1
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Chen Y, Gustafsson J, Yang J, Nielsen J, Kerkhoven EJ. Single-cell omics analysis with genome-scale metabolic modeling. Curr Opin Biotechnol 2024; 86:103078. [PMID: 38359604 DOI: 10.1016/j.copbio.2024.103078] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2023] [Accepted: 01/19/2024] [Indexed: 02/17/2024]
Abstract
Single-cell technologies have been widely used in biological studies and generated a plethora of single-cell data to be interpreted. Due to the inclusion of the priori metabolic network knowledge as well as gene-protein-reaction associations, genome-scale metabolic models (GEMs) have been a powerful tool to integrate and thereby interpret various omics data mostly from bulk samples. Here, we first review two common ways to leverage bulk omics data with GEMs and then discuss advances on integrative analysis of single-cell omics data with GEMs. We end by presenting our views on current challenges and perspectives in this field.
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Affiliation(s)
- Yu Chen
- Key Laboratory of Quantitative Synthetic Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China.
| | - Johan Gustafsson
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Department of Medical Biochemistry and Cell Biology, Institute of Biomedicine, Sahlgrenska Academy, University of Gothenburg, SE-405 30 Gothenburg, Sweden; Department of Life Sciences, Chalmers University of Technology, SE-412 96 Gothenburg, Sweden
| | - Jingyu Yang
- Key Laboratory of Quantitative Synthetic Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Jens Nielsen
- Department of Life Sciences, Chalmers University of Technology, SE-412 96 Gothenburg, Sweden; BioInnovation Institute, DK-2200 Copenhagen, Denmark
| | - Eduard J Kerkhoven
- Department of Life Sciences, Chalmers University of Technology, SE-412 96 Gothenburg, Sweden; Novo Nordisk Foundation Center for Biosustainability, Technology University of Denmark, DK-2800 Kgs. Lyngby, Denmark; SciLifeLab, Chalmers University of Technology, SE-412 96 Gothenburg, Sweden.
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2
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Baghdassarian HM, Lewis NE. Resource allocation in mammalian systems. Biotechnol Adv 2024; 71:108305. [PMID: 38215956 DOI: 10.1016/j.biotechadv.2023.108305] [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: 08/03/2023] [Revised: 12/17/2023] [Accepted: 12/18/2023] [Indexed: 01/14/2024]
Abstract
Cells execute biological functions to support phenotypes such as growth, migration, and secretion. Complementarily, each function of a cell has resource costs that constrain phenotype. Resource allocation by a cell allows it to manage these costs and optimize their phenotypes. In fact, the management of resource constraints (e.g., nutrient availability, bioenergetic capacity, and macromolecular machinery production) shape activity and ultimately impact phenotype. In mammalian systems, quantification of resource allocation provides important insights into higher-order multicellular functions; it shapes intercellular interactions and relays environmental cues for tissues to coordinate individual cells to overcome resource constraints and achieve population-level behavior. Furthermore, these constraints, objectives, and phenotypes are context-dependent, with cells adapting their behavior according to their microenvironment, resulting in distinct steady-states. This review will highlight the biological insights gained from probing resource allocation in mammalian cells and tissues.
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Affiliation(s)
- Hratch M Baghdassarian
- Bioinformatics and Systems Biology Graduate Program, University of California, San Diego, La Jolla, CA 92093, USA; Department of Pediatrics, University of California, San Diego, La Jolla, CA 92093, USA
| | - Nathan E Lewis
- Department of Pediatrics, University of California, San Diego, La Jolla, CA 92093, USA; Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA.
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3
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Cerk K, Ugalde‐Salas P, Nedjad CG, Lecomte M, Muller C, Sherman DJ, Hildebrand F, Labarthe S, Frioux C. Community-scale models of microbiomes: Articulating metabolic modelling and metagenome sequencing. Microb Biotechnol 2024; 17:e14396. [PMID: 38243750 PMCID: PMC10832553 DOI: 10.1111/1751-7915.14396] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Revised: 11/27/2023] [Accepted: 12/20/2023] [Indexed: 01/21/2024] Open
Abstract
Building models is essential for understanding the functions and dynamics of microbial communities. Metabolic models built on genome-scale metabolic network reconstructions (GENREs) are especially relevant as a means to decipher the complex interactions occurring among species. Model reconstruction increasingly relies on metagenomics, which permits direct characterisation of naturally occurring communities that may contain organisms that cannot be isolated or cultured. In this review, we provide an overview of the field of metabolic modelling and its increasing reliance on and synergy with metagenomics and bioinformatics. We survey the means of assigning functions and reconstructing metabolic networks from (meta-)genomes, and present the variety and mathematical fundamentals of metabolic models that foster the understanding of microbial dynamics. We emphasise the characterisation of interactions and the scaling of model construction to large communities, two important bottlenecks in the applicability of these models. We give an overview of the current state of the art in metagenome sequencing and bioinformatics analysis, focusing on the reconstruction of genomes in microbial communities. Metagenomics benefits tremendously from third-generation sequencing, and we discuss the opportunities of long-read sequencing, strain-level characterisation and eukaryotic metagenomics. We aim at providing algorithmic and mathematical support, together with tool and application resources, that permit bridging the gap between metagenomics and metabolic modelling.
