1
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Tarzi C, Zampieri G, Sullivan N, Angione C. Emerging methods for genome-scale metabolic modeling of microbial communities. Trends Endocrinol Metab 2024; 35:533-548. [PMID: 38575441 DOI: 10.1016/j.tem.2024.02.018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Revised: 02/28/2024] [Accepted: 02/29/2024] [Indexed: 04/06/2024]
Abstract
Genome-scale metabolic models (GEMs) are consolidating as platforms for studying mixed microbial populations, by combining biological data and knowledge with mathematical rigor. However, deploying these models to answer research questions can be challenging due to the increasing number of available computational tools, the lack of universal standards, and their inherent limitations. Here, we present a comprehensive overview of foundational concepts for building and evaluating genome-scale models of microbial communities. We then compare tools in terms of requirements, capabilities, and applications. Next, we highlight the current pitfalls and open challenges to consider when adopting existing tools and developing new ones. Our compendium can be relevant for the expanding community of modelers, both at the entry and experienced levels.
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Affiliation(s)
- Chaimaa Tarzi
- School of Computing, Engineering and Digital Technologies, Teesside University, Southfield Rd, Middlesbrough, TS1 3BX, North Yorkshire, UK
| | - Guido Zampieri
- Department of Biology, University of Padova, Padova, 35122, Veneto, Italy
| | - Neil Sullivan
- Complement Genomics Ltd, Station Rd, Lanchester, Durham, DH7 0EX, County Durham, UK
| | - Claudio Angione
- School of Computing, Engineering and Digital Technologies, Teesside University, Southfield Rd, Middlesbrough, TS1 3BX, North Yorkshire, UK; Centre for Digital Innovation, Teesside University, Southfield Rd, Middlesbrough, TS1 3BX, North Yorkshire, UK; National Horizons Centre, Teesside University, 38 John Dixon Ln, Darlington, DL1 1HG, North Yorkshire, UK.
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2
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Luo M, Zhu J, Jia J, Zhang H, Zhao J. Progress on network modeling and analysis of gut microecology: a review. Appl Environ Microbiol 2024; 90:e0009224. [PMID: 38415584 PMCID: PMC11207142 DOI: 10.1128/aem.00092-24] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/29/2024] Open
Abstract
The gut microecological network is a complex microbial community within the human body that plays a key role in linking dietary nutrition and host physiology. To understand the complex relationships among microbes and their functions within this community, network analysis has emerged as a powerful tool. By representing the interactions between microbes and their associated omics data as a network, we can gain a comprehensive understanding of the ecological mechanisms that drive the human gut microbiota. In addition, the network-based approach provides a more intuitive analysis of the gut microbiota, simplifying the study of its complex dynamics and interdependencies. This review provides a comprehensive overview of the methods used to construct and analyze networks in the context of gut microecological background. We discuss various types of network modeling approaches, including co-occurrence networks, causal networks, dynamic networks, and multi-omics networks, and describe the analytical techniques used to identify important network properties. We also highlight the challenges and limitations of network modeling in this area, such as data scarcity and heterogeneity, and provide future research directions to overcome these limitations. By exploring these network-based methods, researchers can gain valuable insights into the intricate relationships and functional roles of microbial communities within the gut, ultimately advancing our understanding of the gut microbiota's impact on human health.
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Affiliation(s)
- Meng Luo
- State Key Laboratory of Food Science and Technology, Jiangnan University, Wuxi, Jiangsu, China
- School of Food Science and Technology, Jiangnan University, Wuxi, Jiangsu, China
| | - Jinlin Zhu
- State Key Laboratory of Food Science and Technology, Jiangnan University, Wuxi, Jiangsu, China
- School of Food Science and Technology, Jiangnan University, Wuxi, Jiangsu, China
| | - Jiajia Jia
- Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), Jiangnan University, Wuxi, China
| | - Hao Zhang
- State Key Laboratory of Food Science and Technology, Jiangnan University, Wuxi, Jiangsu, China
- School of Food Science and Technology, Jiangnan University, Wuxi, Jiangsu, China
- National Engineering Research Center for Functional Food, Jiangnan University, Wuxi, Jiangsu, China
- Wuxi Translational Medicine Research Center, Jiangsu Translational Medicine Research Institute Wuxi Branch, Wuxi, China
- (Yangzhou) Institute of Food Biotechnology, Jiangnan University, Yangzhou, China
| | - Jianxin Zhao
- State Key Laboratory of Food Science and Technology, Jiangnan University, Wuxi, Jiangsu, China
- School of Food Science and Technology, Jiangnan University, Wuxi, Jiangsu, China
- Wuxi Translational Medicine Research Center, Jiangsu Translational Medicine Research Institute Wuxi Branch, Wuxi, China
- (Yangzhou) Institute of Food Biotechnology, Jiangnan University, Yangzhou, China
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3
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Zhu J, Yin J, Chen J, Hu M, Lu W, Wang H, Zhang H, Chen W. Integrative analysis with microbial modelling and machine learning uncovers potential alleviators for ulcerative colitis. Gut Microbes 2024; 16:2336877. [PMID: 38563656 PMCID: PMC10989691 DOI: 10.1080/19490976.2024.2336877] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/07/2023] [Accepted: 03/27/2024] [Indexed: 04/04/2024] Open
Abstract
Ulcerative colitis (UC) is a challenging form of inflammatory bowel disease, and its etiology is intricately linked to disturbances in the gut microbiome. To identify the potential alleviators of UC, we employed an integrative analysis combining microbial community modeling with advanced machine learning techniques. Using metagenomics data sourced from the Integrated Human Microbiome Project, we constructed individualized microbiome community models for each participant. Our analysis highlighted a significant decline in both α and β-diversity of strain-level microbial populations in UC subjects compared to controls. Distinct differences were also observed in the predicted fecal metabolite profiles and strain-to-metabolite contributions between the two groups. Using tree-based machine learning models, we successfully identified specific microbial strains and their associated metabolites as potential alleviators of UC. Notably, our experimental validation using a dextran sulfate sodium-induced UC mouse model demonstrated that the administration of Parabacteroides merdae ATCC 43,184 and N-acetyl-D-mannosamine provided notable relief from colitis symptoms. In summary, our study underscores the potential of an integrative approach to identify novel therapeutic avenues for UC, paving the way for future targeted interventions.
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Affiliation(s)
- Jinlin Zhu
- State Key Laboratory of Food Science and Resources, Jiangnan University, Wuxi, China
- School of Food Science and Technology, Jiangnan University, Wuxi, China
| | - Jialin Yin
- State Key Laboratory of Food Science and Resources, Jiangnan University, Wuxi, China
- School of Food Science and Technology, Jiangnan University, Wuxi, China
| | - Jing Chen
- State Key Laboratory of Food Science and Resources, Jiangnan University, Wuxi, China
- School of Food Science and Technology, Jiangnan University, Wuxi, China
| | - Mingyi Hu
- State Key Laboratory of Food Science and Resources, Jiangnan University, Wuxi, China
- School of Food Science and Technology, Jiangnan University, Wuxi, China
| | - Wenwei Lu
- State Key Laboratory of Food Science and Resources, Jiangnan University, Wuxi, China
- School of Food Science and Technology, Jiangnan University, Wuxi, China
- International Joint Research Laboratory for Pharmabiotics & Antibiotic Resistance, Jiangnan University, Wuxi, China
- (Yangzhou) Institute of Food Biotechnology, Jiangnan University, Yangzhou, China
| | - Hongchao Wang
- State Key Laboratory of Food Science and Resources, Jiangnan University, Wuxi, China
- School of Food Science and Technology, Jiangnan University, Wuxi, China
| | - Hao Zhang
- State Key Laboratory of Food Science and Resources, Jiangnan University, Wuxi, China
- School of Food Science and Technology, Jiangnan University, Wuxi, China
- (Yangzhou) Institute of Food Biotechnology, Jiangnan University, Yangzhou, China
- National Engineering Research Center for Functional Food, Jiangnan University, Wuxi, China
- Wuxi Translational Medicine Research Center and Jiangsu Translational Medicine Research Institute Wuxi Branch, Wuxi People’s Hospital, Wuxi, China
| | - Wei Chen
- State Key Laboratory of Food Science and Resources, Jiangnan University, Wuxi, China
- School of Food Science and Technology, Jiangnan University, Wuxi, China
- National Engineering Research Center for Functional Food, Jiangnan University, Wuxi, China
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4
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Hu M, Caldarelli G, Gili T. Inflammatory bowel disease biomarkers revealed by the human gut microbiome network. Sci Rep 2023; 13:19428. [PMID: 37940667 PMCID: PMC10632483 DOI: 10.1038/s41598-023-46184-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2022] [Accepted: 10/29/2023] [Indexed: 11/10/2023] Open
Abstract
Inflammatory bowel diseases (IBDs) are complex medical conditions in which the gut microbiota is attacked by the immune system of genetically predisposed subjects when exposed to yet unclear environmental factors. The complexity of this class of diseases makes them suitable to be represented and studied with network science. In this paper, the metagenomic data of control, Crohn's disease, and ulcerative colitis subjects' gut microbiota were investigated by representing this data as correlation networks and co-expression networks. We obtained correlation networks by calculating Pearson's correlation between gene expression across subjects. A percolation-based procedure was used to threshold and binarize the adjacency matrices. In contrast, co-expression networks involved the construction of the bipartite subjects-genes networks and the monopartite genes-genes projection after binarization of the biadjacency matrix. Centrality measures and community detection were used on the so-built networks to mine data complexity and highlight possible biomarkers of the diseases. The main results were about the modules of Bacteroides, which were connected in the control subjects' correlation network, Faecalibacterium prausnitzii, where co-enzyme A became central in IBD correlation networks and Escherichia coli, whose module has different patterns of integration within the whole network in the different diagnoses.
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Affiliation(s)
- Mirko Hu
- Department of Medicine and Surgery, University of Parma, 43121, Parma, Italy
| | - Guido Caldarelli
- Department of Molecular Science and Nanosystems, Ca' Foscari University of Venice, 30123, Venice, Italy.
- Institute of Complex Systems, National Research Council (ISC-CNR), 00185, Rome, Italy.
- Fondazione per il Futuro delle Città, FFC, 50133, Firenze, Italy.
- European Centre for Living Technology, (ECLT), 30123, Venice, Italy.
| | - Tommaso Gili
- Networks Unit, IMT School for Advanced Studies Lucca, 55100, Lucca, Italy
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5
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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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6
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Chen J, Yuan Z, Tu Y, Hu W, Xie C, Ye L. Experimental and computational models to investigate intestinal drug permeability and metabolism. Xenobiotica 2023; 53:25-45. [PMID: 36779684 DOI: 10.1080/00498254.2023.2180454] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/14/2023]
Abstract
Oral administration is the preferred route for drug administration that leads to better therapy compliance. The intestine plays a key role in the absorption and metabolism of oral drugs, therefore, new intestinal models are being continuously proposed, which contribute to the study of intestinal physiology, drug screening, drug side effects, and drug-drug interactions.Advances in pharmaceutical processes have produced more drug formulations, causing challenges for intestinal models. To adapt to the rapid evolution of pharmaceuticals, more intestinal models have been created. However, because of the complexity of the intestine, few models can take all aspects of the intestine into account, and some functions must be sacrificed to investigate other areas. Therefore, investigators need to choose appropriate models according to the experimental stage and other requirements to obtain the desired results.To help researchers achieve this goal, this review summarised the advantages and disadvantages of current commonly used intestinal models and discusses possible future directions, providing a better understanding of intestinal models.
