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Cortese N, Procopio A, Merola A, Zaffino P, Cosentino C. Applications of genome-scale metabolic models to the study of human diseases: A systematic review. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 256:108397. [PMID: 39232376 DOI: 10.1016/j.cmpb.2024.108397] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/09/2024] [Revised: 08/25/2024] [Accepted: 08/25/2024] [Indexed: 09/06/2024]
Abstract
BACKGROUND AND OBJECTIVES Genome-scale metabolic networks (GEMs) represent a valuable modeling and computational tool in the broad field of systems biology. Their ability to integrate constraints and high-throughput biological data enables the study of intricate metabolic aspects and processes of different cell types and conditions. The past decade has witnessed an increasing number and variety of applications of GEMs for the study of human diseases, along with a huge effort aimed at the reconstruction, integration and analysis of a high number of organisms. This paper presents a systematic review of the scientific literature, to pursue several important questions about the application of constraint-based modeling in the investigation of human diseases. Hopefully, this paper will provide a useful reference for researchers interested in the application of modeling and computational tools for the investigation of metabolic-related human diseases. METHODS This systematic review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Elsevier Scopus®, National Library of Medicine PubMed® and Clarivate Web of Science™ databases were enquired, resulting in 566 scientific articles. After applying exclusion and eligibility criteria, a total of 169 papers were selected and individually examined. RESULTS The reviewed papers offer a thorough and up-to-date picture of the latest modeling and computational approaches, based on genome-scale metabolic models, that can be leveraged for the investigation of a large variety of human diseases. The numerous studies have been categorized according to the clinical research area involved in the examined disease. Furthermore, the paper discusses the most typical approaches employed to derive clinically-relevant information using the computational models. CONCLUSIONS The number of scientific papers, utilizing GEM-based approaches for the investigation of human diseases, suggests an increasing interest in these types of approaches; hopefully, the present review will represent a useful reference for scientists interested in applying computational modeling approaches to investigate the aetiopathology of human diseases; we also hope that this work will foster the development of novel applications and methods for the discovery of clinically-relevant insights on metabolic-related diseases.
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Affiliation(s)
- Nicola Cortese
- Department of Experimental and Clinical Medicine, Università degli Studi Magna Græcia, Catanzaro, 88100, Italy
| | - Anna Procopio
- Department of Experimental and Clinical Medicine, Università degli Studi Magna Græcia, Catanzaro, 88100, Italy
| | - Alessio Merola
- Department of Experimental and Clinical Medicine, Università degli Studi Magna Græcia, Catanzaro, 88100, Italy
| | - Paolo Zaffino
- Department of Experimental and Clinical Medicine, Università degli Studi Magna Græcia, Catanzaro, 88100, Italy
| | - Carlo Cosentino
- Department of Experimental and Clinical Medicine, Università degli Studi Magna Græcia, Catanzaro, 88100, Italy.
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Pavao A, Girinathan B, Peltier J, Altamirano Silva P, Dupuy B, Muti IH, Malloy C, Cheng LL, Bry L. Elucidating dynamic anaerobe metabolism with HRMAS 13C NMR and genome-scale modeling. Nat Chem Biol 2023; 19:556-564. [PMID: 36894723 PMCID: PMC10154198 DOI: 10.1038/s41589-023-01275-9] [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: 08/24/2022] [Accepted: 01/30/2023] [Indexed: 03/11/2023]
Abstract
Anaerobic microbial metabolism drives critical functions within global ecosystems, host-microbiota interactions, and industrial applications, yet remains ill-defined. Here we advance a versatile approach to elaborate cellular metabolism in obligate anaerobes using the pathogen Clostridioides difficile, an amino acid and carbohydrate-fermenting Clostridia. High-resolution magic angle spinning nuclear magnetic resonance (NMR) spectroscopy of C. difficile, grown with fermentable 13C substrates, informed dynamic flux balance analysis (dFBA) of the pathogen's genome-scale metabolism. Analyses identified dynamic recruitment of oxidative and supporting reductive pathways, with integration of high-flux amino acid and glycolytic metabolism at alanine's biosynthesis to support efficient energy generation, nitrogen handling and biomass generation. Model predictions informed an approach leveraging the sensitivity of 13C NMR spectroscopy to simultaneously track cellular carbon and nitrogen flow from [U-13C]glucose and [15N]leucine, confirming the formation of [13C,15N]alanine. Findings identify metabolic strategies used by C. difficile to support its rapid colonization and expansion in gut ecosystems.
