1
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Hackmann TJ. The vast landscape of carbohydrate fermentation in prokaryotes. FEMS Microbiol Rev 2024; 48:fuae016. [PMID: 38821505 DOI: 10.1093/femsre/fuae016] [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: 04/05/2024] [Revised: 05/23/2024] [Accepted: 05/29/2024] [Indexed: 06/02/2024] Open
Abstract
Fermentation is a type of metabolism carried out by organisms in environments without oxygen. Despite being studied for over 185 years, the diversity and complexity of this metabolism are just now becoming clear. Our review starts with the definition of fermentation, which has evolved over the years and which we help further refine. We then examine the range of organisms that carry out fermentation and their traits. Over one-fourth of all prokaryotes are fermentative, use more than 40 substrates, and release more than 50 metabolic end products. These insights come from studies analyzing records of thousands of organisms. Next, our review examines the complexity of fermentation at the biochemical level. We map out pathways of glucose fermentation in unprecedented detail, covering over 120 biochemical reactions. We also review recent studies coupling genomics and enzymology to reveal new pathways and enzymes. Our review concludes with practical applications for agriculture, human health, and industry. All these areas depend on fermentation and could be improved through manipulating fermentative microbes and enzymes. We discuss potential approaches for manipulation, including genetic engineering, electrofermentation, probiotics, and enzyme inhibitors. We hope our review underscores the importance of fermentation research and stimulates the next 185 years of study.
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Affiliation(s)
- Timothy J Hackmann
- Department of Animal Science, University of California, Davis, CA 95616, United States
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2
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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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3
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Li B, Zu M, Jiang A, Cao Y, Wu J, Shahbazi MA, Shi X, Reis RL, Kundu SC, Xiao B. Magnetic natural lipid nanoparticles for oral treatment of colorectal cancer through potentiated antitumor immunity and microbiota metabolite regulation. Biomaterials 2024; 307:122530. [PMID: 38493672 DOI: 10.1016/j.biomaterials.2024.122530] [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: 11/15/2023] [Revised: 02/10/2024] [Accepted: 03/08/2024] [Indexed: 03/19/2024]
Abstract
The therapeutic efficacy of oral nanotherapeutics against colorectal cancer (CRC) is restricted by inadequate drug accumulation, immunosuppressive microenvironment, and intestinal microbiota imbalance. To overcome these challenges, we elaborately constructed 6-gingerol (Gin)-loaded magnetic mesoporous silicon nanoparticles and functionalized their surface with mulberry leaf-extracted lipids (MLLs) and Pluronic F127 (P127). In vitro experiments revealed that P127 functionalization and alternating magnetic fields (AMFs) promoted internalization of the obtained P127-MLL@Gins by colorectal tumor cells and induced their apoptosis/ferroptosis through Gin/ferrous ion-induced oxidative stress and magneto-thermal effect. After oral administration, P127-MLL@Gins safely passed to the colorectal lumen, infiltrated the mucus barrier, and penetrated into the deep tumors under the influence of AMFs. Subsequently, the P127-MLL@Gin (+ AMF) treatment activated antitumor immunity and suppressed tumor growth. We also found that this therapeutic modality significantly increased the abundance of beneficial bacteria (e.g., Bacillus and unclassified-c-Bacilli), reduced the proportions of harmful bacteria (e.g., Bacteroides and Alloprevotella), and increased lipid oxidation metabolites. Strikingly, checkpoint blockers synergistically improved the therapeutic outcomes of P127-MLL@Gins (+ AMF) against orthotopic and distant colorectal tumors and significantly prolonged mouse life spans. Overall, this oral therapeutic platform is a promising modality for synergistic treatment of CRC.
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Affiliation(s)
- Baoyi Li
- State Key Laboratory of Resource Insects, College of Sericulture, Textile, and Biomass Sciences, Southwest University, Chongqing, 400715, China
| | - Menghang Zu
- State Key Laboratory of Resource Insects, College of Sericulture, Textile, and Biomass Sciences, Southwest University, Chongqing, 400715, China
| | - Aodi Jiang
- State Key Laboratory of Resource Insects, College of Sericulture, Textile, and Biomass Sciences, Southwest University, Chongqing, 400715, China
| | - Yingui Cao
- State Key Laboratory of Resource Insects, College of Sericulture, Textile, and Biomass Sciences, Southwest University, Chongqing, 400715, China
| | - Jiaxue Wu
- State Key Laboratory of Resource Insects, College of Sericulture, Textile, and Biomass Sciences, Southwest University, Chongqing, 400715, China
| | - Mohammad-Ali Shahbazi
- Department of Biomedical Engineering, University Medical Center Groningen, University of Groningen, Antonius Deusinglaan 1, 9713 AV, Groningen, Netherlands; W.J. Kolff Institute for Biomedical Engineering and Materials Science, University of Groningen, Antonius Deusinglaan 1, 9713 AV, Groningen, Netherlands
| | - Xiaoxiao Shi
- State Key Laboratory of Resource Insects, College of Sericulture, Textile, and Biomass Sciences, Southwest University, Chongqing, 400715, China.
| | - Rui L Reis
- 3Bs Research Group, I3Bs - Research Institute on Biomaterials, Biodegradables and Biomimetics, University of Minho, Headquarters of the European Institute of Excellence on Tissue Engineering and Regenerative Medicine, AvePark, Barco, Guimarães, 4805-017, Portugal; ICVS/3B's-PT Government Associate Laboratory, Braga, Guimarães, 4800-058, Portugal
| | - Subhas C Kundu
- 3Bs Research Group, I3Bs - Research Institute on Biomaterials, Biodegradables and Biomimetics, University of Minho, Headquarters of the European Institute of Excellence on Tissue Engineering and Regenerative Medicine, AvePark, Barco, Guimarães, 4805-017, Portugal; ICVS/3B's-PT Government Associate Laboratory, Braga, Guimarães, 4800-058, Portugal
| | - Bo Xiao
- State Key Laboratory of Resource Insects, College of Sericulture, Textile, and Biomass Sciences, Southwest University, Chongqing, 400715, China.
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4
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Wang Y, Han Y, Yang C, Bai T, Zhang C, Wang Z, Sun Y, Hu Y, Besenbacher F, Chen C, Yu M. Long-term relapse-free survival enabled by integrating targeted antibacteria in antitumor treatment. Nat Commun 2024; 15:4194. [PMID: 38760364 PMCID: PMC11101653 DOI: 10.1038/s41467-024-48662-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: 05/11/2023] [Accepted: 05/09/2024] [Indexed: 05/19/2024] Open
Abstract
The role of tumor-resident intracellular microbiota (TRIM) in carcinogenesis has sparked enormous interest. Nevertheless, the impact of TRIM-targeted antibacteria on tumor inhibition and immune regulation in the tumor microenvironment (TME) remains unexplored. Herein, we report long-term relapse-free survival by coordinating antibacteria with antitumor treatment, addressing the aggravated immunosuppression and tumor overgrowth induced by TRIM using breast and prostate cancer models. Combining Ag+ release with a Fenton-like reaction and photothermal conversion, simultaneous bacteria killing and multimodal antitumor therapy are enabled by a single agent. Free of immune-stimulating drugs, the agent restores antitumor immune surveillance and activates immunological responses. Secondary inoculation and distal tumor analysis confirm lasting immunological memory and systemic immune responses. A relapse-free survival of >700 days is achieved. This work unravels the crucial role of TRIM-targeted antibacteria in tumor inhibition and unlocks an unconventional route for immune regulation in TME and a complete cure for cancer.
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Affiliation(s)
- Yuanlin Wang
- State Key Laboratory of Urban Water Resource and Environment, School of Chemistry and Chemical Engineering, Harbin Institute of Technology, Harbin, 150001, China
| | - Yaqian Han
- School of Instrumentation Science and Technology, Harbin Institute of Technology, Harbin, 150001, China
| | - Chenhui Yang
- State Key Laboratory of Urban Water Resource and Environment, School of Chemistry and Chemical Engineering, Harbin Institute of Technology, Harbin, 150001, China
| | - Tiancheng Bai
- State Key Laboratory of Urban Water Resource and Environment, School of Chemistry and Chemical Engineering, Harbin Institute of Technology, Harbin, 150001, China
| | - Chenggang Zhang
- School of Instrumentation Science and Technology, Harbin Institute of Technology, Harbin, 150001, China
| | - Zhaotong Wang
- School of Instrumentation Science and Technology, Harbin Institute of Technology, Harbin, 150001, China
| | - Ye Sun
- School of Instrumentation Science and Technology, Harbin Institute of Technology, Harbin, 150001, China.
| | - Ying Hu
- School of Life Science and Technology, Harbin Institute of Technology, Harbin, 150001, China
| | - Flemming Besenbacher
- Interdisciplinary Nanoscience Center (iNANO), Aarhus University, Aarhus, 8000, Denmark
| | - Chunying Chen
- National Center for Nanoscience and Technology, Chinese Academy of Sciences, Beijing, 100190, China
| | - Miao Yu
- State Key Laboratory of Urban Water Resource and Environment, School of Chemistry and Chemical Engineering, Harbin Institute of Technology, Harbin, 150001, China.
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5
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Ratiner K, Ciocan D, Abdeen SK, Elinav E. Utilization of the microbiome in personalized medicine. Nat Rev Microbiol 2024; 22:291-308. [PMID: 38110694 DOI: 10.1038/s41579-023-00998-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/17/2023] [Indexed: 12/20/2023]
Abstract
Inter-individual human variability, driven by various genetic and environmental factors, complicates the ability to develop effective population-based early disease detection, treatment and prognostic assessment. The microbiome, consisting of diverse microorganism communities including viruses, bacteria, fungi and eukaryotes colonizing human body surfaces, has recently been identified as a contributor to inter-individual variation, through its person-specific signatures. As such, the microbiome may modulate disease manifestations, even among individuals with similar genetic disease susceptibility risks. Information stored within microbiomes may therefore enable early detection and prognostic assessment of disease in at-risk populations, whereas microbiome modulation may constitute an effective and safe treatment tailored to the individual. In this Review, we explore recent advances in the application of microbiome data in precision medicine across a growing number of human diseases. We also discuss the challenges, limitations and prospects of analysing microbiome data for personalized patient care.
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Affiliation(s)
- Karina Ratiner
- Systems Immunology Department, Weizmann Institute of Science, Rehovot, Israel
| | - Dragos Ciocan
- Systems Immunology Department, Weizmann Institute of Science, Rehovot, Israel
| | - Suhaib K Abdeen
- Systems Immunology Department, Weizmann Institute of Science, Rehovot, Israel.
| | - Eran Elinav
- Systems Immunology Department, Weizmann Institute of Science, Rehovot, Israel.
- Division of Cancer-Microbiome Research, DKFZ, Heidelberg, Germany.
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6
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Santangelo BE, Apgar M, Colorado ASB, Martin CG, Sterrett J, Wall E, Joachimiak MP, Hunter LE, Lozupone CA. Integrating biological knowledge for mechanistic inference in the host-associated microbiome. Front Microbiol 2024; 15:1351678. [PMID: 38638909 PMCID: PMC11024261 DOI: 10.3389/fmicb.2024.1351678] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2023] [Accepted: 02/26/2024] [Indexed: 04/20/2024] Open
Abstract
Advances in high-throughput technologies have enhanced our ability to describe microbial communities as they relate to human health and disease. Alongside the growth in sequencing data has come an influx of resources that synthesize knowledge surrounding microbial traits, functions, and metabolic potential with knowledge of how they may impact host pathways to influence disease phenotypes. These knowledge bases can enable the development of mechanistic explanations that may underlie correlations detected between microbial communities and disease. In this review, we survey existing resources and methodologies for the computational integration of broad classes of microbial and host knowledge. We evaluate these knowledge bases in their access methods, content, and source characteristics. We discuss challenges of the creation and utilization of knowledge bases including inconsistency of nomenclature assignment of taxa and metabolites across sources, whether the biological entities represented are rooted in ontologies or taxonomies, and how the structure and accessibility limit the diversity of applications and user types. We make this information available in a code and data repository at: https://github.com/lozuponelab/knowledge-source-mappings. Addressing these challenges will allow for the development of more effective tools for drawing from abundant knowledge to find new insights into microbial mechanisms in disease by fostering a systematic and unbiased exploration of existing information.
