare F, Tralau T, Viant MR, van de Laan JW, Yauk C. Applying genomics in regulatory toxicology: a report of the ECETOC workshop on omics threshold on non-adversity. Arch Toxicol 2023; 97:2291-2302. [PMID: 37296313 PMCID: PMC10322787 DOI: 10.1007/s00204-023-03522-3] [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: 01/23/2023] [Accepted: 05/11/2023] [Indexed: 06/12/2023]
Abstract
In a joint effort involving scientists from academia, industry and regulatory agencies, ECETOC's activities in Omics have led to conceptual proposals for: (1) A framework that assures data quality for reporting and inclusion of Omics data in regulatory assessments; and (2) an approach to robustly quantify these data, prior to interpretation for regulatory use. In continuation of these activities this workshop explored and identified areas of need to facilitate robust interpretation of such data in the context of deriving points of departure (POD) for risk assessment and determining an adverse change from normal variation. ECETOC was amongst the first to systematically explore the application of Omics methods, now incorporated into the group of methods known as New Approach Methodologies (NAMs), to regulatory toxicology. This support has been in the form of both projects (primarily with CEFIC/LRI) and workshops. Outputs have led to projects included in the workplan of the Extended Advisory Group on Molecular Screening and Toxicogenomics (EAGMST) group of the Organisation for Economic Co-operation and Development (OECD) and to the drafting of OECD Guidance Documents for Omics data reporting, with potentially more to follow on data transformation and interpretation. The current workshop was the last in a series of technical methods development workshops, with a sub-focus on the derivation of a POD from Omics data. Workshop presentations demonstrated that Omics data developed within robust frameworks for both scientific data generation and analysis can be used to derive a POD. The issue of noise in the data was discussed as an important consideration for identifying robust Omics changes and deriving a POD. Such variability or "noise" can comprise technical or biological variation within a dataset and should clearly be distinguished from homeostatic responses. Adverse outcome pathways (AOPs) were considered a useful framework on which to assemble Omics methods, and a number of case examples were presented in illustration of this point. What is apparent is that high dimension data will always be subject to varying processing pipelines and hence interpretation, depending on the context they are used in. Yet, they can provide valuable input for regulatory toxicology, with the pre-condition being robust methods for the collection and processing of data together with a comprehensive description how the data were interpreted, and conclusions reached.
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Affiliation(s)
- Timothy W Gant
- United Kingdom Health Security Agency, Harwell Science Campus, Didcot, Oxfordshire, United Kingdom.
- Imperial College London School of Public Health, London, United Kingdom.
| | - Scott S Auerbach
- Division of Translational Toxicology, National Institute of Environmental Health Sciences, RTP, Durham, NC, USA
| | - Martin Von Bergen
- Department for Molecular Systems Biology, Helmholtz Centre for Environmental Research, Leipzig, Germany
| | | | | | - Florian Caiment
- Department of Toxicogenomics, Maastricht University, Maastricht, The Netherlands
| | | | - Joshua Harrill
- Cellular and Molecular Toxicologist, Center for Computational Toxicology and Exposure (CCTE), U.S. Environmental Protection Agency, Durham, NC, USA
| | - Kamin Johnson
- Predictive Safety Center, Corteva Agriscience, Indianapolis, IN, USA
| | - Dongying Li
- National Center for Toxicological Research, U.S. Food and Drug Administration (FDA), 3900 NCTR Road, Jefferson, AR, 72079, USA
| | - David Rouquie
- Bayer SAS, Bayer Crop Science, 355 Rue Dostoïevski, CS 90153, 06906, Valbonne Sophia-Antipolis, France
| | | | | | - Tewes Tralau
- Department of Pesticides Safety, German Federal Institute for Risk Assessment (BfR), Berlin, Germany
| | - Mark R Viant
- School of Biosciences, University of Birmingham, Birmingham, UK
| | | | - Carole Yauk
- Department of Biology, University of Ottawa, Ottawa, Canada
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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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Yao X, Sun S, Zi Y, Liu Y, Yang J, Ren L, Chen G, Cao Z, Hou W, Song Y, Shang J, Jiang H, Li Z, Wang H, Zhang P, Shi L, Li QZ, Yu Y, Zheng Y. Comprehensive microRNA-seq transcriptomic profiling across 11 organs, 4 ages, and 2 sexes of Fischer 344 rats. Sci Data 2022; 9:201. [PMID: 35551205 PMCID: PMC9098487 DOI: 10.1038/s41597-022-01285-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2021] [Accepted: 03/04/2022] [Indexed: 11/08/2022] Open
Abstract
Rat is one of the most widely-used models in chemical safety evaluation and biomedical research. However, the knowledge about its microRNA (miRNA) expression patterns across multiple organs and various developmental stages is still limited. Here, we constructed a comprehensive rat miRNA expression BodyMap using a diverse collection of 320 RNA samples from 11 organs of both sexes of juvenile, adolescent, adult and aged Fischer 344 rats with four biological replicates per group. Following the Illumina TruSeq Small RNA protocol, an average of 5.1 million 50 bp single-end reads was generated per sample, yielding a total of 1.6 billion reads. The quality of the resulting miRNA-seq data was deemed to be high from raw sequences, mapped sequences, and biological reproducibility. Importantly, aliquots of the same RNA samples have previously been used to construct the mRNA BodyMap. The currently presented miRNA-seq dataset along with the existing mRNA-seq dataset from the same RNA samples provides a unique resource for studying the expression characteristics of existing and novel miRNAs, and for integrative analysis of miRNA-mRNA interactions, thereby facilitating better utilization of rats for biomarker discovery.