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Affiliation(s)
- Jinyuan Chen
- Institute of Scientific Research, Southern Medical University, Guangzhou, P.R. China.,TCM-Integrated Hospital, Southern Medical University, Guangzhou, P.R. China
| | - Ziyun Yuan
- NMPA Key Laboratory for Research and Evaluation of Drug Metabolism, Guangdong Provincial Key Laboratory of New Drug Screening, School of Pharmaceutical Sciences, Southern Medical University, Guangzhou, P.R. China
| | - Yifan Tu
- Boehringer-Ingelheim, Connecticut, P.R. USA
| | - Wanyu Hu
- NMPA Key Laboratory for Research and Evaluation of Drug Metabolism, Guangdong Provincial Key Laboratory of New Drug Screening, School of Pharmaceutical Sciences, Southern Medical University, Guangzhou, P.R. China
| | - Cong Xie
- Clinical Pharmacy Center, Nanfang Hospital, Southern Medical University, Guangzhou, P.R. China
| | - Ling Ye
- TCM-Integrated Hospital, Southern Medical University, Guangzhou, P.R. China
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7
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Garcia S, Ordoñez S, López-Molina VM, Lacruz-Pleguezuelos B, Carrillo de Santa Pau E, Marcos-Zambrano LJ. Citizen science helps to raise awareness about gut microbiome health in people at risk of developing non-communicable diseases. Gut Microbes 2023; 15:2241207. [PMID: 37530428 PMCID: PMC10399471 DOI: 10.1080/19490976.2023.2241207] [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: 03/24/2023] [Accepted: 07/20/2023] [Indexed: 08/03/2023] Open
Abstract
Citizens lack knowledge about the impact of gut microbiota on health and how lifestyle and dietary choices can influence it, leading to Non-Communicable Diseases (NCDs) and affecting overall well-being. Participatory action research (PAR) is a promising approach to enhance communication and encourage individuals to adopt healthier behaviors and improve their health. In this study, we explored the feasibility of integrating the photovoice method with citizen science approaches to assess the impact of social and environmental factors on gut microbiota health. In this context, citizen science approaches entailed the involvement of participants in the collection of samples for subsequent analysis, specifically gut microbiome assessment via 16S rRNA gene sequencing. We recruited 70 volunteers and organized six photovoice groups based on age and educational background. Participants selected 64 photographs that represented the influence of daily habits on gut microbiota health and created four photovoice themes. Analysis of the gut microbiome using 16S rRNA gene sequencing identified 474 taxa, and in-depth microbial analysis revealed three clusters of people based on gut microbiome diversity and body mass index (BMI). Our findings indicate that participants enhanced their knowledge of gut microbiome health through PAR activities, and we found a correlation between lower microbial diversity, higher BMI, and better achievement of learning outcomes. Using PAR as a methodology is an effective way to increase citizens' awareness and engagement in self-care, maintain healthy gut microbiota, and prevent NCD development. These interventions are particularly beneficial for individuals at higher risk of developing NCDs.
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Affiliation(s)
- Silvia Garcia
- Computational Biology Group, Precision Nutrition and Cancer Research Program, IMDEA Food Institute, Madrid, Spain
| | - Sheyla Ordoñez
- Computational Biology Group, Precision Nutrition and Cancer Research Program, IMDEA Food Institute, Madrid, Spain
| | - Victor Manuel López-Molina
- Computational Biology Group, Precision Nutrition and Cancer Research Program, IMDEA Food Institute, Madrid, Spain
| | - Blanca Lacruz-Pleguezuelos
- Computational Biology Group, Precision Nutrition and Cancer Research Program, IMDEA Food Institute, Madrid, Spain
| | | | - Laura Judith Marcos-Zambrano
- Computational Biology Group, Precision Nutrition and Cancer Research Program, IMDEA Food Institute, Madrid, Spain
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8
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Metabolic Modeling and Bidirectional Culturing of Two Gut Microbes Reveal Cross-Feeding Interactions and Protective Effects on Intestinal Cells. mSystems 2022; 7:e0064622. [PMID: 36005398 PMCID: PMC9600892 DOI: 10.1128/msystems.00646-22] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022] Open
Abstract
The gut microbiota is constituted by thousands of microbial interactions, some of which correspond to the exchange of metabolic by-products or cross-feeding. Inulin and xylan are two major dietary polysaccharides that are fermented by members of the human gut microbiota, resulting in different metabolic profiles. Here, we integrated community modeling and bidirectional culturing assays to study the metabolic interactions between two gut microbes, Phocaeicola dorei and Lachnoclostridium symbiosum, growing in inulin or xylan, and how they provide a protective effect in cultured cells. P. dorei (previously belonging to the Bacteroides genus) was able to consume inulin and xylan, while L. symposium only used certain inulin fractions to produce butyrate as a major end product. Constrained-based flux simulations of refined genome-scale metabolic models of both microbes predicted high lactate and succinate cross-feeding fluxes between P. dorei and L. symbiosum when growing in each fiber. Bidirectional culture assays in both substrates revealed that L. symbiosum growth increased in the presence of P. dorei. Carbohydrate consumption analyses showed a faster carbohydrate consumption in cocultures compared to monocultures. Lactate and succinate concentrations in bidirectional cocultures were lower than in monocultures, pointing to cross-feeding as initially suggested by the model. Butyrate concentrations were similar across all conditions. Finally, supernatants from both bacteria cultured in xylan in bioreactors significantly reduced tumor necrosis factor-α-induced inflammation in HT-29 cells and exerted a protective effect against the TcdB toxin in Caco-2 epithelial cells. Surprisingly, this effect was not observed in inulin cocultures. Overall, these results highlight the predictive value of metabolic models integrated with microbial culture assays for probing microbial interactions in the gut microbiota. They also provide an example of how metabolic exchange could lead to potential beneficial effects in the host. IMPORTANCE Microbial interactions represent the inner connections in the gut microbiome. By integrating mathematical modeling tools and microbial bidirectional culturing, we determined how two gut commensals engage in the exchange of cross-feeding metabolites, lactate and succinate, for increased growth in two fibers. These interactions underpinned butyrate production in cocultures, resulting in a significant reduction in cellular inflammation and protection against microbial toxins when applied to cellular models.
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Liu Q, Lee B, Xie L. Small molecule modulation of microbiota: a systems pharmacology perspective. BMC Bioinformatics 2022; 23:403. [PMID: 36175827 PMCID: PMC9523894 DOI: 10.1186/s12859-022-04941-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2022] [Accepted: 09/16/2022] [Indexed: 11/16/2022] Open
Abstract
BACKGROUND Microbes are associated with many human diseases and influence drug efficacy. Small-molecule drugs may revolutionize biomedicine by fine-tuning the microbiota on the basis of individual patient microbiome signatures. However, emerging endeavors in small-molecule microbiome drug discovery continue to follow a conventional "one-drug-one-target-one-disease" process. A systematic pharmacology approach that would suppress multiple interacting pathogenic species in the microbiome, could offer an attractive alternative solution. RESULTS We construct a disease-centric signed microbe-microbe interaction network using curated microbe metabolite information and their effects on host. We develop a Signed Random Walk with Restart algorithm for the accurate prediction of effect of microbes on human health and diseases. With a survey on the druggable and evolutionary space of microbe proteins, we find that 8-10% of them can be targeted by existing drugs or drug-like chemicals and that 25% of them have homologs to human proteins. We demonstrate that drugs for diabetes can be the lead compounds for development of microbiota-targeted therapeutics. We further show that the potential drug targets that specifically exist in pathogenic microbes are periplasmic and cellular outer membrane proteins. CONCLUSION The systematic studies of the polypharmacological landscape of the microbiome network may open a new avenue for the small-molecule drug discovery of the microbiome. We believe that the application of systematic method on the polypharmacological investigation could lead to the discovery of novel drug therapies.
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Affiliation(s)
- Qiao Liu
- Department of Computer Science, Hunter College, The City University of New York, New York, NY, USA
| | - Bohyun Lee
- Ph.D. Program in Computer Science, The City University of New York, New York, NY, USA
| | - Lei Xie
- Department of Computer Science, Hunter College, The City University of New York, New York, NY, USA.
- Ph.D. Program in Computer Science, The City University of New York, New York, NY, USA.
- Ph.D. Program in Biochemistry and Biology, The City University of New York, New York, NY, USA.
- Helen and Robert Appel Alzheimer's Disease Research Institute, Feil Family Brain and Mind Research Institute, Weill Cornell Medicine, Cornell University, New York, NY, USA.
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10
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Chatterjee G, Negi S, Basu S, Faintuch J, O'Donovan A, Shukla P. Microbiome systems biology advancements for natural well-being. THE SCIENCE OF THE TOTAL ENVIRONMENT 2022; 838:155915. [PMID: 35568180 DOI: 10.1016/j.scitotenv.2022.155915] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/23/2022] [Revised: 05/09/2022] [Accepted: 05/09/2022] [Indexed: 06/15/2023]
Abstract
Throughout the years all data from epidemiological, physiological and omics have suggested that the microbial communities play a considerable role in modulating human health. The population of microorganisms residing in the human intestine collectively known as microbiota presents a genetic repertoire that is higher in magnitude than the human genome. They play an essential role in host immunity and neuronal signaling. Rapid enhancement of sequence based screening and development of humanized gnotobiotic model has sparked a great deal of interest among scientists to probe the dynamic interactions of the commensal bacteria. This review focuses on systemic analysis of the gut microbiome to decipher the complexity of the host-microbe intercommunication and gives a special emphasis on the evolution of targeted precision medicine through microbiome engineering. In addition, we have also provided a comprehensive description of how interconnection between metabolism and biochemical reactions in a specific organism can be obtained from a metabolic network or a flux balance analysis and combining multiple datasets helps in the identification of a particular metabolite. The review highlights how genetic modification of the critical components and programming the resident microflora can be employed for targeted precision medicine. Inspite of the ongoing debate on the utility of gut microbiome we have explored on the probable new therapeutic avenues like FMT (Fecal microbiota transplant) can be utilized. This review also recapitulates integrating human-relevant 3D cellular models coupled with computational models and the metadata obtained from interventional and epidemiological studies may decipher the complex interactome of diet-microbiota-disease pathophysiology. In addition, it will also open new avenues for the development of therapeutics derived from microbiome or implementation of personalized nutrition. In addition, the identification of biomarkers can also help towards the development of new diagnostic tools and eventually will lead to strategic management of the disease. Inspite of the ongoing debate on the utility of the gut microbiome we have explored how probable new therapeutic avenues like FMT (Fecal microbiota transplant) can be utilized. This review also summarises integrating human-relevant 3D cellular models coupled with computational models and the metadata obtained from interventional and epidemiological studies may decipher the complex interactome of diet- microbiota-disease pathophysiology. In addition, it will also open new avenues for the development of therapeutics derived from the microbiome or implementation of personalized nutrition. In addition, the identification of biomarkers can also help towards the development of new diagnostic tools and eventually will lead to strategic management of disease.