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Affiliation(s)
- Brook E. Santangelo
- Department of Biomedical Informatics, University of Colorado School of Medicine, Aurora, CO, United States
| | - Madison Apgar
- Department of Biomedical Informatics, University of Colorado School of Medicine, Aurora, CO, United States
| | | | - Casey G. Martin
- Department of Biomedical Informatics, University of Colorado School of Medicine, Aurora, CO, United States
| | - John Sterrett
- Department of Integrative Physiology, University of Colorado, Boulder, CO, United States
| | - Elena Wall
- Department of Biomedical Informatics, University of Colorado School of Medicine, Aurora, CO, United States
| | - Marcin P. Joachimiak
- Lawrence Berkeley National Laboratory, Environmental Genomics and Systems Biology Division, Biosystems Data Science Department, Berkeley, CA, United States
| | - Lawrence E. Hunter
- Department of Biomedical Informatics, University of Colorado School of Medicine, Aurora, CO, United States
| | - Catherine A. Lozupone
- Department of Biomedical Informatics, University of Colorado School of Medicine, Aurora, CO, United States
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7
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Sen P, Fan Y, Schlezinger JJ, Ehrlich SD, Webster TF, Hyötyläinen T, Pedersen O, Orešič M. Exposure to environmental toxicants is associated with gut microbiome dysbiosis, insulin resistance and obesity. ENVIRONMENT INTERNATIONAL 2024; 186:108569. [PMID: 38522229 DOI: 10.1016/j.envint.2024.108569] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/13/2023] [Revised: 03/05/2024] [Accepted: 03/06/2024] [Indexed: 03/26/2024]
Abstract
Environmental toxicants (ETs) are associated with adverse health outcomes. Here we hypothesized that exposures to ETs are linked with obesity and insulin resistance partly through a dysbiotic gut microbiota and changes in the serum levels of secondary bile acids (BAs). Serum BAs, per- and polyfluoroalkyl substances (PFAS) and additional twenty-seven ETs were measured by mass spectrometry in 264 Danes (121 men and 143 women, aged 56.6 ± 7.3 years, BMI 29.7 ± 6.0 kg/m2) using a combination of targeted and suspect screening approaches. Bacterial species were identified based on whole-genome shotgun sequencing (WGS) of DNA extracted from stool samples. Personalized genome-scale metabolic models (GEMs) of gut microbial communities were developed to elucidate regulation of BA pathways. Subsequently, we compared findings from the human study with metabolic implications of exposure to perfluorooctanoic acid (PFOA) in PPARα-humanized mice. Serum levels of twelve ETs were associated with obesity and insulin resistance. High chemical exposure was associated with increased abundance of several bacterial species (spp.) of genus (Anaerotruncus, Alistipes, Bacteroides, Bifidobacterium, Clostridium, Dorea, Eubacterium, Escherichia, Prevotella, Ruminococcus, Roseburia, Subdoligranulum, and Veillonella), particularly in men. Conversely, females in the higher exposure group, showed a decrease abundance of Prevotella copri. High concentrations of ETs were correlated with increased levels of secondary BAs including lithocholic acid (LCA), and decreased levels of ursodeoxycholic acid (UDCA). In silico causal inference analyses suggested that microbiome-derived secondary BAs may act as mediators between ETs and obesity or insulin resistance. Furthermore, these findings were substantiated by the outcome of the murine exposure study. Our combined epidemiological and mechanistic studies suggest that multiple ETs may play a role in the etiology of obesity and insulin resistance. These effects may arise from disruptions in the microbial biosynthesis of secondary BAs.
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Affiliation(s)
- Partho Sen
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, 20520, Turku, Finland; School of Medical Sciences, Faculty of Medicine and Health, Örebro University, 702 81, Örebro, Sweden
| | - Yong Fan
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, 2200, Copenhagen, Denmark
| | - Jennifer J Schlezinger
- Department of Environmental Health, Boston University School of Public Health, Boston, MA, USA
| | - Stanislav D Ehrlich
- Department of Clinical and Movement Neurosciences, UCL Queen Square Institute of Neurology, London WC1N 3RX, UK
| | - Thomas F Webster
- Department of Environmental Health, Boston University School of Public Health, Boston, MA, USA
| | - Tuulia Hyötyläinen
- MTM Research Centre, School of Science and Technology, Örebro University, 702 81, Örebro, Sweden.
| | - Oluf Pedersen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, 2200, Copenhagen, Denmark; Center for Clinical Metabolic Research, Herlev-Gentofte University Hospital, Copenhagen, Denmark.
| | - Matej Orešič
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, 20520, Turku, Finland; School of Medical Sciences, Faculty of Medicine and Health, Örebro University, 702 81, Örebro, Sweden.
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8
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Yurkovich JT, Evans SJ, Rappaport N, Boore JL, Lovejoy JC, Price ND, Hood LE. The transition from genomics to phenomics in personalized population health. Nat Rev Genet 2024; 25:286-302. [PMID: 38093095 DOI: 10.1038/s41576-023-00674-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/03/2023] [Indexed: 03/21/2024]
Abstract
Modern health care faces several serious challenges, including an ageing population and its inherent burden of chronic diseases, rising costs and marginal quality metrics. By assessing and optimizing the health trajectory of each individual using a data-driven personalized approach that reflects their genetics, behaviour and environment, we can start to address these challenges. This assessment includes longitudinal phenome measures, such as the blood proteome and metabolome, gut microbiome composition and function, and lifestyle and behaviour through wearables and questionnaires. Here, we review ongoing large-scale genomics and longitudinal phenomics efforts and the powerful insights they provide into wellness. We describe our vision for the transformation of the current health care from disease-oriented to data-driven, wellness-oriented and personalized population health.
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Affiliation(s)
- James T Yurkovich
- Phenome Health, Seattle, WA, USA
- Center for Phenomic Health, The Buck Institute for Research on Aging, Novato, CA, USA
- Department of Bioengineering, University of Texas at Dallas, Richardson, TX, USA
| | - Simon J Evans
- Phenome Health, Seattle, WA, USA
- Center for Phenomic Health, The Buck Institute for Research on Aging, Novato, CA, USA
| | - Noa Rappaport
- Center for Phenomic Health, The Buck Institute for Research on Aging, Novato, CA, USA
- Institute for Systems Biology, Seattle, WA, USA
| | - Jeffrey L Boore
- Phenome Health, Seattle, WA, USA
- Center for Phenomic Health, The Buck Institute for Research on Aging, Novato, CA, USA
| | - Jennifer C Lovejoy
- Phenome Health, Seattle, WA, USA
- Center for Phenomic Health, The Buck Institute for Research on Aging, Novato, CA, USA
- Institute for Systems Biology, Seattle, WA, USA
| | - Nathan D Price
- Institute for Systems Biology, Seattle, WA, USA
- Thorne HealthTech, New York, NY, USA
- Department of Bioengineering, University of Washington, Seattle, WA, USA
- Paul G. Allen School of Computer Science & Engineering, University of Washington, Seattle, WA, USA
| | - Leroy E Hood
- Phenome Health, Seattle, WA, USA.
- Center for Phenomic Health, The Buck Institute for Research on Aging, Novato, CA, USA.
- Institute for Systems Biology, Seattle, WA, USA.
- Department of Bioengineering, University of Washington, Seattle, WA, USA.
- Paul G. Allen School of Computer Science & Engineering, University of Washington, Seattle, WA, USA.
- Department of Immunology, University of Washington, Seattle, WA, USA.
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9
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Predl M, Mießkes M, Rattei T, Zanghellini J. PyCoMo: a python package for community metabolic model creation and analysis. Bioinformatics 2024; 40:btae153. [PMID: 38532295 PMCID: PMC10990682 DOI: 10.1093/bioinformatics/btae153] [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: 10/09/2023] [Revised: 12/29/2023] [Accepted: 03/25/2024] [Indexed: 03/28/2024] Open
Abstract
SUMMARY PyCoMo is a python package for quick and easy generation of genome-scale compartmentalized community metabolic models that are compliant with current openCOBRA file formats. The resulting models can be used to predict (i) the maximum growth rate at a given abundance profile, (ii) the feasible community compositions at a given growth rate, and (iii) all exchange metabolites and cross-feeding interactions in a community metabolic model independent of the abundance profile; we demonstrate PyCoMo's capability by analysing methane production in a previously published simplified biogas community metabolic model. AVAILABILITY AND IMPLEMENTATION PyCoMo is freely available under an MIT licence at http://github.com/univieCUBE/PyCoMo, the Python Package Index, and Zenodo.
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Affiliation(s)
- Michael Predl
- Department of Microbiology and Ecosystem Science, Division of Computational Systems Biology, Centre for Microbiology and Environmental Systems Science, University of Vienna, 1030 Vienna, Austria
- Doctoral School in Microbiology and Environmental Science, University of Vienna, 1030 Vienna, Austria
| | - Marianne Mießkes
- Department of Analytical Chemistry, Faculty of Chemistry, University of Vienna, 1090 Vienna, Austria
- Austrian Centre of Industrial Biotechnology, 1190 Vienna, Austria
| | - Thomas Rattei
- Department of Microbiology and Ecosystem Science, Division of Computational Systems Biology, Centre for Microbiology and Environmental Systems Science, University of Vienna, 1030 Vienna, Austria
- Doctoral School in Microbiology and Environmental Science, University of Vienna, 1030 Vienna, Austria
| | - Jürgen Zanghellini
- Department of Analytical Chemistry, Faculty of Chemistry, University of Vienna, 1090 Vienna, Austria
- Austrian Centre of Industrial Biotechnology, 1190 Vienna, Austria
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10
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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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11
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Jimonet P, Druart C, Blanquet-Diot S, Boucinha L, Kourula S, Le Vacon F, Maubant S, Rabot S, Van de Wiele T, Schuren F, Thomas V, Walther B, Zimmermann M. Gut Microbiome Integration in Drug Discovery and Development of Small Molecules. Drug Metab Dispos 2024; 52:274-287. [PMID: 38307852 DOI: 10.1124/dmd.123.001605] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2023] [Revised: 01/30/2024] [Accepted: 01/31/2024] [Indexed: 02/04/2024] Open
Abstract
Human microbiomes, particularly in the gut, could have a major impact on the efficacy and toxicity of drugs. However, gut microbial metabolism is often neglected in the drug discovery and development process. Medicen, a Paris-based human health innovation cluster, has gathered more than 30 international leading experts from pharma, academia, biotech, clinical research organizations, and regulatory science to develop proposals to facilitate the integration of microbiome science into drug discovery and development. Seven subteams were formed to cover the complementary expertise areas of 1) pharma experience and case studies, 2) in silico microbiome-drug interaction, 3) in vitro microbial stability screening, 4) gut fermentation models, 5) animal models, 6) microbiome integration in clinical and regulatory aspects, and 7) microbiome ecosystems and models. Each expert team produced a state-of-the-art report of their respective field highlighting existing microbiome-related tools at every stage of drug discovery and development. The most critical limitations are the growing, but still limited, drug-microbiome interaction data to produce predictive models and the lack of agreed-upon standards despite recent progress. In this paper we will report on and share proposals covering 1) how microbiome tools can support moving a compound from drug discovery to clinical proof-of-concept studies and alert early on potential undesired properties stemming from microbiome-induced drug metabolism and 2) how microbiome data can be generated and integrated in pharmacokinetic models that are predictive of the human situation. Examples of drugs metabolized by the microbiome will be discussed in detail to support recommendations from the working group. SIGNIFICANCE STATEMENT: Gut microbial metabolism is often neglected in the drug discovery and development process despite growing evidence of drugs' efficacy and safety impacted by their interaction with the microbiome. This paper will detail existing microbiome-related tools covering every stage of drug discovery and development, current progress, and limitations, as well as recommendations to integrate them into the drug discovery and development process.
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Affiliation(s)
- Patrick Jimonet
- Medicen Paris Région, Paris, France (P.J.); Pharmabiotic Research Institute, Narbonne, France (C.D.); UMR 454 MEDIS, Université Clermont Auvergne, Clermont-Ferrand, France (S.B.D.); Global Bioinformatics, Evotec ID, Lyon, France (L.B.); Preclinical Sciences & Translational Safety, JNJ Innovative Medicine, Beerse, Belgium (S.K.); Biofortis, Saint-Herblain, France (F.L.V.); Translational Pharmacology Department, Oncodesign Services, Dijon, France (S.M.); Université Paris-Saclay, INRAE, AgroParisTech, Micalis Institute, Jouy-en-Josas, France (S.R.); Center of Microbial Ecology and Technology, Faculty of Bioscience Engineering, Ghent University, Gent, Belgium (T.V.W.); TNO, Leiden, The Netherlands (F.S.); Lallemand Health Solutions, Blagnac, France (V.T.); Servier, Saclay, France (B.W.); and Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany (M.Z.)
| | - Céline Druart
- Medicen Paris Région, Paris, France (P.J.); Pharmabiotic Research Institute, Narbonne, France (C.D.); UMR 454 MEDIS, Université Clermont Auvergne, Clermont-Ferrand, France (S.B.D.); Global Bioinformatics, Evotec ID, Lyon, France (L.B.); Preclinical Sciences & Translational Safety, JNJ Innovative Medicine, Beerse, Belgium (S.K.); Biofortis, Saint-Herblain, France (F.L.V.); Translational Pharmacology Department, Oncodesign Services, Dijon, France (S.M.); Université Paris-Saclay, INRAE, AgroParisTech, Micalis Institute, Jouy-en-Josas, France (S.R.); Center of Microbial Ecology and Technology, Faculty of Bioscience Engineering, Ghent University, Gent, Belgium (T.V.W.); TNO, Leiden, The Netherlands (F.S.); Lallemand Health Solutions, Blagnac, France (V.T.); Servier, Saclay, France (B.W.); and Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany (M.Z.)
| | - Stéphanie Blanquet-Diot
- Medicen Paris Région, Paris, France (P.J.); Pharmabiotic Research Institute, Narbonne, France (C.D.); UMR 454 MEDIS, Université Clermont Auvergne, Clermont-Ferrand, France (S.B.D.); Global Bioinformatics, Evotec ID, Lyon, France (L.B.); Preclinical Sciences & Translational Safety, JNJ Innovative Medicine, Beerse, Belgium (S.K.); Biofortis, Saint-Herblain, France (F.L.V.); Translational Pharmacology Department, Oncodesign Services, Dijon, France (S.M.); Université Paris-Saclay, INRAE, AgroParisTech, Micalis Institute, Jouy-en-Josas, France (S.R.); Center of Microbial Ecology and Technology, Faculty of Bioscience Engineering, Ghent University, Gent, Belgium (T.V.W.); TNO, Leiden, The Netherlands (F.S.); Lallemand Health Solutions, Blagnac, France (V.T.); Servier, Saclay, France (B.W.); and Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany (M.Z.)