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Affiliation(s)
- Xintong Yao
- State Key Laboratory of Genetic Engineering, Human Phenome Institute, School of Life Sciences and Shanghai Cancer Center, Fudan University, Shanghai, 200438, China
| | - Shanyue Sun
- State Key Laboratory of Genetic Engineering, Human Phenome Institute, School of Life Sciences and Shanghai Cancer Center, Fudan University, Shanghai, 200438, China
| | - Yi Zi
- State Key Laboratory of Genetic Engineering, Human Phenome Institute, School of Life Sciences and Shanghai Cancer Center, Fudan University, Shanghai, 200438, China
| | - Yaqing Liu
- State Key Laboratory of Genetic Engineering, Human Phenome Institute, School of Life Sciences and Shanghai Cancer Center, Fudan University, Shanghai, 200438, China
| | - Jingcheng Yang
- State Key Laboratory of Genetic Engineering, Human Phenome Institute, School of Life Sciences and Shanghai Cancer Center, Fudan University, Shanghai, 200438, China
| | - Luyao Ren
- State Key Laboratory of Genetic Engineering, Human Phenome Institute, School of Life Sciences and Shanghai Cancer Center, Fudan University, Shanghai, 200438, China
| | - Guangchun Chen
- Department of Immunology, Microarray and Immune Phenotyping Core Facility, University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA
| | - Zehui Cao
- State Key Laboratory of Genetic Engineering, Human Phenome Institute, School of Life Sciences and Shanghai Cancer Center, Fudan University, Shanghai, 200438, China
| | - Wanwan Hou
- State Key Laboratory of Genetic Engineering, Human Phenome Institute, School of Life Sciences and Shanghai Cancer Center, Fudan University, Shanghai, 200438, China
| | - Yueqiang Song
- State Key Laboratory of Genetic Engineering, Human Phenome Institute, School of Life Sciences and Shanghai Cancer Center, Fudan University, Shanghai, 200438, China
| | - Jun Shang
- State Key Laboratory of Genetic Engineering, Human Phenome Institute, School of Life Sciences and Shanghai Cancer Center, Fudan University, Shanghai, 200438, China
| | - He Jiang
- State Key Laboratory of Genetic Engineering, Human Phenome Institute, School of Life Sciences and Shanghai Cancer Center, Fudan University, Shanghai, 200438, China
| | - Zhihui Li
- State Key Laboratory of Genetic Engineering, Human Phenome Institute, School of Life Sciences and Shanghai Cancer Center, Fudan University, Shanghai, 200438, China
| | - Haiyan Wang
- State Key Laboratory of Genetic Engineering, Human Phenome Institute, School of Life Sciences and Shanghai Cancer Center, Fudan University, Shanghai, 200438, China
| | - Peipei Zhang
- State Key Laboratory of Genetic Engineering, Human Phenome Institute, School of Life Sciences and Shanghai Cancer Center, Fudan University, Shanghai, 200438, China
| | - Leming Shi
- State Key Laboratory of Genetic Engineering, Human Phenome Institute, School of Life Sciences and Shanghai Cancer Center, Fudan University, Shanghai, 200438, China
| | - Quan-Zhen Li
- Department of Immunology, Microarray and Immune Phenotyping Core Facility, University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA.
| | - Ying Yu
- State Key Laboratory of Genetic Engineering, Human Phenome Institute, School of Life Sciences and Shanghai Cancer Center, Fudan University, Shanghai, 200438, China.
| | - Yuanting Zheng
- State Key Laboratory of Genetic Engineering, Human Phenome Institute, School of Life Sciences and Shanghai Cancer Center, Fudan University, Shanghai, 200438, China.
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R-ODAF: Omics data analysis framework for regulatory application. Regul Toxicol Pharmacol 2022; 131:105143. [PMID: 35247516 DOI: 10.1016/j.yrtph.2022.105143] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2021] [Revised: 12/20/2021] [Accepted: 02/14/2022] [Indexed: 12/12/2022]
Abstract
Despite the widespread use of transcriptomics technologies in toxicology research, acceptance of the data by regulatory agencies to support the hazard assessment is still limited. Fundamental issues contributing to this are the lack of reproducibility in transcriptomics data analysis arising from variance in the methods used to generate data and differences in the data processing. While research applications are flexible in the way the data are generated and interpreted, this is not the case for regulatory applications where an unambiguous answer, possibly later subject to legal scrutiny, is required. A reference analysis framework would give greater credibility to the data and allow the practitioners to justify their use of an alternative bioinformatic process by referring to a standard. In this publication, we propose a method called omics data analysis framework for regulatory application (R-ODAF), which has been built as a user-friendly pipeline to analyze raw transcriptomics data from microarray and next-generation sequencing. In the R-ODAF, we also propose additional statistical steps to remove the number of false positives obtained from standard data analysis pipelines for RNA-sequencing. We illustrate the added value of R-ODAF, compared to a standard workflow, using a typical toxicogenomics dataset of hepatocytes exposed to paracetamol.
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Kleinstreuer NC, Tetko IV, Tong W. Introduction to Special Issue: Computational Toxicology. Chem Res Toxicol 2021; 34:171-175. [PMID: 33583184 DOI: 10.1021/acs.chemrestox.1c00032] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
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