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Affiliation(s)
- Aidan Pavao
- Massachusetts Host-Microbiome Center, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Brintha Girinathan
- Massachusetts Host-Microbiome Center, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Ginkgo Bioworks, The Innovation and Design Building, Boston, MA, USA
| | - Johann Peltier
- Laboratoire Pathogenèse des Bactéries Anaérobies, F-75015, Institut Pasteur, Université Paris-Cité, UMR-CNRS 6047, Paris, France
- Institute for Integrative Biology of the Cell (I2BC), 91198, University of Paris-Saclay, CEA, CNRS, Gif-sur-Yvette, France
| | - Pamela Altamirano Silva
- Centre for Investigations in Tropical Diseases, Faculty of Microbiology, University of Costa Rica, San José, Costa Rica
| | - Bruno Dupuy
- Laboratoire Pathogenèse des Bactéries Anaérobies, F-75015, Institut Pasteur, Université Paris-Cité, UMR-CNRS 6047, Paris, France
| | - Isabella H Muti
- Departments of Radiology and Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Craig Malloy
- Department of Radiology, The University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Leo L Cheng
- Departments of Radiology and Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
| | - Lynn Bry
- Massachusetts Host-Microbiome Center, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
- Clinical Microbiology Laboratory, Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
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3
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Jenior ML, Papin JA. Computational approaches to understanding Clostridioides difficile metabolism and virulence. Curr Opin Microbiol 2022; 65:108-115. [PMID: 34839237 PMCID: PMC8792252 DOI: 10.1016/j.mib.2021.11.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2021] [Revised: 11/06/2021] [Accepted: 11/08/2021] [Indexed: 02/03/2023]
Abstract
The progress of infection by Clostridioides difficile is strongly influenced by metabolic cues it encounters as it colonizes the gastrointestinal tract. Both colonization and regulation of virulence have a multi-factorial interaction between host, microbiome, and gene expression cascades. While these connections with metabolism have been understood for some time, many mechanisms of control have remained difficult to directly assay due to high metabolic variability among C. difficile isolates and difficult genetic systems. Computational systems offer a means to interrogate structure of complex or noisy datasets and generate useful, tractable hypotheses to be tested in the laboratory. Recently, in silico techniques have provided powerful insights into metabolic elements of C. difficile infection ranging from virulence regulation to interactions with the gut microbiota. In this review, we introduce and provide context to the methods of computational modeling that have been applied to C. difficile metabolism and virulence thus far. The techniques discussed here have laid the foundation for future multi-scale efforts aimed at understanding the complex interplay of metabolic activity between pathogen, host, and surrounding microbial community in the regulation of C. difficile pathogenesis.
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Affiliation(s)
- Matthew L Jenior
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, USA,denotes co-corresponding author
| | - Jason A Papin
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, USA, Department of Medicine, Division of Infectious Diseases & International Health, University of Virginia, Charlottesville, VA, USA, Department of Biochemistry & Molecular Genetics, University of Virginia, Charlottesville, VA, USA,denotes co-corresponding author
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4
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Arrieta-Ortiz ML, Immanuel SRC, Turkarslan S, Wu WJ, Girinathan BP, Worley JN, DiBenedetto N, Soutourina O, Peltier J, Dupuy B, Bry L, Baliga NS. Predictive regulatory and metabolic network models for systems analysis of Clostridioides difficile. Cell Host Microbe 2021; 29:1709-1723.e5. [PMID: 34637780 PMCID: PMC8595754 DOI: 10.1016/j.chom.2021.09.008] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2021] [Revised: 07/29/2021] [Accepted: 09/16/2021] [Indexed: 12/15/2022]
Abstract
We present predictive models for comprehensive systems analysis of Clostridioides difficile, the etiology of pseudomembranous colitis. By leveraging 151 published transcriptomes, we generated an EGRIN model that organizes 90% of C. difficile genes into a transcriptional regulatory network of 297 co-regulated modules, implicating genes in sporulation, carbohydrate transport, and metabolism. By advancing a metabolic model through addition and curation of metabolic reactions including nutrient uptake, we discovered 14 amino acids, diverse carbohydrates, and 10 metabolic genes as essential for C. difficile growth in the intestinal environment. Finally, we developed a PRIME model to uncover how EGRIN-inferred combinatorial gene regulation by transcription factors, such as CcpA and CodY, modulates essential metabolic processes to enable C. difficile growth relative to commensal colonization. The C. difficile interactive web portal provides access to these model resources to support collaborative systems-level studies of context-specific virulence mechanisms in C. difficile.
