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Drake C, Wehr MM, Zobl W, Koschmann J, De Lucca D, Kühne BA, Hansen T, Knebel J, Ritter D, Boei J, Vrieling H, Bitsch A, Escher SE. Substantiate a read-across hypothesis by using transcriptome data-A case study on volatile diketones. FRONTIERS IN TOXICOLOGY 2023; 5:1155645. [PMID: 37206915 PMCID: PMC10188990 DOI: 10.3389/ftox.2023.1155645] [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: 01/31/2023] [Accepted: 04/17/2023] [Indexed: 05/21/2023] Open
Abstract
This case study explores the applicability of transcriptome data to characterize a common mechanism of action within groups of short-chain aliphatic α-, β-, and γ-diketones. Human reference in vivo data indicate that the α-diketone diacetyl induces bronchiolitis obliterans in workers involved in the preparation of microwave popcorn. The other three α-diketones induced inflammatory responses in preclinical in vivo animal studies, whereas beta and gamma diketones in addition caused neuronal effects. We investigated early transcriptional responses in primary human bronchiolar (PBEC) cell cultures after 24 h and 72 h of air-liquid exposure. Differentially expressed genes (DEGs) were assessed based on transcriptome data generated with the EUToxRisk gene panel of Temp-O-Seq®. For each individual substance, genes were identified displaying a consistent differential expression across dose and exposure duration. The log fold change values of the DEG profiles indicate that α- and β-diketones are more active compared to γ-diketones. α-diketones in particular showed a highly concordant expression pattern, which may serve as a first indication of the shared mode of action. In order to gain a better mechanistic understanding, the resultant DEGs were submitted to a pathway analysis using ConsensusPathDB. The four α-diketones showed very similar results with regard to the number of activated and shared pathways. Overall, the number of signaling pathways decreased from α-to β-to γ-diketones. Additionally, we reconstructed networks of genes that interact with one another and are associated with different adverse outcomes such as fibrosis, inflammation or apoptosis using the TRANSPATH-database. Transcription factor enrichment and upstream analyses with the geneXplain platform revealed highly interacting gene products (called master regulators, MRs) per case study compound. The mapping of the resultant MRs on the reconstructed networks, visualized similar gene regulation with regard to fibrosis, inflammation and apoptosis. This analysis showed that transcriptome data can strengthen the similarity assessment of compounds, which is of particular importance, e.g., in read-across approaches. It is one important step towards grouping of compounds based on biological profiles.
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Affiliation(s)
- Christina Drake
- Fraunhofer Institute for Toxicology and Experimental Medicine, Chemical Safety and Toxicology, Hannover, Germany
- *Correspondence: Christina Drake,
| | - Matthias M. Wehr
- Fraunhofer Institute for Toxicology and Experimental Medicine, Chemical Safety and Toxicology, Hannover, Germany
| | - Walter Zobl
- Fraunhofer Institute for Toxicology and Experimental Medicine, Chemical Safety and Toxicology, Hannover, Germany
| | | | | | - Britta A. Kühne
- Fraunhofer Institute for Toxicology and Experimental Medicine, Chemical Safety and Toxicology, Hannover, Germany
| | - Tanja Hansen
- Fraunhofer Institute for Toxicology and Experimental Medicine, Chemical Safety and Toxicology, Hannover, Germany
| | - Jan Knebel
- Fraunhofer Institute for Toxicology and Experimental Medicine, Chemical Safety and Toxicology, Hannover, Germany
| | - Detlef Ritter
- Fraunhofer Institute for Toxicology and Experimental Medicine, Chemical Safety and Toxicology, Hannover, Germany
| | - Jan Boei
- Leiden University Medical Center, Leiden, Netherlands
| | | | - Annette Bitsch
- Fraunhofer Institute for Toxicology and Experimental Medicine, Chemical Safety and Toxicology, Hannover, Germany
| | - Sylvia E. Escher
- Fraunhofer Institute for Toxicology and Experimental Medicine, Chemical Safety and Toxicology, Hannover, Germany
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Stegmaier P, Voss N, Meier T, Kel A, Wingender E, Borlak J. Advanced computational biology methods identify molecular switches for malignancy in an EGF mouse model of liver cancer. PLoS One 2011; 6:e17738. [PMID: 21464922 PMCID: PMC3065454 DOI: 10.1371/journal.pone.0017738] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2010] [Accepted: 02/09/2011] [Indexed: 01/04/2023] Open
Abstract
The molecular causes by which the epidermal growth factor receptor tyrosine kinase induces malignant transformation are largely unknown. To better understand EGFs' transforming capacity whole genome scans were applied to a transgenic mouse model of liver cancer and subjected to advanced methods of computational analysis to construct de novo gene regulatory networks based on a combination of sequence analysis and entrained graph-topological algorithms. Here we identified transcription factors, processes, key nodes and molecules to connect as yet unknown interacting partners at the level of protein-DNA interaction. Many of those could be confirmed by electromobility band shift assay at recognition sites of gene specific promoters and by western blotting of nuclear proteins. A novel cellular regulatory circuitry could therefore be proposed that connects cell cycle regulated genes with components of the EGF signaling pathway. Promoter analysis of differentially expressed genes suggested the majority of regulated transcription factors to display specificity to either the pre-tumor or the tumor state. Subsequent search for signal transduction key nodes upstream of the identified transcription factors and their targets suggested the insulin-like growth factor pathway to render the tumor cells independent of EGF receptor activity. Notably, expression of IGF2 in addition to many components of this pathway was highly upregulated in tumors. Together, we propose a switch in autocrine signaling to foster tumor growth that was initially triggered by EGF and demonstrate the knowledge gain form promoter analysis combined with upstream key node identification.
