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Dougherty BV, Moore CJ, Rawls KD, Jenior ML, Chun B, Nagdas S, Saucerman JJ, Kolling GL, Wallqvist A, Papin JA. Identifying metabolic adaptations characteristic of cardiotoxicity using paired transcriptomics and metabolomics data integrated with a computational model of heart metabolism. PLoS Comput Biol 2024; 20:e1011919. [PMID: 38422168 DOI: 10.1371/journal.pcbi.1011919] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2022] [Revised: 03/12/2024] [Accepted: 02/15/2024] [Indexed: 03/02/2024] Open
Abstract
Improvements in the diagnosis and treatment of cancer have revealed long-term side effects of chemotherapeutics, particularly cardiotoxicity. Here, we present paired transcriptomics and metabolomics data characterizing in vitro cardiotoxicity to three compounds: 5-fluorouracil, acetaminophen, and doxorubicin. Standard gene enrichment and metabolomics approaches identify some commonly affected pathways and metabolites but are not able to readily identify metabolic adaptations in response to cardiotoxicity. The paired data was integrated with a genome-scale metabolic network reconstruction of the heart to identify shifted metabolic functions, unique metabolic reactions, and changes in flux in metabolic reactions in response to these compounds. Using this approach, we confirm previously seen changes in the p53 pathway by doxorubicin and RNA synthesis by 5-fluorouracil, we find evidence for an increase in phospholipid metabolism in response to acetaminophen, and we see a shift in central carbon metabolism suggesting an increase in metabolic demand after treatment with doxorubicin and 5-fluorouracil.
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Affiliation(s)
- Bonnie V Dougherty
- Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, United States of America
| | - Connor J Moore
- Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, United States of America
| | - Kristopher D Rawls
- Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, United States of America
| | - Matthew L Jenior
- Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, United States of America
| | - Bryan Chun
- Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, United States of America
| | - Sarbajeet Nagdas
- Department of Microbiology, Immunology, and Cancer Biology, University of Virginia Health System, Charlottesville, Virginia, United States of America
| | - Jeffrey J Saucerman
- Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, United States of America
| | - Glynis L Kolling
- Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, United States of America
- Department of Medicine, Division of Infectious Diseases and International Health, University of Virginia, Charlottesville, Virginia, United States of America
| | - 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, Maryland, United States of America
| | - Jason A Papin
- Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, United States of America
- Department of Medicine, Division of Infectious Diseases and International Health, University of Virginia, Charlottesville, Virginia, United States of America
- Department of Biochemistry & Molecular Genetics, University of Virginia, Charlottesville, Virginia, United States of America
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Abdik E, Çakır T. Transcriptome-based biomarker prediction for Parkinson's disease using genome-scale metabolic modeling. Sci Rep 2024; 14:585. [PMID: 38182712 PMCID: PMC10770157 DOI: 10.1038/s41598-023-51034-y] [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: 09/15/2023] [Accepted: 12/29/2023] [Indexed: 01/07/2024] Open
Abstract
Parkinson's disease (PD) is the second most common neurodegenerative disease in the world. Identification of PD biomarkers is crucial for early diagnosis and to develop target-based therapeutic agents. Integrative analysis of genome-scale metabolic models (GEMs) and omics data provides a computational approach for the prediction of metabolite biomarkers. Here, we applied the TIMBR (Transcriptionally Inferred Metabolic Biomarker Response) algorithm and two modified versions of TIMBR to investigate potential metabolite biomarkers for PD. To this end, we mapped thirteen post-mortem PD transcriptome datasets from the substantia nigra region onto Human-GEM. We considered a metabolite as a candidate biomarker if its production was predicted to be more efficient by a TIMBR-family algorithm in control or PD case for the majority of the datasets. Different metrics based on well-known PD-related metabolite alterations, PD-associated pathways, and a list of 25 high-confidence PD metabolite biomarkers compiled from the literature were used to compare the prediction performance of the three algorithms tested. The modified algorithm with the highest prediction power based on the metrics was called TAMBOOR, TrAnscriptome-based Metabolite Biomarkers by On-Off Reactions, which was introduced for the first time in this study. TAMBOOR performed better in terms of capturing well-known pathway alterations and metabolite secretion changes in PD. Therefore, our tool has a strong potential to be used for the prediction of novel diagnostic biomarkers for human diseases.
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Affiliation(s)
- Ecehan Abdik
- Department of Bioengineering, Gebze Technical University, Kocaeli, Turkey
| | - Tunahan Çakır
- Department of Bioengineering, Gebze Technical University, Kocaeli, Turkey.