| | - Lilia Boucinha
- Medicen Paris Région, Paris, France (P.J.); Pharmabiotic Research Institute, Narbonne, France (C.D.); UMR 454 MEDIS, Université Clermont Auvergne, Clermont-Ferrand, France (S.B.D.); Global Bioinformatics, Evotec ID, Lyon, France (L.B.); Preclinical Sciences & Translational Safety, JNJ Innovative Medicine, Beerse, Belgium (S.K.); Biofortis, Saint-Herblain, France (F.L.V.); Translational Pharmacology Department, Oncodesign Services, Dijon, France (S.M.); Université Paris-Saclay, INRAE, AgroParisTech, Micalis Institute, Jouy-en-Josas, France (S.R.); Center of Microbial Ecology and Technology, Faculty of Bioscience Engineering, Ghent University, Gent, Belgium (T.V.W.); TNO, Leiden, The Netherlands (F.S.); Lallemand Health Solutions, Blagnac, France (V.T.); Servier, Saclay, France (B.W.); and Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany (M.Z.)
| | - Stephanie Kourula
- Medicen Paris Région, Paris, France (P.J.); Pharmabiotic Research Institute, Narbonne, France (C.D.); UMR 454 MEDIS, Université Clermont Auvergne, Clermont-Ferrand, France (S.B.D.); Global Bioinformatics, Evotec ID, Lyon, France (L.B.); Preclinical Sciences & Translational Safety, JNJ Innovative Medicine, Beerse, Belgium (S.K.); Biofortis, Saint-Herblain, France (F.L.V.); Translational Pharmacology Department, Oncodesign Services, Dijon, France (S.M.); Université Paris-Saclay, INRAE, AgroParisTech, Micalis Institute, Jouy-en-Josas, France (S.R.); Center of Microbial Ecology and Technology, Faculty of Bioscience Engineering, Ghent University, Gent, Belgium (T.V.W.); TNO, Leiden, The Netherlands (F.S.); Lallemand Health Solutions, Blagnac, France (V.T.); Servier, Saclay, France (B.W.); and Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany (M.Z.)
| | - Françoise Le Vacon
- Medicen Paris Région, Paris, France (P.J.); Pharmabiotic Research Institute, Narbonne, France (C.D.); UMR 454 MEDIS, Université Clermont Auvergne, Clermont-Ferrand, France (S.B.D.); Global Bioinformatics, Evotec ID, Lyon, France (L.B.); Preclinical Sciences & Translational Safety, JNJ Innovative Medicine, Beerse, Belgium (S.K.); Biofortis, Saint-Herblain, France (F.L.V.); Translational Pharmacology Department, Oncodesign Services, Dijon, France (S.M.); Université Paris-Saclay, INRAE, AgroParisTech, Micalis Institute, Jouy-en-Josas, France (S.R.); Center of Microbial Ecology and Technology, Faculty of Bioscience Engineering, Ghent University, Gent, Belgium (T.V.W.); TNO, Leiden, The Netherlands (F.S.); Lallemand Health Solutions, Blagnac, France (V.T.); Servier, Saclay, France (B.W.); and Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany (M.Z.)
| | - Sylvie Maubant
- Medicen Paris Région, Paris, France (P.J.); Pharmabiotic Research Institute, Narbonne, France (C.D.); UMR 454 MEDIS, Université Clermont Auvergne, Clermont-Ferrand, France (S.B.D.); Global Bioinformatics, Evotec ID, Lyon, France (L.B.); Preclinical Sciences & Translational Safety, JNJ Innovative Medicine, Beerse, Belgium (S.K.); Biofortis, Saint-Herblain, France (F.L.V.); Translational Pharmacology Department, Oncodesign Services, Dijon, France (S.M.); Université Paris-Saclay, INRAE, AgroParisTech, Micalis Institute, Jouy-en-Josas, France (S.R.); Center of Microbial Ecology and Technology, Faculty of Bioscience Engineering, Ghent University, Gent, Belgium (T.V.W.); TNO, Leiden, The Netherlands (F.S.); Lallemand Health Solutions, Blagnac, France (V.T.); Servier, Saclay, France (B.W.); and Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany (M.Z.)
| | - Sylvie Rabot
- Medicen Paris Région, Paris, France (P.J.); Pharmabiotic Research Institute, Narbonne, France (C.D.); UMR 454 MEDIS, Université Clermont Auvergne, Clermont-Ferrand, France (S.B.D.); Global Bioinformatics, Evotec ID, Lyon, France (L.B.); Preclinical Sciences & Translational Safety, JNJ Innovative Medicine, Beerse, Belgium (S.K.); Biofortis, Saint-Herblain, France (F.L.V.); Translational Pharmacology Department, Oncodesign Services, Dijon, France (S.M.); Université Paris-Saclay, INRAE, AgroParisTech, Micalis Institute, Jouy-en-Josas, France (S.R.); Center of Microbial Ecology and Technology, Faculty of Bioscience Engineering, Ghent University, Gent, Belgium (T.V.W.); TNO, Leiden, The Netherlands (F.S.); Lallemand Health Solutions, Blagnac, France (V.T.); Servier, Saclay, France (B.W.); and Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany (M.Z.)
| | - Tom Van de Wiele
- Medicen Paris Région, Paris, France (P.J.); Pharmabiotic Research Institute, Narbonne, France (C.D.); UMR 454 MEDIS, Université Clermont Auvergne, Clermont-Ferrand, France (S.B.D.); Global Bioinformatics, Evotec ID, Lyon, France (L.B.); Preclinical Sciences & Translational Safety, JNJ Innovative Medicine, Beerse, Belgium (S.K.); Biofortis, Saint-Herblain, France (F.L.V.); Translational Pharmacology Department, Oncodesign Services, Dijon, France (S.M.); Université Paris-Saclay, INRAE, AgroParisTech, Micalis Institute, Jouy-en-Josas, France (S.R.); Center of Microbial Ecology and Technology, Faculty of Bioscience Engineering, Ghent University, Gent, Belgium (T.V.W.); TNO, Leiden, The Netherlands (F.S.); Lallemand Health Solutions, Blagnac, France (V.T.); Servier, Saclay, France (B.W.); and Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany (M.Z.)
| | - Frank Schuren
- Medicen Paris Région, Paris, France (P.J.); Pharmabiotic Research Institute, Narbonne, France (C.D.); UMR 454 MEDIS, Université Clermont Auvergne, Clermont-Ferrand, France (S.B.D.); Global Bioinformatics, Evotec ID, Lyon, France (L.B.); Preclinical Sciences & Translational Safety, JNJ Innovative Medicine, Beerse, Belgium (S.K.); Biofortis, Saint-Herblain, France (F.L.V.); Translational Pharmacology Department, Oncodesign Services, Dijon, France (S.M.); Université Paris-Saclay, INRAE, AgroParisTech, Micalis Institute, Jouy-en-Josas, France (S.R.); Center of Microbial Ecology and Technology, Faculty of Bioscience Engineering, Ghent University, Gent, Belgium (T.V.W.); TNO, Leiden, The Netherlands (F.S.); Lallemand Health Solutions, Blagnac, France (V.T.); Servier, Saclay, France (B.W.); and Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany (M.Z.)
| | - Vincent Thomas
- Medicen Paris Région, Paris, France (P.J.); Pharmabiotic Research Institute, Narbonne, France (C.D.); UMR 454 MEDIS, Université Clermont Auvergne, Clermont-Ferrand, France (S.B.D.); Global Bioinformatics, Evotec ID, Lyon, France (L.B.); Preclinical Sciences & Translational Safety, JNJ Innovative Medicine, Beerse, Belgium (S.K.); Biofortis, Saint-Herblain, France (F.L.V.); Translational Pharmacology Department, Oncodesign Services, Dijon, France (S.M.); Université Paris-Saclay, INRAE, AgroParisTech, Micalis Institute, Jouy-en-Josas, France (S.R.); Center of Microbial Ecology and Technology, Faculty of Bioscience Engineering, Ghent University, Gent, Belgium (T.V.W.); TNO, Leiden, The Netherlands (F.S.); Lallemand Health Solutions, Blagnac, France (V.T.); Servier, Saclay, France (B.W.); and Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany (M.Z.)
| | - Bernard Walther
- Medicen Paris Région, Paris, France (P.J.); Pharmabiotic Research Institute, Narbonne, France (C.D.); UMR 454 MEDIS, Université Clermont Auvergne, Clermont-Ferrand, France (S.B.D.); Global Bioinformatics, Evotec ID, Lyon, France (L.B.); Preclinical Sciences & Translational Safety, JNJ Innovative Medicine, Beerse, Belgium (S.K.); Biofortis, Saint-Herblain, France (F.L.V.); Translational Pharmacology Department, Oncodesign Services, Dijon, France (S.M.); Université Paris-Saclay, INRAE, AgroParisTech, Micalis Institute, Jouy-en-Josas, France (S.R.); Center of Microbial Ecology and Technology, Faculty of Bioscience Engineering, Ghent University, Gent, Belgium (T.V.W.); TNO, Leiden, The Netherlands (F.S.); Lallemand Health Solutions, Blagnac, France (V.T.); Servier, Saclay, France (B.W.); and Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany (M.Z.)
| | - Michael Zimmermann
- Medicen Paris Région, Paris, France (P.J.); Pharmabiotic Research Institute, Narbonne, France (C.D.); UMR 454 MEDIS, Université Clermont Auvergne, Clermont-Ferrand, France (S.B.D.); Global Bioinformatics, Evotec ID, Lyon, France (L.B.); Preclinical Sciences & Translational Safety, JNJ Innovative Medicine, Beerse, Belgium (S.K.); Biofortis, Saint-Herblain, France (F.L.V.); Translational Pharmacology Department, Oncodesign Services, Dijon, France (S.M.); Université Paris-Saclay, INRAE, AgroParisTech, Micalis Institute, Jouy-en-Josas, France (S.R.); Center of Microbial Ecology and Technology, Faculty of Bioscience Engineering, Ghent University, Gent, Belgium (T.V.W.); TNO, Leiden, The Netherlands (F.S.); Lallemand Health Solutions, Blagnac, France (V.T.); Servier, Saclay, France (B.W.); and Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany (M.Z.)
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12
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Law SR, Mathes F, Paten AM, Alexandre PA, Regmi R, Reid C, Safarchi A, Shaktivesh S, Wang Y, Wilson A, Rice SA, Gupta VVSR. Life at the borderlands: microbiomes of interfaces critical to One Health. FEMS Microbiol Rev 2024; 48:fuae008. [PMID: 38425054 PMCID: PMC10977922 DOI: 10.1093/femsre/fuae008] [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: 07/26/2023] [Revised: 02/12/2024] [Accepted: 02/27/2024] [Indexed: 03/02/2024] Open
Abstract
Microbiomes are foundational components of the environment that provide essential services relating to food security, carbon sequestration, human health, and the overall well-being of ecosystems. Microbiota exert their effects primarily through complex interactions at interfaces with their plant, animal, and human hosts, as well as within the soil environment. This review aims to explore the ecological, evolutionary, and molecular processes governing the establishment and function of microbiome-host relationships, specifically at interfaces critical to One Health-a transdisciplinary framework that recognizes that the health outcomes of people, animals, plants, and the environment are tightly interconnected. Within the context of One Health, the core principles underpinning microbiome assembly will be discussed in detail, including biofilm formation, microbial recruitment strategies, mechanisms of microbial attachment, community succession, and the effect these processes have on host function and health. Finally, this review will catalogue recent advances in microbiology and microbial ecology methods that can be used to profile microbial interfaces, with particular attention to multi-omic, advanced imaging, and modelling approaches. These technologies are essential for delineating the general and specific principles governing microbiome assembly and functions, mapping microbial interconnectivity across varying spatial and temporal scales, and for the establishment of predictive frameworks that will guide the development of targeted microbiome-interventions to deliver One Health outcomes.
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Affiliation(s)
- Simon R Law
- CSIRO MOSH-Future Science Platform, Australia
- CSIRO Agriculture and Food, Canberra, ACT 2601, Australia
| | - Falko Mathes
- CSIRO MOSH-Future Science Platform, Australia
- CSIRO Environment, Floreat, WA 6014, Australia
| | - Amy M Paten
- CSIRO MOSH-Future Science Platform, Australia
- CSIRO Environment, Canberra, ACT 2601, Australia
| | - Pamela A Alexandre
- CSIRO MOSH-Future Science Platform, Australia
- CSIRO Agriculture and Food, St Lucia, Qld 4072, Australia
| | - Roshan Regmi
- CSIRO MOSH-Future Science Platform, Australia
- CSIRO Agriculture and Food, Urrbrae, SA 5064, Australia
| | - Cameron Reid
- CSIRO MOSH-Future Science Platform, Australia
- CSIRO Environment, Urrbrae, SA 5064, Australia
| | - Azadeh Safarchi
- CSIRO MOSH-Future Science Platform, Australia
- CSIRO Health and Biosecurity, Westmead, NSW 2145, Australia
| | - Shaktivesh Shaktivesh
- CSIRO MOSH-Future Science Platform, Australia
- CSIRO Data 61, Clayton, Vic 3168, Australia
| | - Yanan Wang
- CSIRO MOSH-Future Science Platform, Australia
- CSIRO Health and Biosecurity, Adelaide SA 5000, Australia
| | - Annaleise Wilson
- CSIRO MOSH-Future Science Platform, Australia
- CSIRO Health and Biosecurity, Geelong, Vic 3220, Australia
| | - Scott A Rice
- CSIRO MOSH-Future Science Platform, Australia
- CSIRO Agriculture, and Food, Westmead, NSW 2145, Australia
| | - Vadakattu V S R Gupta
- CSIRO MOSH-Future Science Platform, Australia
- CSIRO Agriculture and Food, Urrbrae, SA 5064, Australia
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13
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Brunner JD, Chia N. Metabolic model-based ecological modeling for probiotic design. eLife 2024; 13:e83690. [PMID: 38380900 PMCID: PMC10942782 DOI: 10.7554/elife.83690] [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: 09/24/2022] [Accepted: 02/19/2024] [Indexed: 02/22/2024] Open
Abstract
The microbial community composition in the human gut has a profound effect on human health. This observation has lead to extensive use of microbiome therapies, including over-the-counter 'probiotic' treatments intended to alter the composition of the microbiome. Despite so much promise and commercial interest, the factors that contribute to the success or failure of microbiome-targeted treatments remain unclear. We investigate the biotic interactions that lead to successful engraftment of a novel bacterial strain introduced to the microbiome as in probiotic treatments. We use pairwise genome-scale metabolic modeling with a generalized resource allocation constraint to build a network of interactions between taxa that appear in an experimental engraftment study. We create induced sub-graphs using the taxa present in individual samples and assess the likelihood of invader engraftment based on network structure. To do so, we use a generalized Lotka-Volterra model, which we show has strong ability to predict if a particular invader or probiotic will successfully engraft into an individual's microbiome. Furthermore, we show that the mechanistic nature of the model is useful for revealing which microbe-microbe interactions potentially drive engraftment.