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Affiliation(s)
| | - Nico Voss
- BIOBASE GmbH, Wolfenbuettel, Germany
| | - Tatiana Meier
- Department Molecular Medicine and Medical Biotechnology, Fraunhofer Institute of Toxicology and Experimental Medicine, Hannover, Germany
- Centre for Pharmacology and Toxicology, Hannover Medical School, Hannover, Germany
| | - Alexander Kel
- BIOBASE GmbH, Wolfenbuettel, Germany
- GeneXplain GmbH, Wolfenbuettel, Germany
- Institute of Chemical Biology and Fundamental Medicine, Novosibirsk, Russia
| | - Edgar Wingender
- BIOBASE GmbH, Wolfenbuettel, Germany
- GeneXplain GmbH, Wolfenbuettel, Germany
- Department of Bioinformatics, University of Goettingen, Goettingen, Germany
| | - Juergen Borlak
- Department Molecular Medicine and Medical Biotechnology, Fraunhofer Institute of Toxicology and Experimental Medicine, Hannover, Germany
- Centre for Pharmacology and Toxicology, Hannover Medical School, Hannover, Germany
- * E-mail:
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Sun X, Hong P. Automatic inference of multicellular regulatory networks using informative priors. INTERNATIONAL JOURNAL OF COMPUTATIONAL BIOLOGY AND DRUG DESIGN 2010; 2:115-33. [PMID: 20090166 DOI: 10.1504/ijcbdd.2009.028820] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
To fully understand the mechanisms governing animal development, computational models and algorithms are needed to enable quantitative studies of the underlying regulatory networks. We developed a mathematical model based on dynamic Bayesian networks to model multicellular regulatory networks that govern cell differentiation processes. A machine-learning method was developed to automatically infer such a model from heterogeneous data. We show that the model inference procedure can be greatly improved by incorporating interaction data across species. The proposed approach was applied to C. elegans vulval induction to reconstruct a model capable of simulating C. elegans vulval induction under 73 different genetic conditions.
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Affiliation(s)
- Xiaoyun Sun
- Department of Computer Science, Brandeis University, Waltham, MA 02454, USA.
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Grinkevich VV, Nikulenkov F, Shi Y, Enge M, Bao W, Maljukova A, Gluch A, Kel A, Sangfelt O, Selivanova G. Ablation of key oncogenic pathways by RITA-reactivated p53 is required for efficient apoptosis. Cancer Cell 2009; 15:441-53. [PMID: 19411072 DOI: 10.1016/j.ccr.2009.03.021] [Citation(s) in RCA: 91] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/16/2008] [Revised: 10/10/2008] [Accepted: 03/24/2009] [Indexed: 12/14/2022]
Abstract
Targeting "oncogene addiction" is a promising strategy for anticancer therapy. We report a potent inhibition of crucial oncogenes by p53 upon reactivation by small-molecule RITA in vitro and in vivo. RITA-activated p53 unleashes the transcriptional repression of antiapoptotic proteins Mcl-1, Bcl-2, MAP4, and survivin; blocks the Akt pathway on several levels; and downregulates c-Myc, cyclin E, and beta-catenin. p53 ablates c-Myc expression via several mechanisms at the transcriptional and posttranscriptional level. We show that the threshold for p53-mediated transrepression of survival genes is higher than for transactivation of proapoptotic targets. Inhibition of oncogenes by p53 reduces the cell's ability to buffer proapoptotic signals and elicits robust apoptosis. Our study highlights the role of transcriptional repression for p53-mediated tumor suppression.
