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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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Mirzaei S, Tefagh M. GEM-based computational modeling for exploring metabolic interactions in a microbial community. PLoS Comput Biol 2024; 20:e1012233. [PMID: 38900842 PMCID: PMC11218945 DOI: 10.1371/journal.pcbi.1012233] [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: 11/16/2023] [Revised: 07/02/2024] [Accepted: 06/03/2024] [Indexed: 06/22/2024] Open
Abstract
Microbial communities play fundamental roles in every complex ecosystem, such as soil, sea and the human body. The stability and diversity of the microbial community depend precisely on the composition of the microbiota. Any change in the composition of these communities affects microbial functions. An important goal of studying the interactions between species is to understand the behavior of microbes and their responses to perturbations. These interactions among species are mediated by the exchange of metabolites within microbial communities. We developed a computational model for the microbial community that has a separate compartment for exchanging metabolites. This model can predict possible metabolites that cause competition, commensalism, and mutual interactions between species within a microbial community. Our constraint-based community metabolic modeling approach provides insights to elucidate the pattern of metabolic interactions for each common metabolite between two microbes. To validate our approach, we used a toy model and a syntrophic co-culture of Desulfovibrio vulgaris and Methanococcus maripaludis, as well as another in co-culture between Geobacter sulfurreducens and Rhodoferax ferrireducens. For a more general evaluation, we applied our algorithm to the honeybee gut microbiome, composed of seven species, and the epiphyte strain Pantoea eucalypti 299R. The epiphyte strain Pe299R has been previously studied and cultured with six different phyllosphere bacteria. Our algorithm successfully predicts metabolites, which imply mutualistic, competitive, or commensal interactions. In contrast to OptCom, MRO, and MICOM algorithms, our COMMA algorithm shows that the potential for competitive interactions between an epiphytic species and Pe299R is not significant. These results are consistent with the experimental measurements of population density and reproductive success of the Pe299R strain.
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Affiliation(s)
- Soraya Mirzaei
- Department of Mathematical Sciences, Sharif University of Technology, Tehran, Iran
| | - Mojtaba Tefagh
- Department of Mathematical Sciences, Sharif University of Technology, Tehran, Iran
- Center for Information Systems & Data Science, Institute for Convergence Science & Technology, Sharif University of Technology, Tehran, Iran
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Silva-Lance F, Montejano-Montelongo I, Bautista E, Nielsen LK, Johansson PI, Marin de Mas I. Integrating Genome-Scale Metabolic Models with Patient Plasma Metabolome to Study Endothelial Metabolism In Situ. Int J Mol Sci 2024; 25:5406. [PMID: 38791446 PMCID: PMC11121795 DOI: 10.3390/ijms25105406] [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: 03/01/2024] [Revised: 04/30/2024] [Accepted: 05/08/2024] [Indexed: 05/26/2024] Open
Abstract
Patient blood samples are invaluable in clinical omics databases, yet current methodologies often fail to fully uncover the molecular mechanisms driving patient pathology. While genome-scale metabolic models (GEMs) show promise in systems medicine by integrating various omics data, having only exometabolomic data remains a limiting factor. To address this gap, we introduce a comprehensive pipeline integrating GEMs with patient plasma metabolome. This pipeline constructs case-specific GEMs using literature-based and patient-specific metabolomic data. Novel computational methods, including adaptive sampling and an in-house developed algorithm for the rational exploration of the sampled space of solutions, enhance integration accuracy while improving computational performance. Model characterization involves task analysis in combination with clustering methods to identify critical cellular functions. The new pipeline was applied to a cohort of trauma patients to investigate shock-induced endotheliopathy using patient plasma metabolome data. By analyzing endothelial cell metabolism comprehensively, the pipeline identified critical therapeutic targets and biomarkers that can potentially contribute to the development of therapeutic strategies. Our study demonstrates the efficacy of integrating patient plasma metabolome data into computational models to analyze endothelial cell metabolism in disease contexts. This approach offers a deeper understanding of metabolic dysregulations and provides insights into diseases with metabolic components and potential treatments.
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Affiliation(s)
- Fernando Silva-Lance
- Novo Nordisk Foundation Center for Biosustainability, Danish Technical University, 2800 Lyngby, Denmark
| | | | - Eric Bautista
- Novo Nordisk Foundation Center for Biosustainability, Danish Technical University, 2800 Lyngby, Denmark
| | - Lars K. Nielsen
- Novo Nordisk Foundation Center for Biosustainability, Danish Technical University, 2800 Lyngby, Denmark
- CAG Center for Endotheliomics, Copenhagen University Hospital, Rigshospitalet, 2100 Copenhagen, Denmark
| | - Pär I. Johansson
- CAG Center for Endotheliomics, Copenhagen University Hospital, Rigshospitalet, 2100 Copenhagen, Denmark
- Department of Clinical Medicine, University of Copenhagen, 2200 Copenhagen, Denmark
| | - Igor Marin de Mas
- Novo Nordisk Foundation Center for Biosustainability, Danish Technical University, 2800 Lyngby, Denmark
- CAG Center for Endotheliomics, Copenhagen University Hospital, Rigshospitalet, 2100 Copenhagen, Denmark
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Hulshof T, Nap B, Martinelli F, Thiele I. Microbial Abundances Retrieved from Sequencing data-automated NCBI Taxonomy (MARS): a pipeline to create relative microbial abundance data for the Microbiome Modelling Toolbox and utilizing homosynonyms for efficient mapping to resources. BIOINFORMATICS ADVANCES 2024; 4:vbae068. [PMID: 38911823 PMCID: PMC11193099 DOI: 10.1093/bioadv/vbae068] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/04/2023] [Revised: 04/18/2024] [Accepted: 05/07/2024] [Indexed: 06/25/2024]
Abstract
Motivation Computational approaches to the functional characterization of the microbiome, such as the Microbiome Modelling Toolbox, require precise information on microbial composition and relative abundances. However, challenges arise from homosynonyms-different names referring to the same taxon, which can hinder the mapping process and lead to missed species mapping when using microbial metabolic reconstruction resources, such as AGORA and APOLLO. Results We introduce the integrated MARS pipeline, a user-friendly Python-based solution that addresses these challenges. MARS automates the extraction of relative abundances from metagenomic reads, maps species and genera onto microbial metabolic reconstructions, and accounts for alternative taxonomic names. It normalizes microbial reads, provides an optional cut-off for low-abundance taxa, and produces relative abundance tables apt for integration with the Microbiome Modelling Toolbox. A sub-component of the pipeline automates the task of identifying homosynonyms, leveraging web scraping to find taxonomic IDs of given species, searching NCBI for alternative names, and cross-reference them with microbial reconstruction resources. Taken together, MARS streamlines the entire process from processed metagenomic reads to relative abundance, thereby significantly reducing time and effort when working with microbiome data. Availability and implementation MARS is implemented in Python. It can be found as an interactive application here: https://mars-pipeline.streamlit.app/along with a detailed documentation here: https://github.com/ThieleLab/mars-pipeline.
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Affiliation(s)
- Tim Hulshof
- School of Medicine, University of Galway, Galway H91 TK33, Ireland
- Digital Metabolic Twin Centre, University of Galway, Galway H91 TK33, Ireland
| | - Bram Nap
- School of Medicine, University of Galway, Galway H91 TK33, Ireland
- Digital Metabolic Twin Centre, University of Galway, Galway H91 TK33, Ireland
| | - Filippo Martinelli
- School of Medicine, University of Galway, Galway H91 TK33, Ireland
- Digital Metabolic Twin Centre, University of Galway, Galway H91 TK33, Ireland
| | - Ines Thiele
- School of Medicine, University of Galway, Galway H91 TK33, Ireland
- Digital Metabolic Twin Centre, University of Galway, Galway H91 TK33, Ireland
- Discipline of Microbiology, University of Galway, Galway H91 TK33, Ireland
- Ryan Institute, University of Galway, Galway H91 TK33, Ireland
- APC Microbiome Ireland, Cork T12 K8AF, Ireland
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Raghu AK, Palanikumar I, Raman K. Designing function-specific minimal microbiomes from large microbial communities. NPJ Syst Biol Appl 2024; 10:46. [PMID: 38702322 PMCID: PMC11068740 DOI: 10.1038/s41540-024-00373-1] [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: 08/09/2023] [Accepted: 04/17/2024] [Indexed: 05/06/2024] Open
Abstract
Microorganisms exist in large communities of diverse species, exhibiting various functionalities. The mammalian gut microbiome, for instance, has the functionality of digesting dietary fibre and producing different short-chain fatty acids. Not all microbes present in a community contribute to a given functionality; it is possible to find a minimal microbiome, which is a subset of the large microbiome, that is capable of performing the functionality while maintaining other community properties such as growth rate and metabolite production. Such a minimal microbiome will also contain keystone species for SCFA production in that community. In this work, we present a systematic constraint-based approach to identify a minimal microbiome from a large community for a user-proposed function. We employ a top-down approach with sequential deletion followed by solving a mixed-integer linear programming problem with the objective of minimising the L1-norm of the membership vector. Notably, we consider quantitative measures of community growth rate and metabolite production rates. We demonstrate the utility of our algorithm by identifying the minimal microbiomes corresponding to three model communities of the gut, and discuss their validity based on the presence of the keystone species in the community. Our approach is generic, flexible and finds application in studying a variety of microbial communities. The algorithm is available from https://github.com/RamanLab/minMicrobiome .
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Affiliation(s)
- Aswathy K Raghu
- Centre for Integrative Biology and Systems mEdicine (IBSE), Indian Institute of Technology (IIT) Madras, Chennai, 600 036, India
- Robert Bosch Centre for Data Science and Artificial Intelligence (RBCDSAI), IIT Madras, Chennai, 600 036, India
- Department of Chemical and Biological Engineering, Northwestern University, IL, 60208, USA
| | - Indumathi Palanikumar
- Centre for Integrative Biology and Systems mEdicine (IBSE), Indian Institute of Technology (IIT) Madras, Chennai, 600 036, India
- Robert Bosch Centre for Data Science and Artificial Intelligence (RBCDSAI), IIT Madras, Chennai, 600 036, India
- Department of Biotechnology, Bhupat Jyoti Mehta School of Biosciences, IIT Madras, Chennai, 600 036, India
| | - Karthik Raman
- Centre for Integrative Biology and Systems mEdicine (IBSE), Indian Institute of Technology (IIT) Madras, Chennai, 600 036, India.
- Robert Bosch Centre for Data Science and Artificial Intelligence (RBCDSAI), IIT Madras, Chennai, 600 036, India.
- Department of Biotechnology, Bhupat Jyoti Mehta School of Biosciences, IIT Madras, Chennai, 600 036, India.
- Department of Data Science and AI, Wadhwani School of Data Science and AI, IIT Madras, Chennai, 600 036, India.
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Blasco T, Balzerani F, Valcárcel LV, Larrañaga P, Bielza C, Francino MP, Rufián-Henares JÁ, Planes FJ, Pérez-Burillo S. BN-BacArena: Bayesian network extension of BacArena for the dynamic simulation of microbial communities. Bioinformatics 2024; 40:btae266. [PMID: 38688585 PMCID: PMC11082422 DOI: 10.1093/bioinformatics/btae266] [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: 08/29/2023] [Revised: 03/11/2024] [Accepted: 04/29/2024] [Indexed: 05/02/2024] Open
Abstract
MOTIVATION Simulating gut microbial dynamics is extremely challenging. Several computational tools, notably the widely used BacArena, enable modeling of dynamic changes in the microbial environment. These methods, however, do not comprehensively account for microbe-microbe stimulant or inhibitory effects or for nutrient-microbe inhibitory effects, typically observed in different compounds present in the daily diet. RESULTS Here, we present BN-BacArena, an extension of BacArena consisting on the incorporation within the native computational framework of a Bayesian network model that accounts for microbe-microbe and nutrient-microbe interactions. Using in vitro experiments, 16S rRNA gene sequencing data and nutritional composition of 55 foods, the output Bayesian network showed 23 significant nutrient-bacteria interactions, suggesting the importance of compounds such as polyols, ascorbic acid, polyphenols and other phytochemicals, and 40 bacteria-bacteria significant relationships. With test data, BN-BacArena demonstrates a statistically significant improvement over BacArena to predict the time-dependent relative abundance of bacterial species involved in the gut microbiota upon different nutritional interventions. As a result, BN-BacArena opens new avenues for the dynamic modeling and simulation of the human gut microbiota metabolism. AVAILABILITY AND IMPLEMENTATION MATLAB and R code are available in https://github.com/PlanesLab/BN-BacArena.
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Affiliation(s)
- Telmo Blasco
- Department of Biomedical Engineering and Sciences, Tecnun School of Engineering, University of Navarra, San Sebastián 20018, Spain
| | - Francesco Balzerani
- Department of Biomedical Engineering and Sciences, Tecnun School of Engineering, University of Navarra, San Sebastián 20018, Spain
| | - Luis V Valcárcel
- Department of Biomedical Engineering and Sciences, Tecnun School of Engineering, University of Navarra, San Sebastián 20018, Spain
- Biomedical Engineering Center, Campus Universitario, University of Navarra, Pamplona, Navarra 31009, Spain
- Instituto de Ciencia de los Datos e Inteligencia Artificial (DATAI), Campus Universitario, University of Navarra, Pamplona 31080, Spain
| | - Pedro Larrañaga
- Departamento de Inteligencia Artificial, Universidad Politécnica de Madrid, Madrid 28660, Spain
| | - Concha Bielza
- Departamento de Inteligencia Artificial, Universidad Politécnica de Madrid, Madrid 28660, Spain
| | - María Pilar Francino
- Area de Genómica y Salud, Fundación para el Fomento de la Investigación Sanitaria y Biomédica de la Comunitat Valenciana-Salud Pública, Valencia 46020, Spain
- CIBER en Epidemiología y Salud Pública, Instituto de Salud Carlos III, Madrid 28029, Spain
| | - José Ángel Rufián-Henares
- Departamento de Nutrición y Bromatología, Centro de Investigación Biomédica, Instituto de Nutrición y Tecnología de los Alimentos, Universidad de Granada, Granada 18016, Spain
- Instituto de Investigación Biosanitaria ibs. GRANADA, Universidad de Granada, Granada 18012, Spain
| | - Francisco J Planes
- Department of Biomedical Engineering and Sciences, Tecnun School of Engineering, University of Navarra, San Sebastián 20018, Spain
- Biomedical Engineering Center, Campus Universitario, University of Navarra, Pamplona, Navarra 31009, Spain
- Instituto de Ciencia de los Datos e Inteligencia Artificial (DATAI), Campus Universitario, University of Navarra, Pamplona 31080, Spain
| | - Sergio Pérez-Burillo
- Department of Biomedical Engineering and Sciences, Tecnun School of Engineering, University of Navarra, San Sebastián 20018, Spain
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Scoditti E, Sabatini S, Carli F, Gastaldelli A. Hepatic glucose metabolism in the steatotic liver. Nat Rev Gastroenterol Hepatol 2024; 21:319-334. [PMID: 38308003 DOI: 10.1038/s41575-023-00888-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 12/13/2023] [Indexed: 02/04/2024]
Abstract
The liver is central in regulating glucose homeostasis, being the major contributor to endogenous glucose production and the greatest reserve of glucose as glycogen. It is both a target and regulator of the action of glucoregulatory hormones. Hepatic metabolic functions are altered in and contribute to the highly prevalent steatotic liver disease (SLD), including metabolic dysfunction-associated SLD (MASLD) and metabolic dysfunction-associated steatohepatitis (MASH). In this Review, we describe the dysregulation of hepatic glucose metabolism in MASLD and MASH and associated metabolic comorbidities, and how advances in techniques and models for the assessment of hepatic glucose fluxes in vivo have led to the identification of the mechanisms related to the alterations in glucose metabolism in MASLD and comorbidities. These fluxes can ultimately increase hepatic glucose production concomitantly with fat accumulation and alterations in the secretion and action of glucoregulatory hormones. No pharmacological treatment has yet been approved for MASLD or MASH, but some antihyperglycaemic drugs approved for treating type 2 diabetes have shown positive effects on hepatic glucose metabolism and hepatosteatosis. A deep understanding of how MASLD affects glucose metabolic fluxes and glucoregulatory hormones might assist in the early identification of at-risk individuals and the use or development of targeted therapies.
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Affiliation(s)
- Egeria Scoditti
- Institute of Clinical Physiology, National Research Council, Lecce, Italy
| | - Silvia Sabatini
- Institute of Clinical Physiology, National Research Council, Pisa, Italy
| | - Fabrizia Carli
- Institute of Clinical Physiology, National Research Council, Pisa, Italy
| | - Amalia Gastaldelli
- Institute of Clinical Physiology, National Research Council, Pisa, Italy.
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Gong Z, Chen J, Jiao X, Gong H, Pan D, Liu L, Zhang Y, Tan T. Genome-scale metabolic network models for industrial microorganisms metabolic engineering: Current advances and future prospects. Biotechnol Adv 2024; 72:108319. [PMID: 38280495 DOI: 10.1016/j.biotechadv.2024.108319] [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: 12/03/2023] [Revised: 01/04/2024] [Accepted: 01/18/2024] [Indexed: 01/29/2024]
Abstract
The construction of high-performance microbial cell factories (MCFs) is the centerpiece of biomanufacturing. However, the complex metabolic regulatory network of microorganisms poses great challenges for the efficient design and construction of MCFs. The genome-scale metabolic network models (GSMs) can systematically simulate the metabolic regulation process of microorganisms in silico, providing effective guidance for the rapid design and construction of MCFs. In this review, we summarized the development status of 16 important industrial microbial GSMs, and further outline the technologies or methods that continuously promote high-quality GSMs construction from five aspects: I) Databases and modeling tools facilitate GSMs reconstruction; II) evolving gap-filling technologies; III) constraint-based model reconstruction; IV) advances in algorithms; and V) developed visualization tools. In addition, we also summarized the applications of GSMs in guiding metabolic engineering from four aspects: I) exploring and explaining metabolic features; II) predicting the effects of genetic perturbations on metabolism; III) predicting the optimal phenotype; IV) guiding cell factories construction in practical experiment. Finally, we discussed the development of GSMs, aiming to provide a reference for efficiently reconstructing GSMs and guiding metabolic engineering.