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Affiliation(s)
- James D Brunner
- Biosciences Division, Los Alamos National LaboratoryLos AlamosUnited States
- Center for Nonlinear Studies, Los Alamos National LaboratoryLos AlamosUnited States
| | - Nicholas Chia
- Data Science and Learning, Argonne National LaboratoryLemontUnited States
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14
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Machado D. A benchmark of optimization solvers for genome-scale metabolic modeling of organisms and communities. mSystems 2024; 9:e0083323. [PMID: 38251879 PMCID: PMC10878033 DOI: 10.1128/msystems.00833-23] [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: 08/08/2023] [Accepted: 12/11/2023] [Indexed: 01/23/2024] Open
Abstract
Genome-scale metabolic modeling is a powerful framework for predicting metabolic phenotypes of any organism with an annotated genome. For two decades, this framework has been used for the rational design of microbial cell factories. In the last decade, the range of applications has exploded, and new frontiers have emerged, including the study of the gut microbiome and its health implications and the role of microbial communities in global ecosystems. However, all the critical steps in this framework, from model construction to simulation, require the use of powerful linear optimization solvers, with the choice often relying on commercial solvers for their well-known computational efficiency. In this work, I benchmark a total of six solvers (two commercial and four open source) and measure their performance to solve linear and mixed-integer linear problems of increasing complexity. Although commercial solvers are still the fastest, at least two open-source solvers show comparable performance. These results show that genome-scale metabolic modeling does not need to be hindered by commercial licensing schemes and can become a truly open science framework for solving urgent societal challenges.IMPORTANCEModeling the metabolism of organisms and communities allows for computational exploration of their metabolic capabilities and testing their response to genetic and environmental perturbations. This holds the potential to address multiple societal issues related to human health and the environment. One of the current limitations is the use of commercial optimization solvers with restrictive licenses for academic and non-academic use. This work compares the performance of several commercial and open-source solvers to solve some of the most complex problems in the field. Benchmarking results show that, although commercial solvers are indeed faster, some of the open-source options can also efficiently tackle the hardest problems, showing great promise for the development of open science applications.
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Affiliation(s)
- Daniel Machado
- Department of Biotechnology and Food Science, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
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Cheon H, Kim JH, Kim JS, Park JB. Valorization of single-carbon chemicals by using carboligases as key enzymes. Curr Opin Biotechnol 2024; 85:103047. [PMID: 38128199 DOI: 10.1016/j.copbio.2023.103047] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Revised: 11/23/2023] [Accepted: 11/24/2023] [Indexed: 12/23/2023]
Abstract
Single-carbon (C1) biorefinery plays a key role in the consumption of global greenhouse gases and a circular carbon economy. Thereby, we have focused on the valorization of C1 compounds (e.g. methanol, formaldehyde, and formate) into multicarbon products, including bioplastic monomers, glycolate, and ethylene glycol. For instance, methanol, derived from the oxidation of CH4, can be converted into glycolate, ethylene glycol, or erythrulose via formaldehyde and glycolaldehyde, employing C1 and/or C2 carboligases as essential enzymes. Escherichia coli was engineered to convert formate, produced from CO via CO2 or from CO2 directly, into glycolate. Recent progress in the design of biotransformation pathways, enzyme discovery, and engineering, as well as whole-cell biocatalyst engineering for C1 biorefinery, was addressed in this review.
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Affiliation(s)
- Huijin Cheon
- Department of Food Science and Biotechnology, Ewha Womans University, Seoul 03760, Republic of Korea
| | - Jun-Hong Kim
- Department of Chemistry, Chonnam National University, Gwangju 61186, Republic of Korea
| | - Jeong-Sun Kim
- Department of Chemistry, Chonnam National University, Gwangju 61186, Republic of Korea.
| | - Jin-Byung Park
- Department of Food Science and Biotechnology, Ewha Womans University, Seoul 03760, Republic of Korea.
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16
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Occhipinti A, Verma S, Doan LMT, Angione C. Mechanism-aware and multimodal AI: beyond model-agnostic interpretation. Trends Cell Biol 2024; 34:85-89. [PMID: 38087709 DOI: 10.1016/j.tcb.2023.11.002] [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: 07/24/2023] [Revised: 11/03/2023] [Accepted: 11/07/2023] [Indexed: 02/04/2024]
Abstract
Artificial intelligence (AI) is widely used for exploiting multimodal biomedical data, with increasingly accurate predictions and model-agnostic interpretations, which are however also agnostic to biological mechanisms. Combining metabolic modelling, 'omics, and imaging data via multimodal AI can generate predictions that can be interpreted mechanistically and transparently, therefore with significantly higher therapeutic potential.
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Affiliation(s)
- Annalisa Occhipinti
- School of Computing, Engineering and Digital Technologies, Teesside University, Middlesborough, UK; Centre for Digital Innovation, Teesside University, Middlesborough, UK; National Horizons Centre, Teesside University, Darlington, UK
| | - Suraj Verma
- School of Computing, Engineering and Digital Technologies, Teesside University, Middlesborough, UK
| | - Le Minh Thao Doan
- School of Computing, Engineering and Digital Technologies, Teesside University, Middlesborough, UK
| | - Claudio Angione
- School of Computing, Engineering and Digital Technologies, Teesside University, Middlesborough, UK; Centre for Digital Innovation, Teesside University, Middlesborough, UK; National Horizons Centre, Teesside University, Darlington, UK.
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17
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Ricaurte D, Huang Y, Sheth RU, Gelsinger DR, Kaufman A, Wang HH. High-throughput transcriptomics of 409 bacteria-drug pairs reveals drivers of gut microbiota perturbation. Nat Microbiol 2024; 9:561-575. [PMID: 38233648 DOI: 10.1038/s41564-023-01581-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Accepted: 12/08/2023] [Indexed: 01/19/2024]
Abstract
Many drugs can perturb the gut microbiome, potentially leading to negative health consequences. However, mechanisms of most microorganism-drug responses have not been elucidated at the genetic level. Using high-throughput bacterial transcriptomics, we systematically characterized the gene expression profiles of prevalent human gut bacteria exposed to the most frequently prescribed orally administered pharmaceuticals. Across >400 drug-microorganism pairs, significant and reproducible transcriptional responses were observed, including pathways involved in multidrug resistance, metabolite transport, tartrate metabolism and riboflavin biosynthesis. Importantly, we discovered that statin-mediated upregulation of the AcrAB-TolC efflux pump in Bacteroidales species enhances microbial sensitivity to vitamin A and secondary bile acids. Moreover, gut bacteria carrying acrAB-tolC genes are depleted in patients taking simvastatin, suggesting that drug-efflux interactions generate collateral toxicity that depletes pump-containing microorganisms from patient microbiomes. This study provides a resource to further understand the drivers of drug-mediated microbiota shifts for better informed clinical interventions.
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Affiliation(s)
- Deirdre Ricaurte
- Department of Systems Biology, Columbia University, New York, NY, USA
- Integrated Program in Cellular, Molecular, and Biomedical Studies, Columbia University, New York, NY, USA
| | - Yiming Huang
- Department of Systems Biology, Columbia University, New York, NY, USA
- Integrated Program in Cellular, Molecular, and Biomedical Studies, Columbia University, New York, NY, USA
| | - Ravi U Sheth
- Department of Systems Biology, Columbia University, New York, NY, USA
- Integrated Program in Cellular, Molecular, and Biomedical Studies, Columbia University, New York, NY, USA
| | | | - Andrew Kaufman
- Department of Systems Biology, Columbia University, New York, NY, USA
| | - Harris H Wang
- Department of Systems Biology, Columbia University, New York, NY, USA.
- Department of Pathology and Cell Biology, Columbia University, New York, NY, USA.
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18
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Verdegaal AA, Goodman AL. Integrating the gut microbiome and pharmacology. Sci Transl Med 2024; 16:eadg8357. [PMID: 38295186 DOI: 10.1126/scitranslmed.adg8357] [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: 02/13/2023] [Accepted: 01/11/2024] [Indexed: 02/02/2024]
Abstract
The gut microbiome harbors trillions of organisms that contribute to human health and disease. These bacteria can also affect the properties of medical drugs used to treat these diseases, and drugs, in turn, can reshape the microbiome. Research addressing interdependent microbiome-host-drug interactions thus has broad impact. In this Review, we discuss these interactions from the perspective of drug bioavailability, absorption, metabolism, excretion, toxicity, and drug-mediated microbiome modulation. We survey approaches that aim to uncover the mechanisms underlying these effects and opportunities to translate this knowledge into new strategies to improve the development, administration, and monitoring of medical drugs.
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Affiliation(s)
- Andrew A Verdegaal
- Department of Microbial Pathogenesis and Microbial Sciences Institute, Yale University School of Medicine, New Haven, CT 06536, USA
| | - Andrew L Goodman
- Department of Microbial Pathogenesis and Microbial Sciences Institute, Yale University School of Medicine, New Haven, CT 06536, USA
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19
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Gelbach PE, Cetin H, Finley SD. Flux sampling in genome-scale metabolic modeling of microbial communities. BMC Bioinformatics 2024; 25:45. [PMID: 38287239 PMCID: PMC10826046 DOI: 10.1186/s12859-024-05655-3] [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: 06/21/2023] [Accepted: 01/15/2024] [Indexed: 01/31/2024] Open
Abstract
BACKGROUND Microbial communities play a crucial role in ecosystem function through metabolic interactions. Genome-scale modeling is a promising method to understand these interactions and identify strategies to optimize the community. Flux balance analysis (FBA) is most often used to predict the flux through all reactions in a genome-scale model; however, the fluxes predicted by FBA depend on a user-defined cellular objective. Flux sampling is an alternative to FBA, as it provides the range of fluxes possible within a microbial community. Furthermore, flux sampling can capture additional heterogeneity across a population, especially when cells exhibit sub-maximal growth rates. RESULTS In this study, we simulate the metabolism of microbial communities and compare the metabolic characteristics found with FBA and flux sampling. With sampling, we find significant differences in the predicted metabolism, including an increase in cooperative interactions and pathway-specific changes in predicted flux. CONCLUSIONS Our results suggest the importance of sampling-based approaches to evaluate metabolic interactions. Furthermore, we emphasize the utility of flux sampling in quantitatively studying interactions between cells and organisms.
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Affiliation(s)
- Patrick E Gelbach
- Alfred E. Mann Department of Biomedical Engineering, University of Southern California, Los Angeles, CA, 90089, USA
| | - Handan Cetin
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA, 90089, USA
| | - Stacey D Finley
- Alfred E. Mann Department of Biomedical Engineering, University of Southern California, Los Angeles, CA, 90089, USA.
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA, 90089, USA.
- Mork Family Department of Chemical Engineering and Materials Science, University of Southern California, Los Angeles, CA, 90089, USA.
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20
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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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21
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Wang S, Ju D, Zeng X. Mechanisms and Clinical Implications of Human Gut Microbiota-Drug Interactions in the Precision Medicine Era. Biomedicines 2024; 12:194. [PMID: 38255298 PMCID: PMC10813426 DOI: 10.3390/biomedicines12010194] [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: 11/05/2023] [Revised: 12/09/2023] [Accepted: 12/12/2023] [Indexed: 01/24/2024] Open
Abstract
The human gut microbiota, comprising trillions of microorganisms residing in the gastrointestinal tract, has emerged as a pivotal player in modulating various aspects of human health and disease. Recent research has shed light on the intricate relationship between the gut microbiota and pharmaceuticals, uncovering profound implications for drug metabolism, efficacy, and safety. This review depicted the landscape of molecular mechanisms and clinical implications of dynamic human gut Microbiota-Drug Interactions (MDI), with an emphasis on the impact of MDI on drug responses and individual variations. This review also discussed the therapeutic potential of modulating the gut microbiota or harnessing its metabolic capabilities to optimize clinical treatments and advance personalized medicine, as well as the challenges and future directions in this emerging field.