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Affiliation(s)
- Klara Cerk
- Quadram Institute BioscienceNorwichUK
- Earlham InstituteNorwichUK
| | | | - Chabname Ghassemi Nedjad
- Inria, University of Bordeaux, INRAETalenceFrance
- University of Bordeaux, CNRS, Bordeaux INP, LaBRI, UMR 5800TalenceFrance
| | - Maxime Lecomte
- Inria, University of Bordeaux, INRAETalenceFrance
- INRAE STLO¸University of RennesRennesFrance
| | | | | | - Falk Hildebrand
- Quadram Institute BioscienceNorwichUK
- Earlham InstituteNorwichUK
| | - Simon Labarthe
- Inria, University of Bordeaux, INRAETalenceFrance
- INRAE, University of Bordeaux, BIOGECO, UMR 1202CestasFrance
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4
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Demicco M, Liu XZ, Leithner K, Fendt SM. Metabolic heterogeneity in cancer. Nat Metab 2024; 6:18-38. [PMID: 38267631 DOI: 10.1038/s42255-023-00963-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/12/2022] [Accepted: 12/06/2023] [Indexed: 01/26/2024]
Abstract
Cancer cells rewire their metabolism to survive during cancer progression. In this context, tumour metabolic heterogeneity arises and develops in response to diverse environmental factors. This metabolic heterogeneity contributes to cancer aggressiveness and impacts therapeutic opportunities. In recent years, technical advances allowed direct characterisation of metabolic heterogeneity in tumours. In addition to the metabolic heterogeneity observed in primary tumours, metabolic heterogeneity temporally evolves along with tumour progression. In this Review, we summarize the mechanisms of environment-induced metabolic heterogeneity. In addition, we discuss how cancer metabolism and the key metabolites and enzymes temporally and functionally evolve during the metastatic cascade and treatment.
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Affiliation(s)
- Margherita Demicco
- Laboratory of Cellular Metabolism and Metabolic Regulation, VIB-KU Leuven Center for Cancer Biology, Leuven, Belgium
- Laboratory of Cellular Metabolism and Metabolic Regulation, Department of Oncology, KU Leuven and Leuven Cancer Institute (LKI), Leuven, Belgium
| | - Xiao-Zheng Liu
- Laboratory of Cellular Metabolism and Metabolic Regulation, VIB-KU Leuven Center for Cancer Biology, Leuven, Belgium
- Laboratory of Cellular Metabolism and Metabolic Regulation, Department of Oncology, KU Leuven and Leuven Cancer Institute (LKI), Leuven, Belgium
| | - Katharina Leithner
- Division of Pulmonology, Department of Internal Medicine, Medical University of Graz, Graz, Austria
- BioTechMed-Graz, Graz, Austria
| | - Sarah-Maria Fendt
- Laboratory of Cellular Metabolism and Metabolic Regulation, VIB-KU Leuven Center for Cancer Biology, Leuven, Belgium.
- Laboratory of Cellular Metabolism and Metabolic Regulation, Department of Oncology, KU Leuven and Leuven Cancer Institute (LKI), Leuven, Belgium.
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5
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Singh S, Sarma DK, Verma V, Nagpal R, Kumar M. Unveiling the future of metabolic medicine: omics technologies driving personalized solutions for precision treatment of metabolic disorders. Biochem Biophys Res Commun 2023; 682:1-20. [PMID: 37788525 DOI: 10.1016/j.bbrc.2023.09.064] [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: 07/07/2023] [Revised: 09/13/2023] [Accepted: 09/21/2023] [Indexed: 10/05/2023]
Abstract
Metabolic disorders are increasingly prevalent worldwide, leading to high rates of morbidity and mortality. The variety of metabolic illnesses can be addressed through personalized medicine. The goal of personalized medicine is to give doctors the ability to anticipate the best course of treatment for patients with metabolic problems. By analyzing a patient's metabolomic, proteomic, genetic profile, and clinical data, physicians can identify relevant diagnostic, and predictive biomarkers and develop treatment plans and therapy for acute and chronic metabolic diseases. To achieve this goal, real-time modeling of clinical data and multiple omics is essential to pinpoint underlying biological mechanisms, risk factors, and possibly useful data to promote early diagnosis and prevention of complex diseases. Incorporating cutting-edge technologies like artificial intelligence and machine learning is crucial for consolidating diverse forms of data, examining multiple variables, establishing databases of clinical indicators to aid decision-making, and formulating ethical protocols to address concerns. This review article aims to explore the potential of personalized medicine utilizing omics approaches for the treatment of metabolic disorders. It focuses on the recent advancements in genomics, epigenomics, proteomics, metabolomics, and nutrigenomics, emphasizing their role in revolutionizing personalized medicine.