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Affiliation(s)
- Zhijin Gong
- National Energy R&D Center for Biorefinery, College of Life Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China; Beijing Key Laboratory of Bioprocess, College of Life Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China
| | - Jiayao Chen
- National Energy R&D Center for Biorefinery, College of Life Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China; Beijing Key Laboratory of Bioprocess, College of Life Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China
| | - Xinyu Jiao
- National Energy R&D Center for Biorefinery, College of Life Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China; Beijing Key Laboratory of Bioprocess, College of Life Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China
| | - Hao Gong
- National Energy R&D Center for Biorefinery, College of Life Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China; Beijing Key Laboratory of Bioprocess, College of Life Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China; College of Mathematics and Physics, Beijing University of Chemical Technology, Beijing 100029, China
| | - Danzi Pan
- National Energy R&D Center for Biorefinery, College of Life Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China; Beijing Key Laboratory of Bioprocess, College of Life Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China; College of Mathematics and Physics, Beijing University of Chemical Technology, Beijing 100029, China
| | - Lingli Liu
- National Energy R&D Center for Biorefinery, College of Life Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China; Beijing Key Laboratory of Bioprocess, College of Life Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China; College of Mathematics and Physics, Beijing University of Chemical Technology, Beijing 100029, China
| | - Yang Zhang
- National Energy R&D Center for Biorefinery, College of Life Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China; Beijing Key Laboratory of Bioprocess, College of Life Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China
| | - Tianwei Tan
- National Energy R&D Center for Biorefinery, College of Life Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China; Beijing Key Laboratory of Bioprocess, College of Life Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China.
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Manoli MT, Gargantilla-Becerra Á, Del Cerro Sánchez C, Rivero-Buceta V, Prieto MA, Nogales J. A model-driven approach to upcycling recalcitrant feedstocks in Pseudomonas putida by decoupling PHA production from nutrient limitation. Cell Rep 2024; 43:113979. [PMID: 38517887 DOI: 10.1016/j.celrep.2024.113979] [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: 08/09/2023] [Revised: 01/29/2024] [Accepted: 03/06/2024] [Indexed: 03/24/2024] Open
Abstract
Bacterial polyhydroxyalkanoates (PHAs) have emerged as promising eco-friendly alternatives to petroleum-based plastics since they are synthesized from renewable resources and offer exceptional properties. However, their production is limited to the stationary growth phase under nutrient-limited conditions, requiring customized strategies and costly two-phase bioprocesses. In this study, we tackle these challenges by employing a model-driven approach to reroute carbon flux and remove regulatory constraints using synthetic biology. We construct a collection of Pseudomonas putida-overproducing strains at the expense of plastics and lignin-related compounds using growth-coupling approaches. PHA production was successfully achieved during growth phase, resulting in the production of up to 46% PHA/cell dry weight while maintaining a balanced carbon-to-nitrogen ratio. Our strains are additionally validated under an upcycling scenario using enzymatically hydrolyzed polyethylene terephthalate as a feedstock. These findings have the potential to revolutionize PHA production and address the global plastic crisis by overcoming the complexities of traditional PHA production bioprocesses.
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Affiliation(s)
- Maria-Tsampika Manoli
- Polymer Biotechnology Group, Department of Microbial and Plant Biotechnology, Margarita Salas Center for Biological Research (CIB-CSIC), 28040 Madrid, Spain; Interdisciplinary Platform for Sustainable Plastics towards a Circular Economy-Spanish National Research Council (SusPlast-CSIC), Madrid, Spain
| | - Álvaro Gargantilla-Becerra
- Interdisciplinary Platform for Sustainable Plastics towards a Circular Economy-Spanish National Research Council (SusPlast-CSIC), Madrid, Spain; 3Systems Biotechnology Group, Department of Systems Biology, Centro Nacional de Biotecnología, CSIC, Madrid 28049, Spain
| | - Carlos Del Cerro Sánchez
- Polymer Biotechnology Group, Department of Microbial and Plant Biotechnology, Margarita Salas Center for Biological Research (CIB-CSIC), 28040 Madrid, Spain; Interdisciplinary Platform for Sustainable Plastics towards a Circular Economy-Spanish National Research Council (SusPlast-CSIC), Madrid, Spain
| | - Virginia Rivero-Buceta
- Polymer Biotechnology Group, Department of Microbial and Plant Biotechnology, Margarita Salas Center for Biological Research (CIB-CSIC), 28040 Madrid, Spain; Interdisciplinary Platform for Sustainable Plastics towards a Circular Economy-Spanish National Research Council (SusPlast-CSIC), Madrid, Spain
| | - M Auxiliadora Prieto
- Polymer Biotechnology Group, Department of Microbial and Plant Biotechnology, Margarita Salas Center for Biological Research (CIB-CSIC), 28040 Madrid, Spain; Interdisciplinary Platform for Sustainable Plastics towards a Circular Economy-Spanish National Research Council (SusPlast-CSIC), Madrid, Spain.
| | - Juan Nogales
- Interdisciplinary Platform for Sustainable Plastics towards a Circular Economy-Spanish National Research Council (SusPlast-CSIC), Madrid, Spain; 3Systems Biotechnology Group, Department of Systems Biology, Centro Nacional de Biotecnología, CSIC, Madrid 28049, Spain; CNB DNA Biofoundry (CNBio), CSIC, Madrid, Spain.
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60
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Paulay A, Grimaud GM, Caballero R, Laroche B, Leclerc M, Labarthe S, Maguin E. Design of a proteolytic module for improved metabolic modeling of Bacteroides caccae. mSystems 2024; 9:e0015324. [PMID: 38517169 PMCID: PMC11019848 DOI: 10.1128/msystems.00153-24] [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: 02/14/2024] [Accepted: 02/27/2024] [Indexed: 03/23/2024] Open
Abstract
The gut microbiota plays a crucial role in health and is significantly modulated by human diets. In addition to Western diets which are rich in proteins, high-protein diets are used for specific populations or indications, mainly weight loss. In this study, we investigated the effect of protein supplementation on Bacteroides caccae, a Gram-negative gut symbiont. The supplementation with whey proteins led to a significant increase in growth rate, final biomass, and short-chain fatty acids production. A comprehensive genomic analysis revealed that B. caccae possesses a set of 156 proteases with putative intracellular and extracellular localization and allowed to identify amino acid transporters and metabolic pathways. We developed a fully curated genome-scale metabolic model of B. caccae that incorporated its proteolytic activity and simulated its growth and production of fermentation-related metabolites in response to the different growth media. We validated the model by comparing the predicted phenotype to experimental data. The model accurately predicted B. caccae's growth and metabolite production (R2 = 0.92 for the training set and R2 = 0.89 for the validation set). We found that accounting for both ATP consumption related to proteolysis, and whey protein accessibility is necessary for accurate predictions of metabolites production. These results provide insights into B. caccae's adaptation to a high-protein diet and its ability to utilize proteins as a source of nutrition. The proposed model provides a useful tool for understanding the feeding mechanism of B. caccae in the gut microbiome.IMPORTANCEMicrobial proteolysis is understudied despite the availability of dietary proteins for the gut microbiota. Here, the proteolytic potential of the gut symbiont Bacteroides caccae was analyzed for the first time using pan-genomics. This sketches a well-equipped bacteria for protein breakdown, capable of producing 156 different proteases with a broad spectrum of cleavage targets. This functional potential was confirmed by the enhancement of growth and metabolic activities at high protein levels. Proteolysis was included in a B. caccae metabolic model which was fitted with the experiments and validated on external data. This model pinpoints the links between protein availability and short-chain fatty acids production, and the importance for B. caccae to gain access to glutamate and asparagine to promote growth. This integrated approach can be generalized to other symbionts and upscaled to complex microbiota to get insights into the ecological impact of proteins on the gut microbiota.
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Affiliation(s)
- Amandine Paulay
- Université Paris-Saclay, INRAE, AgroParisTech, UMR1319 Micalis Institute, Jouy-en-Josas, France
- Biomathematica, Ajaccio, France
- Université Paris-Saclay, INRAE, MaIAGE, Jouy-en-Josas, France
| | | | - Raphaël Caballero
- Université Paris-Saclay, INRAE, AgroParisTech, UMR1319 Micalis Institute, Jouy-en-Josas, France
| | - Béatrice Laroche
- Université Paris-Saclay, INRAE, MaIAGE, Jouy-en-Josas, France
- Université Paris-Saclay, Inria, Centre Inria de Saclay, Palaiseau, France
| | - Marion Leclerc
- Université Paris-Saclay, INRAE, AgroParisTech, UMR1319 Micalis Institute, Jouy-en-Josas, France
- Pendulum Therapeutics, San Francisco, California, USA
| | - Simon Labarthe
- Université Paris-Saclay, INRAE, MaIAGE, Jouy-en-Josas, France
- University of Bordeaux, INRAE, BIOGECO, Cestas, France
- Inria, Univ. Bordeaux, INRAE, Talence, France
| | - Emmanuelle Maguin
- Université Paris-Saclay, INRAE, AgroParisTech, UMR1319 Micalis Institute, Jouy-en-Josas, France
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Hernandez DM, Marzouk M, Cole M, Fortoul MC, Kethireddy SR, Contractor R, Islam H, Moulder T, Kalifa AR, Meneses EM, Mendoza MB, Thomas R, Masud S, Pubien S, Milanes P, Diaz-Tang G, Lopatkin AJ, Smith RP. Purine and pyrimidine synthesis differently affect the strength of the inoculum effect for aminoglycoside and β-lactam antibiotics. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.09.588696. [PMID: 38645041 PMCID: PMC11030397 DOI: 10.1101/2024.04.09.588696] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/23/2024]
Abstract
The inoculum effect has been observed for nearly all antibiotics and bacterial species. However, explanations accounting for its occurrence and strength are lacking. We previously found that growth productivity, which captures the relationship between [ATP] and growth, can account for the strength of the inoculum effect for bactericidal antibiotics. However, the molecular pathway(s) underlying this relationship, and therefore determining the inoculum effect, remain undiscovered. We show that nucleotide synthesis can determine the relationship between [ATP] and growth, and thus the strength of inoculum effect in an antibiotic class-dependent manner. Specifically, and separate from activity through the tricarboxylic acid cycle, we find that transcriptional activity of genes involved in purine and pyrimidine synthesis can predict the strength of the inoculum effect for β-lactam and aminoglycosides antibiotics, respectively. Our work highlights the antibiotic class-specific effect of purine and pyrimidine synthesis on the severity of the inoculum effect and paves the way for intervention strategies to reduce the inoculum effect in the clinic.
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Affiliation(s)
- Daniella M. Hernandez
- Cell Therapy Institute, Kiran Patel College of Allopathic Medicine, Nova Southeastern University, Fort Lauderdale, FL, 33314
| | - Melissa Marzouk
- Cell Therapy Institute, Kiran Patel College of Allopathic Medicine, Nova Southeastern University, Fort Lauderdale, FL, 33314
- Department of Biological Sciences, Halmos College of Arts and Science, Nova Southeastern University, Fort Lauderdale, FL, 33314
| | - Madeline Cole
- Department of Medical Education, Kiran Patel College of Allopathic Medicine, Nova Southeastern University, Fort Lauderdale, FL, 33314
| | - Marla C. Fortoul
- Department of Medical Education, Kiran Patel College of Allopathic Medicine, Nova Southeastern University, Fort Lauderdale, FL, 33314
| | - Saipranavi Reddy Kethireddy
- Department of Biological Sciences, Halmos College of Arts and Science, Nova Southeastern University, Fort Lauderdale, FL, 33314
| | - Rehan Contractor
- Department of Biological Sciences, Halmos College of Arts and Science, Nova Southeastern University, Fort Lauderdale, FL, 33314
| | - Habibul Islam
- Department of Chemical Engineering, University of Rochester; Rochester, NY 14627; USA
| | - Trent Moulder
- Department of Biological Sciences, Halmos College of Arts and Science, Nova Southeastern University, Fort Lauderdale, FL, 33314
| | - Ariane R. Kalifa
- Cell Therapy Institute, Kiran Patel College of Allopathic Medicine, Nova Southeastern University, Fort Lauderdale, FL, 33314
- Department of Biological Sciences, Halmos College of Arts and Science, Nova Southeastern University, Fort Lauderdale, FL, 33314
| | - Estefania Marin Meneses
- Cell Therapy Institute, Kiran Patel College of Allopathic Medicine, Nova Southeastern University, Fort Lauderdale, FL, 33314
- Department of Biological Sciences, Halmos College of Arts and Science, Nova Southeastern University, Fort Lauderdale, FL, 33314
| | - Maximiliano Barbosa Mendoza
- Cell Therapy Institute, Kiran Patel College of Allopathic Medicine, Nova Southeastern University, Fort Lauderdale, FL, 33314
| | - Ruth Thomas
- Department of Biological Sciences, Halmos College of Arts and Science, Nova Southeastern University, Fort Lauderdale, FL, 33314
| | - Saad Masud
- Department of Medical Education, Kiran Patel College of Allopathic Medicine, Nova Southeastern University, Fort Lauderdale, FL, 33314
| | - Sheena Pubien
- Department of Medical Education, Kiran Patel College of Allopathic Medicine, Nova Southeastern University, Fort Lauderdale, FL, 33314
| | - Patricia Milanes
- Department of Medical Education, Kiran Patel College of Allopathic Medicine, Nova Southeastern University, Fort Lauderdale, FL, 33314
| | - Gabriela Diaz-Tang
- Cell Therapy Institute, Kiran Patel College of Allopathic Medicine, Nova Southeastern University, Fort Lauderdale, FL, 33314
- Department of Biological Sciences, Halmos College of Arts and Science, Nova Southeastern University, Fort Lauderdale, FL, 33314
| | - Allison J. Lopatkin
- Department of Chemical Engineering, University of Rochester; Rochester, NY 14627; USA
- Department of Microbiology and Immunology, University of Rochester Medical Center; Rochester, NY 14627; USA
- Department of Biomedical Engineering, University of Rochester Medical Center; Rochester, NY 14627; USA
| | - Robert P. Smith
- Cell Therapy Institute, Kiran Patel College of Allopathic Medicine, Nova Southeastern University, Fort Lauderdale, FL, 33314
- Department of Medical Education, Kiran Patel College of Allopathic Medicine, Nova Southeastern University, Fort Lauderdale, FL, 33314
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Sertbas M, Ulgen KO. Uncovering the Effect of SARS-CoV-2 on Liver Metabolism via Genome-Scale Metabolic Modeling for Reprogramming and Therapeutic Strategies. ACS OMEGA 2024; 9:15535-15546. [PMID: 38585079 PMCID: PMC10993323 DOI: 10.1021/acsomega.4c00392] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/11/2024] [Revised: 02/29/2024] [Accepted: 03/04/2024] [Indexed: 04/09/2024]
Abstract
Genome-scale metabolic models (GEMs) are promising computational tools that contribute to elucidating host-virus interactions at the system level and developing therapeutic strategies against viral infection. In this study, the effect of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) on liver metabolism was investigated using integrated GEMs of human hepatocytes and SARS-CoV-2. They were generated for uninfected and infected hepatocytes using transcriptome data. Reporter metabolite analysis resulted in significant transcriptional changes around several metabolites involved in xenobiotics, drugs, arachidonic acid, and leukotriene metabolisms due to SARS-CoV-2 infection. Flux balance analysis and minimization of metabolic adjustment approaches unraveled possible virus-induced hepatocellular reprogramming in fatty acid, glycerophospholipid, sphingolipid cholesterol, and folate metabolisms, bile acid biosynthesis, and carnitine shuttle among others. Reaction knockout analysis provided critical reactions in glycolysis, oxidative phosphorylation, purine metabolism, and reactive oxygen species detoxification subsystems. Computational analysis also showed that administration of dopamine, glucosamine, D-xylose, cysteine, and (R)-3-hydroxybutanoate contributes to alleviating viral infection. In essence, the reconstructed host-virus GEM helps us understand metabolic programming and develop therapeutic strategies to battle SARS-CoV-2.
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Affiliation(s)
- Mustafa Sertbas
- Department of Chemical Engineering, Bogazici University, 34342 Istanbul, Turkey
| | - Kutlu O. Ulgen
- Department of Chemical Engineering, Bogazici University, 34342 Istanbul, Turkey
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63
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Chen Y, Gustafsson J, Yang J, Nielsen J, Kerkhoven EJ. Single-cell omics analysis with genome-scale metabolic modeling. Curr Opin Biotechnol 2024; 86:103078. [PMID: 38359604 DOI: 10.1016/j.copbio.2024.103078] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2023] [Accepted: 01/19/2024] [Indexed: 02/17/2024]
Abstract
Single-cell technologies have been widely used in biological studies and generated a plethora of single-cell data to be interpreted. Due to the inclusion of the priori metabolic network knowledge as well as gene-protein-reaction associations, genome-scale metabolic models (GEMs) have been a powerful tool to integrate and thereby interpret various omics data mostly from bulk samples. Here, we first review two common ways to leverage bulk omics data with GEMs and then discuss advances on integrative analysis of single-cell omics data with GEMs. We end by presenting our views on current challenges and perspectives in this field.
