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Zaunseder E, Mütze U, Okun JG, Hoffmann GF, Kölker S, Heuveline V, Thiele I. Personalized metabolic whole-body models for newborns and infants predict growth and biomarkers of inherited metabolic diseases. Cell Metab 2024; 36:1882-1897.e7. [PMID: 38834070 DOI: 10.1016/j.cmet.2024.05.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/20/2023] [Revised: 03/13/2024] [Accepted: 05/09/2024] [Indexed: 06/06/2024]
Abstract
Comprehensive whole-body models (WBMs) accounting for organ-specific dynamics have been developed to simulate adult metabolism, but such models do not exist for infants. Here, we present a resource of 360 organ-resolved, sex-specific models of newborn and infant metabolism (infant-WBMs) spanning the first 180 days of life. These infant-WBMs were parameterized to represent the distinct metabolic characteristics of newborns and infants, including nutrition, energy requirements, and thermoregulation. We demonstrate that the predicted infant growth was consistent with the recommendation by the World Health Organization. We assessed the infant-WBMs' reliability and capabilities for personalization by simulating 10,000 newborns based on their blood metabolome and birth weight. Furthermore, the infant-WBMs accurately predicted changes in known biomarkers over time and metabolic responses to treatment strategies for inherited metabolic diseases. The infant-WBM resource holds promise for personalized medicine, as the infant-WBMs could be a first step to digital metabolic twins for newborn and infant metabolism.
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Affiliation(s)
- Elaine Zaunseder
- School of Medicine, University of Galway, Galway, Ireland; Engineering Mathematics and Computing Lab (EMCL), Interdisciplinary Center for Scientific Computing (IWR), Heidelberg University, Heidelberg, Germany; Data Mining and Uncertainty Quantification (DMQ), Heidelberg Institute for Theoretical Studies (HITS), Heidelberg, Germany
| | - Ulrike Mütze
- Division of Child Neurology and Metabolic Medicine, Center for Child and Adolescent Medicine, Heidelberg University, Medical Faculty, Heidelberg, Germany
| | - Jürgen G Okun
- Division of Child Neurology and Metabolic Medicine, Center for Child and Adolescent Medicine, Heidelberg University, Medical Faculty, Heidelberg, Germany
| | - Georg F Hoffmann
- Division of Child Neurology and Metabolic Medicine, Center for Child and Adolescent Medicine, Heidelberg University, Medical Faculty, Heidelberg, Germany
| | - Stefan Kölker
- Division of Child Neurology and Metabolic Medicine, Center for Child and Adolescent Medicine, Heidelberg University, Medical Faculty, Heidelberg, Germany
| | - Vincent Heuveline
- School of Medicine, University of Galway, Galway, Ireland; Engineering Mathematics and Computing Lab (EMCL), Interdisciplinary Center for Scientific Computing (IWR), Heidelberg University, Heidelberg, Germany
| | - Ines Thiele
- School of Medicine, University of Galway, Galway, Ireland; Discipline of Microbiology, University of Galway, Galway, Ireland; Digital Metabolic Twin Centre, University of Galway, Ireland; Ryan Institute, University of Galway, Galway, Ireland; APC Microbiome Ireland, Cork, Ireland.
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2
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Verma S, Magazzù G, Eftekhari N, Lou T, Gilhespy A, Occhipinti A, Angione C. Cross-attention enables deep learning on limited omics-imaging-clinical data of 130 lung cancer patients. CELL REPORTS METHODS 2024; 4:100817. [PMID: 38981473 PMCID: PMC11294841 DOI: 10.1016/j.crmeth.2024.100817] [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: 08/29/2023] [Revised: 04/18/2024] [Accepted: 06/17/2024] [Indexed: 07/11/2024]
Abstract
Deep-learning tools that extract prognostic factors derived from multi-omics data have recently contributed to individualized predictions of survival outcomes. However, the limited size of integrated omics-imaging-clinical datasets poses challenges. Here, we propose two biologically interpretable and robust deep-learning architectures for survival prediction of non-small cell lung cancer (NSCLC) patients, learning simultaneously from computed tomography (CT) scan images, gene expression data, and clinical information. The proposed models integrate patient-specific clinical, transcriptomic, and imaging data and incorporate Kyoto Encyclopedia of Genes and Genomes (KEGG) and Reactome pathway information, adding biological knowledge within the learning process to extract prognostic gene biomarkers and molecular pathways. While both models accurately stratify patients in high- and low-risk groups when trained on a dataset of only 130 patients, introducing a cross-attention mechanism in a sparse autoencoder significantly improves the performance, highlighting tumor regions and NSCLC-related genes as potential biomarkers and thus offering a significant methodological advancement when learning from small imaging-omics-clinical samples.
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Affiliation(s)
- Suraj Verma
- School of Computing, Engineering and Digital Technologies, Teesside University, Middlesbrough, UK
| | | | | | - Thai Lou
- Gateshead Health NHS Foundation Trust, Gateshead, UK
| | - Alex Gilhespy
- South Tyneside and Sunderland NHS Foundation Trust, Sunderland, UK
| | - Annalisa Occhipinti
- School of Computing, Engineering and Digital Technologies, Teesside University, Middlesbrough, UK; Centre for Digital Innovation, Teesside University, Middlesbrough, UK; National Horizons Centre, Teesside University, Darlington, UK
| | - Claudio Angione
- School of Computing, Engineering and Digital Technologies, Teesside University, Middlesbrough, UK; Centre for Digital Innovation, Teesside University, Middlesbrough, UK; National Horizons Centre, Teesside University, Darlington, UK.
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3
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Tarzi C, Zampieri G, Sullivan N, Angione C. Emerging methods for genome-scale metabolic modeling of microbial communities. Trends Endocrinol Metab 2024; 35:533-548. [PMID: 38575441 DOI: 10.1016/j.tem.2024.02.018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Revised: 02/28/2024] [Accepted: 02/29/2024] [Indexed: 04/06/2024]
Abstract
Genome-scale metabolic models (GEMs) are consolidating as platforms for studying mixed microbial populations, by combining biological data and knowledge with mathematical rigor. However, deploying these models to answer research questions can be challenging due to the increasing number of available computational tools, the lack of universal standards, and their inherent limitations. Here, we present a comprehensive overview of foundational concepts for building and evaluating genome-scale models of microbial communities. We then compare tools in terms of requirements, capabilities, and applications. Next, we highlight the current pitfalls and open challenges to consider when adopting existing tools and developing new ones. Our compendium can be relevant for the expanding community of modelers, both at the entry and experienced levels.
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Affiliation(s)
- Chaimaa Tarzi
- School of Computing, Engineering and Digital Technologies, Teesside University, Southfield Rd, Middlesbrough, TS1 3BX, North Yorkshire, UK
| | - Guido Zampieri
- Department of Biology, University of Padova, Padova, 35122, Veneto, Italy
| | - Neil Sullivan
- Complement Genomics Ltd, Station Rd, Lanchester, Durham, DH7 0EX, County Durham, UK
| | - Claudio Angione
- School of Computing, Engineering and Digital Technologies, Teesside University, Southfield Rd, Middlesbrough, TS1 3BX, North Yorkshire, UK; Centre for Digital Innovation, Teesside University, Southfield Rd, Middlesbrough, TS1 3BX, North Yorkshire, UK; National Horizons Centre, Teesside University, 38 John Dixon Ln, Darlington, DL1 1HG, North Yorkshire, UK.
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4
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Narayanan B, Weilandt D, Masid M, Miskovic L, Hatzimanikatis V. Rational strain design with minimal phenotype perturbation. Nat Commun 2024; 15:723. [PMID: 38267425 PMCID: PMC10808392 DOI: 10.1038/s41467-024-44831-0] [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: 12/06/2022] [Accepted: 01/08/2024] [Indexed: 01/26/2024] Open
Abstract
Devising genetic interventions for desired cellular phenotypes remains challenging regarding time and resources. Kinetic models can accelerate this task by simulating metabolic responses to genetic perturbations. However, exhaustive design evaluations with kinetic models are computationally impractical, especially when targeting multiple enzymes. Here, we introduce a framework for efficiently scouting the design space while respecting cellular physiological requirements. The framework employs mixed-integer linear programming and nonlinear simulations with large-scale nonlinear kinetic models to devise genetic interventions while accounting for the network effects of these perturbations. Importantly, it ensures the engineered strain's robustness by maintaining its phenotype close to that of the reference strain. The framework, applied to improve the anthranilate production in E. coli, devises designs for experimental implementation, including eight previously experimentally validated targets. We expect this framework to play a crucial role in future design-build-test-learn cycles, significantly expediting the strain design compared to exhaustive design enumeration.
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Affiliation(s)
- Bharath Narayanan
- Laboratory of Computational Systems Biology (LCSB), Ecole Polytechnique Fédérale de Lausanne (EPFL), CH-1015, Lausanne, Switzerland
- Department of Oncology, University of Cambridge, Cambridge, CB2 0XZ, UK
| | - Daniel Weilandt
- Laboratory of Computational Systems Biology (LCSB), Ecole Polytechnique Fédérale de Lausanne (EPFL), CH-1015, Lausanne, Switzerland
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, 08544, USA
| | - Maria Masid
- Ludwig Institute for Cancer Research, Department of Oncology, University of Lausanne, and Lausanne University Hospital (CHUV), Lausanne, Switzerland
| | - Ljubisa Miskovic
- Laboratory of Computational Systems Biology (LCSB), Ecole Polytechnique Fédérale de Lausanne (EPFL), CH-1015, Lausanne, Switzerland.
| | - Vassily Hatzimanikatis
- Laboratory of Computational Systems Biology (LCSB), Ecole Polytechnique Fédérale de Lausanne (EPFL), CH-1015, Lausanne, Switzerland.
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Batagov A, Dalan R, Wu A, Lai W, Tan CS, Eisenhaber F. Generalized metabolic flux analysis framework provides mechanism-based predictions of ophthalmic complications in type 2 diabetes patients. Health Inf Sci Syst 2023; 11:18. [PMID: 37008895 PMCID: PMC10060506 DOI: 10.1007/s13755-023-00218-x] [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] [Received: 03/08/2022] [Accepted: 02/19/2023] [Indexed: 03/31/2023] Open
Abstract
Chronic metabolic diseases arise from changes in metabolic fluxes through biomolecular pathways and gene networks accumulated over the lifetime of an individual. While clinical and biochemical profiles present just real-time snapshots of the patients' health, efficient computation models of the pathological disturbance of biomolecular processes are required to achieve individualized mechanistic insights into disease progression. Here, we describe the Generalized metabolic flux analysis (GMFA) for addressing this gap. Suitably grouping individual metabolites/fluxes into pools simplifies the analysis of the resulting more coarse-grain network. We also map non-metabolic clinical modalities onto the network with additional edges. Instead of using the time coordinate, the system status (metabolite concentrations and fluxes) is quantified as function of a generalized extent variable (a coordinate in the space of generalized metabolites) that represents the system's coordinate along its evolution path and evaluates the degree of change between any two states on that path. We applied GMFA to analyze Type 2 Diabetes Mellitus (T2DM) patients from two cohorts: EVAS (289 patients from Singapore) and NHANES (517) from the USA. Personalized systems biology models (digital twins) were constructed. We deduced disease dynamics from the individually parameterized metabolic network and predicted the evolution path of the metabolic health state. For each patient, we obtained an individual description of disease dynamics and predict an evolution path of the metabolic health state. Our predictive models achieve an ROC-AUC in the range 0.79-0.95 (sensitivity 80-92%, specificity 62-94%) in identifying phenotypes at the baseline and predicting future development of diabetic retinopathy and cataract progression among T2DM patients within 3 years from the baseline. The GMFA method is a step towards realizing the ultimate goal to develop practical predictive computational models for diagnostics based on systems biology. This tool has potential use in chronic disease management in medical practice. Supplementary Information The online version contains supplementary material available at 10.1007/s13755-023-00218-x.
