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Wu S, Qu Z, Chen D, Wu H, Caiyin Q, Qiao J. Deciphering and designing microbial communities by genome-scale metabolic modelling. Comput Struct Biotechnol J 2024; 23:1990-2000. [PMID: 38765607 PMCID: PMC11098673 DOI: 10.1016/j.csbj.2024.04.055] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2024] [Revised: 04/21/2024] [Accepted: 04/21/2024] [Indexed: 05/22/2024] Open
Abstract
Microbial communities are shaped by the complex interactions among organisms and the environment. Genome-scale metabolic models (GEMs) can provide deeper insights into the complexity and ecological properties of various microbial communities, revealing their intricate interactions. Many researchers have modified GEMs for the microbial communities based on specific needs. Thus, GEMs need to be comprehensively summarized to better understand the trends in their development. In this review, we summarized the key developments in deciphering and designing microbial communities using different GEMs. A timeline of selected highlights in GEMs indicated that this area is evolving from the single-strain level to the microbial community level. Then, we outlined a framework for constructing GEMs of microbial communities. We also summarized the models and resources of static and dynamic community-level GEMs. We focused on the role of external environmental and intracellular resources in shaping the assembly of microbial communities. Finally, we discussed the key challenges and future directions of GEMs, focusing on the integration of GEMs with quorum sensing mechanisms, microbial ecology interactions, machine learning algorithms, and automatic modeling, all of which contribute to consortia-based applications in different fields.
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Affiliation(s)
- Shengbo Wu
- School of Chemical Engineering and Technology, Tianjin University, Tianjin 300072, China
- Zhejiang Shaoxing Research Institute of Tianjin University, Shaoxing 312300, China
| | - Zheping Qu
- School of Chemical Engineering and Technology, Tianjin University, Tianjin 300072, China
| | - Danlei Chen
- School of Chemical Engineering and Technology, Tianjin University, Tianjin 300072, China
- Zhejiang Shaoxing Research Institute of Tianjin University, Shaoxing 312300, China
| | - Hao Wu
- School of Chemical Engineering and Technology, Tianjin University, Tianjin 300072, China
- Zhejiang Shaoxing Research Institute of Tianjin University, Shaoxing 312300, China
| | - Qinggele Caiyin
- School of Chemical Engineering and Technology, Tianjin University, Tianjin 300072, China
- Zhejiang Shaoxing Research Institute of Tianjin University, Shaoxing 312300, China
- Key Laboratory of Systems Bioengineering, Ministry of Education (Tianjin University), Tianjin 300072, China
| | - Jianjun Qiao
- School of Chemical Engineering and Technology, Tianjin University, Tianjin 300072, China
- Zhejiang Shaoxing Research Institute of Tianjin University, Shaoxing 312300, China
- Key Laboratory of Systems Bioengineering, Ministry of Education (Tianjin University), Tianjin 300072, China
- Frontiers Science Center for Synthetic Biology (Ministry of Education), Tianjin University, Tianjin 300072, China
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Malat I, Drancourt M, Grine G. Methanobrevibacter smithii cell variants in human physiology and pathology: A review. Heliyon 2024; 10:e36742. [PMID: 39347381 PMCID: PMC11437934 DOI: 10.1016/j.heliyon.2024.e36742] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2024] [Revised: 08/14/2024] [Accepted: 08/21/2024] [Indexed: 10/01/2024] Open
Abstract
Methanobrevibacter smithii (M. smithii), initially isolated from human feces, has been recognised as a distinct taxon within the Archaea domain following comprehensive phenotypic, genetic, and genomic analyses confirming its uniqueness among methanogens. Its diversity, encompassing 15 genotypes, mirrors that of biotic and host-associated ecosystems in which M. smithii plays a crucial role in detoxifying hydrogen from bacterial fermentations, converting it into mechanically expelled gaseous methane. In microbiota in contact with host epithelial mucosae, M. smithii centres metabolism-driven microbial networks with Bacteroides, Prevotella, Ruminococcus, Veillonella, Enterococcus, Escherichia, Enterobacter, Klebsiella, whereas symbiotic association with the nanoarchaea Candidatus Nanopusillus phoceensis determines small and large cell variants of M. smithii. The former translocate with bacteria to induce detectable inflammatory and serological responses and are co-cultured from blood, urine, and tissular abscesses with bacteria, prototyping M. smithii as a model organism for pathogenicity by association. The sources, mechanisms and dynamics of in utero and lifespan M. smithii acquisition, its diversity, and its susceptibility to molecules of environmental, veterinary, and medical interest still have to be deeply investigated, as only four strains of M. smithii are available in microbial collections, despite the pivotal role this neglected microorganism plays in microbiota physiology and pathologies.
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Affiliation(s)
- Ihab Malat
- IHU Méditerranée Infection, Marseille, France
- Aix-Marseille-Université, MEPHI, IHU Méditerranée Infection, France
| | - Michel Drancourt
- IHU Méditerranée Infection, Marseille, France
- Aix-Marseille-Université, MEPHI, IHU Méditerranée Infection, France
| | - Ghiles Grine
- IHU Méditerranée Infection, Marseille, France
- Aix-Marseille-Université, MEPHI, IHU Méditerranée Infection, France
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3
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Tian F, Wang Y, Qian ZM, Ran S, Zhang Z, Wang C, McMillin SE, Chavan NR, Lin H. Plasma metabolomic signature of healthy lifestyle, structural brain reserve and risk of dementia. Brain 2024:awae257. [PMID: 39324695 DOI: 10.1093/brain/awae257] [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: 04/04/2024] [Revised: 06/18/2024] [Accepted: 07/18/2024] [Indexed: 09/27/2024] Open
Abstract
Although the association between healthy lifestyle and dementia risk has been documented, the relationship between a metabolic signature indicative of healthy lifestyle and dementia risk and the mediating role of structural brain impairment remain unknown. We retrieved 136 628 dementia-free participants from UK Biobank. Elastic net regression was used to obtain a metabolic signature that represented lifestyle behaviours. Cox proportional hazard models were fitted to explore the associations of lifestyle-associated metabolic signature with incident dementia. Causal associations between identified metabolites and dementia were investigated using Mendelian randomization. Mediation analysis was also conducted to uncover the potential mechanisms involving 19 imaging-derived phenotypes (brain volume, grey matter volume, white matter volume and regional grey matter volumes). During a follow-up of 12.55 years, 1783 incident cases of all-cause dementia were identified, including 725 cases of Alzheimer's dementia and 418 cases of vascular dementia. We identified 83 metabolites that could represent healthy lifestyle behaviours using elastic net regression. The metabolic signature was associated with a lower dementia risk, and for each standard deviation increment in metabolic signature, the hazard ratio was 0.89 [95% confidence interval (CI): 0.85, 0.93] for all-cause dementia, 0.95 (95% CI: 0.88, 1.03) for Alzheimer's dementia and 0.84 (95% CI: 0.77, 0.91) for vascular dementia. Mendelian randomization revealed potential causal associations between the identified metabolites and risk of dementia. In addition, the specific structural brain reserve, including the hippocampus, grey matter in the hippocampus, parahippocampal gyrus and middle temporal gyrus, were detected to mediate the effects of metabolic signature on dementia risk (mediated proportion ranging from 6.21% to 11.98%). The metabolic signature associated with a healthy lifestyle is inversely associated with dementia risk, and greater structural brain reserve plays an important role in mediating this relationship. These findings have significant implications for understanding the intricate connections between lifestyle, metabolism and brain health.
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Affiliation(s)
- Fei Tian
- Department of Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China
| | - Yuhua Wang
- Department of Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China
| | - Zhengmin Min Qian
- Department of Epidemiology and Biostatistics, College for Public Health & Social Justice, Saint Louis University, Saint Louis, MO 63104, USA
| | - Shanshan Ran
- Department of Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China
| | - Zilong Zhang
- Department of Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China
| | - Chongjian Wang
- Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou 450001, China
| | | | - Niraj R Chavan
- Department of Obstetrics, Gynecology and Women's Health, School of Medicine Saint Louis University, Saint Louis, MO 63117, USA
| | - Hualiang Lin
- Department of Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China
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4
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Mardinoglu A, Palsson BØ. Genome-scale models in human metabologenomics. Nat Rev Genet 2024:10.1038/s41576-024-00768-0. [PMID: 39300314 DOI: 10.1038/s41576-024-00768-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/29/2024] [Indexed: 09/22/2024]
Abstract
Metabologenomics integrates metabolomics with other omics data types to comprehensively study the genetic and environmental factors that influence metabolism. These multi-omics data can be incorporated into genome-scale metabolic models (GEMs), which are highly curated knowledge bases that explicitly account for genes, transcripts, proteins and metabolites. By including all known biochemical reactions catalysed by enzymes and transporters encoded in the human genome, GEMs analyse and predict the behaviour of complex metabolic networks. Continued advancements to the scale and scope of GEMs - from cells and tissues to microbiomes and the whole body - have helped to design effective treatments and develop better diagnostic tools for metabolic diseases. Furthermore, increasing amounts of multi-omics data are incorporated into GEMs to better identify the underlying mechanisms, biomarkers and potential drug targets of metabolic diseases.
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Affiliation(s)
- Adil Mardinoglu
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden.
- Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral and Craniofacial Sciences, King's College London, London, UK.
| | - Bernhard Ø Palsson
- Bioinformatics and Systems Biology Program, University of California, San Diego, La Jolla, CA, USA.
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA.
- Department of Paediatrics, University of California, San Diego, La Jolla, CA, USA.
- Center for Microbiome Innovation, University of California, San Diego, La Jolla, CA, USA.
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kongens Lyngby, Denmark.
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Majzoub ME, Luu LDW, Haifer C, Paramsothy S, Borody TJ, Leong RW, Thomas T, Kaakoush NO. Refining microbial community metabolic models derived from metagenomics using reference-based taxonomic profiling. mSystems 2024; 9:e0074624. [PMID: 39136455 PMCID: PMC11406951 DOI: 10.1128/msystems.00746-24] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2024] [Accepted: 07/10/2024] [Indexed: 09/18/2024] Open
Abstract
Characterization of microbial community metabolic output is crucial to understanding their functions. Construction of genome-scale metabolic models from metagenome-assembled genomes (MAG) has enabled prediction of metabolite production by microbial communities, yet little is known about their accuracy. Here, we examined the performance of two approaches for metabolite prediction from metagenomes, one that is MAG-guided and another that is taxonomic reference-guided. We applied both on shotgun metagenomics data from human and environmental samples, and validated findings in the human samples using untargeted metabolomics. We found that in human samples, where taxonomic profiling is optimized and reference genomes are readily available, when number of input taxa was normalized, the reference-guided approach predicted more metabolites than the MAG-guided approach. The two approaches showed significant overlap but each identified metabolites not predicted in the other. Pathway enrichment analyses identified significant differences in inferences derived from data based on the approach, highlighting the need for caution in interpretation. In environmental samples, when the number of input taxa was normalized, the reference-guided approach predicted more metabolites than the MAG-guided approach for total metabolites in both sample types and non-redundant metabolites in seawater samples. Nonetheless, as was observed for the human samples, the approaches overlapped substantially but also predicted metabolites not observed in the other. Our findings report on utility of a complementary input to genome-scale metabolic model construction that is less computationally intensive forgoing MAG assembly and refinement, and that can be applied on shallow shotgun sequencing where MAGs cannot be generated.IMPORTANCELittle is known about the accuracy of genome-scale metabolic models (GEMs) of microbial communities despite their influence on inferring community metabolic outputs and culture conditions. The performance of GEMs for metabolite prediction from metagenomes was assessed by applying two approaches on shotgun metagenomics data from human and environmental samples, and validating findings in the human samples using untargeted metabolomics. The performance of the approach was found to be dependent on sample type, but collectively, the reference-guided approach predicted more metabolites than the MAG-guided approach. Despite the differences, the predictions from the approaches overlapped substantially but each identified metabolites not predicted in the other. We found significant differences in biological inferences based on the approach, with some examples of uniquely enriched pathways in one group being invalidated when using the alternative approach, highlighting the need for caution in interpretation of GEMs.
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Affiliation(s)
- Marwan E Majzoub
- School of Biomedical Sciences, Faculty of Medicine and Health, UNSW Sydney, Sydney, New South Wales, Australia
| | - Laurence D W Luu
- School of Biomedical Sciences, Faculty of Medicine and Health, UNSW Sydney, Sydney, New South Wales, Australia
| | - Craig Haifer
- School of Clinical Medicine, Faculty of Medicine and Health, UNSW Sydney, Sydney, New South Wales, Australia
- Department of Gastroenterology, St. Vincent's Hospital, Sydney, New South Wales, Australia
| | - Sudarshan Paramsothy
- Concord Clinical School, University of Sydney, Sydney, New South Wales, Australia
- Department of Gastroenterology, Concord Repatriation General Hospital, Sydney, New South Wales, Australia
| | - Thomas J Borody
- Centre for Digestive Diseases, Sydney, New South Wales, Australia
| | - Rupert W Leong
- Concord Clinical School, University of Sydney, Sydney, New South Wales, Australia
- Department of Gastroenterology, Concord Repatriation General Hospital, Sydney, New South Wales, Australia
| | - Torsten Thomas
- Centre for Marine Science and Innovation, School of Biological, Earth and Environmental Sciences, Faculty of Science, UNSW Sydney, Sydney, New South Wales, Australia
| | - Nadeem O Kaakoush
- School of Biomedical Sciences, Faculty of Medicine and Health, UNSW Sydney, Sydney, New South Wales, Australia
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6
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Lee S, Meslier V, Bidkhori G, Garcia-Guevara F, Etienne-Mesmin L, Clasen F, Park J, Plaza Oñate F, Cai H, Le Chatelier E, Pons N, Pereira M, Seifert M, Boulund F, Engstrand L, Lee D, Proctor G, Mardinoglu A, Blanquet-Diot S, Moyes D, Almeida M, Ehrlich SD, Uhlen M, Shoaie S. Transient colonizing microbes promote gut dysbiosis and functional impairment. NPJ Biofilms Microbiomes 2024; 10:80. [PMID: 39245657 PMCID: PMC11381545 DOI: 10.1038/s41522-024-00561-1] [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: 11/20/2023] [Accepted: 08/27/2024] [Indexed: 09/10/2024] Open
Abstract
Species composition of the healthy adult gut microbiota tends to be stable over time. Destabilization of the gut microbiome under the influence of different factors is the main driver of the microbial dysbiosis and subsequent impacts on host physiology. Here, we used metagenomics data from a Swedish longitudinal cohort, to determine the stability of the gut microbiome and uncovered two distinct microbial species groups; persistent colonizing species (PCS) and transient colonizing species (TCS). We validated the continuation of this grouping, generating gut metagenomics data for additional time points from the same Swedish cohort. We evaluated the existence of PCS/TCS across different geographical regions and observed they are globally conserved features. To characterize PCS/TCS phenotypes, we performed bioreactor fermentation with faecal samples and metabolic modeling. Finally, using chronic disease gut metagenome and other multi-omics data, we identified roles of TCS in microbial dysbiosis and link with abnormal changes to host physiology.