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Affiliation(s)
- Yu Chen
- Key Laboratory of Quantitative Synthetic Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China.
| | - Johan Gustafsson
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Department of Medical Biochemistry and Cell Biology, Institute of Biomedicine, Sahlgrenska Academy, University of Gothenburg, SE-405 30 Gothenburg, Sweden; Department of Life Sciences, Chalmers University of Technology, SE-412 96 Gothenburg, Sweden
| | - Jingyu Yang
- Key Laboratory of Quantitative Synthetic Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Jens Nielsen
- Department of Life Sciences, Chalmers University of Technology, SE-412 96 Gothenburg, Sweden; BioInnovation Institute, DK-2200 Copenhagen, Denmark
| | - Eduard J Kerkhoven
- Department of Life Sciences, Chalmers University of Technology, SE-412 96 Gothenburg, Sweden; Novo Nordisk Foundation Center for Biosustainability, Technology University of Denmark, DK-2800 Kgs. Lyngby, Denmark; SciLifeLab, Chalmers University of Technology, SE-412 96 Gothenburg, Sweden.
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64
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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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65
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Olaverri-Mendizabal D, Valcárcel LV, Barrena N, Rodríguez CJ, Planes FJ. Review and meta-analysis of the genetic Minimal Cut Set approach for gene essentiality prediction in cancer metabolism. Brief Bioinform 2024; 25:bbae115. [PMID: 38546323 PMCID: PMC10976907 DOI: 10.1093/bib/bbae115] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2023] [Revised: 02/15/2024] [Accepted: 02/26/2024] [Indexed: 06/15/2024] Open
Abstract
Cancer metabolism is a marvellously complex topic, in part, due to the reprogramming of its pathways to self-sustain the malignant phenotype in the disease, to the detriment of its healthy counterpart. Understanding these adjustments can provide novel targeted therapies that could disrupt and impair proliferation of cancerous cells. For this very purpose, genome-scale metabolic models (GEMs) have been developed, with Human1 being the most recent reconstruction of the human metabolism. Based on GEMs, we introduced the genetic Minimal Cut Set (gMCS) approach, an uncontextualized methodology that exploits the concepts of synthetic lethality to predict metabolic vulnerabilities in cancer. gMCSs define a set of genes whose knockout would render the cell unviable by disrupting an essential metabolic task in GEMs, thus, making cellular proliferation impossible. Here, we summarize the gMCS approach and review the current state of the methodology by performing a systematic meta-analysis based on two datasets of gene essentiality in cancer. First, we assess several thresholds and distinct methodologies for discerning highly and lowly expressed genes. Then, we address the premise that gMCSs of distinct length should have the same predictive power. Finally, we question the importance of a gene partaking in multiple gMCSs and analyze the importance of all the essential metabolic tasks defined in Human1. Our meta-analysis resulted in parameter evaluation to increase the predictive power for the gMCS approach, as well as a significant reduction of computation times by only selecting the crucial gMCS lengths, proposing the pertinency of particular parameters for the peak processing of gMCS.
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Affiliation(s)
- Danel Olaverri-Mendizabal
- Tecnun School of Engineering, University of Navarra, Manuel de Lardizábal 13, San Sebastián 20018, Spain
| | - Luis V Valcárcel
- Tecnun School of Engineering, University of Navarra, Manuel de Lardizábal 13, San Sebastián 20018, Spain
- Biomedical Engineering Center, University of Navarra, Campus Universitario, Pamplona, Navarra 31009, Spain
- Instituto de Ciencia de los Datos e Inteligencia Artificial (DATAI), University of Navarra, Campus Universitario, Pamplona 31080, Spain
| | - Naroa Barrena
- Tecnun School of Engineering, University of Navarra, Manuel de Lardizábal 13, San Sebastián 20018, Spain
| | - Carlos J Rodríguez
- Tecnun School of Engineering, University of Navarra, Manuel de Lardizábal 13, San Sebastián 20018, Spain
| | - Francisco J Planes
- Tecnun School of Engineering, University of Navarra, Manuel de Lardizábal 13, San Sebastián 20018, Spain
- Biomedical Engineering Center, University of Navarra, Campus Universitario, Pamplona, Navarra 31009, Spain
- Instituto de Ciencia de los Datos e Inteligencia Artificial (DATAI), University of Navarra, Campus Universitario, Pamplona 31080, Spain
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66
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Dvořák P, Burýšková B, Popelářová B, Ebert BE, Botka T, Bujdoš D, Sánchez-Pascuala A, Schöttler H, Hayen H, de Lorenzo V, Blank LM, Benešík M. Synthetically-primed adaptation of Pseudomonas putida to a non-native substrate D-xylose. Nat Commun 2024; 15:2666. [PMID: 38531855 DOI: 10.1038/s41467-024-46812-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2023] [Accepted: 03/11/2024] [Indexed: 03/28/2024] Open
Abstract
To broaden the substrate scope of microbial cell factories towards renewable substrates, rational genetic interventions are often combined with adaptive laboratory evolution (ALE). However, comprehensive studies enabling a holistic understanding of adaptation processes primed by rational metabolic engineering remain scarce. The industrial workhorse Pseudomonas putida was engineered to utilize the non-native sugar D-xylose, but its assimilation into the bacterial biochemical network via the exogenous xylose isomerase pathway remained unresolved. Here, we elucidate the xylose metabolism and establish a foundation for further engineering followed by ALE. First, native glycolysis is derepressed by deleting the local transcriptional regulator gene hexR. We then enhance the pentose phosphate pathway by implanting exogenous transketolase and transaldolase into two lag-shortened strains and allow ALE to finetune the rewired metabolism. Subsequent multilevel analysis and reverse engineering provide detailed insights into the parallel paths of bacterial adaptation to the non-native carbon source, highlighting the enhanced expression of transaldolase and xylose isomerase along with derepressed glycolysis as key events during the process.
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Affiliation(s)
- Pavel Dvořák
- Department of Experimental Biology, Faculty of Science, Masaryk University, Kamenice 753/5, 62500, Brno, Czech Republic.
| | - Barbora Burýšková
- Department of Experimental Biology, Faculty of Science, Masaryk University, Kamenice 753/5, 62500, Brno, Czech Republic
| | - Barbora Popelářová
- Department of Experimental Biology, Faculty of Science, Masaryk University, Kamenice 753/5, 62500, Brno, Czech Republic
| | - Birgitta E Ebert
- Australian Institute for Bioengineering and Nanotechnology, The University of Queensland, Cnr College Rd & Cooper Rd, St Lucia, QLD, QLD 4072, Australia
| | - Tibor Botka
- Department of Experimental Biology, Faculty of Science, Masaryk University, Kamenice 753/5, 62500, Brno, Czech Republic
| | - Dalimil Bujdoš
- APC Microbiome Ireland, University College Cork, College Rd, Cork, T12 YT20, Ireland
- School of Microbiology, University College Cork, College Rd, Cork, T12 Y337, Ireland
| | - Alberto Sánchez-Pascuala
- Department of Biochemistry and Synthetic Metabolism, Max Planck Institute for Terrestrial Microbiology, Karl-von-Frisch-Straße 10, 35043, Marburg, Germany
| | - Hannah Schöttler
- Institute of Inorganic and Analytical Chemistry, University of Münster, Corrensstraße 48, 48149, Münster, Germany
| | - Heiko Hayen
- Institute of Inorganic and Analytical Chemistry, University of Münster, Corrensstraße 48, 48149, Münster, Germany
| | - Víctor de Lorenzo
- Systems and Synthetic Biology Program, Centro Nacional de Biotecnología CNB-CSIC, Cantoblanco, Darwin 3, 28049, Madrid, Spain
| | - Lars M Blank
- Institute of Applied Microbiology, RWTH Aachen University, Worringer Weg 1, 52074, Aachen, Germany
| | - Martin Benešík
- Department of Experimental Biology, Faculty of Science, Masaryk University, Kamenice 753/5, 62500, Brno, Czech Republic
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67
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Hao T, Song Z, Zhang M, Zhang L, Yang J, Li J, Sun J. Reconstruction of Metabolic-Protein Interaction Integrated Network of Eriocheir sinensis and Analysis of Ecdysone Synthesis. Genes (Basel) 2024; 15:410. [PMID: 38674345 PMCID: PMC11049885 DOI: 10.3390/genes15040410] [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: 02/28/2024] [Revised: 03/24/2024] [Accepted: 03/25/2024] [Indexed: 04/28/2024] Open
Abstract
Integrated networks have become a new interest in genome-scale network research due to their ability to comprehensively reflect and analyze the molecular processes in cells. Currently, none of the integrated networks have been reported for higher organisms. Eriocheir sinensis is a typical aquatic animal that grows through ecdysis. Ecdysone has been identified to be a crucial regulator of ecdysis, but the influence factors and regulatory mechanisms of ecdysone synthesis in E. sinensis are still unclear. In this work, the genome-scale metabolic network and protein-protein interaction network of E. sinensis were integrated to reconstruct a metabolic-protein interaction integrated network (MPIN). The MPIN was used to analyze the influence factors of ecdysone synthesis through flux variation analysis. In total, 236 integrated reactions (IRs) were found to influence the ecdysone synthesis of which 16 IRs had a significant impact. These IRs constitute three ecdysone synthesis routes. It is found that there might be alternative pathways to obtain cholesterol for ecdysone synthesis in E. sinensis instead of absorbing it directly from the feeds. The MPIN reconstructed in this work is the first integrated network for higher organisms. The analysis based on the MPIN supplies important information for the mechanism analysis of ecdysone synthesis in E. sinensis.
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Affiliation(s)
- Tong Hao
- Tianjin Key Laboratory of Animal and Plant Resistance, College of Life Sciences, Tianjin Normal University, Tianjin 300387, China; (T.H.); (Z.S.); (M.Z.); (L.Z.); (J.Y.)
| | - Zhentao Song
- Tianjin Key Laboratory of Animal and Plant Resistance, College of Life Sciences, Tianjin Normal University, Tianjin 300387, China; (T.H.); (Z.S.); (M.Z.); (L.Z.); (J.Y.)
| | - Mingzhi Zhang
- Tianjin Key Laboratory of Animal and Plant Resistance, College of Life Sciences, Tianjin Normal University, Tianjin 300387, China; (T.H.); (Z.S.); (M.Z.); (L.Z.); (J.Y.)
| | - Lingrui Zhang
- Tianjin Key Laboratory of Animal and Plant Resistance, College of Life Sciences, Tianjin Normal University, Tianjin 300387, China; (T.H.); (Z.S.); (M.Z.); (L.Z.); (J.Y.)
| | - Jiarui Yang
- Tianjin Key Laboratory of Animal and Plant Resistance, College of Life Sciences, Tianjin Normal University, Tianjin 300387, China; (T.H.); (Z.S.); (M.Z.); (L.Z.); (J.Y.)
| | - Jingjing Li
- Tianjin Fisheries Research Institute, Tianjin 300211, China;
| | - Jinsheng Sun
- Tianjin Key Laboratory of Animal and Plant Resistance, College of Life Sciences, Tianjin Normal University, Tianjin 300387, China; (T.H.); (Z.S.); (M.Z.); (L.Z.); (J.Y.)
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68
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Ye A, Shen JN, Li Y, Lian X, Ma BG, Guo FB. Reconstruction of the genome-scale metabolic network model of Sinorhizobium fredii CCBAU45436 for free-living and symbiotic states. Front Bioeng Biotechnol 2024; 12:1377334. [PMID: 38590605 PMCID: PMC10999553 DOI: 10.3389/fbioe.2024.1377334] [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: 01/27/2024] [Accepted: 03/11/2024] [Indexed: 04/10/2024] Open
Abstract
Sinorhizobium fredii CCBAU45436 is an excellent rhizobium that plays an important role in agricultural production. However, there still needs more comprehensive understanding of the metabolic system of S. fredii CCBAU45436, which hinders its application in agriculture. Therefore, based on the first-generation metabolic model iCC541 we developed a new genome-scale metabolic model iAQY970, which contains 970 genes, 1,052 reactions, 942 metabolites and is scored 89% in the MEMOTE test. Cell growth phenotype predicted by iAQY970 is 81.7% consistent with the experimental data. The results of mapping the proteome data under free-living and symbiosis conditions to the model showed that the biomass production rate in the logarithmic phase was faster than that in the stable phase, and the nitrogen fixation efficiency of rhizobia parasitized in cultivated soybean was higher than that in wild-type soybean, which was consistent with the actual situation. In the symbiotic condition, there are 184 genes that would affect growth, of which 94 are essential; In the free-living condition, there are 143 genes that influence growth, of which 78 are essential. Among them, 86 of the 94 essential genes in the symbiotic condition were consistent with the prediction of iCC541, and 44 essential genes were confirmed by literature information; meanwhile, 30 genes were identified by DEG and 33 genes were identified by Geptop. In addition, we extracted four key nitrogen fixation modules from the model and predicted that sulfite reductase (EC 1.8.7.1) and nitrogenase (EC 1.18.6.1) as the target enzymes to enhance nitrogen fixation by MOMA, which provided a potential focus for strain optimization. Through the comprehensive metabolic model, we can better understand the metabolic capabilities of S. fredii CCBAU45436 and make full use of it in the future.
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Affiliation(s)
- Anqiang Ye
- Department of Respiratory and Critical Care Medicine, Zhongnan Hospital of Wuhan University, School of Pharmaceutical Sciences, Wuhan University, Wuhan, China
- Key Laboratory of Combinatorial Biosynthesis and Drug Discovery, Ministry of Education, Wuhan University, Wuhan, China
| | - Jian-Ning Shen
- Department of Respiratory and Critical Care Medicine, Zhongnan Hospital of Wuhan University, School of Pharmaceutical Sciences, Wuhan University, Wuhan, China
| | - Yong Li
- Department of Respiratory and Critical Care Medicine, Zhongnan Hospital of Wuhan University, School of Pharmaceutical Sciences, Wuhan University, Wuhan, China
| | - Xiang Lian
- Department of Respiratory and Critical Care Medicine, Zhongnan Hospital of Wuhan University, School of Pharmaceutical Sciences, Wuhan University, Wuhan, China
| | - Bin-Guang Ma
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, State Key Laboratory of Agricultural Microbiology, Huazhong Agricultural University, Wuhan, China
| | - Feng-Biao Guo
- Department of Respiratory and Critical Care Medicine, Zhongnan Hospital of Wuhan University, School of Pharmaceutical Sciences, Wuhan University, Wuhan, China
- Key Laboratory of Combinatorial Biosynthesis and Drug Discovery, Ministry of Education, Wuhan University, Wuhan, China
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69
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Luo M, Zhu J, Jia J, Zhang H, Zhao J. Progress on network modeling and analysis of gut microecology: a review. Appl Environ Microbiol 2024; 90:e0009224. [PMID: 38415584 PMCID: PMC11207142 DOI: 10.1128/aem.00092-24] [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] [Indexed: 02/29/2024] Open
Abstract
The gut microecological network is a complex microbial community within the human body that plays a key role in linking dietary nutrition and host physiology. To understand the complex relationships among microbes and their functions within this community, network analysis has emerged as a powerful tool. By representing the interactions between microbes and their associated omics data as a network, we can gain a comprehensive understanding of the ecological mechanisms that drive the human gut microbiota. In addition, the network-based approach provides a more intuitive analysis of the gut microbiota, simplifying the study of its complex dynamics and interdependencies. This review provides a comprehensive overview of the methods used to construct and analyze networks in the context of gut microecological background. We discuss various types of network modeling approaches, including co-occurrence networks, causal networks, dynamic networks, and multi-omics networks, and describe the analytical techniques used to identify important network properties. We also highlight the challenges and limitations of network modeling in this area, such as data scarcity and heterogeneity, and provide future research directions to overcome these limitations. By exploring these network-based methods, researchers can gain valuable insights into the intricate relationships and functional roles of microbial communities within the gut, ultimately advancing our understanding of the gut microbiota's impact on human health.
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Affiliation(s)
- Meng Luo
- State Key Laboratory of Food Science and Technology, Jiangnan University, Wuxi, Jiangsu, China
- School of Food Science and Technology, Jiangnan University, Wuxi, Jiangsu, China
| | - Jinlin Zhu
- State Key Laboratory of Food Science and Technology, Jiangnan University, Wuxi, Jiangsu, China
- School of Food Science and Technology, Jiangnan University, Wuxi, Jiangsu, China
| | - Jiajia Jia
- Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), Jiangnan University, Wuxi, China
| | - Hao Zhang
- State Key Laboratory of Food Science and Technology, Jiangnan University, Wuxi, Jiangsu, China
- School of Food Science and Technology, Jiangnan University, Wuxi, Jiangsu, China
- National Engineering Research Center for Functional Food, Jiangnan University, Wuxi, Jiangsu, China
- Wuxi Translational Medicine Research Center, Jiangsu Translational Medicine Research Institute Wuxi Branch, Wuxi, China
- (Yangzhou) Institute of Food Biotechnology, Jiangnan University, Yangzhou, China
| | - Jianxin Zhao
- State Key Laboratory of Food Science and Technology, Jiangnan University, Wuxi, Jiangsu, China
- School of Food Science and Technology, Jiangnan University, Wuxi, Jiangsu, China
- Wuxi Translational Medicine Research Center, Jiangsu Translational Medicine Research Institute Wuxi Branch, Wuxi, China
- (Yangzhou) Institute of Food Biotechnology, Jiangnan University, Yangzhou, China
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70
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Román L, Melis-Arcos F, Pröschle T, Saa PA, Garrido D. Genome-scale metabolic modeling of the human milk oligosaccharide utilization by Bifidobacterium longum subsp. infantis. mSystems 2024; 9:e0071523. [PMID: 38363147 PMCID: PMC10949479 DOI: 10.1128/msystems.00715-23] [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: 07/25/2023] [Accepted: 01/10/2024] [Indexed: 02/17/2024] Open
Abstract
Bifidobacterium longum subsp. infantis is a representative and dominant species in the infant gut and is considered a beneficial microbe. This organism displays multiple adaptations to thrive in the infant gut, regarded as a model for human milk oligosaccharides (HMOs) utilization. These carbohydrates are abundant in breast milk and include different molecules based on lactose. They contain fucose, sialic acid, and N-acetylglucosamine. Bifidobacterium metabolism is complex, and a systems view of relevant metabolic pathways and exchange metabolites during HMO consumption is missing. To address this limitation, a refined genome-scale network reconstruction of this bacterium is presented using a previous reconstruction of B. infantis ATCC 15967 as a template. The latter was expanded based on an extensive revision of genome annotations, current literature, and transcriptomic data integration. The metabolic reconstruction (iLR578) accounted for 578 genes, 1,047 reactions, and 924 metabolites. Starting from this reconstruction, we built context-specific genome-scale metabolic models using RNA-seq data from cultures growing in lactose and three HMOs. The models revealed notable differences in HMO metabolism depending on the functional characteristics of the substrates. Particularly, fucosyl-lactose showed a divergent metabolism due to a fucose moiety. High yields of lactate and acetate were predicted under growth rate maximization in all conditions, whereas formate, ethanol, and 1,2-propanediol were substantially lower. Similar results were also obtained under near-optimal growth on each substrate when varying the empirically observed acetate-to-lactate production ratio. Model predictions displayed reasonable agreement between central carbon metabolism fluxes and expression data across all conditions. Flux coupling analysis revealed additional connections between succinate exchange and arginine and sulfate metabolism and a strong coupling between central carbon reactions and adenine metabolism. More importantly, specific networks of coupled reactions under each carbon source were derived and analyzed. Overall, the presented network reconstruction constitutes a valuable platform for probing the metabolism of this prominent infant gut bifidobacteria.IMPORTANCEThis work presents a detailed reconstruction of the metabolism of Bifidobacterium longum subsp. infantis, a prominent member of the infant gut microbiome, providing a systems view of its metabolism of human milk oligosaccharides.