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Affiliation(s)
| | | | | | - Wei-Ju Wu
- Institute for Systems Biology, Seattle, WA 98109, USA
| | - Brintha P Girinathan
- Massachusetts Host-Microbiome Center, Brigham & Women's Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Jay N Worley
- Massachusetts Host-Microbiome Center, Brigham & Women's Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Nicholas DiBenedetto
- Massachusetts Host-Microbiome Center, Brigham & Women's Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Olga Soutourina
- Université Paris-Saclay, CEA, CNRS, Institute for Integrative Biology of the Cell (I2BC), Gif-sur-yvette 91198, France
| | - Johann Peltier
- Université Paris-Saclay, CEA, CNRS, Institute for Integrative Biology of the Cell (I2BC), Gif-sur-yvette 91198, France
| | - Bruno Dupuy
- Laboratoire Pathogenèse des Bactéries anaérobies, Institut Pasteur, Université de Paris, UMR CNRS 2001, Paris 75015, France
| | - Lynn Bry
- Massachusetts Host-Microbiome Center, Brigham & Women's Hospital, Harvard Medical School, Boston, MA 02115, USA
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5
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Jenior ML, Leslie JL, Powers DA, Garrett EM, Walker KA, Dickenson ME, Petri WA, Tamayo R, Papin JA. Novel Drivers of Virulence in Clostridioides difficile Identified via Context-Specific Metabolic Network Analysis. mSystems 2021; 6:e0091921. [PMID: 34609164 PMCID: PMC8547418 DOI: 10.1128/msystems.00919-21] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2021] [Accepted: 09/17/2021] [Indexed: 12/20/2022] Open
Abstract
The pathogen Clostridioides difficile causes toxin-mediated diarrhea and is the leading cause of hospital-acquired infection in the United States. Due to growing antibiotic resistance and recurrent infection, targeting C. difficile metabolism presents a new approach to combat this infection. Genome-scale metabolic network reconstructions (GENREs) have been used to identify therapeutic targets and uncover properties that determine cellular behaviors. Thus, we constructed C. difficile GENREs for a hypervirulent isolate (strain [str.] R20291) and a historic strain (str. 630), validating both with in vitro and in vivo data sets. Growth simulations revealed significant correlations with measured carbon source usage (positive predictive value [PPV] ≥ 92.7%), and single-gene deletion analysis showed >89.0% accuracy. Next, we utilized each GENRE to identify metabolic drivers of both sporulation and biofilm formation. Through contextualization of each model using transcriptomes generated from in vitro and infection conditions, we discovered reliance on the pentose phosphate pathway as well as increased usage of cytidine and N-acetylneuraminate when virulence expression is reduced, which was subsequently supported experimentally. Our results highlight the ability of GENREs to identify novel metabolite signals in higher-order phenotypes like bacterial pathogenesis. IMPORTANCE Clostridioides difficile has become the leading single cause of hospital-acquired infections. Numerous studies have demonstrated the importance of specific metabolic pathways in aspects of C. difficile pathophysiology, from initial colonization to regulation of virulence factors. In the past, genome-scale metabolic network reconstruction (GENRE) analysis of bacteria has enabled systematic investigation of the genetic and metabolic properties that contribute to downstream virulence phenotypes. With this in mind, we generated and extensively curated C. difficile GENREs for both a well-studied laboratory strain (str. 630) and a more recently characterized hypervirulent isolate (str. R20291). In silico validation of both GENREs revealed high degrees of agreement with experimental gene essentiality and carbon source utilization data sets. Subsequent exploration of context-specific metabolism during both in vitro growth and infection revealed consistent patterns of metabolism which corresponded with experimentally measured increases in virulence factor expression. Our results support that differential C. difficile virulence is associated with distinct metabolic programs related to use of carbon sources and provide a platform for identification of novel therapeutic targets.