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Affiliation(s)
- Samradhi Singh
- ICMR- National Institute for Research in Environmental Health, Bhopal Bypass Road, Bhouri, Bhopal, 462030, Madhya Pradesh, India
| | - Devojit Kumar Sarma
- ICMR- National Institute for Research in Environmental Health, Bhopal Bypass Road, Bhouri, Bhopal, 462030, Madhya Pradesh, India
| | - Vinod Verma
- Stem Cell Research Centre, Department of Hematology, Sanjay Gandhi Post-Graduate Institute of Medical Sciences, Lucknow, 226014, Uttar Pradesh, India
| | - Ravinder Nagpal
- Department of Nutrition and Integrative Physiology, College of Health and Human Sciences, Florida State University, Tallahassee, FL, 32306, USA
| | - Manoj Kumar
- ICMR- National Institute for Research in Environmental Health, Bhopal Bypass Road, Bhouri, Bhopal, 462030, Madhya Pradesh, India.
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6
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Alexandrov T, Saez‐Rodriguez J, Saka SK. Enablers and challenges of spatial omics, a melting pot of technologies. Mol Syst Biol 2023; 19:e10571. [PMID: 37842805 PMCID: PMC10632737 DOI: 10.15252/msb.202110571] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2021] [Revised: 07/31/2023] [Accepted: 08/03/2023] [Indexed: 10/17/2023] Open
Abstract
Spatial omics has emerged as a rapidly growing and fruitful field with hundreds of publications presenting novel methods for obtaining spatially resolved information for any omics data type on spatial scales ranging from subcellular to organismal. From a technology development perspective, spatial omics is a highly interdisciplinary field that integrates imaging and omics, spatial and molecular analyses, sequencing and mass spectrometry, and image analysis and bioinformatics. The emergence of this field has not only opened a window into spatial biology, but also created multiple novel opportunities, questions, and challenges for method developers. Here, we provide the perspective of technology developers on what makes the spatial omics field unique. After providing a brief overview of the state of the art, we discuss technological enablers and challenges and present our vision about the future applications and impact of this melting pot.
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Affiliation(s)
- Theodore Alexandrov
- Structural and Computational Biology UnitEuropean Molecular Biology LaboratoryHeidelbergGermany
- Molecular Medicine Partnership UnitEuropean Molecular Biology LaboratoryHeidelbergGermany
- BioInnovation InstituteCopenhagenDenmark
| | - Julio Saez‐Rodriguez
- Molecular Medicine Partnership UnitEuropean Molecular Biology LaboratoryHeidelbergGermany
- Faculty of Medicine and Heidelberg University Hospital, Institute for Computational BiomedicineHeidelberg UniversityHeidelbergGermany
| | - Sinem K Saka
- Genome Biology UnitEuropean Molecular Biology LaboratoryHeidelbergGermany
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7
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Zhang Z, Zhu H, Dang P, Wang J, Chang W, Wang X, Alghamdi N, Lu A, Zang Y, Wu W, Wang Y, Zhang Y, Cao S, Zhang C. FLUXestimator: a webserver for predicting metabolic flux and variations using transcriptomics data. Nucleic Acids Res 2023; 51:W180-W190. [PMID: 37216602 PMCID: PMC10320190 DOI: 10.1093/nar/gkad444] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Revised: 04/29/2023] [Accepted: 05/11/2023] [Indexed: 05/24/2023] Open
Abstract
Quantitative assessment of single cell fluxome is critical for understanding the metabolic heterogeneity in diseases. Unfortunately, laboratory-based single cell fluxomics is currently impractical, and the current computational tools for flux estimation are not designed for single cell-level prediction. Given the well-established link between transcriptomic and metabolomic profiles, leveraging single cell transcriptomics data to predict single cell fluxome is not only feasible but also an urgent task. In this study, we present FLUXestimator, an online platform for predicting metabolic fluxome and variations using single cell or general transcriptomics data of large sample-size. The FLUXestimator webserver implements a recently developed unsupervised approach called single cell flux estimation analysis (scFEA), which uses a new neural network architecture to estimate reaction rates from transcriptomics data. To the best of our knowledge, FLUXestimator is the first web-based tool dedicated to predicting cell-/sample-wise metabolic flux and metabolite variations using transcriptomics data of human, mouse and 15 other common experimental organisms. The FLUXestimator webserver is available at http://scFLUX.org/, and stand-alone tools for local use are available at https://github.com/changwn/scFEA. Our tool provides a new avenue for studying metabolic heterogeneity in diseases and has the potential to facilitate the development of new therapeutic strategies.