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Affiliation(s)
- Loreto Román
- Department of Chemical and Bioprocess Engineering, School of Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Felipe Melis-Arcos
- Department of Chemical and Bioprocess Engineering, School of Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Tomás Pröschle
- Department of Chemical and Bioprocess Engineering, School of Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Pedro A. Saa
- Department of Chemical and Bioprocess Engineering, School of Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile
- Institute for Mathematical and Computational Engineering, Pontificia Universidad Católica de Chile, Vicuña Mackenna, Santiago, Chile
| | - Daniel Garrido
- Department of Chemical and Bioprocess Engineering, School of Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile
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71
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Versluis DM, Schoemaker R, Looijesteijn E, Geurts JM, Merks RM. 2'-Fucosyllactose helps butyrate producers outgrow competitors in infant gut microbiota simulations. iScience 2024; 27:109085. [PMID: 38380251 PMCID: PMC10877688 DOI: 10.1016/j.isci.2024.109085] [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: 04/25/2023] [Revised: 11/16/2023] [Accepted: 01/29/2024] [Indexed: 02/22/2024] Open
Abstract
A reduced capacity for butyrate production by the early infant gut microbiota is associated with negative health effects, such as inflammation and the development of allergies. Here, we develop new hypotheses on the effect of the prebiotic galacto-oligosaccharides (GOS) or 2'-fucosyllactose (2'-FL) on butyrate production by the infant gut microbiota using a multiscale, spatiotemporal mathematical model of the infant gut. The model simulates a community of cross-feeding gut bacteria in metabolic detail. It represents the community as a grid of bacterial populations that exchange metabolites, using 20 different subspecies-specific metabolic networks taken from the AGORA database. The simulations predict that both GOS and 2'-FL promote the growth of Bifidobacterium, whereas butyrate producing bacteria are only consistently abundant in the presence of propane-1,2-diol, a product of 2'-FL metabolism. In absence of prebiotics or in presence of only GOS, however, Bacteroides vulgatus and Cutibacterium acnes outcompete butyrate producers by consuming intermediate metabolites.
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Affiliation(s)
- David M. Versluis
- Leiden University, Institute of Biology, 2300 RA Leiden, the Netherlands
| | | | | | | | - Roeland M.H. Merks
- Leiden University, Institute of Biology, 2300 RA Leiden, the Netherlands
- Leiden University, Mathematical Institute, 2300 RA Leiden, the Netherlands
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72
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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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73
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Banerjee D, Yunus IS, Wang X, Kim J, Srinivasan A, Menchavez R, Chen Y, Gin JW, Petzold CJ, Martin HG, Magnuson JK, Adams PD, Simmons BA, Mukhopadhyay A, Kim J, Lee TS. Genome-scale and pathway engineering for the sustainable aviation fuel precursor isoprenol production in Pseudomonas putida. Metab Eng 2024; 82:157-170. [PMID: 38369052 DOI: 10.1016/j.ymben.2024.02.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Revised: 01/10/2024] [Accepted: 02/10/2024] [Indexed: 02/20/2024]
Abstract
Sustainable aviation fuel (SAF) will significantly impact global warming in the aviation sector, and important SAF targets are emerging. Isoprenol is a precursor for a promising SAF compound DMCO (1,4-dimethylcyclooctane) and has been produced in several engineered microorganisms. Recently, Pseudomonas putida has gained interest as a future host for isoprenol bioproduction as it can utilize carbon sources from inexpensive plant biomass. Here, we engineer metabolically versatile host P. putida for isoprenol production. We employ two computational modeling approaches (Bilevel optimization and Constrained Minimal Cut Sets) to predict gene knockout targets and optimize the "IPP-bypass" pathway in P. putida to maximize isoprenol production. Altogether, the highest isoprenol production titer from P. putida was achieved at 3.5 g/L under fed-batch conditions. This combination of computational modeling and strain engineering on P. putida for an advanced biofuels production has vital significance in enabling a bioproduction process that can use renewable carbon streams.
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Affiliation(s)
- Deepanwita Banerjee
- Joint BioEnergy Institute, 5885 Hollis St., Emeryville, CA, 94608, USA; Biological Systems & Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
| | - Ian S Yunus
- Joint BioEnergy Institute, 5885 Hollis St., Emeryville, CA, 94608, USA; Biological Systems & Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
| | - Xi Wang
- Joint BioEnergy Institute, 5885 Hollis St., Emeryville, CA, 94608, USA; Biological Systems & Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
| | - Jinho Kim
- Joint BioEnergy Institute, 5885 Hollis St., Emeryville, CA, 94608, USA; Biological Systems & Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
| | - Aparajitha Srinivasan
- Joint BioEnergy Institute, 5885 Hollis St., Emeryville, CA, 94608, USA; Biological Systems & Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
| | - Russel Menchavez
- Joint BioEnergy Institute, 5885 Hollis St., Emeryville, CA, 94608, USA; Biological Systems & Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
| | - Yan Chen
- Joint BioEnergy Institute, 5885 Hollis St., Emeryville, CA, 94608, USA; Biological Systems & Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
| | - Jennifer W Gin
- Joint BioEnergy Institute, 5885 Hollis St., Emeryville, CA, 94608, USA; Biological Systems & Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
| | - Christopher J Petzold
- Joint BioEnergy Institute, 5885 Hollis St., Emeryville, CA, 94608, USA; Biological Systems & Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
| | - Hector Garcia Martin
- Joint BioEnergy Institute, 5885 Hollis St., Emeryville, CA, 94608, USA; Biological Systems & Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
| | - Jon K Magnuson
- Joint BioEnergy Institute, 5885 Hollis St., Emeryville, CA, 94608, USA; Energy Processes & Materials Division, Pacific Northwest National Laboratory, Richland, WA, 99354, USA
| | - Paul D Adams
- Joint BioEnergy Institute, 5885 Hollis St., Emeryville, CA, 94608, USA; Molecular Biophysics & Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
| | - Blake A Simmons
- Joint BioEnergy Institute, 5885 Hollis St., Emeryville, CA, 94608, USA; Biological Systems & Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
| | - Aindrila Mukhopadhyay
- Joint BioEnergy Institute, 5885 Hollis St., Emeryville, CA, 94608, USA; Biological Systems & Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA; Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
| | - Joonhoon Kim
- Joint BioEnergy Institute, 5885 Hollis St., Emeryville, CA, 94608, USA; Energy Processes & Materials Division, Pacific Northwest National Laboratory, Richland, WA, 99354, USA.
| | - Taek Soon Lee
- Joint BioEnergy Institute, 5885 Hollis St., Emeryville, CA, 94608, USA; Biological Systems & Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA.
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74
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Wu D, Xu F, Xu Y, Huang M, Li Z, Chu J. Towards a hybrid model-driven platform based on flux balance analysis and a machine learning pipeline for biosystem design. Synth Syst Biotechnol 2024; 9:33-42. [PMID: 38234412 PMCID: PMC10793177 DOI: 10.1016/j.synbio.2023.12.004] [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: 07/18/2023] [Revised: 12/22/2023] [Accepted: 12/22/2023] [Indexed: 01/19/2024] Open
Abstract
Metabolic modeling and machine learning (ML) are crucial components of the evolving next-generation tools in systems and synthetic biology, aiming to unravel the intricate relationship between genotype, phenotype, and the environment. Nonetheless, the comprehensive exploration of integrating these two frameworks, and fully harnessing the potential of fluxomic data, remains an unexplored territory. In this study, we present, rigorously evaluate, and compare ML-based techniques for data integration. The hybrid model revealed that the overexpression of six target genes and the knockout of seven target genes contribute to enhanced ethanol production. Specifically, we investigated the influence of succinate dehydrogenase (SDH) on ethanol biosynthesis in Saccharomyces cerevisiae through shake flask experiments. The findings indicate a noticeable increase in ethanol yield, ranging from 6 % to 10 %, in SDH subunit gene knockout strains compared to the wild-type strain. Moreover, in pursuit of a high-yielding strain for ethanol production, dual-gene deletion experiments were conducted targeting glycerol-3-phosphate dehydrogenase (GPD) and SDH. The results unequivocally demonstrate significant enhancements in ethanol production for the engineered strains Δsdh4Δgpd1, Δsdh5Δgpd1, Δsdh6Δgpd1, Δsdh4Δgpd2, Δsdh5Δgpd2, and Δsdh6Δgpd2, with improvements of 21.6 %, 27.9 %, and 22.7 %, respectively. Overall, the results highlighted that integrating mechanistic flux features substantially improves the prediction of gene knockout strains not accounted for in metabolic reconstructions. In addition, the finding in this study delivers valuable tools for comprehending and manipulating intricate phenotypes, thereby enhancing prediction accuracy and facilitating deeper insights into mechanistic aspects within the field of synthetic biology.
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Affiliation(s)
| | | | - Yaying Xu
- State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, 130 Meilong Road, Shanghai, 200237, People's Republic of China
| | - Mingzhi Huang
- State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, 130 Meilong Road, Shanghai, 200237, People's Republic of China
| | - Zhimin Li
- State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, 130 Meilong Road, Shanghai, 200237, People's Republic of China
| | - Ju Chu
- State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, 130 Meilong Road, Shanghai, 200237, People's Republic of China
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75
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Chen Y, Gustafsson J, Tafur Rangel A, Anton M, Domenzain I, Kittikunapong C, Li F, Yuan L, Nielsen J, Kerkhoven EJ. Reconstruction, simulation and analysis of enzyme-constrained metabolic models using GECKO Toolbox 3.0. Nat Protoc 2024; 19:629-667. [PMID: 38238583 DOI: 10.1038/s41596-023-00931-7] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2023] [Accepted: 10/13/2023] [Indexed: 03/10/2024]
Abstract
Genome-scale metabolic models (GEMs) are computational representations that enable mathematical exploration of metabolic behaviors within cellular and environmental constraints. Despite their wide usage in biotechnology, biomedicine and fundamental studies, there are many phenotypes that GEMs are unable to correctly predict. GECKO is a method to improve the predictive power of a GEM by incorporating enzymatic constraints using kinetic and omics data. GECKO has enabled reconstruction of enzyme-constrained metabolic models (ecModels) for diverse organisms, which show better predictive performance than conventional GEMs. In this protocol, we describe how to use the latest version GECKO 3.0; the procedure has five stages: (1) expansion from a starting metabolic model to an ecModel structure, (2) integration of enzyme turnover numbers into the ecModel structure, (3) model tuning, (4) integration of proteomics data into the ecModel and (5) simulation and analysis of ecModels. GECKO 3.0 incorporates deep learning-predicted enzyme kinetics, paving the way for improved metabolic models for virtually any organism and cell line in the absence of experimental data. The time of running the whole protocol is organism dependent, e.g., ~5 h for yeast.
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Affiliation(s)
- Yu Chen
- Department of Life Sciences, Chalmers University of Technology, Gothenburg, Sweden
- Key Laboratory of Quantitative Synthetic Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Johan Gustafsson
- Department of Life Sciences, Chalmers University of Technology, Gothenburg, Sweden
| | - Albert Tafur Rangel
- Department of Life Sciences, Chalmers University of Technology, Gothenburg, Sweden
- Novo Nordisk Foundation Center for Biosustainability, Technology University of Denmark, Lyngby, Denmark
| | - Mihail Anton
- Department of Life Sciences, National Bioinformatics Infrastructure Sweden, Science for Life Laboratory, Chalmers University of Technology, Gothenburg, Sweden
| | - Iván Domenzain
- Department of Life Sciences, Chalmers University of Technology, Gothenburg, Sweden
| | | | - Feiran Li
- Department of Life Sciences, Chalmers University of Technology, Gothenburg, Sweden
- Institute of Biopharmaceutical and Health Engineering, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China
| | - Le Yuan
- Department of Life Sciences, Chalmers University of Technology, Gothenburg, Sweden
| | - Jens Nielsen
- Department of Life Sciences, Chalmers University of Technology, Gothenburg, Sweden
- BioInnovation Institute, Copenhagen, Denmark
| | - Eduard J Kerkhoven
- Department of Life Sciences, Chalmers University of Technology, Gothenburg, Sweden.
- Novo Nordisk Foundation Center for Biosustainability, Technology University of Denmark, Lyngby, Denmark.
- SciLifeLab, Chalmers University of Technology, SE-412 96, Gothenburg, Sweden.
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76
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Liang C, Murray S, Li Y, Lee R, Low A, Sasaki S, Chiang AWT, Lin WJ, Mathews J, Barnes W, Lewis NE. LipidSIM: Inferring mechanistic lipid biosynthesis perturbations from lipidomics with a flexible, low-parameter, Markov modeling framework. Metab Eng 2024; 82:110-122. [PMID: 38311182 PMCID: PMC11163374 DOI: 10.1016/j.ymben.2024.01.004] [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: 08/01/2023] [Revised: 01/03/2024] [Accepted: 01/21/2024] [Indexed: 02/10/2024]
Abstract
Lipid metabolism is a complex and dynamic system involving numerous enzymes at the junction of multiple metabolic pathways. Disruption of these pathways leads to systematic dyslipidemia, a hallmark of many pathological developments, such as nonalcoholic steatohepatitis and diabetes. Recent advances in computational tools can provide insights into the dysregulation of lipid biosynthesis, but limitations remain due to the complexity of lipidomic data, limited knowledge of interactions among involved enzymes, and technical challenges in standardizing across different lipid types. Here, we present a low-parameter, biologically interpretable framework named Lipid Synthesis Investigative Markov model (LipidSIM), which models and predicts the source of perturbations in lipid biosynthesis from lipidomic data. LipidSIM achieves this by accounting for the interdependency between the lipid species via the lipid biosynthesis network and generates testable hypotheses regarding changes in lipid biosynthetic reactions. This feature allows the integration of lipidomics with other omics types, such as transcriptomics, to elucidate the direct driving mechanisms of altered lipidomes due to treatments or disease progression. To demonstrate the value of LipidSIM, we first applied it to hepatic lipidomics following Keap1 knockdown and found that changes in mRNA expression of the lipid pathways were consistent with the LipidSIM-predicted fluxes. Second, we used it to study lipidomic changes following intraperitoneal injection of CCl4 to induce fast NAFLD/NASH development and the progression of fibrosis and hepatic cancer. Finally, to show the power of LipidSIM for classifying samples with dyslipidemia, we used a Dgat2-knockdown study dataset. Thus, we show that as it demands no a priori knowledge of enzyme kinetics, LipidSIM is a valuable and intuitive framework for extracting biological insights from complex lipidomic data.
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Affiliation(s)
- Chenguang Liang
- Department of Bioengineering, University of California, La Jolla, CA, 92093, USA
| | - Sue Murray
- Ionis Pharmaceuticals, Inc., Carlsbad, CA, 92010, USA
| | - Yang Li
- Ionis Pharmaceuticals, Inc., Carlsbad, CA, 92010, USA
| | - Richard Lee
- Ionis Pharmaceuticals, Inc., Carlsbad, CA, 92010, USA
| | - Audrey Low
- Ionis Pharmaceuticals, Inc., Carlsbad, CA, 92010, USA
| | - Shruti Sasaki
- Ionis Pharmaceuticals, Inc., Carlsbad, CA, 92010, USA
| | - Austin W T Chiang
- Department of Pediatrics, University of California, La Jolla, CA, 92093, USA
| | - Wen-Jen Lin
- Graduate Institute of Biomedical Science, China Medical University, Taichung 404333, Taiwan
| | - Joel Mathews
- Ionis Pharmaceuticals, Inc., Carlsbad, CA, 92010, USA
| | - Will Barnes
- Ionis Pharmaceuticals, Inc., Carlsbad, CA, 92010, USA
| | - Nathan E Lewis
- Department of Bioengineering, University of California, La Jolla, CA, 92093, USA; Department of Pediatrics, University of California, La Jolla, CA, 92093, USA.