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Affiliation(s)
| | - Sangeeta Negi
- NMC Biolab, New Mexico Consortium, Los Alamos, NM, USA; Los Alamos National Laboratory, Los Alamos, NM 87544, USA
| | - Supratim Basu
- NMC Biolab, New Mexico Consortium, Los Alamos, NM, USA
| | - Joel Faintuch
- Department of Gastroenterology, Sao Paulo University Medical School, São Paulo, SP 01246-903, Brazil
| | | | - Pratyoosh Shukla
- Enzyme Technology and Protein Bioinformatics Laboratory, School of Biotechnology, Institute of Science, Banaras Hindu University, Varanasi 221005, India.
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11
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Abstract
The gastrointestinal ecosystem is formed from interactions between the host, indigenous gut microbiota, and external world. When colonizing the gut, bacteria must overcome barriers imposed by the intestinal environment, such as host immune responses and microbiota-mediated nutrient limitation. Thus, understanding bacterial colonization requires determining how the gut landscape interacts with microbes attempting to establish within the ecosystem. However, the complicated network of interactions between elements of the intestinal environment makes it challenging to uncover emergent properties of the system using only reductionist methods. A systems biology approach, which aims to investigate complex systems by examining the behavior and relationships of all elements of the system, may afford a more holistic perspective of the colonization process. Here, we examine the confluence between the gut landscape and bacterial colonization through the lens of systems biology. We offer an overview of the conceptual and methodological underpinnings of systems biology, followed by a discussion of key elements of the gut ecosystem as they pertain to bacterial establishment and growth. We conclude by reintegrating these elements to guide future comprehensive investigations of the ecosystem in the context of bacterial intestinal colonization.
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Affiliation(s)
- Madeline R. Barron
- Department of Microbiology & Immunology, University of Michigan Medical School, Ann Arbor, Michigan, USA
| | - Vincent B. Young
- Department of Microbiology & Immunology, University of Michigan Medical School, Ann Arbor, Michigan, USA
- Department of Internal Medicine, Division of Infectious Diseases, University of Michigan Medical School, Ann Arbor, Michigan, USA
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12
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Ternes D, Tsenkova M, Pozdeev VI, Meyers M, Koncina E, Atatri S, Schmitz M, Karta J, Schmoetten M, Heinken A, Rodriguez F, Delbrouck C, Gaigneaux A, Ginolhac A, Nguyen TTD, Grandmougin L, Frachet-Bour A, Martin-Gallausiaux C, Pacheco M, Neuberger-Castillo L, Miranda P, Zuegel N, Ferrand JY, Gantenbein M, Sauter T, Slade DJ, Thiele I, Meiser J, Haan S, Wilmes P, Letellier E. The gut microbial metabolite formate exacerbates colorectal cancer progression. Nat Metab 2022; 4:458-475. [PMID: 35437333 PMCID: PMC9046088 DOI: 10.1038/s42255-022-00558-0] [Citation(s) in RCA: 127] [Impact Index Per Article: 63.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/15/2021] [Accepted: 02/25/2022] [Indexed: 02/07/2023]
Abstract
The gut microbiome is a key player in the immunomodulatory and protumorigenic microenvironment during colorectal cancer (CRC), as different gut-derived bacteria can induce tumour growth. However, the crosstalk between the gut microbiome and the host in relation to tumour cell metabolism remains largely unexplored. Here we show that formate, a metabolite produced by the CRC-associated bacterium Fusobacterium nucleatum, promotes CRC development. We describe molecular signatures linking CRC phenotypes with Fusobacterium abundance. Cocultures of F. nucleatum with patient-derived CRC cells display protumorigenic effects, along with a metabolic shift towards increased formate secretion and cancer glutamine metabolism. We further show that microbiome-derived formate drives CRC tumour invasion by triggering AhR signalling, while increasing cancer stemness. Finally, F. nucleatum or formate treatment in mice leads to increased tumour incidence or size, and Th17 cell expansion, which can favour proinflammatory profiles. Moving beyond observational studies, we identify formate as a gut-derived oncometabolite that is relevant for CRC progression.
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Affiliation(s)
- Dominik Ternes
- Molecular Disease Mechanisms Group, Department of Life Sciences and Medicine, Faculty of Science, Technology and Medicine, University of Luxembourg, Belvaux, Luxembourg
| | - Mina Tsenkova
- Molecular Disease Mechanisms Group, Department of Life Sciences and Medicine, Faculty of Science, Technology and Medicine, University of Luxembourg, Belvaux, Luxembourg
| | - Vitaly Igorevich Pozdeev
- Molecular Disease Mechanisms Group, Department of Life Sciences and Medicine, Faculty of Science, Technology and Medicine, University of Luxembourg, Belvaux, Luxembourg
| | - Marianne Meyers
- Molecular Disease Mechanisms Group, Department of Life Sciences and Medicine, Faculty of Science, Technology and Medicine, University of Luxembourg, Belvaux, Luxembourg
| | - Eric Koncina
- Molecular Disease Mechanisms Group, Department of Life Sciences and Medicine, Faculty of Science, Technology and Medicine, University of Luxembourg, Belvaux, Luxembourg
| | - Sura Atatri
- Molecular Disease Mechanisms Group, Department of Life Sciences and Medicine, Faculty of Science, Technology and Medicine, University of Luxembourg, Belvaux, Luxembourg
| | - Martine Schmitz
- Molecular Disease Mechanisms Group, Department of Life Sciences and Medicine, Faculty of Science, Technology and Medicine, University of Luxembourg, Belvaux, Luxembourg
| | - Jessica Karta
- Molecular Disease Mechanisms Group, Department of Life Sciences and Medicine, Faculty of Science, Technology and Medicine, University of Luxembourg, Belvaux, Luxembourg
| | - Maryse Schmoetten
- Molecular Disease Mechanisms Group, Department of Life Sciences and Medicine, Faculty of Science, Technology and Medicine, University of Luxembourg, Belvaux, Luxembourg
| | - Almut Heinken
- School of Medicine, National University of Ireland, Galway, Ireland
- Ryan Institute, National University of Galway, Galway, Ireland
| | - Fabien Rodriguez
- Molecular Disease Mechanisms Group, Department of Life Sciences and Medicine, Faculty of Science, Technology and Medicine, University of Luxembourg, Belvaux, Luxembourg
| | - Catherine Delbrouck
- Cancer Metabolism Group, Department of Cancer Research, Luxembourg Institute of Health, Luxembourg, Luxembourg
| | - Anthoula Gaigneaux
- Molecular Disease Mechanisms Group, Department of Life Sciences and Medicine, Faculty of Science, Technology and Medicine, University of Luxembourg, Belvaux, Luxembourg
| | - Aurelien Ginolhac
- Molecular Disease Mechanisms Group, Department of Life Sciences and Medicine, Faculty of Science, Technology and Medicine, University of Luxembourg, Belvaux, Luxembourg
| | - Tam Thuy Dan Nguyen
- Department of Biochemistry, Virginia Polytechnic Institute and State University, Blacksburg, VA, USA
| | - Lea Grandmougin
- Systems Ecology Group, Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Audrey Frachet-Bour
- Systems Ecology Group, Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Camille Martin-Gallausiaux
- Systems Ecology Group, Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Maria Pacheco
- Molecular Disease Mechanisms Group, Department of Life Sciences and Medicine, Faculty of Science, Technology and Medicine, University of Luxembourg, Belvaux, Luxembourg
| | | | - Paulo Miranda
- National Center of Pathology, Laboratoire National de Santé, Dudelange, Luxembourg
| | - Nikolaus Zuegel
- Department of Surgery, Centre Hospitalier Emile Mayrisch, Esch-sur-Alzette, Luxembourg
| | - Jean-Yves Ferrand
- Clinical and Epidemiological Investigation Center, Department of Population Health, Luxembourg Institute of Health, Luxembourg, Luxembourg
| | - Manon Gantenbein
- Clinical and Epidemiological Investigation Center, Department of Population Health, Luxembourg Institute of Health, Luxembourg, Luxembourg
| | - Thomas Sauter
- Molecular Disease Mechanisms Group, Department of Life Sciences and Medicine, Faculty of Science, Technology and Medicine, University of Luxembourg, Belvaux, Luxembourg
| | - Daniel Joseph Slade
- Department of Biochemistry, Virginia Polytechnic Institute and State University, Blacksburg, VA, USA
| | - Ines Thiele
- School of Medicine, National University of Ireland, Galway, Ireland
- Ryan Institute, National University of Galway, Galway, Ireland
- Discipline of Microbiology, School of Natural Sciences, National University of Ireland, Galway, Ireland
- APC Microbiome, Cork, Ireland
| | - Johannes Meiser
- Cancer Metabolism Group, Department of Cancer Research, Luxembourg Institute of Health, Luxembourg, Luxembourg
| | - Serge Haan
- Molecular Disease Mechanisms Group, Department of Life Sciences and Medicine, Faculty of Science, Technology and Medicine, University of Luxembourg, Belvaux, Luxembourg
| | - Paul Wilmes
- Molecular Disease Mechanisms Group, Department of Life Sciences and Medicine, Faculty of Science, Technology and Medicine, University of Luxembourg, Belvaux, Luxembourg
- Systems Ecology Group, Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Elisabeth Letellier
- Molecular Disease Mechanisms Group, Department of Life Sciences and Medicine, Faculty of Science, Technology and Medicine, University of Luxembourg, Belvaux, Luxembourg.
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13
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Mohammad FK, Palukuri MV, Shivakumar S, Rengaswamy R, Sahoo S. A Computational Framework for Studying Gut-Brain Axis in Autism Spectrum Disorder. Front Physiol 2022; 13:760753. [PMID: 35330929 PMCID: PMC8940246 DOI: 10.3389/fphys.2022.760753] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2021] [Accepted: 01/17/2022] [Indexed: 12/28/2022] Open
Abstract
Introduction The integrity of the intestinal epithelium is crucial for human health and is harmed in autism spectrum disorder (ASD). An aberrant gut microbial composition resulting in gut-derived metabolic toxins was found to damage the intestinal epithelium, jeopardizing tissue integrity. These toxins further reach the brain via the gut-brain axis, disrupting the normal function of the brain. A mechanistic understanding of metabolic disturbances in the brain and gut is essential to design effective therapeutics and early intervention to block disease progression. Herein, we present a novel computational framework integrating constraint based tissue specific metabolic (CBM) model and whole-body physiological pharmacokinetics (PBPK) modeling for ASD. Furthermore, the role of gut microbiota, diet, and oxidative stress is analyzed in ASD. Methods A representative gut model capturing host-bacteria and bacteria-bacteria interaction was developed using CBM techniques and patient data. Simultaneously, a PBPK model of toxin metabolism was assembled, incorporating multi-scale metabolic information. Furthermore, dynamic flux balance analysis was performed to integrate CBM and PBPK. The effectiveness of a probiotic and dietary intervention to improve autism symptoms was tested on the integrated model. Results The model accurately highlighted critical metabolic pathways of the gut and brain that are associated with ASD. These include central carbon, nucleotide, and vitamin metabolism in the host gut, and mitochondrial energy and amino acid metabolisms in the brain. The proposed dietary intervention revealed that a high-fiber diet is more effective than a western diet in reducing toxins produced inside the gut. The addition of probiotic bacteria Lactobacillus acidophilus, Bifidobacterium longum longum, Akkermansia muciniphila, and Prevotella ruminicola to the diet restores gut microbiota balance, thereby lowering oxidative stress in the gut and brain. Conclusion The proposed computational framework is novel in its applicability, as demonstrated by the determination of the whole-body distribution of ROS toxins and metabolic association in ASD. In addition, it emphasized the potential for developing novel therapeutic strategies to alleviate autism symptoms. Notably, the presented integrated model validates the importance of combining PBPK modeling with COBRA -specific tissue details for understanding disease pathogenesis.