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Affiliation(s)
| | - Dianwen Ju
- Department of Biological Medicines & Shanghai Engineering Research Center of Immunotherapeutics, School of Pharmacy, Fudan University, Shanghai 201203, China;
| | - Xian Zeng
- Department of Biological Medicines & Shanghai Engineering Research Center of Immunotherapeutics, School of Pharmacy, Fudan University, Shanghai 201203, China;
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22
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Heinken A, El Kouche S, Guéant-Rodriguez RM, Guéant JL. Towards personalized genome-scale modeling of inborn errors of metabolism for systems medicine applications. Metabolism 2024; 150:155738. [PMID: 37981189 DOI: 10.1016/j.metabol.2023.155738] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Revised: 11/09/2023] [Accepted: 11/12/2023] [Indexed: 11/21/2023]
Abstract
Inborn errors of metabolism (IEMs) are a group of more than 1000 inherited diseases that are individually rare but have a cumulative global prevalence of 50 per 100,000 births. Recently, it has been recognized that like common diseases, patients with rare diseases can greatly vary in the manifestation and severity of symptoms. Here, we review omics-driven approaches that enable an integrated, holistic view of metabolic phenotypes in IEM patients. We focus on applications of Constraint-based Reconstruction and Analysis (COBRA), a widely used mechanistic systems biology approach, to model the effects of inherited diseases. Moreover, we review evidence that the gut microbiome is also altered in rare diseases. Finally, we outline an approach using personalized metabolic models of IEM patients for the prediction of biomarkers and tailored therapeutic or dietary interventions. Such applications could pave the way towards personalized medicine not just for common, but also for rare diseases.
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Affiliation(s)
- Almut Heinken
- Inserm UMRS 1256 NGERE - Nutrition, Genetics, and Environmental Risk Exposure, University of Lorraine, Nancy F-54000, France.
| | - Sandra El Kouche
- Inserm UMRS 1256 NGERE - Nutrition, Genetics, and Environmental Risk Exposure, University of Lorraine, Nancy F-54000, France
| | - Rosa-Maria Guéant-Rodriguez
- Inserm UMRS 1256 NGERE - Nutrition, Genetics, and Environmental Risk Exposure, University of Lorraine, Nancy F-54000, France; National Center of Inborn Errors of Metabolism, University Regional Hospital Center of Nancy, Nancy F-54000, France
| | - Jean-Louis Guéant
- Inserm UMRS 1256 NGERE - Nutrition, Genetics, and Environmental Risk Exposure, University of Lorraine, Nancy F-54000, France; National Center of Inborn Errors of Metabolism, University Regional Hospital Center of Nancy, Nancy F-54000, France
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23
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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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24
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Zhu J, Yin J, Chen J, Hu M, Lu W, Wang H, Zhang H, Chen W. Integrative analysis with microbial modelling and machine learning uncovers potential alleviators for ulcerative colitis. Gut Microbes 2024; 16:2336877. [PMID: 38563656 PMCID: PMC10989691 DOI: 10.1080/19490976.2024.2336877] [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: 11/07/2023] [Accepted: 03/27/2024] [Indexed: 04/04/2024] Open
Abstract
Ulcerative colitis (UC) is a challenging form of inflammatory bowel disease, and its etiology is intricately linked to disturbances in the gut microbiome. To identify the potential alleviators of UC, we employed an integrative analysis combining microbial community modeling with advanced machine learning techniques. Using metagenomics data sourced from the Integrated Human Microbiome Project, we constructed individualized microbiome community models for each participant. Our analysis highlighted a significant decline in both α and β-diversity of strain-level microbial populations in UC subjects compared to controls. Distinct differences were also observed in the predicted fecal metabolite profiles and strain-to-metabolite contributions between the two groups. Using tree-based machine learning models, we successfully identified specific microbial strains and their associated metabolites as potential alleviators of UC. Notably, our experimental validation using a dextran sulfate sodium-induced UC mouse model demonstrated that the administration of Parabacteroides merdae ATCC 43,184 and N-acetyl-D-mannosamine provided notable relief from colitis symptoms. In summary, our study underscores the potential of an integrative approach to identify novel therapeutic avenues for UC, paving the way for future targeted interventions.
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Affiliation(s)
- Jinlin Zhu
- State Key Laboratory of Food Science and Resources, Jiangnan University, Wuxi, China
- School of Food Science and Technology, Jiangnan University, Wuxi, China
| | - Jialin Yin
- State Key Laboratory of Food Science and Resources, Jiangnan University, Wuxi, China
- School of Food Science and Technology, Jiangnan University, Wuxi, China
| | - Jing Chen
- State Key Laboratory of Food Science and Resources, Jiangnan University, Wuxi, China
- School of Food Science and Technology, Jiangnan University, Wuxi, China
| | - Mingyi Hu
- State Key Laboratory of Food Science and Resources, Jiangnan University, Wuxi, China
- School of Food Science and Technology, Jiangnan University, Wuxi, China
| | - Wenwei Lu
- State Key Laboratory of Food Science and Resources, Jiangnan University, Wuxi, China
- School of Food Science and Technology, Jiangnan University, Wuxi, China
- International Joint Research Laboratory for Pharmabiotics & Antibiotic Resistance, Jiangnan University, Wuxi, China
- (Yangzhou) Institute of Food Biotechnology, Jiangnan University, Yangzhou, China
| | - Hongchao Wang
- State Key Laboratory of Food Science and Resources, Jiangnan University, Wuxi, China
- School of Food Science and Technology, Jiangnan University, Wuxi, China
| | - Hao Zhang
- State Key Laboratory of Food Science and Resources, Jiangnan University, Wuxi, China
- School of Food Science and Technology, Jiangnan University, Wuxi, China
- (Yangzhou) Institute of Food Biotechnology, Jiangnan University, Yangzhou, China
- National Engineering Research Center for Functional Food, Jiangnan University, Wuxi, China
- Wuxi Translational Medicine Research Center and Jiangsu Translational Medicine Research Institute Wuxi Branch, Wuxi People’s Hospital, Wuxi, China
| | - Wei Chen
- State Key Laboratory of Food Science and Resources, Jiangnan University, Wuxi, China
- School of Food Science and Technology, Jiangnan University, Wuxi, China
- National Engineering Research Center for Functional Food, Jiangnan University, Wuxi, China
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25
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Speckmann B, Ehring E, Hu J, Rodriguez Mateos A. Exploring substrate-microbe interactions: a metabiotic approach toward developing targeted synbiotic compositions. Gut Microbes 2024; 16:2305716. [PMID: 38300741 PMCID: PMC10841028 DOI: 10.1080/19490976.2024.2305716] [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: 09/14/2023] [Accepted: 01/11/2024] [Indexed: 02/03/2024] Open
Abstract
Gut microbiota is an important modulator of human health and contributes to high inter-individual variation in response to food and pharmaceutical ingredients. The clinical outcomes of interventions with prebiotics, probiotics, and synbiotics have been mixed and often unpredictable, arguing for novel approaches for developing microbiome-targeted therapeutics. Here, we review how the gut microbiota determines the fate of and individual responses to dietary and xenobiotic compounds via its immense metabolic potential. We highlight that microbial metabolites play a crucial role as targetable mediators in the microbiota-host health relationship. With this in mind, we expand the concept of synbiotics beyond prebiotics' role in facilitating growth and engraftment of probiotics, by focusing on microbial metabolism as a vital mode of action thereof. Consequently, we discuss synbiotic compositions that enable the guided metabolism of dietary or co-formulated ingredients by specific microbes leading to target molecules with beneficial functions. A workflow to develop novel synbiotics is presented, including the selection of promising target metabolites (e.g. equol, urolithin A, spermidine, indole-3 derivatives), identification of suitable substrates and producer strains applying bioinformatic tools, gut models, and eventually human trials.In conclusion, we propose that discovering and enabling specific substrate-microbe interactions is a valuable strategy to rationally design synbiotics that could establish a new category of hybrid nutra-/pharmaceuticals.
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Affiliation(s)
| | | | - Jiaying Hu
- Department of Nutritional Sciences, School of Life Course and Population Sciences, Faculty of Life Sciences and Medicine, King’s College London, London, UK
| | - Ana Rodriguez Mateos
- Department of Nutritional Sciences, School of Life Course and Population Sciences, Faculty of Life Sciences and Medicine, King’s College London, London, UK
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26
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Muñoz-Tamayo R, Davoudkhani M, Fakih I, Robles-Rodriguez CE, Rubino F, Creevey CJ, Forano E. Review: Towards the next-generation models of the rumen microbiome for enhancing predictive power and guiding sustainable production strategies. Animal 2023; 17 Suppl 5:100984. [PMID: 37821326 DOI: 10.1016/j.animal.2023.100984] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2022] [Revised: 09/01/2023] [Accepted: 09/07/2023] [Indexed: 10/13/2023] Open
Abstract
The rumen ecosystem harbours a galaxy of microbes working in syntrophy to carry out a metabolic cascade of hydrolytic and fermentative reactions. This fermentation process allows ruminants to harvest nutrients from a wide range of feedstuff otherwise inaccessible to the host. The interconnection between the ruminant and its rumen microbiota shapes key animal phenotypes such as feed efficiency and methane emissions and suggests the potential of reducing methane emissions and enhancing feed conversion into animal products by manipulating the rumen microbiota. Whilst significant technological progress in omics techniques has increased our knowledge of the rumen microbiota and its genome (microbiome), translating omics knowledge into effective microbial manipulation strategies remains a great challenge. This challenge can be addressed by modelling approaches integrating causality principles and thus going beyond current correlation-based approaches applied to analyse rumen microbial genomic data. However, existing rumen models are not yet adapted to capitalise on microbial genomic information. This gap between the rumen microbiota available omics data and the way microbial metabolism is represented in the existing rumen models needs to be filled to enhance rumen understanding and produce better predictive models with capabilities for guiding nutritional strategies. To fill this gap, the integration of computational biology tools and mathematical modelling frameworks is needed to translate the information of the metabolic potential of the rumen microbes (inferred from their genomes) into a mathematical object. In this paper, we aim to discuss the potential use of two modelling approaches for the integration of microbial genomic information into dynamic models. The first modelling approach explores the theory of state observers to integrate microbial time series data into rumen fermentation models. The second approach is based on the genome-scale network reconstructions of rumen microbes. For a given microorganism, the network reconstruction produces a stoichiometry matrix of the metabolism. This matrix is the core of the so-called genome-scale metabolic models which can be exploited by a plethora of methods comprised within the constraint-based reconstruction and analysis approaches. We will discuss how these methods can be used to produce the next-generation models of the rumen microbiome.
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Affiliation(s)
- R Muñoz-Tamayo
- Université Paris-Saclay, INRAE, AgroParisTech, UMR Modélisation Systémique Appliquée aux Ruminants, 91120 Palaiseau, France.
| | - M Davoudkhani
- Université Paris-Saclay, INRAE, AgroParisTech, UMR Modélisation Systémique Appliquée aux Ruminants, 91120 Palaiseau, France
| | - I Fakih
- Université Paris-Saclay, INRAE, AgroParisTech, UMR Modélisation Systémique Appliquée aux Ruminants, 91120 Palaiseau, France; Université Clermont Auvergne, INRAE, UMR 454 MEDIS, Clermont-Ferrand, France
| | | | - F Rubino
- Institute of Global Food Security, School of Biological Sciences, Queen's University Belfast, BT9 5DL Northern Ireland, UK
| | - C J Creevey
- Institute of Global Food Security, School of Biological Sciences, Queen's University Belfast, BT9 5DL Northern Ireland, UK
| | - E Forano
- Université Clermont Auvergne, INRAE, UMR 454 MEDIS, Clermont-Ferrand, France
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27
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Unni R, Andreani NA, Vallier M, Heinzmann SS, Taubenheim J, Guggeis MA, Tran F, Vogler O, Künzel S, Hövener JB, Rosenstiel P, Kaleta C, Dempfle A, Unterweger D, Baines JF. Evolution of E. coli in a mouse model of inflammatory bowel disease leads to a disease-specific bacterial genotype and trade-offs with clinical relevance. Gut Microbes 2023; 15:2286675. [PMID: 38059748 PMCID: PMC10730162 DOI: 10.1080/19490976.2023.2286675] [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: 09/04/2023] [Accepted: 11/17/2023] [Indexed: 12/08/2023] Open
Abstract
Inflammatory bowel disease (IBD) is a persistent inflammatory condition that affects the gastrointestinal tract and presents significant challenges in its management and treatment. Despite the knowledge that within-host bacterial evolution occurs in the intestine, the disease has rarely been studied from an evolutionary perspective. In this study, we aimed to investigate the evolution of resident bacteria during intestinal inflammation and whether- and how disease-related bacterial genetic changes may present trade-offs with potential therapeutic importance. Here, we perform an in vivo evolution experiment of E. coli in a gnotobiotic mouse model of IBD, followed by multiomic analyses to identify disease-specific genetic and phenotypic changes in bacteria that evolved in an inflamed versus a non-inflamed control environment. Our results demonstrate distinct evolutionary changes in E. coli specific to inflammation, including a single nucleotide variant that independently reached high frequency in all inflamed mice. Using ex vivo fitness assays, we find that these changes are associated with a higher fitness in an inflamed environment compared to isolates derived from non-inflamed mice. Further, using large-scale phenotypic assays, we show that bacterial adaptation to inflammation results in clinically relevant phenotypes, which intriguingly include collateral sensitivity to antibiotics. Bacterial evolution in an inflamed gut yields specific genetic and phenotypic signatures. These results may serve as a basis for developing novel evolution-informed treatment approaches for patients with intestinal inflammation.