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Sjöberg G, Reķēna A, Fornstad M, Lahtvee PJ, van Maris AJA. Evaluation of enzyme-constrained genome-scale model through metabolic engineering of anaerobic co-production of 2,3-butanediol and glycerol by Saccharomyces cerevisiae. Metab Eng 2024; 82:49-59. [PMID: 38309619 DOI: 10.1016/j.ymben.2024.01.007] [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: 08/21/2023] [Revised: 11/27/2023] [Accepted: 01/24/2024] [Indexed: 02/05/2024]
Abstract
Enzyme-constrained genome-scale models (ecGEMs) have potential to predict phenotypes in a variety of conditions, such as growth rates or carbon sources. This study investigated if ecGEMs can guide metabolic engineering efforts to swap anaerobic redox-neutral ATP-providing pathways in yeast from alcoholic fermentation to equimolar co-production of 2,3-butanediol and glycerol. With proven pathways and low product toxicity, the ecGEM solution space aligned well with observed phenotypes. Since this catabolic pathway provides only one-third of the ATP of alcoholic fermentation (2/3 versus 2 ATP per glucose), the ecGEM predicted a growth decrease from 0.36 h-1 in the reference to 0.175 h-1 in the engineered strain. However, this <3-fold decrease would require the specific glucose consumption rate to increase. Surprisingly, after the pathway swap the engineered strain immediately grew at 0.15 h-1 with a glucose consumption rate of 29 mmol (g CDW)-1 h-1, which was indeed higher than reference (23 mmol (g CDW)-1 h-1) and one of the highest reported for S. cerevisiae. The accompanying 2,3-butanediol- (15.8 mmol (g CDW)-1 h-1) and glycerol (19.6 mmol (g CDW)-1 h-1) production rates were close to predicted values. Proteomics confirmed that this increased consumption rate was facilitated by enzyme reallocation from especially ribosomes (from 25.5 to 18.5 %) towards glycolysis (from 28.7 to 43.5 %). Subsequently, 200 generations of sequential transfer did not improve growth of the engineered strain, showing the use of ecGEMs in predicting opportunity space for laboratory evolution. The observations in this study illustrate both the current potential, as well as future improvements, of ecGEMs as a tool for both metabolic engineering and laboratory evolution.
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Affiliation(s)
- Gustav Sjöberg
- Department of Industrial Biotechnology, School of Engineering Sciences in Chemistry, Biotechnology and Health, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Alīna Reķēna
- Department of Chemistry and Biotechnology, Tallinn University of Technology, Tallinn, Estonia
| | - Matilda Fornstad
- Department of Industrial Biotechnology, School of Engineering Sciences in Chemistry, Biotechnology and Health, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Petri-Jaan Lahtvee
- Department of Chemistry and Biotechnology, Tallinn University of Technology, Tallinn, Estonia
| | - Antonius J A van Maris
- Department of Industrial Biotechnology, School of Engineering Sciences in Chemistry, Biotechnology and Health, KTH Royal Institute of Technology, Stockholm, Sweden.
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78
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Burcham ZM, Belk AD, McGivern BB, Bouslimani A, Ghadermazi P, Martino C, Shenhav L, Zhang AR, Shi P, Emmons A, Deel HL, Xu ZZ, Nieciecki V, Zhu Q, Shaffer M, Panitchpakdi M, Weldon KC, Cantrell K, Ben-Hur A, Reed SC, Humphry GC, Ackermann G, McDonald D, Chan SHJ, Connor M, Boyd D, Smith J, Watson JMS, Vidoli G, Steadman D, Lynne AM, Bucheli S, Dorrestein PC, Wrighton KC, Carter DO, Knight R, Metcalf JL. A conserved interdomain microbial network underpins cadaver decomposition despite environmental variables. Nat Microbiol 2024; 9:595-613. [PMID: 38347104 PMCID: PMC10914610 DOI: 10.1038/s41564-023-01580-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2023] [Accepted: 12/08/2023] [Indexed: 03/07/2024]
Abstract
Microbial breakdown of organic matter is one of the most important processes on Earth, yet the controls of decomposition are poorly understood. Here we track 36 terrestrial human cadavers in three locations and show that a phylogenetically distinct, interdomain microbial network assembles during decomposition despite selection effects of location, climate and season. We generated a metagenome-assembled genome library from cadaver-associated soils and integrated it with metabolomics data to identify links between taxonomy and function. This universal network of microbial decomposers is characterized by cross-feeding to metabolize labile decomposition products. The key bacterial and fungal decomposers are rare across non-decomposition environments and appear unique to the breakdown of terrestrial decaying flesh, including humans, swine, mice and cattle, with insects as likely important vectors for dispersal. The observed lockstep of microbial interactions further underlies a robust microbial forensic tool with the potential to aid predictions of the time since death.
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Affiliation(s)
- Zachary M Burcham
- Department of Animal Sciences, Colorado State University, Fort Collins, CO, USA
- Department of Microbiology, University of Tennessee, Knoxville, TN, USA
| | - Aeriel D Belk
- Department of Animal Sciences, Colorado State University, Fort Collins, CO, USA
- Department of Animal Sciences, Auburn University, Auburn, AL, USA
| | - Bridget B McGivern
- Department of Soil and Crop Sciences, Colorado State University, Fort Collins, CO, USA
| | - Amina Bouslimani
- Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, San Diego, CA, USA
| | - Parsa Ghadermazi
- Department of Chemical and Biological Engineering, Colorado State University, Fort Collins, CO, USA
| | - Cameron Martino
- Department of Pediatrics, University of California San Diego, La Jolla, California, USA
| | - Liat Shenhav
- Center for Studies in Physics and Biology, Rockefeller University, New York, NY, USA
- Institute for Systems Genetics, New York Grossman School of Medicine, New York University, New York, NY, USA
- Department of Computer Science, New York University, New York, NY, USA
| | - Anru R Zhang
- Department of Biostatistics and Bioinformatics, Duke University, Durham, NC, USA
- Department of Computer Science, Duke University, Durham, NC, USA
| | - Pixu Shi
- Department of Biostatistics and Bioinformatics, Duke University, Durham, NC, USA
| | - Alexandra Emmons
- Department of Animal Sciences, Colorado State University, Fort Collins, CO, USA
| | - Heather L Deel
- Graduate Program in Cell and Molecular Biology, Colorado State University, Fort Collins, CO, USA
| | - Zhenjiang Zech Xu
- School of Food Science and Technology, Nanchang University, Nanchang, Jiangxi, China
| | - Victoria Nieciecki
- Department of Animal Sciences, Colorado State University, Fort Collins, CO, USA
- Graduate Program in Cell and Molecular Biology, Colorado State University, Fort Collins, CO, USA
| | - Qiyun Zhu
- Department of Pediatrics, University of California San Diego, La Jolla, California, USA
- School of Life Sciences, Arizona State University, Tempe, AZ, USA
- Center for Fundamental and Applied Microbiomics, Arizona State University, Tempe, AZ, USA
| | - Michael Shaffer
- Department of Soil and Crop Sciences, Colorado State University, Fort Collins, CO, USA
| | - Morgan Panitchpakdi
- Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, San Diego, CA, USA
| | - Kelly C Weldon
- Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, San Diego, CA, USA
| | - Kalen Cantrell
- Department of Computer Science and Engineering, University of California San Diego, La Jolla, CA, USA
| | - Asa Ben-Hur
- Department of Computer Science, Colorado State University, Fort Collins, CO, USA
| | - Sasha C Reed
- U.S. Geological Survey, Southwest Biological Science Center, Moab, UT, USA
| | - Greg C Humphry
- Department of Pediatrics, University of California San Diego, La Jolla, California, USA
| | - Gail Ackermann
- Department of Pediatrics, University of California San Diego, La Jolla, California, USA
| | - Daniel McDonald
- Department of Pediatrics, University of California San Diego, La Jolla, California, USA
| | - Siu Hung Joshua Chan
- Department of Chemical and Biological Engineering, Colorado State University, Fort Collins, CO, USA
| | - Melissa Connor
- Forensic Investigation Research Station, Colorado Mesa University, Grand Junction, CO, USA
| | - Derek Boyd
- Forensic Anthropology Center, Department of Anthropology, University of Tennessee, Knoxville, TN, USA
- Department of Social, Cultural, and Justice Studies, University of Tennessee at Chattanooga, Chattanooga, TN, USA
| | - Jake Smith
- Forensic Anthropology Center, Department of Anthropology, University of Tennessee, Knoxville, TN, USA
- Mid-America College of Funeral Service, Jeffersonville, IN, USA
| | - Jenna M S Watson
- Forensic Anthropology Center, Department of Anthropology, University of Tennessee, Knoxville, TN, USA
| | - Giovanna Vidoli
- Forensic Anthropology Center, Department of Anthropology, University of Tennessee, Knoxville, TN, USA
| | - Dawnie Steadman
- Forensic Anthropology Center, Department of Anthropology, University of Tennessee, Knoxville, TN, USA
| | - Aaron M Lynne
- Department of Biological Sciences, Sam Houston State University, Huntsville, TX, USA
| | - Sibyl Bucheli
- Department of Biological Sciences, Sam Houston State University, Huntsville, TX, USA
| | - Pieter C Dorrestein
- Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, San Diego, CA, USA
| | - Kelly C Wrighton
- Department of Soil and Crop Sciences, Colorado State University, Fort Collins, CO, USA
| | - David O Carter
- Laboratory of Forensic Taphonomy, Forensic Sciences Unit, School of Natural Sciences and Mathematics, Chaminade University of Honolulu, Honolulu, HI, USA
| | - Rob Knight
- Department of Pediatrics, University of California San Diego, La Jolla, California, 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
- Department of Bioengineering, University of California San Diego, La Jolla, CA, USA
| | - Jessica L Metcalf
- Department of Animal Sciences, Colorado State University, Fort Collins, CO, USA.
- Graduate Program in Cell and Molecular Biology, Colorado State University, Fort Collins, CO, USA.
- Humans and the Microbiome Program, Canadian Institute for Advanced Research, Toronto, Ontario, Canada.
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79
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Solfisburg QS, Baldini F, Baldwin-Hunter B, Austin GI, Lee HH, Park H, Freedberg DE, Lightdale CJ, Korem T, Abrams JA. The Salivary Microbiome and Predicted Metabolite Production Are Associated with Barrett's Esophagus and High-Grade Dysplasia or Adenocarcinoma. Cancer Epidemiol Biomarkers Prev 2024; 33:371-380. [PMID: 38117184 PMCID: PMC10955687 DOI: 10.1158/1055-9965.epi-23-0652] [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: 06/05/2023] [Revised: 09/05/2023] [Accepted: 12/18/2023] [Indexed: 12/21/2023] Open
Abstract
BACKGROUND Esophageal adenocarcinoma (EAC) is rising in incidence, and established risk factors do not explain this trend. Esophageal microbiome alterations have been associated with Barrett's esophagus (BE) and dysplasia and EAC. The oral microbiome is tightly linked to the esophageal microbiome; this study aimed to identify salivary microbiome-related factors associated with BE, dysplasia, and EAC. METHODS Clinical data and oral health history were collected from patients with and without BE. The salivary microbiome was characterized, assessing differential relative abundance of taxa by 16S rRNA gene sequencing and associations between microbiome composition and clinical features. Microbiome metabolic modeling was used to predict metabolite production. RESULTS A total of 244 patients (125 non-BE and 119 BE) were analyzed. Patients with high-grade dysplasia (HGD)/EAC had a significantly higher prevalence of tooth loss (P = 0.001). There were significant shifts with increased dysbiosis associated with HGD/EAC, independent of tooth loss, with the largest shifts within the genus Streptococcus. Modeling predicted significant shifts in the microbiome metabolic capacities, including increases in L-lactic acid and decreases in butyric acid and L-tryptophan production in HGD/EAC. CONCLUSIONS Marked dysbiosis in the salivary microbiome is associated with HGD and EAC, with notable increases within the genus Streptococcus and accompanying changes in predicted metabolite production. Further work is warranted to identify the biological significance of these alterations and to validate metabolic shifts. IMPACT There is an association between oral dysbiosis and HGD/EAC. Further work is needed to establish the diagnostic, predictive, and causal potential of this relationship.
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Affiliation(s)
- Quinn S Solfisburg
- Department of Medicine, Boston University School of Medicine, Boston, MA, USA
| | - Federico Baldini
- Program for Mathematical Genomics, Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA
| | | | - George I Austin
- Program for Mathematical Genomics, Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA
| | - Harry H Lee
- Program for Mathematical Genomics, Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA
| | - Heekuk Park
- Department of Medicine, Columbia University Irving Medical Center, New York, NY, USA
- Microbiome and Pathogen Genomics Collaborative Center, Department of Medicine, Columbia University Irving Medical Center, New York, NY, USA
| | - Daniel E Freedberg
- Department of Medicine, Columbia University Irving Medical Center, New York, NY, USA
- Digestive and Liver Disease Research Center, Columbia University Irving Medical Center, New York, NY, USA
| | - Charles J Lightdale
- Department of Medicine, Columbia University Irving Medical Center, New York, NY, USA
| | - Tal Korem
- Program for Mathematical Genomics, Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA
- Department of Obstetrics and Gynecology, Columbia University Irving Medical Center, New York, NY, USA
- CIFAR Azrieli Global Scholars Program, CIFAR, Toronto, Canada
| | - Julian A Abrams
- Department of Medicine, Columbia University Irving Medical Center, New York, NY, USA
- Digestive and Liver Disease Research Center, Columbia University Irving Medical Center, New York, NY, USA
- Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, NY USA
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80
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Qian J, Ye C. Development and applications of genome-scale metabolic network models. ADVANCES IN APPLIED MICROBIOLOGY 2024; 126:1-26. [PMID: 38637105 DOI: 10.1016/bs.aambs.2024.02.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/20/2024]
Abstract
The genome-scale metabolic network model is an effective tool for characterizing the gene-protein-response relationship in the entire metabolic pathway of an organism. By combining various algorithms, the genome-scale metabolic network model can effectively simulate the influence of a specific environment on the physiological state of cells, optimize the culture conditions of strains, and predict the targets of genetic modification to achieve targeted modification of strains. In this review, we summarize the whole process of model building, sort out the various tools that may be involved in the model building process, and explain the role of various algorithms in model analysis. In addition, we also summarized the application of GSMM in network characteristics, cell phenotypes, metabolic engineering, etc. Finally, we discuss the current challenges facing GSMM.
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Affiliation(s)
- Jinyi Qian
- Ministry of Education Key Laboratory of NSLSCS, Nanjing Normal University, Nanjing, PR China
| | - Chao Ye
- Ministry of Education Key Laboratory of NSLSCS, Nanjing Normal University, Nanjing, PR China; School of Food Science and Pharmaceutical Engineering, Nanjing Normal University, Nanjing, PR China.
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81
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Cruz F, Capela J, Ferreira EC, Rocha M, Dias O. BioISO: An Objective-Oriented Application for Assisting the Curation of Genome-Scale Metabolic Models. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2024; 21:215-226. [PMID: 38170658 DOI: 10.1109/tcbb.2023.3339972] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/05/2024]
Abstract
As the reconstruction of Genome-Scale Metabolic Models (GEMs) becomes standard practice in systems biology, the number of organisms having at least one metabolic model is peaking at an unprecedented scale. The automation of laborious tasks, such as gap-finding and gap-filling, allowed the development of GEMs for poorly described organisms. However, the quality of these models can be compromised by the automation of several steps, which may lead to erroneous phenotype simulations. Biological networks constraint-based In Silico Optimisation (BioISO) is a computational tool aimed at accelerating the reconstruction of GEMs. This tool facilitates manual curation steps by reducing the large search spaces often met when debugging in silico biological models. BioISO uses a recursive relation-like algorithm and Flux Balance Analysis (FBA) to evaluate and guide debugging of in silico phenotype simulations. The potential of BioISO to guide the debugging of model reconstructions was showcased and compared with the results of two other state-of-the-art gap-filling tools (Meneco and fastGapFill). In this assessment, BioISO is better suited to reducing the search space for errors and gaps in metabolic networks by identifying smaller ratios of dead-end metabolites. Furthermore, BioISO was used as Meneco's gap-finding algorithm to reduce the number of proposed solutions for filling the gaps.
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82
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Marbehan X, Roger M, Fournier F, Infossi P, Guedon E, Delecourt L, Lebrun R, Giudici-Orticoni MT, Delaunay S. Combining metabolic flux analysis with proteomics to shed light on the metabolic flexibility: the case of Desulfovibrio vulgaris Hildenborough. Front Microbiol 2024; 15:1336360. [PMID: 38463485 PMCID: PMC10920352 DOI: 10.3389/fmicb.2024.1336360] [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: 11/10/2023] [Accepted: 01/24/2024] [Indexed: 03/12/2024] Open
Abstract
Introduction Desulfovibrio vulgaris Hildenborough is a gram-negative anaerobic bacterium belonging to the sulfate-reducing bacteria that exhibits highly versatile metabolism. By switching from one energy mode to another depending on nutrients availability in the environments" it plays a central role in shaping ecosystems. Despite intensive efforts to study D. vulgaris energy metabolism at the genomic, biochemical and ecological level, bioenergetics in this microorganism remain far from being fully understood. Alternatively, metabolic modeling is a powerful tool to understand bioenergetics. However, all the current models for D. vulgaris appeared to be not easily adaptable to various environmental conditions. Methods To lift off these limitations, here we constructed a novel transparent and robust metabolic model to explain D. vulgaris bioenergetics by combining whole-cell proteomic analysis with modeling approaches (Flux Balance Analysis). Results The iDvu71 model showed over 0.95 correlation with experimental data. Further simulations allowed a detailed description of D. vulgaris metabolism in various conditions of growth. Altogether, the simulations run in this study highlighted the sulfate-to-lactate consumption ratio as a pivotal factor in D. vulgaris energy metabolism. Discussion In particular, the impact on the hydrogen/formate balance and biomass synthesis is discussed. Overall, this study provides a novel insight into D. vulgaris metabolic flexibility.