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Affiliation(s)
- Sunjae Lee
- Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King's College London, London, SE1 9RT, UK
- School of Life Sciences, Gwangju Institute of Science and Technology, Jouy-en-Josas, 61005, Republic of Korea
| | - Victoria Meslier
- University Paris-Saclay, INRAE, MetaGenoPolis, 78350, Jouy-en-Josas, France
| | - Gholamreza Bidkhori
- Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King's College London, London, SE1 9RT, UK
| | - Fernando Garcia-Guevara
- Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King's College London, London, SE1 9RT, UK
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, SE-171 21, Sweden
| | - Lucie Etienne-Mesmin
- Université Clermont Auvergne, INRAE, UMR 454 MEDIS, 28 place Henri Dunant, F-63000, Clermont-Ferrand, France
| | - Frederick Clasen
- Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King's College London, London, SE1 9RT, UK
| | - Junseok Park
- Department of Bio and Brain Engineering, KAIST, 291 Daehak-ro, Yuseong-gu, Daejeon, 305-701, Republic of Korea
| | | | - Haizhuang Cai
- Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King's College London, London, SE1 9RT, UK
| | | | - Nicolas Pons
- University Paris-Saclay, INRAE, MetaGenoPolis, 78350, Jouy-en-Josas, France
| | - Marcela Pereira
- Centre for Translational Microbiome Research, Department of Microbiology, Tumour and Cell Biology, Karolinska Institutet, Stockholm, SE-171 77, Sweden
| | - Maike Seifert
- Centre for Translational Microbiome Research, Department of Microbiology, Tumour and Cell Biology, Karolinska Institutet, Stockholm, SE-171 77, Sweden
| | - Fredrik Boulund
- Centre for Translational Microbiome Research, Department of Microbiology, Tumour and Cell Biology, Karolinska Institutet, Stockholm, SE-171 77, Sweden
| | - Lars Engstrand
- Centre for Translational Microbiome Research, Department of Microbiology, Tumour and Cell Biology, Karolinska Institutet, Stockholm, SE-171 77, Sweden
| | - Doheon Lee
- Department of Bio and Brain Engineering, KAIST, 291 Daehak-ro, Yuseong-gu, Daejeon, 305-701, Republic of Korea
| | - Gordon Proctor
- Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King's College London, London, SE1 9RT, UK
| | - Adil Mardinoglu
- Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King's College London, London, SE1 9RT, UK
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, SE-171 21, Sweden
| | - Stéphanie Blanquet-Diot
- Université Clermont Auvergne, INRAE, UMR 454 MEDIS, 28 place Henri Dunant, F-63000, Clermont-Ferrand, France
| | - David Moyes
- Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King's College London, London, SE1 9RT, UK
| | - Mathieu Almeida
- University Paris-Saclay, INRAE, MetaGenoPolis, 78350, Jouy-en-Josas, France
| | - S Dusko Ehrlich
- University Paris-Saclay, INRAE, MetaGenoPolis, 78350, Jouy-en-Josas, France
| | - Mathias Uhlen
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, SE-171 21, Sweden
| | - Saeed Shoaie
- Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King's College London, London, SE1 9RT, UK.
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, SE-171 21, Sweden.
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Bertorello S, Cei F, Fink D, Niccolai E, Amedei A. The Future Exploring of Gut Microbiome-Immunity Interactions: From In Vivo/Vitro Models to In Silico Innovations. Microorganisms 2024; 12:1828. [PMID: 39338502 PMCID: PMC11434319 DOI: 10.3390/microorganisms12091828] [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: 08/14/2024] [Revised: 08/29/2024] [Accepted: 09/02/2024] [Indexed: 09/30/2024] Open
Abstract
Investigating the complex interactions between microbiota and immunity is crucial for a fruitful understanding progress of human health and disease. This review assesses animal models, next-generation in vitro models, and in silico approaches that are used to decipher the microbiome-immunity axis, evaluating their strengths and limitations. While animal models provide a comprehensive biological context, they also raise ethical and practical concerns. Conversely, modern in vitro models reduce animal involvement but require specific costs and materials. When considering the environmental impact of these models, in silico approaches emerge as promising for resource reduction, but they require robust experimental validation and ongoing refinement. Their potential is significant, paving the way for a more sustainable and ethical future in microbiome-immunity research.
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Affiliation(s)
- Sara Bertorello
- Department of Experimental and Clinical Medicine, University of Florence, 50139 Florence, Italy; (S.B.); (F.C.); (D.F.); (A.A.)
| | - Francesco Cei
- Department of Experimental and Clinical Medicine, University of Florence, 50139 Florence, Italy; (S.B.); (F.C.); (D.F.); (A.A.)
| | - Dorian Fink
- Department of Experimental and Clinical Medicine, University of Florence, 50139 Florence, Italy; (S.B.); (F.C.); (D.F.); (A.A.)
| | - Elena Niccolai
- Department of Experimental and Clinical Medicine, University of Florence, 50139 Florence, Italy; (S.B.); (F.C.); (D.F.); (A.A.)
- Laboratorio Congiunto MIA-LAB (Microbiome-Immunity Axis Research for a Circular Health), University of Florence, 50134 Florence, Italy
| | - Amedeo Amedei
- Department of Experimental and Clinical Medicine, University of Florence, 50139 Florence, Italy; (S.B.); (F.C.); (D.F.); (A.A.)
- Laboratorio Congiunto MIA-LAB (Microbiome-Immunity Axis Research for a Circular Health), University of Florence, 50134 Florence, Italy
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Zaunseder E, Mütze U, Okun JG, Hoffmann GF, Kölker S, Heuveline V, Thiele I. Personalized metabolic whole-body models for newborns and infants predict growth and biomarkers of inherited metabolic diseases. Cell Metab 2024; 36:1882-1897.e7. [PMID: 38834070 DOI: 10.1016/j.cmet.2024.05.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/20/2023] [Revised: 03/13/2024] [Accepted: 05/09/2024] [Indexed: 06/06/2024]
Abstract
Comprehensive whole-body models (WBMs) accounting for organ-specific dynamics have been developed to simulate adult metabolism, but such models do not exist for infants. Here, we present a resource of 360 organ-resolved, sex-specific models of newborn and infant metabolism (infant-WBMs) spanning the first 180 days of life. These infant-WBMs were parameterized to represent the distinct metabolic characteristics of newborns and infants, including nutrition, energy requirements, and thermoregulation. We demonstrate that the predicted infant growth was consistent with the recommendation by the World Health Organization. We assessed the infant-WBMs' reliability and capabilities for personalization by simulating 10,000 newborns based on their blood metabolome and birth weight. Furthermore, the infant-WBMs accurately predicted changes in known biomarkers over time and metabolic responses to treatment strategies for inherited metabolic diseases. The infant-WBM resource holds promise for personalized medicine, as the infant-WBMs could be a first step to digital metabolic twins for newborn and infant metabolism.
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Affiliation(s)
- Elaine Zaunseder
- School of Medicine, University of Galway, Galway, Ireland; Engineering Mathematics and Computing Lab (EMCL), Interdisciplinary Center for Scientific Computing (IWR), Heidelberg University, Heidelberg, Germany; Data Mining and Uncertainty Quantification (DMQ), Heidelberg Institute for Theoretical Studies (HITS), Heidelberg, Germany
| | - Ulrike Mütze
- Division of Child Neurology and Metabolic Medicine, Center for Child and Adolescent Medicine, Heidelberg University, Medical Faculty, Heidelberg, Germany
| | - Jürgen G Okun
- Division of Child Neurology and Metabolic Medicine, Center for Child and Adolescent Medicine, Heidelberg University, Medical Faculty, Heidelberg, Germany
| | - Georg F Hoffmann
- Division of Child Neurology and Metabolic Medicine, Center for Child and Adolescent Medicine, Heidelberg University, Medical Faculty, Heidelberg, Germany
| | - Stefan Kölker
- Division of Child Neurology and Metabolic Medicine, Center for Child and Adolescent Medicine, Heidelberg University, Medical Faculty, Heidelberg, Germany
| | - Vincent Heuveline
- School of Medicine, University of Galway, Galway, Ireland; Engineering Mathematics and Computing Lab (EMCL), Interdisciplinary Center for Scientific Computing (IWR), Heidelberg University, Heidelberg, Germany
| | - Ines Thiele
- School of Medicine, University of Galway, Galway, Ireland; Discipline of Microbiology, University of Galway, Galway, Ireland; Digital Metabolic Twin Centre, University of Galway, Ireland; Ryan Institute, University of Galway, Galway, Ireland; APC Microbiome Ireland, Cork, Ireland.
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9
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Wu H, Wei J, Wang S, Chen L, Zhang J, Wang N, Tan X. Dietary pattern modifies the risk of MASLD through metabolomic signature. JHEP Rep 2024; 6:101133. [PMID: 39081700 PMCID: PMC11286987 DOI: 10.1016/j.jhepr.2024.101133] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/26/2024] [Revised: 05/24/2024] [Accepted: 05/30/2024] [Indexed: 08/02/2024] Open
Abstract
Background & Aims The EAT-Lancet Commission in 2019 advocated a plant-centric diet for health and environmental benefits, but its relation to metabolic dysfunction-associated steatotic liver disease (MASLD) is unclear. We aimed to discover the metabolite profile linked to the EAT-Lancet diet and its association with MASLD risk, considering genetic predisposition. Methods We analyzed data from 105,752 UK Biobank participants with detailed dietary and metabolomic information. We constructed an EAT-Lancet diet index and derived a corresponding metabolomic signature through elastic net regression. A weighted polygenic risk score for MASLD was computed from associated risk variants. The Cox proportional hazards model was employed to estimate hazard ratios (HRs) and 95% CIs for the risk of MASLD (defined as hospital admission or death). Results During a median follow-up period of 11.6 years, 1,138 cases of MASLD were documented. Participants in the highest group for the EAT-Lancet diet index had a multivariable HR of 0.79 (95% CI 0.66-0.95) for MASLD compared to the lowest group. The diet's impact was unaffected by genetic predisposition to MASLD (p = 0.42). Moreover, a robust correlation was found between the metabolomic signature and the EAT-Lancet diet index (Pearson r = 0.29; p <0.0001). Participants in the highest group for the metabolomic signature had a multivariable HR of 0.46 (95% CI 0.37-0.58) for MASLD, in comparison to those in the lowest group. Conclusions Higher intake of the EAT-Lancet diet and its associated metabolite signature are both linked to a reduced risk of MASLD, independently of traditional risk factors. Impact and implications Our analysis leveraging the UK Biobank study showed higher adherence to the EAT-Lancet diet was associated with a reduced risk of metabolic dysfunction-associated steatotic liver disease (MASLD). We identified a unique metabolite signature comprising 81 metabolites associated with the EAT-Lancet diet, potentially underlying the diet's protective mechanism against MASLD. These findings suggest the EAT-Lancet diet may offer substantial protective benefits against MASLD.
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Affiliation(s)
- Hanzhang Wu
- Department of Big Data in Health Science, Zhejiang University School of Public Health, Hangzhou, China. Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
- The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Hangzhou, China
| | - Jiahe Wei
- Department of Big Data in Health Science, Zhejiang University School of Public Health, Hangzhou, China. Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
- The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Hangzhou, China
| | - Shuai Wang
- Department of Big Data in Health Science, Zhejiang University School of Public Health, Hangzhou, China. Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
- The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Hangzhou, China
| | - Liangkai Chen
- Department of Nutrition and Food Hygiene, Hubei Key Laboratory of Food Nutrition and Safety, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Jihui Zhang
- Center for Sleep and Circadian Medicine, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, China; Department of Psychiatry, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Ningjian Wang
- Institute and Department of Endocrinology and Metabolism, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Xiao Tan
- Department of Big Data in Health Science, Zhejiang University School of Public Health, Hangzhou, China. Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
- The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Hangzhou, China
- Department of Medical Sciences, Uppsala University, Uppsala, Sweden
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10
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Dinakis E, O'Donnell JA, Marques FZ. The gut-immune axis during hypertension and cardiovascular diseases. Acta Physiol (Oxf) 2024; 240:e14193. [PMID: 38899764 DOI: 10.1111/apha.14193] [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: 02/02/2024] [Revised: 05/04/2024] [Accepted: 06/06/2024] [Indexed: 06/21/2024]
Abstract
The gut-immune axis is a relatively novel phenomenon that provides mechanistic links between the gut microbiome and the immune system. A growing body of evidence supports it is key in how the gut microbiome contributes to several diseases, including hypertension and cardiovascular diseases (CVDs). Evidence over the past decade supports a causal link of the gut microbiome in hypertension and its complications, including myocardial infarction, atherosclerosis, heart failure, and stroke. Perturbations in gut homeostasis such as dysbiosis (i.e., alterations in gut microbial composition) may trigger immune responses that lead to chronic low-grade inflammation and, ultimately, the development and progression of these conditions. This is unsurprising, as the gut harbors one of the largest numbers of immune cells in the body, yet is a phenomenon not entirely understood in the context of cardiometabolic disorders. In this review, we discuss the role of the gut microbiome, the immune system, and inflammation in the context of hypertension and CVD, and consolidate current evidence of this complex interplay, whilst highlighting gaps in the literature. We focus on diet as one of the major modulators of the gut microbiota, and explain key microbial-derived metabolites (e.g., short-chain fatty acids, trimethylamine N-oxide) as potential mediators of the communication between the gut and peripheral organs such as the heart, arteries, kidneys, and the brain via the immune system. Finally, we explore the dual role of both the gut microbiome and the immune system, and how they work together to not only contribute, but also mitigate hypertension and CVD.
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Affiliation(s)
- Evany Dinakis
- Hypertension Research Laboratory, School of Biological Sciences, Monash University, Melbourne, Victoria, Australia
| | - Joanne A O'Donnell
- Hypertension Research Laboratory, School of Biological Sciences, Monash University, Melbourne, Victoria, Australia
| | - Francine Z Marques
- Hypertension Research Laboratory, School of Biological Sciences, Monash University, Melbourne, Victoria, Australia
- Heart Failure Research Group, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia
- Victorian Heart Institute, Monash University, Melbourne, Victoria, Australia
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11
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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 modelling predicts personalized short-chain fatty acid production profiles in the human gut. Nat Microbiol 2024; 9:1700-1712. [PMID: 38914826 DOI: 10.1038/s41564-024-01728-4] [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: 03/09/2023] [Accepted: 05/09/2024] [Indexed: 06/26/2024]
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. Here we use a microbial community-scale metabolic modelling (MCMM) approach to predict individual-specific SCFA production profiles to assess the impact of different dietary, prebiotic and probiotic inputs. We evaluate the quantitative accuracy of our MCMMs using in vitro and ex vivo data, plus published human cohort data. We find that 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 aimed at optimizing SCFA production in the gut. Our model represents an approach to direct gut microbiome engineering for precision health and nutrition.