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Affiliation(s)
- Arsen Batagov
- Mesh Bio Pte. Ltd., 10 Anson Rd, #22-02, 079903 Singapore, Singapore
| | - Rinkoo Dalan
- Department of Endocrinology, Tan Tock Seng Hospital, Singapore, Singapore
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore
| | - Andrew Wu
- Mesh Bio Pte. Ltd., 10 Anson Rd, #22-02, 079903 Singapore, Singapore
| | - Wenbin Lai
- Mesh Bio Pte. Ltd., 10 Anson Rd, #22-02, 079903 Singapore, Singapore
| | - Colin S. Tan
- Fundus Image Reading Center, National Healthcare Group Eye Institute, Singapore, Singapore
- Tan Tock Seng Hospital, National Healthcare Group Eye Institute, Singapore, Singapore
- Duke-NUS Medical School, Singapore, Singapore
| | - Frank Eisenhaber
- Bioinformatics Institute (BII), Agency for Science, Technology and Research (A*STAR), Singapore, Singapore
- Genome Institute of Singapore (GIS), Agency for Science, Technology and Research (A*STAR), Singapore, Singapore
- School of Biological Science (SBS), Nanyang Technological University, Singapore, Singapore
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6
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Chen K, Wang F. Cell-specific genome-scale metabolic modeling of SARS-CoV-2-infected lung to identify antiviral enzymes. FEBS Open Bio 2023; 13:2172-2186. [PMID: 37734920 PMCID: PMC10699103 DOI: 10.1002/2211-5463.13710] [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: 06/21/2023] [Revised: 08/09/2023] [Accepted: 09/19/2023] [Indexed: 09/23/2023] Open
Abstract
Computational systems biology plays a key role in the discovery of suitable antiviral targets. We designed a cell-specific, constraint-based modeling technique for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-infected lungs. We used the gene sequence of the alpha variant of SARS-CoV-2 to build a viral biomass reaction (VBR). We also used the mass proportion of lipids between the viral biomass and its host cell to estimate the stoichiometric coefficients of viral lipids in the reaction. We then integrated the VBR, the gene expression of the alpha variant of SARS-CoV-2, and the generic human metabolic network Recon3D to reconstruct a cell-specific genome-scale metabolic model. An antiviral target discovery (AVTD) platform was introduced using this model to identify therapeutic drug targets for combating COVID-19. The AVTD platform not only identified antiviral genes for eliminating viral replication but also predicted side effects of treatments. Our computational results revealed that knocking out dihydroorotate dehydrogenase (DHODH) might reduce the synthesis rate of cytidine-5'-triphosphate and uridine-5'-triphosphate, which terminate the viral building blocks of DNA and RNA for SARS-CoV-2 replication. Our results also indicated that DHODH is a promising antiviral target that causes minor side effects, which is consistent with the results of recent reports. Moreover, we discovered that the genes that participate in the de novo biosynthesis of glycerophospholipids and ceramides become unidentifiable if the VBR does not involve the stoichiometry of lipids.
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Affiliation(s)
- Ke‐Lin Chen
- Department of Chemical EngineeringNational Chung Cheng UniversityChiayiTaiwan
| | - Feng‐Sheng Wang
- Department of Chemical EngineeringNational Chung Cheng UniversityChiayiTaiwan
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7
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Le Bars S, Bolteau M, Bourdon J, Guziolowski C. Predicting weighted unobserved nodes in a regulatory network using answer set programming. BMC Bioinformatics 2023; 24:321. [PMID: 37626282 PMCID: PMC10463596 DOI: 10.1186/s12859-023-05429-3] [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: 03/14/2022] [Accepted: 07/24/2023] [Indexed: 08/27/2023] Open
Abstract
BACKGROUND The impact of a perturbation, over-expression, or repression of a key node on an organism, can be modelled based on a regulatory and/or metabolic network. Integration of these two networks could improve our global understanding of biological mechanisms triggered by a perturbation. This study focuses on improving the modelling of the regulatory network to facilitate a possible integration with the metabolic network. Previously proposed methods that study this problem fail to deal with a real-size regulatory network, computing predictions sensitive to perturbation and quantifying the predicted species behaviour more finely. RESULTS To address previously mentioned limitations, we develop a new method based on Answer Set Programming, MajS. It takes a regulatory network and a discrete partial set of observations as input. MajS tests the consistency between the input data, proposes minimal repairs on the network to establish consistency, and finally computes weighted and signed predictions over the network species. We tested MajS by comparing the HIF-1 signalling pathway with two gene-expression datasets. Our results show that MajS can predict 100% of unobserved species. When comparing MajS with two similar (discrete and quantitative) tools, we observed that compared with the discrete tool, MajS proposes a better coverage of the unobserved species, is more sensitive to system perturbations, and proposes predictions closer to real data. Compared to the quantitative tool, MajS provides more refined discrete predictions that agree with the dynamic proposed by the quantitative tool. CONCLUSIONS MajS is a new method to test the consistency between a regulatory network and a dataset that provides computational predictions on unobserved network species. It provides fine-grained discrete predictions by outputting the weight of the predicted sign as a piece of additional information. MajS' output, thanks to its weight, could easily be integrated with metabolic network modelling.
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Affiliation(s)
- Sophie Le Bars
- École Centrale Nantes, CNRS, LS2N, UMR 6004, Nantes Université, Nantes, 44000 France
| | - Mathieu Bolteau
- École Centrale Nantes, CNRS, LS2N, UMR 6004, Nantes Université, Nantes, 44000 France
| | - Jérémie Bourdon
- École Centrale Nantes, CNRS, LS2N, UMR 6004, Nantes Université, Nantes, 44000 France
| | - Carito Guziolowski
- École Centrale Nantes, CNRS, LS2N, UMR 6004, Nantes Université, Nantes, 44000 France
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8
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Chen C, Wang J, Pan D, Wang X, Xu Y, Yan J, Wang L, Yang X, Yang M, Liu G. Applications of multi-omics analysis in human diseases. MedComm (Beijing) 2023; 4:e315. [PMID: 37533767 PMCID: PMC10390758 DOI: 10.1002/mco2.315] [Citation(s) in RCA: 29] [Impact Index Per Article: 29.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2023] [Revised: 05/25/2023] [Accepted: 05/31/2023] [Indexed: 08/04/2023] Open
Abstract
Multi-omics usually refers to the crossover application of multiple high-throughput screening technologies represented by genomics, transcriptomics, single-cell transcriptomics, proteomics and metabolomics, spatial transcriptomics, and so on, which play a great role in promoting the study of human diseases. Most of the current reviews focus on describing the development of multi-omics technologies, data integration, and application to a particular disease; however, few of them provide a comprehensive and systematic introduction of multi-omics. This review outlines the existing technical categories of multi-omics, cautions for experimental design, focuses on the integrated analysis methods of multi-omics, especially the approach of machine learning and deep learning in multi-omics data integration and the corresponding tools, and the application of multi-omics in medical researches (e.g., cancer, neurodegenerative diseases, aging, and drug target discovery) as well as the corresponding open-source analysis tools and databases, and finally, discusses the challenges and future directions of multi-omics integration and application in precision medicine. With the development of high-throughput technologies and data integration algorithms, as important directions of multi-omics for future disease research, single-cell multi-omics and spatial multi-omics also provided a detailed introduction. This review will provide important guidance for researchers, especially who are just entering into multi-omics medical research.
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Affiliation(s)
- Chongyang Chen
- Key Laboratory of Nuclear MedicineMinistry of HealthJiangsu Key Laboratory of Molecular Nuclear MedicineJiangsu Institute of Nuclear MedicineWuxiChina
- Co‐innovation Center of NeurodegenerationNantong UniversityNantongChina
| | - Jing Wang
- Shenzhen Key Laboratory of Modern ToxicologyShenzhen Medical Key Discipline of Health Toxicology (2020–2024)Shenzhen Center for Disease Control and PreventionShenzhenChina
| | - Donghui Pan
- Key Laboratory of Nuclear MedicineMinistry of HealthJiangsu Key Laboratory of Molecular Nuclear MedicineJiangsu Institute of Nuclear MedicineWuxiChina
| | - Xinyu Wang
- Key Laboratory of Nuclear MedicineMinistry of HealthJiangsu Key Laboratory of Molecular Nuclear MedicineJiangsu Institute of Nuclear MedicineWuxiChina
| | - Yuping Xu
- Key Laboratory of Nuclear MedicineMinistry of HealthJiangsu Key Laboratory of Molecular Nuclear MedicineJiangsu Institute of Nuclear MedicineWuxiChina
| | - Junjie Yan
- Key Laboratory of Nuclear MedicineMinistry of HealthJiangsu Key Laboratory of Molecular Nuclear MedicineJiangsu Institute of Nuclear MedicineWuxiChina
| | - Lizhen Wang
- Key Laboratory of Nuclear MedicineMinistry of HealthJiangsu Key Laboratory of Molecular Nuclear MedicineJiangsu Institute of Nuclear MedicineWuxiChina
| | - Xifei Yang
- Shenzhen Key Laboratory of Modern ToxicologyShenzhen Medical Key Discipline of Health Toxicology (2020–2024)Shenzhen Center for Disease Control and PreventionShenzhenChina
| | - Min Yang
- Key Laboratory of Nuclear MedicineMinistry of HealthJiangsu Key Laboratory of Molecular Nuclear MedicineJiangsu Institute of Nuclear MedicineWuxiChina
| | - Gong‐Ping Liu
- Co‐innovation Center of NeurodegenerationNantong UniversityNantongChina
- Department of PathophysiologySchool of Basic MedicineKey Laboratory of Ministry of Education of China and Hubei Province for Neurological DisordersTongji Medical CollegeHuazhong University of Science and TechnologyWuhanChina
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9
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Sen P, Orešič M. Integrating Omics Data in Genome-Scale Metabolic Modeling: A Methodological Perspective for Precision Medicine. Metabolites 2023; 13:855. [PMID: 37512562 PMCID: PMC10383060 DOI: 10.3390/metabo13070855] [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] [Received: 06/15/2023] [Revised: 07/11/2023] [Accepted: 07/17/2023] [Indexed: 07/30/2023] Open
Abstract
Recent advancements in omics technologies have generated a wealth of biological data. Integrating these data within mathematical models is essential to fully leverage their potential. Genome-scale metabolic models (GEMs) provide a robust framework for studying complex biological systems. GEMs have significantly contributed to our understanding of human metabolism, including the intrinsic relationship between the gut microbiome and the host metabolism. In this review, we highlight the contributions of GEMs and discuss the critical challenges that must be overcome to ensure their reproducibility and enhance their prediction accuracy, particularly in the context of precision medicine. We also explore the role of machine learning in addressing these challenges within GEMs. The integration of omics data with GEMs has the potential to lead to new insights, and to advance our understanding of molecular mechanisms in human health and disease.
