1
|
Meng W, Pan H, Sha Y, Zhai X, Xing A, Lingampelly SS, Sripathi SR, Wang Y, Li K. Metabolic Connectome and Its Role in the Prediction, Diagnosis, and Treatment of Complex Diseases. Metabolites 2024; 14:93. [PMID: 38392985 PMCID: PMC10890086 DOI: 10.3390/metabo14020093] [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: 12/15/2023] [Revised: 01/17/2024] [Accepted: 01/25/2024] [Indexed: 02/25/2024] Open
Abstract
The interconnectivity of advanced biological systems is essential for their proper functioning. In modern connectomics, biological entities such as proteins, genes, RNA, DNA, and metabolites are often represented as nodes, while the physical, biochemical, or functional interactions between them are represented as edges. Among these entities, metabolites are particularly significant as they exhibit a closer relationship to an organism's phenotype compared to genes or proteins. Moreover, the metabolome has the ability to amplify small proteomic and transcriptomic changes, even those from minor genomic changes. Metabolic networks, which consist of complex systems comprising hundreds of metabolites and their interactions, play a critical role in biological research by mediating energy conversion and chemical reactions within cells. This review provides an introduction to common metabolic network models and their construction methods. It also explores the diverse applications of metabolic networks in elucidating disease mechanisms, predicting and diagnosing diseases, and facilitating drug development. Additionally, it discusses potential future directions for research in metabolic networks. Ultimately, this review serves as a valuable reference for researchers interested in metabolic network modeling, analysis, and their applications.
Collapse
Affiliation(s)
- Weiyu Meng
- Center for Artificial Intelligence Driven Drug Discovery, Faculty of Applied Sciences, Macao Polytechnic University, Macau SAR 999078, China
| | - Hongxin Pan
- Center for Artificial Intelligence Driven Drug Discovery, Faculty of Applied Sciences, Macao Polytechnic University, Macau SAR 999078, China
| | - Yuyang Sha
- Center for Artificial Intelligence Driven Drug Discovery, Faculty of Applied Sciences, Macao Polytechnic University, Macau SAR 999078, China
| | - Xiaobing Zhai
- Center for Artificial Intelligence Driven Drug Discovery, Faculty of Applied Sciences, Macao Polytechnic University, Macau SAR 999078, China
| | - Abao Xing
- Center for Artificial Intelligence Driven Drug Discovery, Faculty of Applied Sciences, Macao Polytechnic University, Macau SAR 999078, China
| | | | - Srinivasa R Sripathi
- Henderson Ocular Stem Cell Laboratory, Retina Foundation of the Southwest, Dallas, TX 75231, USA
| | - Yuefei Wang
- National Key Laboratory of Chinese Medicine Modernization, State Key Laboratory of Component-Based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China
- Haihe Laboratory of Modern Chinese Medicine, Tianjin 301617, China
| | - Kefeng Li
- Center for Artificial Intelligence Driven Drug Discovery, Faculty of Applied Sciences, Macao Polytechnic University, Macau SAR 999078, China
| |
Collapse
|
2
|
Pillai M, Hojel E, Jolly MK, Goyal Y. Unraveling non-genetic heterogeneity in cancer with dynamical models and computational tools. NATURE COMPUTATIONAL SCIENCE 2023; 3:301-313. [PMID: 38177938 DOI: 10.1038/s43588-023-00427-0] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/30/2022] [Accepted: 03/03/2023] [Indexed: 01/06/2024]
Abstract
Individual cells within an otherwise genetically homogenous population constantly undergo fluctuations in their molecular state, giving rise to non-genetic heterogeneity. Such diversity is being increasingly implicated in cancer therapy resistance and metastasis. Identifying the origins of non-genetic heterogeneity is therefore crucial for making clinical breakthroughs. We discuss with examples how dynamical models and computational tools have provided critical multiscale insights into the nature and consequences of non-genetic heterogeneity in cancer. We demonstrate how mechanistic modeling has been pivotal in establishing key concepts underlying non-genetic diversity at various biological scales, from population dynamics to gene regulatory networks. We discuss advances in single-cell longitudinal profiling techniques to reveal patterns of non-genetic heterogeneity, highlighting the ongoing efforts and challenges in statistical frameworks to robustly interpret such multimodal datasets. Moving forward, we stress the need for data-driven statistical and mechanistically motivated dynamical frameworks to come together to develop predictive cancer models and inform therapeutic strategies.
Collapse
Affiliation(s)
- Maalavika Pillai
- Department of Cell and Developmental Biology, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
- Center for Synthetic Biology, Northwestern University, Chicago, IL, USA
- Robert H. Lurie Comprehensive Cancer Center, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
- Centre for BioSystems Science and Engineering, Indian Institute of Science, Bangalore, India
| | - Emilia Hojel
- Department of Cell and Developmental Biology, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
- Center for Synthetic Biology, Northwestern University, Chicago, IL, USA
- Department of Biomedical Engineering, Northwestern University McCormick School of Engineering, Evanston, IL, USA
| | - Mohit Kumar Jolly
- Centre for BioSystems Science and Engineering, Indian Institute of Science, Bangalore, India.
| | - Yogesh Goyal
- Department of Cell and Developmental Biology, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA.
- Center for Synthetic Biology, Northwestern University, Chicago, IL, USA.
- Robert H. Lurie Comprehensive Cancer Center, Northwestern University Feinberg School of Medicine, Chicago, IL, USA.