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Affiliation(s)
- Vera V Grinkevich
- Department of Microbiology, Tumor and Cell Biology, Karolinska Institutet, 17177, Stockholm, Sweden
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Michael H, Hogan J, Kel A, Kel-Margoulis O, Schacherer F, Voss N, Wingender E. Building a knowledge base for systems pathology. Brief Bioinform 2008; 9:518-31. [PMID: 19073714 DOI: 10.1093/bib/bbn038] [Citation(s) in RCA: 12] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2023] Open
Abstract
Translating the exponentially growing amount of omics data into knowledge usable for a personalized medicine approach poses a formidable challenge. In this article-taking diabetes as a use case-we present strategies for developing data repositories into computer-accessible knowledge sources that can be used for a systemic view on the molecular causes of diseases, thus laying the foundation for systems pathology.
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Affiliation(s)
- Holger Michael
- Department of Bioinformatics, Goldschmidtstr. 1, D-37077 Göttingen, Germany
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Venter M, Warnich L. In silico promoters: modelling of cis-regulatory context facilitates target predictio. J Cell Mol Med 2008; 13:270-8. [PMID: 18505473 PMCID: PMC3823354 DOI: 10.1111/j.1582-4934.2008.00371.x] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Elucidation of gene regulatory complexity holds much promise towards aiding therapeutic interventions in medical research. It has become progressively more evident that the characterization of highly conserved regulatory modules within promoters may assist in the elucidation of distinct cis-motif and trans-element regulatory interactions, shared in response to stimulus-evoked pathological changes. With special emphasis on the promoter, accurate analyses of cis-motif architecture combined with integrative in silico modelling might serve as a more refined approach for prediction and study of regulatory targets and major regulators governing transcriptional control. In this review, we have highlighted key examples and recent advances implementing in silico promoter models that could serve as essential contributions for future research in molecular medicine.
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Affiliation(s)
- Mauritz Venter
- Department of Genetics, Stellenbosch University, Matieland, South Africa.
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Kel AE, Niehof M, Matys V, Zemlin R, Borlak J. Genome wide prediction of HNF4alpha functional binding sites by the use of local and global sequence context. Genome Biol 2008; 9:R36. [PMID: 18291023 PMCID: PMC2374721 DOI: 10.1186/gb-2008-9-2-r36] [Citation(s) in RCA: 13] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2007] [Revised: 11/09/2007] [Accepted: 02/21/2008] [Indexed: 11/16/2022] Open
Abstract
An application of machine learning algorithms enables prediction of the functional context of transcription factor binding sites in the human genome. We report an application of machine learning algorithms that enables prediction of the functional context of transcription factor binding sites in the human genome. We demonstrate that our method allowed de novo identification of hepatic nuclear factor (HNF)4α binding sites and significantly improved an overall recognition of faithful HNF4α targets. When applied to published findings, an unprecedented high number of false positives were identified. The technique can be applied to any transcription factor.
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Affiliation(s)
- Alexander E Kel
- BIOBASE GmbH, Halchtersche Str, 38304 Wolfenbüttel, Germany.
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Dönitz J, Goemann B, Lizé M, Michael H, Sasse N, Wingender E, Potapov AP. EndoNet: an information resource about regulatory networks of cell-to-cell communication. Nucleic Acids Res 2007; 36:D689-94. [PMID: 18045786 PMCID: PMC2238947 DOI: 10.1093/nar/gkm940] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
EndoNet is an information resource about intercellular regulatory communication. It provides information about hormones, hormone receptors, the sources (i.e. cells, tissues and organs) where the hormones are synthesized and secreted, and where the respective receptors are expressed. The database focuses on the regulatory relations between them. An elementary communication is displayed as a causal link from a cell that secretes a particular hormone to those cells which express the corresponding hormone receptor and respond to the hormone. Whenever expression, synthesis and/or secretion of another hormone are part of this response, it renders the corresponding cell an internal node of the resulting network. This intercellular communication network coordinates the function of different organs. Therefore, the database covers the hierarchy of cellular organization of tissues and organs as it has been modeled in the Cytomer ontology, which has now been directly embedded into EndoNet. The user can query the database; the results can be used to visualize the intercellular information flow. A newly implemented hormone classification enables to browse the database and may be used as alternative entry point. EndoNet is accessible at: http://endonet.bioinf.med.uni-goettingen.de/
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Affiliation(s)
- Jürgen Dönitz
- Department of Bioinformatics, University of Göttingen/Medical School, D-37077 Göttingen
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