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Affiliation(s)
- Partho Sen
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, FI-20520 Turku, Finland
- School of Medical Sciences, Faculty of Medicine and Health, Örebro University, 702 81 Örebro, Sweden
| | - Matej Orešič
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, FI-20520 Turku, Finland
- School of Medical Sciences, Faculty of Medicine and Health, Örebro University, 702 81 Örebro, Sweden
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10
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González-Arrué N, Inostroza I, Conejeros R, Rivas-Astroza M. Phenotype-specific estimation of metabolic fluxes using gene expression data. iScience 2023; 26:106201. [PMID: 36915687 PMCID: PMC10006673 DOI: 10.1016/j.isci.2023.106201] [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: 09/22/2022] [Revised: 11/30/2022] [Accepted: 02/10/2023] [Indexed: 02/17/2023] Open
Abstract
A cell's genome influences its metabolism via the expression of enzyme-related genes, but transcriptome and fluxome are not perfectly correlated as post-transcriptional mechanisms also regulate reaction's kinetics. Here, we addressed the question: given a transcriptome, how unobserved mechanisms of reaction kinetics should be systematically accounted for when inferring the fluxome? To infer the most likely and least biased fluxome, we present Pheflux, a constraint-based model maximizing Shannon's entropy of fluxes per mRNA. Benchmarked against 13C fluxes of yeast and bacteria, Pheflux accurately estimates the carbon core metabolism. We applied Pheflux to thousands of normal and tumor cell transcriptomes obtained from The Cancer Genome Atlas. Pheflux showed statistically significantly higher glucose yields on lactate in breast, kidney, and bronchus-lung tumoral cells than their normal counterparts. Results are consistent with the Warburg effect, a hallmark of cancer metabolism, suggesting that Pheflux can be efficiently used to study the metabolism of eukaryotic cells.
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Affiliation(s)
- Nicolás González-Arrué
- Universidad Tecnológica Metropolitana, Departamento de Biotecnología, Ñuñoa, Santiago 7800003, Chile
| | - Isidora Inostroza
- Universidad Tecnológica Metropolitana, Departamento de Biotecnología, Ñuñoa, Santiago 7800003, Chile
| | - Raúl Conejeros
- Pontificia Universidad Católica de Valparaíso, Escuela de Ingeniería Bioquímica, Valparaíso, 2362803, Chile
| | - Marcelo Rivas-Astroza
- Universidad Tecnológica Metropolitana, Departamento de Biotecnología, Ñuñoa, Santiago 7800003, Chile
- Corresponding author
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11
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Shi T, Yu H, Blair RH. Integrated regulatory and metabolic networks of the tumor microenvironment for therapeutic target prioritization. Stat Appl Genet Mol Biol 2023; 22:sagmb-2022-0054. [PMID: 37988745 DOI: 10.1515/sagmb-2022-0054] [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: 11/05/2022] [Accepted: 09/28/2023] [Indexed: 11/23/2023]
Abstract
Translation of genomic discovery, such as single-cell sequencing data, to clinical decisions remains a longstanding bottleneck in the field. Meanwhile, computational systems biological models, such as cellular metabolism models and cell signaling pathways, have emerged as powerful approaches to provide efficient predictions in metabolites and gene expression levels, respectively. However, there has been limited research on the integration between these two models. This work develops a methodology for integrating computational models of probabilistic gene regulatory networks with a constraint-based metabolism model. By using probabilistic reasoning with Bayesian Networks, we aim to predict cell-specific changes under different interventions, which are embedded into the constraint-based models of metabolism. Applications to single-cell sequencing data of glioblastoma brain tumors generate predictions about the effects of pharmaceutical interventions on the regulatory network and downstream metabolisms in different cell types from the tumor microenvironment. The model presents possible insights into treatments that could potentially suppress anaerobic metabolism in malignant cells with minimal impact on other cell types' metabolism. The proposed integrated model can guide therapeutic target prioritization, the formulation of combination therapies, and future drug discovery. This model integration framework is also generalizable to other applications, such as different cell types, organisms, and diseases.
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Affiliation(s)
- Tiange Shi
- University at Buffalo, Biostatistics, Buffalo, USA
| | - Han Yu
- Roswell Park Comprehensive Cancer Center, Biostatistics and Bioinformatics, Buffalo, USA
| | - Rachael Hageman Blair
- University at Buffalo, Biostatistics, Institute for Artificial Intelligence and Data Science, Buffalo, USA
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12
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Doan LMT, Angione C, Occhipinti A. Machine Learning Methods for Survival Analysis with Clinical and Transcriptomics Data of Breast Cancer. Methods Mol Biol 2023; 2553:325-393. [PMID: 36227551 DOI: 10.1007/978-1-0716-2617-7_16] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Breast cancer is one of the most common cancers in women worldwide, which causes an enormous number of deaths annually. However, early diagnosis of breast cancer can improve survival outcomes enabling simpler and more cost-effective treatments. The recent increase in data availability provides unprecedented opportunities to apply data-driven and machine learning methods to identify early-detection prognostic factors capable of predicting the expected survival and potential sensitivity to treatment of patients, with the final aim of enhancing clinical outcomes. This tutorial presents a protocol for applying machine learning models in survival analysis for both clinical and transcriptomic data. We show that integrating clinical and mRNA expression data is essential to explain the multiple biological processes driving cancer progression. Our results reveal that machine-learning-based models such as random survival forests, gradient boosted survival model, and survival support vector machine can outperform the traditional statistical methods, i.e., Cox proportional hazard model. The highest C-index among the machine learning models was recorded when using survival support vector machine, with a value 0.688, whereas the C-index recorded using the Cox model was 0.677. Shapley Additive Explanation (SHAP) values were also applied to identify the feature importance of the models and their impact on the prediction outcomes.
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Affiliation(s)
- Le Minh Thao Doan
- School of Computing, Engineering and Digital Technologies, Teesside University, Middlesbrough, UK
| | - Claudio Angione
- School of Computing, Engineering and Digital Technologies, Teesside University, Middlesbrough, UK
- Centre for Digital Innovation, Teesside University, Middlesbrough, UK
- Healthcare Innovation Centre, Teesside University, Middlesbrough, UK
- National Horizons Centre, Teesside University, Darlington, UK
| | - Annalisa Occhipinti
- School of Computing, Engineering and Digital Technologies, Teesside University, Middlesbrough, UK.
- Centre for Digital Innovation, Teesside University, Middlesbrough, UK.
- National Horizons Centre, Teesside University, Darlington, UK.
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13
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Hussain S, Yates C, Campbell MJ. Vitamin D and Systems Biology. Nutrients 2022; 14:nu14245197. [PMID: 36558356 PMCID: PMC9782494 DOI: 10.3390/nu14245197] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Revised: 11/28/2022] [Accepted: 12/01/2022] [Indexed: 12/12/2022] Open
Abstract
The biological actions of the vitamin D receptor (VDR) have been investigated intensively for over 100 years and has led to the identification of significant insights into the repertoire of its biological actions. These were initially established to be centered on the regulation of calcium transport in the colon and deposition in bone. Beyond these well-known calcemic roles, other roles have emerged in the regulation of cell differentiation processes and have an impact on metabolism. The purpose of the current review is to consider where applying systems biology (SB) approaches may begin to generate a more precise understanding of where the VDR is, and is not, biologically impactful. Two SB approaches have been developed and begun to reveal insight into VDR biological functions. In a top-down SB approach genome-wide scale data are statistically analyzed, and from which a role for the VDR emerges in terms of being a hub in a biological network. Such approaches have confirmed significant roles, for example, in myeloid differentiation and the control of inflammation and innate immunity. In a bottom-up SB approach, current biological understanding is built into a kinetic model which is then applied to existing biological data to explain the function and identify unknown behavior. To date, this has not been applied to the VDR, but has to the related ERα and identified previously unknown mechanisms of control. One arena where applying top-down and bottom-up SB approaches may be informative is in the setting of prostate cancer health disparities.
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Affiliation(s)
- Shahid Hussain
- Division of Pharmaceutics and Pharmaceutical Chemistry, College of Pharmacy, The Ohio State University, Columbus, OH 43210, USA
| | - Clayton Yates
- Department of Biology and Center for Cancer Research, Tuskegee University, Tuskegee, AL 36088, USA
- Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
- Department of Oncology Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
| | - Moray J. Campbell
- Division of Pharmaceutics and Pharmaceutical Chemistry, College of Pharmacy, The Ohio State University, Columbus, OH 43210, USA
- Correspondence:
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14
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Chen Z, Zhang F, Lu Y, Li Y, Liu G, Shan J, Liu Q. Bioelectronic modulation of single-wavelength localized surface plasmon resonance (LSPR) for the detection of electroactive biomolecules. CHINESE CHEM LETT 2022. [DOI: 10.1016/j.cclet.2021.10.027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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15
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Methods for Stratification and Validation Cohorts: A Scoping Review. J Pers Med 2022; 12:jpm12050688. [PMID: 35629113 PMCID: PMC9144352 DOI: 10.3390/jpm12050688] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2022] [Revised: 03/31/2022] [Accepted: 04/15/2022] [Indexed: 12/12/2022] Open
Abstract
Personalized medicine requires large cohorts for patient stratification and validation of patient clustering. However, standards and harmonized practices on the methods and tools to be used for the design and management of cohorts in personalized medicine remain to be defined. This study aims to describe the current state-of-the-art in this area. A scoping review was conducted searching in PubMed, EMBASE, Web of Science, Psycinfo and Cochrane Library for reviews about tools and methods related to cohorts used in personalized medicine. The search focused on cancer, stroke and Alzheimer’s disease and was limited to reports in English, French, German, Italian and Spanish published from 2005 to April 2020. The screening process was reported through a PRISMA flowchart. Fifty reviews were included, mostly including information about how data were generated (25/50) and about tools used for data management and analysis (24/50). No direct information was found about the quality of data and the requirements to monitor associated clinical data. A scarcity of information and standards was found in specific areas such as sample size calculation. With this information, comprehensive guidelines could be developed in the future to improve the reproducibility and robustness in the design and management of cohorts in personalized medicine studies.
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16
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Hrovatin K, Fischer DS, Theis FJ. Toward modeling metabolic state from single-cell transcriptomics. Mol Metab 2022; 57:101396. [PMID: 34785394 PMCID: PMC8829761 DOI: 10.1016/j.molmet.2021.101396] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/05/2021] [Revised: 10/21/2021] [Accepted: 11/09/2021] [Indexed: 12/31/2022] Open
Abstract
BACKGROUND Single-cell metabolic studies bring new insights into cellular function, which can often not be captured on other omics layers. Metabolic information has wide applicability, such as for the study of cellular heterogeneity or for the understanding of drug mechanisms and biomarker development. However, metabolic measurements on single-cell level are limited by insufficient scalability and sensitivity, as well as resource intensiveness, and are currently not possible in parallel with measuring transcript state, commonly used to identify cell types. Nevertheless, because omics layers are strongly intertwined, it is possible to make metabolic predictions based on measured data of more easily measurable omics layers together with prior metabolic network knowledge. SCOPE OF REVIEW We summarize the current state of single-cell metabolic measurement and modeling approaches, motivating the use of computational techniques. We review three main classes of computational methods used for prediction of single-cell metabolism: pathway-level analysis, constraint-based modeling, and kinetic modeling. We describe the unique challenges arising when transitioning from bulk to single-cell modeling. Finally, we propose potential model extensions and computational methods that could be leveraged to achieve these goals. MAJOR CONCLUSIONS Single-cell metabolic modeling is a rising field that provides a new perspective for understanding cellular functions. The presented modeling approaches vary in terms of input requirements and assumptions, scalability, modeled metabolic layers, and newly gained insights. We believe that the use of prior metabolic knowledge will lead to more robust predictions and will pave the way for mechanistic and interpretable machine-learning models.