- Department of Biomedical Engineering, Northwestern University McCormick School of Engineering, Evanston, IL, USA.
| |
Collapse
|
3
|
Ng RH, Lee JW, Baloni P, Diener C, Heath JR, Su Y. Constraint-Based Reconstruction and Analyses of Metabolic Models: Open-Source Python Tools and Applications to Cancer. Front Oncol 2022; 12:914594. [PMID: 35875150 PMCID: PMC9303011 DOI: 10.3389/fonc.2022.914594] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2022] [Accepted: 05/30/2022] [Indexed: 11/13/2022] Open
Abstract
The influence of metabolism on signaling, epigenetic markers, and transcription is highly complex yet important for understanding cancer physiology. Despite the development of high-resolution multi-omics technologies, it is difficult to infer metabolic activity from these indirect measurements. Fortunately, genome-scale metabolic models and constraint-based modeling provide a systems biology framework to investigate the metabolic states and define the genotype-phenotype associations by integrations of multi-omics data. Constraint-Based Reconstruction and Analysis (COBRA) methods are used to build and simulate metabolic networks using mathematical representations of biochemical reactions, gene-protein reaction associations, and physiological and biochemical constraints. These methods have led to advancements in metabolic reconstruction, network analysis, perturbation studies as well as prediction of metabolic state. Most computational tools for performing these analyses are written for MATLAB, a proprietary software. In order to increase accessibility and handle more complex datasets and models, community efforts have started to develop similar open-source tools in Python. To date there is a comprehensive set of tools in Python to perform various flux analyses and visualizations; however, there are still missing algorithms in some key areas. This review summarizes the availability of Python software for several components of COBRA methods and their applications in cancer metabolism. These tools are evolving rapidly and should offer a readily accessible, versatile way to model the intricacies of cancer metabolism for identifying cancer-specific metabolic features that constitute potential drug targets.
Collapse
Affiliation(s)
- Rachel H. Ng
- Institute for Systems Biology, Seattle, WA, United States
- Department of Bioengineering, University of Washington, Seattle, WA, United States
| | - Jihoon W. Lee
- Medical Scientist Training Program, University of Washington, Seattle, WA, United States
- Program in Immunology, Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, WA, United States
| | | | | | - James R. Heath
- Institute for Systems Biology, Seattle, WA, United States
- Department of Bioengineering, University of Washington, Seattle, WA, United States
| | - Yapeng Su
- Program in Immunology, Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, WA, United States
- Herbold Computational Biology Program, Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA, United States
| |
Collapse
|
4
|
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.
Collapse
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.
| |
Collapse
|
5
|
Lange J, Gillham O, Alkharji R, Eaton S, Ferrari G, Madej M, Flower M, Tedesco FS, Muntoni F, Ferretti P. Dystrophin deficiency affects human astrocyte properties and response to damage. Glia 2022; 70:466-490. [PMID: 34773297 DOI: 10.1002/glia.24116] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2019] [Revised: 10/21/2021] [Accepted: 10/22/2021] [Indexed: 01/03/2023]
Abstract
In addition to progressive muscular degeneration due to dystrophin mutations, 1/3 of Duchenne muscular dystrophy (DMD) patients present cognitive deficits. However, there is currently an incomplete understanding about the function of the multiple dystrophin isoforms in human brains. Here, we tested the hypothesis that dystrophin deficiency affects glial function in DMD and could therefore contribute to neural impairment. We investigated human dystrophin isoform expression with development and differentiation and response to damage in human astrocytes from control and induced pluripotent stem cells from DMD patients. In control cells, short dystrophin isoforms were up-regulated with development and their expression levels changed differently upon neuronal and astrocytic differentiation, as well as in 2-dimensional versus 3-dimensional astrocyte cultures. All DMD-astrocytes tested displayed altered morphology, proliferative activity and AQP4 expression. Furthermore, they did not show any morphological change in response to inflammatory stimuli and their number was significantly lower as compared to stimulated healthy astrocytes. Finally, DMD-astrocytes appeared to be more sensitive than controls to oxidative damage as shown by their increased cell death. Behavioral and metabolic defects in DMD-astrocytes were consistent with gene pathway dysregulation shared by lines with different mutations as demonstrated by bulk RNA-seq analysis. Together, our DMD model provides evidence for altered astrocyte function in DMD suggesting that defective astrocyte responses may contribute to neural impairment and might provide additional potential therapeutic targets.
Collapse
Affiliation(s)
- Jenny Lange
- Department of Developmental Biology and Cancer, Stem Cells and Regenerative Medicine Section, UCL Great Ormond Street Institute of Child Health, University College London, London, UK
| | - Olivia Gillham
- Department of Developmental Biology and Cancer, Stem Cells and Regenerative Medicine Section, UCL Great Ormond Street Institute of Child Health, University College London, London, UK
| | - Reem Alkharji
- Department of Developmental Biology and Cancer, Stem Cells and Regenerative Medicine Section, UCL Great Ormond Street Institute of Child Health, University College London, London, UK
| | - Simon Eaton
- Department of Developmental Biology and Cancer, Stem Cells and Regenerative Medicine Section, UCL Great Ormond Street Institute of Child Health, University College London, London, UK
| | - Giulia Ferrari
- Department of Cell and Developmental Biology, University College London, London, UK
| | - Monika Madej
- Department of Neuromuscular Diseases, UCL Queen Square Institute of Neurology, London, UK
| | - Michael Flower
- UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Francesco Saverio Tedesco
- Department of Cell and Developmental Biology, University College London, London, UK
- Dubowitz Neuromuscular Centre, UCL Great Ormond Street Institute of Child Health, University College London, London, UK
- The Francis Crick Institute, 1 Midland Road, London, UK
| | - Francesco Muntoni
- Dubowitz Neuromuscular Centre, UCL Great Ormond Street Institute of Child Health, University College London, London, UK
- NIHR Great Ormond Street Hospital Biomedical Research Centre, Great Ormond Street Institute of Child Health, University College London, & Great Ormond Street Hospital Trust, London, UK
| | - Patrizia Ferretti
- Department of Developmental Biology and Cancer, Stem Cells and Regenerative Medicine Section, UCL Great Ormond Street Institute of Child Health, University College London, London, UK
| |
Collapse
|
6
|
Di Filippo M, Pescini D, Galuzzi BG, Bonanomi M, Gaglio D, Mangano E, Consolandi C, Alberghina L, Vanoni M, Damiani C. INTEGRATE: Model-based multi-omics data integration to characterize multi-level metabolic regulation. PLoS Comput Biol 2022; 18:e1009337. [PMID: 35130273 PMCID: PMC8853556 DOI: 10.1371/journal.pcbi.1009337] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2021] [Revised: 02/17/2022] [Accepted: 01/13/2022] [Indexed: 12/12/2022] Open
Abstract
Metabolism is directly and indirectly fine-tuned by a complex web of interacting regulatory mechanisms that fall into two major classes. On the one hand, the expression level of the catalyzing enzyme sets the maximal theoretical flux level (i.e., the net rate of the reaction) for each enzyme-controlled reaction. On the other hand, metabolic regulation controls the metabolic flux through the interactions of metabolites (substrates, cofactors, allosteric modulators) with the responsible enzyme. High-throughput data, such as metabolomics and transcriptomics data, if analyzed separately, do not accurately characterize the hierarchical regulation of metabolism outlined above. They must be integrated to disassemble the interdependence between different regulatory layers controlling metabolism. To this aim, we propose INTEGRATE, a computational pipeline that integrates metabolomics and transcriptomics data, using constraint-based stoichiometric metabolic models as a scaffold. We compute differential reaction expression from transcriptomics data and use constraint-based modeling to predict if the differential expression of metabolic enzymes directly originates differences in metabolic fluxes. In parallel, we use metabolomics to predict how differences in substrate availability translate into differences in metabolic fluxes. We discriminate fluxes regulated at the metabolic and/or gene expression level by intersecting these two output datasets. We demonstrate the pipeline using a set of immortalized normal and cancer breast cell lines. In a clinical setting, knowing the regulatory level at which a given metabolic reaction is controlled will be valuable to inform targeted, truly personalized therapies in cancer patients.