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Affiliation(s)
- Rahul Unni
- Section Evolutionary Medicine, Max Planck Institute for Evolutionary Biology, Plön, Germany
- Section of Evolutionary Medicine, Institute for Experimental Medicine, Kiel University, Kiel, Germany
| | - Nadia Andrea Andreani
- Section Evolutionary Medicine, Max Planck Institute for Evolutionary Biology, Plön, Germany
- Section of Evolutionary Medicine, Institute for Experimental Medicine, Kiel University, Kiel, Germany
| | - Marie Vallier
- Section Evolutionary Medicine, Max Planck Institute for Evolutionary Biology, Plön, Germany
- Section of Evolutionary Medicine, Institute for Experimental Medicine, Kiel University, Kiel, Germany
| | - Silke S. Heinzmann
- Research Unit Analytical BioGeoChemistry, Helmholtz Munich, Neuherberg, Germany
| | - Jan Taubenheim
- Research Group Medical Systems Biology, Institute for Experimental Medicine, Kiel University, Kiel, Germany
| | - Martina A. Guggeis
- Institute of Clinical Molecular Biology, Christian-Albrechts-University Kiel and University Medical Center Schleswig-Holstein, Kiel, Germany
- Department of Internal Medicine I, University Medical Center Schleswig-Holstein, Kiel, Germany
| | - Florian Tran
- Institute of Clinical Molecular Biology, Christian-Albrechts-University Kiel and University Medical Center Schleswig-Holstein, Kiel, Germany
- Department of Internal Medicine I, University Medical Center Schleswig-Holstein, Kiel, Germany
| | - Olga Vogler
- Section Evolutionary Medicine, Max Planck Institute for Evolutionary Biology, Plön, Germany
| | - Sven Künzel
- Section Evolutionary Medicine, Max Planck Institute for Evolutionary Biology, Plön, Germany
| | - Jan-Bernd Hövener
- Section Biomedical Imaging, Molecular Imaging North Competence Center (MOIN CC), Department of Radiology and Neuroradiology, University Medical Center Kiel, Kiel, Germany
| | - Philip Rosenstiel
- Institute of Clinical Molecular Biology, Christian-Albrechts-University Kiel and University Medical Center Schleswig-Holstein, Kiel, Germany
| | - Christoph Kaleta
- Research Group Medical Systems Biology, Institute for Experimental Medicine, Kiel University, Kiel, Germany
| | - Astrid Dempfle
- Institute of Medical Informatics and Statistics, Kiel University, Kiel, Germany
| | - Daniel Unterweger
- Section Evolutionary Medicine, Max Planck Institute for Evolutionary Biology, Plön, Germany
- Section of Evolutionary Medicine, Institute for Experimental Medicine, Kiel University, Kiel, Germany
| | - John F. Baines
- Section Evolutionary Medicine, Max Planck Institute for Evolutionary Biology, Plön, Germany
- Section of Evolutionary Medicine, Institute for Experimental Medicine, Kiel University, Kiel, Germany
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28
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Quinn-Bohmann N, Wilmanski T, Sarmiento KR, Levy L, Lampe JW, Gurry T, Rappaport N, Ostrem EM, Venturelli OS, Diener C, Gibbons SM. Microbial community-scale metabolic modeling predicts personalized short chain fatty acid production profiles in the human gut. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.02.28.530516. [PMID: 36909644 PMCID: PMC10002715 DOI: 10.1101/2023.02.28.530516] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/06/2023]
Abstract
Microbially-derived short chain fatty acids (SCFAs) in the human gut are tightly coupled to host metabolism, immune regulation, and integrity of the intestinal epithelium. However, the production of SCFAs can vary widely between individuals consuming the same diet, with lower levels often associated with disease. A systems-scale mechanistic understanding of this heterogeneity is lacking. We present a microbial community-scale metabolic modeling (MCMM) approach to predict individual-specific SCFA production profiles. We assess the quantitative accuracy of our MCMMs using in vitro, ex vivo, and in vivo data. Next, we show how MCMM SCFA predictions are significantly associated with blood-derived clinical chemistries, including cardiometabolic and immunological health markers, across a large human cohort. Finally, we demonstrate how MCMMs can be leveraged to design personalized dietary, prebiotic, and probiotic interventions that optimize SCFA production in the gut. Our results represent an important advance in engineering gut microbiome functional outputs for precision health and nutrition.
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Affiliation(s)
- Nick Quinn-Bohmann
- Institute for Systems Biology, Seattle, WA 98109, USA
- Molecular Engineering Graduate Program, University of Washington, Seattle, WA 98195, USA
| | | | | | - Lisa Levy
- Fred Hutchinson Cancer Center, Seattle, WA 98109, USA
| | | | - Thomas Gurry
- Pharmaceutical Biochemistry Group, School of Pharmaceutical Sciences, University of Geneva, Switzerland
- Myota GmbH, Berlin, Germany
| | - Noa Rappaport
- Center for Phenomic Health, Buck Institute for Research on Aging, Novato, CA 94945, USA
| | - Erin M. Ostrem
- Department of Biochemistry, University of Wisconsin-Madison, Madison, WI 53706, USA
| | - Ophelia S. Venturelli
- Department of Biochemistry, University of Wisconsin-Madison, Madison, WI 53706, USA
- Department of Biomedical Engineering, University of Wisconsin-Madison, Madison, WI 53706, USA
- Department of Chemical & Biological Engineering, University of Wisconsin-Madison, Madison, WI 53706, USA
- Department of Bacteriology, University of Wisconsin-Madison, Madison, WI 53706, USA
| | | | - Sean M. Gibbons
- Institute for Systems Biology, Seattle, WA 98109, USA
- Molecular Engineering Graduate Program, University of Washington, Seattle, WA 98195, USA
- Department of Bioengineering, University of Washington, Seattle, WA 98195, USA
- Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA
- eScience Institute, University of Washington, Seattle, WA 98195, USA
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29
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Bäuerle F, Döbel GO, Camus L, Heilbronner S, Dräger A. Genome-scale metabolic models consistently predict in vitro characteristics of Corynebacterium striatum. FRONTIERS IN BIOINFORMATICS 2023; 3:1214074. [PMID: 37936955 PMCID: PMC10626998 DOI: 10.3389/fbinf.2023.1214074] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Accepted: 10/02/2023] [Indexed: 11/09/2023] Open
Abstract
Introduction: Genome-scale metabolic models (GEMs) are organism-specific knowledge bases which can be used to unravel pathogenicity or improve production of specific metabolites in biotechnology applications. However, the validity of predictions for bacterial proliferation in in vitro settings is hardly investigated. Methods: The present work combines in silico and in vitro approaches to create and curate strain-specific genome-scale metabolic models of Corynebacterium striatum. Results: We introduce five newly created strain-specific genome-scale metabolic models (GEMs) of high quality, satisfying all contemporary standards and requirements. All these models have been benchmarked using the community standard test suite Metabolic Model Testing (MEMOTE) and were validated by laboratory experiments. For the curation of those models, the software infrastructure refineGEMs was developed to work on these models in parallel and to comply with the quality standards for GEMs. The model predictions were confirmed by experimental data and a new comparison metric based on the doubling time was developed to quantify bacterial growth. Discussion: Future modeling projects can rely on the proposed software, which is independent of specific environmental conditions. The validation approach based on the growth rate calculation is now accessible and closely aligned with biological questions. The curated models are freely available via BioModels and a GitHub repository and can be used. The open-source software refineGEMs is available from https://github.com/draeger-lab/refinegems.
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Affiliation(s)
- Famke Bäuerle
- Computational Systems Biology of Infections and Antimicrobial-Resistant Pathogens, Institute for Bioinformatics and Medical Informatics (IBMI), Eberhard Karl University of Tübingen, Tübingen, Germany
- Interfaculty Institute of Microbiology and Infection Medicine Tübingen (IMIT), Eberhard Karl University of Tübingen, Tübingen, Germany
- Department of Computer Science, Eberhard Karl University of Tübingen, Tübingen, Germany
| | - Gwendolyn O. Döbel
- Computational Systems Biology of Infections and Antimicrobial-Resistant Pathogens, Institute for Bioinformatics and Medical Informatics (IBMI), Eberhard Karl University of Tübingen, Tübingen, Germany
- Department of Computer Science, Eberhard Karl University of Tübingen, Tübingen, Germany
- German Center for Infection Research (DZIF), Partner Site Tübingen, Tübingen, Germany
| | - Laura Camus
- Interfaculty Institute of Microbiology and Infection Medicine Tübingen (IMIT), Eberhard Karl University of Tübingen, Tübingen, Germany
- German Center for Infection Research (DZIF), Partner Site Tübingen, Tübingen, Germany
- Cluster of Excellence “Controlling Microbes to Fight Infections (CMFI)”, Eberhard Karl University of Tübingen, Tübingen, Germany
| | - Simon Heilbronner
- Interfaculty Institute of Microbiology and Infection Medicine Tübingen (IMIT), Eberhard Karl University of Tübingen, Tübingen, Germany
- German Center for Infection Research (DZIF), Partner Site Tübingen, Tübingen, Germany
- Cluster of Excellence “Controlling Microbes to Fight Infections (CMFI)”, Eberhard Karl University of Tübingen, Tübingen, Germany
- Faculty of Biology, Microbiology, Ludwig Maximilian University of Munich, Munich, Germany
| | - Andreas Dräger
- Computational Systems Biology of Infections and Antimicrobial-Resistant Pathogens, Institute for Bioinformatics and Medical Informatics (IBMI), Eberhard Karl University of Tübingen, Tübingen, Germany
- Department of Computer Science, Eberhard Karl University of Tübingen, Tübingen, Germany
- German Center for Infection Research (DZIF), Partner Site Tübingen, Tübingen, Germany
- Cluster of Excellence “Controlling Microbes to Fight Infections (CMFI)”, Eberhard Karl University of Tübingen, Tübingen, Germany
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30
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Hertel J, Heinken A, Fässler D, Thiele I. Causal inference on microbiome-metabolome relations in observational host-microbiome data via in silico in vivo association pattern analyses. CELL REPORTS METHODS 2023; 3:100615. [PMID: 37848031 PMCID: PMC10626217 DOI: 10.1016/j.crmeth.2023.100615] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/18/2022] [Revised: 05/23/2023] [Accepted: 09/20/2023] [Indexed: 10/19/2023]
Abstract
Understanding the effects of the microbiome on the host's metabolism is core to enlightening the role of the microbiome in health and disease. Herein, we develop the paradigm of in silico in vivo association pattern analyses, combining microbiome metabolome association studies with in silico constraint-based community modeling. Via theoretical dissection of confounding and causal paths, we show that in silico in vivo association pattern analyses allow for causal inference on microbiome-metabolome relations in observational data. We justify the corresponding theoretical criterion by structural equation modeling of host-microbiome systems, integrating deterministic microbiome community modeling into population statistics approaches. We show the feasibility of our approach on a published multi-omics dataset (n = 347), demonstrating causal microbiome-metabolite relations for 26 out of 54 fecal metabolites. In summary, we generate a promising approach for causal inference in metabolic host-microbiome interactions by integrating hypothesis-free screening association studies with knowledge-based in silico modeling.
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Affiliation(s)
- Johannes Hertel
- School of Medicine, University of Galway, Galway, Ireland; Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Greifswald, Germany
| | - Almut Heinken
- School of Medicine, University of Galway, Galway, Ireland; UMRS Inserm 1256 NGERE (Nutrition-Genetics-Environmental Risks), Institute of Medical Research (Pôle BMS) - University of Lorraine, Vandoeuvre-les-Nancy, France
| | - Daniel Fässler
- Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Greifswald, Germany
| | - Ines Thiele
- School of Medicine, University of Galway, Galway, Ireland; Discipline of Microbiology, University of Galway, Galway, Ireland; APC Microbiome Ireland, University College Cork, Cork, Ireland; Ryan Institute, University of Galway, Galway, Ireland.
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31
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Marcelino VR, Welsh C, Diener C, Gulliver EL, Rutten EL, Young RB, Giles EM, Gibbons SM, Greening C, Forster SC. Disease-specific loss of microbial cross-feeding interactions in the human gut. Nat Commun 2023; 14:6546. [PMID: 37863966 PMCID: PMC10589287 DOI: 10.1038/s41467-023-42112-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Accepted: 09/27/2023] [Indexed: 10/22/2023] Open
Abstract
Many gut microorganisms critical to human health rely on nutrients produced by each other for survival; however, these cross-feeding interactions are still challenging to quantify and remain poorly characterized. Here, we introduce a Metabolite Exchange Score (MES) to quantify those interactions. Using metabolic models of prokaryotic metagenome-assembled genomes from over 1600 individuals, MES allows us to identify and rank metabolic interactions that are significantly affected by a loss of cross-feeding partners in 10 out of 11 diseases. When applied to a Crohn's disease case-control study, our approach identifies a lack of species with the ability to consume hydrogen sulfide as the main distinguishing microbiome feature of disease. We propose that our conceptual framework will help prioritize in-depth analyses, experiments and clinical targets, and that targeting the restoration of microbial cross-feeding interactions is a promising mechanism-informed strategy to reconstruct a healthy gut ecosystem.
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Affiliation(s)
- Vanessa R Marcelino
- Department of Molecular and Translational Sciences, Monash University, Clayton, VIC, 3168, Australia.
- Centre for Innate Immunity and Infectious Diseases, Hudson Institute of Medical Research, Clayton, VIC, 3168, Australia.
- Melbourne Integrative Genomics, School of BioSciences, University of Melbourne, Parkville, VIC, 3010, Australia.
