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Paul RD, Jadebeck JF, Stratmann A, Wiechert W, Nöh K. hopsy - a methods marketplace for convex polytope sampling in Python. BIOINFORMATICS (OXFORD, ENGLAND) 2024; 40:btae430. [PMID: 38950177 PMCID: PMC11245314 DOI: 10.1093/bioinformatics/btae430] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/22/2023] [Revised: 05/10/2024] [Accepted: 06/28/2024] [Indexed: 07/03/2024]
Abstract
SUMMARY Effective collaboration between developers of Bayesian inference methods and users is key to advance our quantitative understanding of biosystems. We here present hopsy, a versatile open-source platform designed to provide convenient access to powerful Markov chain Monte Carlo sampling algorithms tailored to models defined on convex polytopes (CP). Based on the high-performance C++ sampling library HOPS, hopsy inherits its strengths and extends its functionalities with the accessibility of the Python programming language. A versatile plugin-mechanism enables seamless integration with domain-specific models, providing method developers with a framework for testing, benchmarking, and distributing CP samplers to approach real-world inference tasks. We showcase hopsy by solving common and newly composed domain-specific sampling problems, highlighting important design choices. By likening hopsy to a marketplace, we emphasize its role in bringing together users and developers, where users get access to state-of-the-art methods, and developers contribute their own innovative solutions for challenging domain-specific inference problems. AVAILABILITY AND IMPLEMENTATION Sources, documentation and a continuously updated list of sampling algorithms are available at https://jugit.fz-juelich.de/IBG-1/ModSim/hopsy, with Linux, Windows and MacOS binaries at https://pypi.org/project/hopsy/.
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Affiliation(s)
- Richard D Paul
- Institute of Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich, 52428 Jülich, Germany
- Institute of Advanced Simulations, IAS-8: Data Analytics and Machine Learning, Forschungszentrum Jülich, 52428 Jülich, Germany
| | - Johann F Jadebeck
- Institute of Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich, 52428 Jülich, Germany
- Computational Systems Biotechnology (AVT.CSB), RWTH Aachen University, 52074 Aachen, Germany
| | - Anton Stratmann
- Institute of Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich, 52428 Jülich, Germany
- Computational Systems Biotechnology (AVT.CSB), RWTH Aachen University, 52074 Aachen, Germany
| | - Wolfgang Wiechert
- Institute of Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich, 52428 Jülich, Germany
- Computational Systems Biotechnology (AVT.CSB), RWTH Aachen University, 52074 Aachen, Germany
| | - Katharina Nöh
- Institute of Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich, 52428 Jülich, Germany
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2
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Luo X, Liu Y, Balck A, Klein C, Fleming RMT. Identification of metabolites reproducibly associated with Parkinson's Disease via meta-analysis and computational modelling. NPJ Parkinsons Dis 2024; 10:126. [PMID: 38951523 PMCID: PMC11217404 DOI: 10.1038/s41531-024-00732-z] [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: 07/27/2023] [Accepted: 05/30/2024] [Indexed: 07/03/2024] Open
Abstract
Many studies have reported metabolomic analysis of different bio-specimens from Parkinson's disease (PD) patients. However, inconsistencies in reported metabolite concentration changes make it difficult to draw conclusions as to the role of metabolism in the occurrence or development of Parkinson's disease. We reviewed the literature on metabolomic analysis of PD patients. From 74 studies that passed quality control metrics, 928 metabolites were identified with significant changes in PD patients, but only 190 were replicated with the same changes in more than one study. Of these metabolites, 60 exclusively increased, such as 3-methoxytyrosine and glycine, 54 exclusively decreased, such as pantothenic acid and caffeine, and 76 inconsistently changed in concentration in PD versus control subjects, such as ornithine and tyrosine. A genome-scale metabolic model of PD and corresponding metabolic map linking most of the replicated metabolites enabled a better understanding of the dysfunctional pathways of PD and the prediction of additional potential metabolic markers from pathways with consistent metabolite changes to target in future studies.
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Affiliation(s)
- Xi Luo
- School of Medicine, University of Galway, University Rd, Galway, Ireland
| | - Yanjun Liu
- School of Medicine, University of Galway, University Rd, Galway, Ireland
| | - Alexander Balck
- Institute of Neurogenetics and Department of Neurology, University of Luebeck and University Hospital Schleswig-Holstein, Luebeck, Germany
| | - Christine Klein
- Institute of Neurogenetics and Department of Neurology, University of Luebeck and University Hospital Schleswig-Holstein, Luebeck, Germany
| | - Ronan M T Fleming
- School of Medicine, University of Galway, University Rd, Galway, Ireland.
- Division of Systems Biomedicine and Pharmacology, Leiden Academic Centre for Drug Research, Leiden University, Leiden, Netherlands.
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3
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Lange E, Kranert L, Krüger J, Benndorf D, Heyer R. Microbiome modeling: a beginner's guide. Front Microbiol 2024; 15:1368377. [PMID: 38962127 PMCID: PMC11220171 DOI: 10.3389/fmicb.2024.1368377] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2024] [Accepted: 05/27/2024] [Indexed: 07/05/2024] Open
Abstract
Microbiomes, comprised of diverse microbial species and viruses, play pivotal roles in human health, environmental processes, and biotechnological applications and interact with each other, their environment, and hosts via ecological interactions. Our understanding of microbiomes is still limited and hampered by their complexity. A concept improving this understanding is systems biology, which focuses on the holistic description of biological systems utilizing experimental and computational methods. An important set of such experimental methods are metaomics methods which analyze microbiomes and output lists of molecular features. These lists of data are integrated, interpreted, and compiled into computational microbiome models, to predict, optimize, and control microbiome behavior. There exists a gap in understanding between microbiologists and modelers/bioinformaticians, stemming from a lack of interdisciplinary knowledge. This knowledge gap hinders the establishment of computational models in microbiome analysis. This review aims to bridge this gap and is tailored for microbiologists, researchers new to microbiome modeling, and bioinformaticians. To achieve this goal, it provides an interdisciplinary overview of microbiome modeling, starting with fundamental knowledge of microbiomes, metaomics methods, common modeling formalisms, and how models facilitate microbiome control. It concludes with guidelines and repositories for modeling. Each section provides entry-level information, example applications, and important references, serving as a valuable resource for comprehending and navigating the complex landscape of microbiome research and modeling.
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Affiliation(s)
- Emanuel Lange
- Multidimensional Omics Data Analysis, Department for Bioanalytics, Leibniz-Institut für Analytische Wissenschaften - ISAS - e.V., Dortmund, Germany
- Graduate School Digital Infrastructure for the Life Sciences, Bielefeld Institute for Bioinformatics Infrastructure (BIBI), Faculty of Technology, Bielefeld University, Bielefeld, Germany
| | - Lena Kranert
- Institute for Automation Engineering, Otto von Guericke University Magdeburg, Magdeburg, Germany
| | - Jacob Krüger
- Engineering of Software-Intensive Systems, Department of Mathematics and Computer Science, Eindhoven University of Technology, Eindhoven, Netherlands
| | - Dirk Benndorf
- Applied Biosciences and Bioprocess Engineering, Anhalt University of Applied Sciences, Köthen, Germany
| | - Robert Heyer
- Multidimensional Omics Data Analysis, Department for Bioanalytics, Leibniz-Institut für Analytische Wissenschaften - ISAS - e.V., Dortmund, Germany
- Graduate School Digital Infrastructure for the Life Sciences, Bielefeld Institute for Bioinformatics Infrastructure (BIBI), Faculty of Technology, Bielefeld University, Bielefeld, Germany
- Multidimensional Omics Data Analysis, Faculty of Technology, Bielefeld University, Bielefeld, Germany
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4
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Tarzi C, Zampieri G, Sullivan N, Angione C. Emerging methods for genome-scale metabolic modeling of microbial communities. Trends Endocrinol Metab 2024; 35:533-548. [PMID: 38575441 DOI: 10.1016/j.tem.2024.02.018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Revised: 02/28/2024] [Accepted: 02/29/2024] [Indexed: 04/06/2024]
Abstract
Genome-scale metabolic models (GEMs) are consolidating as platforms for studying mixed microbial populations, by combining biological data and knowledge with mathematical rigor. However, deploying these models to answer research questions can be challenging due to the increasing number of available computational tools, the lack of universal standards, and their inherent limitations. Here, we present a comprehensive overview of foundational concepts for building and evaluating genome-scale models of microbial communities. We then compare tools in terms of requirements, capabilities, and applications. Next, we highlight the current pitfalls and open challenges to consider when adopting existing tools and developing new ones. Our compendium can be relevant for the expanding community of modelers, both at the entry and experienced levels.
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Affiliation(s)
- Chaimaa Tarzi
- School of Computing, Engineering and Digital Technologies, Teesside University, Southfield Rd, Middlesbrough, TS1 3BX, North Yorkshire, UK
| | - Guido Zampieri
- Department of Biology, University of Padova, Padova, 35122, Veneto, Italy
| | - Neil Sullivan
- Complement Genomics Ltd, Station Rd, Lanchester, Durham, DH7 0EX, County Durham, UK
| | - Claudio Angione
- School of Computing, Engineering and Digital Technologies, Teesside University, Southfield Rd, Middlesbrough, TS1 3BX, North Yorkshire, UK; Centre for Digital Innovation, Teesside University, Southfield Rd, Middlesbrough, TS1 3BX, North Yorkshire, UK; National Horizons Centre, Teesside University, 38 John Dixon Ln, Darlington, DL1 1HG, North Yorkshire, UK.
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5
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Zaunseder E, Mütze U, Okun JG, Hoffmann GF, Kölker S, Heuveline V, Thiele I. Personalized metabolic whole-body models for newborns and infants predict growth and biomarkers of inherited metabolic diseases. Cell Metab 2024:S1550-4131(24)00182-7. [PMID: 38834070 DOI: 10.1016/j.cmet.2024.05.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/20/2023] [Revised: 03/13/2024] [Accepted: 05/09/2024] [Indexed: 06/06/2024]
Abstract
Comprehensive whole-body models (WBMs) accounting for organ-specific dynamics have been developed to simulate adult metabolism, but such models do not exist for infants. Here, we present a resource of 360 organ-resolved, sex-specific models of newborn and infant metabolism (infant-WBMs) spanning the first 180 days of life. These infant-WBMs were parameterized to represent the distinct metabolic characteristics of newborns and infants, including nutrition, energy requirements, and thermoregulation. We demonstrate that the predicted infant growth was consistent with the recommendation by the World Health Organization. We assessed the infant-WBMs' reliability and capabilities for personalization by simulating 10,000 newborns based on their blood metabolome and birth weight. Furthermore, the infant-WBMs accurately predicted changes in known biomarkers over time and metabolic responses to treatment strategies for inherited metabolic diseases. The infant-WBM resource holds promise for personalized medicine, as the infant-WBMs could be a first step to digital metabolic twins for newborn and infant metabolism.
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Affiliation(s)
- Elaine Zaunseder
- School of Medicine, University of Galway, Galway, Ireland; Engineering Mathematics and Computing Lab (EMCL), Interdisciplinary Center for Scientific Computing (IWR), Heidelberg University, Heidelberg, Germany; Data Mining and Uncertainty Quantification (DMQ), Heidelberg Institute for Theoretical Studies (HITS), Heidelberg, Germany
| | - Ulrike Mütze
- Division of Child Neurology and Metabolic Medicine, Center for Child and Adolescent Medicine, Heidelberg University, Medical Faculty, Heidelberg, Germany
| | - Jürgen G Okun
- Division of Child Neurology and Metabolic Medicine, Center for Child and Adolescent Medicine, Heidelberg University, Medical Faculty, Heidelberg, Germany
| | - Georg F Hoffmann
- Division of Child Neurology and Metabolic Medicine, Center for Child and Adolescent Medicine, Heidelberg University, Medical Faculty, Heidelberg, Germany
| | - Stefan Kölker
- Division of Child Neurology and Metabolic Medicine, Center for Child and Adolescent Medicine, Heidelberg University, Medical Faculty, Heidelberg, Germany
| | - Vincent Heuveline
- School of Medicine, University of Galway, Galway, Ireland; Engineering Mathematics and Computing Lab (EMCL), Interdisciplinary Center for Scientific Computing (IWR), Heidelberg University, Heidelberg, Germany
| | - Ines Thiele
- School of Medicine, University of Galway, Galway, Ireland; Discipline of Microbiology, University of Galway, Galway, Ireland; Digital Metabolic Twin Centre, University of Galway, Ireland; Ryan Institute, University of Galway, Galway, Ireland; APC Microbiome Ireland, Cork, Ireland.
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6
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Turanli B, Gulfidan G, Aydogan OO, Kula C, Selvaraj G, Arga KY. Genome-scale metabolic models in translational medicine: the current status and potential of machine learning in improving the effectiveness of the models. Mol Omics 2024; 20:234-247. [PMID: 38444371 DOI: 10.1039/d3mo00152k] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/07/2024]
Abstract
The genome-scale metabolic model (GEM) has emerged as one of the leading modeling approaches for systems-level metabolic studies and has been widely explored for a broad range of organisms and applications. Owing to the development of genome sequencing technologies and available biochemical data, it is possible to reconstruct GEMs for model and non-model microorganisms as well as for multicellular organisms such as humans and animal models. GEMs will evolve in parallel with the availability of biological data, new mathematical modeling techniques and the development of automated GEM reconstruction tools. The use of high-quality, context-specific GEMs, a subset of the original GEM in which inactive reactions are removed while maintaining metabolic functions in the extracted model, for model organisms along with machine learning (ML) techniques could increase their applications and effectiveness in translational research in the near future. Here, we briefly review the current state of GEMs, discuss the potential contributions of ML approaches for more efficient and frequent application of these models in translational research, and explore the extension of GEMs to integrative cellular models.
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Affiliation(s)
- Beste Turanli
- Marmara University, Faculty of Engineering, Department of Bioengineering, Istanbul, Turkey.
- Health Biotechnology Joint Research and Application Center of Excellence, Istanbul, Turkey
| | - Gizem Gulfidan
- Marmara University, Faculty of Engineering, Department of Bioengineering, Istanbul, Turkey.
| | - Ozge Onluturk Aydogan
- Marmara University, Faculty of Engineering, Department of Bioengineering, Istanbul, Turkey.
| | - Ceyda Kula
- Marmara University, Faculty of Engineering, Department of Bioengineering, Istanbul, Turkey.
- Health Biotechnology Joint Research and Application Center of Excellence, Istanbul, Turkey
| | - Gurudeeban Selvaraj
- Concordia University, Centre for Research in Molecular Modeling & Department of Chemistry and Biochemistry, Quebec, Canada
- Saveetha Institute of Medical and Technical Sciences (SIMATS), Saveetha Dental College and Hospital, Department of Biomaterials, Bioinformatics Unit, Chennai, India
| | - Kazim Yalcin Arga
- Marmara University, Faculty of Engineering, Department of Bioengineering, Istanbul, Turkey.
- Health Biotechnology Joint Research and Application Center of Excellence, Istanbul, Turkey
- Marmara University, Genetic and Metabolic Diseases Research and Investigation Center, Istanbul, Turkey
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7
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Mahout M, Carlson RP, Simon L, Peres S. Logic programming-based Minimal Cut Sets reveal consortium-level therapeutic targets for chronic wound infections. NPJ Syst Biol Appl 2024; 10:34. [PMID: 38565568 PMCID: PMC10987626 DOI: 10.1038/s41540-024-00360-6] [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: 07/29/2023] [Accepted: 03/13/2024] [Indexed: 04/04/2024] Open
Abstract
Minimal Cut Sets (MCSs) identify sets of reactions which, when removed from a metabolic network, disable certain cellular functions. The traditional search for MCSs within genome-scale metabolic models (GSMMs) targets cellular growth, identifies reaction sets resulting in a lethal phenotype if disrupted, and retrieves a list of corresponding gene, mRNA, or enzyme targets. Using the dual link between MCSs and Elementary Flux Modes (EFMs), our logic programming-based tool aspefm was able to compute MCSs of any size from GSMMs in acceptable run times. The tool demonstrated better performance when computing large-sized MCSs than the mixed-integer linear programming methods. We applied the new MCSs methodology to a medically-relevant consortium model of two cross-feeding bacteria, Staphylococcus aureus and Pseudomonas aeruginosa. aspefm constraints were used to bias the computation of MCSs toward exchanged metabolites that could complement lethal phenotypes in individual species. We found that interspecies metabolite exchanges could play an essential role in rescuing single-species growth, for instance inosine could complement lethal reaction knock-outs in the purine synthesis, glycolysis, and pentose phosphate pathways of both bacteria. Finally, MCSs were used to derive a list of promising enzyme targets for consortium-level therapeutic applications that cannot be circumvented via interspecies metabolite exchange.
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Affiliation(s)
- Maxime Mahout
- Université Paris-Saclay, CNRS, Laboratoire Interdisciplinaire des Sciences du Numérique, 91405, Orsay, France
| | - Ross P Carlson
- Department of Chemical and Biological Engineering, Center for Biofilm Engineering, Microbiology and Immunology, Montana State University, Bozeman, MT, 59717, USA
| | - Laurent Simon
- Bordeaux-INP, Université Bordeaux, LaBRI, 33405, Talence Cedex, France
| | - Sabine Peres
- UMR CNRS 5558, Laboratoire de Biométrie et de Biologie Évolutive, Université Claude Bernard Lyon 1, 69100, Villeurbanne, France.
- INRIA Lyon Centre, 69100, Villeurbanne, France.