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Affiliation(s)
- Karin Hrovatin
- Institute of Computational Biology, Helmholtz Center Munich, Ingolstaedter Landstraße 1, Neuherberg, 85764, Germany; TUM School of Life Sciences Weihenstephan, Technical University of Munich, Alte Akademie 8, Freising, 85354, Germany.
| | - David S Fischer
- Institute of Computational Biology, Helmholtz Center Munich, Ingolstaedter Landstraße 1, Neuherberg, 85764, Germany; TUM School of Life Sciences Weihenstephan, Technical University of Munich, Alte Akademie 8, Freising, 85354, Germany.
| | - Fabian J Theis
- Institute of Computational Biology, Helmholtz Center Munich, Ingolstaedter Landstraße 1, Neuherberg, 85764, Germany; TUM School of Life Sciences Weihenstephan, Technical University of Munich, Alte Akademie 8, Freising, 85354, Germany; Department of Mathematics, Technical University of Munich, Boltzmannstr. 3, Garching bei München, 85748, Germany.
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17
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Wang FS, Chen KL, Chu SW. Human/SARS-CoV-2 Genome-Scale Metabolic Modeling to Discover Potential Antiviral Targets for COVID-19. J Taiwan Inst Chem Eng 2022; 133:104273. [PMID: 35186172 PMCID: PMC8843340 DOI: 10.1016/j.jtice.2022.104273] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2021] [Revised: 02/06/2022] [Accepted: 02/11/2022] [Indexed: 12/20/2022]
Affiliation(s)
- Feng-Sheng Wang
- Department of Chemical Engineering, National Chung Cheng University, Chiayi 621301, Taiwan
| | - Ke-Lin Chen
- Department of Chemical Engineering, National Chung Cheng University, Chiayi 621301, Taiwan
| | - Sz-Wei Chu
- Department of Chemical Engineering, National Chung Cheng University, Chiayi 621301, Taiwan
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18
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Sebestyén A, Dankó T, Sztankovics D, Moldvai D, Raffay R, Cervi C, Krencz I, Zsiros V, Jeney A, Petővári G. The role of metabolic ecosystem in cancer progression — metabolic plasticity and mTOR hyperactivity in tumor tissues. Cancer Metastasis Rev 2022; 40:989-1033. [PMID: 35029792 PMCID: PMC8825419 DOI: 10.1007/s10555-021-10006-2] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/06/2021] [Accepted: 11/26/2021] [Indexed: 12/14/2022]
Abstract
Despite advancements in cancer management, tumor relapse and metastasis are associated with poor outcomes in many cancers. Over the past decade, oncogene-driven carcinogenesis, dysregulated cellular signaling networks, dynamic changes in the tissue microenvironment, epithelial-mesenchymal transitions, protein expression within regulatory pathways, and their part in tumor progression are described in several studies. However, the complexity of metabolic enzyme expression is considerably under evaluated. Alterations in cellular metabolism determine the individual phenotype and behavior of cells, which is a well-recognized hallmark of cancer progression, especially in the adaptation mechanisms underlying therapy resistance. In metabolic symbiosis, cells compete, communicate, and even feed each other, supervised by tumor cells. Metabolic reprogramming forms a unique fingerprint for each tumor tissue, depending on the cellular content and genetic, epigenetic, and microenvironmental alterations of the developing cancer. Based on its sensing and effector functions, the mechanistic target of rapamycin (mTOR) kinase is considered the master regulator of metabolic adaptation. Moreover, mTOR kinase hyperactivity is associated with poor prognosis in various tumor types. In situ metabolic phenotyping in recent studies highlights the importance of metabolic plasticity, mTOR hyperactivity, and their role in tumor progression. In this review, we update recent developments in metabolic phenotyping of the cancer ecosystem, metabolic symbiosis, and plasticity which could provide new research directions in tumor biology. In addition, we suggest pathomorphological and analytical studies relating to metabolic alterations, mTOR activity, and their associations which are necessary to improve understanding of tumor heterogeneity and expand the therapeutic management of cancer.
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19
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Redhu N, Thakur Z. Network biology and applications. Bioinformatics 2022. [DOI: 10.1016/b978-0-323-89775-4.00024-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022] Open
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20
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Advancing Tumor Microenvironment Research by Combining Organs-on-Chips and Biosensors. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2022; 1379:171-203. [DOI: 10.1007/978-3-031-04039-9_7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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21
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Vijayakumar S, Magazzù G, Moon P, Occhipinti A, Angione C. A Practical Guide to Integrating Multimodal Machine Learning and Metabolic Modeling. Methods Mol Biol 2022; 2399:87-122. [PMID: 35604554 DOI: 10.1007/978-1-0716-1831-8_5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Complex, distributed, and dynamic sets of clinical biomedical data are collectively referred to as multimodal clinical data. In order to accommodate the volume and heterogeneity of such diverse data types and aid in their interpretation when they are combined with a multi-scale predictive model, machine learning is a useful tool that can be wielded to deconstruct biological complexity and extract relevant outputs. Additionally, genome-scale metabolic models (GSMMs) are one of the main frameworks striving to bridge the gap between genotype and phenotype by incorporating prior biological knowledge into mechanistic models. Consequently, the utilization of GSMMs as a foundation for the integration of multi-omic data originating from different domains is a valuable pursuit towards refining predictions. In this chapter, we show how cancer multi-omic data can be analyzed via multimodal machine learning and metabolic modeling. Firstly, we focus on the merits of adopting an integrative systems biology led approach to biomedical data mining. Following this, we propose how constraint-based metabolic models can provide a stable yet adaptable foundation for the integration of multimodal data with machine learning. Finally, we provide a step-by-step tutorial for the combination of machine learning and GSMMs, which includes: (i) tissue-specific constraint-based modeling; (ii) survival analysis using time-to-event prediction for cancer; and (iii) classification and regression approaches for multimodal machine learning. The code associated with the tutorial can be found at https://github.com/Angione-Lab/Tutorials_Combining_ML_and_GSMM .
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Affiliation(s)
- Supreeta Vijayakumar
- Computational Systems Biology and Data Analytics Research Group, Teesside University, Middlebrough, UK
| | - Giuseppe Magazzù
- Computational Systems Biology and Data Analytics Research Group, Teesside University, Middlebrough, UK
| | - Pradip Moon
- Computational Systems Biology and Data Analytics Research Group, Teesside University, Middlebrough, UK
| | - Annalisa Occhipinti
- Computational Systems Biology and Data Analytics Research Group, Middlebrough, UK
- Centre for Digital Innovation, Teesside University, Middlesbrough, UK
| | - Claudio Angione
- Computational Systems Biology and Data Analytics Research Group, Teesside University, Middlebrough, UK.
- Centre for Digital Innovation, Teesside University, Middlesbrough, UK.
- Healthcare Innovation Centre, Teesside University, Middlesbrough, UK.
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22
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Barata T, Vieira V, Rodrigues R, Neves RPD, Rocha M. Reconstruction of tissue-specific genome-scale metabolic models for human cancer stem cells. Comput Biol Med 2021; 142:105177. [PMID: 35026576 DOI: 10.1016/j.compbiomed.2021.105177] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2021] [Revised: 12/23/2021] [Accepted: 12/24/2021] [Indexed: 02/07/2023]
Abstract
Cancer Stem Cells (CSCs) contribute to cancer aggressiveness, metastasis, chemo/radio-therapy resistance, and tumor recurrence. Recent studies emphasized the importance of metabolic reprogramming of CSCs for the maintenance and progression of the cancer phenotype through both the fulfillment of the energetic requirements and the supply of substrates fundamental for fast-cell growth, as well as through metabolite-induced epigenetic regulation. Therefore, it is of paramount importance to develop therapeutic strategies tailored to target the metabolism of CSCs. In this work, we built computational Genome-Scale Metabolic Models (GSMMs) for CSCs of different tissues. Flux simulations were then used to predict metabolic phenotypes, identify potential therapeutic targets, and spot already-known Transcription Factors (TFs), miRNAs and antimetabolites that could be used as part of drug repurposing strategies against cancer. Results were in accordance with experimental evidence, provided insights of new metabolic mechanisms for already known agents, and allowed for the identification of potential new targets and compounds that could be interesting for further in vitro and in vivo validation.
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Affiliation(s)
- Tânia Barata
- CNC - Center for Neuroscience and Cell Biology, CIBB - Centre for Innovative Biomedicine and Biotechnology, University of Coimbra, 3004-517, Coimbra, Portugal
| | - Vítor Vieira
- Centre of Biological Engineering, University of Minho - Campus de Gualtar, Braga, Portugal
| | - Rúben Rodrigues
- Centre of Biological Engineering, University of Minho - Campus de Gualtar, Braga, Portugal
| | - Ricardo Pires das Neves
- CNC - Center for Neuroscience and Cell Biology, CIBB - Centre for Innovative Biomedicine and Biotechnology, University of Coimbra, 3004-517, Coimbra, Portugal; IIIUC-Institute of Interdisciplinary Research, University of Coimbra, 3030-789, Coimbra, Portugal.
| | - Miguel Rocha
- Centre of Biological Engineering, University of Minho - Campus de Gualtar, Braga, Portugal; Department of Informatics, University of Minho.
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23
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Vijayakumar S, Angione C. Protocol for hybrid flux balance, statistical, and machine learning analysis of multi-omic data from the cyanobacterium Synechococcus sp. PCC 7002. STAR Protoc 2021; 2:100837. [PMID: 34632416 PMCID: PMC8488602 DOI: 10.1016/j.xpro.2021.100837] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Abstract
Combining a computational framework for flux balance analysis with machine learning improves the accuracy of predicting metabolic activity across conditions, while enabling mechanistic interpretation. This protocol presents a guide to condition-specific metabolic modeling that integrates regularized flux balance analysis with machine learning approaches to extract key features from transcriptomic and fluxomic data. We demonstrate the protocol as applied to Synechococcus sp. PCC 7002; we also outline how it can be adapted to any species or community with available multi-omic data. For complete details on the use and execution of this protocol, please refer to Vijayakumar et al. (2020).