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Affiliation(s)
- Matthew L. Jenior
- Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, USA
| | - Jhansi L. Leslie
- Department of Medicine, Division of Infectious Diseases & International Health, University of Virginia, Charlottesville, Virginia, USA
| | - Deborah A. Powers
- Department of Biochemistry & Molecular Genetics, University of Virginia, Charlottesville, Virginia, USA
| | - Elizabeth M. Garrett
- Department of Microbiology & Immunology, University of North Carolina Chapel Hill School of Medicine, Chapel Hill, North Carolina, USA
| | - Kimberly A. Walker
- Department of Microbiology & Immunology, University of North Carolina Chapel Hill School of Medicine, Chapel Hill, North Carolina, USA
| | - Mary E. Dickenson
- Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, USA
| | - William A. Petri
- Department of Medicine, Division of Infectious Diseases & International Health, University of Virginia, Charlottesville, Virginia, USA
- Department of Microbiology, Immunology and Cancer Biology, University of Virginia Health System, Charlottesville, Virginia, USA
- Department of Pathology, University of Virginia Health System, Charlottesville, Virginia, USA
| | - Rita Tamayo
- Department of Microbiology & Immunology, University of North Carolina Chapel Hill School of Medicine, Chapel Hill, North Carolina, USA
| | - Jason A. Papin
- Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, USA
- Department of Medicine, Division of Infectious Diseases & International Health, University of Virginia, Charlottesville, Virginia, USA
- Department of Biochemistry & Molecular Genetics, University of Virginia, Charlottesville, Virginia, USA
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6
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Vijayakumar S, Rahman PK, Angione C. A Hybrid Flux Balance Analysis and Machine Learning Pipeline Elucidates Metabolic Adaptation in Cyanobacteria. iScience 2020; 23:101818. [PMID: 33354660 PMCID: PMC7744713 DOI: 10.1016/j.isci.2020.101818] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2020] [Revised: 10/23/2020] [Accepted: 11/13/2020] [Indexed: 01/20/2023] Open
Abstract
Machine learning has recently emerged as a promising tool for inferring multi-omic relationships in biological systems. At the same time, genome-scale metabolic models (GSMMs) can be integrated with such multi-omic data to refine phenotypic predictions. In this work, we use a multi-omic machine learning pipeline to analyze a GSMM of Synechococcus sp. PCC 7002, a cyanobacterium with large potential to produce renewable biofuels. We use regularized flux balance analysis to observe flux response between conditions across photosynthesis and energy metabolism. We then incorporate principal-component analysis, k-means clustering, and LASSO regularization to reduce dimensionality and extract key cross-omic features. Our results suggest that combining metabolic modeling with machine learning elucidates mechanisms used by cyanobacteria to cope with fluctuations in light intensity and salinity that cannot be detected using transcriptomics alone. Furthermore, GSMMs introduce critical mechanistic details that improve the performance of omic-based machine learning methods.
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Affiliation(s)
- Supreeta Vijayakumar
- Department of Computer Science and Information Systems, Teesside University, Middlesbrough, North Yorkshire TS1 3BX, UK
| | - Pattanathu K.S.M. Rahman
- Centre for Enzyme Innovation, Institute of Biological and Biomedical Sciences, School of Biological Sciences, University of Portsmouth, Portsmouth, Hampshire PO1 2UP, UK
- Tara Biologics, Woking, Surrey GU21 6BP, UK
| | - Claudio Angione
- Department of Computer Science and Information Systems, Teesside University, Middlesbrough, North Yorkshire TS1 3BX, UK
- Centre for Digital Innovation, Teesside University, Middlesbrough TS1 3BX, UK
- Healthcare Innovation Centre, Teesside University, Middlesbrough TS1 3BX, UK
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7
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Norsigian CJ, Danhof HA, Brand CK, Oezguen N, Midani FS, Palsson BO, Savidge TC, Britton RA, Spinler JK, Monk JM. Systems biology analysis of the Clostridioides difficile core-genome contextualizes microenvironmental evolutionary pressures leading to genotypic and phenotypic divergence. NPJ Syst Biol Appl 2020; 6:31. [PMID: 33082337 PMCID: PMC7576604 DOI: 10.1038/s41540-020-00151-9] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2020] [Accepted: 08/26/2020] [Indexed: 12/15/2022] Open
Abstract
Hospital acquired Clostridioides (Clostridium) difficile infection is exacerbated by the continued evolution of C. difficile strains, a phenomenon studied by multiple laboratories using stock cultures specific to each laboratory. Intralaboratory evolution of strains contributes to interlaboratory variation in experimental results adding to the challenges of scientific rigor and reproducibility. To explore how microevolution of C. difficile within laboratories influences the metabolic capacity of an organism, three different laboratory stock isolates of the C. difficile 630 reference strain were whole-genome sequenced and profiled in over 180 nutrient environments using phenotypic microarrays. The results identified differences in growth dynamics for 32 carbon sources including trehalose, fructose, and mannose. An updated genome-scale model for C. difficile 630 was constructed and used to contextualize the 28 unique mutations observed between the stock cultures. The integration of phenotypic screens with model predictions identified pathways enabling catabolism of ethanolamine, salicin, arbutin, and N-acetyl-galactosamine that differentiated individual C. difficile 630 laboratory isolates. The reconstruction was used as a framework to analyze the core-genome of 415 publicly available C. difficile genomes and identify areas of metabolism prone to evolution within the species. Genes encoding enzymes and transporters involved in starch metabolism and iron acquisition were more variable while C. difficile distinct metabolic functions like Stickland fermentation were more consistent. A substitution in the trehalose PTS system was identified with potential implications in strain virulence. Thus, pairing genome-scale models with large-scale physiological and genomic data enables a mechanistic framework for studying the evolution of pathogens within microenvironments and will lead to predictive modeling to combat pathogen emergence.