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8
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Kishk A, Pires Pacheco M, Heurtaux T, Sauter T. Metabolic models predict fotemustine and the combination of eflornithine/rifamycin and adapalene/cannabidiol for the treatment of gliomas. Brief Bioinform 2024; 25:bbae199. [PMID: 38701414 PMCID: PMC11066901 DOI: 10.1093/bib/bbae199] [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: 01/15/2024] [Revised: 03/15/2024] [Accepted: 04/15/2024] [Indexed: 05/05/2024] Open
Abstract
Gliomas are the most common type of malignant brain tumors, with glioblastoma multiforme (GBM) having a median survival of 15 months due to drug resistance and relapse. The treatment of gliomas relies on surgery, radiotherapy and chemotherapy. Only 12 anti-brain tumor chemotherapies (AntiBCs), mostly alkylating agents, have been approved so far. Glioma subtype-specific metabolic models were reconstructed to simulate metabolite exchanges, in silico knockouts and the prediction of drug and drug combinations for all three subtypes. The simulations were confronted with literature, high-throughput screenings (HTSs), xenograft and clinical trial data to validate the workflow and further prioritize the drug candidates. The three subtype models accurately displayed different degrees of dependencies toward glutamine and glutamate. Furthermore, 33 single drugs, mainly antimetabolites and TXNRD1-inhibitors, as well as 17 drug combinations were predicted as potential candidates for gliomas. Half of these drug candidates have been previously tested in HTSs. Half of the tested drug candidates reduce proliferation in cell lines and two-thirds in xenografts. Most combinations were predicted to be efficient for all three glioma types. However, eflornithine/rifamycin and cannabidiol/adapalene were predicted specifically for GBM and low-grade glioma, respectively. Most drug candidates had comparable efficiency in preclinical tests, cerebrospinal fluid bioavailability and mode-of-action to AntiBCs. However, fotemustine and valganciclovir alone and eflornithine and celecoxib in combination with AntiBCs improved the survival compared to AntiBCs in two-arms, phase I/II and higher glioma clinical trials. Our work highlights the potential of metabolic modeling in advancing glioma drug discovery, which accurately predicted metabolic vulnerabilities, repurposable drugs and combinations for the glioma subtypes.
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Affiliation(s)
- Ali Kishk
- Department of Life Sciences and Medicine, University of Luxembourg, L-4367 Belvaux, Luxembourg
| | - Maria Pires Pacheco
- Department of Life Sciences and Medicine, University of Luxembourg, L-4367 Belvaux, Luxembourg
| | - Tony Heurtaux
- Department of Life Sciences and Medicine, University of Luxembourg, L-4367 Belvaux, Luxembourg
- Luxembourg Centre of Neuropathology, L-3555 Dudelange, Luxembourg
| | - Thomas Sauter
- Department of Life Sciences and Medicine, University of Luxembourg, L-4367 Belvaux, Luxembourg
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9
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Martinelli F, Heinken A, Henning AK, Ulmer MA, Hensen T, González A, Arnold M, Asthana S, Budde K, Engelman CD, Estaki M, Grabe HJ, Heston MB, Johnson S, Kastenmüller G, Martino C, McDonald D, Rey FE, Kilimann I, Peters O, Wang X, Spruth EJ, Schneider A, Fliessbach K, Wiltfang J, Hansen N, Glanz W, Buerger K, Janowitz D, Laske C, Munk MH, Spottke A, Roy N, Nauck M, Teipel S, Knight R, Kaddurah-Daouk RF, Bendlin BB, Hertel J, Thiele I. Whole-body metabolic modelling reveals microbiome and genomic interactions on reduced urine formate levels in Alzheimer's disease. Sci Rep 2024; 14:6095. [PMID: 38480804 PMCID: PMC10937638 DOI: 10.1038/s41598-024-55960-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2023] [Accepted: 02/29/2024] [Indexed: 03/17/2024] Open
Abstract
In this study, we aimed to understand the potential role of the gut microbiome in the development of Alzheimer's disease (AD). We took a multi-faceted approach to investigate this relationship. Urine metabolomics were examined in individuals with AD and controls, revealing decreased formate and fumarate concentrations in AD. Additionally, we utilised whole-genome sequencing (WGS) data obtained from a separate group of individuals with AD and controls. This information allowed us to create and investigate host-microbiome personalised whole-body metabolic models. Notably, AD individuals displayed diminished formate microbial secretion in these models. Additionally, we identified specific reactions responsible for the production of formate in the host, and interestingly, these reactions were linked to genes that have correlations with AD. This study suggests formate as a possible early AD marker and highlights genetic and microbiome contributions to its production. The reduced formate secretion and its genetic associations point to a complex connection between gut microbiota and AD. This holistic understanding might pave the way for novel diagnostic and therapeutic avenues in AD management.
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Affiliation(s)
- Filippo Martinelli
- School of Medicine, University of Galway, Galway, Ireland
- The Ryan Institute, University of Galway, Galway, Ireland
| | - Almut Heinken
- School of Medicine, University of Galway, Galway, Ireland
- The Ryan Institute, University of Galway, Galway, Ireland
- Inserm UMRS 1256 NGERE, University of Lorraine, Nancy, France
| | - Ann-Kristin Henning
- Institute of Clinical Chemistry and Laboratory Medicine, University Medicine Greifswald, Greifswald, Germany
| | - Maria A Ulmer
- Institute of Computational Biology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
| | - Tim Hensen
- School of Medicine, University of Galway, Galway, Ireland
- The Ryan Institute, University of Galway, Galway, Ireland
| | - Antonio González
- Department of Pediatrics, University of California San Diego, La Jolla, CA, USA
| | - Matthias Arnold
- Institute of Computational Biology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
- Department of Psychiatry and Behavioural Sciences, Duke University, Durham, NC, USA
| | - Sanjay Asthana
- Wisconsin Alzheimer's Disease Research Center, School of Medicine and Public Health, University of Wisconsin, Madison, USA
| | - Kathrin Budde
- Institute of Clinical Chemistry and Laboratory Medicine, University Medicine Greifswald, Greifswald, Germany
| | - Corinne D Engelman
- Department of Population Health Sciences, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, USA
| | - Mehrbod Estaki
- Department of Pediatrics, University of California San Diego, La Jolla, CA, USA
| | - Hans-Jörgen Grabe
- German Center of Neurodegenerative Diseases (DZNE), Rostock/Greifswald, Germany
| | - Margo B Heston
- Wisconsin Alzheimer's Disease Research Center, School of Medicine and Public Health, University of Wisconsin, Madison, USA
| | - Sterling Johnson
- Wisconsin Alzheimer's Disease Research Center, School of Medicine and Public Health, University of Wisconsin, Madison, USA
| | - Gabi Kastenmüller
- Institute of Computational Biology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
| | - Cameron Martino
- Department of Pediatrics, University of California San Diego, La Jolla, CA, USA
| | - Daniel McDonald
- Department of Pediatrics, University of California San Diego, La Jolla, CA, USA
| | - Federico E Rey
- Department of Bacteriology, University of Wisconsin-Madison, Madison, WI, USA
| | - Ingo Kilimann
- German Center of Neurodegenerative Diseases (DZNE), Rostock, Germany
- Department of Psychosomatic Medicine, University Medicine Rostock, Rostock, Germany
| | - Olive Peters
- German Center of Neurodegenerative Diseases (DZNE), Berlin, Germany
- Department of Psychiatry, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Xiao Wang
- Department of Psychiatry, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Eike Jakob Spruth
- German Center of Neurodegenerative Diseases (DZNE), Berlin, Germany
- Department of Psychiatry and Psychotherapy, Charité, Berlin, Germany
| | - Anja Schneider
- German Center of Neurodegenerative Diseases (DZNE), Bonn, Germany
- Department of Neurology, University of Bonn, Bonn, Germany
| | - Klaus Fliessbach
- German Center of Neurodegenerative Diseases (DZNE), Bonn, Germany
- Department of Neurology, University of Bonn, Bonn, Germany
| | - Jens Wiltfang
- German Center of Neurodegenerative Diseases (DZNE), Goettingen, Germany
- Department of Psychiatry and Psychotherapy, University of Goettingen, Goettingen, Germany
- Neurosciences and Signaling Group, Department of Medical Sciences, Institute of Biomedicine (iBiMED), University of Aveiro, Aveiro, Portugal
| | - Niels Hansen
- Department of Psychiatry and Psychotherapy, University of Goettingen, Goettingen, Germany
| | - Wenzel Glanz
- German Center of Neurodegenerative Diseases (DZNE), Magdeburg, Germany
| | - Katharina Buerger
- German Center of Neurodegenerative Diseases (DZNE), Munich, Germany
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Munich, Germany
| | - Daniel Janowitz
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Munich, Germany
| | - Christoph Laske
- German Center of Neurodegenerative Diseases (DZNE), Tübingen, Germany
- Section for Dementia Research, Hertie Institute for Clinical Brain Research, Tübingen, Germany
- Section for Dementia Research, Department of Psychiatry and Psychotherapy, University of Tübingen, Tübingen, Germany
| | - Matthias H Munk
- German Center of Neurodegenerative Diseases (DZNE), Tübingen, Germany
- Section for Dementia Research, Department of Psychiatry and Psychotherapy, University of Tübingen, Tübingen, Germany
| | - Annika Spottke
- German Center of Neurodegenerative Diseases (DZNE), Bonn, Germany
- Department of Neurology, University of Bonn, Bonn, Germany
| | - Nina Roy
- German Center of Neurodegenerative Diseases (DZNE), Bonn, Germany
| | - Matthias Nauck
- Institute of Clinical Chemistry and Laboratory Medicine, University Medicine Greifswald, Greifswald, Germany
- DZHK (German Centre for Cardiovascular Research), Partner Site Greifswald, University Medicine, Greifswald, Germany
| | - Stefan Teipel
- German Center of Neurodegenerative Diseases (DZNE), Rostock, Germany
- Department of Psychosomatic Medicine, University Medicine Rostock, Rostock, Germany
| | - Rob Knight
- Department of Pediatrics, University of California San Diego, La Jolla, CA, USA
- Department of Computer Science and Engineering, University of California San Diego, La Jolla, CA, USA
- Center for Microbiome Innovation, University of California San Diego, La Jolla, CA, USA
- Shu Chien-Gene Lay Department of Engineering, University of California San Diego, La Jolla, CA, USA
- Halıcıoğlu Data Science Institute, University of California San Diego, La Jolla, CA, USA
| | | | - Barbara B Bendlin
- Wisconsin Alzheimer's Disease Research Center, School of Medicine and Public Health, University of Wisconsin, Madison, USA
| | - Johannes Hertel
- School of Medicine, University of Galway, Galway, Ireland.
- DZHK (German Centre for Cardiovascular Research), Partner Site Greifswald, University Medicine, Greifswald, Germany.
| | - Ines Thiele
- School of Medicine, University of Galway, Galway, Ireland.
- The Ryan Institute, University of Galway, Galway, Ireland.
- School of Microbiology, University of Galway, Galway, Ireland.
- APC Microbiome Ireland, Cork, Ireland.
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10
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Jimonet P, Druart C, Blanquet-Diot S, Boucinha L, Kourula S, Le Vacon F, Maubant S, Rabot S, Van de Wiele T, Schuren F, Thomas V, Walther B, Zimmermann M. Gut Microbiome Integration in Drug Discovery and Development of Small Molecules. Drug Metab Dispos 2024; 52:274-287. [PMID: 38307852 DOI: 10.1124/dmd.123.001605] [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: 11/07/2023] [Revised: 01/30/2024] [Accepted: 01/31/2024] [Indexed: 02/04/2024] Open
Abstract
Human microbiomes, particularly in the gut, could have a major impact on the efficacy and toxicity of drugs. However, gut microbial metabolism is often neglected in the drug discovery and development process. Medicen, a Paris-based human health innovation cluster, has gathered more than 30 international leading experts from pharma, academia, biotech, clinical research organizations, and regulatory science to develop proposals to facilitate the integration of microbiome science into drug discovery and development. Seven subteams were formed to cover the complementary expertise areas of 1) pharma experience and case studies, 2) in silico microbiome-drug interaction, 3) in vitro microbial stability screening, 4) gut fermentation models, 5) animal models, 6) microbiome integration in clinical and regulatory aspects, and 7) microbiome ecosystems and models. Each expert team produced a state-of-the-art report of their respective field highlighting existing microbiome-related tools at every stage of drug discovery and development. The most critical limitations are the growing, but still limited, drug-microbiome interaction data to produce predictive models and the lack of agreed-upon standards despite recent progress. In this paper we will report on and share proposals covering 1) how microbiome tools can support moving a compound from drug discovery to clinical proof-of-concept studies and alert early on potential undesired properties stemming from microbiome-induced drug metabolism and 2) how microbiome data can be generated and integrated in pharmacokinetic models that are predictive of the human situation. Examples of drugs metabolized by the microbiome will be discussed in detail to support recommendations from the working group. SIGNIFICANCE STATEMENT: Gut microbial metabolism is often neglected in the drug discovery and development process despite growing evidence of drugs' efficacy and safety impacted by their interaction with the microbiome. This paper will detail existing microbiome-related tools covering every stage of drug discovery and development, current progress, and limitations, as well as recommendations to integrate them into the drug discovery and development process.
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Affiliation(s)
- Patrick Jimonet
- Medicen Paris Région, Paris, France (P.J.); Pharmabiotic Research Institute, Narbonne, France (C.D.); UMR 454 MEDIS, Université Clermont Auvergne, Clermont-Ferrand, France (S.B.D.); Global Bioinformatics, Evotec ID, Lyon, France (L.B.); Preclinical Sciences & Translational Safety, JNJ Innovative Medicine, Beerse, Belgium (S.K.); Biofortis, Saint-Herblain, France (F.L.V.); Translational Pharmacology Department, Oncodesign Services, Dijon, France (S.M.); Université Paris-Saclay, INRAE, AgroParisTech, Micalis Institute, Jouy-en-Josas, France (S.R.); Center of Microbial Ecology and Technology, Faculty of Bioscience Engineering, Ghent University, Gent, Belgium (T.V.W.); TNO, Leiden, The Netherlands (F.S.); Lallemand Health Solutions, Blagnac, France (V.T.); Servier, Saclay, France (B.W.); and Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany (M.Z.)
| | - Céline Druart
- Medicen Paris Région, Paris, France (P.J.); Pharmabiotic Research Institute, Narbonne, France (C.D.); UMR 454 MEDIS, Université Clermont Auvergne, Clermont-Ferrand, France (S.B.D.); Global Bioinformatics, Evotec ID, Lyon, France (L.B.); Preclinical Sciences & Translational Safety, JNJ Innovative Medicine, Beerse, Belgium (S.K.); Biofortis, Saint-Herblain, France (F.L.V.); Translational Pharmacology Department, Oncodesign Services, Dijon, France (S.M.); Université Paris-Saclay, INRAE, AgroParisTech, Micalis Institute, Jouy-en-Josas, France (S.R.); Center of Microbial Ecology and Technology, Faculty of Bioscience Engineering, Ghent University, Gent, Belgium (T.V.W.); TNO, Leiden, The Netherlands (F.S.); Lallemand Health Solutions, Blagnac, France (V.T.); Servier, Saclay, France (B.W.); and Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany (M.Z.)
| | - Stéphanie Blanquet-Diot
- Medicen Paris Région, Paris, France (P.J.); Pharmabiotic Research Institute, Narbonne, France (C.D.); UMR 454 MEDIS, Université Clermont Auvergne, Clermont-Ferrand, France (S.B.D.); Global Bioinformatics, Evotec ID, Lyon, France (L.B.); Preclinical Sciences & Translational Safety, JNJ Innovative Medicine, Beerse, Belgium (S.K.); Biofortis, Saint-Herblain, France (F.L.V.); Translational Pharmacology Department, Oncodesign Services, Dijon, France (S.M.); Université Paris-Saclay, INRAE, AgroParisTech, Micalis Institute, Jouy-en-Josas, France (S.R.); Center of Microbial Ecology and Technology, Faculty of Bioscience Engineering, Ghent University, Gent, Belgium (T.V.W.); TNO, Leiden, The Netherlands (F.S.); Lallemand Health Solutions, Blagnac, France (V.T.); Servier, Saclay, France (B.W.); and Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany (M.Z.)
| | - Lilia Boucinha
- Medicen Paris Région, Paris, France (P.J.); Pharmabiotic Research Institute, Narbonne, France (C.D.); UMR 454 MEDIS, Université Clermont Auvergne, Clermont-Ferrand, France (S.B.D.); Global Bioinformatics, Evotec ID, Lyon, France (L.B.); Preclinical Sciences & Translational Safety, JNJ Innovative Medicine, Beerse, Belgium (S.K.); Biofortis, Saint-Herblain, France (F.L.V.); Translational Pharmacology Department, Oncodesign Services, Dijon, France (S.M.); Université Paris-Saclay, INRAE, AgroParisTech, Micalis Institute, Jouy-en-Josas, France (S.R.); Center of Microbial Ecology and Technology, Faculty of Bioscience Engineering, Ghent University, Gent, Belgium (T.V.W.); TNO, Leiden, The Netherlands (F.S.); Lallemand Health Solutions, Blagnac, France (V.T.); Servier, Saclay, France (B.W.); and Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany (M.Z.)
| | - Stephanie Kourula
- Medicen Paris Région, Paris, France (P.J.); Pharmabiotic Research Institute, Narbonne, France (C.D.); UMR 454 MEDIS, Université Clermont Auvergne, Clermont-Ferrand, France (S.B.D.); Global Bioinformatics, Evotec ID, Lyon, France (L.B.); Preclinical Sciences & Translational Safety, JNJ Innovative Medicine, Beerse, Belgium (S.K.); Biofortis, Saint-Herblain, France (F.L.V.); Translational Pharmacology Department, Oncodesign Services, Dijon, France (S.M.); Université Paris-Saclay, INRAE, AgroParisTech, Micalis Institute, Jouy-en-Josas, France (S.R.); Center of Microbial Ecology and Technology, Faculty of Bioscience Engineering, Ghent University, Gent, Belgium (T.V.W.); TNO, Leiden, The Netherlands (F.S.); Lallemand Health Solutions, Blagnac, France (V.T.); Servier, Saclay, France (B.W.); and Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany (M.Z.)
| | - Françoise Le Vacon
- Medicen Paris Région, Paris, France (P.J.); Pharmabiotic Research Institute, Narbonne, France (C.D.); UMR 454 MEDIS, Université Clermont Auvergne, Clermont-Ferrand, France (S.B.D.); Global Bioinformatics, Evotec ID, Lyon, France (L.B.); Preclinical Sciences & Translational Safety, JNJ Innovative Medicine, Beerse, Belgium (S.K.); Biofortis, Saint-Herblain, France (F.L.V.); Translational Pharmacology Department, Oncodesign Services, Dijon, France (S.M.); Université Paris-Saclay, INRAE, AgroParisTech, Micalis Institute, Jouy-en-Josas, France (S.R.); Center of Microbial Ecology and Technology, Faculty of Bioscience Engineering, Ghent University, Gent, Belgium (T.V.W.); TNO, Leiden, The Netherlands (F.S.); Lallemand Health Solutions, Blagnac, France (V.T.); Servier, Saclay, France (B.W.); and Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany (M.Z.)