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Affiliation(s)
- Faiz Khan Mohammad
- Department of Chemical Engineering, Indian Institute of Technology Madras, Chennai, India
| | - Meghana Venkata Palukuri
- Department of Chemical Engineering, Indian Institute of Technology Madras, Chennai, India.,Initiative for Biological Systems Engineering, Indian Institute of Technology Madras, Chennai, India
| | - Shruti Shivakumar
- Department of Chemical Engineering, Indian Institute of Technology Madras, Chennai, India.,Initiative for Biological Systems Engineering, Indian Institute of Technology Madras, Chennai, India
| | - Raghunathan Rengaswamy
- Department of Chemical Engineering, Indian Institute of Technology Madras, Chennai, India.,Initiative for Biological Systems Engineering, Indian Institute of Technology Madras, Chennai, India
| | - Swagatika Sahoo
- Department of Chemical Engineering, Indian Institute of Technology Madras, Chennai, India.,Initiative for Biological Systems Engineering, Indian Institute of Technology Madras, Chennai, India
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14
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Purohit V, Wagner A, Yosef N, Kuchroo VK. Systems-based approaches to study immunometabolism. Cell Mol Immunol 2022; 19:409-420. [PMID: 35121805 PMCID: PMC8891302 DOI: 10.1038/s41423-021-00783-9] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Accepted: 09/17/2021] [Indexed: 02/06/2023] Open
Abstract
Technical advances at the interface of biology and computation, such as single-cell RNA-sequencing (scRNA-seq), reveal new layers of complexity in cellular systems. An emerging area of investigation using the systems biology approach is the study of the metabolism of immune cells. The diverse spectra of immune cell phenotypes, sparsity of immune cell numbers in vivo, limitations in the number of metabolites identified, dynamic nature of cellular metabolism and metabolic fluxes, tissue specificity, and high dependence on the local milieu make investigations in immunometabolism challenging, especially at the single-cell level. In this review, we define the systemic nature of immunometabolism, summarize cell- and system-based approaches, and introduce mathematical modeling approaches for systems interrogation of metabolic changes in immune cells. We close the review by discussing the applications and shortcomings of metabolic modeling techniques. With systems-oriented studies of metabolism expected to become a mainstay of immunological research, an understanding of current approaches toward systems immunometabolism will help investigators make the best use of current resources and push the boundaries of the discipline.
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Affiliation(s)
- Vinee Purohit
- Evergrande Center for Immunologic Diseases and Ann Romney Center for Neurologic Diseases, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, 02141, USA
| | - Allon Wagner
- Department of Electrical Engineering and Computer Science, University of California, Berkeley, CA, 94720, USA
- Center for Computational Biology, University of California, Berkeley, CA, 94720, USA
| | - Nir Yosef
- Department of Electrical Engineering and Computer Science, University of California, Berkeley, CA, 94720, USA
- Center for Computational Biology, University of California, Berkeley, CA, 94720, USA
| | - Vijay K Kuchroo
- Evergrande Center for Immunologic Diseases and Ann Romney Center for Neurologic Diseases, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA.
- Broad Institute of MIT and Harvard, Cambridge, MA, 02141, USA.
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15
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Balaguer-Trias J, Deepika D, Schuhmacher M, Kumar V. Impact of Contaminants on Microbiota: Linking the Gut-Brain Axis with Neurotoxicity. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph19031368. [PMID: 35162390 PMCID: PMC8835190 DOI: 10.3390/ijerph19031368] [Citation(s) in RCA: 33] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/28/2021] [Revised: 01/19/2022] [Accepted: 01/21/2022] [Indexed: 02/06/2023]
Abstract
Over the last years, research has focused on microbiota to establish a missing link between neuronal health and intestine imbalance. Many studies have considered microbiota as critical regulators of the gut–brain axis. The crosstalk between microbiota and the central nervous system is mainly explained through three different pathways: the neural, endocrine, and immune pathways, intricately interconnected with each other. In day-to-day life, human beings are exposed to a wide variety of contaminants that affect our intestinal microbiota and alter the bidirectional communication between the gut and brain, causing neuronal disorders. The interplay between xenobiotics, microbiota and neurotoxicity is still not fully explored, especially for susceptible populations such as pregnant women, neonates, and developing children. Precisely, early exposure to contaminants can trigger neurodevelopmental toxicity and long-term diseases. There is growing but limited research on the specific mechanisms of the microbiota–gut–brain axis (MGBA), making it challenging to understand the effect of environmental pollutants. In this review, we discuss the biological interplay between microbiota–gut–brain and analyse the role of endocrine-disrupting chemicals: Bisphenol A (BPA), Chlorpyrifos (CPF), Diethylhexyl phthalate (DEHP), and Per- and polyfluoroalkyl substances (PFAS) in MGBA perturbations and subsequent neurotoxicity. The complexity of the MGBA and the changing nature of the gut microbiota pose significant challenges for future research. However, emerging in-silico models able to analyse and interpret meta-omics data are a promising option for understanding the processes in this axis and can help prevent neurotoxicity.
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Affiliation(s)
- Jordina Balaguer-Trias
- Environmental Engineering Laboratory, Department of Chemical Engineering, Universitat Rovira i Virgili, 43007 Tarragona, Spain; (J.B.-T.); (D.D.); (M.S.)
| | - Deepika Deepika
- Environmental Engineering Laboratory, Department of Chemical Engineering, Universitat Rovira i Virgili, 43007 Tarragona, Spain; (J.B.-T.); (D.D.); (M.S.)
| | - Marta Schuhmacher
- Environmental Engineering Laboratory, Department of Chemical Engineering, Universitat Rovira i Virgili, 43007 Tarragona, Spain; (J.B.-T.); (D.D.); (M.S.)
| | - Vikas Kumar
- Environmental Engineering Laboratory, Department of Chemical Engineering, Universitat Rovira i Virgili, 43007 Tarragona, Spain; (J.B.-T.); (D.D.); (M.S.)
- IISPV (Pere Virgili Institute for Health Research), Sant Joan University Hospital, Universitat Rovira i Virgili, 43204 Reus, Spain
- Correspondence: ; Tel.: +34977558576
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16
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Ankrah NYD, Bernstein DB, Biggs M, Carey M, Engevik M, García-Jiménez B, Lakshmanan M, Pacheco AR, Sulheim S, Medlock GL. Enhancing Microbiome Research through Genome-Scale Metabolic Modeling. mSystems 2021; 6:e0059921. [PMID: 34904863 PMCID: PMC8670372 DOI: 10.1128/msystems.00599-21] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023] Open
Abstract
Construction and analysis of genome-scale metabolic models (GEMs) is a well-established systems biology approach that can be used to predict metabolic and growth phenotypes. The ability of GEMs to produce mechanistic insight into microbial ecological processes makes them appealing tools that can open a range of exciting opportunities in microbiome research. Here, we briefly outline these opportunities, present current rate-limiting challenges for the trustworthy application of GEMs to microbiome research, and suggest approaches for moving the field forward.
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Affiliation(s)
- Nana Y. D. Ankrah
- State University of New York at Plattsburgh, Plattsburgh, New York, USA
| | | | | | - Maureen Carey
- University of Virginia, Charlottesville, Virginia, USA
| | - Melinda Engevik
- Medical University of South Carolina, Charleston, South Carolina, USA
| | | | - Meiyappan Lakshmanan
- Bioprocessing Technology Institute, Agency for Science, Technology and Research (A*STAR), Singapore
- Bioinformatics Institute, Agency for Science, Technology and Research (A*STAR), Singapore
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17
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Xiang B, Zhao L, Zhang M. Metagenome-Scale Metabolic Network Suggests Folate Produced by Bifidobacterium longum Might Contribute to High-Fiber-Diet-Induced Weight Loss in a Prader-Willi Syndrome Child. Microorganisms 2021; 9:microorganisms9122493. [PMID: 34946095 PMCID: PMC8705902 DOI: 10.3390/microorganisms9122493] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2021] [Revised: 11/13/2021] [Accepted: 11/29/2021] [Indexed: 01/14/2023] Open
Abstract
Gut-microbiota-targeted nutrition intervention has achieved success in the management of obesity, but its underlying mechanism still needs extended exploration. An obese Prader-Willi syndrome boy lost 25.8 kg after receiving a high-fiber dietary intervention for 105 days. The fecal microbiome sequencing data taken from the boy on intervention days 0, 15, 30, 45, 60, 75, and 105, along with clinical indexes, were used to construct a metagenome-scale metabolic network. Firstly, the abundances of the microbial strains were obtained by mapping the sequencing reads onto the assembly of gut organisms through use of reconstruction and analysis (AGORA) genomes. The nutritional components of the diet were obtained through the Virtual Metabolic Human database. Then, a community model was simulated using the Microbiome Modeling Toolbox. Finally, the significant Spearman correlations among the metabolites and the clinical indexes were screened and the strains that were producing these metabolites were identified. The high-fiber diet reduced the overall amount of metabolite secretions, but the secretions of folic acid derivatives by Bifidobacterium longum strains were increased and were significantly relevant to the observed weight loss. Reduced metabolites might also have directly contributed to the weight loss or indirectly contribute by enhancing leptin and decreasing adiponectin. Metagenome-scale metabolic network technology provides a cost-efficient solution for screening the functional microbial strains and metabolic pathways that are responding to nutrition therapy.
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18
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Abstract
The neonatal body provides a range of potential habitats, such as the gut, for microbes. These sites eventually harbor microbial communities (microbiotas). A "complete" (adult) gut microbiota is not acquired by the neonate immediately after birth. Rather, the exclusive, milk-based nutrition of the infant encourages the assemblage of a gut microbiota of low diversity, usually dominated by bifidobacterial species. The maternal fecal microbiota is an important source of bacterial species that colonize the gut of infants, at least in the short-term. However, development of the microbiota is influenced by the use of human milk (breast feeding), infant formula, preterm delivery of infants, caesarean delivery, antibiotic administration, family details and other environmental factors. Following the introduction of weaning (complementary) foods, the gut microbiota develops in complexity due to the availability of a diversity of plant glycans in fruits and vegetables. These glycans provide growth substrates for the bacterial families (such as members of the Ruminococcaceae and Lachnospiraceae) that, in due course, will dominate the gut microbiota of the adult. Although current data are often fragmentary and observational, it can be concluded that the nutrition that a child receives in early life is likely to impinge not only on the development of the microbiota at that time but also on the subsequent lifelong, functional relationships between the microbiota and the human host. The purpose of this review, therefore, is to discuss the importance of promoting the assemblage of functionally robust gut microbiotas at appropriate times in early life.