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Affiliation(s)
- Zixuan Zhang
- College of Software, College of Computer Science and Technology, Jilin University, Changchun 130012, China
- Department of Medical and Molecular Genetics and Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, IN 46202, USA
| | - Haiqi Zhu
- Department of Medical and Molecular Genetics and Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, IN 46202, USA
- Department of Computer Sciences, Indiana University, Bloomington, IN 47405, USA
| | - Pengtao Dang
- Department of Medical and Molecular Genetics and Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, IN 46202, USA
- Department of Electric Computer Engineering, Purdue University, Indianapolis, IN 46202, USA
| | - Jia Wang
- Department of Medical and Molecular Genetics and Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, IN 46202, USA
- Department of Computer Sciences, Indiana University, Bloomington, IN 47405, USA
| | - Wennan Chang
- Department of Medical and Molecular Genetics and Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, IN 46202, USA
| | - Xiao Wang
- Department of Medical and Molecular Genetics and Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, IN 46202, USA
- Department of Computer Sciences, Indiana University, Bloomington, IN 47405, USA
| | - Norah Alghamdi
- Department of Medical and Molecular Genetics and Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, IN 46202, USA
| | - Alex Lu
- Department of Medical and Molecular Genetics and Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, IN 46202, USA
| | - Yong Zang
- Department of Medical and Molecular Genetics and Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, IN 46202, USA
- Department of Biostatistics and Health Data Science, Indiana University School of Medicine, Indianapolis, IN 46202, USA
| | - Wenzhuo Wu
- Department of Industrial Engineering, Purdue University, West Lafayette, IN 47907, USA
| | - Yijie Wang
- Department of Computer Sciences, Indiana University, Bloomington, IN 47405, USA
| | - Yu Zhang
- College of Software, College of Computer Science and Technology, Jilin University, Changchun 130012, China
- Department of Medical and Molecular Genetics and Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, IN 46202, USA
| | - Sha Cao
- Department of Medical and Molecular Genetics and Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, IN 46202, USA
- Department of Biostatistics and Health Data Science, Indiana University School of Medicine, Indianapolis, IN 46202, USA
| | - Chi Zhang
- Department of Medical and Molecular Genetics and Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, IN 46202, USA
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8
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Clemente-Suárez VJ, Martín-Rodríguez A, Redondo-Flórez L, López-Mora C, Yáñez-Sepúlveda R, Tornero-Aguilera JF. New Insights and Potential Therapeutic Interventions in Metabolic Diseases. Int J Mol Sci 2023; 24:10672. [PMID: 37445852 DOI: 10.3390/ijms241310672] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Revised: 06/13/2023] [Accepted: 06/21/2023] [Indexed: 07/15/2023] Open
Abstract
Endocrine homeostasis and metabolic diseases have been the subject of extensive research in recent years. The development of new techniques and insights has led to a deeper understanding of the mechanisms underlying these conditions and opened up new avenues for diagnosis and treatment. In this review, we discussed the rise of metabolic diseases, especially in Western countries, the genetical, psychological, and behavioral basis of metabolic diseases, the role of nutrition and physical activity in the development of metabolic diseases, the role of single-cell transcriptomics, gut microbiota, epigenetics, advanced imaging techniques, and cell-based therapies in metabolic diseases. Finally, practical applications derived from this information are made.
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Affiliation(s)
- Vicente Javier Clemente-Suárez
- Faculty of Sports Sciences, Universidad Europea de Madrid, Tajo Street, s/n, 28670 Madrid, Spain
- Grupo de Investigación en Cultura, Educación y Sociedad, Universidad de la Costa, Barranquilla 080002, Colombia
| | | | - Laura Redondo-Flórez
- Department of Health Sciences, Faculty of Biomedical and Health Sciences, Universidad Europea de Madrid, Tajo Street s/n, 28670 Villaviciosa de Odon, Spain
| | - Clara López-Mora
- Facultad de Ciencias Biomédicas y de la Salud, Universidad Europea de Valencia, Pg. de l'Albereda, 7, 46010 València, Spain
| | - Rodrigo Yáñez-Sepúlveda
- Faculty of Education and Social Sciences, Universidad Andres Bello, Viña del Mar 2520000, Chile
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9
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Mogilenko DA, Sergushichev A, Artyomov MN. Systems Immunology Approaches to Metabolism. Annu Rev Immunol 2023; 41:317-342. [PMID: 37126419 DOI: 10.1146/annurev-immunol-101220-031513] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/02/2023]
Abstract
Over the last decade, immunometabolism has emerged as a novel interdisciplinary field of research and yielded significant fundamental insights into the regulation of immune responses. Multiple classical approaches to interrogate immunometabolism, including bulk metabolic profiling and analysis of metabolic regulator expression, paved the way to appreciating the physiological complexity of immunometabolic regulation in vivo. Studying immunometabolism at the systems level raised the need to transition towards the next-generation technology for metabolic profiling and analysis. Spatially resolved metabolic imaging and computational algorithms for multi-modal data integration are new approaches to connecting metabolism and immunity. In this review, we discuss recent studies that highlight the complex physiological interplay between immune responses and metabolism and give an overview of technological developments that bear the promise of capturing this complexity most directly and comprehensively.