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Affiliation(s)
- Charles J Norsigian
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA
| | - Heather A Danhof
- Department of Molecular Virology and Microbiology, Baylor College of Medicine, Houston, TX, USA.,Alkek Center for Metagenomics and Microbiome Research, Baylor College of Medicine, Houston, TX, USA
| | - Colleen K Brand
- Department of Molecular Virology and Microbiology, Baylor College of Medicine, Houston, TX, USA.,Alkek Center for Metagenomics and Microbiome Research, Baylor College of Medicine, Houston, TX, USA
| | - Numan Oezguen
- Department of Pathology and Immunology, Baylor College of Medicine, Houston, TX, USA
| | - Firas S Midani
- Department of Molecular Virology and Microbiology, Baylor College of Medicine, Houston, TX, USA.,Alkek Center for Metagenomics and Microbiome Research, Baylor College of Medicine, Houston, TX, USA
| | - Bernhard O Palsson
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA
| | - Tor C Savidge
- Department of Pathology and Immunology, Baylor College of Medicine, Houston, TX, USA
| | - Robert A Britton
- Department of Molecular Virology and Microbiology, Baylor College of Medicine, Houston, TX, USA.,Alkek Center for Metagenomics and Microbiome Research, Baylor College of Medicine, Houston, TX, USA
| | - Jennifer K Spinler
- Department of Pathology and Immunology, Baylor College of Medicine, Houston, TX, USA
| | - Jonathan M Monk
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA.
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8
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Yan F, Gao F. A systematic strategy for the investigation of vaccines and drugs targeting bacteria. Comput Struct Biotechnol J 2020; 18:1525-1538. [PMID: 32637049 PMCID: PMC7327267 DOI: 10.1016/j.csbj.2020.06.008] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2020] [Revised: 06/02/2020] [Accepted: 06/03/2020] [Indexed: 02/07/2023] Open
Abstract
Infectious and epidemic diseases induced by bacteria have historically caused great distress to people, and have even resulted in a large number of deaths worldwide. At present, many researchers are working on the discovery of viable drug and vaccine targets for bacteria through multiple methods, including the analyses of comparative subtractive genome, core genome, replication-related proteins, transcriptomics and riboswitches, which plays a significant part in the treatment of infectious and pandemic diseases. The 3D structures of the desired target proteins, drugs and epitopes can be predicted and modeled through target analysis. Meanwhile, molecular dynamics (MD) analysis of the constructed drug/epitope-protein complexes is an important standard for testing the suitability of these screened drugs and vaccines. Currently, target discovery, target analysis and MD analysis are integrated into a systematic set of drug and vaccine analysis strategy for bacteria. We hope that this comprehensive strategy will help in the design of high-performance vaccines and drugs.
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Affiliation(s)
- Fangfang Yan
- Department of Physics, School of Science, Tianjin University, Tianjin 300072, China
| | - Feng Gao
- Department of Physics, School of Science, Tianjin University, Tianjin 300072, China
- Frontiers Science Center for Synthetic Biology and Key Laboratory of Systems Bioengineering (Ministry of Education), Tianjin University, Tianjin 300072, China
- SynBio Research Platform, Collaborative Innovation Center of Chemical Science and Engineering (Tianjin), Tianjin 300072, China
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9
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Vijayakumar S, Conway M, Lió P, Angione C. Seeing the wood for the trees: a forest of methods for optimization and omic-network integration in metabolic modelling. Brief Bioinform 2019; 19:1218-1235. [PMID: 28575143 DOI: 10.1093/bib/bbx053] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2017] [Indexed: 11/13/2022] Open
Abstract
Metabolic modelling has entered a mature phase with dozens of methods and software implementations available to the practitioner and the theoretician. It is not easy for a modeller to be able to see the wood (or the forest) for the trees. Driven by this analogy, we here present a 'forest' of principal methods used for constraint-based modelling in systems biology. This provides a tree-based view of methods available to prospective modellers, also available in interactive version at http://modellingmetabolism.net, where it will be kept updated with new methods after the publication of the present manuscript. Our updated classification of existing methods and tools highlights the most promising in the different branches, with the aim to develop a vision of how existing methods could hybridize and become more complex. We then provide the first hands-on tutorial for multi-objective optimization of metabolic models in R. We finally discuss the implementation of multi-view machine learning approaches in poly-omic integration. Throughout this work, we demonstrate the optimization of trade-offs between multiple metabolic objectives, with a focus on omic data integration through machine learning. We anticipate that the combination of a survey, a perspective on multi-view machine learning and a step-by-step R tutorial should be of interest for both the beginner and the advanced user.