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Pannala VR, Estes SK, Rahim M, Trenary I, O’Brien TP, Shiota C, Printz RL, Reifman J, Shiota M, Young JD, Wallqvist A. Toxicant-Induced Metabolic Alterations in Lipid and Amino Acid Pathways Are Predictive of Acute Liver Toxicity in Rats. Int J Mol Sci 2020; 21:ijms21218250. [PMID: 33158035 PMCID: PMC7663358 DOI: 10.3390/ijms21218250] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2020] [Revised: 10/30/2020] [Accepted: 11/02/2020] [Indexed: 02/07/2023] Open
Abstract
Liver disease and disorders associated with aberrant hepatocyte metabolism can be initiated via drug and environmental toxicant exposures. In this study, we tested the hypothesis that gene and metabolic profiling can reveal commonalities in liver response to different toxicants and provide the capability to identify early signatures of acute liver toxicity. We used Sprague Dawley rats and three classical hepatotoxicants: acetaminophen (2 g/kg), bromobenzene (0.4 g/kg), and carbon tetrachloride (0.3 g/kg), to identify early perturbations in liver metabolism after a single acute exposure dose. We measured changes in liver genes and plasma metabolites at two time points (5 and 10 h) and used genome-scale metabolic models to identify commonalities in liver responses across the three toxicants. We found strong correlations for gene and metabolic profiles between the toxicants, indicative of similarities in the liver response to toxicity. We identified several injury-specific pathways in lipid and amino acid metabolism that changed similarly across the three toxicants. Our findings suggest that several plasma metabolites in lipid and amino acid metabolism are strongly associated with the progression of liver toxicity, and as such, could be targeted and clinically assessed for their potential as early predictors of acute liver toxicity.
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Affiliation(s)
- 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 21702, USA;
- The Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc., Bethesda, MD 20817, USA
- Correspondence: (V.R.P.); (J.D.Y.); (A.W.); Tel.: +1-301-619-1976 (V.R.P.); +1-615-343-4253 (J.D.Y.); +1-301-619-1989 (A.W.); Fax: +301-619-1983 (A.W. & V.R.P.); +615-343-7951 (J.D.Y.)
| | - Shanea K. Estes
- Department of Molecular Physiology and Biophysics, Vanderbilt University School of Medicine, Nashville, TN 37232, USA; (S.K.E.); (T.P.O.); (C.S.); (R.L.P.); (M.S.)
| | - Mohsin Rahim
- Department of Chemical and Biomolecular Engineering, Vanderbilt University School of Engineering, Nashville, TN 37232, USA; (M.R.); (I.T.)
| | - Irina Trenary
- Department of Chemical and Biomolecular Engineering, Vanderbilt University School of Engineering, Nashville, TN 37232, USA; (M.R.); (I.T.)
| | - Tracy P. O’Brien
- Department of Molecular Physiology and Biophysics, Vanderbilt University School of Medicine, Nashville, TN 37232, USA; (S.K.E.); (T.P.O.); (C.S.); (R.L.P.); (M.S.)
| | - Chiyo Shiota
- Department of Molecular Physiology and Biophysics, Vanderbilt University School of Medicine, Nashville, TN 37232, USA; (S.K.E.); (T.P.O.); (C.S.); (R.L.P.); (M.S.)
| | - Richard L. Printz
- Department of Molecular Physiology and Biophysics, Vanderbilt University School of Medicine, Nashville, TN 37232, USA; (S.K.E.); (T.P.O.); (C.S.); (R.L.P.); (M.S.)
| | - Jaques Reifman
- 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 21702, USA;
| | - Masakazu Shiota
- Department of Molecular Physiology and Biophysics, Vanderbilt University School of Medicine, Nashville, TN 37232, USA; (S.K.E.); (T.P.O.); (C.S.); (R.L.P.); (M.S.)
| | - Jamey D. Young
- Department of Molecular Physiology and Biophysics, Vanderbilt University School of Medicine, Nashville, TN 37232, USA; (S.K.E.); (T.P.O.); (C.S.); (R.L.P.); (M.S.)
- Department of Chemical and Biomolecular Engineering, Vanderbilt University School of Engineering, Nashville, TN 37232, USA; (M.R.); (I.T.)
- Correspondence: (V.R.P.); (J.D.Y.); (A.W.); Tel.: +1-301-619-1976 (V.R.P.); +1-615-343-4253 (J.D.Y.); +1-301-619-1989 (A.W.); Fax: +301-619-1983 (A.W. & V.R.P.); +615-343-7951 (J.D.Y.)
| | - 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 21702, USA;
- Correspondence: (V.R.P.); (J.D.Y.); (A.W.); Tel.: +1-301-619-1976 (V.R.P.); +1-615-343-4253 (J.D.Y.); +1-301-619-1989 (A.W.); Fax: +301-619-1983 (A.W. & V.R.P.); +615-343-7951 (J.D.Y.)
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