| | - Sylvie Maubant
- Medicen Paris Région, Paris, France (P.J.); Pharmabiotic Research Institute, Narbonne, France (C.D.); UMR 454 MEDIS, Université Clermont Auvergne, Clermont-Ferrand, France (S.B.D.); Global Bioinformatics, Evotec ID, Lyon, France (L.B.); Preclinical Sciences & Translational Safety, JNJ Innovative Medicine, Beerse, Belgium (S.K.); Biofortis, Saint-Herblain, France (F.L.V.); Translational Pharmacology Department, Oncodesign Services, Dijon, France (S.M.); Université Paris-Saclay, INRAE, AgroParisTech, Micalis Institute, Jouy-en-Josas, France (S.R.); Center of Microbial Ecology and Technology, Faculty of Bioscience Engineering, Ghent University, Gent, Belgium (T.V.W.); TNO, Leiden, The Netherlands (F.S.); Lallemand Health Solutions, Blagnac, France (V.T.); Servier, Saclay, France (B.W.); and Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany (M.Z.)
| | - Sylvie Rabot
- Medicen Paris Région, Paris, France (P.J.); Pharmabiotic Research Institute, Narbonne, France (C.D.); UMR 454 MEDIS, Université Clermont Auvergne, Clermont-Ferrand, France (S.B.D.); Global Bioinformatics, Evotec ID, Lyon, France (L.B.); Preclinical Sciences & Translational Safety, JNJ Innovative Medicine, Beerse, Belgium (S.K.); Biofortis, Saint-Herblain, France (F.L.V.); Translational Pharmacology Department, Oncodesign Services, Dijon, France (S.M.); Université Paris-Saclay, INRAE, AgroParisTech, Micalis Institute, Jouy-en-Josas, France (S.R.); Center of Microbial Ecology and Technology, Faculty of Bioscience Engineering, Ghent University, Gent, Belgium (T.V.W.); TNO, Leiden, The Netherlands (F.S.); Lallemand Health Solutions, Blagnac, France (V.T.); Servier, Saclay, France (B.W.); and Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany (M.Z.)
| | - Tom Van de Wiele
- Medicen Paris Région, Paris, France (P.J.); Pharmabiotic Research Institute, Narbonne, France (C.D.); UMR 454 MEDIS, Université Clermont Auvergne, Clermont-Ferrand, France (S.B.D.); Global Bioinformatics, Evotec ID, Lyon, France (L.B.); Preclinical Sciences & Translational Safety, JNJ Innovative Medicine, Beerse, Belgium (S.K.); Biofortis, Saint-Herblain, France (F.L.V.); Translational Pharmacology Department, Oncodesign Services, Dijon, France (S.M.); Université Paris-Saclay, INRAE, AgroParisTech, Micalis Institute, Jouy-en-Josas, France (S.R.); Center of Microbial Ecology and Technology, Faculty of Bioscience Engineering, Ghent University, Gent, Belgium (T.V.W.); TNO, Leiden, The Netherlands (F.S.); Lallemand Health Solutions, Blagnac, France (V.T.); Servier, Saclay, France (B.W.); and Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany (M.Z.)
| | - Frank Schuren
- Medicen Paris Région, Paris, France (P.J.); Pharmabiotic Research Institute, Narbonne, France (C.D.); UMR 454 MEDIS, Université Clermont Auvergne, Clermont-Ferrand, France (S.B.D.); Global Bioinformatics, Evotec ID, Lyon, France (L.B.); Preclinical Sciences & Translational Safety, JNJ Innovative Medicine, Beerse, Belgium (S.K.); Biofortis, Saint-Herblain, France (F.L.V.); Translational Pharmacology Department, Oncodesign Services, Dijon, France (S.M.); Université Paris-Saclay, INRAE, AgroParisTech, Micalis Institute, Jouy-en-Josas, France (S.R.); Center of Microbial Ecology and Technology, Faculty of Bioscience Engineering, Ghent University, Gent, Belgium (T.V.W.); TNO, Leiden, The Netherlands (F.S.); Lallemand Health Solutions, Blagnac, France (V.T.); Servier, Saclay, France (B.W.); and Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany (M.Z.)
| | - Vincent Thomas
- Medicen Paris Région, Paris, France (P.J.); Pharmabiotic Research Institute, Narbonne, France (C.D.); UMR 454 MEDIS, Université Clermont Auvergne, Clermont-Ferrand, France (S.B.D.); Global Bioinformatics, Evotec ID, Lyon, France (L.B.); Preclinical Sciences & Translational Safety, JNJ Innovative Medicine, Beerse, Belgium (S.K.); Biofortis, Saint-Herblain, France (F.L.V.); Translational Pharmacology Department, Oncodesign Services, Dijon, France (S.M.); Université Paris-Saclay, INRAE, AgroParisTech, Micalis Institute, Jouy-en-Josas, France (S.R.); Center of Microbial Ecology and Technology, Faculty of Bioscience Engineering, Ghent University, Gent, Belgium (T.V.W.); TNO, Leiden, The Netherlands (F.S.); Lallemand Health Solutions, Blagnac, France (V.T.); Servier, Saclay, France (B.W.); and Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany (M.Z.)
| | - Bernard Walther
- Medicen Paris Région, Paris, France (P.J.); Pharmabiotic Research Institute, Narbonne, France (C.D.); UMR 454 MEDIS, Université Clermont Auvergne, Clermont-Ferrand, France (S.B.D.); Global Bioinformatics, Evotec ID, Lyon, France (L.B.); Preclinical Sciences & Translational Safety, JNJ Innovative Medicine, Beerse, Belgium (S.K.); Biofortis, Saint-Herblain, France (F.L.V.); Translational Pharmacology Department, Oncodesign Services, Dijon, France (S.M.); Université Paris-Saclay, INRAE, AgroParisTech, Micalis Institute, Jouy-en-Josas, France (S.R.); Center of Microbial Ecology and Technology, Faculty of Bioscience Engineering, Ghent University, Gent, Belgium (T.V.W.); TNO, Leiden, The Netherlands (F.S.); Lallemand Health Solutions, Blagnac, France (V.T.); Servier, Saclay, France (B.W.); and Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany (M.Z.)
| | - Michael Zimmermann
- Medicen Paris Région, Paris, France (P.J.); Pharmabiotic Research Institute, Narbonne, France (C.D.); UMR 454 MEDIS, Université Clermont Auvergne, Clermont-Ferrand, France (S.B.D.); Global Bioinformatics, Evotec ID, Lyon, France (L.B.); Preclinical Sciences & Translational Safety, JNJ Innovative Medicine, Beerse, Belgium (S.K.); Biofortis, Saint-Herblain, France (F.L.V.); Translational Pharmacology Department, Oncodesign Services, Dijon, France (S.M.); Université Paris-Saclay, INRAE, AgroParisTech, Micalis Institute, Jouy-en-Josas, France (S.R.); Center of Microbial Ecology and Technology, Faculty of Bioscience Engineering, Ghent University, Gent, Belgium (T.V.W.); TNO, Leiden, The Netherlands (F.S.); Lallemand Health Solutions, Blagnac, France (V.T.); Servier, Saclay, France (B.W.); and Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany (M.Z.)
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11
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Baghdassarian HM, Lewis NE. Resource allocation in mammalian systems. Biotechnol Adv 2024; 71:108305. [PMID: 38215956 PMCID: PMC11182366 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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12
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Cooke J, Delmas M, Wieder C, Rodríguez Mier P, Frainay C, Vinson F, Ebbels T, Poupin N, Jourdan F. Genome scale metabolic network modelling for metabolic profile predictions. PLoS Comput Biol 2024; 20:e1011381. [PMID: 38386685 PMCID: PMC10914266 DOI: 10.1371/journal.pcbi.1011381] [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: 07/25/2023] [Revised: 03/05/2024] [Accepted: 01/25/2024] [Indexed: 02/24/2024] Open
Abstract
Metabolic profiling (metabolomics) aims at measuring small molecules (metabolites) in complex samples like blood or urine for human health studies. While biomarker-based assessment often relies on a single molecule, metabolic profiling combines several metabolites to create a more complex and more specific fingerprint of the disease. However, in contrast to genomics, there is no unique metabolomics setup able to measure the entire metabolome. This challenge leads to tedious and resource consuming preliminary studies to be able to design the right metabolomics experiment. In that context, computer assisted metabolic profiling can be of strong added value to design metabolomics studies more quickly and efficiently. We propose a constraint-based modelling approach which predicts in silico profiles of metabolites that are more likely to be differentially abundant under a given metabolic perturbation (e.g. due to a genetic disease), using flux simulation. In genome-scale metabolic networks, the fluxes of exchange reactions, also known as the flow of metabolites through their external transport reactions, can be simulated and compared between control and disease conditions in order to calculate changes in metabolite import and export. These import/export flux differences would be expected to induce changes in circulating biofluid levels of those metabolites, which can then be interpreted as potential biomarkers or metabolites of interest. In this study, we present SAMBA (SAMpling Biomarker Analysis), an approach which simulates fluxes in exchange reactions following a metabolic perturbation using random sampling, compares the simulated flux distributions between the baseline and modulated conditions, and ranks predicted differentially exchanged metabolites as potential biomarkers for the perturbation. We show that there is a good fit between simulated metabolic exchange profiles and experimental differential metabolites detected in plasma, such as patient data from the disease database OMIM, and metabolic trait-SNP associations found in mGWAS studies. These biomarker recommendations can provide insight into the underlying mechanism or metabolic pathway perturbation lying behind observed metabolite differential abundances, and suggest new metabolites as potential avenues for further experimental analyses.
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Affiliation(s)
- Juliette Cooke
- Toxalim (Research Centre in Food Toxicology), Université de Toulouse, INRAE, ENVT, INP-Purpan, UPS, Toulouse, France
| | - Maxime Delmas
- Toxalim (Research Centre in Food Toxicology), Université de Toulouse, INRAE, ENVT, INP-Purpan, UPS, Toulouse, France
- Idiap Research Institute, Martigny, Switzerland
| | - Cecilia Wieder
- Section of Bioinformatics, Division of Systems Medicine, Department of Metabolism, Digestion, and Reproduction, Faculty of Medicine, Imperial College London, London, United Kingdom
| | - Pablo Rodríguez Mier
- Toxalim (Research Centre in Food Toxicology), Université de Toulouse, INRAE, ENVT, INP-Purpan, UPS, Toulouse, France
- Heidelberg University, Faculty of Medicine, Heidelberg University Hospital, Institute for Computational Biomedicine, Bioquant, Heidelberg, Germany
| | - Clément Frainay
- Toxalim (Research Centre in Food Toxicology), Université de Toulouse, INRAE, ENVT, INP-Purpan, UPS, Toulouse, France
| | - Florence Vinson
- Toxalim (Research Centre in Food Toxicology), Université de Toulouse, INRAE, ENVT, INP-Purpan, UPS, Toulouse, France
- MetaToul-MetaboHUB, National Infrastructure of Metabolomics and Fluxomics, Toulouse, France
| | - Timothy Ebbels
- Section of Bioinformatics, Division of Systems Medicine, Department of Metabolism, Digestion, and Reproduction, Faculty of Medicine, Imperial College London, London, United Kingdom
| | - Nathalie Poupin
- Toxalim (Research Centre in Food Toxicology), Université de Toulouse, INRAE, ENVT, INP-Purpan, UPS, Toulouse, France
| | - Fabien Jourdan
- Toxalim (Research Centre in Food Toxicology), Université de Toulouse, INRAE, ENVT, INP-Purpan, UPS, Toulouse, France
- MetaToul-MetaboHUB, National Infrastructure of Metabolomics and Fluxomics, Toulouse, France
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13
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Abo SMC, Casella E, Layton AT. Sexual Dimorphism in Substrate Metabolism During Exercise. Bull Math Biol 2024; 86:17. [PMID: 38228814 DOI: 10.1007/s11538-023-01242-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Accepted: 11/29/2023] [Indexed: 01/18/2024]
Abstract
During aerobic exercise, women oxidize significantly more lipids and less carbohydrates than men. This sexual dimorphism in substrate metabolism has been attributed, in part, to the observed differences in epinephrine and glucagon levels between men and women during exercise. To identify the underpinning candidate physiological mechanisms for these sex differences, we developed a sex-specific multi-scale mathematical model that relates cellular metabolism in the organs to whole-body responses during exercise. We conducted simulations to test the hypothesis that sex differences in the exercise-induced changes to epinephrine and glucagon would result in the sexual dimorphism of hepatic metabolic flux rates via the glucagon-to-insulin ratio (GIR). Indeed, model simulations indicate that the shift towards lipid metabolism in the female model is primarily driven by the liver. The female model liver exhibits resistance to GIR-mediated glycogenolysis, which helps maintain hepatic glycogen levels. This decreases arterial glucose levels and promotes the oxidation of free fatty acids. Furthermore, in the female model, skeletal muscle relies on plasma free fatty acids as the primary fuel source, rather than intramyocellular lipids, whereas the opposite holds true for the male model.
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Affiliation(s)
- Stéphanie M C Abo
- Department of Applied Mathematics, University of Waterloo, 200 University Ave W, Waterloo, ON, N2L 3G1, Canada.
| | - Elisa Casella
- Department of Applied Mathematics, University of Waterloo, 200 University Ave W, Waterloo, ON, N2L 3G1, Canada
| | - Anita T Layton
- Department of Applied Mathematics, University of Waterloo, 200 University Ave W, Waterloo, ON, N2L 3G1, Canada
- Cheriton School of Computer Science, Department of Biology, and School of Pharmacy, University of Waterloo, 200 University Ave W, Waterloo, ON, N2L 3G1, Canada
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14
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Heinken A, El Kouche S, Guéant-Rodriguez RM, Guéant JL. Towards personalized genome-scale modeling of inborn errors of metabolism for systems medicine applications. Metabolism 2024; 150:155738. [PMID: 37981189 DOI: 10.1016/j.metabol.2023.155738] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Revised: 11/09/2023] [Accepted: 11/12/2023] [Indexed: 11/21/2023]
Abstract
Inborn errors of metabolism (IEMs) are a group of more than 1000 inherited diseases that are individually rare but have a cumulative global prevalence of 50 per 100,000 births. Recently, it has been recognized that like common diseases, patients with rare diseases can greatly vary in the manifestation and severity of symptoms. Here, we review omics-driven approaches that enable an integrated, holistic view of metabolic phenotypes in IEM patients. We focus on applications of Constraint-based Reconstruction and Analysis (COBRA), a widely used mechanistic systems biology approach, to model the effects of inherited diseases. Moreover, we review evidence that the gut microbiome is also altered in rare diseases. Finally, we outline an approach using personalized metabolic models of IEM patients for the prediction of biomarkers and tailored therapeutic or dietary interventions. Such applications could pave the way towards personalized medicine not just for common, but also for rare diseases.
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Affiliation(s)
- Almut Heinken
- Inserm UMRS 1256 NGERE - Nutrition, Genetics, and Environmental Risk Exposure, University of Lorraine, Nancy F-54000, France.
| | - Sandra El Kouche
- Inserm UMRS 1256 NGERE - Nutrition, Genetics, and Environmental Risk Exposure, University of Lorraine, Nancy F-54000, France
| | - Rosa-Maria Guéant-Rodriguez
- Inserm UMRS 1256 NGERE - Nutrition, Genetics, and Environmental Risk Exposure, University of Lorraine, Nancy F-54000, France; National Center of Inborn Errors of Metabolism, University Regional Hospital Center of Nancy, Nancy F-54000, France
| | - Jean-Louis Guéant
- Inserm UMRS 1256 NGERE - Nutrition, Genetics, and Environmental Risk Exposure, University of Lorraine, Nancy F-54000, France; National Center of Inborn Errors of Metabolism, University Regional Hospital Center of Nancy, Nancy F-54000, France
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15
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Uatay A, Gall L, Irons L, Tewari SG, Zhu XS, Gibbs M, Kimko H. Physiological Indirect Response Model to Omics-Powered Quantitative Systems Pharmacology Model. J Pharm Sci 2024; 113:11-21. [PMID: 37898164 DOI: 10.1016/j.xphs.2023.10.032] [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: 08/30/2023] [Revised: 10/21/2023] [Accepted: 10/21/2023] [Indexed: 10/30/2023]
Abstract
Over the past several decades, mathematical modeling has been applied to increasingly wider scopes of questions in drug development. Accordingly, the range of modeling tools has also been evolving, as showcased by contributions of Jusko and colleagues: from basic pharmacokinetics/pharmacodynamics (PK/PD) modeling to today's platform-based approach of quantitative systems pharmacology (QSP) modeling. Aimed at understanding the mechanism of action of investigational drugs, QSP models characterize systemic effects by incorporating information about cellular signaling networks, which is often represented by omics data. In this perspective, we share a few examples illustrating approaches for the integration of omics into mechanistic QSP modeling. We briefly overview how the evolution of PK/PD modeling into QSP has been accompanied by an increase in available data and the complexity of mathematical methods that integrate it. We discuss current gaps and challenges of integrating omics data into QSP models and propose several potential areas where integrated QSP and omics modeling may benefit drug development.
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Affiliation(s)
- Aydar Uatay
- Clinical Pharmacology & Quantitative Pharmacology, R&D Biopharmaceuticals, Cambridge, United Kingdom.
| | - Louis Gall
- Clinical Pharmacology & Quantitative Pharmacology, R&D Biopharmaceuticals, Cambridge, United Kingdom
| | - Linda Irons
- Clinical Pharmacology & Quantitative Pharmacology, R&D Biopharmaceuticals, Waltham, MA, United States
| | - Shivendra G Tewari
- Clinical Pharmacology & Quantitative Pharmacology, R&D Biopharmaceuticals, Gaithersburg, MD, United States
| | - Xu Sue Zhu
- Clinical Pharmacology & Quantitative Pharmacology, R&D Biopharmaceuticals, Waltham, MA, United States
| | - Megan Gibbs
- Clinical Pharmacology & Quantitative Pharmacology, R&D Biopharmaceuticals, Waltham, MA, United States
| | - Holly Kimko
- Clinical Pharmacology & Quantitative Pharmacology, R&D Biopharmaceuticals, Gaithersburg, MD, United States.