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Affiliation(s)
- Supreeta Vijayakumar
- School of Computing, Engineering & Digital Technologies, Teesside University, Middlesbrough, North Yorkshire TS1 3BX, UK
| | - Claudio Angione
- School of Computing, Engineering & Digital Technologies, Teesside University, Middlesbrough, North Yorkshire TS1 3BX, UK
- Centre for Digital Innovation, Teesside University, Middlesbrough TS1 3BX, UK
- Healthcare Innovation Centre, Teesside University, Middlesbrough TS1 3BX, UK
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24
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Tourigny DS, Goldberg AP, Karr JR. Simulating single-cell metabolism using a stochastic flux-balance analysis algorithm. Biophys J 2021; 120:5231-5242. [PMID: 34757076 DOI: 10.1016/j.bpj.2021.10.038] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2021] [Revised: 10/01/2021] [Accepted: 10/26/2021] [Indexed: 10/19/2022] Open
Abstract
Stochasticity from gene expression in single cells is known to drive metabolic heterogeneity at the level of cellular populations, which is understood to have important consequences for issues such as microbial drug tolerance and treatment of human diseases like cancer. Despite considerable advancements in profiling the genomes, transcriptomes, and proteomes of single cells, it remains difficult to experimentally characterize their metabolism at the genome scale. Computational methods could bridge this gap toward a systems understanding of single-cell biology. To address this challenge, we developed stochastic simulation algorithm with flux-balance analysis embedded (SSA-FBA), a computational framework for simulating the stochastic dynamics of the metabolism of individual cells using genome-scale metabolic models with experimental estimates of gene expression and enzymatic reaction rate parameters. SSA-FBA extends the constraint-based modeling formalism of metabolic network modeling to the single-cell regime, enabling simulation when experimentation is intractable. We also developed an efficient implementation of SSA-FBA that leverages the topology of embedded flux-balance analysis models to significantly reduce the computational cost of simulation. As a preliminary case study, we built a reduced single-cell model of Mycoplasma pneumoniae and used SSA-FBA to illustrate the role of stochasticity on the dynamics of metabolism at the single-cell level.
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Affiliation(s)
- David S Tourigny
- Irving Institute for Cancer Dynamics, Columbia University, New York, New York; School of Mathematics, University of Birmingham, Birmingham, United Kingdom.
| | - Arthur P Goldberg
- Icahn Institute for Data Science and Genomic Technology, and Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Jonathan R Karr
- Icahn Institute for Data Science and Genomic Technology, and Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York
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25
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Li R, Li L, Xu Y, Yang J. Machine learning meets omics: applications and perspectives. Brief Bioinform 2021; 23:6425809. [PMID: 34791021 DOI: 10.1093/bib/bbab460] [Citation(s) in RCA: 40] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2021] [Revised: 09/29/2021] [Accepted: 10/07/2021] [Indexed: 02/07/2023] Open
Abstract
The innovation of biotechnologies has allowed the accumulation of omics data at an alarming rate, thus introducing the era of 'big data'. Extracting inherent valuable knowledge from various omics data remains a daunting problem in bioinformatics. Better solutions often need some kind of more innovative methods for efficient handlings and effective results. Recent advancements in integrated analysis and computational modeling of multi-omics data helped address such needs in an increasingly harmonious manner. The development and application of machine learning have largely advanced our insights into biology and biomedicine and greatly promoted the development of therapeutic strategies, especially for precision medicine. Here, we propose a comprehensive survey and discussion on what happened, is happening and will happen when machine learning meets omics. Specifically, we describe how artificial intelligence can be applied to omics studies and review recent advancements at the interface between machine learning and the ever-widest range of omics including genomics, transcriptomics, proteomics, metabolomics, radiomics, as well as those at the single-cell resolution. We also discuss and provide a synthesis of ideas, new insights, current challenges and perspectives of machine learning in omics.
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Affiliation(s)
- Rufeng Li
- Department of Cell Biology and Genetics, School of Basic Medical Sciences, Xi'an Jiaotong University Health Science Center, Xi'an 710061, P. R. China
| | - Lixin Li
- Department of Cell Biology and Genetics, School of Basic Medical Sciences, Xi'an Jiaotong University Health Science Center, Xi'an 710061, P. R. China
| | - Yungang Xu
- School of Electronics and Information, Northwestern Polytechnical University, Xi'an, 710129, China
| | - Juan Yang
- Department of Cell Biology and Genetics, School of Basic Medical Sciences, Xi'an Jiaotong University Health Science Center, Xi'an 710061, P. R. China.,Key Laboratory of Environment and Genes Related to Diseases (Xi'an Jiaotong University), Ministry of Education of China, Xi'an 710061, P. R. China
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26
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Occhipinti A, Hamadi Y, Kugler H, Wintersteiger CM, Yordanov B, Angione C. Discovering Essential Multiple Gene Effects Through Large Scale Optimization: An Application to Human Cancer Metabolism. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2021; 18:2339-2352. [PMID: 32248120 DOI: 10.1109/tcbb.2020.2973386] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Computational modelling of metabolic processes has proven to be a useful approach to formulate our knowledge and improve our understanding of core biochemical systems that are crucial to maintaining cellular functions. Towards understanding the broader role of metabolism on cellular decision-making in health and disease conditions, it is important to integrate the study of metabolism with other core regulatory systems and omics within the cell, including gene expression patterns. After quantitatively integrating gene expression profiles with a genome-scale reconstruction of human metabolism, we propose a set of combinatorial methods to reverse engineer gene expression profiles and to find pairs and higher-order combinations of genetic modifications that simultaneously optimize multi-objective cellular goals. This enables us to suggest classes of transcriptomic profiles that are most suitable to achieve given metabolic phenotypes. We demonstrate how our techniques are able to compute beneficial, neutral or "toxic" combinations of gene expression levels. We test our methods on nine tissue-specific cancer models, comparing our outcomes with the corresponding normal cells, identifying genes as targets for potential therapies. Our methods open the way to a broad class of applications that require an understanding of the interplay among genotype, metabolism, and cellular behaviour, at scale.
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27
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Yaneske E, Zampieri G, Bertoldi L, Benvenuto G, Angione C. Genome-scale metabolic modelling of SARS-CoV-2 in cancer cells reveals an increased shift to glycolytic energy production. FEBS Lett 2021; 595:2350-2365. [PMID: 34409594 PMCID: PMC8427129 DOI: 10.1002/1873-3468.14180] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2021] [Revised: 08/02/2021] [Accepted: 08/15/2021] [Indexed: 01/08/2023]
Abstract
Cancer is considered a high‐risk condition for severe illness resulting from COVID‐19. The interaction between severe acute respiratory syndrome coronavirus‐2 (SARS‐CoV‐2) and human metabolism is key to elucidating the risk posed by COVID‐19 for cancer patients and identifying effective treatments, yet it is largely uncharacterised on a mechanistic level. We present a genome‐scale map of short‐term metabolic alterations triggered by SARS‐CoV‐2 infection of cancer cells. Through transcriptomic‐ and proteomic‐informed genome‐scale metabolic modelling, we characterise the role of RNA and fatty acid biosynthesis in conjunction with a rewiring in energy production pathways and enhanced cytokine secretion. These findings link together complementary aspects of viral invasion of cancer cells, while providing mechanistic insights that can inform the development of treatment strategies.
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Affiliation(s)
- Elisabeth Yaneske
- School of Computing, Engineering and Digital Technologies, Teesside University, Middlesbrough, UK
| | - Guido Zampieri
- School of Computing, Engineering and Digital Technologies, Teesside University, Middlesbrough, UK.,Department of Biology, University of Padua, Italy
| | | | | | - Claudio Angione
- School of Computing, Engineering and Digital Technologies, Teesside University, Middlesbrough, UK.,Healthcare Innovation Centre, Teesside University, Middlesbrough, UK.,Centre for Digital Innovation, Teesside University, Middlesbrough, UK
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28
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Bonilla DA, Moreno Y, Rawson ES, Forero DA, Stout JR, Kerksick CM, Roberts MD, Kreider RB. A Convergent Functional Genomics Analysis to Identify Biological Regulators Mediating Effects of Creatine Supplementation. Nutrients 2021; 13:2521. [PMID: 34444681 PMCID: PMC8397972 DOI: 10.3390/nu13082521] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2021] [Accepted: 07/21/2021] [Indexed: 12/12/2022] Open
Abstract
Creatine (Cr) and phosphocreatine (PCr) are physiologically essential molecules for life, given they serve as rapid and localized support of energy- and mechanical-dependent processes. This evolutionary advantage is based on the action of creatine kinase (CK) isozymes that connect places of ATP synthesis with sites of ATP consumption (the CK/PCr system). Supplementation with creatine monohydrate (CrM) can enhance this system, resulting in well-known ergogenic effects and potential health or therapeutic benefits. In spite of our vast knowledge about these molecules, no integrative analysis of molecular mechanisms under a systems biology approach has been performed to date; thus, we aimed to perform for the first time a convergent functional genomics analysis to identify biological regulators mediating the effects of Cr supplementation in health and disease. A total of 35 differentially expressed genes were analyzed. We identified top-ranked pathways and biological processes mediating the effects of Cr supplementation. The impact of CrM on miRNAs merits more research. We also cautiously suggest two dose-response functional pathways (kinase- and ubiquitin-driven) for the regulation of the Cr uptake. Our functional enrichment analysis, the knowledge-based pathway reconstruction, and the identification of hub nodes provide meaningful information for future studies. This work contributes to a better understanding of the well-reported benefits of Cr in sports and its potential in health and disease conditions, although further clinical research is needed to validate the proposed mechanisms.
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Affiliation(s)
- Diego A. Bonilla
- Research Division, Dynamical Business & Science Society—DBSS International SAS, Bogotá 110861, Colombia;
- Research Group in Biochemistry and Molecular Biology, Universidad Distrital Francisco José de Caldas, Bogotá 110311, Colombia
- Research Group in Physical Activity, Sports and Health Sciences (GICAFS), Universidad de Córdoba, Montería 230002, Colombia
- kDNA Genomics, Joxe Mari Korta Research Center, University of the Basque Country UPV/EHU, 20018 Donostia-San Sebastián, Spain
| | - Yurany Moreno
- Research Division, Dynamical Business & Science Society—DBSS International SAS, Bogotá 110861, Colombia;
- Research Group in Biochemistry and Molecular Biology, Universidad Distrital Francisco José de Caldas, Bogotá 110311, Colombia
| | - Eric S. Rawson
- Department of Health, Nutrition and Exercise Science, Messiah University, Mechanicsburg, PA 17055, USA;
| | - Diego A. Forero
- Professional Program in Sport Training, School of Health and Sport Sciences, Fundación Universitaria del Área Andina, Bogotá 111221, Colombia;
| | - Jeffrey R. Stout
- Physiology of Work and Exercise Response (POWER) Laboratory, Institute of Exercise Physiology and Rehabilitation Science, University of Central Florida, Orlando, FL 32816, USA;
| | - Chad M. Kerksick
- Exercise and Performance Nutrition Laboratory, School of Health Sciences, Lindenwood University, Saint Charles, MO 63301, USA;
| | - Michael D. Roberts
- School of Kinesiology, Auburn University, Auburn, AL 36849, USA;
- Edward via College of Osteopathic Medicine, Auburn, AL 36849, USA
| | - Richard B. Kreider
- Exercise & Sport Nutrition Laboratory, Human Clinical Research Facility, Texas A&M University, College Station, TX 77843, USA;
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Tarazona S, Arzalluz-Luque A, Conesa A. Undisclosed, unmet and neglected challenges in multi-omics studies. NATURE COMPUTATIONAL SCIENCE 2021; 1:395-402. [PMID: 38217236 DOI: 10.1038/s43588-021-00086-z] [Citation(s) in RCA: 54] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/18/2021] [Accepted: 05/17/2021] [Indexed: 01/15/2024]
Abstract
Multi-omics approaches have become a reality in both large genomics projects and small laboratories. However, the multi-omics research community still faces a number of issues that have either not been sufficiently discussed or for which current solutions are still limited. In this Perspective, we elaborate on these limitations and suggest points of attention for future research. We finally discuss new opportunities and challenges brought to the field by the rapid development of single-cell high-throughput molecular technologies.