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Affiliation(s)
- Nick Quinn-Bohmann
- Institute for Systems Biology, Seattle, WA, USA
- Molecular Engineering Graduate Program, University of Washington, Seattle, WA, USA
| | | | | | - Lisa Levy
- Fred Hutchinson Cancer Center, Seattle, WA, USA
| | | | - Thomas Gurry
- Pharmaceutical Biochemistry Group, School of Pharmaceutical Sciences, University of Geneva, Geneva, Switzerland
- Myota GmbH, Berlin, Germany
| | - Noa Rappaport
- Center for Phenomic Health, Buck Institute for Research on Aging, Novato, CA, USA
| | - Erin M Ostrem
- Department of Biochemistry, University of Wisconsin-Madison, Madison, WI, USA
| | - Ophelia S Venturelli
- Department of Biochemistry, University of Wisconsin-Madison, Madison, WI, USA
- Department of Biomedical Engineering, University of Wisconsin-Madison, Madison, WI, USA
- Department of Chemical & Biological Engineering, University of Wisconsin-Madison, Madison, WI, USA
- Department of Bacteriology, University of Wisconsin-Madison, Madison, WI, USA
| | - Christian Diener
- Institute for Systems Biology, Seattle, WA, USA.
- Diagnostic and Research Institute of Hygiene, Microbiology and Environmental Medicine, Medical University of Graz, Graz, Austria.
| | - Sean M Gibbons
- Institute for Systems Biology, Seattle, WA, USA.
- Molecular Engineering Graduate Program, University of Washington, Seattle, WA, USA.
- Department of Bioengineering, University of Washington, Seattle, WA, USA.
- Department of Genome Sciences, University of Washington, Seattle, WA, USA.
- eScience Institute, University of Washington, Seattle, WA, USA.
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12
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Kim N, Ma J, Kim W, Kim J, Belenky P, Lee I. Genome-resolved metagenomics: a game changer for microbiome medicine. Exp Mol Med 2024; 56:1501-1512. [PMID: 38945961 PMCID: PMC11297344 DOI: 10.1038/s12276-024-01262-7] [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: 12/13/2023] [Revised: 03/06/2024] [Accepted: 03/25/2024] [Indexed: 07/02/2024] Open
Abstract
Recent substantial evidence implicating commensal bacteria in human diseases has given rise to a new domain in biomedical research: microbiome medicine. This emerging field aims to understand and leverage the human microbiota and derivative molecules for disease prevention and treatment. Despite the complex and hierarchical organization of this ecosystem, most research over the years has relied on 16S amplicon sequencing, a legacy of bacterial phylogeny and taxonomy. Although advanced sequencing technologies have enabled cost-effective analysis of entire microbiota, translating the relatively short nucleotide information into the functional and taxonomic organization of the microbiome has posed challenges until recently. In the last decade, genome-resolved metagenomics, which aims to reconstruct microbial genomes directly from whole-metagenome sequencing data, has made significant strides and continues to unveil the mysteries of various human-associated microbial communities. There has been a rapid increase in the volume of whole metagenome sequencing data and in the compilation of novel metagenome-assembled genomes and protein sequences in public depositories. This review provides an overview of the capabilities and methods of genome-resolved metagenomics for studying the human microbiome, with a focus on investigating the prokaryotic microbiota of the human gut. Just as decoding the human genome and its variations marked the beginning of the genomic medicine era, unraveling the genomes of commensal microbes and their sequence variations is ushering us into the era of microbiome medicine. Genome-resolved metagenomics stands as a pivotal tool in this transition and can accelerate our journey toward achieving these scientific and medical milestones.
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Affiliation(s)
- Nayeon Kim
- Department of Biotechnology, College of Life Science and Biotechnology, Yonsei University, Seoul, 03722, Republic of Korea
| | - Junyeong Ma
- Department of Biotechnology, College of Life Science and Biotechnology, Yonsei University, Seoul, 03722, Republic of Korea
| | - Wonjong Kim
- Department of Biotechnology, College of Life Science and Biotechnology, Yonsei University, Seoul, 03722, Republic of Korea
| | - Jungyeon Kim
- Department of Biotechnology, College of Life Science and Biotechnology, Yonsei University, Seoul, 03722, Republic of Korea
| | - Peter Belenky
- Department of Molecular Microbiology and Immunology, Brown University, Providence, RI, 02912, USA.
| | - Insuk Lee
- Department of Biotechnology, College of Life Science and Biotechnology, Yonsei University, Seoul, 03722, Republic of Korea.
- POSTECH Biotech Center, Pohang University of Science and Technology (POSTECH), Pohang, 37673, Republic of Korea.
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13
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Tian S, Liao X, Chen S, Wu Y, Chen M. Genetic association of the gut microbiota with epigenetic clocks mediated by inflammatory cytokines: a Mendelian randomization analysis. Front Immunol 2024; 15:1339722. [PMID: 38903525 PMCID: PMC11186987 DOI: 10.3389/fimmu.2024.1339722] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2023] [Accepted: 05/24/2024] [Indexed: 06/22/2024] Open
Abstract
Background A new aging biomarker epigenetic clock has been developed. There exists a close link between aging and gut microbiota, which may be mediated by inflammatory cytokines. However, the relationship between the epigenetic clock, gut microbiota, and the mediating substances is unclear. Methods Two large genome-wide association meta-analyses were analyzed by two-sample Mendelian randomization. The results between gut microbiota and epigenetic clock were investigated using the four methods (Inverse variance weighted, MR-Egger, weighted median, MR-PRESSO). Genetic correlation was measured by Linked disequilibrium score regression (LDSC). The correctness of the study direction was checked by the Steiger test. Cochran's Q statistic and MR-Egger intercept were used as sensitivity analyses of the study. The two-step method was used to examine the mediating role of inflammatory cytokines. We use the Benjamini-Hochberg correction method to correct the P value. Results After FDR correction, multiple bacterial genera were significantly or suggestively associated with four epigenetic clocks (GrimAge, HannumAge, IEAA, PhenoAge). And we detected several inflammatory factors acting as mediators of gut microbiota and epigenetic clocks. Conclusion This study provides genetic evidence for a positive and negative link between gut microbiota and aging risk. We hope that by elucidating the genetic relationship and potential mechanisms between aging and gut microbiota, we will provide new avenues for continuing aging-related research and treatment.
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Affiliation(s)
- Siyu Tian
- School of Clinical Medicine, Chengdu University of Traditional Chinese Medicine (TCM), Chengdu, China
| | - Xingyu Liao
- School of Clinical Medicine, Chengdu University of Traditional Chinese Medicine (TCM), Chengdu, China
| | - Siqi Chen
- School of Clinical Medicine, Chengdu University of Traditional Chinese Medicine (TCM), Chengdu, China
| | - Yu Wu
- School of Clinical Medicine, Chengdu University of Traditional Chinese Medicine (TCM), Chengdu, China
| | - Min Chen
- Department of Colorectal Surgery, Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, China
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14
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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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15
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Sen P, Prandovszky E, Honkanen JK, Chen O, Yolken R, Suvisaari J. Dysregulation of Microbiota in Patients With First-Episode Psychosis Is Associated With Symptom Severity and Treatment Response. Biol Psychiatry 2024; 95:370-379. [PMID: 38061464 DOI: 10.1016/j.biopsych.2023.10.024] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/22/2023] [Revised: 08/21/2023] [Accepted: 10/23/2023] [Indexed: 01/16/2024]
Abstract
BACKGROUND The gut microbiome has been implicated in the pathogenesis of mental disorders where the gut-brain axis acts as a bidirectional communication network. METHODS Herein, we investigated the compositional and functional differences of gut microbiome between patients with first-episode psychosis (FEP) (n = 26) and healthy control participants (n = 22) using whole-genome shotgun sequencing. In addition, we assessed the oral microbiome in patients with FEP (n = 13) and listed their taxonomic diversity. RESULTS Our findings suggest that there is a dysbiosis of gut microbiota in patients with FEP. Relative abundance of Bifidobacterium adolescentis, Prevotella copri, and Turicibacter sanguinis was markedly increased (linear discriminant analysis scores [log10] > 1, and Mann-Whitney U test; false discovery rate-adjusted p values < .05) in the FEP group compared with the healthy control participants. Pathway analysis indicated that several metabolic pathways, particularly deoxyribonucleotide biosynthesis, branched-chain amino acid biosynthesis, tricarboxylic acid cycle, and fatty acid elongation and biosynthesis, were dysregulated in the FEP group compared with the healthy control group. In addition, this preliminary study was able to identify specific gut microbes (at baseline) that were predictive of weight gain in the FEP group at a 1-year follow-up. Bacteroides dorei, Bifidobacterium adolescentis, Turicibacter sanguinis, Roseburia spp., and Ruminococcus lactaris were positively associated (eXtreme gradient boosting, XGBoost regression model, Shapley additive explanations, R2 = 0.82) with weight gain. CONCLUSIONS Our findings may suggest the involvement of gut microbiota in the pathogenesis of psychosis. The benefit of modulation of the gut microbiome in the treatment of psychotic disorders should be explored further.
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Affiliation(s)
- Partho Sen
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland
| | - Emese Prandovszky
- Stanley Division of Developmental Neurovirology, Department of Pediatrics, Johns Hopkins School of Medicine, Baltimore, Maryland
| | - Jarno K Honkanen
- Translational Immunology Program, Research Programs Unit, University of Helsinki, Helsinki, Finland
| | - Ou Chen
- Stanley Division of Developmental Neurovirology, Department of Pediatrics, Johns Hopkins School of Medicine, Baltimore, Maryland
| | - Robert Yolken
- Stanley Division of Developmental Neurovirology, Department of Pediatrics, Johns Hopkins School of Medicine, Baltimore, Maryland
| | - Jaana Suvisaari
- Research Program for Clinical and Molecular Metabolism, University of Helsinki, Helsinki, Finland; Finnish Institute for Health and Welfare, Helsinki, Finland.
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16
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Shtossel O, Koren O, Shai I, Rinott E, Louzoun Y. Gut microbiome-metabolome interactions predict host condition. MICROBIOME 2024; 12:24. [PMID: 38336867 PMCID: PMC10858481 DOI: 10.1186/s40168-023-01737-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/04/2023] [Accepted: 12/10/2023] [Indexed: 02/12/2024]
Abstract
BACKGROUND The effect of microbes on their human host is often mediated through changes in metabolite concentrations. As such, multiple tools have been proposed to predict metabolite concentrations from microbial taxa frequencies. Such tools typically fail to capture the dependence of the microbiome-metabolite relation on the environment. RESULTS We propose to treat the microbiome-metabolome relation as the equilibrium of a complex interaction and to relate the host condition to a latent representation of the interaction between the log concentration of the metabolome and the log frequencies of the microbiome. We develop LOCATE (Latent variables Of miCrobiome And meTabolites rElations), a machine learning tool to predict the metabolite concentration from the microbiome composition and produce a latent representation of the interaction. This representation is then used to predict the host condition. LOCATE's accuracy in predicting the metabolome is higher than all current predictors. The metabolite concentration prediction accuracy significantly decreases cross datasets, and cross conditions, especially in 16S data. LOCATE's latent representation predicts the host condition better than either the microbiome or the metabolome. This representation is strongly correlated with host demographics. A significant improvement in accuracy (0.793 vs. 0.724 average accuracy) is obtained even with a small number of metabolite samples ([Formula: see text]). CONCLUSION These results suggest that a latent representation of the microbiome-metabolome interaction leads to a better association with the host condition than any of the two separated or the simple combination of the two. Video Abstract.
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Affiliation(s)
- Oshrit Shtossel
- Department of Mathematics, Bar-Ilan University, Ramat Gan, 52900, Israel
| | - Omry Koren
- The Azrieli Faculty of Medicine, Bar-Ilan University, Safed, Israel
| | - Iris Shai
- Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer-Sheva, Israel
| | - Ehud Rinott
- Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer-Sheva, Israel
| | - Yoram Louzoun
- Department of Mathematics, Bar-Ilan University, Ramat Gan, 52900, Israel.
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17
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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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18
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Zhu H, Hao H, Yu L. Identifying disease-related microbes based on multi-scale variational graph autoencoder embedding Wasserstein distance. BMC Biol 2023; 21:294. [PMID: 38115088 PMCID: PMC10731776 DOI: 10.1186/s12915-023-01796-8] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2023] [Accepted: 12/05/2023] [Indexed: 12/21/2023] Open
Abstract
BACKGROUND Enormous clinical and biomedical researches have demonstrated that microbes are crucial to human health. Identifying associations between microbes and diseases can not only reveal potential disease mechanisms, but also facilitate early diagnosis and promote precision medicine. Due to the data perturbation and unsatisfactory latent representation, there is a significant room for improvement. RESULTS In this work, we proposed a novel framework, Multi-scale Variational Graph AutoEncoder embedding Wasserstein distance (MVGAEW) to predict disease-related microbes, which had the ability to resist data perturbation and effectively generate latent representations for both microbes and diseases from the perspective of distribution. First, we calculated multiple similarities and integrated them through similarity network confusion. Subsequently, we obtained node latent representations by improved variational graph autoencoder. Ultimately, XGBoost classifier was employed to predict potential disease-related microbes. We also introduced multi-order node embedding reconstruction to enhance the representation capacity. We also performed ablation studies to evaluate the contribution of each section of our model. Moreover, we conducted experiments on common drugs and case studies, including Alzheimer's disease, Crohn's disease, and colorectal neoplasms, to validate the effectiveness of our framework. CONCLUSIONS Significantly, our model exceeded other currently state-of-the-art methods, exhibiting a great improvement on the HMDAD database.
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Affiliation(s)
- Huan Zhu
- School of Computer Science and Technology, Xidian University, Xi'an, China
| | - Hongxia Hao
- School of Computer Science and Technology, Xidian University, Xi'an, China.
| | - Liang Yu
- School of Computer Science and Technology, Xidian University, Xi'an, China.