Collapse
Affiliation(s)
- Marzia Di Filippo
- Department of Statistics and Quantitative Methods, University of Milan-Bicocca, Milan, Italy
- ISBE/SYSBIO Centre of Systems Biology, Milan, Italy
| | - Dario Pescini
- Department of Statistics and Quantitative Methods, University of Milan-Bicocca, Milan, Italy
- ISBE/SYSBIO Centre of Systems Biology, Milan, Italy
| | - Bruno Giovanni Galuzzi
- ISBE/SYSBIO Centre of Systems Biology, Milan, Italy
- Department of Biotechnology and Biosciences, University of Milan-Bicocca, Milan, Italy
| | - Marcella Bonanomi
- ISBE/SYSBIO Centre of Systems Biology, Milan, Italy
- Department of Biotechnology and Biosciences, University of Milan-Bicocca, Milan, Italy
| | - Daniela Gaglio
- ISBE/SYSBIO Centre of Systems Biology, Milan, Italy
- Institute of Molecular Bioimaging and Physiology (IBFM), National Research Council (CNR), Segrate, Italy
| | - Eleonora Mangano
- Institute for Biomedical Technologies (ITB), National Research Council (CNR), Segrate, Italy
| | - Clarissa Consolandi
- Institute for Biomedical Technologies (ITB), National Research Council (CNR), Segrate, Italy
| | - Lilia Alberghina
- ISBE/SYSBIO Centre of Systems Biology, Milan, Italy
- Department of Biotechnology and Biosciences, University of Milan-Bicocca, Milan, Italy
| | - Marco Vanoni
- ISBE/SYSBIO Centre of Systems Biology, Milan, Italy
- Department of Biotechnology and Biosciences, University of Milan-Bicocca, Milan, Italy
| | - Chiara Damiani
- ISBE/SYSBIO Centre of Systems Biology, Milan, Italy
- Department of Biotechnology and Biosciences, University of Milan-Bicocca, Milan, Italy
- * E-mail:
| |
Collapse
|
7
|
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.
Collapse
|
8
|
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 .
Collapse
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.
| |
Collapse
|
9
|
GPRuler: Metabolic gene-protein-reaction rules automatic reconstruction. PLoS Comput Biol 2021; 17:e1009550. [PMID: 34748537 PMCID: PMC8601613 DOI: 10.1371/journal.pcbi.1009550] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2021] [Revised: 11/18/2021] [Accepted: 10/11/2021] [Indexed: 11/19/2022] Open
Abstract
Metabolic network models are increasingly being used in health care and industry. As a consequence, many tools have been released to automate their reconstruction process de novo. In order to enable gene deletion simulations and integration of gene expression data, these networks must include gene-protein-reaction (GPR) rules, which describe with a Boolean logic relationships between the gene products (e.g., enzyme isoforms or subunits) associated with the catalysis of a given reaction. Nevertheless, the reconstruction of GPRs still remains a largely manual and time consuming process. Aiming at fully automating the reconstruction process of GPRs for any organism, we propose the open-source python-based framework GPRuler. By mining text and data from 9 different biological databases, GPRuler can reconstruct GPRs starting either from just the name of the target organism or from an existing metabolic model. The performance of the developed tool is evaluated at small-scale level for a manually curated metabolic model, and at genome-scale level for three metabolic models related to Homo sapiens and Saccharomyces cerevisiae organisms. By exploiting these models as benchmarks, the proposed tool shown its ability to reproduce the original GPR rules with a high level of accuracy. In all the tested scenarios, after a manual investigation of the mismatches between the rules proposed by GPRuler and the original ones, the proposed approach revealed to be in many cases more accurate than the original models. By complementing existing tools for metabolic network reconstruction with the possibility to reconstruct GPRs quickly and with a few resources, GPRuler paves the way to the study of context-specific metabolic networks, representing the active portion of the complete network in given conditions, for organisms of industrial or biomedical interest that have not been characterized metabolically yet.