- Department of Microbiology and Immunology at the Peter Doherty Institute for Infection and Immunity, University of Melbourne, Parkville, VIC, 3010, Australia.
| | - Caitlin Welsh
- Department of Microbiology, Biomedicine Discovery Institute, Clayton, VIC, 3800, Australia
| | | | - Emily L Gulliver
- Department of Molecular and Translational Sciences, Monash University, Clayton, VIC, 3168, Australia
- Centre for Innate Immunity and Infectious Diseases, Hudson Institute of Medical Research, Clayton, VIC, 3168, Australia
| | - Emily L Rutten
- Department of Molecular and Translational Sciences, Monash University, Clayton, VIC, 3168, Australia
- Centre for Innate Immunity and Infectious Diseases, Hudson Institute of Medical Research, Clayton, VIC, 3168, Australia
| | - Remy B Young
- Department of Molecular and Translational Sciences, Monash University, Clayton, VIC, 3168, Australia
- Centre for Innate Immunity and Infectious Diseases, Hudson Institute of Medical Research, Clayton, VIC, 3168, Australia
| | - Edward M Giles
- Centre for Innate Immunity and Infectious Diseases, Hudson Institute of Medical Research, Clayton, VIC, 3168, Australia
- Department of Paediatrics, Monash University, Clayton, VIC, 3168, Australia
| | - Sean M Gibbons
- Institute for Systems Biology, Seattle, WA, 98109, USA
- Department of Bioengineering, University of Washington, Seattle, WA, 98195, USA
- Department of Genome Sciences, University of Washington, Seattle, WA, 98195, USA
- eScience Institute, University of Washington, Seattle, WA, 98195, USA
| | - Chris Greening
- Department of Microbiology, Biomedicine Discovery Institute, Clayton, VIC, 3800, Australia
| | - Samuel C Forster
- Department of Molecular and Translational Sciences, Monash University, Clayton, VIC, 3168, Australia.
- Centre for Innate Immunity and Infectious Diseases, Hudson Institute of Medical Research, Clayton, VIC, 3168, Australia.
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32
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Kim K, Kang M, Cho BK. Systems and synthetic biology-driven engineering of live bacterial therapeutics. Front Bioeng Biotechnol 2023; 11:1267378. [PMID: 37929193 PMCID: PMC10620806 DOI: 10.3389/fbioe.2023.1267378] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Accepted: 10/09/2023] [Indexed: 11/07/2023] Open
Abstract
The past decade has seen growing interest in bacterial engineering for therapeutically relevant applications. While early efforts focused on repurposing genetically tractable model strains, such as Escherichia coli, engineering gut commensals is gaining traction owing to their innate capacity to survive and stably propagate in the intestine for an extended duration. Although limited genetic tractability has been a major roadblock, recent advances in systems and synthetic biology have unlocked our ability to effectively harness native gut commensals for therapeutic and diagnostic purposes, ranging from the rational design of synthetic microbial consortia to the construction of synthetic cells that execute "sense-and-respond" logic operations that allow real-time detection and therapeutic payload delivery in response to specific signals in the intestine. In this review, we outline the current progress and latest updates on microbial therapeutics, with particular emphasis on gut commensal engineering driven by synthetic biology and systems understanding of their molecular phenotypes. Finally, the challenges and prospects of engineering gut commensals for therapeutic applications are discussed.
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Affiliation(s)
- Kangsan Kim
- Department of Biological Sciences, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea
- KAIST Institute for the BioCentury, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea
| | - Minjeong Kang
- Department of Biological Sciences, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea
- KAIST Institute for the BioCentury, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea
| | - Byung-Kwan Cho
- Department of Biological Sciences, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea
- KAIST Institute for the BioCentury, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea
- Graduate School of Engineering Biology, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea
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33
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Wohlgemuth R. Synthesis of Metabolites and Metabolite-like Compounds Using Biocatalytic Systems. Metabolites 2023; 13:1097. [PMID: 37887422 PMCID: PMC10608848 DOI: 10.3390/metabo13101097] [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: 08/16/2023] [Revised: 10/13/2023] [Accepted: 10/15/2023] [Indexed: 10/28/2023] Open
Abstract
Methodologies for the synthesis and purification of metabolites, which have been developed following their discovery, analysis, and structural identification, have been involved in numerous life science milestones. The renewed focus on the small molecule domain of biological cells has also created an increasing awareness of the rising gap between the metabolites identified and the metabolites which have been prepared as pure compounds. The design and engineering of resource-efficient and straightforward synthetic methodologies for the production of the diverse and numerous metabolites and metabolite-like compounds have attracted much interest. The variety of metabolic pathways in biological cells provides a wonderful blueprint for designing simplified and resource-efficient synthetic routes to desired metabolites. Therefore, biocatalytic systems have become key enabling tools for the synthesis of an increasing number of metabolites, which can then be utilized as standards, enzyme substrates, inhibitors, or other products, or for the discovery of novel biological functions.
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Affiliation(s)
- Roland Wohlgemuth
- MITR, Institute of Applied Radiation Chemistry, Faculty of Chemistry, Lodz University of Technology, Zeromskiego Street 116, 90-924 Lodz, Poland;
- Swiss Coordination Committee Biotechnology (SKB), 8021 Zurich, Switzerland
- European Society of Applied Biocatalysis (ESAB), 1000 Brussels, Belgium
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34
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Heinken A, Hulshof TO, Nap B, Martinelli F, Basile A, O'Brolchain A, O’Sullivan NF, Gallagher C, Magee E, McDonagh F, Lalor I, Bergin M, Evans P, Daly R, Farrell R, Delaney RM, Hill S, McAuliffe SR, Kilgannon T, Fleming RM, Thinnes CC, Thiele I. APOLLO: A genome-scale metabolic reconstruction resource of 247,092 diverse human microbes spanning multiple continents, age groups, and body sites. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.02.560573. [PMID: 37873072 PMCID: PMC10592896 DOI: 10.1101/2023.10.02.560573] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/25/2023]
Abstract
Computational modelling of microbiome metabolism has proved instrumental to catalyse our understanding of diet-host-microbiome-disease interactions through the interrogation of mechanistic, strain- and molecule-resolved metabolic models. We present APOLLO, a resource of 247,092 human microbial genome-scale metabolic reconstructions spanning 19 phyla and accounting for microbial genomes from 34 countries, all age groups, and five body sites. We explored the metabolic potential of the reconstructed strains and developed a machine learning classifier able to predict with high accuracy the taxonomic strain assignments. We also built 14,451 sample-specific microbial community models, which could be stratified by body site, age, and disease states. Finally, we predicted faecal metabolites enriched or depleted in gut microbiomes of people with Crohn's disease, Parkinson disease, and undernourished children. APOLLO is compatible with the human whole-body models, and thus, provide unprecedented opportunities for systems-level modelling of personalised host-microbiome co-metabolism. APOLLO will be freely available under https://www.vmh.life/.
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Affiliation(s)
- Almut Heinken
- School of Medicine, University of Galway, Galway, Ireland
- Ryan Institute, University of Galway, Galway, Ireland
- Inserm UMRS 1256 NGERE, University of Lorraine, Nancy, France
| | - Timothy Otto Hulshof
- School of Medicine, University of Galway, Galway, Ireland
- Ryan Institute, University of Galway, Galway, Ireland
| | - Bram Nap
- School of Medicine, University of Galway, Galway, Ireland
- Ryan Institute, University of Galway, Galway, Ireland
| | - Filippo Martinelli
- School of Medicine, University of Galway, Galway, Ireland
- Ryan Institute, University of Galway, Galway, Ireland
| | - Arianna Basile
- School of Medicine, University of Galway, Galway, Ireland
- Department of Biology, University of Padova, Padova, Italy
| | | | | | | | | | | | - Ian Lalor
- University of Galway, Galway, Ireland
| | | | | | | | | | | | | | | | | | | | - Cyrille C. Thinnes
- School of Medicine, University of Galway, Galway, Ireland
- Ryan Institute, University of Galway, Galway, Ireland
| | - Ines Thiele
- School of Medicine, University of Galway, Galway, Ireland
- Ryan Institute, University of Galway, Galway, Ireland
- Division of Microbiology, University of Galway, Galway, Ireland
- APC Microbiome Ireland, Cork, Ireland
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35
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Gralka M, Pollak S, Cordero OX. Genome content predicts the carbon catabolic preferences of heterotrophic bacteria. Nat Microbiol 2023; 8:1799-1808. [PMID: 37653010 DOI: 10.1038/s41564-023-01458-z] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2023] [Accepted: 07/24/2023] [Indexed: 09/02/2023]
Abstract
Heterotrophic bacteria-bacteria that utilize organic carbon sources-are taxonomically and functionally diverse across environments. It is challenging to map metabolic interactions and niches within microbial communities due to the large number of metabolites that could serve as potential carbon and energy sources for heterotrophs. Whether their metabolic niches can be understood using general principles, such as a small number of simplified metabolic categories, is unclear. Here we perform high-throughput metabolic profiling of 186 marine heterotrophic bacterial strains cultured in media containing one of 135 carbon substrates to determine growth rates, lag times and yields. We show that, despite high variability at all levels of taxonomy, the catabolic niches of heterotrophic bacteria can be understood in terms of their preference for either glycolytic (sugars) or gluconeogenic (amino and organic acids) carbon sources. This preference is encoded by the total number of genes found in pathways that feed into the two modes of carbon utilization and can be predicted using a simple linear model based on gene counts. This allows for coarse-grained descriptions of microbial communities in terms of prevalent modes of carbon catabolism. The sugar-acid preference is also associated with genomic GC content and thus with the carbon-nitrogen requirements of their encoded proteome. Our work reveals how the evolution of bacterial genomes is structured by fundamental constraints rooted in metabolism.
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Affiliation(s)
- Matti Gralka
- Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA.
- Systems Biology Group, Amsterdam Institute for Life and Environment (A-LIFE) and Amsterdam Institute of Molecular and Life Sciences (AIMMS), Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.
| | - Shaul Pollak
- Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
- Division of Microbial Ecology, Centre for Microbiology and Environmental Systems Science, University of Vienna, Vienna, Austria
| | - Otto X Cordero
- Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA.
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36
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Hackmann TJ, Zhang B. The phenotype and genotype of fermentative prokaryotes. SCIENCE ADVANCES 2023; 9:eadg8687. [PMID: 37756392 PMCID: PMC10530074 DOI: 10.1126/sciadv.adg8687] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/08/2023] [Accepted: 08/25/2023] [Indexed: 09/29/2023]
Abstract
Fermentation is a type of metabolism pervasive in oxygen-deprived environments. Despite its importance, we know little about the range and traits of organisms that carry out this metabolism. Our study addresses this gap with a comprehensive analysis of the phenotype and genotype of fermentative prokaryotes. We assembled a dataset with phenotypic records of 8350 organisms plus 4355 genomes and 13.6 million genes. Our analysis reveals fermentation is both widespread (in ~30% of prokaryotes) and complex (forming ~300 combinations of metabolites). Furthermore, it points to previously uncharacterized proteins involved in this metabolism. Previous studies suggest that metabolic pathways for fermentation are well understood, but metabolic models built in our study show gaps in our knowledge. This study demonstrates the complexity of fermentation while showing that there is still much to learn about this metabolism. All resources in our study can be explored by the scientific community with an online, interactive tool.
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Affiliation(s)
| | - Bo Zhang
- Department of Chemical Engineering, University of California, Santa Barbara, CA, USA
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37
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Meydan Y, Baldini F, Korem T. pymgpipe: microbiome metabolic modeling in Python. JOURNAL OF OPEN SOURCE SOFTWARE 2023; 8:5545. [PMID: 37885608 PMCID: PMC10600976 DOI: 10.21105/joss.05545] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 10/28/2023]
Affiliation(s)
- Yoli Meydan
- Program for Mathematical Genomics, Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, United States of America
| | - Federico Baldini
- Program for Mathematical Genomics, Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, United States of America
| | - Tal Korem
- Program for Mathematical Genomics, Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, United States of America
- Department of Obstetrics and Gynecology, Columbia University Irving Medical Center, New York, NY, United States of America
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38
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Mirhakkak MH, Chen X, Ni Y, Heinekamp T, Sae-Ong T, Xu LL, Kurzai O, Barber AE, Brakhage AA, Boutin S, Schäuble S, Panagiotou G. Genome-scale metabolic modeling of Aspergillus fumigatus strains reveals growth dependencies on the lung microbiome. Nat Commun 2023; 14:4369. [PMID: 37474497 PMCID: PMC10359302 DOI: 10.1038/s41467-023-39982-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2022] [Accepted: 07/03/2023] [Indexed: 07/22/2023] Open
Abstract
Aspergillus fumigatus, an opportunistic human pathogen, frequently infects the lungs of people with cystic fibrosis and is one of the most common causes of infectious-disease death in immunocompromised patients. Here, we construct 252 strain-specific, genome-scale metabolic models of this important fungal pathogen to study and better understand the metabolic component of its pathogenic versatility. The models show that 23.1% of A. fumigatus metabolic reactions are not conserved across strains and are mainly associated with amino acid, nucleotide, and nitrogen metabolism. Profiles of non-conserved reactions and growth-supporting reaction fluxes are sufficient to differentiate strains, for example by environmental or clinical origin. In addition, shotgun metagenomics analysis of sputum from 40 cystic fibrosis patients (15 females, 25 males) before and after diagnosis with an A. fumigatus colonization suggests that the fungus shapes the lung microbiome towards a more beneficial fungal growth environment associated with aromatic amino acid availability and the shikimate pathway. Our findings are starting points for the development of drugs or microbiome intervention strategies targeting fungal metabolic needs for survival and colonization in the non-native environment of the human lung.