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19
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Lagoumintzis G, Patrinos GP. Triangulating nutrigenomics, metabolomics and microbiomics toward personalized nutrition and healthy living. Hum Genomics 2023; 17:109. [PMID: 38062537 PMCID: PMC10704648 DOI: 10.1186/s40246-023-00561-w] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Accepted: 12/02/2023] [Indexed: 12/18/2023] Open
Abstract
The unique physiological and genetic characteristics of individuals influence their reactions to different dietary constituents and nutrients. This notion is the foundation of personalized nutrition. The field of nutrigenetics has witnessed significant progress in understanding the impact of genetic variants on macronutrient and micronutrient levels and the individual's responsiveness to dietary intake. These variants hold significant value in facilitating the development of personalized nutritional interventions, thereby enabling the effective translation from conventional dietary guidelines to genome-guided nutrition. Nevertheless, certain obstacles could impede the extensive implementation of individualized nutrition, which is still in its infancy, such as the polygenic nature of nutrition-related pathologies. Consequently, many disorders are susceptible to the collective influence of multiple genes and environmental interplay, wherein each gene exerts a moderate to modest effect. Furthermore, it is widely accepted that diseases emerge because of the intricate interplay between genetic predisposition and external environmental influences. In the context of this specific paradigm, the utilization of advanced "omic" technologies, including epigenomics, transcriptomics, proteomics, metabolomics, and microbiome analysis, in conjunction with comprehensive phenotyping, has the potential to unveil hitherto undisclosed hereditary elements and interactions between genes and the environment. This review aims to provide up-to-date information regarding the fundamentals of personalized nutrition, specifically emphasizing the complex triangulation interplay among microbiota, dietary metabolites, and genes. Furthermore, it highlights the intestinal microbiota's unique makeup, its influence on nutrigenomics, and the tailoring of dietary suggestions. Finally, this article provides an overview of genotyping versus microbiomics, focusing on investigating the potential applications of this knowledge in the context of tailored dietary plans that aim to improve human well-being and overall health.
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Affiliation(s)
- George Lagoumintzis
- Division of Pharmacology and Biosciences, Department of Pharmacy, School of Health Sciences, University of Patras, 26504, Patras, Greece.
| | - George P Patrinos
- Division of Pharmacology and Biosciences, Department of Pharmacy, School of Health Sciences, University of Patras, 26504, Patras, Greece.
- Department of Genetics and Genomics, College of Medicine and Health Sciences, United Arab Emirates University, Al-Ain, Abu Dhabi, UAE.
- Zayed Center for Health Sciences, United Arab Emirates University, Al-Ain, Abu Dhabi, UAE.
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20
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Zhou H, Yu B, Sun J, Chen H, Liu Z, Ge L, Chen D. Comparison of maternal and neonatal gut microbial community and function in a porcine model. Anim Biotechnol 2023; 34:2972-2978. [PMID: 36165762 DOI: 10.1080/10495398.2022.2126367] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/01/2022]
Abstract
Our knowledge of the difference in maternal and neonatal gut microbiota composition is not fully understood. Using the Bama miniature pig model, the bacterial community in the feces from sows and piglets was analyzed on an IonS5TMXL platform targeting the single-end reads strategy. Results revealed that the maternal and neonatal bacteria profile in the pig model was distinct. Compared with the piglets, sows had higher proportions of bacteria in Spirochetes, Clostridiales, and Spirochaetales (p < 0.10) and had a lower abundance of bacteria in Tyzzerella (p < 0.05) and Alistipes (p < 0.10). Meanwhile, the proportions of bacteria in Oscillibacter and the index of Chao1, Shannon, and observed_species increased in the sows compared with those in the piglets (p < 0.05). Moreover, the abundance of bacteria associated with the human disease was higher (p < 0.05) and the population of bacteria associated with cellular processes was lower (p < 0.05) in the piglets compared with those in the sows. Collectively, the diversity and beneficial bacteria populations in the sow fecal microbiota exhibit more than those in the piglets. This study indicates that maternal fecal microbiota may be a beneficial source of transplanted bacteria to promote healthy function in neonates.
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Affiliation(s)
- Hua Zhou
- Key Laboratory of Animal Disease-Resistance Nutrition, Animal Nutrition Institute, Sichuan Agricultural University, Chengdu, China
- Key Laboratory of Animal Nutrition, College of Animal Science and Technology, Jiangxi Agricultural University, Nanchang, China
| | - Bing Yu
- Key Laboratory of Animal Disease-Resistance Nutrition, Animal Nutrition Institute, Sichuan Agricultural University, Chengdu, China
| | - Jing Sun
- Chongqing Academy of Animal Sciences, Rongchang, China
| | - Hong Chen
- College of Food Science, Sichuan Agricultural University, Ya'an, China
| | - Zuohua Liu
- Chongqing Academy of Animal Sciences, Rongchang, China
| | - Liangpeng Ge
- Chongqing Academy of Animal Sciences, Rongchang, China
| | - Daiwen Chen
- Key Laboratory of Animal Disease-Resistance Nutrition, Animal Nutrition Institute, Sichuan Agricultural University, Chengdu, China
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21
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Timofeeva AM, Galyamova MR, Sedykh SE. Plant Growth-Promoting Bacteria of Soil: Designing of Consortia Beneficial for Crop Production. Microorganisms 2023; 11:2864. [PMID: 38138008 PMCID: PMC10745983 DOI: 10.3390/microorganisms11122864] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2023] [Revised: 11/23/2023] [Accepted: 11/25/2023] [Indexed: 12/24/2023] Open
Abstract
Plant growth-promoting bacteria are commonly used in agriculture, particularly for seed inoculation. Multispecies consortia are believed to be the most promising form of these bacteria. However, designing and modeling bacterial consortia to achieve desired phenotypic outcomes in plants is challenging. This review aims to address this challenge by exploring key antimicrobial interactions. Special attention is given to approaches for developing soil plant growth-promoting bacteria consortia. Additionally, advanced omics-based methods are analyzed that allow soil microbiomes to be characterized, providing an understanding of the molecular and functional aspects of these microbial communities. A comprehensive discussion explores the utilization of bacterial preparations in biofertilizers for agricultural applications, focusing on the intricate design of synthetic bacterial consortia with these preparations. Overall, the review provides valuable insights and strategies for intentionally designing bacterial consortia to enhance plant growth and development.
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Affiliation(s)
- Anna M. Timofeeva
- SB RAS Institute of Chemical Biology and Fundamental Medicine, 630090 Novosibirsk, Russia;
- Faculty of Natural Sciences, Novosibirsk State University, 630090 Novosibirsk, Russia;
| | - Maria R. Galyamova
- Faculty of Natural Sciences, Novosibirsk State University, 630090 Novosibirsk, Russia;
| | - Sergey E. Sedykh
- SB RAS Institute of Chemical Biology and Fundamental Medicine, 630090 Novosibirsk, Russia;
- Faculty of Natural Sciences, Novosibirsk State University, 630090 Novosibirsk, Russia;
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22
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Heinken A, Hulshof TO, Nap B, Martinelli F, Basile A, O'Brolchain A, O’Sullivan NF, Gallagher C, Magee E, McDonagh F, Lalor I, Bergin M, Evans P, Daly R, Farrell R, Delaney RM, Hill S, McAuliffe SR, Kilgannon T, Fleming RM, Thinnes CC, Thiele I. APOLLO: A genome-scale metabolic reconstruction resource of 247,092 diverse human microbes spanning multiple continents, age groups, and body sites. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.02.560573. [PMID: 37873072 PMCID: PMC10592896 DOI: 10.1101/2023.10.02.560573] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/25/2023]
Abstract
Computational modelling of microbiome metabolism has proved instrumental to catalyse our understanding of diet-host-microbiome-disease interactions through the interrogation of mechanistic, strain- and molecule-resolved metabolic models. We present APOLLO, a resource of 247,092 human microbial genome-scale metabolic reconstructions spanning 19 phyla and accounting for microbial genomes from 34 countries, all age groups, and five body sites. We explored the metabolic potential of the reconstructed strains and developed a machine learning classifier able to predict with high accuracy the taxonomic strain assignments. We also built 14,451 sample-specific microbial community models, which could be stratified by body site, age, and disease states. Finally, we predicted faecal metabolites enriched or depleted in gut microbiomes of people with Crohn's disease, Parkinson disease, and undernourished children. APOLLO is compatible with the human whole-body models, and thus, provide unprecedented opportunities for systems-level modelling of personalised host-microbiome co-metabolism. APOLLO will be freely available under https://www.vmh.life/.
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Affiliation(s)
- Almut Heinken
- School of Medicine, University of Galway, Galway, Ireland
- Ryan Institute, University of Galway, Galway, Ireland
- Inserm UMRS 1256 NGERE, University of Lorraine, Nancy, France
| | - Timothy Otto Hulshof
- School of Medicine, University of Galway, Galway, Ireland
- Ryan Institute, University of Galway, Galway, Ireland
| | - Bram Nap
- School of Medicine, University of Galway, Galway, Ireland
- Ryan Institute, University of Galway, Galway, Ireland
| | - Filippo Martinelli
- School of Medicine, University of Galway, Galway, Ireland
- Ryan Institute, University of Galway, Galway, Ireland
| | - Arianna Basile
- School of Medicine, University of Galway, Galway, Ireland
- Department of Biology, University of Padova, Padova, Italy
| | | | | | | | | | | | - Ian Lalor
- University of Galway, Galway, Ireland
| | | | | | | | | | | | | | | | | | | | - Cyrille C. Thinnes
- School of Medicine, University of Galway, Galway, Ireland
- Ryan Institute, University of Galway, Galway, Ireland
| | - Ines Thiele
- School of Medicine, University of Galway, Galway, Ireland
- Ryan Institute, University of Galway, Galway, Ireland
- Division of Microbiology, University of Galway, Galway, Ireland
- APC Microbiome Ireland, Cork, Ireland
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23
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Li P, Roos S, Luo H, Ji B, Nielsen J. Metabolic engineering of human gut microbiome: Recent developments and future perspectives. Metab Eng 2023; 79:1-13. [PMID: 37364774 DOI: 10.1016/j.ymben.2023.06.006] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2023] [Revised: 06/10/2023] [Accepted: 06/10/2023] [Indexed: 06/28/2023]
Abstract
Many studies have demonstrated that the gut microbiota is associated with human health and disease. Manipulation of the gut microbiota, e.g. supplementation of probiotics, has been suggested to be feasible, but subject to limited therapeutic efficacy. To develop efficient microbiota-targeted diagnostic and therapeutic strategies, metabolic engineering has been applied to construct genetically modified probiotics and synthetic microbial consortia. This review mainly discusses commonly adopted strategies for metabolic engineering in the human gut microbiome, including the use of in silico, in vitro, or in vivo approaches for iterative design and construction of engineered probiotics or microbial consortia. Especially, we highlight how genome-scale metabolic models can be applied to advance our understanding of the gut microbiota. Also, we review the recent applications of metabolic engineering in gut microbiome studies as well as discuss important challenges and opportunities.
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Affiliation(s)
- Peishun Li
- Department of Biology and Biological Engineering, Chalmers University of Technology, SE41296, Gothenburg, Sweden
| | - Stefan Roos
- Department of Molecular Sciences, Uppsala BioCenter, Swedish University of Agricultural Sciences, SE75007, Uppsala, Sweden
| | - Hao Luo
- Department of Biology and Biological Engineering, Chalmers University of Technology, SE41296, Gothenburg, Sweden
| | - Boyang Ji
- BioInnovation Institute, Ole Maaløes Vej 3, DK2200, Copenhagen, Denmark
| | - Jens Nielsen
- Department of Biology and Biological Engineering, Chalmers University of Technology, SE41296, Gothenburg, Sweden; BioInnovation Institute, Ole Maaløes Vej 3, DK2200, Copenhagen, Denmark.
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24
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Griesemer M, Navid A. Uses of Multi-Objective Flux Analysis for Optimization of Microbial Production of Secondary Metabolites. Microorganisms 2023; 11:2149. [PMID: 37763993 PMCID: PMC10536367 DOI: 10.3390/microorganisms11092149] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2023] [Revised: 08/07/2023] [Accepted: 08/16/2023] [Indexed: 09/29/2023] Open
Abstract
Secondary metabolites are not essential for the growth of microorganisms, but they play a critical role in how microbes interact with their surroundings. In addition to this important ecological role, secondary metabolites also have a variety of agricultural, medicinal, and industrial uses, and thus the examination of secondary metabolism of plants and microbes is a growing scientific field. While the chemical production of certain secondary metabolites is possible, industrial-scale microbial production is a green and economically attractive alternative. This is even more true, given the advances in bioengineering that allow us to alter the workings of microbes in order to increase their production of compounds of interest. This type of engineering requires detailed knowledge of the "chassis" organism's metabolism. Since the resources and the catalytic capacity of enzymes in microbes is finite, it is important to examine the tradeoffs between various bioprocesses in an engineered system and alter its working in a manner that minimally perturbs the robustness of the system while allowing for the maximum production of a product of interest. The in silico multi-objective analysis of metabolism using genome-scale models is an ideal method for such examinations.
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Affiliation(s)
| | - Ali Navid
- Lawrence Livermore National Laboratory, Biosciences & Biotechnology Division, Physical & Life Sciences Directorate, Livermore, CA 94550, USA
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25
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Scott WT, Benito-Vaquerizo S, Zimmermann J, Bajić D, Heinken A, Suarez-Diez M, Schaap PJ. A structured evaluation of genome-scale constraint-based modeling tools for microbial consortia. PLoS Comput Biol 2023; 19:e1011363. [PMID: 37578975 PMCID: PMC10449394 DOI: 10.1371/journal.pcbi.1011363] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Revised: 08/24/2023] [Accepted: 07/17/2023] [Indexed: 08/16/2023] Open
Abstract
Harnessing the power of microbial consortia is integral to a diverse range of sectors, from healthcare to biotechnology to environmental remediation. To fully realize this potential, it is critical to understand the mechanisms behind the interactions that structure microbial consortia and determine their functions. Constraint-based reconstruction and analysis (COBRA) approaches, employing genome-scale metabolic models (GEMs), have emerged as the state-of-the-art tool to simulate the behavior of microbial communities from their constituent genomes. In the last decade, many tools have been developed that use COBRA approaches to simulate multi-species consortia, under either steady-state, dynamic, or spatiotemporally varying scenarios. Yet, these tools have not been systematically evaluated regarding their software quality, most suitable application, and predictive power. Hence, it is uncertain which tools users should apply to their system and what are the most urgent directions that developers should take in the future to improve existing capacities. This study conducted a systematic evaluation of COBRA-based tools for microbial communities using datasets from two-member communities as test cases. First, we performed a qualitative assessment in which we evaluated 24 published tools based on a list of FAIR (Findability, Accessibility, Interoperability, and Reusability) features essential for software quality. Next, we quantitatively tested the predictions in a subset of 14 of these tools against experimental data from three different case studies: a) syngas fermentation by C. autoethanogenum and C. kluyveri for the static tools, b) glucose/xylose fermentation with engineered E. coli and S. cerevisiae for the dynamic tools, and c) a Petri dish of E. coli and S. enterica for tools incorporating spatiotemporal variation. Our results show varying performance levels of the best qualitatively assessed tools when examining the different categories of tools. The differences in the mathematical formulation of the approaches and their relation to the results were also discussed. Ultimately, we provide recommendations for refining future GEM microbial modeling tools.