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Affiliation(s)
- Sonia Tarazona
- Department of Applied Statistics, Operations Research and Quality, Universitat Politècnica de València, Valencia, Spain
| | - Angeles Arzalluz-Luque
- Department of Applied Statistics, Operations Research and Quality, Universitat Politècnica de València, Valencia, Spain
| | - Ana Conesa
- Microbiology and Cell Science Department, Institute for Food and Agricultural Research, University of Florida, Gainesville, FL, USA.
- Genetics Institute, University of Florida, Gainesville, FL, USA.
- Institute for Integrative Systems Biology, Spanish National Research Council, Valencia, Spain.
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30
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Abnormal brain cholesterol homeostasis in Alzheimer's disease-a targeted metabolomic and transcriptomic study. NPJ Aging Mech Dis 2021; 7:11. [PMID: 34075056 PMCID: PMC8169871 DOI: 10.1038/s41514-021-00064-9] [Citation(s) in RCA: 61] [Impact Index Per Article: 20.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2020] [Accepted: 03/18/2021] [Indexed: 02/02/2023] Open
Abstract
The role of brain cholesterol metabolism in Alzheimer's disease (AD) remains unclear. Peripheral and brain cholesterol levels are largely independent due to the impermeability of the blood brain barrier (BBB), highlighting the importance of studying the role of brain cholesterol homeostasis in AD. We first tested whether metabolite markers of brain cholesterol biosynthesis and catabolism were altered in AD and associated with AD pathology using linear mixed-effects models in two brain autopsy samples from the Baltimore Longitudinal Study of Aging (BLSA) and the Religious Orders Study (ROS). We next tested whether genetic regulators of brain cholesterol biosynthesis and catabolism were altered in AD using the ANOVA test in publicly available brain tissue transcriptomic datasets. Finally, using regional brain transcriptomic data, we performed genome-scale metabolic network modeling to assess alterations in cholesterol biosynthesis and catabolism reactions in AD. We show that AD is associated with pervasive abnormalities in cholesterol biosynthesis and catabolism. Using transcriptomic data from Parkinson's disease (PD) brain tissue samples, we found that gene expression alterations identified in AD were not observed in PD, suggesting that these changes may be specific to AD. Our results suggest that reduced de novo cholesterol biosynthesis may occur in response to impaired enzymatic cholesterol catabolism and efflux to maintain brain cholesterol levels in AD. This is accompanied by the accumulation of nonenzymatically generated cytotoxic oxysterols. Our results set the stage for experimental studies to address whether abnormalities in cholesterol metabolism are plausible therapeutic targets in AD.
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31
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Exploring the Metabolic Heterogeneity of Cancers: A Benchmark Study of Context-Specific Models. J Pers Med 2021; 11:jpm11060496. [PMID: 34205912 PMCID: PMC8229374 DOI: 10.3390/jpm11060496] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2021] [Revised: 05/25/2021] [Accepted: 05/28/2021] [Indexed: 12/18/2022] Open
Abstract
Metabolic heterogeneity is a hallmark of cancer and can distinguish a normal phenotype from a cancer phenotype. In the systems biology domain, context-specific models facilitate extracting physiologically relevant information from high-quality data. Here, to utilize the heterogeneity of metabolic patterns to discover biomarkers of all cancers, we benchmarked thousands of context-specific models using well-established algorithms for the integration of omics data into the generic human metabolic model Recon3D. By analyzing the active reactions capable of carrying flux and their magnitude through flux balance analysis, we proved that the metabolic pattern of each cancer is unique and could act as a cancer metabolic fingerprint. Subsequently, we searched for proper feature selection methods to cluster the flux states characterizing each cancer. We employed PCA-based dimensionality reduction and a random forest learning algorithm to reveal reactions containing the most relevant information in order to effectively identify the most influential fluxes. Conclusively, we discovered different pathways that are probably the main sources for metabolic heterogeneity in cancers. We designed the GEMbench website to interactively present the data, methods, and analysis results.
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32
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Nazem-Bokaee H, Hom EFY, Warden AC, Mathews S, Gueidan C. Towards a Systems Biology Approach to Understanding the Lichen Symbiosis: Opportunities and Challenges of Implementing Network Modelling. Front Microbiol 2021; 12:667864. [PMID: 34012428 PMCID: PMC8126723 DOI: 10.3389/fmicb.2021.667864] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2021] [Accepted: 04/09/2021] [Indexed: 11/16/2022] Open
Abstract
Lichen associations, a classic model for successful and sustainable interactions between micro-organisms, have been studied for many years. However, there are significant gaps in our understanding about how the lichen symbiosis operates at the molecular level. This review addresses opportunities for expanding current knowledge on signalling and metabolic interplays in the lichen symbiosis using the tools and approaches of systems biology, particularly network modelling. The largely unexplored nature of symbiont recognition and metabolic interdependency in lichens could benefit from applying a holistic approach to understand underlying molecular mechanisms and processes. Together with ‘omics’ approaches, the application of signalling and metabolic network modelling could provide predictive means to gain insights into lichen signalling and metabolic pathways. First, we review the major signalling and recognition modalities in the lichen symbioses studied to date, and then describe how modelling signalling networks could enhance our understanding of symbiont recognition, particularly leveraging omics techniques. Next, we highlight the current state of knowledge on lichen metabolism. We also discuss metabolic network modelling as a tool to simulate flux distribution in lichen metabolic pathways and to analyse the co-dependence between symbionts. This is especially important given the growing number of lichen genomes now available and improved computational tools for reconstructing such models. We highlight the benefits and possible bottlenecks for implementing different types of network models as applied to the study of lichens.
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Affiliation(s)
- Hadi Nazem-Bokaee
- CSIRO Australian National Herbarium, Centre for Australian National Biodiversity Research, National Research Collections Australia, NCMI, Canberra, ACT, Australia.,CSIRO Land and Water, Canberra, ACT, Australia.,CSIRO Synthetic Biology Future Science Platform, Canberra, ACT, Australia
| | - Erik F Y Hom
- Department of Biology and Center for Biodiversity and Conservation Research, The University of Mississippi, University City, MS, United States
| | | | - Sarah Mathews
- Department of Biological Sciences, Louisiana State University, Baton Rouge, LA, United States
| | - Cécile Gueidan
- CSIRO Australian National Herbarium, Centre for Australian National Biodiversity Research, National Research Collections Australia, NCMI, Canberra, ACT, Australia
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33
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Karta J, Bossicard Y, Kotzamanis K, Dolznig H, Letellier E. Mapping the Metabolic Networks of Tumor Cells and Cancer-Associated Fibroblasts. Cells 2021; 10:304. [PMID: 33540679 PMCID: PMC7912987 DOI: 10.3390/cells10020304] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2020] [Revised: 01/20/2021] [Accepted: 01/26/2021] [Indexed: 12/12/2022] Open
Abstract
Metabolism is considered to be the core of all cellular activity. Thus, extensive studies of metabolic processes are ongoing in various fields of biology, including cancer research. Cancer cells are known to adapt their metabolism to sustain high proliferation rates and survive in unfavorable environments with low oxygen and nutrient concentrations. Hence, targeting cancer cell metabolism is a promising therapeutic strategy in cancer research. However, cancers consist not only of genetically altered tumor cells but are interwoven with endothelial cells, immune cells and fibroblasts, which together with the extracellular matrix (ECM) constitute the tumor microenvironment (TME). Cancer-associated fibroblasts (CAFs), which are linked to poor prognosis in different cancer types, are one important component of the TME. CAFs play a significant role in reprogramming the metabolic landscape of tumor cells, but how, and in what manner, this interaction takes place remains rather unclear. This review aims to highlight the metabolic landscape of tumor cells and CAFs, including their recently identified subtypes, in different tumor types. In addition, we discuss various in vitro and in vivo metabolic techniques as well as different in silico computational tools that can be used to identify and characterize CAF-tumor cell interactions. Finally, we provide our view on how mapping the complex metabolic networks of stromal-tumor metabolism will help in finding novel metabolic targets for cancer treatment.
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Affiliation(s)
- Jessica Karta
- Molecular Disease Mechanisms Group, Department of Life Sciences and Medicine, Faculty of Science, Technology and Medicine, University of Luxembourg, 6 avenue du Swing, L-4367 Belval, Luxembourg; (J.K.); (Y.B.); (K.K.)
| | - Ysaline Bossicard
- Molecular Disease Mechanisms Group, Department of Life Sciences and Medicine, Faculty of Science, Technology and Medicine, University of Luxembourg, 6 avenue du Swing, L-4367 Belval, Luxembourg; (J.K.); (Y.B.); (K.K.)
| | - Konstantinos Kotzamanis
- Molecular Disease Mechanisms Group, Department of Life Sciences and Medicine, Faculty of Science, Technology and Medicine, University of Luxembourg, 6 avenue du Swing, L-4367 Belval, Luxembourg; (J.K.); (Y.B.); (K.K.)
| | - Helmut Dolznig
- Tumor Stroma Interaction Group, Institute of Medical Genetics, Medical University of Vienna, Währinger Strasse 10, 1090 Vienna, Austria;
| | - Elisabeth Letellier
- Molecular Disease Mechanisms Group, Department of Life Sciences and Medicine, Faculty of Science, Technology and Medicine, University of Luxembourg, 6 avenue du Swing, L-4367 Belval, Luxembourg; (J.K.); (Y.B.); (K.K.)
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34
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Van Pelt DW, Kharaz YA, Sarver DC, Eckhardt LR, Dzierzawski JT, Disser NP, Piacentini AN, Comerford E, McDonagh B, Mendias CL. Multiomics analysis of the mdx/mTR mouse model of Duchenne muscular dystrophy. Connect Tissue Res 2021; 62:24-39. [PMID: 32664808 DOI: 10.1080/03008207.2020.1791103] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
Abstract
PURPOSE/AIM Duchenne muscular dystrophy (DMD) is a progressive neuromuscular disease characterized by extensive muscle weakness. Patients with DMD lack a functional dystrophin protein, which transmits force and organizes the cytoskeleton of skeletal muscle. Multiomic studies have been proposed as a way to obtain novel insight about disease processes from preclinical models, and we used this approach to study pathological changes in dystrophic muscles. MATERIALS AND METHODS We evaluated hindlimb muscles of male mdx/mTR mice, which lack a functional dystrophin protein and have deficits in satellite cell abundance and proliferative capacity. Wild type (WT) C57BL/6 J mice served as controls. Muscle fiber contractility was measured, along with changes in the transcriptome using RNA sequencing, and in the proteome, metabolome, and lipidome using mass spectrometry. RESULTS While mdx/mTR mice displayed gross pathological changes and continued cycles of degeneration and regeneration, we found no differences in permeabilized fiber contractility between strains. However, there were numerous changes in the transcriptome and proteome related to protein balance, contractile elements, extracellular matrix, and metabolism. There was only a 53% agreement in fold-change data between the proteome and transcriptome. Numerous changes in markers of skeletal muscle metabolism were observed, with dystrophic muscles exhibiting elevated glycolytic metabolites such as 6-phosphoglycerate, fructose-6-phosphate and glucose-6-phosphate, fructose bisphosphate, phosphorylated hexoses, and phosphoenolpyruvate. CONCLUSIONS These findings highlight the utility of multiomics in studying muscle disease, and provide additional insight into the pathological changes in dystrophic muscles that might help to indirectly guide evidence-based nutritional or exercise prescription in DMD patients.