Collapse
|
10
|
Heinken A, Basile A, Hertel J, Thinnes C, Thiele I. Genome-Scale Metabolic Modeling of the Human Microbiome in the Era of Personalized Medicine. Annu Rev Microbiol 2021; 75:199-222. [PMID: 34314593 DOI: 10.1146/annurev-micro-060221-012134] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
The human microbiome plays an important role in human health and disease. Meta-omics analyses provide indispensable data for linking changes in microbiome composition and function to disease etiology. Yet, the lack of a mechanistic understanding of, e.g., microbiome-metabolome links hampers the translation of these findings into effective, novel therapeutics. Here, we propose metabolic modeling of microbial communities through constraint-based reconstruction and analysis (COBRA) as a complementary approach to meta-omics analyses. First, we highlight the importance of microbial metabolism in cardiometabolic diseases, inflammatory bowel disease, colorectal cancer, Alzheimer disease, and Parkinson disease. Next, we demonstrate that microbial community modeling can stratify patients and controls, mechanistically link microbes with fecal metabolites altered in disease, and identify host pathways affected by the microbiome. Finally, we outline our vision for COBRA modeling combined with meta-omics analyses and multivariate statistical analyses to inform and guide clinical trials, yield testable hypotheses, and ultimately propose novel dietary and therapeutic interventions. Expected final online publication date for the Annual Review of Microbiology, Volume 75 is October 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.
Collapse
Affiliation(s)
- Almut Heinken
- School of Medicine, National University of Ireland, Galway, H91 TK33, Ireland;
| | - Arianna Basile
- Department of Biology, University of Padua, Padua 35121, Italy
| | - Johannes Hertel
- School of Medicine, National University of Ireland, Galway, H91 TK33, Ireland; .,Department of Psychiatry and Psychotherapy, University of Greifswald, 17489 Greifswald, Germany
| | - Cyrille Thinnes
- School of Medicine, National University of Ireland, Galway, H91 TK33, Ireland;
| | - Ines Thiele
- School of Medicine, National University of Ireland, Galway, H91 TK33, Ireland; .,Division of Microbiology, National University of Ireland, Galway, H91 TK33, Ireland.,APC Microbiome Ireland, University College Cork, Cork, T12 K8AF, Ireland
| |
Collapse
|
11
|
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.
Collapse
|
12
|
Towards the routine use of in silico screenings for drug discovery using metabolic modelling. Biochem Soc Trans 2021; 48:955-969. [PMID: 32369553 PMCID: PMC7329353 DOI: 10.1042/bst20190867] [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: 01/15/2020] [Revised: 04/01/2020] [Accepted: 04/06/2020] [Indexed: 12/12/2022]
Abstract
Currently, the development of new effective drugs for cancer therapy is not only hindered by development costs, drug efficacy, and drug safety but also by the rapid occurrence of drug resistance in cancer. Hence, new tools are needed to study the underlying mechanisms in cancer. Here, we discuss the current use of metabolic modelling approaches to identify cancer-specific metabolism and find possible new drug targets and drugs for repurposing. Furthermore, we list valuable resources that are needed for the reconstruction of cancer-specific models by integrating various available datasets with genome-scale metabolic reconstructions using model-building algorithms. We also discuss how new drug targets can be determined by using gene essentiality analysis, an in silico method to predict essential genes in a given condition such as cancer and how synthetic lethality studies could greatly benefit cancer patients by suggesting drug combinations with reduced side effects.
Collapse
|
13
|
Masid M, Ataman M, Hatzimanikatis V. Analysis of human metabolism by reducing the complexity of the genome-scale models using redHUMAN. Nat Commun 2020; 11:2821. [PMID: 32499584 PMCID: PMC7272419 DOI: 10.1038/s41467-020-16549-2] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2019] [Accepted: 05/07/2020] [Indexed: 01/31/2023] Open
Abstract
Altered metabolism is associated with many human diseases. Human genome-scale metabolic models (GEMs) were reconstructed within systems biology to study the biochemistry occurring in human cells. However, the complexity of these networks hinders a consistent and concise physiological representation. We present here redHUMAN, a workflow for reconstructing reduced models that focus on parts of the metabolism relevant to a specific physiology using the recently established methods redGEM and lumpGEM. The reductions include the thermodynamic properties of compounds and reactions guaranteeing the consistency of predictions with the bioenergetics of the cell. We introduce a method (redGEMX) to incorporate the pathways used by cells to adapt to the medium. We provide the thermodynamic curation of the human GEMs Recon2 and Recon3D and we apply the redHUMAN workflow to derive leukemia-specific reduced models. The reduced models are powerful platforms for studying metabolic differences between phenotypes, such as diseased and healthy cells.
Collapse
Affiliation(s)
- Maria Masid
- Laboratory of Computational Systems Biotechnology, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Meric Ataman
- Computational and Systems Biology, Biozentrum, University of Basel, Basel, Switzerland
| | - Vassily Hatzimanikatis
- Laboratory of Computational Systems Biotechnology, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.
| |
Collapse
|
14
|
Damiani C, Rovida L, Maspero D, Sala I, Rosato L, Di Filippo M, Pescini D, Graudenzi A, Antoniotti M, Mauri G. MaREA4Galaxy: Metabolic reaction enrichment analysis and visualization of RNA-seq data within Galaxy. Comput Struct Biotechnol J 2020; 18:993-999. [PMID: 32373287 PMCID: PMC7191582 DOI: 10.1016/j.csbj.2020.04.008] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2019] [Revised: 03/20/2020] [Accepted: 04/08/2020] [Indexed: 12/21/2022] Open
Abstract
We present MaREA4Galaxy, a user-friendly tool that allows a user to characterize and to graphically compare groups of samples with different transcriptional regulation of metabolism, as estimated from cross-sectional RNA-seq data. The tool is available as plug-in for the widely-used Galaxy platform for comparative genomics and bioinformatics analyses. MaREA4Galaxy combines three modules. The Expression2RAS module, which, for each reaction of a specified set, computes a Reaction Activity Score (RAS) as a function of the expression level of genes encoding for the associated enzyme. The MaREA (Metabolic Reaction Enrichment Analysis) module that allows to highlight significant differences in reaction activities between specified groups of samples. The Clustering module which employs the RAS computed before as a metric for unsupervised clustering of samples into distinct metabolic subgroups; the Clustering tool provides different clustering techniques and implements standard methods to evaluate the goodness of the results.