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Affiliation(s)
| | - Magali Roger
- BIP-UMR 7281, Laboratoire de Bioénergétique et Ingénierie des Protéines, Aix-Marseille Université, CNRS, Marseille, France
| | | | - Pascale Infossi
- BIP-UMR 7281, Laboratoire de Bioénergétique et Ingénierie des Protéines, Aix-Marseille Université, CNRS, Marseille, France
| | | | - Louis Delecourt
- BIP-UMR 7281, Laboratoire de Bioénergétique et Ingénierie des Protéines, Aix-Marseille Université, CNRS, Marseille, France
- LISM-UMR 7255, Laboratoire d’Ingénierie des Systèmes Macromoléculaires, Aix-Marseille Université, CNRS, Marseille, France
| | - Régine Lebrun
- IMM-FR3479, Marseille Protéomique, Aix-Marseille Université, CNRS, Marseille, France
| | - Marie-Thérèse Giudici-Orticoni
- BIP-UMR 7281, Laboratoire de Bioénergétique et Ingénierie des Protéines, Aix-Marseille Université, CNRS, Marseille, France
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83
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Soommat P, Raethong N, Ruengsang R, Thananusak R, Laomettachit T, Laoteng K, Saithong T, Vongsangnak W. Light-Exposed Metabolic Responses of Cordyceps militaris through Transcriptome-Integrated Genome-Scale Modeling. BIOLOGY 2024; 13:139. [PMID: 38534409 DOI: 10.3390/biology13030139] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/30/2024] [Revised: 02/18/2024] [Accepted: 02/19/2024] [Indexed: 03/28/2024]
Abstract
The genome-scale metabolic model (GSMM) of Cordyceps militaris provides a comprehensive basis of carbon assimilation for cell growth and metabolite production. However, the model with a simple mass balance concept shows limited capability to probe the metabolic responses of C. militaris under light exposure. This study, therefore, employed the transcriptome-integrated GSMM approach to extend the investigation of C. militaris's metabolism under light conditions. Through the gene inactivity moderated by metabolism and expression (GIMME) framework, the iPS1474-tiGSMM model was furnished with the transcriptome data, thus providing a simulation that described reasonably well the metabolic responses underlying the phenotypic observation of C. militaris under the particular light conditions. The iPS1474-tiGSMM obviously showed an improved prediction of metabolic fluxes in correlation with the expressed genes involved in the cordycepin and carotenoid biosynthetic pathways under the sucrose culturing conditions. Further analysis of reporter metabolites suggested that the central carbon, purine, and fatty acid metabolisms towards carotenoid biosynthesis were the predominant metabolic processes responsible in light conditions. This finding highlights the key responsive processes enabling the acclimatization of C. militaris metabolism in varying light conditions. This study provides a valuable perspective on manipulating metabolic genes and fluxes towards the target metabolite production of C. militaris.
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Affiliation(s)
- Panyawarin Soommat
- Genetic Engineering and Bioinformatics Program, Graduate School, Kasetsart University, Bangkok 10900, Thailand
| | - Nachon Raethong
- Institute of Nutrition, Mahidol University, Nakhon Pathom 73170, Thailand
| | - Ratchaprapa Ruengsang
- Bioinformatics and Systems Biology Program, School of Bioresources and Technology and School of Information Technology, King Mongkut's University of Technology Thonburi (Bang Khun Thian), Bangkok 10150, Thailand
| | - Roypim Thananusak
- Omics Center for Agriculture, Bioresources, Food, and Health, Kasetsart University (OmiKU), Bangkok 10900, Thailand
| | - Teeraphan Laomettachit
- Bioinformatics and Systems Biology Program, School of Bioresources and Technology and School of Information Technology, King Mongkut's University of Technology Thonburi (Bang Khun Thian), Bangkok 10150, Thailand
| | - Kobkul Laoteng
- Industrial Bioprocess Technology Research Team, Functional Ingredients and Food Innovation Research Group, National Center for Genetic Engineering and Biotechnology (BIOTEC), National Science and Technology Development Agency (NSTDA), Pathum Thani 12120, Thailand
| | - Treenut Saithong
- Bioinformatics and Systems Biology Program, School of Bioresources and Technology and School of Information Technology, King Mongkut's University of Technology Thonburi (Bang Khun Thian), Bangkok 10150, Thailand
- Center for Agricultural Systems Biology (CASB), Systems Biology and Bioinformatics Research Group, Pilot Plant Development and Training Institute, King Mongkut's University of Technology Thonburi (Bang Khun Thian), Bangkok 10150, Thailand
| | - Wanwipa Vongsangnak
- Omics Center for Agriculture, Bioresources, Food, and Health, Kasetsart University (OmiKU), Bangkok 10900, Thailand
- Department of Zoology, Faculty of Science, Kasetsart University, Bangkok 10900, Thailand
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84
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Qin N, Li L, Wan X, Ji X, Chen Y, Li C, Liu P, Zhang Y, Yang W, Jiang J, Xia J, Shi S, Tan T, Nielsen J, Chen Y, Liu Z. Increased CO 2 fixation enables high carbon-yield production of 3-hydroxypropionic acid in yeast. Nat Commun 2024; 15:1591. [PMID: 38383540 PMCID: PMC10881976 DOI: 10.1038/s41467-024-45557-9] [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: 08/31/2023] [Accepted: 01/28/2024] [Indexed: 02/23/2024] Open
Abstract
CO2 fixation plays a key role to make biobased production cost competitive. Here, we use 3-hydroxypropionic acid (3-HP) to showcase how CO2 fixation enables approaching theoretical-yield production. Using genome-scale metabolic models to calculate the production envelope, we demonstrate that the provision of bicarbonate, formed from CO2, restricts previous attempts for high yield production of 3-HP. We thus develop multiple strategies for bicarbonate uptake, including the identification of Sul1 as a potential bicarbonate transporter, domain swapping of malonyl-CoA reductase, identification of Esbp6 as a potential 3-HP exporter, and deletion of Uga1 to prevent 3-HP degradation. The combined rational engineering increases 3-HP production from 0.14 g/L to 11.25 g/L in shake flask using 20 g/L glucose, approaching the maximum theoretical yield with concurrent biomass formation. The engineered yeast forms the basis for commercialization of bio-acrylic acid, while our CO2 fixation strategies pave the way for CO2 being used as the sole carbon source.
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Affiliation(s)
- Ning Qin
- College of Life Science and Technology, Beijing Advanced Innovation Center for Soft Matter Science and Engineering, Beijing University of Chemical Technology, Beijing, 100029, China
| | - Lingyun Li
- College of Life Science and Technology, Beijing Advanced Innovation Center for Soft Matter Science and Engineering, Beijing University of Chemical Technology, Beijing, 100029, China
- Department of Life Sciences, Chalmers University of Technology, SE412 96, Gothenburg, Sweden
| | - Xiaozhen Wan
- College of Life Science and Technology, Beijing Advanced Innovation Center for Soft Matter Science and Engineering, Beijing University of Chemical Technology, Beijing, 100029, China
| | - Xu Ji
- College of Life Science and Technology, Beijing Advanced Innovation Center for Soft Matter Science and Engineering, Beijing University of Chemical Technology, Beijing, 100029, China
| | - Yu Chen
- Key Laboratory of Quantitative Synthetic Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
| | - Chaokun Li
- Stem Cells and Metabolism Research Program, Faculty of Medicine, University of Helsinki, 00014, Helsinki, Finland
| | - Ping Liu
- The State Key Laboratory of Chemical Resource Engineering, College of Chemical Engineering, Beijing University of Chemical Technology, Beijing, 100029, China
| | - Yijie Zhang
- College of Life Science and Technology, Beijing Advanced Innovation Center for Soft Matter Science and Engineering, Beijing University of Chemical Technology, Beijing, 100029, China
| | - Weijie Yang
- College of Life Science and Technology, Beijing Advanced Innovation Center for Soft Matter Science and Engineering, Beijing University of Chemical Technology, Beijing, 100029, China
| | - Junfeng Jiang
- Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, 300308, China
| | - Jianye Xia
- Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, 300308, China
| | - Shuobo Shi
- College of Life Science and Technology, Beijing Advanced Innovation Center for Soft Matter Science and Engineering, Beijing University of Chemical Technology, Beijing, 100029, China
| | - Tianwei Tan
- College of Life Science and Technology, Beijing Advanced Innovation Center for Soft Matter Science and Engineering, Beijing University of Chemical Technology, Beijing, 100029, China
| | - Jens Nielsen
- College of Life Science and Technology, Beijing Advanced Innovation Center for Soft Matter Science and Engineering, Beijing University of Chemical Technology, Beijing, 100029, China.
- Department of Life Sciences, Chalmers University of Technology, SE412 96, Gothenburg, Sweden.
- BioInnovation Institute, Ole Maaløes Vej 3, DK2200, Copenhagen, Denmark.
| | - Yun Chen
- Department of Life Sciences, Chalmers University of Technology, SE412 96, Gothenburg, Sweden.
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800, Kongens, Lyngby, Denmark.
| | - Zihe Liu
- College of Life Science and Technology, Beijing Advanced Innovation Center for Soft Matter Science and Engineering, Beijing University of Chemical Technology, Beijing, 100029, China.
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85
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Ziari N, Hellerstein MK. PyMIDA: A Graphical User Interface for Mass Isotopomer Distribution Analysis. Anal Chem 2024; 96:2303-2308. [PMID: 38306584 DOI: 10.1021/acs.analchem.3c02211] [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: 02/04/2024]
Abstract
Mass isotopomer distribution analysis (MIDA) is an analytical technique that measures the synthesis rate of biological polymers using combinatorial probabilities and stable isotope labeling. Over the past few decades, this method has been developed and applied to a wide range of uses that have increased our understanding of metabolism and the etiology and monitoring of disease. There is currently no publicly available piece of software for performing MIDA calculations in a targeted manner without its functionality being limited to a specific use case. We present a cross-platform Python graphical user interface implementation for research to obtain kinetic parameters easily from stable-isotope labeling studies and provide the code and user manual on GitHub.
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Affiliation(s)
- Naveed Ziari
- Department of Nutritional Sciences & Toxicology, College of Natural Resources, University of California, Berkeley, 94720 California, United States
| | - Marc K Hellerstein
- Department of Nutritional Sciences & Toxicology, College of Natural Resources, University of California, Berkeley, 94720 California, United States
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Hou C, Song X, Xiong Z, Wang G, Xia Y, Ai L. Genome-scale reconstruction of the metabolic network in Streptococcus thermophilus S-3 and assess urea metabolism. JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE 2024; 104:1458-1469. [PMID: 37814322 DOI: 10.1002/jsfa.13026] [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: 03/19/2023] [Revised: 08/16/2023] [Accepted: 10/01/2023] [Indexed: 10/11/2023]
Abstract
BACKGROUND Streptococcus thermophilus is an important strain widely used in dairy fermentation, with distinct urea metabolism characteristics compared to other lactic acid bacteria. The conversion of urea by S. thermophilus has been shown to affect the flavor and acidification characteristics of milk. Additionally, urea metabolism has been found to significantly increase the number of cells and reduce cell damage under acidic pH conditions, resulting in higher activity. However, the physiological role of urea metabolism in S. thermophilus has not been fully evaluated. A deep understanding of this metabolic feature is of great significance for its production and application. Genome-scale metabolic network models (GEMs) are effective tools for investigating the metabolic network of organisms using computational biology methods. Constructing an organism-specific GEM can assist us in comprehending its characteristic metabolism at a systemic level. RESULTS In the present study, we reconstructed a high-quality GEM of S. thermophilus S-3 (iCH492), which contains 492 genes, 608 metabolites and 642 reactions. Growth phenotyping experiments were employed to validate the model both qualitatively and quantitatively, yielding satisfactory predictive accuracy (95.83%), sensitivity (93.33%) and specificity (100%). Subsequently, a systematic evaluation of urea metabolism in S. thermophilus was performed using iCH492. The results showed that urea metabolism reduces intracellular hydrogen ions and creates membrane potential by producing and transporting ammonium ions. This activation of glycolytic fluxes and ATP synthase produces more ATP for biomass synthesis. The regulation of fluxes of reactions involving NAD(P)H by urea metabolism improves redox balance. CONCLUSION Model iCH492 represents the most comprehensive knowledge-base of S. thermophilus to date, serving as a potent tool. The evaluation of urea metabolism led to novel insights regarding the role of urease. © 2023 Society of Chemical Industry.
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Affiliation(s)
- Chengjie Hou
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, China
| | - Xin Song
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, China
| | - Zhiqiang Xiong
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, China
| | - Guangqiang Wang
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, China
| | - Yongjun Xia
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, China
| | - Lianzhong Ai
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, China
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87
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Liu W, Wang H, Zhao Q, Tao C, Qu W, Hou Y, Huang R, Sun Z, Zhu G, Jiang X, Fang Y, Gao J, Wu X, Yang Z, Ping R, Chen J, Yang R, Chu T, Zhou J, Fan J, Tang Z, Yang D, Shi Y. Multiomics analysis reveals metabolic subtypes and identifies diacylglycerol kinase α (DGKA) as a potential therapeutic target for intrahepatic cholangiocarcinoma. Cancer Commun (Lond) 2024; 44:226-250. [PMID: 38143235 PMCID: PMC10876206 DOI: 10.1002/cac2.12513] [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: 07/04/2023] [Revised: 11/23/2023] [Accepted: 12/14/2023] [Indexed: 12/26/2023] Open
Abstract
BACKGROUND Intrahepatic cholangiocarcinoma (iCCA) is a highly heterogeneous and lethal hepatobiliary tumor with few therapeutic strategies. The metabolic reprogramming of tumor cells plays an essential role in the development of tumors, while the metabolic molecular classification of iCCA is largely unknown. Here, we performed an integrated multiomics analysis and metabolic classification to depict differences in metabolic characteristics of iCCA patients, hoping to provide a novel perspective to understand and treat iCCA. METHODS We performed integrated multiomics analysis in 116 iCCA samples, including whole-exome sequencing, bulk RNA-sequencing and proteome analysis. Based on the non-negative matrix factorization method and the protein abundance of metabolic genes in human genome-scale metabolic models, the metabolic subtype of iCCA was determined. Survival and prognostic gene analyses were used to compare overall survival (OS) differences between metabolic subtypes. Cell proliferation analysis, 5-ethynyl-2'-deoxyuridine (EdU) assay, colony formation assay, RNA-sequencing and Western blotting were performed to investigate the molecular mechanisms of diacylglycerol kinase α (DGKA) in iCCA cells. RESULTS Three metabolic subtypes (S1-S3) with subtype-specific biomarkers of iCCA were identified. These metabolic subtypes presented with distinct prognoses, metabolic features, immune microenvironments, and genetic alterations. The S2 subtype with the worst survival showed the activation of some special metabolic processes, immune-suppressed microenvironment and Kirsten rat sarcoma viral oncogene homolog (KRAS)/AT-rich interactive domain 1A (ARID1A) mutations. Among the S2 subtype-specific upregulated proteins, DGKA was further identified as a potential drug target for iCCA, which promoted cell proliferation by enhancing phosphatidic acid (PA) metabolism and activating mitogen-activated protein kinase (MAPK) signaling. CONCLUSION Via multiomics analyses, we identified three metabolic subtypes of iCCA, revealing that the S2 subtype exhibited the poorest survival outcomes. We further identified DGKA as a potential target for the S2 subtype.