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Affiliation(s)
- Gerald W. Tannock
- Department of Microbiology and Immunology, University of Otago, Dunedin, New Zealand
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19
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Sahu A, Blätke MA, Szymański JJ, Töpfer N. Advances in flux balance analysis by integrating machine learning and mechanism-based models. Comput Struct Biotechnol J 2021; 19:4626-4640. [PMID: 34471504 PMCID: PMC8382995 DOI: 10.1016/j.csbj.2021.08.004] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2021] [Revised: 08/03/2021] [Accepted: 08/03/2021] [Indexed: 02/08/2023] Open
Abstract
The availability of multi-omics data sets and genome-scale metabolic models for various organisms provide a platform for modeling and analyzing genotype-to-phenotype relationships. Flux balance analysis is the main tool for predicting flux distributions in genome-scale metabolic models and various data-integrative approaches enable modeling context-specific network behavior. Due to its linear nature, this optimization framework is readily scalable to multi-tissue or -organ and even multi-organism models. However, both data and model size can hamper a straightforward biological interpretation of the estimated fluxes. Moreover, flux balance analysis simulates metabolism at steady-state and thus, in its most basic form, does not consider kinetics or regulatory events. The integration of flux balance analysis with complementary data analysis and modeling techniques offers the potential to overcome these challenges. In particular machine learning approaches have emerged as the tool of choice for data reduction and selection of most important variables in big data sets. Kinetic models and formal languages can be used to simulate dynamic behavior. This review article provides an overview of integrative studies that combine flux balance analysis with machine learning approaches, kinetic models, such as physiology-based pharmacokinetic models, and formal graphical modeling languages, such as Petri nets. We discuss the mathematical aspects and biological applications of these integrated approaches and outline challenges and future perspectives.
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Affiliation(s)
- Ankur Sahu
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Corrensstraße 3, 06466 Gatersleben, Germany
| | - Mary-Ann Blätke
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Corrensstraße 3, 06466 Gatersleben, Germany
| | - Jędrzej Jakub Szymański
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Corrensstraße 3, 06466 Gatersleben, Germany
| | - Nadine Töpfer
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Corrensstraße 3, 06466 Gatersleben, Germany
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20
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Blasco T, Pérez-Burillo S, Balzerani F, Hinojosa-Nogueira D, Lerma-Aguilera A, Pastoriza S, Cendoya X, Rubio Á, Gosalbes MJ, Jiménez-Hernández N, Pilar Francino M, Apaolaza I, Rufián-Henares JÁ, Planes FJ. An extended reconstruction of human gut microbiota metabolism of dietary compounds. Nat Commun 2021; 12:4728. [PMID: 34354065 PMCID: PMC8342455 DOI: 10.1038/s41467-021-25056-x] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2020] [Accepted: 07/21/2021] [Indexed: 02/07/2023] Open
Abstract
Understanding how diet and gut microbiota interact in the context of human health is a key question in personalized nutrition. Genome-scale metabolic networks and constraint-based modeling approaches are promising to systematically address this complex problem. However, when applied to nutritional questions, a major issue in existing reconstructions is the limited information about compounds in the diet that are metabolized by the gut microbiota. Here, we present AGREDA, an extended reconstruction of diet metabolism in the human gut microbiota. AGREDA adds the degradation pathways of 209 compounds present in the human diet, mainly phenolic compounds, a family of metabolites highly relevant for human health and nutrition. We show that AGREDA outperforms existing reconstructions in predicting diet-specific output metabolites from the gut microbiota. Using 16S rRNA gene sequencing data of faecal samples from Spanish children representing different clinical conditions, we illustrate the potential of AGREDA to establish relevant metabolic interactions between diet and gut microbiota.
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Affiliation(s)
- Telmo Blasco
- Tecnun, University of Navarra, San Sebastián, Spain
- Biomedical Engineering Center, University of Navarra, Campus Universitario, Pamplona, Navarra, Spain
| | - Sergio Pérez-Burillo
- Departamento de Nutrición y Bromatología, Instituto de Nutrición y Tecnología de los Alimentos, Centro de Investigación Biomédica, Universidad de Granada, Granada, Spain
| | - Francesco Balzerani
- Tecnun, University of Navarra, San Sebastián, Spain
- Biomedical Engineering Center, University of Navarra, Campus Universitario, Pamplona, Navarra, Spain
| | - Daniel Hinojosa-Nogueira
- Departamento de Nutrición y Bromatología, Instituto de Nutrición y Tecnología de los Alimentos, Centro de Investigación Biomédica, Universidad de Granada, Granada, Spain
| | - Alberto Lerma-Aguilera
- Área de Genòmica i Salut, Fundación para el Fomento de la Investigación Sanitaria y Biomédica de la Comunitat Valenciana-Salud Pública, Valencia, Spain
- CIBER en Epidemiología y Salud Pública, Madrid, Spain
| | - Silvia Pastoriza
- Departamento de Nutrición y Bromatología, Instituto de Nutrición y Tecnología de los Alimentos, Centro de Investigación Biomédica, Universidad de Granada, Granada, Spain
| | - Xabier Cendoya
- Tecnun, University of Navarra, San Sebastián, Spain
- Biomedical Engineering Center, University of Navarra, Campus Universitario, Pamplona, Navarra, Spain
| | - Ángel Rubio
- Tecnun, University of Navarra, San Sebastián, Spain
- Biomedical Engineering Center, University of Navarra, Campus Universitario, Pamplona, Navarra, Spain
| | - María José Gosalbes
- Área de Genòmica i Salut, Fundación para el Fomento de la Investigación Sanitaria y Biomédica de la Comunitat Valenciana-Salud Pública, Valencia, Spain
- CIBER en Epidemiología y Salud Pública, Madrid, Spain
| | - Nuria Jiménez-Hernández
- Área de Genòmica i Salut, Fundación para el Fomento de la Investigación Sanitaria y Biomédica de la Comunitat Valenciana-Salud Pública, Valencia, Spain
- CIBER en Epidemiología y Salud Pública, Madrid, Spain
| | - M Pilar Francino
- Área de Genòmica i Salut, Fundación para el Fomento de la Investigación Sanitaria y Biomédica de la Comunitat Valenciana-Salud Pública, Valencia, Spain.
- CIBER en Epidemiología y Salud Pública, Madrid, Spain.
| | - Iñigo Apaolaza
- Tecnun, University of Navarra, San Sebastián, Spain.
- Biomedical Engineering Center, University of Navarra, Campus Universitario, Pamplona, Navarra, Spain.
| | - José Ángel Rufián-Henares
- Departamento de Nutrición y Bromatología, Instituto de Nutrición y Tecnología de los Alimentos, Centro de Investigación Biomédica, Universidad de Granada, Granada, Spain.
- Instituto de Investigación Biosanitaria ibs.GRANADA, Universidad de Granada, Granada, Spain.
| | - Francisco J Planes
- Tecnun, University of Navarra, San Sebastián, Spain.
- Biomedical Engineering Center, University of Navarra, Campus Universitario, Pamplona, Navarra, Spain.
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21
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Wagner A, Wang C, Fessler J, DeTomaso D, Avila-Pacheco J, Kaminski J, Zaghouani S, Christian E, Thakore P, Schellhaass B, Akama-Garren E, Pierce K, Singh V, Ron-Harel N, Douglas VP, Bod L, Schnell A, Puleston D, Sobel RA, Haigis M, Pearce EL, Soleimani M, Clish C, Regev A, Kuchroo VK, Yosef N. Metabolic modeling of single Th17 cells reveals regulators of autoimmunity. Cell 2021; 184:4168-4185.e21. [PMID: 34216539 PMCID: PMC8621950 DOI: 10.1016/j.cell.2021.05.045] [Citation(s) in RCA: 213] [Impact Index Per Article: 71.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2020] [Revised: 02/15/2021] [Accepted: 05/27/2021] [Indexed: 12/11/2022]
Abstract
Metabolism is a major regulator of immune cell function, but it remains difficult to study the metabolic status of individual cells. Here, we present Compass, an algorithm to characterize cellular metabolic states based on single-cell RNA sequencing and flux balance analysis. We applied Compass to associate metabolic states with T helper 17 (Th17) functional variability (pathogenic potential) and recovered a metabolic switch between glycolysis and fatty acid oxidation, akin to known Th17/regulatory T cell (Treg) differences, which we validated by metabolic assays. Compass also predicted that Th17 pathogenicity was associated with arginine and downstream polyamine metabolism. Indeed, polyamine-related enzyme expression was enhanced in pathogenic Th17 and suppressed in Treg cells. Chemical and genetic perturbation of polyamine metabolism inhibited Th17 cytokines, promoted Foxp3 expression, and remodeled the transcriptome and epigenome of Th17 cells toward a Treg-like state. In vivo perturbations of the polyamine pathway altered the phenotype of encephalitogenic T cells and attenuated tissue inflammation in CNS autoimmunity.
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Affiliation(s)
- Allon Wagner
- Department of Electrical Engineering and Computer Science, University of California, Berkeley, Berkeley, CA 94720, USA; Center for Computational Biology, University of California, Berkeley, Berkeley, CA 94720, USA
| | - Chao Wang
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Evergrande Center for Immunologic Diseases, Harvard Medical School and Brigham and Women's Hospital, Boston, MA 02115, USA; Biological Sciences Platform, Sunnybrook Research Institute, Toronto, ON M4N 3M5, Canada; Department of Immunology, University of Toronto, Toronto, ON M5S 1A8, Canada.
| | - Johannes Fessler
- Evergrande Center for Immunologic Diseases, Harvard Medical School and Brigham and Women's Hospital, Boston, MA 02115, USA
| | - David DeTomaso
- Center for Computational Biology, University of California, Berkeley, Berkeley, CA 94720, USA
| | | | - James Kaminski
- Center for Computational Biology, University of California, Berkeley, Berkeley, CA 94720, USA
| | - Sarah Zaghouani
- Evergrande Center for Immunologic Diseases, Harvard Medical School and Brigham and Women's Hospital, Boston, MA 02115, USA
| | - Elena Christian
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | | | - Brandon Schellhaass
- Department of Electrical Engineering and Computer Science, University of California, Berkeley, Berkeley, CA 94720, USA
| | - Elliot Akama-Garren
- Evergrande Center for Immunologic Diseases, Harvard Medical School and Brigham and Women's Hospital, Boston, MA 02115, USA
| | - Kerry Pierce
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | | | - Noga Ron-Harel
- Department of Cell Biology, Blavatnik Institute, Harvard Medical School, Boston, MA 02115, USA; Department of Biology, Technion, Israel Institute of Technology, Haifa 3200003, Israel
| | - Vivian Paraskevi Douglas
- Massachusetts Eye and Ear, Department of Ophthalmology, Harvard Medical School, Boston, MA 02114, USA
| | - Lloyd Bod
- Evergrande Center for Immunologic Diseases, Harvard Medical School and Brigham and Women's Hospital, Boston, MA 02115, USA
| | - Alexandra Schnell
- Evergrande Center for Immunologic Diseases, Harvard Medical School and Brigham and Women's Hospital, Boston, MA 02115, USA
| | - Daniel Puleston
- Max Planck Institute of Immunobiology and Epigenetics, 79108 Freiburg, Germany
| | - Raymond A Sobel
- Palo Alto Veteran's Administration Health Care System and Department of Pathology, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Marcia Haigis
- Department of Cell Biology, Blavatnik Institute, Harvard Medical School, Boston, MA 02115, USA
| | - Erika L Pearce
- Max Planck Institute of Immunobiology and Epigenetics, 79108 Freiburg, Germany
| | - Manoocher Soleimani
- Department of Medicine, University of New Mexico Health Sciences Center, Albuquerque, NM 87121, USA
| | - Clary Clish
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Aviv Regev
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Department of Biology, Howard Hughes Medical Institute, Massachusetts Institute of Technology, Cambridge, MA 02140, USA
| | - Vijay K Kuchroo
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Evergrande Center for Immunologic Diseases, Harvard Medical School and Brigham and Women's Hospital, Boston, MA 02115, USA.
| | - Nir Yosef
- Department of Electrical Engineering and Computer Science, University of California, Berkeley, Berkeley, CA 94720, USA; Center for Computational Biology, University of California, Berkeley, Berkeley, CA 94720, USA; Chan-Zuckerberg Biohub, San Francisco, CA 94158, USA; Ragon Institute of MGH, MIT, and Harvard, Cambridge, MA 02139, USA.