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Affiliation(s)
| | - Max Conway
- Computer Laboratory, University of Cambridge, UK
| | - Pietro Lió
- Computer Laboratory, University of Cambridge, UK
| | - Claudio Angione
- Department of Computer Science and Information Systems, Teesside University, UK
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10
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Human Systems Biology and Metabolic Modelling: A Review-From Disease Metabolism to Precision Medicine. BIOMED RESEARCH INTERNATIONAL 2019; 2019:8304260. [PMID: 31281846 PMCID: PMC6590590 DOI: 10.1155/2019/8304260] [Citation(s) in RCA: 42] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/01/2018] [Revised: 02/07/2019] [Accepted: 05/20/2019] [Indexed: 01/06/2023]
Abstract
In cell and molecular biology, metabolism is the only system that can be fully simulated at genome scale. Metabolic systems biology offers powerful abstraction tools to simulate all known metabolic reactions in a cell, therefore providing a snapshot that is close to its observable phenotype. In this review, we cover the 15 years of human metabolic modelling. We show that, although the past five years have not experienced large improvements in the size of the gene and metabolite sets in human metabolic models, their accuracy is rapidly increasing. We also describe how condition-, tissue-, and patient-specific metabolic models shed light on cell-specific changes occurring in the metabolic network, therefore predicting biomarkers of disease metabolism. We finally discuss current challenges and future promising directions for this research field, including machine/deep learning and precision medicine. In the omics era, profiling patients and biological processes from a multiomic point of view is becoming more common and less expensive. Starting from multiomic data collected from patients and N-of-1 trials where individual patients constitute different case studies, methods for model-building and data integration are being used to generate patient-specific models. Coupled with state-of-the-art machine learning methods, this will allow characterizing each patient's disease phenotype and delivering precision medicine solutions, therefore leading to preventative medicine, reduced treatment, and in silico clinical trials.
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11
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Neumann-Schaal M, Jahn D, Schmidt-Hohagen K. Metabolism the Difficile Way: The Key to the Success of the Pathogen Clostridioides difficile. Front Microbiol 2019; 10:219. [PMID: 30828322 PMCID: PMC6384274 DOI: 10.3389/fmicb.2019.00219] [Citation(s) in RCA: 87] [Impact Index Per Article: 17.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2018] [Accepted: 01/28/2019] [Indexed: 12/11/2022] Open
Abstract
Strains of Clostridioides difficile cause detrimental diarrheas with thousands of deaths worldwide. The infection process by the Gram-positive, strictly anaerobic gut bacterium is directly related to its unique metabolism, using multiple Stickland-type amino acid fermentation reactions coupled to Rnf complex-mediated sodium/proton gradient formation for ATP generation. Major pathways utilize phenylalanine, leucine, glycine and proline with the formation of 3-phenylproprionate, isocaproate, butyrate, 5-methylcaproate, valerate and 5-aminovalerate. In parallel a versatile sugar catabolism including pyruvate formate-lyase as a central enzyme and an incomplete tricarboxylic acid cycle to prevent unnecessary NADH formation completes the picture. However, a complex gene regulatory network that carefully mediates the continuous adaptation of this metabolism to changing environmental conditions is only partially elucidated. It involves the pleiotropic regulators CodY and SigH, the known carbon metabolism regulator CcpA, the proline regulator PrdR, the iron regulator Fur, the small regulatory RNA CsrA and potentially the NADH-responsive regulator Rex. Here, we describe the current knowledge of the metabolic principles of energy generation by C. difficile and the underlying gene regulatory scenarios.