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Affiliation(s)
- Weiren Liu
- Department of Liver Surgery and TransplantationLiver Cancer Institute, Zhongshan HospitalFudan UniversityKey Laboratory of Carcinogenesis and Cancer Invasion of Ministry of EducationShanghaiP. R. China
- Research Unit of Liver cancer Recurrence and Metastasis, Chinese Academy of Medical SciencesBeijingP. R. China
| | - Huqiang Wang
- State Key Laboratory of Medical Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of LifeomicsBeijingP. R. China
| | - Qianfu Zhao
- Department of Liver Surgery and TransplantationLiver Cancer Institute, Zhongshan HospitalFudan UniversityKey Laboratory of Carcinogenesis and Cancer Invasion of Ministry of EducationShanghaiP. R. China
- Research Unit of Liver cancer Recurrence and Metastasis, Chinese Academy of Medical SciencesBeijingP. R. China
| | - Chenyang Tao
- Department of Liver Surgery and TransplantationLiver Cancer Institute, Zhongshan HospitalFudan UniversityKey Laboratory of Carcinogenesis and Cancer Invasion of Ministry of EducationShanghaiP. R. China
- Research Unit of Liver cancer Recurrence and Metastasis, Chinese Academy of Medical SciencesBeijingP. R. China
| | - Weifeng Qu
- Department of Liver Surgery and TransplantationLiver Cancer Institute, Zhongshan HospitalFudan UniversityKey Laboratory of Carcinogenesis and Cancer Invasion of Ministry of EducationShanghaiP. R. China
- Research Unit of Liver cancer Recurrence and Metastasis, Chinese Academy of Medical SciencesBeijingP. R. China
| | - Yushan Hou
- State Key Laboratory of Medical Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of LifeomicsBeijingP. R. China
| | - Run Huang
- Department of Liver Surgery and TransplantationLiver Cancer Institute, Zhongshan HospitalFudan UniversityKey Laboratory of Carcinogenesis and Cancer Invasion of Ministry of EducationShanghaiP. R. China
- Research Unit of Liver cancer Recurrence and Metastasis, Chinese Academy of Medical SciencesBeijingP. R. China
| | - Zimei Sun
- State Key Laboratory of Medical Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of LifeomicsBeijingP. R. China
| | - Guiqi Zhu
- Department of Liver Surgery and TransplantationLiver Cancer Institute, Zhongshan HospitalFudan UniversityKey Laboratory of Carcinogenesis and Cancer Invasion of Ministry of EducationShanghaiP. R. China
- Research Unit of Liver cancer Recurrence and Metastasis, Chinese Academy of Medical SciencesBeijingP. R. China
| | - Xifei Jiang
- Department of Liver Surgery and TransplantationLiver Cancer Institute, Zhongshan HospitalFudan UniversityKey Laboratory of Carcinogenesis and Cancer Invasion of Ministry of EducationShanghaiP. R. China
- Research Unit of Liver cancer Recurrence and Metastasis, Chinese Academy of Medical SciencesBeijingP. R. China
| | - Yuan Fang
- Department of Liver Surgery and TransplantationLiver Cancer Institute, Zhongshan HospitalFudan UniversityKey Laboratory of Carcinogenesis and Cancer Invasion of Ministry of EducationShanghaiP. R. China
- Research Unit of Liver cancer Recurrence and Metastasis, Chinese Academy of Medical SciencesBeijingP. R. China
| | - Jun Gao
- Department of Liver Surgery and TransplantationLiver Cancer Institute, Zhongshan HospitalFudan UniversityKey Laboratory of Carcinogenesis and Cancer Invasion of Ministry of EducationShanghaiP. R. China
- Research Unit of Liver cancer Recurrence and Metastasis, Chinese Academy of Medical SciencesBeijingP. R. China
| | - Xiaoling Wu
- Department of Liver Surgery and TransplantationLiver Cancer Institute, Zhongshan HospitalFudan UniversityKey Laboratory of Carcinogenesis and Cancer Invasion of Ministry of EducationShanghaiP. R. China
- Research Unit of Liver cancer Recurrence and Metastasis, Chinese Academy of Medical SciencesBeijingP. R. China
| | - Zhixiang Yang
- State Key Laboratory of Medical Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of LifeomicsBeijingP. R. China
| | - Rongyu Ping
- State Key Laboratory of Medical Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of LifeomicsBeijingP. R. China
| | - Jiafeng Chen
- Department of Liver Surgery and TransplantationLiver Cancer Institute, Zhongshan HospitalFudan UniversityKey Laboratory of Carcinogenesis and Cancer Invasion of Ministry of EducationShanghaiP. R. China
- Research Unit of Liver cancer Recurrence and Metastasis, Chinese Academy of Medical SciencesBeijingP. R. China
| | - Rui Yang
- Department of Liver Surgery and TransplantationLiver Cancer Institute, Zhongshan HospitalFudan UniversityKey Laboratory of Carcinogenesis and Cancer Invasion of Ministry of EducationShanghaiP. R. China
- Research Unit of Liver cancer Recurrence and Metastasis, Chinese Academy of Medical SciencesBeijingP. R. China
| | - Tianhao Chu
- Department of Liver Surgery and TransplantationLiver Cancer Institute, Zhongshan HospitalFudan UniversityKey Laboratory of Carcinogenesis and Cancer Invasion of Ministry of EducationShanghaiP. R. China
- Research Unit of Liver cancer Recurrence and Metastasis, Chinese Academy of Medical SciencesBeijingP. R. China
| | - Jian Zhou
- Department of Liver Surgery and TransplantationLiver Cancer Institute, Zhongshan HospitalFudan UniversityKey Laboratory of Carcinogenesis and Cancer Invasion of Ministry of EducationShanghaiP. R. China
- Research Unit of Liver cancer Recurrence and Metastasis, Chinese Academy of Medical SciencesBeijingP. R. China
| | - Jia Fan
- Department of Liver Surgery and TransplantationLiver Cancer Institute, Zhongshan HospitalFudan UniversityKey Laboratory of Carcinogenesis and Cancer Invasion of Ministry of EducationShanghaiP. R. China
- Research Unit of Liver cancer Recurrence and Metastasis, Chinese Academy of Medical SciencesBeijingP. R. China
| | - Zheng Tang
- Department of Liver Surgery and TransplantationLiver Cancer Institute, Zhongshan HospitalFudan UniversityKey Laboratory of Carcinogenesis and Cancer Invasion of Ministry of EducationShanghaiP. R. China
- Research Unit of Liver cancer Recurrence and Metastasis, Chinese Academy of Medical SciencesBeijingP. R. China
| | - Dong Yang
- State Key Laboratory of Medical Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of LifeomicsBeijingP. R. China
| | - Yinghong Shi
- Department of Liver Surgery and TransplantationLiver Cancer Institute, Zhongshan HospitalFudan UniversityKey Laboratory of Carcinogenesis and Cancer Invasion of Ministry of EducationShanghaiP. R. China
- Research Unit of Liver cancer Recurrence and Metastasis, Chinese Academy of Medical SciencesBeijingP. R. China
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Immanuel A, Yennamalli RM, Ulaganathan V. Targeting the Bottlenecks in Levan Biosynthesis Pathway in Bacillus subtilis and Strain Optimization by Computational Modeling and Omics Integration. OMICS : A JOURNAL OF INTEGRATIVE BIOLOGY 2024; 28:49-58. [PMID: 38315781 DOI: 10.1089/omi.2023.0277] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/07/2024]
Abstract
Levan is a fructan polymer with many industrial applications such as the formulation of hydrogels, drug delivery, and wound healing, among others. To this end, metabolic systems engineering is a valuable method to improve the yield of a specific metabolite in a wide range of bacterial and eukaryotic organisms. In this study, we report a systems biology approach integrating genomics data for the Bacillus subtilis model, wherein the metabolic pathway for levan biosynthesis is unpacked. We analyzed a revised genome-scale enzyme-constrained metabolic model (ecGEM) and performed simulations to increase levan biopolymer production capacity in B. subtilis. We used the model ec_iYO844_lvn to (1) identify the essential genes and bottlenecks in levan production, and (2) specifically design an engineered B. subtilis strain capable of producing higher levan yields. The FBA and FVA analysis showed the maximal growth rate of the organism up to 0.624 hr-1 at 20 mmol gDw-1 hr-1 of sucrose intake. Gene knockout analyses were performed to identify gene knockout targets to increase the levan flux in B. subtilis. Importantly, we found that the pgk and ctaD genes are the two target genes for the knockout. The perturbation of these two genes has flux gains for levan production reactions with 1.3- and 1.4-fold the relative flux span in the mutant strains, respectively, compared to the wild type. In all, this work identifies the bottlenecks in the production of levan and possible ways to overcome them. Our results provide deeper insights on the bacterium's physiology and new avenues for strain engineering.
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Affiliation(s)
- Aruldoss Immanuel
- Molecular Motors Lab, Department of Biotechnology, School of Chemical & Biotechnology, SASTRA Deemed to be University, Thanjavur, India
| | - Ragothaman M Yennamalli
- Department of Bioinformatics, School of Chemical & Biotechnology, SASTRA Deemed to be University, Thanjavur, India
| | - Venkatasubramanian Ulaganathan
- Molecular Motors Lab, Department of Biotechnology, School of Chemical & Biotechnology, SASTRA Deemed to be University, Thanjavur, India
- Department of Bioinformatics, School of Chemical & Biotechnology, SASTRA Deemed to be University, Thanjavur, India
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89
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Hassani L, Moosavi MR, Setoodeh P, Zare H. FastKnock: an efficient next-generation approach to identify all knockout strategies for strain optimization. Microb Cell Fact 2024; 23:37. [PMID: 38287320 PMCID: PMC10823710 DOI: 10.1186/s12934-023-02277-x] [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: 06/30/2023] [Accepted: 12/15/2023] [Indexed: 01/31/2024] Open
Abstract
Overproduction of desired native or nonnative biochemical(s) in (micro)organisms can be achieved through metabolic engineering. Appropriate rewiring of cell metabolism is performed by making rational changes such as insertion, up-/down-regulation and knockout of genes and consequently metabolic reactions. Finding appropriate targets (including proper sets of reactions to be knocked out) for metabolic engineering to design optimal production strains has been the goal of a number of computational algorithms. We developed FastKnock, an efficient next-generation algorithm for identifying all possible knockout strategies (with a predefined maximum number of reaction deletions) for the growth-coupled overproduction of biochemical(s) of interest. We achieve this by developing a special depth-first traversal algorithm that allows us to prune the search space significantly. This leads to a drastic reduction in execution time. We evaluate the performance of the FastKnock algorithm using various Escherichia coli genome-scale metabolic models in different conditions (minimal and rich mediums) for the overproduction of a number of desired metabolites. FastKnock efficiently prunes the search space to less than 0.2% for quadruple- and 0.02% for quintuple-reaction knockouts. Compared to the classic approaches such as OptKnock and the state-of-the-art techniques such as MCSEnumerator methods, FastKnock found many more beneficial and important practical solutions. The availability of all the solutions provides the opportunity to further characterize, rank and select the most appropriate intervention strategy based on any desired evaluation index. Our implementation of the FastKnock method in Python is publicly available at https://github.com/leilahsn/FastKnock .
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Affiliation(s)
- Leila Hassani
- Department of Computer Science and Engineering and IT, School of Electrical and Computer Engineering, Shiraz University, Shiraz, Iran
| | - Mohammad R Moosavi
- Department of Computer Science and Engineering and IT, School of Electrical and Computer Engineering, Shiraz University, Shiraz, Iran.
| | - Payam Setoodeh
- Department of Chemical Engineering, School of Chemical, Petroleum and Gas Engineering, Shiraz University, Shiraz, Iran
- Booth School of Engineering Practice and Technology, McMaster University, Hamilton, ON, Canada
| | - Habil Zare
- Department of Cell Systems and Anatomy, University of Texas Health Science Center, San Antonio, TX, USA.
- Glenn Biggs Institute for Alzheimer's & Neurodegenerative Diseases, University of Texas Health Science Center, San Antonio, USA.
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90
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Jacopin E, Sakamoto Y, Nishida K, Kaizu K, Takahashi K. An architecture for collaboration in systems biology at the age of the Metaverse. NPJ Syst Biol Appl 2024; 10:12. [PMID: 38280851 PMCID: PMC10821884 DOI: 10.1038/s41540-024-00334-8] [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: 08/02/2023] [Accepted: 01/10/2024] [Indexed: 01/29/2024] Open
Abstract
As the current state of the Metaverse is largely driven by corporate interests, which may not align with scientific goals and values, academia should play a more active role in its development. Here, we present the challenges and solutions for building a Metaverse that supports systems biology research and collaboration. Our solution consists of two components: Kosmogora, a server ensuring biological data access, traceability, and integrity in the context of a highly collaborative environment such as a metaverse; and ECellDive, a virtual reality application to explore, interact, and build upon the data managed by Kosmogora. We illustrate the synergy between the two components by visualizing a metabolic network and its flux balance analysis. We also argue that the Metaverse of systems biology will foster closer communication and cooperation between experimentalists and modelers in the field.
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Affiliation(s)
- Eliott Jacopin
- RIKEN, Center for Biosystems Dynamics Research, 6-2-3 Furuedai, Suita, Osaka, 565-0874, Japan.
| | - Yuki Sakamoto
- RIKEN, Center for Biosystems Dynamics Research, 6-2-3 Furuedai, Suita, Osaka, 565-0874, Japan
| | - Kozo Nishida
- RIKEN, Center for Integrative Medical Sciences, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa, 230-0045, Japan
- Tokyo University of Agriculture and Technology, Department of Biotechnology and Life Science, 2-24-16 Nakamachi, Koganei, Tokyo, 184-8588, Japan
| | - Kazunari Kaizu
- RIKEN, Center for Biosystems Dynamics Research, 6-2-3 Furuedai, Suita, Osaka, 565-0874, Japan
| | - Koichi Takahashi
- RIKEN, Center for Biosystems Dynamics Research, 6-2-3 Furuedai, Suita, Osaka, 565-0874, Japan
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91
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Jóhannsson F, Yurkovich JT, Guðmundsson S, Sigurjónsson ÓE, Rolfsson Ó. Temperature Dependence of Platelet Metabolism. Metabolites 2024; 14:91. [PMID: 38392983 PMCID: PMC10890334 DOI: 10.3390/metabo14020091] [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: 11/30/2023] [Revised: 01/16/2024] [Accepted: 01/19/2024] [Indexed: 02/25/2024] Open
Abstract
Temperature plays a fundamental role in biology, influencing cellular function, chemical reaction rates, molecular structures, and interactions. While the temperature dependence of many biochemical reactions is well defined in vitro, the effect of temperature on metabolic function at the network level is poorly understood, and it remains an important challenge in optimizing the storage of cells and tissues at lower temperatures. Here, we used time-course metabolomic data and systems biology approaches to characterize the effects of storage temperature on human platelets (PLTs) in a platelet additive solution. We observed that changes to the metabolome with storage time do not simply scale with temperature but instead display complex temperature dependence, with only a small subset of metabolites following an Arrhenius-type relationship. Investigation of PLT energy metabolism through integration with computational modeling revealed that oxidative metabolism is more sensitive to temperature changes than glycolysis. The increased contribution of glycolysis to ATP turnover at lower temperatures indicates a stronger glycolytic phenotype with decreasing storage temperature. More broadly, these results demonstrate that the temperature dependence of the PLT metabolic network is not uniform, suggesting that efforts to improve the health of stored PLTs could be targeted at specific pathways.
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Affiliation(s)
- Freyr Jóhannsson
- Center for Systems Biology, University of Iceland, Sturlugata 8, 102 Reykjavik, Iceland
- School of Health Sciences, Medical Department, University of Iceland, Sturlugata 8, 102 Reykjavik, Iceland
| | - James T Yurkovich
- Department of Bioengineering, University of California San Diego, La Jolla, CA 92093, USA
- Phenome Health, Seattle, WA 98109, USA
- Center for Phenomic Health, The Buck Institute for Research on Aging, Novato, CA 94945, USA
| | - Steinn Guðmundsson
- Center for Systems Biology, University of Iceland, Sturlugata 8, 102 Reykjavik, Iceland
- Faculty of Industrial Engineering, Mechanical Engineering and Computer Science, University of Iceland, Dunhagi 3, 107 Reykjavik, Iceland
| | - Ólafur E Sigurjónsson
- The Blood Bank, Landspitali-University Hospital, Snorrabraut 60, 101 Reykjavik, Iceland
- School of Science and Engineering, Reykjavik University, Menntavegur 1, 102 Reykjavik, Iceland
| | - Óttar Rolfsson
- Center for Systems Biology, University of Iceland, Sturlugata 8, 102 Reykjavik, Iceland
- School of Health Sciences, Medical Department, University of Iceland, Sturlugata 8, 102 Reykjavik, Iceland
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92
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Zulfiqar M, Singh V, Steinbeck C, Sorokina M. Review on computer-assisted biosynthetic capacities elucidation to assess metabolic interactions and communication within microbial communities. Crit Rev Microbiol 2024:1-40. [PMID: 38270170 DOI: 10.1080/1040841x.2024.2306465] [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: 03/13/2023] [Accepted: 01/12/2024] [Indexed: 01/26/2024]
Abstract
Microbial communities thrive through interactions and communication, which are challenging to study as most microorganisms are not cultivable. To address this challenge, researchers focus on the extracellular space where communication events occur. Exometabolomics and interactome analysis provide insights into the molecules involved in communication and the dynamics of their interactions. Advances in sequencing technologies and computational methods enable the reconstruction of taxonomic and functional profiles of microbial communities using high-throughput multi-omics data. Network-based approaches, including community flux balance analysis, aim to model molecular interactions within and between communities. Despite these advances, challenges remain in computer-assisted biosynthetic capacities elucidation, requiring continued innovation and collaboration among diverse scientists. This review provides insights into the current state and future directions of computer-assisted biosynthetic capacities elucidation in studying microbial communities.
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Affiliation(s)
- Mahnoor Zulfiqar
- Institute for Inorganic and Analytical Chemistry, Friedrich Schiller University, Jena, Germany
- Cluster of Excellence Balance of the Microverse, Friedrich Schiller University Jena, Jena, Germany
| | - Vinay Singh
- Institute for Inorganic and Analytical Chemistry, Friedrich Schiller University, Jena, Germany
| | - Christoph Steinbeck
- Institute for Inorganic and Analytical Chemistry, Friedrich Schiller University, Jena, Germany
- Cluster of Excellence Balance of the Microverse, Friedrich Schiller University Jena, Jena, Germany
| | - Maria Sorokina
- Institute for Inorganic and Analytical Chemistry, Friedrich Schiller University, Jena, Germany
- Data Science and Artificial Intelligence, Research and Development, Pharmaceuticals, Bayer, Berlin, Germany
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93
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Joseph C, Zafeiropoulos H, Bernaerts K, Faust K. Predicting microbial interactions with approaches based on flux balance analysis: an evaluation. BMC Bioinformatics 2024; 25:36. [PMID: 38262921 PMCID: PMC10804772 DOI: 10.1186/s12859-024-05651-7] [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: 03/23/2023] [Accepted: 01/11/2024] [Indexed: 01/25/2024] Open
Abstract
BACKGROUND Given a genome-scale metabolic model (GEM) of a microorganism and criteria for optimization, flux balance analysis (FBA) predicts the optimal growth rate and its corresponding flux distribution for a specific medium. FBA has been extended to microbial consortia and thus can be used to predict interactions by comparing in-silico growth rates for co- and monocultures. Although FBA-based methods for microbial interaction prediction are becoming popular, a systematic evaluation of their accuracy has not yet been performed. RESULTS Here, we evaluate the accuracy of FBA-based predictions of human and mouse gut bacterial interactions using growth data from the literature. For this, we collected 26 GEMs from the semi-curated AGORA database as well as four previously published curated GEMs. We tested the accuracy of three tools (COMETS, Microbiome Modeling Toolbox and MICOM) by comparing growth rates predicted in mono- and co-culture to growth rates extracted from the literature and also investigated the impact of different tool settings and media. We found that except for curated GEMs, predicted growth rates and their ratios (i.e. interaction strengths) do not correlate with growth rates and interaction strengths obtained from in vitro data. CONCLUSIONS Prediction of growth rates with FBA using semi-curated GEMs is currently not sufficiently accurate to predict interaction strengths reliably.