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22
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Pérez-Burillo S, Molino S, Navajas-Porras B, Valverde-Moya ÁJ, Hinojosa-Nogueira D, López-Maldonado A, Pastoriza S, Rufián-Henares JÁ. An in vitro batch fermentation protocol for studying the contribution of food to gut microbiota composition and functionality. Nat Protoc 2021; 16:3186-3209. [PMID: 34089022 DOI: 10.1038/s41596-021-00537-x] [Citation(s) in RCA: 96] [Impact Index Per Article: 32.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2020] [Accepted: 03/18/2021] [Indexed: 02/05/2023]
Abstract
Knowledge of the effect of foods on gut microbiota composition and functionality is expanding. To isolate the effect of single foods and/or single nutrients (i.e., fiber, polyphenols), this protocol describes an in vitro batch fermentation procedure to be carried out after an in vitro gastrointestinal digestion. Therefore, this is an extension of the previous protocol described by Brodkorb et al. (2019) for studying in vitro digestion. The current protocol uses an oligotrophic fermentation medium with peptone and a high concentration of fecal inoculum from human fecal samples both to provide the microbiota and as the main source of nutrients for the bacteria. This protocol is recommended for screening work to be performed when many food samples are to be studied. It has been used successfully to study gut microbiota fermentation of different foodstuffs, giving insights into their functionality, community structure or ability to degrade particular substances, which can contribute to the development of personalized nutrition strategies. The procedure does not require a specific level of expertise. The protocol takes 4-6 h for preparation of fermentation tubes and 20 h for incubation.
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Affiliation(s)
- Sergio Pérez-Burillo
- Departamento de Nutrición y Bromatología, Instituto de Nutrición y Tecnología de Alimentos, Centro de Investigación Biomédica, Universidad de Granada, Granada, Spain
| | - Silvia Molino
- Departamento de Nutrición y Bromatología, Instituto de Nutrición y Tecnología de Alimentos, Centro de Investigación Biomédica, Universidad de Granada, Granada, Spain
| | - Beatriz Navajas-Porras
- Departamento de Nutrición y Bromatología, Instituto de Nutrición y Tecnología de Alimentos, Centro de Investigación Biomédica, Universidad de Granada, Granada, Spain
| | - Álvaro Jesús Valverde-Moya
- Departamento de Nutrición y Bromatología, Instituto de Nutrición y Tecnología de Alimentos, Centro de Investigación Biomédica, Universidad de Granada, Granada, Spain
| | - Daniel Hinojosa-Nogueira
- Departamento de Nutrición y Bromatología, Instituto de Nutrición y Tecnología de Alimentos, Centro de Investigación Biomédica, Universidad de Granada, Granada, Spain
| | - Alicia López-Maldonado
- Departamento de Nutrición y Bromatología, Instituto de Nutrición y Tecnología de Alimentos, Centro de Investigación Biomédica, Universidad de Granada, Granada, Spain
| | - Silvia Pastoriza
- Departamento de Nutrición y Bromatología, Instituto de Nutrición y Tecnología de Alimentos, Centro de Investigación Biomédica, Universidad de Granada, Granada, Spain
| | - José Ángel Rufián-Henares
- Departamento de Nutrición y Bromatología, Instituto de Nutrición y Tecnología de Alimentos, Centro de Investigación Biomédica, Universidad de Granada, Granada, Spain. .,Instituto de Investigación Biosanitaria ibs.GRANADA, Universidad de Granada, Granada, Spain.
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23
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Ankrah NYD, Barker BE, Song J, Wu C, McMullen JG, Douglas AE. Predicted Metabolic Function of the Gut Microbiota of Drosophila melanogaster. mSystems 2021. [PMID: 33947801 DOI: 10.1101/2021.01.20.427455] [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] [Indexed: 05/12/2023] Open
Abstract
An important goal for many nutrition-based microbiome studies is to identify the metabolic function of microbes in complex microbial communities and their impact on host physiology. This research can be confounded by poorly understood effects of community composition and host diet on the metabolic traits of individual taxa. Here, we investigated these multiway interactions by constructing and analyzing metabolic models comprising every combination of five bacterial members of the Drosophila gut microbiome (from single taxa to the five-member community of Acetobacter and Lactobacillus species) under three nutrient regimes. We show that the metabolic function of Drosophila gut bacteria is dynamic, influenced by community composition, and responsive to dietary modulation. Furthermore, we show that ecological interactions such as competition and mutualism identified from the growth patterns of gut bacteria are underlain by a diversity of metabolic interactions, and show that the bacteria tend to compete for amino acids and B vitamins more frequently than for carbon sources. Our results reveal that, in addition to fermentation products such as acetate, intermediates of the tricarboxylic acid (TCA) cycle, including 2-oxoglutarate and succinate, are produced at high flux and cross-fed between bacterial taxa, suggesting important roles for TCA cycle intermediates in modulating Drosophila gut microbe interactions and the potential to influence host traits. These metabolic models provide specific predictions of the patterns of ecological and metabolic interactions among gut bacteria under different nutrient regimes, with potentially important consequences for overall community metabolic function and nutritional interactions with the host.IMPORTANCE Drosophila is an important model for microbiome research partly because of the low complexity of its mostly culturable gut microbiota. Our current understanding of how Drosophila interacts with its gut microbes and how these interactions influence host traits derives almost entirely from empirical studies that focus on individual microbial taxa or classes of metabolites. These studies have failed to capture fully the complexity of metabolic interactions that occur between host and microbe. To overcome this limitation, we reconstructed and analyzed 31 metabolic models for every combination of the five principal bacterial taxa in the gut microbiome of Drosophila This revealed that metabolic interactions between Drosophila gut bacterial taxa are highly dynamic and influenced by cooccurring bacteria and nutrient availability. Our results generate testable hypotheses about among-microbe ecological interactions in the Drosophila gut and the diversity of metabolites available to influence host traits.
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Affiliation(s)
- Nana Y D Ankrah
- Department of Entomology, Cornell University, Ithaca, New York, USA
| | - Brandon E Barker
- Center for Advanced Computing, Cornell University, Ithaca, New York, USA
| | - Joan Song
- School of Electrical and Computer Engineering, Cornell University, Ithaca, New York, USA
| | - Cindy Wu
- Robert Frederick Smith School of Chemical and Biomolecular Engineering, Cornell University, Ithaca, New York, USA
| | - John G McMullen
- Department of Entomology, Cornell University, Ithaca, New York, USA
| | - Angela E Douglas
- Department of Entomology, Cornell University, Ithaca, New York, USA
- Department of Molecular Biology and Genetics, Cornell University, Ithaca, New York, USA
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24
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Ankrah NYD, Barker BE, Song J, Wu C, McMullen JG, Douglas AE. Predicted Metabolic Function of the Gut Microbiota of Drosophila melanogaster. mSystems 2021; 6:e01369-20. [PMID: 33947801 PMCID: PMC8269265 DOI: 10.1128/msystems.01369-20] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2021] [Accepted: 04/01/2021] [Indexed: 12/28/2022] Open
Abstract
An important goal for many nutrition-based microbiome studies is to identify the metabolic function of microbes in complex microbial communities and their impact on host physiology. This research can be confounded by poorly understood effects of community composition and host diet on the metabolic traits of individual taxa. Here, we investigated these multiway interactions by constructing and analyzing metabolic models comprising every combination of five bacterial members of the Drosophila gut microbiome (from single taxa to the five-member community of Acetobacter and Lactobacillus species) under three nutrient regimes. We show that the metabolic function of Drosophila gut bacteria is dynamic, influenced by community composition, and responsive to dietary modulation. Furthermore, we show that ecological interactions such as competition and mutualism identified from the growth patterns of gut bacteria are underlain by a diversity of metabolic interactions, and show that the bacteria tend to compete for amino acids and B vitamins more frequently than for carbon sources. Our results reveal that, in addition to fermentation products such as acetate, intermediates of the tricarboxylic acid (TCA) cycle, including 2-oxoglutarate and succinate, are produced at high flux and cross-fed between bacterial taxa, suggesting important roles for TCA cycle intermediates in modulating Drosophila gut microbe interactions and the potential to influence host traits. These metabolic models provide specific predictions of the patterns of ecological and metabolic interactions among gut bacteria under different nutrient regimes, with potentially important consequences for overall community metabolic function and nutritional interactions with the host.IMPORTANCE Drosophila is an important model for microbiome research partly because of the low complexity of its mostly culturable gut microbiota. Our current understanding of how Drosophila interacts with its gut microbes and how these interactions influence host traits derives almost entirely from empirical studies that focus on individual microbial taxa or classes of metabolites. These studies have failed to capture fully the complexity of metabolic interactions that occur between host and microbe. To overcome this limitation, we reconstructed and analyzed 31 metabolic models for every combination of the five principal bacterial taxa in the gut microbiome of Drosophila This revealed that metabolic interactions between Drosophila gut bacterial taxa are highly dynamic and influenced by cooccurring bacteria and nutrient availability. Our results generate testable hypotheses about among-microbe ecological interactions in the Drosophila gut and the diversity of metabolites available to influence host traits.
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Affiliation(s)
- Nana Y D Ankrah
- Department of Entomology, Cornell University, Ithaca, New York, USA
| | - Brandon E Barker
- Center for Advanced Computing, Cornell University, Ithaca, New York, USA
| | - Joan Song
- School of Electrical and Computer Engineering, Cornell University, Ithaca, New York, USA
| | - Cindy Wu
- Robert Frederick Smith School of Chemical and Biomolecular Engineering, Cornell University, Ithaca, New York, USA
| | - John G McMullen
- Department of Entomology, Cornell University, Ithaca, New York, USA
| | - Angela E Douglas
- Department of Entomology, Cornell University, Ithaca, New York, USA
- Department of Molecular Biology and Genetics, Cornell University, Ithaca, New York, USA
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25
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Renz A, Widerspick L, Dräger A. First Genome-Scale Metabolic Model of Dolosigranulum pigrum Confirms Multiple Auxotrophies. Metabolites 2021; 11:232. [PMID: 33918864 PMCID: PMC8069353 DOI: 10.3390/metabo11040232] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2021] [Revised: 03/21/2021] [Accepted: 04/06/2021] [Indexed: 12/11/2022] Open
Abstract
Dolosigranulum pigrum is a quite recently discovered Gram-positive coccus. It has gained increasing attention due to its negative correlation with Staphylococcus aureus, which is one of the most successful modern pathogens causing severe infections with tremendous morbidity and mortality due to its multiple resistances. As the possible mechanisms behind its inhibition of S. aureus remain unclear, a genome-scale metabolic model (GEM) is of enormous interest and high importance to better study its role in this fight. This article presents the first GEM of D. pigrum, which was curated using automated reconstruction tools and extensive manual curation steps to yield a high-quality GEM. It was evaluated and validated using all currently available experimental data of D. pigrum. With this model, already predicted auxotrophies and biosynthetic pathways could be verified. The model was used to define a minimal medium for further laboratory experiments and to predict various carbon sources' growth capacities. This model will pave the way to better understand D. pigrum's role in the fight against S. aureus.