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Affiliation(s)
- Meina Neumann-Schaal
- Leibniz Institute DSMZ, German Collection of Microorganisms and Cell Cultures, Braunschweig, Germany.,Integrated Centre of Systems Biology (BRICS), Braunschweig University of Technology, Braunschweig, Germany
| | - Dieter Jahn
- Integrated Centre of Systems Biology (BRICS), Braunschweig University of Technology, Braunschweig, Germany.,Institute of Microbiology, Braunschweig University of Technology, Braunschweig, Germany
| | - Kerstin Schmidt-Hohagen
- Integrated Centre of Systems Biology (BRICS), Braunschweig University of Technology, Braunschweig, Germany.,Department of Bioinformatics and Biochemistry, Braunschweig University of Technology, Braunschweig, Germany
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12
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Modular Metabolic Engineering for Biobased Chemical Production. Trends Biotechnol 2019; 37:152-166. [DOI: 10.1016/j.tibtech.2018.07.003] [Citation(s) in RCA: 72] [Impact Index Per Article: 14.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2018] [Revised: 07/03/2018] [Accepted: 07/05/2018] [Indexed: 11/21/2022]
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Sarkar D, Mueller TJ, Liu D, Pakrasi HB, Maranas CD. A diurnal flux balance model of Synechocystis sp. PCC 6803 metabolism. PLoS Comput Biol 2019; 15:e1006692. [PMID: 30677028 PMCID: PMC6364703 DOI: 10.1371/journal.pcbi.1006692] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2017] [Revised: 02/05/2019] [Accepted: 12/03/2018] [Indexed: 11/26/2022] Open
Abstract
Phototrophic organisms such as cyanobacteria utilize the sun's energy to convert atmospheric carbon dioxide into organic carbon, resulting in diurnal variations in the cell's metabolism. Flux balance analysis is a widely accepted constraint-based optimization tool for analyzing growth and metabolism, but it is generally used in a time-invariant manner with no provisions for sequestering different biomass components at different time periods. Here we present CycleSyn, a periodic model of Synechocystis sp. PCC 6803 metabolism that spans a 12-hr light/12-hr dark cycle by segmenting it into 12 Time Point Models (TPMs) with a uniform duration of two hours. The developed framework allows for the flow of metabolites across TPMs while inventorying metabolite levels and only allowing for the utilization of currently or previously produced compounds. The 12 TPMs allow for the incorporation of time-dependent constraints that capture the cyclic nature of cellular processes. Imposing bounds on reactions informed by temporally-segmented transcriptomic data enables simulation of phototrophic growth as a single linear programming (LP) problem. The solution provides the time varying reaction fluxes over a 24-hour cycle and the accumulation/consumption of metabolites. The diurnal rhythm of metabolic gene expression driven by the circadian clock and its metabolic consequences is explored. Predicted flux and metabolite pools are in line with published studies regarding the temporal organization of phototrophic growth in Synechocystis PCC 6803 paving the way for constructing time-resolved genome-scale models (GSMs) for organisms with a circadian clock. In addition, the metabolic reorganization that would be required to enable Synechocystis PCC 6803 to temporally separate photosynthesis from oxygen-sensitive nitrogen fixation is also explored using the developed model formalism.
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Affiliation(s)
- Debolina Sarkar
- Department of Chemical Engineering, Pennsylvania State University,
University Park, Pennsylvania, United States of America
| | - Thomas J. Mueller
- Department of Chemical Engineering, Pennsylvania State University,
University Park, Pennsylvania, United States of America
| | - Deng Liu
- Department of Biology, Washington University, St. Louis, Missouri, United
States of America
| | - Himadri B. Pakrasi
- Department of Biology, Washington University, St. Louis, Missouri, United
States of America
| | - Costas D. Maranas
- Department of Chemical Engineering, Pennsylvania State University,
University Park, Pennsylvania, United States of America
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Subramanian A, Sarkar RR. Perspectives on Leishmania Species and Stage-specific Adaptive Mechanisms. Trends Parasitol 2018; 34:1068-1081. [DOI: 10.1016/j.pt.2018.09.004] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2018] [Revised: 09/10/2018] [Accepted: 09/21/2018] [Indexed: 12/23/2022]
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Abstract
BACKGROUND Ageing can be classified in two different ways, chronological ageing and biological ageing. While chronological age is a measure of the time that has passed since birth, biological (also known as transcriptomic) ageing is defined by how time and the environment affect an individual in comparison to other individuals of the same chronological age. Recent research studies have shown that transcriptomic age is associated with certain genes, and that each of those genes has an effect size. Using these effect sizes we can calculate the transcriptomic age of an individual from their age-associated gene expression levels. The limitation of this approach is that it does not consider how these changes in gene expression affect the metabolism of individuals and hence their observable cellular phenotype. RESULTS We propose a method based on poly-omic constraint-based models and machine learning in order to further the understanding of transcriptomic ageing. We use normalised CD4 T-cell gene expression data from peripheral blood mononuclear cells in 499 healthy individuals to create individual metabolic models. These models are then combined with a transcriptomic age predictor and chronological age to provide new insights into the differences between transcriptomic and chronological ageing. As a result, we propose a novel metabolic age predictor. CONCLUSIONS We show that our poly-omic predictors provide a more detailed analysis of transcriptomic ageing compared to gene-based approaches, and represent a basis for furthering our knowledge of the ageing mechanisms in human cells.