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Affiliation(s)
- William T. Scott
- Laboratory of Systems and Synthetic Biology, Wageningen University & Research, Wageningen, the Netherlands
- UNLOCK, Wageningen University & Research and Delft University of Technology, Wageningen, the Netherlands
| | - Sara Benito-Vaquerizo
- Laboratory of Systems and Synthetic Biology, Wageningen University & Research, Wageningen, the Netherlands
| | - Johannes Zimmermann
- Christian-Albrechts-University Kiel, Institute of Experimental Medicine, Research Group Medical Systems Biology, Kiel, Germany
| | - Djordje Bajić
- Department of Biotechnology, Delft University of Technology, Delft, the Netherlands
| | - Almut Heinken
- Inserm U1256 Laboratoire nGERE, Université de Lorraine, Nancy, France
| | - Maria Suarez-Diez
- Laboratory of Systems and Synthetic Biology, Wageningen University & Research, Wageningen, the Netherlands
| | - Peter J. Schaap
- Laboratory of Systems and Synthetic Biology, Wageningen University & Research, Wageningen, the Netherlands
- UNLOCK, Wageningen University & Research and Delft University of Technology, Wageningen, the Netherlands
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26
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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: 7] [Impact Index Per Article: 7.0] [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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27
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Kussmann M, Abe Cunha DH, Berciano S. Bioactive compounds for human and planetary health. Front Nutr 2023; 10:1193848. [PMID: 37545571 PMCID: PMC10400358 DOI: 10.3389/fnut.2023.1193848] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2023] [Accepted: 06/15/2023] [Indexed: 08/08/2023] Open
Abstract
Bioactive compounds found in edible plants and foods are vital for human and planetary health, yet their significance remains underappreciated. These natural bioactives, as part of whole diets, ingredients, or supplements, can modulate multiple aspects of human health and wellness. Recent advancements in omic sciences and computational biology, combined with the development of Precision Nutrition, have contributed to the convergence of nutrition and medicine, as well as more efficient and affordable healthcare solutions that harness the power of food for prevention and therapy. Innovation in this field is crucial to feed a growing global population sustainably and healthily. This requires significant changes in our food system, spanning agriculture, production, distribution and consumption. As we are facing pressing planetary health challenges, investing in bioactive-based solutions is an opportunity to protect biodiversity and the health of our soils, waters, and the atmosphere, while also creating value for consumers, patients, communities, and stakeholders. Such research and innovation targets include alternative proteins, such as cellular agriculture and plant-derived protein; natural extracts that improve shelf-life as natural preservatives; upcycling of agricultural by-products to reduce food waste; and the development of natural alternatives to synthetic fertilizers and pesticides. Translational research and innovation in the field of natural bioactives are currently being developed at two levels, using a systems-oriented approach. First, at the biological level, the interplay between these compounds and the human host and microbiome is being elucidated through omics research, big data and artificial intelligence, to accelerate both discovery and validation. Second, at the ecosystem level, efforts are focused on producing diverse nutrient-rich, flavorful, and resilient, yet high-yield agricultural crops, and educating consumers to make informed choices that benefit both their health and the planet. Adopting a system-oriented perspective helps: unravel the intricate and dynamic relationships between bioactives, nutrition, and sustainability outcomes, harnessing the power of nature to promote human health and wellbeing; foster sustainable agriculture and protect the ecosystem. Interdisciplinary collaboration in this field is needed for a new era of research and development of practical food-based solutions for some of the most pressing challenges humanity and our planet are facing today.
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Affiliation(s)
- Martin Kussmann
- Kompetenzzentrum für Ernährung (KErn), Freising, Germany
- Kussmann Biotech GmbH, Nordkirchen, Germany
| | - David Henrique Abe Cunha
- Ideatomik Creative Industries, Botucatu, Brazil
- Institute of Biosciences, São Paulo State University, Rio Claro, Brazil
| | - Silvia Berciano
- Friedman School of Nutrition Science and Policy, Tufts University, Boston, MA, United States
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28
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Li H, Gao X, Chen Y, Wang M, Xu C, Yu Q, Jin Y, Song J, Zhu Q. Potential risk of tamoxifen: gut microbiota and inflammation in mice with breast cancer. Front Oncol 2023; 13:1121471. [PMID: 37469407 PMCID: PMC10353877 DOI: 10.3389/fonc.2023.1121471] [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: 12/11/2022] [Accepted: 05/31/2023] [Indexed: 07/21/2023] Open
Abstract
Objective Tamoxifen is an effective anti-tumor medicine, but evidence has been provided on tamoxifen-related inflammation as well as its impact on gut microbiota. In this study, we aimed to investigate tamoxifen-induced gut microbiota and inflammation alteration. Methods We established a BC xenograft mouse model using the MCF-7 cell line. 16S rRNA gene sequencing was used to investigate gut microbiota. qRT-PCR, western blotting, and cytometric bead array were used to investigate inflammation-related biomarkers. Various bioinformatic approaches were used to analyze the data. Results Significant differences in gut microbial composition, characteristic taxa, and microbiome phenotype prediction were observed between control, model, and tamoxifen-treated mice. Furthermore, protein expression of IL-6 and TLR5 was up-regulated in tamoxifen-treated mice, while the mRNA of Tlr5 and Il-6, as well as protein expression of IL-6 and TLR5 in the model group, were down-regulated in the colon. The concentration of IFN-γ, IL-6, and IL12P70 in serum was up-regulated in tamoxifen-treated mice. Moreover, correlation-based clustering analysis demonstrated that inflammation-negatively correlated taxa, including Lachnospiraceae-UCG-006 and Anaerotruncus, were enriched in the model group, while inflammation-positively correlated taxa, including Prevotellaceae_UCG_001 and Akkermansia, were enriched in the tamoxifen-treated group. Finally, colon histologic damage was observed in tamoxifen-treated mice. Conclusion Tamoxifen treatment significantly altered gut microbiota and increased inflammation in the breast cancer xenograft mice model. This may be related to tamoxifen-induced intestinal epithelial barrier damage and TLR5 up-regulation.
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Affiliation(s)
- Hailong Li
- School of Green Intelligent Pharmaceutical Industry, Zhejiang Guangsha Vocational and Technical University of Construction, Dongyang, Zhejiang, China
| | - Xiufei Gao
- Department of Breast Surgery, The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Chinese Medicine), Hangzhou, Zhejiang, China
| | - Yian Chen
- First Clinical Medical College, Zhejiang Chinese Medical University, Hangzhou, Zhejiang, China
| | - Mengqian Wang
- First Clinical Medical College, Zhejiang Chinese Medical University, Hangzhou, Zhejiang, China
| | - Chuchu Xu
- First Clinical Medical College, Zhejiang Chinese Medical University, Hangzhou, Zhejiang, China
| | - Qinghong Yu
- First Clinical Medical College, Zhejiang Chinese Medical University, Hangzhou, Zhejiang, China
| | - Ying Jin
- First Clinical Medical College, Zhejiang Chinese Medical University, Hangzhou, Zhejiang, China
| | - Jiaqing Song
- First Clinical Medical College, Zhejiang Chinese Medical University, Hangzhou, Zhejiang, China
| | - Qi Zhu
- Department of Breast Surgery, The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Chinese Medicine), Hangzhou, Zhejiang, China
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29
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Shen Y, Wang P, Yang X, Chen M, Dong Y, Li J. A cross-sectional study identifying disparities in serum metabolic profiles among hypertensive patients with ISH, IDH and SDH subtypes. Front Cardiovasc Med 2023; 10:1102754. [PMID: 37215555 PMCID: PMC10192909 DOI: 10.3389/fcvm.2023.1102754] [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: 11/19/2022] [Accepted: 04/20/2023] [Indexed: 05/24/2023] Open
Abstract
Background It has been well acknowledged that disordered intestinal microflora and their fermented products play crucial role during the development of hypertension (HTN). Aberrant profiles of fecal bacteria have been documented in subjects with isolated systolic HTN (ISH) and isolated diastolic HTN (IDH) previously. Nevertheless, evidence regarding the association of metabolic products in the bloodstream with ISH, IDH and combined systolic and diastolic HTN (SDH) remains scarce. Methods We performed a cross-sectional study and conducted untargeted liquid chromatography-mass spectrometry (LC/MS) analysis on serum samples of 119 participants, including 13 subjects with normotension (SBP < 120/DBP < 80 mm Hg), 11 individuals with ISH (SBP ≥ 130/DBP < 80 mm Hg), 27 patients with IDH (SBP < 130/DBP ≥ 80 mm Hg), and 68 SDH patients (SBP ≥ 130, DBP ≥ 80 mm Hg). Results Here, the results showed clearly separated clusters in PLS-DA and OPLS-DA score plots for patients suffering from ISH, IDH and SDH when compared with normotension controls. The ISH group was characterized by elevated levels of 3,5-tetradecadien carnitine and notable reduction of maleic acid. While IDH patients were enriched with metabolites in L-lactic acid and depleted in citric acid. Stearoylcarnitine was identified to be specifically enriched in SDH group. The differentially abundant metabolites between ISH and controls were involved in tyrosine metabolism pathways, and in biosynthesis of phenylalanine for those between SDH and controls. Potential linkages between the gut microbial and serum metabolic signatures were detected within ISH, IDH and SDH groups. Furthermore, we found the association of discriminatory metabolites with the characteristics of patients. Conclusion Our findings demonstrate disparate blood metabolomics signatures across ISH, IDH and SDH, with differentially enriched metabolites and potential functional pathways identified, reveal the underlying microbiome and metabolome network in HTN subtypes, and provide potential targets for disease classification and therapeutic strategy in clinical practice.
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Affiliation(s)
- Yang Shen
- Department of Nephrology, Beijing Chaoyang Hospital, Capital Medical University, Beijing, China
| | - Pan Wang
- Heart Center & Beijing Key Laboratory of Hypertension, Beijing Chaoyang Hospital, Capital Medical University, Beijing, China
- Department of Cardiology, Beijing Chaoyang Hospital, Capital Medical University, Beijing, China
| | - Xinchun Yang
- Heart Center & Beijing Key Laboratory of Hypertension, Beijing Chaoyang Hospital, Capital Medical University, Beijing, China
- Department of Cardiology, Beijing Chaoyang Hospital, Capital Medical University, Beijing, China
| | - Mulei Chen
- Heart Center & Beijing Key Laboratory of Hypertension, Beijing Chaoyang Hospital, Capital Medical University, Beijing, China
- Department of Cardiology, Beijing Chaoyang Hospital, Capital Medical University, Beijing, China
| | - Ying Dong
- Heart Center & Beijing Key Laboratory of Hypertension, Beijing Chaoyang Hospital, Capital Medical University, Beijing, China
- Department of Cardiology, Beijing Chaoyang Hospital, Capital Medical University, Beijing, China
| | - Jing Li
- Heart Center & Beijing Key Laboratory of Hypertension, Beijing Chaoyang Hospital, Capital Medical University, Beijing, China
- Department of Cardiology, Beijing Chaoyang Hospital, Capital Medical University, Beijing, China
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30
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Noerman S, Landberg R. Blood metabolite profiles linking dietary patterns with health-Toward precision nutrition. J Intern Med 2023; 293:408-432. [PMID: 36484466 DOI: 10.1111/joim.13596] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Diet is one of the most important exposures that may affect health throughout life span. Investigations on dietary patterns rather than single food components are gaining in popularity because they take the complexity of the whole dietary context into account. Adherence to such dietary patterns can be measured by using metabolomics, which allows measurements of thousands of molecules simultaneously. Derived metabolite signatures of dietary patterns may reflect the consumption of specific groups of foods or their constituents originating from the dietary pattern per se, or the physiological response toward the food-derived metabolites, their interaction with endogenous metabolism, and exogenous factors such as gut microbiota. Here, we review and discuss blood metabolite fingerprints of healthy dietary patterns. The plasma concentration of several food-derived metabolites-such as betaines from whole grains and n - 3 polyunsaturated fatty acids and furan fatty acids from fish-seems to consistently reflect the intake of common foods of several healthy dietary patterns. The metabolites reflecting shared features of different healthy food indices form biomarker panels for which specific, targeted assays could be developed. The specificity of such biomarker panels would need to be validated, and proof-of-concept feeding trials are needed to evaluate to what extent the panels may mediate the effects of dietary patterns on disease risk indicators or if they are merely food intake biomarkers. Metabolites mediating health effects may represent novel targets for precision prevention strategies of clinical relevance to be verified in future studies.
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Affiliation(s)
- Stefania Noerman
- Department of Biology and Biological Engineering, Division of Food and Nutrition Science, Chalmers University of Technology, Gothenburg, Sweden
| | - Rikard Landberg
- Department of Biology and Biological Engineering, Division of Food and Nutrition Science, Chalmers University of Technology, Gothenburg, Sweden
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31
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Clasen F, Nunes PM, Bidkhori G, Bah N, Boeing S, Shoaie S, Anastasiou D. Systematic diet composition swap in a mouse genome-scale metabolic model reveals determinants of obesogenic diet metabolism in liver cancer. iScience 2023; 26:106040. [PMID: 36844450 PMCID: PMC9947310 DOI: 10.1016/j.isci.2023.106040] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2022] [Revised: 09/08/2022] [Accepted: 01/20/2023] [Indexed: 01/26/2023] Open
Abstract
Dietary nutrient availability and gene expression, together, influence tissue metabolic activity. Here, we explore whether altering dietary nutrient composition in the context of mouse liver cancer suffices to overcome chronic gene expression changes that arise from tumorigenesis and western-style diet (WD). We construct a mouse genome-scale metabolic model and estimate metabolic fluxes in liver tumors and non-tumoral tissue after computationally varying the composition of input diet. This approach, called Systematic Diet Composition Swap (SyDiCoS), revealed that, compared to a control diet, WD increases production of glycerol and succinate irrespective of specific tissue gene expression patterns. Conversely, differences in fatty acid utilization pathways between tumor and non-tumor liver are amplified with WD by both dietary carbohydrates and lipids together. Our data suggest that combined dietary component modifications may be required to normalize the distinctive metabolic patterns that underlie selective targeting of tumor metabolism.