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Affiliation(s)
- Mohammad H Mirhakkak
- Department of Microbiome Dynamics, Leibniz Institute for Natural Product Research and Infection Biology (Leibniz-HKI), 07745, Jena, Germany
| | - Xiuqiang Chen
- Department of Microbiome Dynamics, Leibniz Institute for Natural Product Research and Infection Biology (Leibniz-HKI), 07745, Jena, Germany
| | - Yueqiong Ni
- Department of Microbiome Dynamics, Leibniz Institute for Natural Product Research and Infection Biology (Leibniz-HKI), 07745, Jena, Germany
| | - Thorsten Heinekamp
- Department of Molecular and Applied Microbiology, Leibniz Institute for Natural Product Research and Infection Biology (Leibniz-HKI), 07745, Jena, Germany
| | - Tongta Sae-Ong
- Department of Microbiome Dynamics, Leibniz Institute for Natural Product Research and Infection Biology (Leibniz-HKI), 07745, Jena, Germany
| | - Lin-Lin Xu
- Department of Microbiome Dynamics, Leibniz Institute for Natural Product Research and Infection Biology (Leibniz-HKI), 07745, Jena, Germany
| | - Oliver Kurzai
- Institute for Hygiene and Microbiology, University of Würzburg, 97080, Würzburg, Germany
- Research Group Fungal Septomics, Leibniz Institute of Natural Product Research and Infection Biology (Leibniz-HKI), 07745, Jena, Germany
- National Reference Center for Invasive Fungal Infections (NRZMyk), Leibniz Institute of Natural Product Research and Infection Biology (Leibniz-HKI), 07745, Jena, Germany
| | - Amelia E Barber
- Junior Research Group Fungal Informatics, Institute of Microbiology, Friedrich-Schiller-University Jena, 07745, Jena, Germany
| | - Axel A Brakhage
- Department of Molecular and Applied Microbiology, Leibniz Institute for Natural Product Research and Infection Biology (Leibniz-HKI), 07745, Jena, Germany
- Institute of Microbiology, Friedrich Schiller University Jena, 07745, Jena, Germany
| | - Sebastien Boutin
- Department of Infectious Diseases and Microbiology, University of Lübeck, 23562, Lübeck, Germany
- Translational Lung Research Center Heidelberg (TLRC), German Center for Lung Research (DZL), University of Heidelberg, 69120, Heidelberg, Germany
| | - Sascha Schäuble
- Department of Microbiome Dynamics, Leibniz Institute for Natural Product Research and Infection Biology (Leibniz-HKI), 07745, Jena, Germany.
| | - Gianni Panagiotou
- Department of Microbiome Dynamics, Leibniz Institute for Natural Product Research and Infection Biology (Leibniz-HKI), 07745, Jena, Germany.
- Department of Medicine and State Key Laboratory of Pharmaceutical Biotechnology, University of Hong Kong, Hong Kong, China.
- Friedrich Schiller University, Faculty of Biological Sciences, Jena, 07745, Germany.
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Sen P, Orešič M. Integrating Omics Data in Genome-Scale Metabolic Modeling: A Methodological Perspective for Precision Medicine. Metabolites 2023; 13:855. [PMID: 37512562 PMCID: PMC10383060 DOI: 10.3390/metabo13070855] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Revised: 07/11/2023] [Accepted: 07/17/2023] [Indexed: 07/30/2023] Open
Abstract
Recent advancements in omics technologies have generated a wealth of biological data. Integrating these data within mathematical models is essential to fully leverage their potential. Genome-scale metabolic models (GEMs) provide a robust framework for studying complex biological systems. GEMs have significantly contributed to our understanding of human metabolism, including the intrinsic relationship between the gut microbiome and the host metabolism. In this review, we highlight the contributions of GEMs and discuss the critical challenges that must be overcome to ensure their reproducibility and enhance their prediction accuracy, particularly in the context of precision medicine. We also explore the role of machine learning in addressing these challenges within GEMs. The integration of omics data with GEMs has the potential to lead to new insights, and to advance our understanding of molecular mechanisms in human health and disease.
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Affiliation(s)
- Partho Sen
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, FI-20520 Turku, Finland
- School of Medical Sciences, Faculty of Medicine and Health, Örebro University, 702 81 Örebro, Sweden
| | - Matej Orešič
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, FI-20520 Turku, Finland
- School of Medical Sciences, Faculty of Medicine and Health, Örebro University, 702 81 Örebro, Sweden
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Kim SK, Lee M, Lee YQ, Lee HJ, Rho M, Kim Y, Seo JY, Youn SH, Hwang SJ, Kang NG, Lee CH, Park SY, Lee DY. Genome-scale metabolic modeling and in silico analysis of opportunistic skin pathogen Cutibacterium acnes. Front Cell Infect Microbiol 2023; 13:1099314. [PMID: 37520435 PMCID: PMC10374032 DOI: 10.3389/fcimb.2023.1099314] [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: 11/15/2022] [Accepted: 06/29/2023] [Indexed: 08/01/2023] Open
Abstract
Cutibacterium acnes, one of the most abundant skin microbes found in the sebaceous gland, is known to contribute to the development of acne vulgaris when its strains become imbalanced. The current limitations of acne treatment using antibiotics have caused an urgent need to develop a systematic strategy for selectively targeting C. acnes, which can be achieved by characterizing their cellular behaviors under various skin environments. To this end, we developed a genome-scale metabolic model (GEM) of virulent C. acnes, iCA843, based on the genome information of a relevant strain from ribotype 5 to comprehensively understand the pathogenic traits of C. acnes in the skin environment. We validated the model qualitatively by demonstrating its accuracy prediction of propionate and acetate production patterns, which were consistent with experimental observations. Additionally, we identified unique biosynthetic pathways for short-chain fatty acids in C. acnes compared to other GEMs of acne-inducing skin pathogens. By conducting constraint-based flux analysis under endogenous carbon sources in human skin, we discovered that the Wood-Werkman cycle is highly activated under acnes-associated skin condition for the regeneration of NAD, resulting in enhanced propionate production. Finally, we proposed potential anti-C. acnes targets by using the model-guided systematic framework based on gene essentiality analysis and protein sequence similarity search with abundant skin microbiome taxa.
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Affiliation(s)
- Su-Kyung Kim
- School of Chemical Engineering, Sungkyunkwan University, Suwon, Gyeonggi-do, Republic of Korea
| | - Minouk Lee
- School of Chemical Engineering, Sungkyunkwan University, Suwon, Gyeonggi-do, Republic of Korea
| | - Yi Qing Lee
- School of Chemical Engineering, Sungkyunkwan University, Suwon, Gyeonggi-do, Republic of Korea
| | - Hyun Jun Lee
- Department of Biomedical Informatics, Hanyang University, Seoul, Republic of Korea
| | - Mina Rho
- Department of Biomedical Informatics, Hanyang University, Seoul, Republic of Korea
- Department of Computer Science, Hanyang University, Seoul, Republic of Korea
| | - Yunkwan Kim
- R&D Center, LG Household & Healthcare (LG H&H), Seoul, Republic of Korea
| | - Jung Yeon Seo
- R&D Center, LG Household & Healthcare (LG H&H), Seoul, Republic of Korea
| | - Sung Hun Youn
- R&D Center, LG Household & Healthcare (LG H&H), Seoul, Republic of Korea
| | - Seung Jin Hwang
- R&D Center, LG Household & Healthcare (LG H&H), Seoul, Republic of Korea
| | - Nae Gyu Kang
- R&D Center, LG Household & Healthcare (LG H&H), Seoul, Republic of Korea
| | - Choong-Hwan Lee
- Department of Bioscience and Biotechnology, Konkuk University, Seoul, Republic of Korea
| | - Seo-Young Park
- School of Chemical Engineering, Sungkyunkwan University, Suwon, Gyeonggi-do, Republic of Korea
| | - Dong-Yup Lee
- School of Chemical Engineering, Sungkyunkwan University, Suwon, Gyeonggi-do, Republic of Korea
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Mousa S, Sarfraz M, Mousa WK. The Interplay between Gut Microbiota and Oral Medications and Its Impact on Advancing Precision Medicine. Metabolites 2023; 13:metabo13050674. [PMID: 37233715 DOI: 10.3390/metabo13050674] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2023] [Revised: 05/14/2023] [Accepted: 05/16/2023] [Indexed: 05/27/2023] Open
Abstract
Trillions of diverse microbes reside in the gut and are deeply interwoven with the human physiological process, from food digestion, immune system maturation, and fighting invading pathogens, to drug metabolism. Microbial drug metabolism has a profound impact on drug absorption, bioavailability, stability, efficacy, and toxicity. However, our knowledge of specific gut microbial strains, and their genes that encode enzymes involved in the metabolism, is limited. The microbiome encodes over 3 million unique genes contributing to a huge enzymatic capacity, vastly expanding the traditional drug metabolic reactions that occur in the liver, manipulating their pharmacological effect, and, ultimately, leading to variation in drug response. For example, the microbial deactivation of anticancer drugs such as gemcitabine can lead to resistance to chemotherapeutics or the crucial role of microbes in modulating the efficacy of the anticancer drug, cyclophosphamide. On the other hand, recent findings show that many drugs can shape the composition, function, and gene expression of the gut microbial community, making it harder to predict the outcome of drug-microbiota interactions. In this review, we discuss the recent understanding of the multidirectional interaction between the host, oral medications, and gut microbiota, using traditional and machine-learning approaches. We analyze gaps, challenges, and future promises of personalized medicine that consider gut microbes as a crucial player in drug metabolism. This consideration will enable the development of personalized therapeutic regimes with an improved outcome, ultimately leading to precision medicine.
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Affiliation(s)
- Sara Mousa
- College of Pharmacy, Al Ain University, Abu Dhabi P.O. Box 112612, United Arab Emirates
| | - Muhammad Sarfraz
- College of Pharmacy, Al Ain University, Abu Dhabi P.O. Box 112612, United Arab Emirates
| | - Walaa K Mousa
- College of Pharmacy, Al Ain University, Abu Dhabi P.O. Box 112612, United Arab Emirates
- College of Pharmacy, Mansoura University, Mansoura 35516, Egypt
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Gelbach PE, Finley SD. Flux Sampling in Genome-scale Metabolic Modeling of Microbial Communities. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.04.18.537368. [PMID: 37197028 PMCID: PMC10173371 DOI: 10.1101/2023.04.18.537368] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 05/19/2023]
Abstract
Microbial communities play a crucial role in ecosystem function through metabolic interactions. Genome-scale modeling is a promising method to understand these interactions. Flux balance analysis (FBA) is most often used to predict the flux through all reactions in a genome-scale model. However, the fluxes predicted by FBA depend on a user-defined cellular objective. Flux sampling is an alternative to FBA, as it provides the range of fluxes possible within a microbial community. Furthermore, flux sampling may capture additional heterogeneity across cells, especially when cells exhibit sub-maximal growth rates. In this study, we simulate the metabolism of microbial communities and compare the metabolic characteristics found with FBA and flux sampling. We find significant differences in the predicted metabolism with sampling, including increased cooperative interactions and pathway-specific changes in predicted flux. Our results suggest the importance of sampling-based and objective function-independent approaches to evaluate metabolic interactions and emphasize their utility in quantitatively studying interactions between cells and organisms.
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Affiliation(s)
- Patrick E. Gelbach
- Alfred E. Mann Department of Biomedical Engineering, University of Southern California, Los Angeles, CA 90089, USA
| | - Stacey D. Finley
- Alfred E. Mann Department of Biomedical Engineering, University of Southern California, Los Angeles, CA 90089, USA
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA 90089, USA
- Mork Family Department of Chemical Engineering and Materials Science, University of Southern California, Los Angeles, CA 90089, USA
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Boccuto L, Tack J, Ianiro G, Abenavoli L, Scarpellini E. Human Genes Involved in the Interaction between Host and Gut Microbiome: Regulation and Pathogenic Mechanisms. Genes (Basel) 2023; 14:genes14040857. [PMID: 37107615 PMCID: PMC10137629 DOI: 10.3390/genes14040857] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Revised: 03/24/2023] [Accepted: 03/29/2023] [Indexed: 04/05/2023] Open
Abstract
Introduction: The umbrella term “human gut microbiota” describes the complex ecosystem harboring our gut. It includes bacteria, viruses, protozoa, archaea, fungi, and yeasts. This taxonomic classification does not describe its functions, which encompass nutrients digestion and absorption, immune system regulation, and host metabolism. “Gut microbiome” indicates instead the genome belonging to these “microbes” actively involved in these functions. However, the interaction between the host genome and the microbial ones determines the fine functioning of our organism. Methods: We reviewed the data available in the scientific literature on the definition of gut microbiota, gut microbiome, and the data on human genes involved in the interaction with the latter. We consulted the main medical databases using the following keywords, acronyms, and their associations: gut microbiota, gut microbiome, human genes, immune function, and metabolism. Results: Candidate human genes encoding enzymes, inflammatory cytokines, and proteins show similarity with those included in the gut microbiome. These findings have become available through newer artificial intelligence (AI) algorithms allowing big data analysis. From an evolutionary point of view, these pieces of evidence explain the strict and sophisticated interaction at the basis of human metabolism and immunity regulation in humans. They unravel more and more physiopathologic pathways included in human health and disease. Discussion: Several lines of evidence also obtained through big data analysis support the bi-directional role of gut microbiome and human genome in host metabolism and immune system regulation.
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Affiliation(s)
- Luigi Boccuto
- School of Nursing, Healthcare Genetics Program, Clemson University, Clemson University School of Health Research, Clemson, SC 29631, USA
| | - Jan Tack
- Translational Research Center for Gastrointestinal Disorders (T.A.R.G.I.D.), Gasthuisberg University Hospital, KU Leuven, Herestraat 49, 3000 Lueven, Belgium
| | - Gianluca Ianiro
- Department of Medical and Surgical Sciences, Digestive Disease Center, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, 00168 Rome, Italy
- Department of Translational Medicine and Surgery, Università Cattolica del Sacro Cuore, 00168 Rome, Italy
| | - Ludovico Abenavoli
- Department of Health Sciences, Magna Graecia University, 88100 Catanzaro, Italy
| | - Emidio Scarpellini
- Translational Research Center for Gastrointestinal Disorders (T.A.R.G.I.D.), Gasthuisberg University Hospital, KU Leuven, Herestraat 49, 3000 Lueven, Belgium
- Clinical Nutrition and Hepatology Unit, San Benedetto del Tronto General Hospital, 63074 San Benedetto del Tronto, Italy
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