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Affiliation(s)
- Denis A Mogilenko
- Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, Missouri, USA; ,
- Current affiliation: Department of Medicine, Department of Pathology, Microbiology, and Immunology, and Vanderbilt Center for Immunobiology, Vanderbilt University Medical Center, Nashville, Tennessee, USA;
| | - Alexey Sergushichev
- Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, Missouri, USA; ,
- Computer Technologies Laboratory, ITMO University, Saint Petersburg, Russia
| | - Maxim N Artyomov
- Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, Missouri, USA; ,
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10
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Danzi F, Pacchiana R, Mafficini A, Scupoli MT, Scarpa A, Donadelli M, Fiore A. To metabolomics and beyond: a technological portfolio to investigate cancer metabolism. Signal Transduct Target Ther 2023; 8:137. [PMID: 36949046 PMCID: PMC10033890 DOI: 10.1038/s41392-023-01380-0] [Citation(s) in RCA: 29] [Impact Index Per Article: 29.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Revised: 02/08/2023] [Accepted: 02/15/2023] [Indexed: 03/24/2023] Open
Abstract
Tumour cells have exquisite flexibility in reprogramming their metabolism in order to support tumour initiation, progression, metastasis and resistance to therapies. These reprogrammed activities include a complete rewiring of the bioenergetic, biosynthetic and redox status to sustain the increased energetic demand of the cells. Over the last decades, the cancer metabolism field has seen an explosion of new biochemical technologies giving more tools than ever before to navigate this complexity. Within a cell or a tissue, the metabolites constitute the direct signature of the molecular phenotype and thus their profiling has concrete clinical applications in oncology. Metabolomics and fluxomics, are key technological approaches that mainly revolutionized the field enabling researchers to have both a qualitative and mechanistic model of the biochemical activities in cancer. Furthermore, the upgrade from bulk to single-cell analysis technologies provided unprecedented opportunity to investigate cancer biology at cellular resolution allowing an in depth quantitative analysis of complex and heterogenous diseases. More recently, the advent of functional genomic screening allowed the identification of molecular pathways, cellular processes, biomarkers and novel therapeutic targets that in concert with other technologies allow patient stratification and identification of new treatment regimens. This review is intended to be a guide for researchers to cancer metabolism, highlighting current and emerging technologies, emphasizing advantages, disadvantages and applications with the potential of leading the development of innovative anti-cancer therapies.
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Affiliation(s)
- Federica Danzi
- Department of Neurosciences, Biomedicine and Movement Sciences, Section of Biochemistry, University of Verona, Verona, Italy
| | - Raffaella Pacchiana
- Department of Neurosciences, Biomedicine and Movement Sciences, Section of Biochemistry, University of Verona, Verona, Italy
| | - Andrea Mafficini
- Department of Diagnostics and Public Health, University of Verona, Verona, Italy
| | - Maria T Scupoli
- Department of Neurosciences, Biomedicine and Movement Sciences, Biology and Genetics Section, University of Verona, Verona, Italy
| | - Aldo Scarpa
- Department of Diagnostics and Public Health, University of Verona, Verona, Italy
- ARC-NET Research Centre, University and Hospital Trust of Verona, Verona, Italy
| | - Massimo Donadelli
- Department of Neurosciences, Biomedicine and Movement Sciences, Section of Biochemistry, University of Verona, Verona, Italy.
| | - Alessandra Fiore
- Department of Neurosciences, Biomedicine and Movement Sciences, Section of Biochemistry, University of Verona, Verona, Italy
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11
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Raddatz AD, Furdui CM, Bey EA, Kemp ML. Single-Cell Kinetic Modeling of β-Lapachone Metabolism in Head and Neck Squamous Cell Carcinoma. Antioxidants (Basel) 2023; 12:741. [PMID: 36978989 PMCID: PMC10045120 DOI: 10.3390/antiox12030741] [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: 01/30/2023] [Revised: 03/13/2023] [Accepted: 03/14/2023] [Indexed: 03/19/2023] Open
Abstract
Head and neck squamous cell carcinoma (HNSCC) cells are highly heterogeneous in their metabolism and typically experience elevated reactive oxygen species (ROS) levels such as superoxide and hydrogen peroxide (H2O2) in the tumor microenvironment. Tumor cells survive under these chronic oxidative conditions by upregulating antioxidant systems. To investigate the heterogeneity of cellular responses to chemotherapeutic H2O2 generation in tumor and healthy tissue, we leveraged single-cell RNA-sequencing (scRNA-seq) data to perform redox systems-level simulations of quinone-cycling β-lapachone treatment as a source of NQO1-dependent rapid superoxide and hydrogen peroxide (H2O2) production. Transcriptomic data from 10 HNSCC patient tumors was used to populate over 4000 single-cell antioxidant enzymatic network models of drug metabolism. The simulations reflected significant systems-level differences between the redox states of healthy and cancer cells, demonstrating in some patient samples a targetable cancer cell population or in others statistically indistinguishable effects between non-malignant and malignant cells. Subsequent multivariate analyses between healthy and malignant cellular models pointed to distinct contributors of redox responses between these phenotypes. This model framework provides a mechanistic basis for explaining mixed outcomes of NAD(P)H:quinone oxidoreductase 1 (NQO1)-bioactivatable therapeutics despite the tumor specificity of these drugs as defined by NQO1/catalase expression and highlights the role of alternate antioxidant components in dictating drug-induced oxidative stress.