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Affiliation(s)
- Frederick Clasen
- Cancer Metabolism Laboratory, The Francis Crick Institute, 1 Midland Road, London NW1 1AT, UK
- Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral and Craniofacial Sciences, King’s College London, London SE1 9RT, UK
| | - Patrícia M. Nunes
- Cancer Metabolism Laboratory, The Francis Crick Institute, 1 Midland Road, London NW1 1AT, UK
| | - Gholamreza Bidkhori
- Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral and Craniofacial Sciences, King’s College London, London SE1 9RT, UK
| | - Nourdine Bah
- Bioinformatics and Biostatistics Science Technology Platform, Francis Crick Institute, 1 Midland Road, London NW1 1AT, UK
| | - Stefan Boeing
- Bioinformatics and Biostatistics Science Technology Platform, Francis Crick Institute, 1 Midland Road, London NW1 1AT, UK
| | - Saeed Shoaie
- Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral and Craniofacial Sciences, King’s College London, London SE1 9RT, UK
- Science for Life Laboratory (SciLifeLab), KTH - Royal Institute of Technology, Tomtebodavägen 23, 171 65 Solna, Stockholm, Sweden
| | - Dimitrios Anastasiou
- Cancer Metabolism Laboratory, The Francis Crick Institute, 1 Midland Road, London NW1 1AT, UK
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Rios Garza D, Gonze D, Zafeiropoulos H, Liu B, Faust K. Metabolic models of human gut microbiota: Advances and challenges. Cell Syst 2023; 14:109-121. [PMID: 36796330 DOI: 10.1016/j.cels.2022.11.002] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2022] [Revised: 08/24/2022] [Accepted: 11/04/2022] [Indexed: 02/17/2023]
Abstract
The human gut is a complex ecosystem consisting of hundreds of microbial species interacting with each other and with the human host. Mathematical models of the gut microbiome integrate our knowledge of this system and help to formulate hypotheses to explain observations. The generalized Lotka-Volterra model has been widely used for this purpose, but it does not describe interaction mechanisms and thus does not account for metabolic flexibility. Recently, models that explicitly describe gut microbial metabolite production and consumption have become popular. These models have been used to investigate the factors that shape gut microbial composition and to link specific gut microorganisms to changes in metabolite concentrations found in diseases. Here, we review how such models are built and what we have learned so far from their application to human gut microbiome data. In addition, we discuss current challenges of these models and how these can be addressed in the future.
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Affiliation(s)
- Daniel Rios Garza
- KU Leuven, Department of Microbiology, Immunology and Transplantation, Rega Institute for Medical Research, Laboratory of Molecular Bacteriology, 3000 Leuven, Belgium
| | - Didier Gonze
- Unité de Chronobiologie Théorique, Faculté des Sciences, CP 231, Université Libre de Bruxelles, Bvd du Triomphe, 1050 Bruxelles, Belgium
| | - Haris Zafeiropoulos
- Biology Department, University of Crete, Heraklion 700 13, Greece; Institute of Marine Biology, Biotechnology and Aquaculture (IMBBC), Hellenic Centre for Marine Research (HCMR), Former U.S. Base of Gournes P.O. Box 2214, 71003, Heraklion, Crete, Greece
| | - Bin Liu
- KU Leuven, Department of Microbiology, Immunology and Transplantation, Rega Institute for Medical Research, Laboratory of Molecular Bacteriology, 3000 Leuven, Belgium
| | - Karoline Faust
- KU Leuven, Department of Microbiology, Immunology and Transplantation, Rega Institute for Medical Research, Laboratory of Molecular Bacteriology, 3000 Leuven, Belgium.
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Shah RV, Steffen LM, Nayor M, Reis JP, Jacobs DR, Allen NB, Lloyd-Jones D, Meyer K, Cole J, Piaggi P, Vasan RS, Clish CB, Murthy VL. Dietary metabolic signatures and cardiometabolic risk. Eur Heart J 2023; 44:557-569. [PMID: 36424694 PMCID: PMC10169425 DOI: 10.1093/eurheartj/ehac446] [Citation(s) in RCA: 18] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/06/2022] [Revised: 06/23/2022] [Accepted: 07/28/2022] [Indexed: 11/27/2022] Open
Abstract
AIMS Observational studies of diet in cardiometabolic-cardiovascular disease (CM-CVD) focus on self-reported consumption of food or dietary pattern, with limited information on individual metabolic responses to dietary intake linked to CM-CVD. Here, machine learning approaches were used to identify individual metabolic patterns related to diet and relation to long-term CM-CVD in early adulthood. METHODS AND RESULTS In 2259 White and Black adults (age 32.1 ± 3.6 years, 45% women, 44% Black) in the Coronary Artery Risk Development in Young Adults (CARDIA) study, multivariate models were employed to identify metabolite signatures of food group and composite dietary intake across 17 food groups, 2 nutrient groups, and healthy eating index-2015 (HEI2015) diet quality score. A broad array of metabolites associated with diet were uncovered, reflecting food-related components/catabolites (e.g. fish and long-chain unsaturated triacylglycerols), interactions with host features (microbiome), or pathways broadly implicated in CM-CVD (e.g. ceramide/sphingomyelin lipid metabolism). To integrate diet with metabolism, penalized machine learning models were used to define a metabolite signature linked to a putative CM-CVD-adverse diet (e.g. high in red/processed meat, refined grains), which was subsequently associated with long-term diabetes and CVD risk numerically more strongly than HEI2015 in CARDIA [e.g. diabetes: standardized hazard ratio (HR): 1.62, 95% confidence interval (CI): 1.32-1.97, P < 0.0001; CVD: HR: 1.55, 95% CI: 1.12-2.14, P = 0.008], with associations replicated for diabetes (P < 0.0001) in the Framingham Heart Study. CONCLUSION Metabolic signatures of diet are associated with long-term CM-CVD independent of lifestyle and traditional risk factors. Metabolomics improves precision to identify adverse consequences and pathways of diet-related CM-CVD.
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Affiliation(s)
- Ravi V Shah
- Vanderbilt University Medical Center, Vanderbilt Clinical and Translational Research Center (VTRACC), Nashville, TN, USA
| | - Lyn M Steffen
- Division of Epidemiology and Community Health, University of Minnesota School of Public Health, Minneapolis, MN, USA
| | - Matthew Nayor
- Cardiology Division, Boston University School of Medicine, Boston, MA, USA
| | - Jared P Reis
- Epidemiology Branch, National Heart, Lung, and Blood Institute, Bethesda, MD, USA
| | - David R Jacobs
- Division of Epidemiology and Community Health, University of Minnesota School of Public Health, Minneapolis, MN, USA
| | - Norrina B Allen
- Department of Preventive Medicine, Northwestern University, Chicago, IL, USA
| | - Donald Lloyd-Jones
- Department of Preventive Medicine, Northwestern University, Chicago, IL, USA
| | - Katie Meyer
- Nutrition Department, UNC Chapel Hill, Chapel Hill, NC, USA
| | - Joanne Cole
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Paolo Piaggi
- Department of Information Engineering, University of Pisa, Pisa, Italy
| | - Ramachandran S Vasan
- Sections of Preventive Medicine and Epidemiology and Cardiovascular Medicine, Department of Medicine, and Department of Epidemiology, Boston University Schools of Medicine and Public Health, Boston, MA, USA
- The Framingham Heart Study, Framingham, MA, USA
| | - Clary B Clish
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Venkatesh L Murthy
- Department of Medicine and Radiology, University of Michigan, 1338 Cardiovascular Center, Ann Arbor, MI 48109-5873, USA
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Ezzamouri B, Rosario D, Bidkhori G, Lee S, Uhlen M, Shoaie S. Metabolic modelling of the human gut microbiome in type 2 diabetes patients in response to metformin treatment. NPJ Syst Biol Appl 2023; 9:2. [PMID: 36681701 PMCID: PMC9867701 DOI: 10.1038/s41540-022-00261-6] [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: 03/31/2022] [Accepted: 11/08/2022] [Indexed: 01/22/2023] Open
Abstract
The human gut microbiome has been associated with several metabolic disorders including type 2 diabetes mellitus. Understanding metabolic changes in the gut microbiome is important to elucidate the role of gut bacteria in regulating host metabolism. Here, we used available metagenomics data from a metformin study, together with genome-scale metabolic modelling of the key bacteria in individual and community-level to investigate the mechanistic role of the gut microbiome in response to metformin. Individual modelling predicted that species that are increased after metformin treatment have higher growth rates in comparison to species that are decreased after metformin treatment. Gut microbial enrichment analysis showed prior to metformin treatment pathways related to the hypoglycemic effect were enriched. Our observations highlight how the key bacterial species after metformin treatment have commensal and competing behavior, and how their cellular metabolism changes due to different nutritional environment. Integrating different diets showed there were specific microbial alterations between different diets. These results show the importance of the nutritional environment and how dietary guidelines may improve drug efficiency through the gut microbiota.
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Affiliation(s)
- Bouchra Ezzamouri
- grid.13097.3c0000 0001 2322 6764Centre for Host–Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King’s College London, SE1 9RT London, UK ,grid.420545.20000 0004 0489 3985Unit for Population-Based Dermatology, St John’s Institute of Dermatology, King’s College London and Guy’s and St Thomas’ NHS Foundation Trust, London, UK
| | - Dorines Rosario
- grid.13097.3c0000 0001 2322 6764Centre for Host–Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King’s College London, SE1 9RT London, UK
| | - Gholamreza Bidkhori
- grid.13097.3c0000 0001 2322 6764Centre for Host–Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King’s College London, SE1 9RT London, UK ,Present Address: AIVIVO Ltd. Unit 25, Bio-innovation centre, Cambridge Science Park, Cambridge, UK
| | - Sunjae Lee
- grid.13097.3c0000 0001 2322 6764Centre for Host–Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King’s College London, SE1 9RT London, UK
| | - Mathias Uhlen
- grid.5037.10000000121581746Science for Life Laboratory, KTH - Royal Institute of Technology, 171 21 Stockholm, Sweden
| | - Saeed Shoaie
- grid.13097.3c0000 0001 2322 6764Centre for Host–Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King’s College London, SE1 9RT London, UK ,grid.5037.10000000121581746Science for Life Laboratory, KTH - Royal Institute of Technology, 171 21 Stockholm, Sweden
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Effects of Ilisha elongata protein, soy protein and whey protein on growth characteristics and adhesion of probiotics. Curr Res Food Sci 2022; 5:2125-2134. [DOI: 10.1016/j.crfs.2022.10.024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2022] [Revised: 09/08/2022] [Accepted: 10/22/2022] [Indexed: 11/06/2022] Open
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Mostolizadeh R, Glöckler M, Dräger A. Towards the human nasal microbiome: Simulating D. pigrum and S. aureus. Front Cell Infect Microbiol 2022; 12:925215. [PMID: 36605126 PMCID: PMC9810029 DOI: 10.3389/fcimb.2022.925215] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2022] [Accepted: 08/15/2022] [Indexed: 01/12/2023] Open
Abstract
The human nose harbors various microbes that decisively influence the wellbeing and health of their host. Among the most threatening pathogens in this habitat is Staphylococcus aureus. Multiple epidemiological studies identify Dolosigranulum pigrum as a likely beneficial bacterium based on its positive association with health, including negative associations with S. aureus. Carefully curated GEMs are available for both bacterial species that reliably simulate their growth behavior in isolation. To unravel the mutual effects among bacteria, building community models for simulating co-culture growth is necessary. However, modeling microbial communities remains challenging. This article illustrates how applying the NCMW fosters our understanding of two microbes' joint growth conditions in the nasal habitat and their intricate interplay from a metabolic modeling perspective. The resulting community model combines the latest available curated GEMs of D. pigrum and S. aureus. This uses case illustrates how to incorporate genuine GEM of participating microorganisms and creates a basic community model mimicking the human nasal environment. Our analysis supports the role of negative microbe-microbe interactions involving D. pigrum examined experimentally in the lab. By this, we identify and characterize metabolic exchange factors involved in a specific interaction between D. pigrum and S. aureus as an in silico candidate factor for a deep insight into the associated species. This method may serve as a blueprint for developing more complex microbial interaction models. Its direct application suggests new ways to prevent disease-causing infections by inhibiting the growth of pathogens such as S. aureus through microbe-microbe interactions.
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Affiliation(s)
- Reihaneh Mostolizadeh
- Computational Systems Biology of Infections and Antimicrobial-Resistant Pathogens, Institute for Bioinformatics and Medical Informatics (IBMI), University of Tübingen, Tübingen, Germany,Department of Computer Science, University of Tübingen, Tübingen, Germany,German Center for Infection Research (DZIF), Partner site, Tübingen, Germany,Cluster of Excellence ‘Controlling Microbes to Fight Infections’, University of Tübingen, Tübingen, Germany,*Correspondence: Reihaneh Mostolizadeh,
| | - Manuel Glöckler
- Department of Computer Science, University of Tübingen, Tübingen, Germany
| | - Andreas Dräger
- Computational Systems Biology of Infections and Antimicrobial-Resistant Pathogens, Institute for Bioinformatics and Medical Informatics (IBMI), University of Tübingen, Tübingen, Germany,Department of Computer Science, University of Tübingen, Tübingen, Germany,German Center for Infection Research (DZIF), Partner site, Tübingen, Germany,Cluster of Excellence ‘Controlling Microbes to Fight Infections’, University of Tübingen, Tübingen, Germany
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Abstract
We are host to an assembly of microorganisms that vary in structure and function along the length of the gut and from the lumen to the mucosa. This ecosystem is collectively known as the gut microbiota and significant efforts have been spent during the past 2 decades to catalog and functionally describe the normal gut microbiota and how it varies during a wide spectrum of disease states. The gut microbiota is altered in several cardiometabolic diseases and recent work has established microbial signatures that may advance disease. However, most research has focused on identifying associations between the gut microbiota and human diseases states and to investigate causality and potential mechanisms using cells and animals. Since the gut microbiota functions on the intersection between diet and host metabolism, and can contribute to inflammation, several microbially produced metabolites and molecules may modulate cardiometabolic diseases. Here we discuss how the gut bacterial composition is altered in, and can contribute to, cardiometabolic disease, as well as how the gut bacteria can be targeted to treat and prevent metabolic diseases.
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Affiliation(s)
- Louise E Olofsson
- Wallenberg Laboratory, Department of Molecular and Clinical Medicine, Institute of Medicine, University of Gothenburg, Sweden
| | - Fredrik Bäckhed
- Wallenberg Laboratory, Department of Molecular and Clinical Medicine, Institute of Medicine, University of Gothenburg, Sweden.,Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health Sciences, University of Copenhagen, Denmark.,Region Västra Götaland, Sahlgrenska University Hospital, Department of Clinical Physiology, Gothenburg, Sweden
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Martínez-López YE, Esquivel-Hernández DA, Sánchez-Castañeda JP, Neri-Rosario D, Guardado-Mendoza R, Resendis-Antonio O. Type 2 diabetes, gut microbiome, and systems biology: A novel perspective for a new era. Gut Microbes 2022; 14:2111952. [PMID: 36004400 PMCID: PMC9423831 DOI: 10.1080/19490976.2022.2111952] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/04/2023] Open
Abstract
The association between the physio-pathological variables of type 2 diabetes (T2D) and gut microbiota composition suggests a new avenue to track the disease and improve the outcomes of pharmacological and non-pharmacological treatments. This enterprise requires new strategies to elucidate the metabolic disturbances occurring in the gut microbiome as the disease progresses. To this end, physiological knowledge and systems biology pave the way for characterizing microbiota and identifying strategies in a move toward healthy compositions. Here, we dissect the recent associations between gut microbiota and T2D. In addition, we discuss recent advances in how drugs, diet, and exercise modulate the microbiome to favor healthy stages. Finally, we present computational approaches for disentangling the metabolic activity underlying host-microbiota codependence. Altogether, we envision that the combination of physiology and computational modeling of microbiota metabolism will drive us to optimize the diagnosis and treatment of T2D patients in a personalized way.