Collapse
Affiliation(s)
- Chiara Damiani
- Dept. of Biotechnology and Biosciences, Universitá degli Studi di Milano-Bicocca, Milan, Italy
- Dept. of Informatics, Systems and Communication, Universitá degli Studi di Milano-Bicocca, Milan, Italy
- SYSBE-IT/SYSBIO Centre of Systems Biology, Universitá degli Studi di Milano-Bicocca, Milan, Italy
| | - Lorenzo Rovida
- Dept. of Informatics, Systems and Communication, Universitá degli Studi di Milano-Bicocca, Milan, Italy
| | - Davide Maspero
- Dept. of Informatics, Systems and Communication, Universitá degli Studi di Milano-Bicocca, Milan, Italy
- Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy
| | - Irene Sala
- Dept. of Informatics, Systems and Communication, Universitá degli Studi di Milano-Bicocca, Milan, Italy
| | - Luca Rosato
- Dept. of Informatics, Systems and Communication, Universitá degli Studi di Milano-Bicocca, Milan, Italy
| | - Marzia Di Filippo
- SYSBE-IT/SYSBIO Centre of Systems Biology, Universitá degli Studi di Milano-Bicocca, Milan, Italy
- Department of Statistics and Quantitative Methods, Universitá degli Studi di Milano-Bicocca, Milan, Italy
| | - Dario Pescini
- SYSBE-IT/SYSBIO Centre of Systems Biology, Universitá degli Studi di Milano-Bicocca, Milan, Italy
- Department of Statistics and Quantitative Methods, Universitá degli Studi di Milano-Bicocca, Milan, Italy
| | - Alex Graudenzi
- Institute of Molecular Bioimaging and Physiology, Consiglio Nazionale delle Ricerche (IBFM-CNR), Segrate, Milan, Italy
| | - Marco Antoniotti
- Dept. of Informatics, Systems and Communication, Universitá degli Studi di Milano-Bicocca, Milan, Italy
| | - Giancarlo Mauri
- Dept. of Informatics, Systems and Communication, Universitá degli Studi di Milano-Bicocca, Milan, Italy
- SYSBE-IT/SYSBIO Centre of Systems Biology, Universitá degli Studi di Milano-Bicocca, Milan, Italy
| |
Collapse
|
15
|
Abstract
Laboratory models derived from clinical samples represent a solid platform in preclinical research for drug testing and investigation of disease mechanisms. The integration of these laboratory models with their digital counterparts (i.e., predictive mathematical models) allows to set up digital twins essential to fully exploit their potential to face the enormous molecular complexity of human organisms. In particular, due to the close integration of cell metabolism with all other cellular processes, any perturbation in cellular physiology typically reflect on altered cells metabolic profiling. In this regard, changes in metabolism have been shown, also in our laboratory, to drive a causal role in the emergence of cancer disease. Nevertheless, a unique metabolic program does not describe the altered metabolic profile of all tumour cells due to many causes from genetic variability to intratumour heterogeneous dependency on nutrients consumption and metabolism by multiple co-existing subclones. Currently, fluxomics approaches just match with the necessity of characterizing the overall flux distribution of cells within given samples, by disregarding possible heterogeneous behaviors. For the purpose of stratifying cancer heterogeneous subpopulations, quantification of fluxes at the single-cell level is needed. To this aim, we here present a new computational framework called single-cell Flux Balance Analysis (scFBA) that aims to set up digital metabolic twins in the perspective of being better exploited within a framework that makes also use of laboratory patient cell models. In particular, scFBA aims at integrating single-cell RNA-seq data within computational population models in order to depict a snapshot of the corresponding single-cell metabolic phenotypes at a given moment, together with an unsupervised identification of metabolic subpopulations.
Collapse
|
16
|
Damiani C, Maspero D, Di Filippo M, Colombo R, Pescini D, Graudenzi A, Westerhoff HV, Alberghina L, Vanoni M, Mauri G. Integration of single-cell RNA-seq data into population models to characterize cancer metabolism. PLoS Comput Biol 2019; 15:e1006733. [PMID: 30818329 PMCID: PMC6413955 DOI: 10.1371/journal.pcbi.1006733] [Citation(s) in RCA: 53] [Impact Index Per Article: 10.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2018] [Revised: 03/12/2019] [Accepted: 12/22/2018] [Indexed: 02/07/2023] Open
Abstract
Metabolic reprogramming is a general feature of cancer cells. Regrettably, the comprehensive quantification of metabolites in biological specimens does not promptly translate into knowledge on the utilization of metabolic pathways. By estimating fluxes across metabolic pathways, computational models hold the promise to bridge this gap between data and biological functionality. These models currently portray the average behavior of cell populations however, masking the inherent heterogeneity that is part and parcel of tumorigenesis as much as drug resistance. To remove this limitation, we propose single-cell Flux Balance Analysis (scFBA) as a computational framework to translate single-cell transcriptomes into single-cell fluxomes. We show that the integration of single-cell RNA-seq profiles of cells derived from lung adenocarcinoma and breast cancer patients into a multi-scale stoichiometric model of a cancer cell population: significantly 1) reduces the space of feasible single-cell fluxomes; 2) allows to identify clusters of cells with different growth rates within the population; 3) points out the possible metabolic interactions among cells via exchange of metabolites. The scFBA suite of MATLAB functions is available at https://github.com/BIMIB-DISCo/scFBA, as well as the case study datasets. Cytotoxicity of chemotherapeutic agents and resistance to targeted treatments are the main reasons why cancer is still one of the top causes of death. As tumor cells are intrinsically resistant to therapies that target signaling pathways, targeting the metabolic hallmarks of cancer holds promise for more incisive treatments. Regrettably, the heterogeneity of cancer metabolism hinders the identification of effective treatments. To fully uncover the metabolic heterogeneity within tumors, characterization of metabolic programs (metabolic flux distributions) at the single-cell level is required. To fill the gap between current technologies for genomics and future technologies for fluxomics, both at the single-cell and the genome-wide scale, we propose to integrate cancer data from: 1) single-cell transcriptomics and 2) bulk metabolomics, into a multi-scale stoichiometric model, to deliver for the first time metabolic fluxomes at the single-cell level. To this end, we introduce a new paradigm for flux balance analysis and data integration in cancer metabolism to: 1) characterize metabolic heterogeneity, not only at the inter-, but also at the intra-tumor level 2) identify the metabolic interactions between cancer populations, whose role in resistance to metabolic treatments has been recently recognized 3) predict the collective response to drug targeting of metabolism.