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Affiliation(s)
- Alina Renz
- Computational Systems Biology of Infections and Antimicrobial-Resistant Pathogens, Institute for Bioinformatics and Medical Informatics (IBMI), University of Tübingen, 72076 Tübingen, Germany; (A.R.); (L.W.)
- Department of Computer Science, University of Tübingen, 72076 Tübingen, Germany
- Cluster of Excellence ‘Controlling Microbes to Fight Infections’, University of Tübingen, 72076 Tübingen, Germany
| | - Lina Widerspick
- Computational Systems Biology of Infections and Antimicrobial-Resistant Pathogens, Institute for Bioinformatics and Medical Informatics (IBMI), University of Tübingen, 72076 Tübingen, Germany; (A.R.); (L.W.)
| | - Andreas Dräger
- Computational Systems Biology of Infections and Antimicrobial-Resistant Pathogens, Institute for Bioinformatics and Medical Informatics (IBMI), University of Tübingen, 72076 Tübingen, Germany; (A.R.); (L.W.)
- Department of Computer Science, University of Tübingen, 72076 Tübingen, Germany
- Cluster of Excellence ‘Controlling Microbes to Fight Infections’, University of Tübingen, 72076 Tübingen, Germany
- German Center for Infection Research (DZIF), Partner site Tübingen, 72076 Tübingen, Germany
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26
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Wang J, Carper DL, Burdick LH, Shrestha HK, Appidi MR, Abraham PE, Timm CM, Hettich RL, Pelletier DA, Doktycz MJ. Formation, characterization and modeling of emergent synthetic microbial communities. Comput Struct Biotechnol J 2021; 19:1917-1927. [PMID: 33995895 PMCID: PMC8079826 DOI: 10.1016/j.csbj.2021.03.034] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2020] [Revised: 03/22/2021] [Accepted: 03/25/2021] [Indexed: 01/04/2023] Open
Abstract
Microbial communities colonize plant tissues and contribute to host function. How these communities form and how individual members contribute to shaping the microbial community are not well understood. Synthetic microbial communities, where defined individual isolates are combined, can serve as valuable model systems for uncovering the organizational principles of communities. Using genome-defined organisms, systematic analysis by computationally-based network reconstruction can lead to mechanistic insights and the metabolic interactions between species. In this study, 10 bacterial strains isolated from the Populus deltoides rhizosphere were combined and passaged in two different media environments to form stable microbial communities. The membership and relative abundances of the strains stabilized after around 5 growth cycles and resulted in just a few dominant strains that depended on the medium. To unravel the underlying metabolic interactions, flux balance analysis was used to model microbial growth and identify potential metabolic exchanges involved in shaping the microbial communities. These analyses were complemented by growth curves of the individual isolates, pairwise interaction screens, and metaproteomics of the community. A fast growth rate is identified as one factor that can provide an advantage for maintaining presence in the community. Final community selection can also depend on selective antagonistic relationships and metabolic exchanges. Revealing the mechanisms of interaction among plant-associated microorganisms provides insights into strategies for engineering microbial communities that can potentially increase plant growth and disease resistance. Further, deciphering the membership and metabolic potentials of a bacterial community will enable the design of synthetic communities with desired biological functions.
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Affiliation(s)
- Jia Wang
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA
| | - Dana L. Carper
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA
| | - Leah H. Burdick
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA
| | - Him K. Shrestha
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA
- Graduate School of Genome Science and Technology, University of Tennessee, Knoxville, TN, USA
| | - Manasa R. Appidi
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA
- Graduate School of Genome Science and Technology, University of Tennessee, Knoxville, TN, USA
| | - Paul E. Abraham
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA
| | - Collin M. Timm
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA
| | - Robert L. Hettich
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA
| | - Dale A. Pelletier
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA
- Corresponding authors.
| | - Mitchel J. Doktycz
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA
- Corresponding authors.
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27
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Liu Z, Ma A, Mathé E, Merling M, Ma Q, Liu B. Network analyses in microbiome based on high-throughput multi-omics data. Brief Bioinform 2021; 22:1639-1655. [PMID: 32047891 PMCID: PMC7986608 DOI: 10.1093/bib/bbaa005] [Citation(s) in RCA: 44] [Impact Index Per Article: 14.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2019] [Revised: 01/07/2020] [Accepted: 01/08/2020] [Indexed: 02/06/2023] Open
Abstract
Together with various hosts and environments, ubiquitous microbes interact closely with each other forming an intertwined system or community. Of interest, shifts of the relationships between microbes and their hosts or environments are associated with critical diseases and ecological changes. While advances in high-throughput Omics technologies offer a great opportunity for understanding the structures and functions of microbiome, it is still challenging to analyse and interpret the omics data. Specifically, the heterogeneity and diversity of microbial communities, compounded with the large size of the datasets, impose a tremendous challenge to mechanistically elucidate the complex communities. Fortunately, network analyses provide an efficient way to tackle this problem, and several network approaches have been proposed to improve this understanding recently. Here, we systemically illustrate these network theories that have been used in biological and biomedical research. Then, we review existing network modelling methods of microbial studies at multiple layers from metagenomics to metabolomics and further to multi-omics. Lastly, we discuss the limitations of present studies and provide a perspective for further directions in support of the understanding of microbial communities.
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Affiliation(s)
- Zhaoqian Liu
- Department of Biomedical Informatics, College of Medicine, the Ohio State University, Columbus, OH 43210, USA
| | - Anjun Ma
- Department of Biomedical Informatics, College of Medicine, the Ohio State University, Columbus, OH 43210, USA
| | - Ewy Mathé
- Department of Biomedical Informatics, College of Medicine, the Ohio State University, Columbus, OH 43210, USA
| | - Marlena Merling
- Department of Biomedical Informatics, College of Medicine, the Ohio State University, Columbus, OH 43210, USA
| | - Qin Ma
- Department of Biomedical Informatics, College of Medicine, the Ohio State University, Columbus, OH 43210, USA
| | - Bingqiang Liu
- Department of Biomedical Informatics, College of Medicine, the Ohio State University, Columbus, OH 43210, USA
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28
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Modulating the Gut Microbiota of Humans by Dietary Intervention with Plant Glycans. Appl Environ Microbiol 2021; 87:AEM.02757-20. [PMID: 33355114 DOI: 10.1128/aem.02757-20] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
Abstract
The human colon contains a community of microbial species, mostly bacteria, which is often referred to as the gut microbiota. The community is considered essential to human well-being by conferring additional energy-harvesting capacity, niche exclusion of pathogens, and molecular signaling activities that are integrated into human physiological processes. Plant polysaccharides (glycans, dietary fiber) are an important source of carbon and energy that supports the maintenance and functioning of the gut microbiota. Therefore, the daily quantity and quality of plant glycans consumed by the human host have the potential to influence health. Members of the gut microbiota differ in ability to utilize different types of plant glycans. Dietary interventions with specific glycans could modulate the microbiota, counteracting ecological perturbations that disrupt the intricate relationships between microbiota and host (dysbiosis). This review considers prospects and research options for modulation of the gut microbiota by the formulation of diets that, when consumed habitually, would correct dysbiosis by building diverse consortia that boost functional resilience. Traditional "prebiotics" favor bifidobacteria and lactobacilli, whereas dietary mixtures of plant glycans that are varied in chemical complexity would promote high-diversity microbiotas. It is concluded that research should aim at improving knowledge of bacterial consortia that, through shared nourishment, degrade and ferment plant glycans. The consortia may vary in composition from person to person, but functional outputs will be consistent in a given context because of metabolic redundancy among bacteria. Thus, the individuality of gut microbiotas could be encompassed, functional resilience encouraged, and correction of dysbiosis achieved.
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29
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Sharma P, Wu G, Kumaraswamy D, Burchat N, Ye H, Gong Y, Zhao L, Lam YY, Sampath H. Sex-Dependent Effects of 7,8-Dihydroxyflavone on Metabolic Health Are Associated with Alterations in the Host Gut Microbiome. Nutrients 2021; 13:nu13020637. [PMID: 33669347 PMCID: PMC7920311 DOI: 10.3390/nu13020637] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2020] [Revised: 02/05/2021] [Accepted: 02/09/2021] [Indexed: 12/12/2022] Open
Abstract
7,8-Dihydroxyflavone (DHF) is a naturally occurring flavonoid that has been reported to protect against a variety of pathologies. Chronic administration of DHF prevents high-fat diet (HFD)-induced obesity in female, but not male, mice. However, the mechanisms underlying this sexual dimorphism have not been elucidated. We have discovered that oral DHF supplementation significantly attenuates fat mass, hepatic lipid accumulation, and adipose tissue inflammation in female mice. In contrast, male mice were not protected from adiposity, and had a paradoxical worsening of hepatic lipid accumulation and adipose tissue inflammation upon DHF supplementation. Consistent with these sexually dimorphic effects on body weight and metabolic health, 7,8-DHF induced early and stable remodeling of the female intestinal microbiome. DHF supplementation significantly increased gut microbial diversity, and suppressed potentially detrimental bacteria, particularly Desulfovibrionaceae, which are pro-inflammatory and positively associated with obesity and inflammation. Changes in the female gut microbiome preceded alterations in body weights, and in silico analyses indicated that these early microbial changes were highly predictive of subsequent weight gain in female mice. While some alterations in the intestinal microbiome were also observed in male DHF-supplemented mice, these changes were distinct from those in females and, importantly, were not predictive of subsequent body weight changes in male animals. The temporality of microbial changes preceding alterations in body weight in female mice suggests a role for the gut microbiome in mediating the sexually dimorphic effects of DHF on body weight. Given the significant clinical interest in this flavonoid across a wide range of pathologies, further elucidation of these sexually dimorphic effects will aid the development of effective clinical therapies.
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Affiliation(s)
- Priyanka Sharma
- Rutgers Center for Lipid Research, New Jersey Institute for Food, Nutrition, and Health, New Brunswick, NJ 08901, USA; (P.S.); (D.K.); (N.B.); (H.Y.)
- Center for Microbiome, Nutrition, and Health, New Jersey Institute for Food, Nutrition, and Health, New Brunswick, NJ 08901, USA; (G.W.); (Y.G.); (L.Z.)
| | - Guojun Wu
- Center for Microbiome, Nutrition, and Health, New Jersey Institute for Food, Nutrition, and Health, New Brunswick, NJ 08901, USA; (G.W.); (Y.G.); (L.Z.)
- Department of Biochemistry and Microbiology, Rutgers University, New Brunswick, NJ 08901, USA
| | - Deeptha Kumaraswamy
- Rutgers Center for Lipid Research, New Jersey Institute for Food, Nutrition, and Health, New Brunswick, NJ 08901, USA; (P.S.); (D.K.); (N.B.); (H.Y.)