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Affiliation(s)
- Douglas W Van Pelt
- Department of Rehabilitation Sciences, College of Health Sciences, University of Kentucky , Lexington, KY, USA
| | - Yalda A Kharaz
- Department of Musculoskeletal Biology, Institute of Ageing and Chronic Disease, University of Liverpool , Liverpool, UK
| | - Dylan C Sarver
- Department of Orthopaedic Surgery, University of Michigan Medical School , Ann Arbor, MI, USA
| | - Logan R Eckhardt
- Department of Orthopaedic Surgery, University of Michigan Medical School , Ann Arbor, MI, USA
| | - Justin T Dzierzawski
- Department of Orthopaedic Surgery, University of Michigan Medical School , Ann Arbor, MI, USA
| | | | - Alex N Piacentini
- Research Institute, Hospital for Special Surgery , New York, NY, USA
| | - Eithne Comerford
- Department of Musculoskeletal Biology, Institute of Ageing and Chronic Disease, University of Liverpool , Liverpool, UK
| | - Brian McDonagh
- Department of Physiology, School of Medicine, National University of Ireland , Galway, Ireland
| | - Christopher L Mendias
- Department of Orthopaedic Surgery, University of Michigan Medical School , Ann Arbor, MI, USA.,Research Institute, Hospital for Special Surgery , New York, NY, USA.,Department of Physiology & Biophysics, Weill Cornell Medical College , New York, NY, USA
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35
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Armstrong LE, Bergeron MF, Lee EC, Mershon JE, Armstrong EM. Overtraining Syndrome as a Complex Systems Phenomenon. FRONTIERS IN NETWORK PHYSIOLOGY 2021; 1:794392. [PMID: 36925581 PMCID: PMC10013019 DOI: 10.3389/fnetp.2021.794392] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/13/2021] [Accepted: 12/13/2021] [Indexed: 12/29/2022]
Abstract
The phenomenon of reduced athletic performance following sustained, intense training (Overtraining Syndrome, and OTS) was first recognized more than 90 years ago. Although hundreds of scientific publications have focused on OTS, a definitive diagnosis, reliable biomarkers, and effective treatments remain unknown. The present review considers existing models of OTS, acknowledges the individualized and sport-specific nature of signs/symptoms, describes potential interacting predisposing factors, and proposes that OTS will be most effectively characterized and evaluated via the underlying complex biological systems. Complex systems in nature are not aptly characterized or successfully analyzed using the classic scientific method (i.e., simplifying complex problems into single variables in a search for cause-and-effect) because they result from myriad (often non-linear) concomitant interactions of multiple determinants. Thus, this review 1) proposes that OTS be viewed from the perspectives of complex systems and network physiology, 2) advocates for and recommends that techniques such as trans-omic analyses and machine learning be widely employed, and 3) proposes evidence-based areas for future OTS investigations, including concomitant multi-domain analyses incorporating brain neural networks, dysfunction of hypothalamic-pituitary-adrenal responses to training stress, the intestinal microbiota, immune factors, and low energy availability. Such an inclusive and modern approach will measurably help in prevention and management of OTS.
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Affiliation(s)
- Lawrence E Armstrong
- Human Performance Laboratory, University of Connecticut, Storrs, CT, United States
| | - Michael F Bergeron
- Sport Sciences and Medicine and Performance Health, WTA Women's Tennis Association, St. Petersburg, FL, United States
| | - Elaine C Lee
- Human Performance Laboratory, University of Connecticut, Storrs, CT, United States
| | - James E Mershon
- Department of Energy and Renewables, Heriot-Watt University, Stromness, United Kingdom
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36
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Voit EO. Metabolic Systems. SYSTEMS MEDICINE 2021. [DOI: 10.1016/b978-0-12-801238-3.11619-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
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37
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Vijayakumar S, Rahman PK, Angione C. A Hybrid Flux Balance Analysis and Machine Learning Pipeline Elucidates Metabolic Adaptation in Cyanobacteria. iScience 2020; 23:101818. [PMID: 33354660 PMCID: PMC7744713 DOI: 10.1016/j.isci.2020.101818] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2020] [Revised: 10/23/2020] [Accepted: 11/13/2020] [Indexed: 01/20/2023] Open
Abstract
Machine learning has recently emerged as a promising tool for inferring multi-omic relationships in biological systems. At the same time, genome-scale metabolic models (GSMMs) can be integrated with such multi-omic data to refine phenotypic predictions. In this work, we use a multi-omic machine learning pipeline to analyze a GSMM of Synechococcus sp. PCC 7002, a cyanobacterium with large potential to produce renewable biofuels. We use regularized flux balance analysis to observe flux response between conditions across photosynthesis and energy metabolism. We then incorporate principal-component analysis, k-means clustering, and LASSO regularization to reduce dimensionality and extract key cross-omic features. Our results suggest that combining metabolic modeling with machine learning elucidates mechanisms used by cyanobacteria to cope with fluctuations in light intensity and salinity that cannot be detected using transcriptomics alone. Furthermore, GSMMs introduce critical mechanistic details that improve the performance of omic-based machine learning methods.
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Affiliation(s)
- Supreeta Vijayakumar
- Department of Computer Science and Information Systems, Teesside University, Middlesbrough, North Yorkshire TS1 3BX, UK
| | - Pattanathu K.S.M. Rahman
- Centre for Enzyme Innovation, Institute of Biological and Biomedical Sciences, School of Biological Sciences, University of Portsmouth, Portsmouth, Hampshire PO1 2UP, UK
- Tara Biologics, Woking, Surrey GU21 6BP, UK
| | - Claudio Angione
- Department of Computer Science and Information Systems, Teesside University, Middlesbrough, North Yorkshire TS1 3BX, UK
- Centre for Digital Innovation, Teesside University, Middlesbrough TS1 3BX, UK
- Healthcare Innovation Centre, Teesside University, Middlesbrough TS1 3BX, UK
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38
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Jiang L, Wang M, Lin S, Jian R, Li X, Chan J, Dong G, Fang H, Robinson AE, Snyder MP. A Quantitative Proteome Map of the Human Body. Cell 2020; 183:269-283.e19. [PMID: 32916130 PMCID: PMC7575058 DOI: 10.1016/j.cell.2020.08.036] [Citation(s) in RCA: 217] [Impact Index Per Article: 54.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2020] [Revised: 07/14/2020] [Accepted: 08/19/2020] [Indexed: 02/06/2023]
Abstract
Determining protein levels in each tissue and how they compare with RNA levels is important for understanding human biology and disease as well as regulatory processes that control protein levels. We quantified the relative protein levels from over 12,000 genes across 32 normal human tissues. Tissue-specific or tissue-enriched proteins were identified and compared to transcriptome data. Many ubiquitous transcripts are found to encode tissue-specific proteins. Discordance of RNA and protein enrichment revealed potential sites of synthesis and action of secreted proteins. The tissue-specific distribution of proteins also provides an in-depth view of complex biological events that require the interplay of multiple tissues. Most importantly, our study demonstrated that protein tissue-enrichment information can explain phenotypes of genetic diseases, which cannot be obtained by transcript information alone. Overall, our results demonstrate how understanding protein levels can provide insights into regulation, secretome, metabolism, and human diseases.
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Affiliation(s)
- Lihua Jiang
- Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Meng Wang
- Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Shin Lin
- Division of Cardiology, University of Washington, Seattle, WA 98195, USA
| | - Ruiqi Jian
- Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Xiao Li
- Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Joanne Chan
- Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Guanlan Dong
- Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Huaying Fang
- Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Aaron E Robinson
- Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Michael P Snyder
- Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA.
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39
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Yilmaz LS, Li X, Nanda S, Fox B, Schroeder F, Walhout AJ. Modeling tissue-relevant Caenorhabditis elegans metabolism at network, pathway, reaction, and metabolite levels. Mol Syst Biol 2020; 16:e9649. [PMID: 33022146 PMCID: PMC7537831 DOI: 10.15252/msb.20209649] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2020] [Revised: 09/10/2020] [Accepted: 09/11/2020] [Indexed: 01/04/2023] Open
Abstract
Metabolism is a highly compartmentalized process that provides building blocks for biomass generation during development, homeostasis, and wound healing, and energy to support cellular and organismal processes. In metazoans, different cells and tissues specialize in different aspects of metabolism. However, studying the compartmentalization of metabolism in different cell types in a whole animal and for a particular stage of life is difficult. Here, we present MEtabolic models Reconciled with Gene Expression (MERGE), a computational pipeline that we used to predict tissue-relevant metabolic function at the network, pathway, reaction, and metabolite levels based on single-cell RNA-sequencing (scRNA-seq) data from the nematode Caenorhabditis elegans. Our analysis recapitulated known tissue functions in C. elegans, captured metabolic properties that are shared with similar tissues in human, and provided predictions for novel metabolic functions. MERGE is versatile and applicable to other systems. We envision this work as a starting point for the development of metabolic network models for individual cells as scRNA-seq continues to provide higher-resolution gene expression data.
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Affiliation(s)
- Lutfu Safak Yilmaz
- Program in Systems Biology, Program in Molecular Medicine, University of Massachusetts Medical School, Worcester, MA, USA
| | - Xuhang Li
- Program in Systems Biology, Program in Molecular Medicine, University of Massachusetts Medical School, Worcester, MA, USA
| | - Shivani Nanda
- Program in Systems Biology, Program in Molecular Medicine, University of Massachusetts Medical School, Worcester, MA, USA
| | - Bennett Fox
- Boyce Thompson Institute, Department of Chemistry and Chemical Biology, Cornell University, Ithaca, NY, USA
| | - Frank Schroeder
- Boyce Thompson Institute, Department of Chemistry and Chemical Biology, Cornell University, Ithaca, NY, USA
| | - Albertha Jm Walhout
- Program in Systems Biology, Program in Molecular Medicine, University of Massachusetts Medical School, Worcester, MA, USA
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40
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Balcerczyk A, Damblon C, Elena-Herrmann B, Panthu B, Rautureau GJP. Metabolomic Approaches to Study Chemical Exposure-Related Metabolism Alterations in Mammalian Cell Cultures. Int J Mol Sci 2020; 21:E6843. [PMID: 32961865 PMCID: PMC7554780 DOI: 10.3390/ijms21186843] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2020] [Revised: 09/11/2020] [Accepted: 09/14/2020] [Indexed: 12/12/2022] Open
Abstract
Biological organisms are constantly exposed to an immense repertoire of molecules that cover environmental or food-derived molecules and drugs, triggering a continuous flow of stimuli-dependent adaptations. The diversity of these chemicals as well as their concentrations contribute to the multiplicity of induced effects, including activation, stimulation, or inhibition of physiological processes and toxicity. Metabolism, as the foremost phenotype and manifestation of life, has proven to be immensely sensitive and highly adaptive to chemical stimuli. Therefore, studying the effect of endo- or xenobiotics over cellular metabolism delivers valuable knowledge to apprehend potential cellular activity of individual molecules and evaluate their acute or chronic benefits and toxicity. The development of modern metabolomics technologies such as mass spectrometry or nuclear magnetic resonance spectroscopy now offers unprecedented solutions for the rapid and efficient determination of metabolic profiles of cells and more complex biological systems. Combined with the availability of well-established cell culture techniques, these analytical methods appear perfectly suited to determine the biological activity and estimate the positive and negative effects of chemicals in a variety of cell types and models, even at hardly detectable concentrations. Metabolic phenotypes can be estimated from studying intracellular metabolites at homeostasis in vivo, while in vitro cell cultures provide additional access to metabolites exchanged with growth media. This article discusses analytical solutions available for metabolic phenotyping of cell culture metabolism as well as the general metabolomics workflow suitable for testing the biological activity of molecular compounds. We emphasize how metabolic profiling of cell supernatants and intracellular extracts can deliver valuable and complementary insights for evaluating the effects of xenobiotics on cellular metabolism. We note that the concepts and methods discussed primarily for xenobiotics exposure are widely applicable to drug testing in general, including endobiotics that cover active metabolites, nutrients, peptides and proteins, cytokines, hormones, vitamins, etc.