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Affiliation(s)
- Andrew D. Raddatz
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Emory University, Atlanta, GA 30332, USA
| | - Cristina M. Furdui
- Department of Internal Medicine, Section on Molecular Medicine, Wake Forest School of Medicine, Winston-Salem, NC 27101, USA
| | - Erik A. Bey
- Wood Hudson Cancer Research Laboratory, Newport, KY 41071, USA
| | - Melissa L. Kemp
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Emory University, Atlanta, GA 30332, USA
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12
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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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Clasen F, Nunes PM, Bidkhori G, Bah N, Boeing S, Shoaie S, Anastasiou D. Systematic diet composition swap in a mouse genome-scale metabolic model reveals determinants of obesogenic diet metabolism in liver cancer. iScience 2023; 26:106040. [PMID: 36844450 PMCID: PMC9947310 DOI: 10.1016/j.isci.2023.106040] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2022] [Revised: 09/08/2022] [Accepted: 01/20/2023] [Indexed: 01/26/2023] Open
Abstract
Dietary nutrient availability and gene expression, together, influence tissue metabolic activity. Here, we explore whether altering dietary nutrient composition in the context of mouse liver cancer suffices to overcome chronic gene expression changes that arise from tumorigenesis and western-style diet (WD). We construct a mouse genome-scale metabolic model and estimate metabolic fluxes in liver tumors and non-tumoral tissue after computationally varying the composition of input diet. This approach, called Systematic Diet Composition Swap (SyDiCoS), revealed that, compared to a control diet, WD increases production of glycerol and succinate irrespective of specific tissue gene expression patterns. Conversely, differences in fatty acid utilization pathways between tumor and non-tumor liver are amplified with WD by both dietary carbohydrates and lipids together. Our data suggest that combined dietary component modifications may be required to normalize the distinctive metabolic patterns that underlie selective targeting of tumor metabolism.
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Affiliation(s)
- Frederick Clasen
- Cancer Metabolism Laboratory, The Francis Crick Institute, 1 Midland Road, London NW1 1AT, UK
- Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral and Craniofacial Sciences, King’s College London, London SE1 9RT, UK
| | - Patrícia M. Nunes
- Cancer Metabolism Laboratory, The Francis Crick Institute, 1 Midland Road, London NW1 1AT, UK
| | - Gholamreza Bidkhori
- Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral and Craniofacial Sciences, King’s College London, London SE1 9RT, UK
| | - Nourdine Bah
- Bioinformatics and Biostatistics Science Technology Platform, Francis Crick Institute, 1 Midland Road, London NW1 1AT, UK
| | - Stefan Boeing
- Bioinformatics and Biostatistics Science Technology Platform, Francis Crick Institute, 1 Midland Road, London NW1 1AT, UK
| | - Saeed Shoaie
- Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral and Craniofacial Sciences, King’s College London, London SE1 9RT, UK
- Science for Life Laboratory (SciLifeLab), KTH - Royal Institute of Technology, Tomtebodavägen 23, 171 65 Solna, Stockholm, Sweden
| | - Dimitrios Anastasiou
- Cancer Metabolism Laboratory, The Francis Crick Institute, 1 Midland Road, London NW1 1AT, UK
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14
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Wei J, Yu W, Chen J, Huang G, Zhang L, Chen Z, Hu M, Gong X, Du H. Single-cell and spatial analyses reveal the association between gene expression of glutamine synthetase with the immunosuppressive phenotype of APOE+CTSZ+TAM in cancers. Mol Oncol 2023; 17:611-628. [PMID: 36587392 PMCID: PMC10061288 DOI: 10.1002/1878-0261.13373] [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: 10/03/2022] [Revised: 11/28/2022] [Accepted: 12/30/2022] [Indexed: 01/02/2023] Open
Abstract
An immunosuppressive state is regulated by various factors in the tumor microenvironment (TME), including, but not limited to, metabolic plasticity of immunosuppressive cells and cytokines secreted by these cells. We used single-cell RNA-sequencing (scRNA-seq) data and applied single-cell flux estimation analysis to characterize the link between metabolism and cellular function within the hypoxic TME of colorectal (CRC) and lung cancer. In terms of metabolic heterogeneity, we found myeloid cells potentially inclined to accumulate glutamine but tumor cells inclined to accumulate glutamate. In particular, we uncovered a tumor-associated macrophage (TAM) subpopulation, APOE+CTSZ+TAM, that was present in high proportions in tumor samples and exhibited immunosuppressive characteristics through upregulating the expression of anti-inflammatory genes. The proportion of APOE+CTSZ+TAM and regulatory T cells (Treg) were positively correlated across CRC scRNA-seq samples. APOE+CTSZ+TAM potentially interacted with Treg via CXCL16-CCR6 signals, as seen by ligand-receptor interactions analysis. Notably, glutamate-to-glutamine metabolic flux score and glutamine synthetase (GLUL) expression were uniquely higher in APOE+CTSZ+TAM, compared with other cell types within the TME. GLUL expression in macrophages was positively correlated with anti-inflammatory score and was higher in high-grade and invasive tumor samples. Moreover, spatial transcriptome and multiplex immunofluorescence staining of samples showed that APOE+CTSZ+TAM and Treg potentially colocalized in the tissue sections from CRC clinical samples. These results highlight the specific role and metabolic characteristic of the APOE+CTSZ+TAM subpopulation and provide a new perspective for macrophage subcluster-targeted therapeutic interventions or metabolic checkpoint-based cancer therapies.