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Affiliation(s)
- Clémence Joseph
- Department of Microbiology, Immunology and Transplantation, Rega Institute for Medical Research, Laboratory of Molecular Bacteriology, KU Leuven, 3000, Leuven, Belgium
| | - Haris Zafeiropoulos
- Department of Microbiology, Immunology and Transplantation, Rega Institute for Medical Research, Laboratory of Molecular Bacteriology, KU Leuven, 3000, Leuven, Belgium
| | - Kristel Bernaerts
- Department of Chemical Engineering, Chemical and Biochemical Reactor Engineering and Safety (CREaS), KU Leuven, 3001, Leuven, Belgium
| | - Karoline Faust
- Department of Microbiology, Immunology and Transplantation, Rega Institute for Medical Research, Laboratory of Molecular Bacteriology, KU Leuven, 3000, Leuven, Belgium.
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94
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Han S, Kim D, Kim Y, Yoon SH. Genome-scale metabolic network model and phenome of solvent-tolerant Pseudomonas putida S12. BMC Genomics 2024; 25:63. [PMID: 38229031 DOI: 10.1186/s12864-023-09940-y] [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: 07/24/2023] [Accepted: 12/25/2023] [Indexed: 01/18/2024] Open
Abstract
BACKGROUND Pseudomonas putida S12 is a gram-negative bacterium renowned for its high tolerance to organic solvents and metabolic versatility, making it attractive for various applications, including bioremediation and the production of aromatic compounds, bioplastics, biofuels, and value-added compounds. However, a metabolic model of S12 has yet to be developed. RESULTS In this study, we present a comprehensive and highly curated genome-scale metabolic network model of S12 (iSH1474), containing 1,474 genes, 1,436 unique metabolites, and 2,938 metabolic reactions. The model was constructed by leveraging existing metabolic models and conducting comparative analyses of genomes and phenomes. Approximately 2,000 different phenotypes were measured for S12 and its closely related KT2440 strain under various nutritional and environmental conditions. These phenotypic data, combined with the reported experimental data, were used to refine and validate the reconstruction. Model predictions quantitatively agreed well with in vivo flux measurements and the batch cultivation of S12, which demonstrated that iSH1474 accurately represents the metabolic capabilities of S12. Furthermore, the model was simulated to investigate the maximum theoretical metabolic capacity of S12 growing on toxic organic solvents. CONCLUSIONS iSH1474 represents a significant advancement in our understanding of the cellular metabolism of P. putida S12. The combined results of metabolic simulation and comparative genome and phenome analyses identified the genetic and metabolic determinants of the characteristic phenotypes of S12. This study could accelerate the development of this versatile organism as an efficient cell factory for various biotechnological applications.
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Affiliation(s)
- Sol Han
- Department of Bioscience and Biotechnology, Konkuk University, Seoul, 05029, Republic of Korea
| | - Dohyeon Kim
- Department of Bioscience and Biotechnology, Konkuk University, Seoul, 05029, Republic of Korea
| | - Youngshin Kim
- Department of Bioscience and Biotechnology, Konkuk University, Seoul, 05029, Republic of Korea
| | - Sung Ho Yoon
- Department of Bioscience and Biotechnology, Konkuk University, Seoul, 05029, Republic of Korea.
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95
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Wutkowska M, Tláskal V, Bordel S, Stein LY, Nweze JA, Daebeler A. Leveraging genome-scale metabolic models to understand aerobic methanotrophs. THE ISME JOURNAL 2024; 18:wrae102. [PMID: 38861460 PMCID: PMC11195481 DOI: 10.1093/ismejo/wrae102] [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: 03/04/2024] [Revised: 05/20/2024] [Accepted: 06/10/2024] [Indexed: 06/13/2024]
Abstract
Genome-scale metabolic models (GEMs) are valuable tools serving systems biology and metabolic engineering. However, GEMs are still an underestimated tool in informing microbial ecology. Since their first application for aerobic gammaproteobacterial methane oxidizers less than a decade ago, GEMs have substantially increased our understanding of the metabolism of methanotrophs, a microbial guild of high relevance for the natural and biotechnological mitigation of methane efflux to the atmosphere. Particularly, GEMs helped to elucidate critical metabolic and regulatory pathways of several methanotrophic strains, predicted microbial responses to environmental perturbations, and were used to model metabolic interactions in cocultures. Here, we conducted a systematic review of GEMs exploring aerobic methanotrophy, summarizing recent advances, pointing out weaknesses, and drawing out probable future uses of GEMs to improve our understanding of the ecology of methane oxidizers. We also focus on their potential to unravel causes and consequences when studying interactions of methane-oxidizing bacteria with other methanotrophs or members of microbial communities in general. This review aims to bridge the gap between applied sciences and microbial ecology research on methane oxidizers as model organisms and to provide an outlook for future studies.
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Affiliation(s)
- Magdalena Wutkowska
- Institute of Soil Biology and Biogeochemistry, Biology Centre CAS, 370 05 České Budějovice, Czech Republic
| | - Vojtěch Tláskal
- Institute of Soil Biology and Biogeochemistry, Biology Centre CAS, 370 05 České Budějovice, Czech Republic
| | - Sergio Bordel
- Department of Chemical Engineering and Environmental Technology, School of Industrial Engineering, University of Valladolid, Valladolid 47011, Spain
- Institute of Sustainable Processes, Valladolid 47011, Spain
| | - Lisa Y Stein
- Department of Biological Sciences, Faculty of Science, University of Alberta, Edmonton, AB T6G 2E9, Canada
| | - Justus Amuche Nweze
- Institute of Soil Biology and Biogeochemistry, Biology Centre CAS, 370 05 České Budějovice, Czech Republic
- Department of Ecosystem Biology, Faculty of Science, University of South Bohemia, 370 05 České Budějovice, Czech Republic
- Department of Science Laboratory Technology, Faculty of Physical Sciences, University of Nigeria, Nsukka 410001, Nigeria
| | - Anne Daebeler
- Institute of Soil Biology and Biogeochemistry, Biology Centre CAS, 370 05 České Budějovice, Czech Republic
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96
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Xu X, Li C, Cao W, Yan L, Cao L, Han Q, Gao M, Chen Y, Shen Z, Jiang J, Chen C. Bacterial growth and environmental adaptation via thiamine biosynthesis and thiamine-mediated metabolic interactions. THE ISME JOURNAL 2024; 18:wrae157. [PMID: 39129674 PMCID: PMC11346370 DOI: 10.1093/ismejo/wrae157] [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: 03/19/2024] [Revised: 07/24/2024] [Accepted: 08/11/2024] [Indexed: 08/13/2024]
Abstract
Understanding the ancestral transition from anaerobic to aerobic lifestyles is essential for comprehending life's early evolution. However, the biological adaptations occurring during this crucial transition remain largely unexplored. Thiamine is an important cofactor involved in central carbon metabolism and aerobic respiration. Here, we explored the phylogenetic and global distribution of thiamine-auxotrophic and thiamine-prototrophic bacteria based on the thiamine biosynthetic pathway in 154 838 bacterial genomes. We observed strong coincidences of the origin of thiamine-synthetic bacteria with the "Great Oxygenation Event," indicating that thiamine biosynthesis in bacteria emerged as an adaptation to aerobic respiration. Furthermore, we demonstrated that thiamine-mediated metabolic interactions are fundamental factors influencing the assembly and diversity of bacterial communities by a global survey across 4245 soil samples. Through our newly established stable isotope probing-metabolic modeling method, we uncovered the active utilization of thiamine-mediated metabolic interactions by bacterial communities in response to changing environments, thus revealing an environmental adaptation strategy employed by bacteria at the community level. Our study demonstrates the widespread thiamine-mediated metabolic interactions in bacterial communities and their crucial roles in setting the stage for an evolutionary transition from anaerobic to aerobic lifestyles and subsequent environmental adaptation. These findings provide new insights into early bacterial evolution and their subsequent growth and adaptations to environments.
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Affiliation(s)
- Xihui Xu
- College of Life Sciences, Nanjing Agricultural University, Nanjing 210095, China
| | - Can Li
- College of Life Sciences, Nanjing Agricultural University, Nanjing 210095, China
| | - Weimiao Cao
- College of Life Sciences, Nanjing Agricultural University, Nanjing 210095, China
| | - Lulu Yan
- College of Life Sciences, Nanjing Agricultural University, Nanjing 210095, China
| | - Lulu Cao
- College of Life Sciences, Nanjing Agricultural University, Nanjing 210095, China
| | - Qi Han
- College of Life Sciences, Nanjing Agricultural University, Nanjing 210095, China
| | - Minling Gao
- Department of Materials and Environmental Engineering, Shantou University, Shantou 515063, China
| | - Yahua Chen
- College of Life Sciences, Nanjing Agricultural University, Nanjing 210095, China
- Jiangsu Collaborative Innovation Center for Solid Organic Waste Resource Utilization, Nanjing Agricultural University, Nanjing 210095, China
| | - Zhenguo Shen
- College of Life Sciences, Nanjing Agricultural University, Nanjing 210095, China
- Jiangsu Collaborative Innovation Center for Solid Organic Waste Resource Utilization, Nanjing Agricultural University, Nanjing 210095, China
| | - Jiandong Jiang
- College of Life Sciences, Nanjing Agricultural University, Nanjing 210095, China
| | - Chen Chen
- College of Life Sciences, Nanjing Agricultural University, Nanjing 210095, China
- Jiangsu Collaborative Innovation Center for Solid Organic Waste Resource Utilization, Nanjing Agricultural University, Nanjing 210095, China
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97
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Saa PA, Zapararte S, Drovandi CC, Nielsen LK. LooplessFluxSampler: an efficient toolbox for sampling the loopless flux solution space of metabolic models. BMC Bioinformatics 2024; 25:3. [PMID: 38166586 PMCID: PMC10763395 DOI: 10.1186/s12859-023-05616-2] [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: 04/20/2023] [Accepted: 12/13/2023] [Indexed: 01/04/2024] Open
Abstract
BACKGROUND Uniform random sampling of mass-balanced flux solutions offers an unbiased appraisal of the capabilities of metabolic networks. Unfortunately, it is impossible to avoid thermodynamically infeasible loops in flux samples when using convex samplers on large metabolic models. Current strategies for randomly sampling the non-convex loopless flux space display limited efficiency and lack theoretical guarantees. RESULTS Here, we present LooplessFluxSampler, an efficient algorithm for exploring the loopless mass-balanced flux solution space of metabolic models, based on an Adaptive Directions Sampling on a Box (ADSB) algorithm. ADSB is rooted in the general Adaptive Direction Sampling (ADS) framework, specifically the Parallel ADS, for which theoretical convergence and irreducibility results are available for sampling from arbitrary distributions. By sampling directions that adapt to the target distribution, ADSB traverses more efficiently the sample space achieving faster mixing than other methods. Importantly, the presented algorithm is guaranteed to target the uniform distribution over convex regions, and it provably converges on the latter distribution over more general (non-convex) regions provided the sample can have full support. CONCLUSIONS LooplessFluxSampler enables scalable statistical inference of the loopless mass-balanced solution space of large metabolic models. Grounded in a theoretically sound framework, this toolbox provides not only efficient but also reliable results for exploring the properties of the almost surely non-convex loopless flux space. Finally, LooplessFluxSampler includes a Markov Chain diagnostics suite for assessing the quality of the final sample and the performance of the algorithm.
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Affiliation(s)
- Pedro A Saa
- Department of Chemical and Bioprocess Engineering, School of Engineering, Pontifical Catholic University of Chile, Av. Vicuña Mackenna 4860, 7820436, Santiago, Chile
- Institute for Mathematical and Computational Engineering, Pontifical Catholic University of Chile, Av. Vicuña Mackenna 4860, 7820436, Santiago, Chile
| | - Sebastian Zapararte
- Department of Chemical and Bioprocess Engineering, School of Engineering, Pontifical Catholic University of Chile, Av. Vicuña Mackenna 4860, 7820436, Santiago, Chile
| | - Christopher C Drovandi
- School of Mathematical Sciences and Centre for Data Science, Queensland University of Technology, 2 George Street, Brisbane, Australia
| | - Lars K Nielsen
- Australian Institute for Bioengineering and Nanotechnology, The University of Queensland, Building 75, Cnr College Rd and Cooper Rd, Brisbane, Australia.
- The Novo Nordisk Foundation Centre for Biosustainability, Technical University of Denmark, Building, Kemitorvet 220, 2800, Kongens Lyngby, Copenhagen, Denmark.
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98
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Morrissey J, Strain B, Kontoravdi C. Flux Balance Analysis of Mammalian Cell Systems. Methods Mol Biol 2024; 2774:119-134. [PMID: 38441762 DOI: 10.1007/978-1-0716-3718-0_9] [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] [Indexed: 03/07/2024]
Abstract
Flux balance analysis (FBA) is a computational methodology to model and analyze the metabolic behavior of cells. In this chapter, we break down the key steps for formulating an FBA model and other FBA-derived methodologies in the context of mammalian cell biology, including strain design, developing cell line-specific models, and conducting flux sampling. We provide annotated COBRApy code for each step to show how it would work in practice.
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Affiliation(s)
- James Morrissey
- Department of Chemical Engineering, Imperial College London, London, UK
| | - Benjamin Strain
- Department of Chemical Engineering, Imperial College London, London, UK
| | - Cleo Kontoravdi
- Department of Chemical Engineering, Imperial College London, London, UK.
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99
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Eames A, Chandrasekaran S. Leveraging metabolic modeling and machine learning to uncover modulators of quiescence depth. PNAS NEXUS 2024; 3:pgae013. [PMID: 38292544 PMCID: PMC10825626 DOI: 10.1093/pnasnexus/pgae013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/01/2023] [Accepted: 12/28/2023] [Indexed: 02/01/2024]
Abstract
Quiescence, a temporary withdrawal from the cell cycle, plays a key role in tissue homeostasis and regeneration. Quiescence is increasingly viewed as a continuum between shallow and deep quiescence, reflecting different potentials to proliferate. The depth of quiescence is altered in a range of diseases and during aging. Here, we leveraged genome-scale metabolic modeling (GEM) to define the metabolic and epigenetic changes that take place with quiescence deepening. We discovered contrasting changes in lipid catabolism and anabolism and diverging trends in histone methylation and acetylation. We then built a multi-cell type machine learning model that accurately predicts quiescence depth in diverse biological contexts. Using both machine learning and genome-scale flux simulations, we performed high-throughput screening of chemical and genetic modulators of quiescence and identified novel small molecule and genetic modulators with relevance to cancer and aging.
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Affiliation(s)
- Alec Eames
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI 48109, USA
| | - Sriram Chandrasekaran
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI 48109, USA
- Program in Chemical Biology, University of Michigan, Ann Arbor, MI 48109, USA
- Rogel Cancer Center, University of Michigan Medical School, Ann Arbor, MI 48109, USA
- Center for Bioinformatics and Computational Medicine, University of Michigan, Ann Arbor, MI 48109, USA
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100
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Cerk K, Ugalde‐Salas P, Nedjad CG, Lecomte M, Muller C, Sherman DJ, Hildebrand F, Labarthe S, Frioux C. Community-scale models of microbiomes: Articulating metabolic modelling and metagenome sequencing. Microb Biotechnol 2024; 17:e14396. [PMID: 38243750 PMCID: PMC10832553 DOI: 10.1111/1751-7915.14396] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Revised: 11/27/2023] [Accepted: 12/20/2023] [Indexed: 01/21/2024] Open
Abstract
Building models is essential for understanding the functions and dynamics of microbial communities. Metabolic models built on genome-scale metabolic network reconstructions (GENREs) are especially relevant as a means to decipher the complex interactions occurring among species. Model reconstruction increasingly relies on metagenomics, which permits direct characterisation of naturally occurring communities that may contain organisms that cannot be isolated or cultured. In this review, we provide an overview of the field of metabolic modelling and its increasing reliance on and synergy with metagenomics and bioinformatics. We survey the means of assigning functions and reconstructing metabolic networks from (meta-)genomes, and present the variety and mathematical fundamentals of metabolic models that foster the understanding of microbial dynamics. We emphasise the characterisation of interactions and the scaling of model construction to large communities, two important bottlenecks in the applicability of these models. We give an overview of the current state of the art in metagenome sequencing and bioinformatics analysis, focusing on the reconstruction of genomes in microbial communities. Metagenomics benefits tremendously from third-generation sequencing, and we discuss the opportunities of long-read sequencing, strain-level characterisation and eukaryotic metagenomics. We aim at providing algorithmic and mathematical support, together with tool and application resources, that permit bridging the gap between metagenomics and metabolic modelling.
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Affiliation(s)
- Klara Cerk
- Quadram Institute BioscienceNorwichUK
- Earlham InstituteNorwichUK
| | | | - Chabname Ghassemi Nedjad
- Inria, University of Bordeaux, INRAETalenceFrance
- University of Bordeaux, CNRS, Bordeaux INP, LaBRI, UMR 5800TalenceFrance
| | - Maxime Lecomte
- Inria, University of Bordeaux, INRAETalenceFrance
- INRAE STLO¸University of RennesRennesFrance
| | | | | | - Falk Hildebrand
- Quadram Institute BioscienceNorwichUK
- Earlham InstituteNorwichUK
| | - Simon Labarthe
- Inria, University of Bordeaux, INRAETalenceFrance
- INRAE, University of Bordeaux, BIOGECO, UMR 1202CestasFrance
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