Collapse
Affiliation(s)
- Chiara Damiani
- Dept. of Informatics, Systems and Communication, University of Milan-Bicocca, 20126, Milan, Italy
- SYSBIO Centre of Systems Biology, 20126, Milan, Italy
- * E-mail:
| | - Davide Maspero
- Dept. of Biotechnology and Biosciences, University of Milan-Bicocca, 20126, Milan, Italy
- Department of Research, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy
| | - Marzia Di Filippo
- SYSBIO Centre of Systems Biology, 20126, Milan, Italy
- Dept. of Biotechnology and Biosciences, University of Milan-Bicocca, 20126, Milan, Italy
| | - Riccardo Colombo
- Dept. of Informatics, Systems and Communication, University of Milan-Bicocca, 20126, Milan, Italy
- SYSBIO Centre of Systems Biology, 20126, Milan, Italy
| | - Dario Pescini
- SYSBIO Centre of Systems Biology, 20126, Milan, Italy
- Dept. of Statistics and Quantitative Methods, University of Milan-Bicocca, 20126, Milan, Italy
| | - Alex Graudenzi
- Dept. of Informatics, Systems and Communication, University of Milan-Bicocca, 20126, Milan, Italy
| | - Hans Victor Westerhoff
- Dept. of Molecular Cell Physiology, Faculty of Earth and Life Sciences, VU University, Amsterdam, The Netherlands
- Manchester Centre for Integrative Systems Biology, School of Chemical Engineering and Analytical Science, University of Manchester, Manchester, United Kingdom
- Swammerdam Institute for Life Sciences, Faculty of Science, University of Amsterdam, Amsterdam, The Netherlands
| | - Lilia Alberghina
- SYSBIO Centre of Systems Biology, 20126, Milan, Italy
- Dept. of Biotechnology and Biosciences, University of Milan-Bicocca, 20126, Milan, Italy
| | - Marco Vanoni
- SYSBIO Centre of Systems Biology, 20126, Milan, Italy
- Dept. of Biotechnology and Biosciences, University of Milan-Bicocca, 20126, Milan, Italy
| | - Giancarlo Mauri
- Dept. of Informatics, Systems and Communication, University of Milan-Bicocca, 20126, Milan, Italy
- SYSBIO Centre of Systems Biology, 20126, Milan, Italy
| |
Collapse
|
17
|
Graudenzi A, Maspero D, Di Filippo M, Gnugnoli M, Isella C, Mauri G, Medico E, Antoniotti M, Damiani C. Integration of transcriptomic data and metabolic networks in cancer samples reveals highly significant prognostic power. J Biomed Inform 2018; 87:37-49. [PMID: 30244122 DOI: 10.1016/j.jbi.2018.09.010] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2018] [Revised: 09/07/2018] [Accepted: 09/14/2018] [Indexed: 12/20/2022]
Abstract
Effective stratification of cancer patients on the basis of their molecular make-up is a key open challenge. Given the altered and heterogenous nature of cancer metabolism, we here propose to use the overall expression of central carbon metabolism as biomarker to characterize groups of patients with important characteristics, such as response to ad-hoc therapeutic strategies and survival expectancy. To this end, we here introduce the data integration framework named Metabolic Reaction Enrichment Analysis (MaREA), which strives to characterize the metabolic deregulations that distinguish cancer phenotypes, by projecting RNA-seq data onto metabolic networks, without requiring metabolic measurements. MaREA computes a score for each network reaction, based on the expression of the set of genes encoding for the associated enzyme(s). The scores are first used as features for cluster analysis and then to rank and visualize in an organized fashion the metabolic deregulations that distinguish cancer sub-types. We applied our method to recent lung and breast cancer RNA-seq datasets from The Cancer Genome Atlas and we were able to identify subgroups of patients with significant differences in survival expectancy. We show how the prognostic power of MaREA improves when an extracted and further curated core model focusing on central carbon metabolism is used rather than the genome-wide reference network. The visualization of the metabolic differences between the groups with best and worst prognosis allowed to identify and analyze key metabolic properties related to cancer aggressiveness. Some of these properties are shared across different cancer (sub) types, e.g., the up-regulation of nucleic acid and amino acid synthesis, whereas some other appear to be tumor-specific, such as the up- or down-regulation of the phosphoenolpyruvate carboxykinase reaction, which display different patterns in distinct tumor (sub)types. These results might be soon employed to deliver highly automated diagnostic and prognostic strategies for cancer patients.
Collapse
Affiliation(s)
- Alex Graudenzi
- Department of Informatics, Systems and Communication, University of Milan-Bicocca, Milan, Italy
| | - Davide Maspero
- Department of Biotechnology and Biosciences, University Milano-Bicocca, Milan, Italy
| | - Marzia Di Filippo
- Department of Biotechnology and Biosciences, University Milano-Bicocca, Milan, Italy; SYSBIO Centre of Systems Biology, University Milano-Bicocca, Milan, Italy
| | - Marco Gnugnoli
- Department of Biotechnology and Biosciences, University Milano-Bicocca, Milan, Italy; SYSBIO Centre of Systems Biology, University Milano-Bicocca, Milan, Italy
| | - Claudio Isella
- University of Torino, Department of Oncology, Candiolo, Torino, Italy; Candiolo Cancer Institute, FPO-IRCCS, Candiolo, Torino, Italy
| | - Giancarlo Mauri
- Department of Informatics, Systems and Communication, University of Milan-Bicocca, Milan, Italy; SYSBIO Centre of Systems Biology, University Milano-Bicocca, Milan, Italy
| | - Enzo Medico
- University of Torino, Department of Oncology, Candiolo, Torino, Italy; Candiolo Cancer Institute, FPO-IRCCS, Candiolo, Torino, Italy
| | - Marco Antoniotti
- Department of Informatics, Systems and Communication, University of Milan-Bicocca, Milan, Italy; Milan Center for Neuroscience, University of Milan-Bicocca, Monza, Italy
| | - Chiara Damiani
- Department of Informatics, Systems and Communication, University of Milan-Bicocca, Milan, Italy; SYSBIO Centre of Systems Biology, University Milano-Bicocca, Milan, Italy.