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Affiliation(s)
- Jinfen Wei
- School of Biology and Biological Engineering, South China University of Technology, Guangzhou, China
| | - Wenqi Yu
- School of Biology and Biological Engineering, South China University of Technology, Guangzhou, China
| | - Juanzhi Chen
- Department of Pathology, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China.,Department of Pathology, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Guanda Huang
- School of Biology and Biological Engineering, South China University of Technology, Guangzhou, China
| | - Lingjie Zhang
- Department of Pathology, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China.,Department of Pathology, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Zixi Chen
- School of Biology and Biological Engineering, South China University of Technology, Guangzhou, China
| | - Meiling Hu
- School of Biology and Biological Engineering, South China University of Technology, Guangzhou, China
| | - Xiaocheng Gong
- School of Biology and Biological Engineering, South China University of Technology, Guangzhou, China
| | - Hongli Du
- School of Biology and Biological Engineering, South China University of Technology, Guangzhou, China
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15
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Metabolomics and modelling approaches for systems metabolic engineering. Metab Eng Commun 2022; 15:e00209. [PMID: 36281261 PMCID: PMC9587336 DOI: 10.1016/j.mec.2022.e00209] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2022] [Revised: 10/13/2022] [Accepted: 10/14/2022] [Indexed: 11/21/2022] Open
Abstract
Metabolic engineering involves the manipulation of microbes to produce desirable compounds through genetic engineering or synthetic biology approaches. Metabolomics involves the quantitation of intracellular and extracellular metabolites, where mass spectrometry and nuclear magnetic resonance based analytical instrumentation are often used. Here, the experimental designs, sample preparations, metabolite quenching and extraction are essential to the quantitative metabolomics workflow. The resultant metabolomics data can then be used with computational modelling approaches, such as kinetic and constraint-based modelling, to better understand underlying mechanisms and bottlenecks in the synthesis of desired compounds, thereby accelerating research through systems metabolic engineering. Constraint-based models, such as genome scale models, have been used successfully to enhance the yield of desired compounds from engineered microbes, however, unlike kinetic or dynamic models, constraint-based models do not incorporate regulatory effects. Nevertheless, the lack of time-series metabolomic data generation has hindered the usefulness of dynamic models till today. In this review, we show that improvements in automation, dynamic real-time analysis and high throughput workflows can drive the generation of more quality data for dynamic models through time-series metabolomics data generation. Spatial metabolomics also has the potential to be used as a complementary approach to conventional metabolomics, as it provides information on the localization of metabolites. However, more effort must be undertaken to identify metabolites from spatial metabolomics data derived through imaging mass spectrometry, where machine learning approaches could prove useful. On the other hand, single-cell metabolomics has also seen rapid growth, where understanding cell-cell heterogeneity can provide more insights into efficient metabolic engineering of microbes. Moving forward, with potential improvements in automation, dynamic real-time analysis, high throughput workflows, and spatial metabolomics, more data can be produced and studied using machine learning algorithms, in conjunction with dynamic models, to generate qualitative and quantitative predictions to advance metabolic engineering efforts.
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16
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Gill GS, Schultz MC. Multienzyme activity profiling for evaluation of cell‐to‐cell variability of metabolic state. FASEB Bioadv 2022; 4:709-723. [PMID: 36349298 PMCID: PMC9635011 DOI: 10.1096/fba.2022-00073] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Revised: 08/25/2022] [Accepted: 08/29/2022] [Indexed: 11/29/2022] Open
Abstract
In solid organs, cells of the same “type” can vary in their molecular phenotype. The basis of this state variation is being revealed by characterizing cell features including the expression pattern of mRNAs and the internal distribution of proteins. Here, the variability of metabolic state between cells is probed by enzyme activity profiling. We study individual cells of types that can be identified during the post‐mitotic phase of oogenesis in Xenopus laevis. Whole‐cell homogenates of isolated oocytes are used for kinetic analysis of enzymes, with a focus on the initial reaction rate. For each oocyte type studied, the activity signatures of glyceraldehyde 3‐phosphate dehydrogenase (GAPDH) and malate dehydrogenase 1 (MDH1) vary more between the homogenates of single oocytes than between repeat samplings of control homogenates. Unexpectedly, the activity signatures of GAPDH and MDH1 strongly co‐vary between oocytes of each type and change in strength of correlation during oogenesis. Therefore, variability of the kinetic behavior of these housekeeping enzymes between “identical” cells is physiologically programmed. Based on these findings, we propose that single‐cell profiling of enzyme kinetics will improve understanding of how metabolic state heterogeneity is related to heterogeneity revealed by omics methods including proteomics, epigenomics, and metabolomics.