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16
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Lakhani A, Kang DH, Kang YE, Park JO. Toward Systems-Level Metabolic Analysis in Endocrine Disorders and Cancer. Endocrinol Metab (Seoul) 2023; 38:619-630. [PMID: 37989266 PMCID: PMC10764991 DOI: 10.3803/enm.2023.1814] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/09/2023] [Revised: 10/27/2023] [Accepted: 11/01/2023] [Indexed: 11/23/2023] Open
Abstract
Metabolism is a dynamic network of biochemical reactions that support systemic homeostasis amidst changing nutritional, environmental, and physical activity factors. The circulatory system facilitates metabolite exchange among organs, while the endocrine system finely tunes metabolism through hormone release. Endocrine disorders like obesity, diabetes, and Cushing's syndrome disrupt this balance, contributing to systemic inflammation and global health burdens. They accompany metabolic changes on multiple levels from molecular interactions to individual organs to the whole body. Understanding how metabolic fluxes relate to endocrine disorders illuminates the underlying dysregulation. Cancer is increasingly considered a systemic disorder because it not only affects cells in localized tumors but also the whole body, especially in metastasis. In tumorigenesis, cancer-specific mutations and nutrient availability in the tumor microenvironment reprogram cellular metabolism to meet increased energy and biosynthesis needs. Cancer cachexia results in metabolic changes to other organs like muscle, adipose tissue, and liver. This review explores the interplay between the endocrine system and systems-level metabolism in health and disease. We highlight metabolic fluxes in conditions like obesity, diabetes, Cushing's syndrome, and cancers. Recent advances in metabolomics, fluxomics, and systems biology promise new insights into dynamic metabolism, offering potential biomarkers, therapeutic targets, and personalized medicine.
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Affiliation(s)
- Aliya Lakhani
- Department of Chemical and Biomolecular Engineering, University of California, Los Angeles, Los Angeles, CA, USA
| | - Da Hyun Kang
- Department of Internal Medicine, Chungnam National University College of Medicine, Daejeon, Korea
| | - Yea Eun Kang
- Department of Internal Medicine, Chungnam National University College of Medicine, Daejeon, Korea
| | - Junyoung O. Park
- Department of Chemical and Biomolecular Engineering, University of California, Los Angeles, Los Angeles, CA, USA
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17
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Ghini V, Vieri W, Celli T, Pecchioli V, Boccia N, Alonso-Vásquez T, Pelagatti L, Fondi M, Luchinat C, Bertini L, Vannucchi V, Landini G, Turano P. COVID-19: A complex disease with a unique metabolic signature. PLoS Pathog 2023; 19:e1011787. [PMID: 37943960 PMCID: PMC10662774 DOI: 10.1371/journal.ppat.1011787] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Revised: 11/21/2023] [Accepted: 10/30/2023] [Indexed: 11/12/2023] Open
Abstract
Plasma of COVID-19 patients contains a strong metabolomic/lipoproteomic signature, revealed by the NMR analysis of a cohort of >500 patients sampled during various waves of COVID-19 infection, corresponding to the spread of different variants, and having different vaccination status. This composite signature highlights common traits of the SARS-CoV-2 infection. The most dysregulated molecules display concentration trends that scale with disease severity and might serve as prognostic markers for fatal events. Metabolomics evidence is then used as input data for a sex-specific multi-organ metabolic model. This reconstruction provides a comprehensive view of the impact of COVID-19 on the entire human metabolism. The human (male and female) metabolic network is strongly impacted by the disease to an extent dictated by its severity. A marked metabolic reprogramming at the level of many organs indicates an increase in the generic energetic demand of the organism following infection. Sex-specific modulation of immune response is also suggested.
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Affiliation(s)
- Veronica Ghini
- Department of Chemistry “Ugo Schiff”, University of Florence, Sesto Fiorentino Florence, Italy
- Magnetic Resonance Center (CERM), University of Florence, Sesto Fiorentino, Florence, Italy
| | - Walter Vieri
- Department of Chemistry “Ugo Schiff”, University of Florence, Sesto Fiorentino Florence, Italy
- Department of Biology, University of Florence, Sesto Fiorentino, Florence, Italy
| | - Tommaso Celli
- Internal Medicine, Santa Maria Nuova Hospital, Florence, Florence, Italy
| | - Valentina Pecchioli
- Department of Chemistry “Ugo Schiff”, University of Florence, Sesto Fiorentino Florence, Italy
- Magnetic Resonance Center (CERM), University of Florence, Sesto Fiorentino, Florence, Italy
| | - Nunzia Boccia
- Internal Medicine, Santa Maria Nuova Hospital, Florence, Florence, Italy
| | - Tania Alonso-Vásquez
- Department of Biology, University of Florence, Sesto Fiorentino, Florence, Italy
| | - Lorenzo Pelagatti
- Internal Medicine, Santa Maria Nuova Hospital, Florence, Florence, Italy
| | - Marco Fondi
- Department of Biology, University of Florence, Sesto Fiorentino, Florence, Italy
| | - Claudio Luchinat
- Department of Chemistry “Ugo Schiff”, University of Florence, Sesto Fiorentino Florence, Italy
- Magnetic Resonance Center (CERM), University of Florence, Sesto Fiorentino, Florence, Italy
- Consorzio Interuniversitario Risonanze Magnetiche di Metallo Proteine (CIRMMP), Sesto Fiorentino Florence, Italy
| | - Laura Bertini
- Internal Medicine, Santa Maria Nuova Hospital, Florence, Florence, Italy
| | - Vieri Vannucchi
- Internal Medicine, Santa Maria Nuova Hospital, Florence, Florence, Italy
| | - Giancarlo Landini
- Internal Medicine, Santa Maria Nuova Hospital, Florence, Florence, Italy
| | - Paola Turano
- Department of Chemistry “Ugo Schiff”, University of Florence, Sesto Fiorentino Florence, Italy
- Magnetic Resonance Center (CERM), University of Florence, Sesto Fiorentino, Florence, Italy
- Consorzio Interuniversitario Risonanze Magnetiche di Metallo Proteine (CIRMMP), Sesto Fiorentino Florence, Italy
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18
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Backman TWH, Schenk C, Radivojevic T, Ando D, Singh J, Czajka JJ, Costello Z, Keasling JD, Tang Y, Akhmatskaya E, Garcia Martin H. BayFlux: A Bayesian method to quantify metabolic Fluxes and their uncertainty at the genome scale. PLoS Comput Biol 2023; 19:e1011111. [PMID: 37948450 PMCID: PMC10664898 DOI: 10.1371/journal.pcbi.1011111] [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: 04/18/2023] [Revised: 11/22/2023] [Accepted: 09/27/2023] [Indexed: 11/12/2023] Open
Abstract
Metabolic fluxes, the number of metabolites traversing each biochemical reaction in a cell per unit time, are crucial for assessing and understanding cell function. 13C Metabolic Flux Analysis (13C MFA) is considered to be the gold standard for measuring metabolic fluxes. 13C MFA typically works by leveraging extracellular exchange fluxes as well as data from 13C labeling experiments to calculate the flux profile which best fit the data for a small, central carbon, metabolic model. However, the nonlinear nature of the 13C MFA fitting procedure means that several flux profiles fit the experimental data within the experimental error, and traditional optimization methods offer only a partial or skewed picture, especially in "non-gaussian" situations where multiple very distinct flux regions fit the data equally well. Here, we present a method for flux space sampling through Bayesian inference (BayFlux), that identifies the full distribution of fluxes compatible with experimental data for a comprehensive genome-scale model. This Bayesian approach allows us to accurately quantify uncertainty in calculated fluxes. We also find that, surprisingly, the genome-scale model of metabolism produces narrower flux distributions (reduced uncertainty) than the small core metabolic models traditionally used in 13C MFA. The different results for some reactions when using genome-scale models vs core metabolic models advise caution in assuming strong inferences from 13C MFA since the results may depend significantly on the completeness of the model used. Based on BayFlux, we developed and evaluated novel methods (P-13C MOMA and P-13C ROOM) to predict the biological results of a gene knockout, that improve on the traditional MOMA and ROOM methods by quantifying prediction uncertainty.
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Affiliation(s)
- Tyler W. H. Backman
- Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, California, United States of America
- Biofuels and Bioproducts Division, Joint BioEnergy Institute, Emeryville, California, United States of America
| | - Christina Schenk
- Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, California, United States of America
- BCAM, Basque Center for Applied Mathematics, Bilbao, Spain
- DOE Agile BioFoundry, Emeryville, California, United States of America
| | - Tijana Radivojevic
- Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, California, United States of America
- Biofuels and Bioproducts Division, Joint BioEnergy Institute, Emeryville, California, United States of America
- DOE Agile BioFoundry, Emeryville, California, United States of America
| | - David Ando
- Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, California, United States of America
- Biofuels and Bioproducts Division, Joint BioEnergy Institute, Emeryville, California, United States of America
| | - Jahnavi Singh
- Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, California, United States of America
| | - Jeffrey J. Czajka
- Department of Energy, Environmental and Chemical Engineering, Washington University in St. Louis, St. Louis, Missouri, United States of America
| | - Zak Costello
- Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, California, United States of America
- Biofuels and Bioproducts Division, Joint BioEnergy Institute, Emeryville, California, United States of America
- DOE Agile BioFoundry, Emeryville, California, United States of America
| | - Jay D. Keasling
- Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, California, United States of America
- Biofuels and Bioproducts Division, Joint BioEnergy Institute, Emeryville, California, United States of America
- Department of Chemical and Biomolecular Engineering, University of California, Berkeley, California, United States of America
- Department of Bioengineering, University of California, Berkeley, California, United States of America
- QB3 Institute, University of California, Berkeley, California, United States of America
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Copenhagen, Denmark
- Center for Synthetic Biochemistry, Institute for Synthetic Biology, Shenzhen Institutes for Advanced Technologies, Shenzhen, China
| | - Yinjie Tang
- Department of Energy, Environmental and Chemical Engineering, Washington University in St. Louis, St. Louis, Missouri, United States of America
| | - Elena Akhmatskaya
- Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, California, United States of America
- BCAM, Basque Center for Applied Mathematics, Bilbao, Spain
- IKERBASQUE, Basque Foundation for Science, Bilbao, Spain
| | - Hector Garcia Martin
- Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, California, United States of America
- Biofuels and Bioproducts Division, Joint BioEnergy Institute, Emeryville, California, United States of America
- BCAM, Basque Center for Applied Mathematics, Bilbao, Spain
- DOE Agile BioFoundry, Emeryville, California, United States of America
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19
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Boris V, Vanessa V. Molecular systems biology approaches to investigate mechanisms of gut-brain communication in neurological diseases. Eur J Neurol 2023; 30:3622-3632. [PMID: 37038632 DOI: 10.1111/ene.15819] [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: 01/05/2023] [Revised: 04/03/2023] [Accepted: 04/05/2023] [Indexed: 04/12/2023]
Abstract
BACKGROUND Whilst the incidence of neurological diseases is increasing worldwide, treatment remains mostly limited to symptom management. The gut-brain axis, which encompasses the communication routes between microbiota, gut and brain, has emerged as a crucial area of investigation for identifying new preventive and therapeutic targets in neurological disease. METHODS Due to the inter-organ, systemic nature of the gut-brain axis, together with the multitude of biomolecules and microbial species involved, molecular systems biology approaches are required to accurately investigate the mechanisms of gut-brain communication. High-throughput omics profiling, together with computational methodologies such as dimensionality reduction or clustering, machine learning, network inference and genome-scale metabolic models, allows novel biomarkers to be discovered and elucidates mechanistic insights. RESULTS In this review, the general concepts of experimental and computational methodologies for gut-brain axis research are introduced and their applications are discussed, mainly in human cohorts. Important aspects are further highlighted concerning rational study design, sampling procedures and data modalities relevant for gut-brain communication, strengths and limitations of methodological approaches and some future perspectives. CONCLUSION Multi-omics analyses, together with advanced data mining, are essential to functionally characterize the gut-brain axis and put forward novel preventive or therapeutic strategies in neurological disease.
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Affiliation(s)
- Vandemoortele Boris
- Laboratory for Computational Biology, Integromics and Gene Regulation (CBIGR), Cancer Research Institute Ghent (CRIG), Ghent, Belgium
- Department of Biomedical Molecular Biology, Ghent University, Ghent, Belgium
- Department of Biomolecular Medicine, Ghent University, Ghent, Belgium
| | - Vermeirssen Vanessa
- Laboratory for Computational Biology, Integromics and Gene Regulation (CBIGR), Cancer Research Institute Ghent (CRIG), Ghent, Belgium
- Department of Biomedical Molecular Biology, Ghent University, Ghent, Belgium
- Department of Biomolecular Medicine, Ghent University, Ghent, Belgium
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20
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Hertel J, Heinken A, Fässler D, Thiele I. Causal inference on microbiome-metabolome relations in observational host-microbiome data via in silico in vivo association pattern analyses. CELL REPORTS METHODS 2023; 3:100615. [PMID: 37848031 PMCID: PMC10626217 DOI: 10.1016/j.crmeth.2023.100615] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/18/2022] [Revised: 05/23/2023] [Accepted: 09/20/2023] [Indexed: 10/19/2023]
Abstract
Understanding the effects of the microbiome on the host's metabolism is core to enlightening the role of the microbiome in health and disease. Herein, we develop the paradigm of in silico in vivo association pattern analyses, combining microbiome metabolome association studies with in silico constraint-based community modeling. Via theoretical dissection of confounding and causal paths, we show that in silico in vivo association pattern analyses allow for causal inference on microbiome-metabolome relations in observational data. We justify the corresponding theoretical criterion by structural equation modeling of host-microbiome systems, integrating deterministic microbiome community modeling into population statistics approaches. We show the feasibility of our approach on a published multi-omics dataset (n = 347), demonstrating causal microbiome-metabolite relations for 26 out of 54 fecal metabolites. In summary, we generate a promising approach for causal inference in metabolic host-microbiome interactions by integrating hypothesis-free screening association studies with knowledge-based in silico modeling.
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Affiliation(s)
- Johannes Hertel
- School of Medicine, University of Galway, Galway, Ireland; Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Greifswald, Germany
| | - Almut Heinken
- School of Medicine, University of Galway, Galway, Ireland; UMRS Inserm 1256 NGERE (Nutrition-Genetics-Environmental Risks), Institute of Medical Research (Pôle BMS) - University of Lorraine, Vandoeuvre-les-Nancy, France
| | - Daniel Fässler
- Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Greifswald, Germany
| | - Ines Thiele
- School of Medicine, University of Galway, Galway, Ireland; Discipline of Microbiology, University of Galway, Galway, Ireland; APC Microbiome Ireland, University College Cork, Cork, Ireland; Ryan Institute, University of Galway, Galway, Ireland.
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21
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Marcelino VR, Welsh C, Diener C, Gulliver EL, Rutten EL, Young RB, Giles EM, Gibbons SM, Greening C, Forster SC. Disease-specific loss of microbial cross-feeding interactions in the human gut. Nat Commun 2023; 14:6546. [PMID: 37863966 PMCID: PMC10589287 DOI: 10.1038/s41467-023-42112-w] [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: 02/14/2023] [Accepted: 09/27/2023] [Indexed: 10/22/2023] Open
Abstract
Many gut microorganisms critical to human health rely on nutrients produced by each other for survival; however, these cross-feeding interactions are still challenging to quantify and remain poorly characterized. Here, we introduce a Metabolite Exchange Score (MES) to quantify those interactions. Using metabolic models of prokaryotic metagenome-assembled genomes from over 1600 individuals, MES allows us to identify and rank metabolic interactions that are significantly affected by a loss of cross-feeding partners in 10 out of 11 diseases. When applied to a Crohn's disease case-control study, our approach identifies a lack of species with the ability to consume hydrogen sulfide as the main distinguishing microbiome feature of disease. We propose that our conceptual framework will help prioritize in-depth analyses, experiments and clinical targets, and that targeting the restoration of microbial cross-feeding interactions is a promising mechanism-informed strategy to reconstruct a healthy gut ecosystem.
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Affiliation(s)
- Vanessa R Marcelino
- Department of Molecular and Translational Sciences, Monash University, Clayton, VIC, 3168, Australia.
- Centre for Innate Immunity and Infectious Diseases, Hudson Institute of Medical Research, Clayton, VIC, 3168, Australia.
- Melbourne Integrative Genomics, School of BioSciences, University of Melbourne, Parkville, VIC, 3010, Australia.
- Department of Microbiology and Immunology at the Peter Doherty Institute for Infection and Immunity, University of Melbourne, Parkville, VIC, 3010, Australia.
| | - Caitlin Welsh
- Department of Microbiology, Biomedicine Discovery Institute, Clayton, VIC, 3800, Australia
| | | | - Emily L Gulliver
- Department of Molecular and Translational Sciences, Monash University, Clayton, VIC, 3168, Australia
- Centre for Innate Immunity and Infectious Diseases, Hudson Institute of Medical Research, Clayton, VIC, 3168, Australia
| | - Emily L Rutten
- Department of Molecular and Translational Sciences, Monash University, Clayton, VIC, 3168, Australia
- Centre for Innate Immunity and Infectious Diseases, Hudson Institute of Medical Research, Clayton, VIC, 3168, Australia
| | - Remy B Young
- Department of Molecular and Translational Sciences, Monash University, Clayton, VIC, 3168, Australia
- Centre for Innate Immunity and Infectious Diseases, Hudson Institute of Medical Research, Clayton, VIC, 3168, Australia
| | - Edward M Giles
- Centre for Innate Immunity and Infectious Diseases, Hudson Institute of Medical Research, Clayton, VIC, 3168, Australia
- Department of Paediatrics, Monash University, Clayton, VIC, 3168, Australia
| | - Sean M Gibbons
- Institute for Systems Biology, Seattle, WA, 98109, USA
- Department of Bioengineering, University of Washington, Seattle, WA, 98195, USA
- Department of Genome Sciences, University of Washington, Seattle, WA, 98195, USA
- eScience Institute, University of Washington, Seattle, WA, 98195, USA
| | - Chris Greening
- Department of Microbiology, Biomedicine Discovery Institute, Clayton, VIC, 3800, Australia
| | - Samuel C Forster
- Department of Molecular and Translational Sciences, Monash University, Clayton, VIC, 3168, Australia.