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Affiliation(s)
- Aneta Balcerczyk
- Department of Molecular Biophysics, Faculty of Biology and Environmental Protection, University of Lodz, Pomorska 141/143, 90-236 Lodz, Poland;
| | - Christian Damblon
- Unité de Recherche MolSys, Faculté des sciences, Université de Liège, 4000 Liège, Belgium;
| | | | - Baptiste Panthu
- CarMeN Laboratory, INSERM, INRA, INSA Lyon, Univ Lyon, Université Claude Bernard Lyon 1, 69921 Oullins CEDEX, France;
- Hospices Civils de Lyon, Faculté de Médecine, Hôpital Lyon Sud, 69921 Oullins CEDEX, France
| | - Gilles J. P. Rautureau
- Centre de Résonance Magnétique Nucléaire à Très Hauts Champs (CRMN FRE 2034 CNRS, UCBL, ENS Lyon), Université Claude Bernard Lyon 1, 69100 Villeurbanne, France
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41
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Sen P, Lamichhane S, Mathema VB, McGlinchey A, Dickens AM, Khoomrung S, Orešič M. Deep learning meets metabolomics: a methodological perspective. Brief Bioinform 2020; 22:1531-1542. [PMID: 32940335 DOI: 10.1093/bib/bbaa204] [Citation(s) in RCA: 45] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2020] [Revised: 08/08/2020] [Accepted: 08/10/2020] [Indexed: 12/15/2022] Open
Abstract
Deep learning (DL), an emerging area of investigation in the fields of machine learning and artificial intelligence, has markedly advanced over the past years. DL techniques are being applied to assist medical professionals and researchers in improving clinical diagnosis, disease prediction and drug discovery. It is expected that DL will help to provide actionable knowledge from a variety of 'big data', including metabolomics data. In this review, we discuss the applicability of DL to metabolomics, while presenting and discussing several examples from recent research. We emphasize the use of DL in tackling bottlenecks in metabolomics data acquisition, processing, metabolite identification, as well as in metabolic phenotyping and biomarker discovery. Finally, we discuss how DL is used in genome-scale metabolic modelling and in interpretation of metabolomics data. The DL-based approaches discussed here may assist computational biologists with the integration, prediction and drawing of statistical inference about biological outcomes, based on metabolomics data.
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Affiliation(s)
- Partho Sen
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, 20520 Turku, Finland.,School of Medical Sciences, Örebro University, 702 81 Örebro, Sweden
| | - Santosh Lamichhane
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, 20520 Turku, Finland
| | - Vivek B Mathema
- Metabolomics and Systems Biology, Department of Biochemistry, and Siriraj Metabolomics and Phenomics Center, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok 10700, Thailand
| | - Aidan McGlinchey
- School of Medical Sciences, Örebro University, 702 81 Örebro, Sweden
| | - Alex M Dickens
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, 20520 Turku, Finland
| | - Sakda Khoomrung
- Metabolomics and Systems Biology, Department of Biochemistry, and Siriraj Metabolomics and Phenomics Center, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok 10700, Thailand.,Center for Innovation in Chemistry (PERCH), Faculty of Science, Mahidol University, Rama 6 Road, Bangkok 10400, Thailand
| | - Matej Orešič
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, 20520 Turku, Finland.,School of Medical Sciences, Örebro University, 702 81 Örebro, Sweden
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42
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Eicher T, Kinnebrew G, Patt A, Spencer K, Ying K, Ma Q, Machiraju R, Mathé EA. Metabolomics and Multi-Omics Integration: A Survey of Computational Methods and Resources. Metabolites 2020; 10:E202. [PMID: 32429287 PMCID: PMC7281435 DOI: 10.3390/metabo10050202] [Citation(s) in RCA: 60] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2020] [Revised: 05/07/2020] [Accepted: 05/13/2020] [Indexed: 02/06/2023] Open
Abstract
As researchers are increasingly able to collect data on a large scale from multiple clinical and omics modalities, multi-omics integration is becoming a critical component of metabolomics research. This introduces a need for increased understanding by the metabolomics researcher of computational and statistical analysis methods relevant to multi-omics studies. In this review, we discuss common types of analyses performed in multi-omics studies and the computational and statistical methods that can be used for each type of analysis. We pinpoint the caveats and considerations for analysis methods, including required parameters, sample size and data distribution requirements, sources of a priori knowledge, and techniques for the evaluation of model accuracy. Finally, for the types of analyses discussed, we provide examples of the applications of corresponding methods to clinical and basic research. We intend that our review may be used as a guide for metabolomics researchers to choose effective techniques for multi-omics analyses relevant to their field of study.
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Affiliation(s)
- Tara Eicher
- Biomedical Informatics Department, The Ohio State University College of Medicine, Columbus, OH 43210, USA; (T.E.); (G.K.); (K.S.); (Q.M.); (R.M.)
- Computer Science and Engineering Department, The Ohio State University College of Engineering, Columbus, OH 43210, USA
| | - Garrett Kinnebrew
- Biomedical Informatics Department, The Ohio State University College of Medicine, Columbus, OH 43210, USA; (T.E.); (G.K.); (K.S.); (Q.M.); (R.M.)
- Comprehensive Cancer Center, The Ohio State University and James Cancer Hospital, Columbus, OH 43210, USA;
- Bioinformatics Shared Resource Group, The Ohio State University, Columbus, OH 43210, USA
| | - Andrew Patt
- Division of Preclinical Innovation, National Center for Advancing Translational Sciences, NIH, 9800 Medical Center Dr., Rockville, MD, 20892, USA;
- Biomedical Sciences Graduate Program, The Ohio State University, Columbus, OH 43210, USA
| | - Kyle Spencer
- Biomedical Informatics Department, The Ohio State University College of Medicine, Columbus, OH 43210, USA; (T.E.); (G.K.); (K.S.); (Q.M.); (R.M.)
- Biomedical Sciences Graduate Program, The Ohio State University, Columbus, OH 43210, USA
- Nationwide Children’s Research Hospital, Columbus, OH 43210, USA
| | - Kevin Ying
- Comprehensive Cancer Center, The Ohio State University and James Cancer Hospital, Columbus, OH 43210, USA;
- Molecular, Cellular and Developmental Biology Program, The Ohio State University, Columbus, OH 43210, USA
| | - Qin Ma
- Biomedical Informatics Department, The Ohio State University College of Medicine, Columbus, OH 43210, USA; (T.E.); (G.K.); (K.S.); (Q.M.); (R.M.)
| | - Raghu Machiraju
- Biomedical Informatics Department, The Ohio State University College of Medicine, Columbus, OH 43210, USA; (T.E.); (G.K.); (K.S.); (Q.M.); (R.M.)
- Computer Science and Engineering Department, The Ohio State University College of Engineering, Columbus, OH 43210, USA
- Department of Pathology, Wexner Medical Center, The Ohio State University, Columbus, OH 43210, USA
- Translational Data Analytics Institute, The Ohio State University, Columbus, OH 43210, USA
| | - Ewy A. Mathé
- Biomedical Informatics Department, The Ohio State University College of Medicine, Columbus, OH 43210, USA; (T.E.); (G.K.); (K.S.); (Q.M.); (R.M.)
- Division of Preclinical Innovation, National Center for Advancing Translational Sciences, NIH, 9800 Medical Center Dr., Rockville, MD, 20892, USA;
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Suliman HM, Osman B, Abdoon IH, Saad AM, Khalid H. Ameliorative activity of Adansonia digitata fruit on high sugar/high fat diet-simulated Metabolic Syndrome model in male Wistar rats. Biomed Pharmacother 2020; 125:109968. [PMID: 32066041 DOI: 10.1016/j.biopha.2020.109968] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2019] [Revised: 01/20/2020] [Accepted: 01/24/2020] [Indexed: 12/11/2022] Open
Abstract
Metabolic syndrome is a complex of metabolic disorders characterized by oxidative stress which compromises cell functions and entails multiple organs pathologies. We investigated the therapeutic and protective potential of Adansonia digitata fruit -a potent antioxidant- in high sugar/high fat diet-simulated metabolic syndrome in Wistar rats. 42 male rats (140-200 g) were randomly divided into 7 groups. G1 was kept on standard laboratory diet (SLD) for all 9 weeks (negative control). 5 groups were fed high Sugar/high fat diet for 6 weeks then switched to SLD for another 3 weeks + oral treatment as follows: G2+ no treatment (positive control), G3-G5 + 200, 400 and 800 mg/kg/day aqueous A. digitata fruit respectively, G6 + 10 mg/kg/day Simvastatin. G7 + HS/HFD + 400 mg/kg/day A. digitata fruit simultaneously and was terminated at W6. Our results showed that G2-G6 develops dyslipidemia, hyperglycaemia, weight gain, elevated hepatic biomarkers, elevated creatinine and urea plus pathological derangements in the heart, liver and kidney tissues compared to negative control at W6. 200 mg/kg/day A. digitata fruit significantly ameliorated the induced dyslipidemia (P ≤ 0.001), hyperglycaemia (P ≤ 0.001) with a significant reduction in the Atherogenic Index of Plasma (P ≤ 0.000) after 3 weeks treatment. The fruit normalized the elevated hepatic biomarkers as well as creatinine and urea. A dose dependent partial reduction in lesion intensity was observed in the hepatic tissue while the heart and kidney showed mostly reversed to normal histology. The inflammatory infiltration was eliminated. Relevant results were observed for the two higher doses. The simultaneous treatment showed significant lower levels in all biomarkers investigated compared to positive control which could be interpreted as protective activity. A reduction of 4-11% in whole body weight was achieved. CONCLUSION: MetS was successfully simulated with a HS/HFD formula in male Wistar rats. Treatment with aqueous A. digitata fruit showed anti-Metabolic Syndrome potential reflected by weight loss, anti-inflammatory, hypolipidemic, hypoglycaemic, renal, hepatic and cardio-protective activities.
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Affiliation(s)
- Hayat Mohamed Suliman
- Department of Pharmacology, Faculty of Pharmacy, University of Khartoum, 1111 Al Qasr Avenue, P.O.B 1996, Khartoum, Sudan.
| | - Bashier Osman
- Department of Pharmacology, Faculty of Pharmacy, University of Khartoum, 1111 Al Qasr Avenue, P.O.B 1996, Khartoum, Sudan
| | - Iman H Abdoon
- Department of Pharmacology, Faculty of Pharmacy, University of Khartoum, 1111 Al Qasr Avenue, P.O.B 1996, Khartoum, Sudan
| | - Amir Mustafa Saad
- Department of Pathology, Faculty of Veterinary Medicine, University of Khartoum, Sudan
| | - Hassan Khalid
- Department of Pharmacognosy, Faculty of Pharmacy, University of Khartoum, Sudan
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Abstract
In this chapter we consider in silico modeling of diseases starting from some simple to some complex (and mathematical) concepts. Examples and applications of in silico modeling for some important categories of diseases (such as for cancers, infectious diseases, and neuronal diseases) are also given.
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