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Affiliation(s)
- Yoscelina Estrella Martínez-López
- Human Systems Biology Laboratory. Instituto Nacional de Medicina Genómica (INMEGEN). México City, México,Programa de Doctorado en Ciencias Médicas, Odontológicas y de la Salud, Universidad Nacional Autónoma de México (UNAM). Ciudad de México, México,Metabolic Research Laboratory, Department of Medicine and Nutrition. University of Guanajuato. León, Guanajuato, México
| | | | - Jean Paul Sánchez-Castañeda
- Human Systems Biology Laboratory. Instituto Nacional de Medicina Genómica (INMEGEN). México City, México,Programa de Maestría en Ciencias Bioquímicas, Universidad Nacional Autónoma de México (UNAM). Ciudad de México, México
| | - Daniel Neri-Rosario
- Human Systems Biology Laboratory. Instituto Nacional de Medicina Genómica (INMEGEN). México City, México,Programa de Maestría en Ciencias Bioquímicas, Universidad Nacional Autónoma de México (UNAM). Ciudad de México, México
| | - Rodolfo Guardado-Mendoza
- Metabolic Research Laboratory, Department of Medicine and Nutrition. University of Guanajuato. León, Guanajuato, México,Research Department, Hospital Regional de Alta Especialidad del Bajío. León, Guanajuato, México,Rodolfo Guardado-Mendoza Metabolic Research Laboratory, Department of Medicine and Nutrition. University of Guanajuato. León, Guanajuato, México
| | - Osbaldo Resendis-Antonio
- Human Systems Biology Laboratory. Instituto Nacional de Medicina Genómica (INMEGEN). México City, México,Coordinación de la Investigación Científica – Red de Apoyo a la Investigación, Universidad Nacional Autónoma de México (UNAM). Ciudad de México, México,CONTACT Osbaldo Resendis-Antonio Centro de Ciencias de la Complejidad, Universidad Nacional Autónoma de México (UNAM), Periferico Sur 4809, Arenal Tepepan, Tlalpan, 14610 Ciudad de México, CDMX
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Zou A, Nadeau K, Xiong X, Wang PW, Copeland JK, Lee JY, Pierre JS, Ty M, Taj B, Brumell JH, Guttman DS, Sharif S, Korver D, Parkinson J. Systematic profiling of the chicken gut microbiome reveals dietary supplementation with antibiotics alters expression of multiple microbial pathways with minimal impact on community structure. MICROBIOME 2022; 10:127. [PMID: 35965349 PMCID: PMC9377095 DOI: 10.1186/s40168-022-01319-7] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/05/2022] [Accepted: 06/29/2022] [Indexed: 06/15/2023]
Abstract
BACKGROUND The emergence of antimicrobial resistance is a major threat to global health and has placed pressure on the livestock industry to eliminate the use of antibiotic growth promotants (AGPs) as feed additives. To mitigate their removal, efficacious alternatives are required. AGPs are thought to operate through modulating the gut microbiome to limit opportunities for colonization by pathogens, increase nutrient utilization, and reduce inflammation. However, little is known concerning the underlying mechanisms. Previous studies investigating the effects of AGPs on the poultry gut microbiome have largely focused on 16S rDNA surveys based on a single gastrointestinal (GI) site, diet, and/or timepoint, resulting in an inconsistent view of their impact on community composition. METHODS In this study, we perform a systematic investigation of both the composition and function of the chicken gut microbiome, in response to AGPs. Birds were raised under two different diets and AGP treatments, and 16S rDNA surveys applied to six GI sites sampled at three key timepoints of the poultry life cycle. Functional investigations were performed through metatranscriptomics analyses and metabolomics. RESULTS Our study reveals a more nuanced view of the impact of AGPs, dependent on age of bird, diet, and intestinal site sampled. Although AGPs have a limited impact on taxonomic abundances, they do appear to redefine influential taxa that may promote the exclusion of other taxa. Microbiome expression profiles further reveal a complex landscape in both the expression and taxonomic representation of multiple pathways including cell wall biogenesis, antimicrobial resistance, and several involved in energy, amino acid, and nucleotide metabolism. Many AGP-induced changes in metabolic enzyme expression likely serve to redirect metabolic flux with the potential to regulate bacterial growth or produce metabolites that impact the host. CONCLUSIONS As alternative feed additives are developed to mimic the action of AGPs, our study highlights the need to ensure such alternatives result in functional changes that are consistent with site-, age-, and diet-associated taxa. The genes and pathways identified in this study are therefore expected to drive future studies, applying tools such as community-based metabolic modeling, focusing on the mechanistic impact of different dietary regimes on the microbiome. Consequently, the data generated in this study will be crucial for the development of next-generation feed additives targeting gut health and poultry production. Video Abstract.
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Affiliation(s)
- Angela Zou
- Department of Biochemistry, University of Toronto, Toronto, ON Canada
- Program in Molecular Medicine, Hospital for Sick Children, Peter Gilgan Center for Research and Learning, 686 Bay Street, Toronto, ON M5G 0A4 Canada
| | - Kerry Nadeau
- Department of Agricultural, Food and Nutritional Science, University of Alberta, Edmonton, AB Canada
| | - Xuejian Xiong
- Program in Molecular Medicine, Hospital for Sick Children, Peter Gilgan Center for Research and Learning, 686 Bay Street, Toronto, ON M5G 0A4 Canada
| | - Pauline W. Wang
- Centre for the Analysis of Genome Evolution & Function, University of Toronto, 25 Willcocks St, Toronto, Ontario Canada
| | - Julia K. Copeland
- Centre for the Analysis of Genome Evolution & Function, University of Toronto, 25 Willcocks St, Toronto, Ontario Canada
| | - Jee Yeon Lee
- Centre for the Analysis of Genome Evolution & Function, University of Toronto, 25 Willcocks St, Toronto, Ontario Canada
| | - James St. Pierre
- Program in Molecular Medicine, Hospital for Sick Children, Peter Gilgan Center for Research and Learning, 686 Bay Street, Toronto, ON M5G 0A4 Canada
- Department of Molecular Genetics, University of Toronto, Toronto, ON Canada
| | - Maxine Ty
- Department of Biochemistry, University of Toronto, Toronto, ON Canada
- Program in Molecular Medicine, Hospital for Sick Children, Peter Gilgan Center for Research and Learning, 686 Bay Street, Toronto, ON M5G 0A4 Canada
| | - Billy Taj
- Program in Molecular Medicine, Hospital for Sick Children, Peter Gilgan Center for Research and Learning, 686 Bay Street, Toronto, ON M5G 0A4 Canada
| | - John H. Brumell
- Department of Molecular Genetics, University of Toronto, Toronto, ON Canada
- Program in Cell Biology, Hospital for Sick Children, Peter Gilgan Center for Research and Learning, 686 Bay Street, Toronto, ON Canada
- Institute of Medical Science, University of Toronto, Toronto, ON Canada
- SickKids IBD Centre, Hospital for Sick Children, Peter Gilgan Center for Research and Learning, 686 Bay Street, Toronto, ON Canada
| | - David S. Guttman
- Centre for the Analysis of Genome Evolution & Function, University of Toronto, 25 Willcocks St, Toronto, Ontario Canada
- Department of Cell and Systems Biology, University of Toronto, Toronto, ON Canada
| | - Shayan Sharif
- Department of Pathobiology, Ontario Veterinary College, University of Guelph, Guelph, ON Canada
| | - Doug Korver
- Department of Agricultural, Food and Nutritional Science, University of Alberta, Edmonton, AB Canada
| | - John Parkinson
- Department of Biochemistry, University of Toronto, Toronto, ON Canada
- Program in Molecular Medicine, Hospital for Sick Children, Peter Gilgan Center for Research and Learning, 686 Bay Street, Toronto, ON M5G 0A4 Canada
- Department of Molecular Genetics, University of Toronto, Toronto, ON Canada
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Thiele I, Fleming RM. Whole-body metabolic modelling predicts isoleucine dependency of SARS-CoV-2 replication. Comput Struct Biotechnol J 2022; 20:4098-4109. [PMID: 35874091 PMCID: PMC9296228 DOI: 10.1016/j.csbj.2022.07.019] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Accepted: 07/10/2022] [Indexed: 11/21/2022] Open
Abstract
We aimed at investigating host-virus co-metabolism during SARS-CoV-2 infection. Therefore, we extended comprehensive sex-specific, whole-body organ resolved models of human metabolism with the necessary reactions to replicate SARS-CoV-2 in the lung as well as selected peripheral organs. Using this comprehensive host-virus model, we obtained the following key results: 1. The predicted maximal possible virus shedding rate was limited by isoleucine availability. 2. The supported initial viral load depended on the increase in CD4+ T-cells, consistent with the literature. 3. During viral infection, the whole-body metabolism changed including the blood metabolome, which agreed well with metabolomic studies from COVID-19 patients and healthy controls. 4. The virus shedding rate could be reduced by either inhibition of the guanylate kinase 1 or availability of amino acids, e.g., in the diet. 5. The virus variants differed in their maximal possible virus shedding rates, which could be inversely linked to isoleucine occurrences in the sequences. Taken together, this study presents the metabolic crosstalk between host and virus and emphasises the role of amino acid metabolism during SARS-CoV-2 infection, in particular of isoleucine. As such, it provides an example of how computational modelling can complement more canonical approaches to gain insight into host-virus crosstalk and to identify potential therapeutic strategies.
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Affiliation(s)
- Ines Thiele
- School of Medicine, National University of Galway, Galway, Ireland
- Ryan Institute, National University of Galway, Galway, Ireland
- Division of Microbiology, National University of Galway, Galway, Ireland
- APC Microbiome Ireland, Cork, Ireland
| | - Ronan M.T. Fleming
- School of Medicine, National University of Galway, Galway, Ireland
- Leiden Academic Centre for Drug Research, Leiden University, Leiden, The Netherlands
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Koppe L, Soulage CO. The impact of dietary nutrient intake on gut microbiota in the progression and complications of chronic kidney disease. Kidney Int 2022; 102:728-739. [PMID: 35870642 DOI: 10.1016/j.kint.2022.06.025] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2021] [Revised: 06/02/2022] [Accepted: 06/10/2022] [Indexed: 11/29/2022]
Abstract
Chronic kidney disease (CKD) has been associated with changes in the function and composition of the gut microbiota. The ecosystem of the human gut consists of trillions of microorganisms forming an authentic metabolically active organ that is fueled by nutrients to produce bioactive compounds. These microbiota-derived metabolites may be protective for kidney function (e.g. short-chain fatty acids from fermentation of dietary fibers) or deleterious (e.g. gut-derived uremic toxins such as trimethylamine N-oxide, p-cresyl sulfate, and indoxyl sulfate from fermentation of amino acids). Although diet is the cornerstone of the management of the patient with CKD, it remains a relatively underused component of the clinician's armamentarium. In this review, we describe the latest advances in understanding diet-microbiota crosstalk in a uremic context, and how this communication might contribute to CKD progression and complications. We then discuss how this knowledge could be harnessed for personalized nutrition strategies to prevent patients with CKD progressing to end-stage kidney disease and its detrimental consequences.
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Affiliation(s)
- Laetitia Koppe
- Department of Nephrology, Hospices Civils de Lyon, Centre Hospitalier Lyon-Sud, F-69495 Pierre-Bénite, France; Univ. Lyon, CarMeN lab, INSA-Lyon, INSERM U1060, INRA, Université Claude Bernard Lyon 1, F-69621 Villeurbanne, France.
| | - Christophe O Soulage
- Univ. Lyon, CarMeN lab, INSA-Lyon, INSERM U1060, INRA, Université Claude Bernard Lyon 1, F-69621 Villeurbanne, France
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42
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Liu Y, Xu P. Quantitative and analytical tools to analyze the spatiotemporal population dynamics of microbial consortia. Curr Opin Biotechnol 2022; 76:102754. [PMID: 35809433 DOI: 10.1016/j.copbio.2022.102754] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Revised: 05/30/2022] [Accepted: 06/06/2022] [Indexed: 12/27/2022]
Abstract
Microorganisms occupy almost every niche on earth. They play critical roles in maintaining ecological balance, atmospheric C/N cycle, and human health. Microbes live in consortia with metabolite exchange or signal communication. Quantitative and analytical tools are becoming increasingly important to study microbial consortia dynamics. We argue that a combined reductionist and holistic approach will be important to understanding the assembly rules and spatiotemporal population dynamics of the microbial community (MICOM). Reductionism allows us to reconstruct complex MICOM from isolated or simple synthetic consortia. Holism allows us to understand microbes as a community with cooperation and competition. Here we review the recent development of quantitative and analytical tools to uncover the underlying principles in microbial communities that govern their spatiotemporal change and interaction dynamics. Mathematical models and analytical tools will continue to provide essential knowledge and expand our capability to manipulate and control microbial consortia.
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Affiliation(s)
- Yugeng Liu
- Department of Chemical Engineering, Guangdong Technion-Israel Institute of Technology, Shantou, Guangdong 515063, China
| | - Peng Xu
- Department of Chemical Engineering, Guangdong Technion-Israel Institute of Technology, Shantou, Guangdong 515063, China; Guangdong Provincial Key Laboratory of Materials and Technologies for Energy Conversion, Guangdong Technion-Israel Institute of Technology, Shantou, Guangdong 515063, China.
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Milenkovic D, Capel F, Combaret L, Comte B, Dardevet D, Evrard B, Guillet C, Monfoulet LE, Pinel A, Polakof S, Pujos-Guillot E, Rémond D, Wittrant Y, Savary-Auzeloux I. Targeting the gut to prevent and counteract metabolic disorders and pathologies during aging. Crit Rev Food Sci Nutr 2022; 63:11185-11210. [PMID: 35730212 DOI: 10.1080/10408398.2022.2089870] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Impairment of gut function is one of the explanatory mechanisms of health status decline in elderly population. These impairments involve a decline in gut digestive physiology, metabolism and immune status, and associated to that, changes in composition and function of the microbiota it harbors. Continuous deteriorations are generally associated with the development of systemic dysregulations and ultimately pathologies that can worsen the initial health status of individuals. All these alterations observed at the gut level can then constitute a wide range of potential targets for development of nutritional strategies that can impact gut tissue or associated microbiota pattern. This can be key, in a preventive manner, to limit gut functionality decline, or in a curative way to help maintaining optimum nutrients bioavailability in a context on increased requirements, as frequently observed in pathological situations. The aim of this review is to give an overview on the alterations that can occur in the gut during aging and lead to the development of altered function in other tissues and organs, ultimately leading to the development of pathologies. Subsequently is discussed how nutritional strategies that target gut tissue and gut microbiota can help to avoid or delay the occurrence of aging-related pathologies.