- Centre for Innate Immunity and Infectious Diseases, Hudson Institute of Medical Research, Clayton, VIC, 3168, Australia.
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22
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Heinken A, Hulshof TO, Nap B, Martinelli F, Basile A, O'Brolchain A, O’Sullivan NF, Gallagher C, Magee E, McDonagh F, Lalor I, Bergin M, Evans P, Daly R, Farrell R, Delaney RM, Hill S, McAuliffe SR, Kilgannon T, Fleming RM, Thinnes CC, Thiele I. APOLLO: A genome-scale metabolic reconstruction resource of 247,092 diverse human microbes spanning multiple continents, age groups, and body sites. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.02.560573. [PMID: 37873072 PMCID: PMC10592896 DOI: 10.1101/2023.10.02.560573] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/25/2023]
Abstract
Computational modelling of microbiome metabolism has proved instrumental to catalyse our understanding of diet-host-microbiome-disease interactions through the interrogation of mechanistic, strain- and molecule-resolved metabolic models. We present APOLLO, a resource of 247,092 human microbial genome-scale metabolic reconstructions spanning 19 phyla and accounting for microbial genomes from 34 countries, all age groups, and five body sites. We explored the metabolic potential of the reconstructed strains and developed a machine learning classifier able to predict with high accuracy the taxonomic strain assignments. We also built 14,451 sample-specific microbial community models, which could be stratified by body site, age, and disease states. Finally, we predicted faecal metabolites enriched or depleted in gut microbiomes of people with Crohn's disease, Parkinson disease, and undernourished children. APOLLO is compatible with the human whole-body models, and thus, provide unprecedented opportunities for systems-level modelling of personalised host-microbiome co-metabolism. APOLLO will be freely available under https://www.vmh.life/.
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Affiliation(s)
- Almut Heinken
- School of Medicine, University of Galway, Galway, Ireland
- Ryan Institute, University of Galway, Galway, Ireland
- Inserm UMRS 1256 NGERE, University of Lorraine, Nancy, France
| | - Timothy Otto Hulshof
- School of Medicine, University of Galway, Galway, Ireland
- Ryan Institute, University of Galway, Galway, Ireland
| | - Bram Nap
- School of Medicine, University of Galway, Galway, Ireland
- Ryan Institute, University of Galway, Galway, Ireland
| | - Filippo Martinelli
- School of Medicine, University of Galway, Galway, Ireland
- Ryan Institute, University of Galway, Galway, Ireland
| | - Arianna Basile
- School of Medicine, University of Galway, Galway, Ireland
- Department of Biology, University of Padova, Padova, Italy
| | | | | | | | | | | | - Ian Lalor
- University of Galway, Galway, Ireland
| | | | | | | | | | | | | | | | | | | | - Cyrille C. Thinnes
- School of Medicine, University of Galway, Galway, Ireland
- Ryan Institute, University of Galway, Galway, Ireland
| | - Ines Thiele
- School of Medicine, University of Galway, Galway, Ireland
- Ryan Institute, University of Galway, Galway, Ireland
- Division of Microbiology, University of Galway, Galway, Ireland
- APC Microbiome Ireland, Cork, Ireland
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23
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Fleming RMT, Haraldsdottir HS, Minh LH, Vuong PT, Hankemeier T, Thiele I. Cardinality optimization in constraint-based modelling: application to human metabolism. Bioinformatics 2023; 39:btad450. [PMID: 37697651 PMCID: PMC10495685 DOI: 10.1093/bioinformatics/btad450] [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: 12/07/2022] [Revised: 05/12/2023] [Indexed: 09/13/2023] Open
Abstract
MOTIVATION Several applications in constraint-based modelling can be mathematically formulated as cardinality optimization problems involving the minimization or maximization of the number of nonzeros in a vector. These problems include testing for stoichiometric consistency, testing for flux consistency, testing for thermodynamic flux consistency, computing sparse solutions to flux balance analysis problems and computing the minimum number of constraints to relax to render an infeasible flux balance analysis problem feasible. Such cardinality optimization problems are computationally complex, with no known polynomial time algorithms capable of returning an exact and globally optimal solution. RESULTS By approximating the zero-norm with nonconvex continuous functions, we reformulate a set of cardinality optimization problems in constraint-based modelling into a difference of convex functions. We implemented and numerically tested novel algorithms that approximately solve the reformulated problems using a sequence of convex programs. We applied these algorithms to various biochemical networks and demonstrate that our algorithms match or outperform existing related approaches. In particular, we illustrate the efficiency and practical utility of our algorithms for cardinality optimization problems that arise when extracting a model ready for thermodynamic flux balance analysis given a human metabolic reconstruction. AVAILABILITY AND IMPLEMENTATION Open source scripts to reproduce the results are here https://github.com/opencobra/COBRA.papers/2023_cardOpt with general purpose functions integrated within the COnstraint-Based Reconstruction and Analysis toolbox: https://github.com/opencobra/cobratoolbox.
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Affiliation(s)
- Ronan M T Fleming
- Metabolomics and Analytics Center, Leiden Academic Centre for Drug Research, Leiden University, Wassenaarseweg 76, Leiden 2333 CC, The Netherlands
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, 6 avenue du Swing, Belvaux L-4362, Luxembourg
- School of Medicine, National University of Ireland, University Rd, Galway H91 TK33, Ireland
| | - Hulda S Haraldsdottir
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, 6 avenue du Swing, Belvaux L-4362, Luxembourg
| | - Le Hoai Minh
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, 6 avenue du Swing, Belvaux L-4362, Luxembourg
| | - Phan Tu Vuong
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, 6 avenue du Swing, Belvaux L-4362, Luxembourg
- Mathematical Sciences School, University of Southampton, University Road, Southampton SO17 1BJ, United Kingdom
| | - Thomas Hankemeier
- Metabolomics and Analytics Center, Leiden Academic Centre for Drug Research, Leiden University, Wassenaarseweg 76, Leiden 2333 CC, The Netherlands
| | - Ines Thiele
- School of Medicine, National University of Ireland, University Rd, Galway H91 TK33, Ireland
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24
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Heinken A, Hertel J, Acharya G, Ravcheev DA, Nyga M, Okpala OE, Hogan M, Magnúsdóttir S, Martinelli F, Nap B, Preciat G, Edirisinghe JN, Henry CS, Fleming RMT, Thiele I. Genome-scale metabolic reconstruction of 7,302 human microorganisms for personalized medicine. Nat Biotechnol 2023; 41:1320-1331. [PMID: 36658342 PMCID: PMC10497413 DOI: 10.1038/s41587-022-01628-0] [Citation(s) in RCA: 42] [Impact Index Per Article: 42.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2021] [Accepted: 11/30/2022] [Indexed: 01/21/2023]
Abstract
The human microbiome influences the efficacy and safety of a wide variety of commonly prescribed drugs. Designing precision medicine approaches that incorporate microbial metabolism would require strain- and molecule-resolved, scalable computational modeling. Here, we extend our previous resource of genome-scale metabolic reconstructions of human gut microorganisms with a greatly expanded version. AGORA2 (assembly of gut organisms through reconstruction and analysis, version 2) accounts for 7,302 strains, includes strain-resolved drug degradation and biotransformation capabilities for 98 drugs, and was extensively curated based on comparative genomics and literature searches. The microbial reconstructions performed very well against three independently assembled experimental datasets with an accuracy of 0.72 to 0.84, surpassing other reconstruction resources and predicted known microbial drug transformations with an accuracy of 0.81. We demonstrate that AGORA2 enables personalized, strain-resolved modeling by predicting the drug conversion potential of the gut microbiomes from 616 patients with colorectal cancer and controls, which greatly varied between individuals and correlated with age, sex, body mass index and disease stages. AGORA2 serves as a knowledge base for the human microbiome and paves the way to personalized, predictive analysis of host-microbiome metabolic interactions.
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Affiliation(s)
- Almut Heinken
- School of Medicine, University of Galway, Galway, Ireland
- Ryan Institute, University of Galway, Galway, Ireland
- INSERM UMRS 1256, Nutrition, Genetics, and Environmental Risk Exposure (NGERE), University of Lorraine, Nancy, France
| | - Johannes Hertel
- School of Medicine, University of Galway, Galway, Ireland
- Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Greifswald, Germany
| | - Geeta Acharya
- Integrated BioBank of Luxembourg, Dudelange, Luxembourg
| | - Dmitry A Ravcheev
- School of Medicine, University of Galway, Galway, Ireland
- Ryan Institute, University of Galway, Galway, Ireland
| | | | | | - Marcus Hogan
- School of Medicine, University of Galway, Galway, Ireland
- Ryan Institute, University of Galway, Galway, Ireland
| | - Stefanía Magnúsdóttir
- Center for Molecular Medicine, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Filippo Martinelli
- School of Medicine, University of Galway, Galway, Ireland
- Ryan Institute, University of Galway, Galway, Ireland
| | - Bram Nap
- School of Medicine, University of Galway, Galway, Ireland
- Ryan Institute, University of Galway, Galway, Ireland
| | - German Preciat
- Leiden Academic Centre for Drug Research, Leiden University, Leiden, the Netherlands
| | - Janaka N Edirisinghe
- Computation Institute, University of Chicago, Chicago, IL, USA
- Mathematics and Computer Science Division, Argonne National Laboratory, Argonne, IL, USA
| | - Christopher S Henry
- Mathematics and Computer Science Division, Argonne National Laboratory, Argonne, IL, USA
| | - Ronan M T Fleming
- School of Medicine, University of Galway, Galway, Ireland
- Leiden Academic Centre for Drug Research, Leiden University, Leiden, the Netherlands
| | - Ines Thiele
- School of Medicine, University of Galway, Galway, Ireland.
- Ryan Institute, University of Galway, Galway, Ireland.
- Division of Microbiology, University of Galway, Galway, Ireland.
- APC Microbiome Ireland, Cork, Ireland.
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25
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Jadebeck JF, Wiechert W, Nöh K. Practical sampling of constraint-based models: Optimized thinning boosts CHRR performance. PLoS Comput Biol 2023; 19:e1011378. [PMID: 37566638 PMCID: PMC10446239 DOI: 10.1371/journal.pcbi.1011378] [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: 01/04/2023] [Revised: 08/23/2023] [Accepted: 07/21/2023] [Indexed: 08/13/2023] Open
Abstract
Thinning is a sub-sampling technique to reduce the memory footprint of Markov chain Monte Carlo. Despite being commonly used, thinning is rarely considered efficient. For sampling constraint-based models, a highly relevant use-case in systems biology, we here demonstrate that thinning boosts computational and, thereby, sampling efficiencies of the widely used Coordinate Hit-and-Run with Rounding (CHRR) algorithm. By benchmarking CHRR with thinning with simplices and genome-scale metabolic networks of up to thousands of dimensions, we find a substantial increase in computational efficiency compared to unthinned CHRR, in our examples by orders of magnitude, as measured by the effective sample size per time (ESS/t), with performance gains growing with polytope (effective network) dimension. Using a set of benchmark models we derive a ready-to-apply guideline for tuning thinning to efficient and effective use of compute resources without requiring additional coding effort. Our guideline is validated using three (out-of-sample) large-scale networks and we show that it allows sampling convex polytopes uniformly to convergence in a fraction of time, thereby unlocking the rigorous investigation of hitherto intractable models. The derivation of our guideline is explained in detail, allowing future researchers to update it as needed as new model classes and more training data becomes available. CHRR with deliberate utilization of thinning thereby paves the way to keep pace with progressing model sizes derived with the constraint-based reconstruction and analysis (COBRA) tool set. Sampling and evaluation pipelines are available at https://jugit.fz-juelich.de/IBG-1/ModSim/fluxomics/chrrt.
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Affiliation(s)
- Johann F. Jadebeck
- Institute of Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich, Jülich, Germany
- Computational Systems Biotechnology (AVT.CSB), RWTH Aachen University, Aachen, Germany
| | - Wolfgang Wiechert
- Institute of Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich, Jülich, Germany
- Computational Systems Biotechnology (AVT.CSB), RWTH Aachen University, Aachen, Germany
| | - Katharina Nöh
- Institute of Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich, Jülich, Germany
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26
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Moore CJ, Holstege CP, Papin JA. Metabolic modeling of sex-specific liver tissue suggests mechanism of differences in toxicological responses. PLoS Comput Biol 2023; 19:e1010927. [PMID: 37603574 PMCID: PMC10470949 DOI: 10.1371/journal.pcbi.1010927] [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: 02/02/2023] [Revised: 08/31/2023] [Accepted: 07/25/2023] [Indexed: 08/23/2023] Open
Abstract
Male subjects in animal and human studies are disproportionately used for toxicological testing. This discrepancy is evidenced in clinical medicine where females are more likely than males to experience liver-related adverse events in response to xenobiotics. While previous work has shown gene expression differences between the sexes, there is a lack of systems-level approaches to understand the direct clinical impact of these differences. Here, we integrate gene expression data with metabolic network models to characterize the impact of transcriptional changes of metabolic genes in the context of sex differences and drug treatment. We used Tasks Inferred from Differential Expression (TIDEs), a reaction-centric approach to analyzing differences in gene expression, to discover that several metabolic pathways exhibit sex differences including glycolysis, fatty acid metabolism, nucleotide metabolism, and xenobiotics metabolism. When TIDEs is used to compare expression differences in treated and untreated hepatocytes, we find several subsystems with differential expression overlap with the sex-altered pathways such as fatty acid metabolism, purine and pyrimidine metabolism, and xenobiotics metabolism. Finally, using sex-specific transcriptomic data, we create individual and averaged male and female liver models and find differences in the pentose phosphate pathway and other metabolic pathways. These results suggest potential sex differences in the contribution of the pentose phosphate pathway to oxidative stress, and we recommend further research into how these reactions respond to hepatotoxic pharmaceuticals.
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Affiliation(s)
- Connor J. Moore
- Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, United States of America
| | - Christopher P. Holstege
- Department of Emergency Medicine, Division of Medical Toxicology, University of Virginia, Charlottesville, Virginia, United States of America
| | - Jason A. Papin
- Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, United States of America
- Department of Biochemistry & Molecular Genetics, University of Virginia, Charlottesville, Virginia, United States of America
- Department of Medicine, Division of Infectious Diseases and International Health, University of Virginia, Charlottesville, Virginia, United States of America
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27
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Sen P, Orešič M. Integrating Omics Data in Genome-Scale Metabolic Modeling: A Methodological Perspective for Precision Medicine. Metabolites 2023; 13:855. [PMID: 37512562 PMCID: PMC10383060 DOI: 10.3390/metabo13070855] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Revised: 07/11/2023] [Accepted: 07/17/2023] [Indexed: 07/30/2023] Open
Abstract
Recent advancements in omics technologies have generated a wealth of biological data. Integrating these data within mathematical models is essential to fully leverage their potential. Genome-scale metabolic models (GEMs) provide a robust framework for studying complex biological systems. GEMs have significantly contributed to our understanding of human metabolism, including the intrinsic relationship between the gut microbiome and the host metabolism. In this review, we highlight the contributions of GEMs and discuss the critical challenges that must be overcome to ensure their reproducibility and enhance their prediction accuracy, particularly in the context of precision medicine. We also explore the role of machine learning in addressing these challenges within GEMs. The integration of omics data with GEMs has the potential to lead to new insights, and to advance our understanding of molecular mechanisms in human health and disease.
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Affiliation(s)
- Partho Sen
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, FI-20520 Turku, Finland
- School of Medical Sciences, Faculty of Medicine and Health, Örebro University, 702 81 Örebro, Sweden
| | - Matej Orešič
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, FI-20520 Turku, Finland
- School of Medical Sciences, Faculty of Medicine and Health, Örebro University, 702 81 Örebro, Sweden
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28
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Correia GD, Marchesi JR, MacIntyre DA. Moving beyond DNA: towards functional analysis of the vaginal microbiome by non-sequencing-based methods. Curr Opin Microbiol 2023; 73:102292. [PMID: 36931094 DOI: 10.1016/j.mib.2023.102292] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Revised: 02/10/2023] [Accepted: 02/14/2023] [Indexed: 03/17/2023]
Abstract
Over the last two decades, sequencing-based methods have revolutionised our understanding of niche-specific microbial complexity. In the lower female reproductive tract, these approaches have enabled identification of bacterial compositional structures associated with health and disease. Application of metagenomics and metatranscriptomics strategies have provided insight into the putative function of these communities but it is increasingly clear that direct measures of microbial and host cell function are required to understand the contribution of microbe-host interactions to pathophysiology. Here we explore and discuss current methods and approaches, many of which rely upon mass-spectrometry, being used to capture functional insight into the vaginal mucosal interface. In addition to improving mechanistic understanding, these methods offer innovative solutions for the development of diagnostic and therapeutic strategies designed to improve women's health.
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Affiliation(s)
- Gonçalo Ds Correia
- Institute of Reproductive and Developmental Biology, Department of Metabolism, Digestion and Reproduction, Imperial College London, London W12 0NN, UK; March of Dimes Prematurity Research Centre at Imperial College London, London, UK
| | - Julian R Marchesi
- March of Dimes Prematurity Research Centre at Imperial College London, London, UK; Centre for Digestive Diseases, Department of Metabolism, Digestion and Reproduction, Imperial College London, Imperial College London, London W2 1NY, UK
| | - David A MacIntyre
- Institute of Reproductive and Developmental Biology, Department of Metabolism, Digestion and Reproduction, Imperial College London, London W12 0NN, UK; March of Dimes Prematurity Research Centre at Imperial College London, London, UK.