| |
Collapse
|
18
|
Colombo R, Damiani C, Gilbert D, Heiner M, Mauri G, Pescini D. Emerging ensembles of kinetic parameters to characterize observed metabolic phenotypes. BMC Bioinformatics 2018; 19:251. [PMID: 30066662 PMCID: PMC6201900 DOI: 10.1186/s12859-018-2181-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Background Determining the value of kinetic constants for a metabolic system in the exact
physiological conditions is an extremely hard task. However, this kind of
information is of pivotal relevance to effectively simulate a biological
phenomenon as complex as metabolism. Results To overcome this issue, we propose to investigate emerging properties of
ensembles of sets of kinetic constants leading to the biological readout observed
in different experimental conditions. To this aim, we exploit information
retrievable from constraint-based analyses (i.e. metabolic flux distributions at
steady state) with the goal to generate feasible values for kinetic constants
exploiting the mass action law. The sets retrieved from the previous step will be
used to parametrize a mechanistic model whose simulation will be performed to
reconstruct the dynamics of the system (until reaching the metabolic steady state)
for each experimental condition. Every parametrization that is in accordance with
the expected metabolic phenotype is collected in an ensemble whose features are
analyzed to determine the emergence of properties of a phenotype. In this work we
apply the proposed approach to identify ensembles of kinetic parameters for five
metabolic phenotypes of E. Coli, by analyzing
five different experimental conditions associated with the ECC2comp model recently
published by Hädicke and collaborators. Conclusions Our results suggest that the parameter values of just few reactions are
responsible for the emergence of a metabolic phenotype. Notably, in contrast with
constraint-based approaches such as Flux Balance Analysis, the methodology used in
this paper does not require to assume that metabolism is optimizing towards a
specific goal. Electronic supplementary material The online version of this article (10.1186/s12859-018-2181-7) contains supplementary material, which is available to authorized
users.
Collapse
Affiliation(s)
- Riccardo Colombo
- Department of Informatics, Systems and Communication, University of Milan - Bicocca, Viale Sarca, 336, Milan, 20126, Italy. .,SYSBIO - Centre of Systems Biology, Piazza della Scienza, 2, Milan, 20126, Italy.
| | - Chiara Damiani
- Department of Informatics, Systems and Communication, University of Milan - Bicocca, Viale Sarca, 336, Milan, 20126, Italy.,SYSBIO - Centre of Systems Biology, Piazza della Scienza, 2, Milan, 20126, Italy
| | - David Gilbert
- College of Engineering, Design and Physical Sciences, Brunel University, Middlesex, London, Uxbridg, UB8 3PH, UK
| | - Monika Heiner
- Computer Science Department, Brandenburg University of Technology Cottbus-Senftenberg, Walther-Pauer-Str. 2, Cottbus, D-03046, Germany
| | - Giancarlo Mauri
- Department of Informatics, Systems and Communication, University of Milan - Bicocca, Viale Sarca, 336, Milan, 20126, Italy.,SYSBIO - Centre of Systems Biology, Piazza della Scienza, 2, Milan, 20126, Italy
| | - Dario Pescini
- SYSBIO - Centre of Systems Biology, Piazza della Scienza, 2, Milan, 20126, Italy.,Department of Statistics and Quantitative Methods, University of Milan - Bicocca, Via Bicocca degli Arcimboldi, 8, Milan, 20126, Italy
| |
Collapse
|
19
|
Damiani C, Di Filippo M, Pescini D, Maspero D, Colombo R, Mauri G. popFBA: tackling intratumour heterogeneity with Flux Balance Analysis. Bioinformatics 2018; 33:i311-i318. [PMID: 28881985 PMCID: PMC5870635 DOI: 10.1093/bioinformatics/btx251] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022] Open
Abstract
Motivation Intratumour heterogeneity poses many challenges to the treatment of cancer. Unfortunately, the transcriptional and metabolic information retrieved by currently available computational and experimental techniques portrays the average behaviour of intermixed and heterogeneous cell subpopulations within a given tumour. Emerging single-cell genomic analyses are nonetheless unable to characterize the interactions among cancer subpopulations. In this study, we propose popFBA, an extension to classic Flux Balance Analysis, to explore how metabolic heterogeneity and cooperation phenomena affect the overall growth of cancer cell populations. Results We show how clones of a metabolic network of human central carbon metabolism, sharing the same stoichiometry and capacity constraints, may follow several different metabolic paths and cooperate to maximize the growth of the total population. We also introduce a method to explore the space of possible interactions, given some constraints on plasma supply of nutrients. We illustrate how alternative nutrients in plasma supply and/or a dishomogeneous distribution of oxygen provision may affect the landscape of heterogeneous phenotypes. We finally provide a technique to identify the most proliferative cells within the heterogeneous population. Availability and implementation the popFBA MATLAB function and the SBML model are available at https://github.com/BIMIB-DISCo/popFBA.