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Affiliation(s)
- Govind S. Gill
- Department of Biochemistry University of Alberta Edmonton Alberta Canada
- Department of Pediatrics & Group on the Molecular and Cell Biology of Lipids University of Alberta Edmonton Alberta Canada
| | - Michael C. Schultz
- Department of Biochemistry University of Alberta Edmonton Alberta Canada
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17
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Zuo F, Yu J, He X. Single-Cell Metabolomics in Hematopoiesis and Hematological Malignancies. Front Oncol 2022; 12:931393. [PMID: 35912231 PMCID: PMC9326066 DOI: 10.3389/fonc.2022.931393] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2022] [Accepted: 06/07/2022] [Indexed: 11/13/2022] Open
Abstract
Aberrant metabolism contributes to tumor initiation, progression, metastasis, and drug resistance. Metabolic dysregulation has emerged as a hallmark of several hematologic malignancies. Decoding the molecular mechanism underlying metabolic rewiring in hematological malignancies would provide promising avenues for novel therapeutic interventions. Single-cell metabolic analysis can directly offer a meaningful readout of the cellular phenotype, allowing us to comprehensively dissect cellular states and access biological information unobtainable from bulk analysis. In this review, we first highlight the unique metabolic properties of hematologic malignancies and underscore potential metabolic vulnerabilities. We then emphasize the emerging single-cell metabolomics techniques, aiming to provide a guide to interrogating metabolism at single-cell resolution. Furthermore, we summarize recent studies demonstrating the power of single-cell metabolomics to uncover the roles of metabolic rewiring in tumor biology, cellular heterogeneity, immunometabolism, and therapeutic resistance. Meanwhile, we describe a practical view of the potential applications of single-cell metabolomics in hematopoiesis and hematological malignancies. Finally, we present the challenges and perspectives of single-cell metabolomics development.
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18
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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] [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.
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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
- *Correspondence: James R. Heath, ; Yapeng Su,
| | - 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
- *Correspondence: James R. Heath, ; Yapeng Su,
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19
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Garrido-Rodriguez M, Zirngibl K, Ivanova O, Lobentanzer S, Saez-Rodriguez J. Integrating knowledge and omics to decipher mechanisms via large-scale models of signaling networks. Mol Syst Biol 2022; 18:e11036. [PMID: 35880747 PMCID: PMC9316933 DOI: 10.15252/msb.202211036] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2022] [Revised: 05/12/2022] [Accepted: 05/31/2022] [Indexed: 11/10/2022] Open
Abstract
Signal transduction governs cellular behavior, and its dysregulation often leads to human disease. To understand this process, we can use network models based on prior knowledge, where nodes represent biomolecules, usually proteins, and edges indicate interactions between them. Several computational methods combine untargeted omics data with prior knowledge to estimate the state of signaling networks in specific biological scenarios. Here, we review, compare, and classify recent network approaches according to their characteristics in terms of input omics data, prior knowledge and underlying methodologies. We highlight existing challenges in the field, such as the general lack of ground truth and the limitations of prior knowledge. We also point out new omics developments that may have a profound impact, such as single‐cell proteomics or large‐scale profiling of protein conformational changes. We provide both an introduction for interested users seeking strategies to study cell signaling on a large scale and an update for seasoned modelers.
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Affiliation(s)
- Martin Garrido-Rodriguez
- Heidelberg University, Faculty of Medicine, and Heidelberg University Hospital, Institute for Computational Biomedicine, Bioquant, Heidelberg, Germany
| | - Katharina Zirngibl
- Heidelberg University, Faculty of Medicine, and Heidelberg University Hospital, Institute for Computational Biomedicine, Bioquant, Heidelberg, Germany
| | - Olga Ivanova
- Heidelberg University, Faculty of Medicine, and Heidelberg University Hospital, Institute for Computational Biomedicine, Bioquant, Heidelberg, Germany
| | - Sebastian Lobentanzer
- Heidelberg University, Faculty of Medicine, and Heidelberg University Hospital, Institute for Computational Biomedicine, Bioquant, Heidelberg, Germany
| | - Julio Saez-Rodriguez
- Heidelberg University, Faculty of Medicine, and Heidelberg University Hospital, Institute for Computational Biomedicine, Bioquant, Heidelberg, Germany
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20
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Subramanian A, Zakeri P, Mousa M, Alnaqbi H, Alshamsi FY, Bettoni L, Damiani E, Alsafar H, Saeys Y, Carmeliet P. Angiogenesis goes computational – The future way forward to discover new angiogenic targets? Comput Struct Biotechnol J 2022; 20:5235-5255. [PMID: 36187917 PMCID: PMC9508490 DOI: 10.1016/j.csbj.2022.09.019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2022] [Revised: 09/09/2022] [Accepted: 09/09/2022] [Indexed: 11/26/2022] Open
Abstract
Multi-omics technologies are being increasingly utilized in angiogenesis research. Yet, computational methods have not been widely used for angiogenic target discovery and prioritization in this field, partly because (wet-lab) vascular biologists are insufficiently familiar with computational biology tools and the opportunities they may offer. With this review, written for vascular biologists who lack expertise in computational methods, we aspire to break boundaries between both fields and to illustrate the potential of these tools for future angiogenic target discovery. We provide a comprehensive survey of currently available computational approaches that may be useful in prioritizing candidate genes, predicting associated mechanisms, and identifying their specificity to endothelial cell subtypes. We specifically highlight tools that use flexible, machine learning frameworks for large-scale data integration and gene prioritization. For each purpose-oriented category of tools, we describe underlying conceptual principles, highlight interesting applications and discuss limitations. Finally, we will discuss challenges and recommend some guidelines which can help to optimize the process of accurate target discovery.
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