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29
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Allegra S, Chiara F, Di Grazia D, Gaspari M, De Francia S. Evaluation of Sex Differences in Preclinical Pharmacology Research: How Far Is Left to Go? Pharmaceuticals (Basel) 2023; 16:786. [PMID: 37375734 DOI: 10.3390/ph16060786] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2023] [Revised: 05/10/2023] [Accepted: 05/23/2023] [Indexed: 06/29/2023] Open
Abstract
Until the last quarter of the 20th century, sex was not recognized as a variable in health research, nor was it believed to be a factor that could affect health and illness. Researchers preferred studying male models for a variety of reasons, such as simplicity, lower costs, hormone confounding effects, and fear of liability from perinatal exposure in case of pregnancy. Equitable representation is imperative for determining the safety, effectiveness, and tolerance of therapeutic agents for all consumers. Decades of female models' underrepresentation in preclinical studies has resulted in inequality in the understanding, diagnosis, and treatment of disease between the sexes. Sex bias has been highlighted as one of the contributing factors to the poor translation and replicability of preclinical research. There have been multiple calls for action, and the inclusion of sex as a biological variable is increasingly supported. However, although there has been substantial progress in the efforts to include more female models in preclinical studies, disparities today remain. In the present review, we consider the current standard practice of the preclinical research setting, why the sex bias exists, why there is the need to include female models, and what risks may arise from continuing this exclusion from experimental design.
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Affiliation(s)
- Sarah Allegra
- Department of Biological and Clinical Sciences, University of Turin, S. Luigi Gonzaga Hospital, 10043 Orbassano, Italy
| | - Francesco Chiara
- Department of Biological and Clinical Sciences, University of Turin, S. Luigi Gonzaga Hospital, 10043 Orbassano, Italy
| | - Daniela Di Grazia
- Department of Biological and Clinical Sciences, University of Turin, S. Luigi Gonzaga Hospital, 10043 Orbassano, Italy
| | - Marco Gaspari
- Department of Biological and Clinical Sciences, University of Turin, S. Luigi Gonzaga Hospital, 10043 Orbassano, Italy
| | - Silvia De Francia
- Department of Biological and Clinical Sciences, University of Turin, S. Luigi Gonzaga Hospital, 10043 Orbassano, Italy
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30
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Kumar T, Sethuraman R, Mitra S, Ravindran B, Narayanan M. MultiCens: Multilayer network centrality measures to uncover molecular mediators of tissue-tissue communication. PLoS Comput Biol 2023; 19:e1011022. [PMID: 37093889 PMCID: PMC10159362 DOI: 10.1371/journal.pcbi.1011022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2022] [Revised: 05/04/2023] [Accepted: 03/12/2023] [Indexed: 04/25/2023] Open
Abstract
With the evolution of multicellularity, communication among cells in different tissues and organs became pivotal to life. Molecular basis of such communication has long been studied, but genome-wide screens for genes and other biomolecules mediating tissue-tissue signaling are lacking. To systematically identify inter-tissue mediators, we present a novel computational approach MultiCens (Multilayer/Multi-tissue network Centrality measures). Unlike single-layer network methods, MultiCens can distinguish within- vs. across-layer connectivity to quantify the "influence" of any gene in a tissue on a query set of genes of interest in another tissue. MultiCens enjoys theoretical guarantees on convergence and decomposability, and performs well on synthetic benchmarks. On human multi-tissue datasets, MultiCens predicts known and novel genes linked to hormones. MultiCens further reveals shifts in gene network architecture among four brain regions in Alzheimer's disease. MultiCens-prioritized hypotheses from these two diverse applications, and potential future ones like "Multi-tissue-expanded Gene Ontology" analysis, can enable whole-body yet molecular-level systems investigations in humans.
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Affiliation(s)
- Tarun Kumar
- Department of Computer Science and Engineering, Indian Institute of Technology (IIT) Madras, Chennai, India
- The Centre for Integrative Biology and Systems medicinE (IBSE), IIT Madras, Chennai, India
- Robert Bosch Center for Data Science and Artificial Intelligence (RBCDSAI), IIT Madras, Chennai, India
| | | | - Sanga Mitra
- Department of Computer Science and Engineering, Indian Institute of Technology (IIT) Madras, Chennai, India
| | - Balaraman Ravindran
- Department of Computer Science and Engineering, Indian Institute of Technology (IIT) Madras, Chennai, India
- The Centre for Integrative Biology and Systems medicinE (IBSE), IIT Madras, Chennai, India
- Robert Bosch Center for Data Science and Artificial Intelligence (RBCDSAI), IIT Madras, Chennai, India
| | - Manikandan Narayanan
- Department of Computer Science and Engineering, Indian Institute of Technology (IIT) Madras, Chennai, India
- The Centre for Integrative Biology and Systems medicinE (IBSE), IIT Madras, Chennai, India
- Robert Bosch Center for Data Science and Artificial Intelligence (RBCDSAI), IIT Madras, Chennai, India
- Multiscale Digital Neuroanatomy (MDN), IIT Madras, Chennai, India
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31
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Pandey AK, Loscalzo J. Network medicine: an approach to complex kidney disease phenotypes. Nat Rev Nephrol 2023:10.1038/s41581-023-00705-0. [PMID: 37041415 DOI: 10.1038/s41581-023-00705-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/13/2023] [Indexed: 04/13/2023]
Abstract
Scientific reductionism has been the basis of disease classification and understanding for more than a century. However, the reductionist approach of characterizing diseases from a limited set of clinical observations and laboratory evaluations has proven insufficient in the face of an exponential growth in data generated from transcriptomics, proteomics, metabolomics and deep phenotyping. A new systematic method is necessary to organize these datasets and build new definitions of what constitutes a disease that incorporates both biological and environmental factors to more precisely describe the ever-growing complexity of phenotypes and their underlying molecular determinants. Network medicine provides such a conceptual framework to bridge these vast quantities of data while providing an individualized understanding of disease. The modern application of network medicine principles is yielding new insights into the pathobiology of chronic kidney diseases and renovascular disorders by expanding the understanding of pathogenic mediators, novel biomarkers and new options for renal therapeutics. These efforts affirm network medicine as a robust paradigm for elucidating new advances in the diagnosis and treatment of kidney disorders.
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Affiliation(s)
- Arvind K Pandey
- Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women's Hospital, and Harvard Medical School, Boston, MA, USA
| | - Joseph Loscalzo
- Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women's Hospital, and Harvard Medical School, Boston, MA, USA.
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32
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Reed MB, Ponce de León M, Vraka C, Rausch I, Godbersen GM, Popper V, Geist BK, Komorowski A, Nics L, Schmidt C, Klug S, Langsteger W, Karanikas G, Traub-Weidinger T, Hahn A, Lanzenberger R, Hacker M. Whole-body metabolic connectivity framework with functional PET. Neuroimage 2023; 271:120030. [PMID: 36925087 DOI: 10.1016/j.neuroimage.2023.120030] [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: 09/16/2022] [Revised: 02/22/2023] [Accepted: 03/13/2023] [Indexed: 03/15/2023] Open
Abstract
The nervous and circulatory system interconnects the various organs of the human body, building hierarchically organized subsystems, enabling fine-tuned, metabolically expensive brain-body and inter-organ crosstalk to appropriately adapt to internal and external demands. A deviation or failure in the function of a single organ or subsystem could trigger unforeseen biases or dysfunctions of the entire network, leading to maladaptive physiological or psychological responses. Therefore, quantifying these networks in healthy individuals and patients may help further our understanding of complex disorders involving body-brain crosstalk. Here we present a generalized framework to automatically estimate metabolic inter-organ connectivity utilizing whole-body functional positron emission tomography (fPET). The developed framework was applied to 16 healthy subjects (mean age ± SD, 25 ± 6 years; 13 female) that underwent one dynamic 18F-FDG PET/CT scan. Multiple procedures of organ segmentation (manual, automatic, circular volumes) and connectivity estimation (polynomial fitting, spatiotemporal filtering, covariance matrices) were compared to provide an optimized thorough overview of the workflow. The proposed approach was able to estimate the metabolic connectivity patterns within brain regions and organs as well as their interactions. Automated organ delineation, but not simplified circular volumes, showed high agreement with manual delineation. Polynomial fitting yielded similar connectivity as spatiotemporal filtering at the individual subject level. Furthermore, connectivity measures and group-level covariance matrices did not match. The strongest brain-body connectivity was observed for the liver and kidneys. The proposed framework offers novel opportunities towards analyzing metabolic function from a systemic, hierarchical perspective in a multitude of physiological pathological states.
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Affiliation(s)
- Murray Bruce Reed
- Department of Psychiatry and Psychotherapy, Comprehensive Center for Clinical Neurosciences and Mental Health (C3NMH), Medical University of Vienna, Austria
| | - Magdalena Ponce de León
- Department of Psychiatry and Psychotherapy, Comprehensive Center for Clinical Neurosciences and Mental Health (C3NMH), Medical University of Vienna, Austria
| | - Chrysoula Vraka
- Division of Nuclear Medicine, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Austria
| | - Ivo Rausch
- QIMP Team, Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria
| | - Godber Mathis Godbersen
- Department of Psychiatry and Psychotherapy, Comprehensive Center for Clinical Neurosciences and Mental Health (C3NMH), Medical University of Vienna, Austria
| | - Valentin Popper
- Department of Psychiatry and Psychotherapy, Comprehensive Center for Clinical Neurosciences and Mental Health (C3NMH), Medical University of Vienna, Austria
| | - Barbara Katharina Geist
- Division of Nuclear Medicine, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Austria
| | - Arkadiusz Komorowski
- Department of Psychiatry and Psychotherapy, Comprehensive Center for Clinical Neurosciences and Mental Health (C3NMH), Medical University of Vienna, Austria
| | - Lukas Nics
- Division of Nuclear Medicine, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Austria
| | - Clemens Schmidt
- Department of Psychiatry and Psychotherapy, Comprehensive Center for Clinical Neurosciences and Mental Health (C3NMH), Medical University of Vienna, Austria
| | - Sebastian Klug
- Department of Psychiatry and Psychotherapy, Comprehensive Center for Clinical Neurosciences and Mental Health (C3NMH), Medical University of Vienna, Austria
| | - Werner Langsteger
- Division of Nuclear Medicine, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Austria
| | - Georgios Karanikas
- Division of Nuclear Medicine, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Austria
| | - Tatjana Traub-Weidinger
- Division of Nuclear Medicine, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Austria
| | - Andreas Hahn
- Department of Psychiatry and Psychotherapy, Comprehensive Center for Clinical Neurosciences and Mental Health (C3NMH), Medical University of Vienna, Austria
| | - Rupert Lanzenberger
- Department of Psychiatry and Psychotherapy, Comprehensive Center for Clinical Neurosciences and Mental Health (C3NMH), Medical University of Vienna, Austria.
| | - Marcus Hacker
- Division of Nuclear Medicine, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Austria
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33
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Sorokin A, Goryanin I. FBA-PRCC. Partial Rank Correlation Coefficient (PRCC) Global Sensitivity Analysis (GSA) in Application to Constraint-Based Models. Biomolecules 2023; 13:biom13030500. [PMID: 36979435 PMCID: PMC10046323 DOI: 10.3390/biom13030500] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2022] [Revised: 02/24/2023] [Accepted: 03/02/2023] [Indexed: 03/11/2023] Open
Abstract
Background: Whole-genome models (GEMs) have become a versatile tool for systems biology, biotechnology, and medicine. GEMs created by automatic and semi-automatic approaches contain a lot of redundant reactions. At the same time, the nonlinearity of the model makes it difficult to evaluate the significance of the reaction for cell growth or metabolite production. Methods: We propose a new way to apply the global sensitivity analysis (GSA) to GEMs in a straightforward parallelizable fashion. Results: We have shown that Partial Rank Correlation Coefficient (PRCC) captures key steps in the metabolic network despite the network distance from the product synthesis reaction. Conclusions: FBA-PRCC is a fast, interpretable, and reliable metric to identify the sign and magnitude of the reaction contribution to various cellular functions.
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Affiliation(s)
- Anatoly Sorokin
- Okinawa Institute of Science and Technology Graduate University, Okinawa 904-0495, Japan
- Correspondence: (A.S.); (I.G.)
| | - Igor Goryanin
- Okinawa Institute of Science and Technology Graduate University, Okinawa 904-0495, Japan
- Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, China
- School of Informatics, The University of Edinburgh, Informatics Forum, Edinburgh EH8 9AB, UK
- Correspondence: (A.S.); (I.G.)
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34
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Multi-dimensional experimental and computational exploration of metabolism pinpoints complex probiotic interactions. Metab Eng 2023; 76:120-132. [PMID: 36720400 DOI: 10.1016/j.ymben.2023.01.008] [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: 06/18/2022] [Revised: 12/13/2022] [Accepted: 01/21/2023] [Indexed: 01/29/2023]
Abstract
Multi-strain probiotics are widely regarded as effective products for improving gut microbiota stability and host health, providing advantages over single-strain probiotics. However, in general, it is unclear to what extent different strains would cooperate or compete for resources, and how the establishment of a common biofilm microenvironment could influence their interactions. In this work, we develop an integrative experimental and computational approach to comprehensively assess the metabolic functionality and interactions of probiotics across growth conditions. Our approach combines co-culture assays with genome-scale modelling of metabolism and multivariate data analysis, thus exploiting complementary data- and knowledge-driven systems biology techniques. To show the advantages of the proposed approach, we apply it to the study of the interactions between two widely used probiotic strains of Lactobacillus reuteri and Saccharomyces boulardii, characterising their production potential for compounds that can be beneficial to human health. Our results show that these strains can establish a mixed cooperative-antagonistic interaction best explained by competition for shared resources, with an increased individual exchange but an often decreased net production of amino acids and short-chain fatty acids. Overall, our work provides a strategy that can be used to explore microbial metabolic fingerprints of biotechnological interest, capable of capturing multifaceted equilibria even in simple microbial consortia.
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35
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Weston BR, Thiele I. A nutrition algorithm to optimize feed and medium composition using genome-scale metabolic models. Metab Eng 2023; 76:167-178. [PMID: 36724839 DOI: 10.1016/j.ymben.2023.01.010] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2022] [Revised: 12/13/2022] [Accepted: 01/25/2023] [Indexed: 01/30/2023]
Abstract
The optimization of animal feeds and cell culture media are problems of interest to a wide range of industries and scientific disciplines. Both problems are dictated by the properties of an organism's metabolism. However, due to the tremendous complexity of metabolic systems, it can be difficult to predict how metabolism will respond to changes in nutrient availability. A common tool used to capture the complexity of metabolism in a computational framework is a genome-scale metabolic model (GEM). GEMs are useful for predicting the fluxes of reactions within an organism's metabolism. To optimize feed or media, in silico experiments can be performed with GEMs by systematically varying nutritional constraints and predicting metabolic activity. In this way, the influence of various nutritional changes on metabolic outcomes can be evaluated. However, this methodology does not guarantee an optimal solution. Here, we develop a nutrition algorithm that utilizes linear programming to search the entire flux solution space of possible dietary intervention strategies to identify the most efficient changes to nutrition for a desirable metabolic outcome. We illustrate the utility of the nutrition algorithm on GEMs of Atlantic salmon (Salmo salar) and Chinese hamster ovary (CHO) cell metabolism and find that the nutrition algorithm makes predictions that not only align with experimental findings but reveal new insights into promising feeding strategies. We show that the nutrition algorithm is highly versatile and customizable to meet the user's needs. For instance, we demonstrate that the nutrition algorithm can be used to predict feed/media compositions that maximize profit margins. While the nutrition algorithm can be used to define an optimal feed/medium ab initio, it can also identify minimal changes to be made to an existing feed/medium to drive the largest metabolic shift. Moreover, the nutrition algorithm can target multiple metabolic pathways simultaneously with only a marginal increase in computational expense. While the nutrition algorithm has its limitations, we believe that this tool can be leveraged in a broad range of biotechnological applications to enhance the feed/medium optimization process.
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Affiliation(s)
- Bronson R Weston
- School of Medicine, University of Galway, Galway, H91 TK33, Ireland; Ryan Institute, University of Galway, Galway, H91 TK33, Ireland
| | - Ines Thiele
- School of Medicine, University of Galway, Galway, H91 TK33, Ireland; Ryan Institute, University of Galway, Galway, H91 TK33, Ireland; Discipline of Microbiology, University of Galway, Galway, H91 TK33, Ireland; APC Microbiome Ireland, University College Cork, Cork, Ireland, Cork, T12 K8AF, Ireland.
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36
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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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Context-Specific Genome-Scale Metabolic Modelling and Its Application to the Analysis of COVID-19 Metabolic Signatures. Metabolites 2023; 13:metabo13010126. [PMID: 36677051 PMCID: PMC9866716 DOI: 10.3390/metabo13010126] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Revised: 12/27/2022] [Accepted: 01/10/2023] [Indexed: 01/19/2023] Open
Abstract
Genome-scale metabolic models (GEMs) have found numerous applications in different domains, ranging from biotechnology to systems medicine. Herein, we overview the most popular algorithms for the automated reconstruction of context-specific GEMs using high-throughput experimental data. Moreover, we describe different datasets applied in the process, and protocols that can be used to further automate the model reconstruction and validation. Finally, we describe recent COVID-19 applications of context-specific GEMs, focusing on the analysis of metabolic implications, identification of biomarkers and potential drug targets.
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Zacharias HU, Kaleta C, Cossais F, Schaeffer E, Berndt H, Best L, Dost T, Glüsing S, Groussin M, Poyet M, Heinzel S, Bang C, Siebert L, Demetrowitsch T, Leypoldt F, Adelung R, Bartsch T, Bosy-Westphal A, Schwarz K, Berg D. Microbiome and Metabolome Insights into the Role of the Gastrointestinal-Brain Axis in Parkinson's and Alzheimer's Disease: Unveiling Potential Therapeutic Targets. Metabolites 2022; 12:metabo12121222. [PMID: 36557259 PMCID: PMC9786685 DOI: 10.3390/metabo12121222] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Revised: 11/25/2022] [Accepted: 11/28/2022] [Indexed: 12/12/2022] Open
Abstract
Neurodegenerative diseases such as Parkinson's (PD) and Alzheimer's disease (AD), the prevalence of which is rapidly rising due to an aging world population and westernization of lifestyles, are expected to put a strong socioeconomic burden on health systems worldwide. Clinical trials of therapies against PD and AD have only shown limited success so far. Therefore, research has extended its scope to a systems medicine point of view, with a particular focus on the gastrointestinal-brain axis as a potential main actor in disease development and progression. Microbiome and metabolome studies have already revealed important insights into disease mechanisms. Both the microbiome and metabolome can be easily manipulated by dietary and lifestyle interventions, and might thus offer novel, readily available therapeutic options to prevent the onset as well as the progression of PD and AD. This review summarizes our current knowledge on the interplay between microbiota, metabolites, and neurodegeneration along the gastrointestinal-brain axis. We further illustrate state-of-the art methods of microbiome and metabolome research as well as metabolic modeling that facilitate the identification of disease pathomechanisms. We conclude with therapeutic options to modulate microbiome composition to prevent or delay neurodegeneration and illustrate potential future research directions to fight PD and AD.