Collapse
Affiliation(s)
- Chiara Damiani
- SYSBIO Centre of Systems Biology, Milan, Italy.,Department of Informatics, Systems and Communication, University Milano-Bicocca, Milan, Italy
| | - Marzia Di Filippo
- SYSBIO Centre of Systems Biology, Milan, Italy.,Department of Biotechnology and Biosciences, University Milano-Bicocca, Milan, Italy
| | - Dario Pescini
- SYSBIO Centre of Systems Biology, Milan, Italy.,Department of Statistics and Quantitative Methods, University Milano-Bicocca, Milan, Italy
| | - Davide Maspero
- Department of Biotechnology and Biosciences, University Milano-Bicocca, Milan, Italy
| | - Riccardo Colombo
- SYSBIO Centre of Systems Biology, Milan, Italy.,Department of Informatics, Systems and Communication, University Milano-Bicocca, Milan, Italy
| | - Giancarlo Mauri
- SYSBIO Centre of Systems Biology, Milan, Italy.,Department of Informatics, Systems and Communication, University Milano-Bicocca, Milan, Italy
| |
Collapse
|
20
|
Smith AC, Eyassu F, Mazat JP, Robinson AJ. MitoCore: a curated constraint-based model for simulating human central metabolism. BMC SYSTEMS BIOLOGY 2017; 11:114. [PMID: 29178872 PMCID: PMC5702245 DOI: 10.1186/s12918-017-0500-7] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/02/2017] [Accepted: 11/13/2017] [Indexed: 01/16/2023]
Abstract
BACKGROUND The complexity of metabolic networks can make the origin and impact of changes in central metabolism occurring during diseases difficult to understand. Computer simulations can help unravel this complexity, and progress has advanced in genome-scale metabolic models. However, many models produce unrealistic results when challenged to simulate abnormal metabolism as they include incorrect specification and localisation of reactions and transport steps, incorrect reaction parameters, and confounding of prosthetic groups and free metabolites in reactions. Other common drawbacks are due to their scale, making them difficult to parameterise and simulation results hard to interpret. Therefore, it remains important to develop smaller, manually curated models. RESULTS We present MitoCore, a manually curated constraint-based computer model of human metabolism that incorporates the complexity of central metabolism and simulates this metabolism successfully under normal and abnormal physiological conditions, including hypoxia and mitochondrial diseases. MitoCore describes 324 metabolic reactions, 83 transport steps between mitochondrion and cytosol, and 74 metabolite inputs and outputs through the plasma membrane, to produce a model of manageable scale for easy interpretation of results. Its key innovations include a more accurate partitioning of metabolism between cytosol and mitochondrial matrix; better modelling of connecting transport steps; differentiation of prosthetic groups and free co-factors in reactions; and a new representation of the respiratory chain and the proton motive force. MitoCore's default parameters simulate normal cardiomyocyte metabolism, and to improve usability and allow comparison with other models and types of analysis, its reactions and metabolites have extensive annotation, and cross-reference identifiers from Virtual Metabolic Human database and KEGG. These innovations-including over 100 reactions absent or modified from Recon 2-are necessary to model central metabolism more accurately. CONCLUSION We anticipate MitoCore as a research tool for scientists, from experimentalists looking to interpret their data and test hypotheses, to experienced modellers predicting the consequences of disease or using computationally intensive methods that are infeasible with larger models, as well as a teaching tool for those new to modelling and needing a small, manageable model on which to learn and experiment.
Collapse
Affiliation(s)
- Anthony C Smith
- Medical Research Council Mitochondrial Biology Unit, University of Cambridge, Cambridge Biomedical Campus, Hills Road, Cambridge, CB2 0XY, UK
| | - Filmon Eyassu
- Medical Research Council Mitochondrial Biology Unit, University of Cambridge, Cambridge Biomedical Campus, Hills Road, Cambridge, CB2 0XY, UK
| | - Jean-Pierre Mazat
- Institute of Biochemistry and Genetics of the Cell, CNRS-UMR5095, 1 Rue Camille Saint Saëns, 33077, Bordeaux cedex, France.,University Bordeaux Segalen, 146 rue Léo Saignat, 33076, Bordeaux cedex, France
| | - Alan J Robinson
- Medical Research Council Mitochondrial Biology Unit, University of Cambridge, Cambridge Biomedical Campus, Hills Road, Cambridge, CB2 0XY, UK.
| |
Collapse
|
21
|
Biomedical applications of cell- and tissue-specific metabolic network models. J Biomed Inform 2017; 68:35-49. [DOI: 10.1016/j.jbi.2017.02.014] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2016] [Revised: 02/21/2017] [Accepted: 02/23/2017] [Indexed: 12/17/2022]
|
22
|
Shen F, Li J, Zhu Y, Wang Z. Systematic investigation of metabolic reprogramming in different cancers based on tissue-specific metabolic models. J Bioinform Comput Biol 2016; 14:1644001. [PMID: 27760488 DOI: 10.1142/s0219720016440017] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
Cancer cells have different metabolism in contrast to normal cells. The advancement in omics measurement technology enables the genome-wide characterization of altered cellular processes in cancers, but the metabolic flux landscape of cancer is still far from understood. In this study, we compared the well-reconstructed tissue-specific models of five cancers, including breast, liver, lung, renal, and urothelial cancer, and their corresponding normal cells. There are similar patterns in majority of significantly regulated pathways and enriched pathways in correlated reaction sets. But the differences among cancers are also explicit. The renal cancer demonstrates more dramatic difference with other cancer models, including the smallest number of reactions, flux distribution patterns, and specifically correlated pathways. We also validated the predicted essential genes and revealed the Warburg effect by in silico simulation in renal cancer, which are consistent with the measurements for renal cancer. In conclusion, the tissue-specific metabolic model is more suitable to investigate the cancer metabolism. The similarity and heterogenicity of metabolic reprogramming in different cancers are crucial for understanding the aberrant mechanisms of cancer proliferation, which is fundamental for identifying drug targets and biomarkers.
Collapse
Affiliation(s)
- Fangzhou Shen
- 1 School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai 200240, P. R. China
| | - Jian Li
- 1 School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai 200240, P. R. China
| | - Ying Zhu
- 1 School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai 200240, P. R. China
| | - Zhuo Wang
- 1 School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai 200240, P. R. China
| |
Collapse
|