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Affiliation(s)
- Elisabeth Yaneske
- Department of Computer Science and Information Systems, Teesside University, Borough Road, Middlesbrough, UK
| | - Claudio Angione
- Department of Computer Science and Information Systems, Teesside University, Borough Road, Middlesbrough, UK
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Abstract
The genome-scale cellular network has become a necessary tool in the systematic analysis of microbes. In a cell, there are several layers (i.e., types) of the molecular networks, for example, genome-scale metabolic network (GMN), transcriptional regulatory network (TRN), and signal transduction network (STN). It has been realized that the limitation and inaccuracy of the prediction exist just using only a single-layer network. Therefore, the integrated network constructed based on the networks of the three types attracts more interests. The function of a biological process in living cells is usually performed by the interaction of biological components. Therefore, it is necessary to integrate and analyze all the related components at the systems level for the comprehensively and correctly realizing the physiological function in living organisms. In this review, we discussed three representative genome-scale cellular networks: GMN, TRN, and STN, representing different levels (i.e., metabolism, gene regulation, and cellular signaling) of a cell’s activities. Furthermore, we discussed the integration of the networks of the three types. With more understanding on the complexity of microbial cells, the development of integrated network has become an inevitable trend in analyzing genome-scale cellular networks of microorganisms.
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Affiliation(s)
- Tong Hao
- Tianjin Key Laboratory of Animal and Plant Resistance, College of Life Sciences, Tianjin Normal University, Tianjin, China
| | - Dan Wu
- Tianjin Key Laboratory of Animal and Plant Resistance, College of Life Sciences, Tianjin Normal University, Tianjin, China
| | - Lingxuan Zhao
- Tianjin Key Laboratory of Animal and Plant Resistance, College of Life Sciences, Tianjin Normal University, Tianjin, China
| | - Qian Wang
- Tianjin Key Laboratory of Animal and Plant Resistance, College of Life Sciences, Tianjin Normal University, Tianjin, China
| | - Edwin Wang
- Tianjin Key Laboratory of Animal and Plant Resistance, College of Life Sciences, Tianjin Normal University, Tianjin, China.,Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Jinsheng Sun
- Tianjin Key Laboratory of Animal and Plant Resistance, College of Life Sciences, Tianjin Normal University, Tianjin, China.,Tianjin Bohai Fisheries Research Institute, Tianjin, China
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Vijayakumar S, Conway M, Lió P, Angione C. Optimization of Multi-Omic Genome-Scale Models: Methodologies, Hands-on Tutorial, and Perspectives. Methods Mol Biol 2018; 1716:389-408. [PMID: 29222764 DOI: 10.1007/978-1-4939-7528-0_18] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
Abstract
Genome-scale metabolic models are valuable tools for assessing the metabolic potential of living organisms. Being downstream of gene expression, metabolism is increasingly being used as an indicator of the phenotypic outcome for drugs and therapies. We here present a review of the principal methods used for constraint-based modelling in systems biology, and explore how the integration of multi-omic data can be used to improve phenotypic predictions of genome-scale metabolic models. We believe that the large-scale comparison of the metabolic response of an organism to different environmental conditions will be an important challenge for genome-scale models. Therefore, within the context of multi-omic methods, we describe a tutorial for multi-objective optimization using the metabolic and transcriptomics adaptation estimator (METRADE), implemented in MATLAB. METRADE uses microarray and codon usage data to model bacterial metabolic response to environmental conditions (e.g., antibiotics, temperatures, heat shock). Finally, we discuss key considerations for the integration of multi-omic networks into metabolic models, towards automatically extracting knowledge from such models.
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Affiliation(s)
- Supreeta Vijayakumar
- Department of Computer Science and Information Systems, Teesside University, Middlesbrough, Tees Valley TS1 3BX, UK
| | - Max Conway
- Computer Laboratory, University of Cambridge, Cambridge, UK
| | - Pietro Lió
- Computer Laboratory, University of Cambridge, Cambridge, UK
| | - Claudio Angione
- Department of Computer Science and Information Systems, Teesside University, Middlesbrough, Tees Valley TS1 3BX, UK.
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