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Affiliation(s)
- Helena U. Zacharias
- Peter L. Reichertz Institute for Medical Informatics of TU Braunschweig and Hannover Medical School, 30625 Hannover, Germany
- Department of Internal Medicine I, University Medical Center Schleswig-Holstein, Campus Kiel, 24105 Kiel, Germany
- Institute of Clinical Molecular Biology, Kiel University and University Medical Center Schleswig-Holstein, Campus Kiel, 24105 Kiel, Germany
- Correspondence: (H.U.Z.); (C.K.)
| | - Christoph Kaleta
- Research Group Medical Systems Biology, Institute for Experimental Medicine, Kiel University, 24105 Kiel, Germany
- Kiel Nano, Surface and Interface Science—KiNSIS, Kiel University, 24118 Kiel, Germany
- Correspondence: (H.U.Z.); (C.K.)
| | | | - Eva Schaeffer
- Department of Neurology, Kiel University and University Medical Center Schleswig-Holstein, Campus Kiel, 24105 Kiel, Germany
| | - Henry Berndt
- Research Group Comparative Immunobiology, Zoological Institute, Kiel University, 24118 Kiel, Germany
| | - Lena Best
- Research Group Medical Systems Biology, Institute for Experimental Medicine, Kiel University, 24105 Kiel, Germany
| | - Thomas Dost
- Research Group Medical Systems Biology, Institute for Experimental Medicine, Kiel University, 24105 Kiel, Germany
| | - Svea Glüsing
- Institute of Human Nutrition and Food Science, Food Technology, Kiel University, 24118 Kiel, Germany
| | - Mathieu Groussin
- Institute of Clinical Molecular Biology, Kiel University and University Medical Center Schleswig-Holstein, Campus Kiel, 24105 Kiel, Germany
| | - Mathilde Poyet
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Sebastian Heinzel
- Department of Neurology, Kiel University and University Medical Center Schleswig-Holstein, Campus Kiel, 24105 Kiel, Germany
- Institute of Medical Informatics and Statistics, Kiel University and University Medical Center Schleswig-Holstein, Campus Kiel, 24105 Kiel, Germany
| | - Corinna Bang
- Institute of Clinical Molecular Biology, Kiel University and University Medical Center Schleswig-Holstein, Campus Kiel, 24105 Kiel, Germany
| | - Leonard Siebert
- Kiel Nano, Surface and Interface Science—KiNSIS, Kiel University, 24118 Kiel, Germany
- Functional Nanomaterials, Department of Materials Science, Kiel University, 24143 Kiel, Germany
| | - Tobias Demetrowitsch
- Institute of Human Nutrition and Food Science, Food Technology, Kiel University, 24118 Kiel, Germany
- Kiel Network of Analytical Spectroscopy and Mass Spectrometry, Kiel University, 24118 Kiel, Germany
| | - Frank Leypoldt
- Department of Neurology, Kiel University and University Medical Center Schleswig-Holstein, Campus Kiel, 24105 Kiel, Germany
- Neuroimmunology, Institute of Clinical Chemistry, University Medical Center Schleswig-Holstein, 24105 Kiel, Germany
| | - Rainer Adelung
- Kiel Nano, Surface and Interface Science—KiNSIS, Kiel University, 24118 Kiel, Germany
- Functional Nanomaterials, Department of Materials Science, Kiel University, 24143 Kiel, Germany
| | - Thorsten Bartsch
- Kiel Nano, Surface and Interface Science—KiNSIS, Kiel University, 24118 Kiel, Germany
- Department of Neurology, Kiel University and University Medical Center Schleswig-Holstein, Campus Kiel, 24105 Kiel, Germany
| | - Anja Bosy-Westphal
- Institute of Human Nutrition and Food Science, Kiel University, 24107 Kiel, Germany
| | - Karin Schwarz
- Kiel Nano, Surface and Interface Science—KiNSIS, Kiel University, 24118 Kiel, Germany
- Institute of Human Nutrition and Food Science, Food Technology, Kiel University, 24118 Kiel, Germany
- Kiel Network of Analytical Spectroscopy and Mass Spectrometry, Kiel University, 24118 Kiel, Germany
| | - Daniela Berg
- Kiel Nano, Surface and Interface Science—KiNSIS, Kiel University, 24118 Kiel, Germany
- Department of Neurology, Kiel University and University Medical Center Schleswig-Holstein, Campus Kiel, 24105 Kiel, Germany
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Bhosle A, Wang Y, Franzosa EA, Huttenhower C. Progress and opportunities in microbial community metabolomics. Curr Opin Microbiol 2022; 70:102195. [PMID: 36063685 DOI: 10.1016/j.mib.2022.102195] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2022] [Revised: 07/20/2022] [Accepted: 07/21/2022] [Indexed: 01/25/2023]
Abstract
The metabolome lies at the interface of host-microbiome crosstalk. Previous work has established links between chemically diverse microbial metabolites and a myriad of host physiological processes and diseases. Coupled with scalable and cost-effective technologies, metabolomics is thus gaining popularity as a tool for characterization of microbial communities, particularly when combined with metagenomics as a window into microbiome function. A systematic interrogation of microbial community metabolomes can uncover key microbial compounds, metabolic capabilities of the microbiome, and also provide critical mechanistic insights into microbiome-linked host phenotypes. In this review, we discuss methods and accompanying resources that have been developed for these purposes. The accomplishments of these methods demonstrate that metabolomes can be used to functionally characterize microbial communities, and that microbial properties can be used to identify and investigate chemical compounds.
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Affiliation(s)
- Amrisha Bhosle
- Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA; Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA, USA; Harvard Chan Microbiome in Public Health Center, Harvard T. H. Chan School of Public Health, Boston, MA, USA
| | - Ya Wang
- Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA, USA; Harvard Chan Microbiome in Public Health Center, Harvard T. H. Chan School of Public Health, Boston, MA, USA
| | - Eric A Franzosa
- Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA; Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA, USA; Harvard Chan Microbiome in Public Health Center, Harvard T. H. Chan School of Public Health, Boston, MA, USA
| | - Curtis Huttenhower
- Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA; Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA, USA; Harvard Chan Microbiome in Public Health Center, Harvard T. H. Chan School of Public Health, Boston, MA, USA.
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40
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Nascentes Melo LM, Lesner NP, Sabatier M, Ubellacker JM, Tasdogan A. Emerging metabolomic tools to study cancer metastasis. Trends Cancer 2022; 8:988-1001. [PMID: 35909026 DOI: 10.1016/j.trecan.2022.07.003] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2022] [Revised: 06/24/2022] [Accepted: 07/06/2022] [Indexed: 12/24/2022]
Abstract
Metastasis is responsible for 90% of deaths in patients with cancer. Understanding the role of metabolism during metastasis has been limited by the development of robust and sensitive technologies that capture metabolic processes in metastasizing cancer cells. We discuss the current technologies available to study (i) metabolism in primary and metastatic cancer cells and (ii) metabolic interactions between cancer cells and the tumor microenvironment (TME) at different stages of the metastatic cascade. We identify advantages and disadvantages of each method and discuss how these tools and technologies will further improve our understanding of metastasis. Studies investigating the complex metabolic rewiring of different cells using state-of-the-art metabolomic technologies have the potential to reveal novel biological processes and therapeutic interventions for human cancers.
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Affiliation(s)
| | - Nicholas P Lesner
- Abramson Family Cancer Research Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Marie Sabatier
- Department of Molecular Metabolism, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Jessalyn M Ubellacker
- Department of Molecular Metabolism, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
| | - Alpaslan Tasdogan
- Department of Dermatology, University Hospital Essen and German Cancer Consortium, Partner Site, Essen, Germany.
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Foguet C, Xu Y, Ritchie SC, Lambert SA, Persyn E, Nath AP, Davenport EE, Roberts DJ, Paul DS, Di Angelantonio E, Danesh J, Butterworth AS, Yau C, Inouye M. Genetically personalised organ-specific metabolic models in health and disease. Nat Commun 2022; 13:7356. [PMID: 36446790 PMCID: PMC9708841 DOI: 10.1038/s41467-022-35017-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2022] [Accepted: 11/15/2022] [Indexed: 11/30/2022] Open
Abstract
Understanding how genetic variants influence disease risk and complex traits (variant-to-function) is one of the major challenges in human genetics. Here we present a model-driven framework to leverage human genome-scale metabolic networks to define how genetic variants affect biochemical reaction fluxes across major human tissues, including skeletal muscle, adipose, liver, brain and heart. As proof of concept, we build personalised organ-specific metabolic flux models for 524,615 individuals of the INTERVAL and UK Biobank cohorts and perform a fluxome-wide association study (FWAS) to identify 4312 associations between personalised flux values and the concentration of metabolites in blood. Furthermore, we apply FWAS to identify 92 metabolic fluxes associated with the risk of developing coronary artery disease, many of which are linked to processes previously described to play in role in the disease. Our work demonstrates that genetically personalised metabolic models can elucidate the downstream effects of genetic variants on biochemical reactions involved in common human diseases.
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Affiliation(s)
- Carles Foguet
- Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK.
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.
- Heart and Lung Research Institute, University of Cambridge, Cambridge, UK.
| | - Yu Xu
- Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Heart and Lung Research Institute, University of Cambridge, Cambridge, UK
| | - Scott C Ritchie
- Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Heart and Lung Research Institute, University of Cambridge, Cambridge, UK
- British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge, UK
- Cambridge Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute, Melbourne, VIC, Australia
| | - Samuel A Lambert
- Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Heart and Lung Research Institute, University of Cambridge, Cambridge, UK
| | - Elodie Persyn
- Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Heart and Lung Research Institute, University of Cambridge, Cambridge, UK
| | - Artika P Nath
- Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Cambridge Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute, Melbourne, VIC, Australia
| | | | - David J Roberts
- BRC Haematology Theme, Radcliffe Department of Medicine, and NHSBT-Oxford, John Radcliffe Hospital, Oxford, UK
- National Institute for Health and Care Research Blood and Transplant Research Unit in Donor Health and Behaviour, University of Cambridge, Cambridge, UK
- NHS Blood and Transplant, John Radcliffe Hospital, Oxford, UK
| | - Dirk S Paul
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Heart and Lung Research Institute, University of Cambridge, Cambridge, UK
- British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge, UK
| | - Emanuele Di Angelantonio
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Heart and Lung Research Institute, University of Cambridge, Cambridge, UK
- British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge, UK
- National Institute for Health and Care Research Blood and Transplant Research Unit in Donor Health and Behaviour, University of Cambridge, Cambridge, UK
- Health Data Science Centre, Human Technopole, Milan, Italy
| | - John Danesh
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Heart and Lung Research Institute, University of Cambridge, Cambridge, UK
- British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge, UK
- Wellcome Sanger Institute, Hinxton, UK
- National Institute for Health and Care Research Blood and Transplant Research Unit in Donor Health and Behaviour, University of Cambridge, Cambridge, UK
| | - Adam S Butterworth
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Heart and Lung Research Institute, University of Cambridge, Cambridge, UK
- British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge, UK
- National Institute for Health and Care Research Blood and Transplant Research Unit in Donor Health and Behaviour, University of Cambridge, Cambridge, UK
| | - Christopher Yau
- Nuffield Department of Women's and Reproductive Health, University of Oxford, Oxford, OX3 9DU, UK
- Health Data Research UK, Gibbs Building, 215 Euston Road, London, NW1 2BE, UK
| | - Michael Inouye
- Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK.
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.
- Heart and Lung Research Institute, University of Cambridge, Cambridge, UK.
- British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge, UK.
- Cambridge Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute, Melbourne, VIC, Australia.
- The Alan Turing Institute, London, UK.
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Combining denoising of RNA-seq data and flux balance analysis for cluster analysis of single cells. BMC Bioinformatics 2022; 23:445. [PMID: 36284276 PMCID: PMC9597960 DOI: 10.1186/s12859-022-04967-6] [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: 09/27/2022] [Accepted: 09/28/2022] [Indexed: 11/17/2022] Open
Abstract
Background Sophisticated methods to properly pre-process and analyze the increasing collection of single-cell RNA sequencing (scRNA-seq) data are increasingly being developed. On the contrary, the best practices to integrate these data into metabolic networks, aiming at describing metabolic phenotypes within a heterogeneous cell population, have been poorly investigated. In this regard, a critical factor is the presence of false zero values in reactions essential for a fundamental metabolic function, such as biomass or energy production. Here, we investigate the role of denoising strategies in mitigating this problem. Methods We applied state-of-the-art denoising strategies - namely MAGIC, ENHANCE, and SAVER - on three public scRNA-seq datasets. We then associated a metabolic flux distribution with every single cell by embedding its noise-free transcriptomics profile in the constraints of the optimization of a core metabolic model. Finally, we used the obtained single-cell optimal metabolic fluxes as features for cluster analysis. We compared the results obtained with different techniques, and with or without the use of denoising. We also investigated the possibility of applying denoising directly on the Reaction Activity Scores, which are metabolic features extracted from the read counts, rather than on the read counts. Results We show that denoising of transcriptomics data improves the clustering of single cells. We also illustrate that denoising restores important metabolic properties, such as the correlation between cell cycle phase and biomass accumulation, and between the RAS scores of reactions belonging to the same metabolic pathway. We show that MAGIC performs better than ENHANCE and SAVER, and that, denoising applied directly on the RAS matrix could be an effective alternative in removing false zero values from essential metabolic reactions. Conclusions Our results indicate that including denoising as a pre-processing operation represents a milestone to integrate scRNA-seq data into Flux Balance Analysis simulations and to perform single-cell cluster analysis with a focus on metabolic phenotypes.
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Jadhav A, Kumar T, Raghavendra M, Loganathan T, Narayanan M. Predicting cross-tissue hormone-gene relations using balanced word embeddings. Bioinformatics 2022; 38:4771-4781. [PMID: 36000859 PMCID: PMC9563690 DOI: 10.1093/bioinformatics/btac578] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2022] [Revised: 07/29/2022] [Accepted: 08/23/2022] [Indexed: 11/12/2022] Open
Abstract
MOTIVATION Inter-organ/inter-tissue communication is central to multi-cellular organisms including humans, and mapping inter-tissue interactions can advance system-level whole-body modeling efforts. Large volumes of biomedical literature have fostered studies that map within-tissue or tissue-agnostic interactions, but literature-mining studies that infer inter-tissue relations, such as between hormones and genes are solely missing. RESULTS We present a first study to predict from biomedical literature the hormone-gene associations mediating inter-tissue signaling in the human body. Our BioEmbedS* models use neural network-based Biomedical word Embeddings with a Support Vector Machine classifier to predict if a hormone-gene pair is associated or not, and whether an associated gene is involved in the hormone's production or response. Model training relies on our unified dataset Hormone-Gene version 1 of ground-truth associations between genes and endocrine hormones, which we compiled and carefully balanced in the embedded space to handle data disparities, such as between poorly- versus well-studied hormones. Our BioEmbedS model recapitulates known gene mediators of tissue-tissue signaling with 70.4% accuracy; predicts novel inter-tissue communication genes in humans, which are enriched for hormone-related disorders; and generalizes well to mouse, thereby holding promise for its extension to other multi-cellular organisms as well. AVAILABILITY AND IMPLEMENTATION Freely available at https://cross-tissue-signaling.herokuapp.com are our model predictions & datasets; https://github.com/BIRDSgroup/BioEmbedS has all relevant code. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Aditya Jadhav
- Department of Computer Science and Engineering, Indian Institute of Technology (IIT) Madras, Chennai, India
| | - Tarun Kumar
- Department of Computer Science and Engineering, Indian Institute of Technology (IIT) Madras, Chennai, India
- Initiative for Biological Systems Engineering, IIT Madras, Chennai, India
- Robert Bosch Centre for Data Science and Artificial Intelligence, IIT Madras, Chennai, India
| | - Mohit Raghavendra
- Department of Information Technology, National Institute of Technology Karnataka, Surathkal, India
| | - Tamizhini Loganathan
- Initiative for Biological Systems Engineering, IIT Madras, Chennai, India
- Robert Bosch Centre for Data Science and Artificial Intelligence, IIT Madras, Chennai, India
| | - Manikandan Narayanan
- Department of Computer Science and Engineering, Indian Institute of Technology (IIT) Madras, Chennai, India
- Initiative for Biological Systems Engineering, IIT Madras, Chennai, India
- Robert Bosch Centre for Data Science and Artificial Intelligence, IIT Madras, Chennai, India
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Thiele I, Preciat G, Fleming RMT. MetaboAnnotator: An efficient toolbox to annotate metabolites in genome-scale metabolic reconstructions. Bioinformatics 2022; 38:4831-4832. [PMID: 36047841 PMCID: PMC9563688 DOI: 10.1093/bioinformatics/btac596] [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: 03/24/2022] [Revised: 08/16/2022] [Accepted: 08/29/2022] [Indexed: 11/14/2022] Open
Abstract
MOTIVATION Genome-scale metabolic reconstructions have been assembled for thousands of organisms using a wide-range of tools. However, metabolite annotations, required to compare and link metabolites between reconstructions remain incomplete. Here, we aim to further extend metabolite annotation coverage using various databases and chemoinformatic approaches. RESULTS We developed a COBRA toolbox extension, deemed MetaboAnnotator, which facilitates the comprehensive annotation of metabolites with database independent and dependent identifiers, obtains molecular structure files, and calculates metabolite formula and charge at pH 7.2. The resulting metabolite annotations allow for subsequent cross-mapping between reconstructions and mapping of, e.g., metabolomic data. AVAILABILITY MetaboAnnotator and tutorials are freely available at https://github.com/opencobra. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Ines Thiele
- School of Medicine, National University of Galway, Galway, Ireland.,Ryan Institute, National University of Galway, Galway, Ireland.,Division of Microbiology, National University of Galway, Galway, Ireland.,APC Microbiome Ireland, University College Cork, Cork, Ireland
| | - German Preciat
- Analytical BioSciences Division, Leiden Academic Centre for Drug Research, Leiden University, The Netherlands
| | - Ronan M T Fleming
- School of Medicine, National University of Galway, Galway, Ireland.,Analytical BioSciences Division, Leiden Academic Centre for Drug Research, Leiden University, The Netherlands
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Martínez-López YE, Esquivel-Hernández DA, Sánchez-Castañeda JP, Neri-Rosario D, Guardado-Mendoza R, Resendis-Antonio O. Type 2 diabetes, gut microbiome, and systems biology: A novel perspective for a new era. Gut Microbes 2022; 14:2111952. [PMID: 36004400 PMCID: PMC9423831 DOI: 10.1080/19490976.2022.2111952] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/04/2023] Open
Abstract
The association between the physio-pathological variables of type 2 diabetes (T2D) and gut microbiota composition suggests a new avenue to track the disease and improve the outcomes of pharmacological and non-pharmacological treatments. This enterprise requires new strategies to elucidate the metabolic disturbances occurring in the gut microbiome as the disease progresses. To this end, physiological knowledge and systems biology pave the way for characterizing microbiota and identifying strategies in a move toward healthy compositions. Here, we dissect the recent associations between gut microbiota and T2D. In addition, we discuss recent advances in how drugs, diet, and exercise modulate the microbiome to favor healthy stages. Finally, we present computational approaches for disentangling the metabolic activity underlying host-microbiota codependence. Altogether, we envision that the combination of physiology and computational modeling of microbiota metabolism will drive us to optimize the diagnosis and treatment of T2D patients in a personalized way.