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Affiliation(s)
- Dragan Milenkovic
- Human Nutrition Unit, UMR1019, University Clermont Auvergne, INRAE, Clermont-Ferrand, France
| | - Frédéric Capel
- Human Nutrition Unit, UMR1019, University Clermont Auvergne, INRAE, Clermont-Ferrand, France
| | - Lydie Combaret
- Human Nutrition Unit, UMR1019, University Clermont Auvergne, INRAE, Clermont-Ferrand, France
| | - Blandine Comte
- Human Nutrition Unit, UMR1019, University Clermont Auvergne, INRAE, Clermont-Ferrand, France
| | - Dominique Dardevet
- Human Nutrition Unit, UMR1019, University Clermont Auvergne, INRAE, Clermont-Ferrand, France
| | - Bertrand Evrard
- Human Nutrition Unit, UMR1019, University Clermont Auvergne, INRAE, Clermont-Ferrand, France
| | - Christelle Guillet
- Human Nutrition Unit, UMR1019, University Clermont Auvergne, INRAE, Clermont-Ferrand, France
| | | | - Alexandre Pinel
- Human Nutrition Unit, UMR1019, University Clermont Auvergne, INRAE, Clermont-Ferrand, France
| | - Sergio Polakof
- Human Nutrition Unit, UMR1019, University Clermont Auvergne, INRAE, Clermont-Ferrand, France
| | - Estelle Pujos-Guillot
- Human Nutrition Unit, UMR1019, University Clermont Auvergne, INRAE, Clermont-Ferrand, France
| | - Didier Rémond
- Human Nutrition Unit, UMR1019, University Clermont Auvergne, INRAE, Clermont-Ferrand, France
| | - Yohann Wittrant
- Human Nutrition Unit, UMR1019, University Clermont Auvergne, INRAE, Clermont-Ferrand, France
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Proffitt C, Bidkhori G, Lee S, Tebani A, Mardinoglu A, Uhlen M, Moyes DL, Shoaie S. Genome-scale metabolic modelling of the human gut microbiome reveals changes of the glyoxylate and dicarboxylate metabolism in metabolic disorders. iScience 2022; 25:104513. [PMID: 35754734 PMCID: PMC9213702 DOI: 10.1016/j.isci.2022.104513] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2021] [Revised: 03/14/2022] [Accepted: 05/27/2022] [Indexed: 11/20/2022] Open
Abstract
The human gut microbiome has been associated with metabolic disorders including obesity, type 2 diabetes, and atherosclerosis. Understanding the contribution of microbiome metabolic changes is important for elucidating the role of gut bacteria in regulating metabolism. We used available metagenomics data from these metabolic disorders, together with genome-scale metabolic modeling of key bacteria in the individual and community-level to investigate the mechanistic role of the gut microbiome in metabolic diseases. Modeling predicted increased levels of glutamate consumption along with the production of ammonia, arginine, and proline in gut bacteria common across the disorders. Abundance profiles and network-dependent analysis identified the enrichment of tartrate dehydrogenase in the disorders. Moreover, independent plasma metabolite levels showed associations between metabolites including proline and tyrosine and an increased tartrate metabolism in healthy obese individuals. We, therefore, propose that an increased tartrate metabolism could be a significant mediator of the microbiome metabolic changes in metabolic disorders. Metagenomic analysis highlights key common bacterial species across metabolic diseases Metabolic models showed higher levels of acetate produced by disease enriched bacteria Reaction analysis revealed increases in the glyoxylate and dicarboxylate pathway Metabolomics and modeling analysis showed the potential role of tartrate metabolism
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Affiliation(s)
- Ceri Proffitt
- Centre for Host–Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King’s College London, London, SE1 9RT, UK
| | - Gholamreza Bidkhori
- Centre for Host–Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King’s College London, London, SE1 9RT, UK
| | - Sunjae Lee
- Centre for Host–Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King’s College London, London, SE1 9RT, UK
| | - Abdellah Tebani
- Science for Life Laboratory, KTH–Royal Institute of Technology, Stockholm, Sweden
| | - Adil Mardinoglu
- Centre for Host–Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King’s College London, London, SE1 9RT, UK
- Science for Life Laboratory, KTH–Royal Institute of Technology, Stockholm, Sweden
| | - Mathias Uhlen
- Science for Life Laboratory, KTH–Royal Institute of Technology, Stockholm, Sweden
| | - David L. Moyes
- Centre for Host–Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King’s College London, London, SE1 9RT, UK
| | - Saeed Shoaie
- Centre for Host–Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King’s College London, London, SE1 9RT, UK
- Science for Life Laboratory, KTH–Royal Institute of Technology, Stockholm, Sweden
- Corresponding author
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45
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Sepich-Poore GD, Guccione C, Laplane L, Pradeu T, Curtius K, Knight R. Cancer's second genome: Microbial cancer diagnostics and redefining clonal evolution as a multispecies process: Humans and their tumors are not aseptic, and the multispecies nature of cancer modulates clinical care and clonal evolution: Humans and their tumors are not aseptic, and the multispecies nature of cancer modulates clinical care and clonal evolution. Bioessays 2022; 44:e2100252. [PMID: 35253252 PMCID: PMC10506734 DOI: 10.1002/bies.202100252] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2021] [Revised: 01/31/2022] [Accepted: 02/16/2022] [Indexed: 12/13/2022]
Abstract
The presence and role of microbes in human cancers has come full circle in the last century. Tumors are no longer considered aseptic, but implications for cancer biology and oncology remain underappreciated. Opportunities to identify and build translational diagnostics, prognostics, and therapeutics that exploit cancer's second genome-the metagenome-are manifold, but require careful consideration of microbial experimental idiosyncrasies that are distinct from host-centric methods. Furthermore, the discoveries of intracellular and intra-metastatic cancer bacteria necessitate fundamental changes in describing clonal evolution and selection, reflecting bidirectional interactions with non-human residents. Reconsidering cancer clonality as a multispecies process similarly holds key implications for understanding metastasis and prognosing therapeutic resistance while providing rational guidance for the next generation of bacterial cancer therapies. Guided by these new findings and challenges, this Review describes opportunities to exploit cancer's metagenome in oncology and proposes an evolutionary framework as a first step towards modeling multispecies cancer clonality. Also see the video abstract here: https://youtu.be/-WDtIRJYZSs.
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Affiliation(s)
| | - Caitlin Guccione
- Division of Biomedical Informatics, Department of Medicine, University of California San Diego, La Jolla, CA 92093, USA
- Bioinformatics and Systems Biology Program, University of California San Diego, La Jolla, CA 92093, USA
- Department of Pediatrics, University of California San Diego, La Jolla, CA 92093, USA
| | - Lucie Laplane
- Institut d’histoire et de philosophie des sciences et des techniques (UMR8590), CNRS & Panthéon-Sorbonne University, 75006 Paris, France
- Hematopoietic stem cells and the development of myeloid malignancies (UMR1287), Gustave Roussy Cancer Campus, 94800 Villejuif, France
| | - Thomas Pradeu
- ImmunoConcept (UMR5164), CNRS & University of Bordeaux, 33076 Bordeaux Cedex, France
| | - Kit Curtius
- Division of Biomedical Informatics, Department of Medicine, University of California San Diego, La Jolla, CA 92093, USA
- Bioinformatics and Systems Biology Program, University of California San Diego, La Jolla, CA 92093, USA
| | - Rob Knight
- Department of Bioengineering, University of California San Diego, La Jolla, CA 92093, USA
- Department of Pediatrics, University of California San Diego, La Jolla, CA 92093, USA
- Department of Computer Science and Engineering, University of California San Diego, La Jolla, CA 92093, USA
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Ghaffari P, Shoaie S, Nielsen LK. Irritable bowel syndrome and microbiome; Switching from conventional diagnosis and therapies to personalized interventions. J Transl Med 2022; 20:173. [PMID: 35410233 PMCID: PMC9004034 DOI: 10.1186/s12967-022-03365-z] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2021] [Accepted: 03/26/2022] [Indexed: 02/08/2023] Open
Abstract
AbstractThe human microbiome has been linked to several diseases. Gastrointestinal diseases are still one of the most prominent area of study in host-microbiome interactions however the underlying microbial mechanisms in these disorders are not fully established. Irritable bowel syndrome (IBS) remains as one of the prominent disorders with significant changes in the gut microbiome composition and without definitive treatment. IBS has a severe impact on socio-economic and patient’s lifestyle. The association studies between the IBS and microbiome have shed a light on relevance of microbial composition, and hence microbiome-based trials were designed. However, there are no clear evidence of potential treatment for IBS. This review summarizes the epidemiology and socioeconomic impact of IBS and then focus on microbiome observational and clinical trials. At the end, we propose a new perspective on using data-driven approach and applying computational modelling and machine learning to design microbiome-aware personalized treatment for IBS.
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In vitro interaction network of a synthetic gut bacterial community. THE ISME JOURNAL 2022; 16:1095-1109. [PMID: 34857933 PMCID: PMC8941000 DOI: 10.1038/s41396-021-01153-z] [Citation(s) in RCA: 48] [Impact Index Per Article: 24.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/26/2021] [Revised: 10/27/2021] [Accepted: 11/10/2021] [Indexed: 02/07/2023]
Abstract
A key challenge in microbiome research is to predict the functionality of microbial communities based on community membership and (meta)-genomic data. As central microbiota functions are determined by bacterial community networks, it is important to gain insight into the principles that govern bacteria-bacteria interactions. Here, we focused on the growth and metabolic interactions of the Oligo-Mouse-Microbiota (OMM12) synthetic bacterial community, which is increasingly used as a model system in gut microbiome research. Using a bottom-up approach, we uncovered the directionality of strain-strain interactions in mono- and pairwise co-culture experiments as well as in community batch culture. Metabolic network reconstruction in combination with metabolomics analysis of bacterial culture supernatants provided insights into the metabolic potential and activity of the individual community members. Thereby, we could show that the OMM12 interaction network is shaped by both exploitative and interference competition in vitro in nutrient-rich culture media and demonstrate how community structure can be shifted by changing the nutritional environment. In particular, Enterococcus faecalis KB1 was identified as an important driver of community composition by affecting the abundance of several other consortium members in vitro. As a result, this study gives fundamental insight into key drivers and mechanistic basis of the OMM12 interaction network in vitro, which serves as a knowledge base for future mechanistic in vivo studies.
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48
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Koduru L, Lakshmanan M, Hoon S, Lee DY, Lee YK, Ow DSW. Systems Biology of Gut Microbiota-Human Receptor Interactions: Toward Anti-inflammatory Probiotics. Front Microbiol 2022; 13:846555. [PMID: 35308387 PMCID: PMC8928190 DOI: 10.3389/fmicb.2022.846555] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2021] [Accepted: 02/11/2022] [Indexed: 12/14/2022] Open
Abstract
The incidence and prevalence of inflammatory disorders have increased globally, and is projected to double in the next decade. Gut microbiome-based therapeutics have shown promise in ameliorating chronic inflammation. However, they are largely experimental, context- or strain-dependent and lack a clear mechanistic basis. This hinders precision probiotics and poses significant risk, especially to individuals with pre-existing conditions. Molecules secreted by gut microbiota act as ligands to several health-relevant receptors expressed in human gut, such as the G-protein coupled receptors (GPCRs), Toll-like receptor 4 (TLR4), pregnane X receptor (PXR), and aryl hydrocarbon receptor (AhR). Among these, the human AhR expressed in different tissues exhibits anti-inflammatory effects and shows activity against a wide range of ligands produced by gut bacteria. However, different AhR ligands induce varying host responses and signaling in a tissue/organ-specific manner, which remain mostly unknown. The emerging systems biology paradigm, with its powerful in silico tool repertoire, provides opportunities for comprehensive and high-throughput strain characterization. In particular, combining metabolic models with machine learning tools can be useful to delineate tissue and ligand-specific signaling and thus their causal mechanisms in disease and health. The knowledge of such a mechanistic basis is indispensable to account for strain heterogeneity and actualize precision probiotics.
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Affiliation(s)
- Lokanand Koduru
- Institute of Molecular and Cell Biology, Agency for Science, Technology and Research (A*STAR), Singapore, Singapore
| | - Meiyappan Lakshmanan
- Bioprocessing Technology Institute, Agency for Science, Technology and Research (A*STAR), Singapore, Singapore
- Bioinformatics Institute, Agency for Science, Technology and Research (A*STAR), Singapore, Singapore
| | - Shawn Hoon
- Institute of Molecular and Cell Biology, Agency for Science, Technology and Research (A*STAR), Singapore, Singapore
| | - Dong-Yup Lee
- School of Chemical Engineering, Sungkyunkwan University, Suwon, South Korea
| | - Yuan Kun Lee
- Department of Microbiology and Immunology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Department of Surgery, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Dave Siak-Wei Ow
- Bioprocessing Technology Institute, Agency for Science, Technology and Research (A*STAR), Singapore, Singapore
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Liu G, Chu M, Xu P, Nie S, Xu X, Ren J. Effects of Ilisha elongata proteins on proliferation and adhesion of Lactobacillus plantarum. Food Chem X 2022; 13:100206. [PMID: 35499024 PMCID: PMC9039923 DOI: 10.1016/j.fochx.2022.100206] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2021] [Revised: 12/27/2021] [Accepted: 01/03/2022] [Indexed: 12/01/2022] Open
Abstract
Ilisha elongata proteins promote the growth afnd proliferation of LP45. Probiotic proliferation of Ie-S protein is higher than that of Ie-W protein. Ilisha elongata proteins can promote LP45 adhesion in the intestinal tract.
The effects of aquatic proteins on the proliferation and adhesion of intestinal probiotic bacteria were investigated by in vitro fermentation and mouse in vitrointestinal tissue models. Compared with the control group, the Illisha elongata protein reduced the growth time of Lactobacillus plantarum (LP45) by 34.25% and increased the total number of colonies by 6.61%. The Ilisha elongata salt-solubale protein performed better than water-soluble protein in vitro proliferation of LP45. Ilisha elongata salt-soluble protein significantly increased the number of viable bacteria adhering to intestinal, and caused changes in the amount of polysaccharides, proteins and biofilms in the intestinal tissue model. These results indicate that the Ilisha elongata protein is beneficial to the proliferation and adhesion of probiotics in the intestinal, and can be used as an active protein beneficial to intestinal health.
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50
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Selway CA, Sudarpa J, Weyrich LS. Moving beyond the gut microbiome: combining systems biology and multi-site microbiome analyses to combat non-communicable diseases. MEDICINE IN MICROECOLOGY 2022. [DOI: 10.1016/j.medmic.2022.100052] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022] Open
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