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Affiliation(s)
- Yoscelina Estrella Martínez-López
- Human Systems Biology Laboratory. Instituto Nacional de Medicina Genómica (INMEGEN). México City, México,Programa de Doctorado en Ciencias Médicas, Odontológicas y de la Salud, Universidad Nacional Autónoma de México (UNAM). Ciudad de México, México,Metabolic Research Laboratory, Department of Medicine and Nutrition. University of Guanajuato. León, Guanajuato, México
| | | | - Jean Paul Sánchez-Castañeda
- Human Systems Biology Laboratory. Instituto Nacional de Medicina Genómica (INMEGEN). México City, México,Programa de Maestría en Ciencias Bioquímicas, Universidad Nacional Autónoma de México (UNAM). Ciudad de México, México
| | - Daniel Neri-Rosario
- Human Systems Biology Laboratory. Instituto Nacional de Medicina Genómica (INMEGEN). México City, México,Programa de Maestría en Ciencias Bioquímicas, Universidad Nacional Autónoma de México (UNAM). Ciudad de México, México
| | - Rodolfo Guardado-Mendoza
- Metabolic Research Laboratory, Department of Medicine and Nutrition. University of Guanajuato. León, Guanajuato, México,Research Department, Hospital Regional de Alta Especialidad del Bajío. León, Guanajuato, México,Rodolfo Guardado-Mendoza Metabolic Research Laboratory, Department of Medicine and Nutrition. University of Guanajuato. León, Guanajuato, México
| | - Osbaldo Resendis-Antonio
- Human Systems Biology Laboratory. Instituto Nacional de Medicina Genómica (INMEGEN). México City, México,Coordinación de la Investigación Científica – Red de Apoyo a la Investigación, Universidad Nacional Autónoma de México (UNAM). Ciudad de México, México,CONTACT Osbaldo Resendis-Antonio Centro de Ciencias de la Complejidad, Universidad Nacional Autónoma de México (UNAM), Periferico Sur 4809, Arenal Tepepan, Tlalpan, 14610 Ciudad de México, CDMX
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Multi-omics personalized network analyses highlight progressive disruption of central metabolism associated with COVID-19 severity. Cell Syst 2022; 13:665-681.e4. [PMID: 35933992 PMCID: PMC9263811 DOI: 10.1016/j.cels.2022.06.006] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2021] [Revised: 04/18/2022] [Accepted: 06/27/2022] [Indexed: 01/26/2023]
Abstract
The clinical outcome and disease severity in coronavirus disease 2019 (COVID-19) are heterogeneous, and the progression or fatality of the disease cannot be explained by a single factor like age or comorbidities. In this study, we used system-wide network-based system biology analysis using whole blood RNA sequencing, immunophenotyping by flow cytometry, plasma metabolomics, and single-cell-type metabolomics of monocytes to identify the potential determinants of COVID-19 severity at personalized and group levels. Digital cell quantification and immunophenotyping of the mononuclear phagocytes indicated a substantial role in coordinating the immune cells that mediate COVID-19 severity. Stratum-specific and personalized genome-scale metabolic modeling indicated monocarboxylate transporter family genes (e.g., SLC16A6), nucleoside transporter genes (e.g., SLC29A1), and metabolites such as α-ketoglutarate, succinate, malate, and butyrate could play a crucial role in COVID-19 severity. Metabolic perturbations targeting the central metabolic pathway (TCA cycle) can be an alternate treatment strategy in severe COVID-19.
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Kishk A, Pacheco MP, Heurtaux T, Sinkkonen L, Pang J, Fritah S, Niclou SP, Sauter T. Review of Current Human Genome-Scale Metabolic Models for Brain Cancer and Neurodegenerative Diseases. Cells 2022; 11:cells11162486. [PMID: 36010563 PMCID: PMC9406599 DOI: 10.3390/cells11162486] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2022] [Revised: 07/28/2022] [Accepted: 08/08/2022] [Indexed: 11/16/2022] Open
Abstract
Brain disorders represent 32% of the global disease burden, with 169 million Europeans affected. Constraint-based metabolic modelling and other approaches have been applied to predict new treatments for these and other diseases. Many recent studies focused on enhancing, among others, drug predictions by generating generic metabolic models of brain cells and on the contextualisation of the genome-scale metabolic models with expression data. Experimental flux rates were primarily used to constrain or validate the model inputs. Bi-cellular models were reconstructed to study the interaction between different cell types. This review highlights the evolution of genome-scale models for neurodegenerative diseases and glioma. We discuss the advantages and drawbacks of each approach and propose improvements, such as building bi-cellular models, tailoring the biomass formulations for glioma and refinement of the cerebrospinal fluid composition.
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Affiliation(s)
- Ali Kishk
- Department of Life Sciences and Medicine, University of Luxembourg, L-4367 Belvaux, Luxembourg
| | - Maria Pires Pacheco
- Department of Life Sciences and Medicine, University of Luxembourg, L-4367 Belvaux, Luxembourg
| | - Tony Heurtaux
- Department of Life Sciences and Medicine, University of Luxembourg, L-4367 Belvaux, Luxembourg
- Luxembourg Center of Neuropathology, L-3555 Dudelange, Luxembourg
| | - Lasse Sinkkonen
- Department of Life Sciences and Medicine, University of Luxembourg, L-4367 Belvaux, Luxembourg
| | - Jun Pang
- Department of Computer Science, University of Luxembourg, L-4364 Esch-sur-Alzette, Luxembourg
| | - Sabrina Fritah
- NORLUX Neuro-Oncology Laboratory, Luxembourg Institute of Health, Department of Cancer Research, L-1526 Luxembourg, Luxembourg
| | - Simone P. Niclou
- NORLUX Neuro-Oncology Laboratory, Luxembourg Institute of Health, Department of Cancer Research, L-1526 Luxembourg, Luxembourg
| | - Thomas Sauter
- Department of Life Sciences and Medicine, University of Luxembourg, L-4367 Belvaux, Luxembourg
- Correspondence:
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Whole-body metabolic modelling predicts isoleucine dependency of SARS-CoV-2 replication. Comput Struct Biotechnol J 2022; 20:4098-4109. [PMID: 35874091 PMCID: PMC9296228 DOI: 10.1016/j.csbj.2022.07.019] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Accepted: 07/10/2022] [Indexed: 11/21/2022] Open
Abstract
We aimed at investigating host-virus co-metabolism during SARS-CoV-2 infection. Therefore, we extended comprehensive sex-specific, whole-body organ resolved models of human metabolism with the necessary reactions to replicate SARS-CoV-2 in the lung as well as selected peripheral organs. Using this comprehensive host-virus model, we obtained the following key results: 1. The predicted maximal possible virus shedding rate was limited by isoleucine availability. 2. The supported initial viral load depended on the increase in CD4+ T-cells, consistent with the literature. 3. During viral infection, the whole-body metabolism changed including the blood metabolome, which agreed well with metabolomic studies from COVID-19 patients and healthy controls. 4. The virus shedding rate could be reduced by either inhibition of the guanylate kinase 1 or availability of amino acids, e.g., in the diet. 5. The virus variants differed in their maximal possible virus shedding rates, which could be inversely linked to isoleucine occurrences in the sequences. Taken together, this study presents the metabolic crosstalk between host and virus and emphasises the role of amino acid metabolism during SARS-CoV-2 infection, in particular of isoleucine. As such, it provides an example of how computational modelling can complement more canonical approaches to gain insight into host-virus crosstalk and to identify potential therapeutic strategies.
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Gibbons SM, Gurry T, Lampe JW, Chakrabarti A, Dam V, Everard A, Goas A, Gross G, Kleerebezem M, Lane J, Maukonen J, Penna ALB, Pot B, Valdes AM, Walton G, Weiss A, Zanzer YC, Venlet NV, Miani M. Perspective: Leveraging the Gut Microbiota to Predict Personalized Responses to Dietary, Prebiotic, and Probiotic Interventions. Adv Nutr 2022; 13:1450-1461. [PMID: 35776947 PMCID: PMC9526856 DOI: 10.1093/advances/nmac075] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Revised: 05/31/2022] [Accepted: 06/28/2022] [Indexed: 01/28/2023] Open
Abstract
Humans often show variable responses to dietary, prebiotic, and probiotic interventions. Emerging evidence indicates that the gut microbiota is a key determinant for this population heterogeneity. Here, we provide an overview of some of the major computational and experimental tools being applied to critical questions of microbiota-mediated personalized nutrition and health. First, we discuss the latest advances in in silico modeling of the microbiota-nutrition-health axis, including the application of statistical, mechanistic, and hybrid artificial intelligence models. Second, we address high-throughput in vitro techniques for assessing interindividual heterogeneity, from ex vivo batch culturing of stool and continuous culturing in anaerobic bioreactors, to more sophisticated organ-on-a-chip models that integrate both host and microbial compartments. Third, we explore in vivo approaches for better understanding of personalized, microbiota-mediated responses to diet, prebiotics, and probiotics, from nonhuman animal models and human observational studies, to human feeding trials and crossover interventions. We highlight examples of existing, consumer-facing precision nutrition platforms that are currently leveraging the gut microbiota. Furthermore, we discuss how the integration of a broader set of the tools and techniques described in this piece can generate the data necessary to support a greater diversity of precision nutrition strategies. Finally, we present a vision of a precision nutrition and healthcare future, which leverages the gut microbiota to design effective, individual-specific interventions.
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Affiliation(s)
| | - Thomas Gurry
- Pharmaceutical Biochemistry group, School of Pharmaceutical Sciences, University of Geneva, Geneva, Switzerland
- Institute of Pharmaceutical Sciences of Western Switzerland (PSI-WS), University of Geneva/University of Lausanne, Geneva, Switzerland
| | - Johanna W Lampe
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | | | - Veerle Dam
- Sensus BV (Royal Cosun), Roosendaal, The Netherlands
| | - Amandine Everard
- Metabolism and Nutrition Research Group, Louvain Drug Research Institute, Walloon Excellence in Life Sciences and BIOtechnology (WELBIO), UCLouvain, Université Catholique de Louvain, Brussels, Belgium
| | - Almudena Goas
- Department of Food, Nutrition, and Exercise Sciences, University of Surrey, Guildford, United Kingdom
| | - Gabriele Gross
- Medical and Scientific Affairs, Reckitt| Mead Johnson Nutrition Institute, Nijmegen, The Netherlands
| | - Michiel Kleerebezem
- Host Microbe Interactomics Group, Wageningen University & Research, Wageningen, The Netherlands
| | - Jonathan Lane
- Health and Happiness Group, H&H Research, Cork, Ireland
| | | | - Ana Lucia Barretto Penna
- Department of Food Engineering and Technology, São Paulo State University, São José do Rio Preto, Brazil
| | - Bruno Pot
- Yakult Europe BV, Almere, The Netherlands
| | - Ana M Valdes
- Nottingham NIHR Biomedical Research Centre at the School of Medicine, University of Nottingham, Nottingham, United Kingdom
| | - Gemma Walton
- Food and Nutritional Sciences, University of Reading, Reading, United Kingdom
| | - Adrienne Weiss
- Yili Innovation Center Europe, Wageningen, The Netherlands
| | | | - Naomi V Venlet
- International Life Sciences Institute, European Branch, Brussels, Belgium
| | - Michela Miani
- International Life Sciences Institute, European Branch, Brussels, Belgium
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50
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Zakhartsev M, Rotnes F, Gulla M, Øyås O, van Dam JCJ, Suarez-Diez M, Grammes F, Hafþórsson RA, van Helvoirt W, Koehorst JJ, Schaap PJ, Jin Y, Mydland LT, Gjuvsland AB, Sandve SR, Martins dos Santos VAP, Vik JO. SALARECON connects the Atlantic salmon genome to growth and feed efficiency. PLoS Comput Biol 2022; 18:e1010194. [PMID: 35687595 PMCID: PMC9223387 DOI: 10.1371/journal.pcbi.1010194] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2021] [Revised: 06/23/2022] [Accepted: 05/10/2022] [Indexed: 11/19/2022] Open
Abstract
Atlantic salmon (Salmo salar) is the most valuable farmed fish globally and there is much interest in optimizing its genetics and rearing conditions for growth and feed efficiency. Marine feed ingredients must be replaced to meet global demand, with challenges for fish health and sustainability. Metabolic models can address this by connecting genomes to metabolism, which converts nutrients in the feed to energy and biomass, but such models are currently not available for major aquaculture species such as salmon. We present SALARECON, a model focusing on energy, amino acid, and nucleotide metabolism that links the Atlantic salmon genome to metabolic fluxes and growth. It performs well in standardized tests and captures expected metabolic (in)capabilities. We show that it can explain observed hypoxic growth in terms of metabolic fluxes and apply it to aquaculture by simulating growth with commercial feed ingredients. Predicted limiting amino acids and feed efficiencies agree with data, and the model suggests that marine feed efficiency can be achieved by supplementing a few amino acids to plant- and insect-based feeds. SALARECON is a high-quality model that makes it possible to simulate Atlantic salmon metabolism and growth. It can be used to explain Atlantic salmon physiology and address key challenges in aquaculture such as development of sustainable feeds. Atlantic salmon aquaculture generates billions of euros annually, but faces challenges of sustainability. Salmon are carnivores by nature, and fish oil and fish meal have become scarce resources in fish feed production. Novel, sustainable feedstuffs are being trialed hand in hand with studies of the genetics of growth and feed efficiency. This calls for a mathematical-biological framework to integrate data with understanding of the effects of novel feeds on salmon physiology and its interplay with genetics. We have developed the SALARECON model of the core salmon metabolic reaction network, linking its genome to metabolic fluxes and growth. Computational analyses show good agreement with observed growth, amino acid limitations, and feed efficiencies, illustrating the potential for in silico studies of potential feed mixtures. In particular, in silico screening of possible diets will enable more efficient animal experiments with improved knowledge gain. We have adopted best practices for test-driven development, virtual experiments to assay metabolic capabilities, revision control, and FAIR data and model management. This facilitates fast, collaborative, reliable development of the model for future applications in sustainable production biology.
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Affiliation(s)
- Maksim Zakhartsev
- Faculty of Chemistry, Biotechnology and Food Science, Norwegian University of Life Sciences (NMBU), Ås, Norway
- Faculty of Biosciences, Norwegian University of Life Sciences (NMBU), Ås, Norway
| | - Filip Rotnes
- Faculty of Chemistry, Biotechnology and Food Science, Norwegian University of Life Sciences (NMBU), Ås, Norway
- Faculty of Biosciences, Norwegian University of Life Sciences (NMBU), Ås, Norway
| | - Marie Gulla
- Faculty of Chemistry, Biotechnology and Food Science, Norwegian University of Life Sciences (NMBU), Ås, Norway
- Faculty of Biosciences, Norwegian University of Life Sciences (NMBU), Ås, Norway
| | - Ove Øyås
- Faculty of Chemistry, Biotechnology and Food Science, Norwegian University of Life Sciences (NMBU), Ås, Norway
- Faculty of Biosciences, Norwegian University of Life Sciences (NMBU), Ås, Norway
| | - Jesse C. J. van Dam
- Laboratory of Systems and Synthetic Biology, Wageningen University & Research (WUR), Wageningen, The Netherlands
| | - Maria Suarez-Diez
- Laboratory of Systems and Synthetic Biology, Wageningen University & Research (WUR), Wageningen, The Netherlands
| | - Fabian Grammes
- Faculty of Biosciences, Norwegian University of Life Sciences (NMBU), Ås, Norway
| | | | - Wout van Helvoirt
- Faculty of Biosciences, Norwegian University of Life Sciences (NMBU), Ås, Norway
| | - Jasper J. Koehorst
- Laboratory of Systems and Synthetic Biology, Wageningen University & Research (WUR), Wageningen, The Netherlands
| | - Peter J. Schaap
- Laboratory of Systems and Synthetic Biology, Wageningen University & Research (WUR), Wageningen, The Netherlands
| | - Yang Jin
- Faculty of Biosciences, Norwegian University of Life Sciences (NMBU), Ås, Norway
| | - Liv Torunn Mydland
- Faculty of Biosciences, Norwegian University of Life Sciences (NMBU), Ås, Norway
| | - Arne B. Gjuvsland
- Faculty of Biosciences, Norwegian University of Life Sciences (NMBU), Ås, Norway
| | - Simen R. Sandve
- Faculty of Biosciences, Norwegian University of Life Sciences (NMBU), Ås, Norway
| | | | - Jon Olav Vik
- Faculty of Chemistry, Biotechnology and Food Science, Norwegian University of Life Sciences (NMBU), Ås, Norway
- Faculty of Biosciences, Norwegian University of Life Sciences (NMBU), Ås, Norway
- * E-mail:
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