- Center for Microbiome, Nutrition, and Health, New Jersey Institute for Food, Nutrition, and Health, New Brunswick, NJ 08901, USA; (G.W.); (Y.G.); (L.Z.)
| | - Natalie Burchat
- Rutgers Center for Lipid Research, New Jersey Institute for Food, Nutrition, and Health, New Brunswick, NJ 08901, USA; (P.S.); (D.K.); (N.B.); (H.Y.)
- Center for Microbiome, Nutrition, and Health, New Jersey Institute for Food, Nutrition, and Health, New Brunswick, NJ 08901, USA; (G.W.); (Y.G.); (L.Z.)
| | - Hong Ye
- Rutgers Center for Lipid Research, New Jersey Institute for Food, Nutrition, and Health, New Brunswick, NJ 08901, USA; (P.S.); (D.K.); (N.B.); (H.Y.)
- Center for Microbiome, Nutrition, and Health, New Jersey Institute for Food, Nutrition, and Health, New Brunswick, NJ 08901, USA; (G.W.); (Y.G.); (L.Z.)
| | - Yongjia Gong
- Center for Microbiome, Nutrition, and Health, New Jersey Institute for Food, Nutrition, and Health, New Brunswick, NJ 08901, USA; (G.W.); (Y.G.); (L.Z.)
- Department of Biochemistry and Microbiology, Rutgers University, New Brunswick, NJ 08901, USA
| | - Liping Zhao
- Center for Microbiome, Nutrition, and Health, New Jersey Institute for Food, Nutrition, and Health, New Brunswick, NJ 08901, USA; (G.W.); (Y.G.); (L.Z.)
- Department of Biochemistry and Microbiology, Rutgers University, New Brunswick, NJ 08901, USA
| | - Yan Y. Lam
- Center for Microbiome, Nutrition, and Health, New Jersey Institute for Food, Nutrition, and Health, New Brunswick, NJ 08901, USA; (G.W.); (Y.G.); (L.Z.)
- Department of Biochemistry and Microbiology, Rutgers University, New Brunswick, NJ 08901, USA
- Correspondence: (Y.Y.L.); (H.S.); Tel.: +1-848-932-0266 (H.S.)
| | - Harini Sampath
- Rutgers Center for Lipid Research, New Jersey Institute for Food, Nutrition, and Health, New Brunswick, NJ 08901, USA; (P.S.); (D.K.); (N.B.); (H.Y.)
- Center for Microbiome, Nutrition, and Health, New Jersey Institute for Food, Nutrition, and Health, New Brunswick, NJ 08901, USA; (G.W.); (Y.G.); (L.Z.)
- Department of Nutritional Sciences, Rutgers University, New Brunswick, NJ 08901, USA
- Correspondence: (Y.Y.L.); (H.S.); Tel.: +1-848-932-0266 (H.S.)
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30
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Wu G, Zhao N, Zhang C, Lam YY, Zhao L. Guild-based analysis for understanding gut microbiome in human health and diseases. Genome Med 2021; 13:22. [PMID: 33563315 PMCID: PMC7874449 DOI: 10.1186/s13073-021-00840-y] [Citation(s) in RCA: 77] [Impact Index Per Article: 25.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2019] [Accepted: 01/26/2021] [Indexed: 12/14/2022] Open
Abstract
To demonstrate the causative role of gut microbiome in human health and diseases, we first need to identify, via next-generation sequencing, potentially important functional members associated with specific health outcomes and disease phenotypes. However, due to the strain-level genetic complexity of the gut microbiota, microbiome datasets are highly dimensional and highly sparse in nature, making it challenging to identify putative causative agents of a particular disease phenotype. Members of an ecosystem seldomly live independently from each other. Instead, they develop local interactions and form inter-member organizations to influence the ecosystem's higher-level patterns and functions. In the ecological study of macro-organisms, members are defined as belonging to the same "guild" if they exploit the same class of resources in a similar way or work together as a coherent functional group. Translating the concept of "guild" to the study of gut microbiota, we redefine guild as a group of bacteria that show consistent co-abundant behavior and likely to work together to contribute to the same ecological function. In this opinion article, we discuss how to use guilds as the aggregation unit to reduce dimensionality and sparsity in microbiome-wide association studies for identifying candidate gut bacteria that may causatively contribute to human health and diseases.
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Affiliation(s)
- Guojun Wu
- Center for Nutrition, Microbiome and Health, New Jersey Institute for Food, Nutrition and Health, Rutgers University, New Brunswick, NJ, USA.,Department of Biochemistry and Microbiology, Rutgers University, New Brunswick, NJ, USA.,Rutgers-Jiaotong Joint Laboratory for Microbiome and Human Health, New Brunswick, NJ, USA
| | - Naisi Zhao
- Center for Nutrition, Microbiome and Health, New Jersey Institute for Food, Nutrition and Health, Rutgers University, New Brunswick, NJ, USA.,Department of Public Health and Community Medicine, School of Medicine, Tufts University, Medford, MA, USA
| | - Chenhong Zhang
- Rutgers-Jiaotong Joint Laboratory for Microbiome and Human Health, New Brunswick, NJ, USA.,State Key Laboratory of Microbial Metabolism, Ministry of Education Laboratory of Systems Biomedicine, Shanghai Jiao Tong University, Shanghai, China
| | - Yan Y Lam
- Center for Nutrition, Microbiome and Health, New Jersey Institute for Food, Nutrition and Health, Rutgers University, New Brunswick, NJ, USA.,Department of Biochemistry and Microbiology, Rutgers University, New Brunswick, NJ, USA.,Rutgers-Jiaotong Joint Laboratory for Microbiome and Human Health, New Brunswick, NJ, USA
| | - Liping Zhao
- Center for Nutrition, Microbiome and Health, New Jersey Institute for Food, Nutrition and Health, Rutgers University, New Brunswick, NJ, USA. .,Department of Biochemistry and Microbiology, Rutgers University, New Brunswick, NJ, USA. .,Rutgers-Jiaotong Joint Laboratory for Microbiome and Human Health, New Brunswick, NJ, USA. .,State Key Laboratory of Microbial Metabolism, Ministry of Education Laboratory of Systems Biomedicine, Shanghai Jiao Tong University, Shanghai, China.
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31
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Vo D, Singh SC, Safa S, Sahoo D. Boolean implication analysis unveils candidate universal relationships in microbiome data. BMC Bioinformatics 2021; 22:49. [PMID: 33546590 PMCID: PMC7863539 DOI: 10.1186/s12859-020-03941-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2020] [Accepted: 12/16/2020] [Indexed: 12/31/2022] Open
Abstract
BACKGROUND Microbiomes consist of bacteria, viruses, and other microorganisms, and are responsible for many different functions in both organisms and the environment. Past analyses of microbiomes focused on using correlation to determine linear relationships between microbes and diseases. Weak correlations due to nonlinearity between microbe pairs may cause researchers to overlook critical components of the data. With the abundance of available microbiome, we need a method that comprehensively studies microbiomes and how they are related to each other. RESULTS We collected publicly available datasets from human, environment, and animal samples to determine both symmetric and asymmetric Boolean implication relationships between a pair of microbes. We then found relationships that are potentially invariants, meaning they will hold in any microbe community. In other words, if we determine there is a relationship between two microbes, we expect the relationship to hold in almost all contexts. We discovered that around 330,000 pairs of microbes universally exhibit the same relationship in almost all the datasets we studied, thus making them good candidates for invariants. Our results also confirm known biological properties and seem promising in terms of disease diagnosis. CONCLUSIONS Since the relationships are likely universal, we expect them to hold in clinical settings, as well as general populations. If these strong invariants are present in disease settings, it may provide insight into prognostic, predictive, or therapeutic properties of clinically relevant diseases. For example, our results indicate that there is a difference in the microbe distributions between patients who have or do not have IBD, eczema and psoriasis. These new analyses may improve disease diagnosis and drug development in terms of accuracy and efficiency.
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Affiliation(s)
- Daniella Vo
- Department of Bioinformatics and Systems Biology, University of California San Diego, La Jolla, CA, 92093-083, USA
| | - Shayal Charisma Singh
- Halıcıoğlu Data Science Institute, University of California San Diego, La Jolla, CA, 92093-083, USA
| | - Sara Safa
- Department of Computer Science and Engineering, Jacob's School of Engineering, University of California San Diego, La Jolla, CA, 92093-083, USA
| | - Debashis Sahoo
- Department of Computer Science and Engineering, Jacob's School of Engineering, University of California San Diego, La Jolla, CA, 92093-083, USA.
- Department of Pediatrics, University of California San Diego, 9500 Gilman Drive, MC 0730, Leichtag Building 132, La Jolla, CA, 92093-083, USA.
- Moores Cancer Center, University of California San Diego, La Jolla, CA, 92093-083, USA.
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Henson MA. Computational modeling of the gut microbiota reveals putative metabolic mechanisms of recurrent Clostridioides difficile infection. PLoS Comput Biol 2021; 17:e1008782. [PMID: 33617526 PMCID: PMC7932513 DOI: 10.1371/journal.pcbi.1008782] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2020] [Revised: 03/04/2021] [Accepted: 02/05/2021] [Indexed: 12/16/2022] Open
Abstract
Approximately 30% of patients who have Clostridioides difficile infection (CDI) will suffer at least one incident of reinfection. While the underlying causes of CDI recurrence are poorly understood, interactions between C. difficile and commensal gut bacteria are thought to play an important role. In this study, an in silico pipeline was used to process 16S rRNA gene amplicon sequence data of 225 stool samples from 93 CDI patients into sample-specific models of bacterial community metabolism. Clustered metabolite production rates generated from post-diagnosis samples generated a high Enterobacteriaceae abundance cluster containing disproportionately large numbers of recurrent samples and patients. This cluster was predicted to have significantly reduced capabilities for secondary bile acid synthesis but elevated capabilities for aromatic amino acid catabolism. When applied to 16S sequence data of 40 samples from fecal microbiota transplantation (FMT) patients suffering from recurrent CDI and their stool donors, the community modeling method generated a high Enterobacteriaceae abundance cluster with a disproportionate large number of pre-FMT samples. This cluster also was predicted to exhibit reduced secondary bile acid synthesis and elevated aromatic amino acid catabolism. Collectively, these in silico predictions suggest that Enterobacteriaceae may create a gut environment favorable for C. difficile spore germination and/or toxin synthesis.
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Affiliation(s)
- Michael A. Henson
- Department of Chemical Engineering and Institute for Applied Life Sciences, University of Massachusetts, Amherst, Massachusetts, United States of America
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33
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Jansma J, El Aidy S. Understanding the host-microbe interactions using metabolic modeling. MICROBIOME 2021; 9:16. [PMID: 33472685 PMCID: PMC7819158 DOI: 10.1186/s40168-020-00955-1] [Citation(s) in RCA: 41] [Impact Index Per Article: 13.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/12/2020] [Accepted: 12/06/2020] [Indexed: 06/12/2023]
Abstract
The human gut harbors an enormous number of symbiotic microbes, which is vital for human health. However, interactions within the complex microbiota community and between the microbiota and its host are challenging to elucidate, limiting development in the treatment for a variety of diseases associated with microbiota dysbiosis. Using in silico simulation methods based on flux balance analysis, those interactions can be better investigated. Flux balance analysis uses an annotated genome-scale reconstruction of a metabolic network to determine the distribution of metabolic fluxes that represent the complete metabolism of a bacterium in a certain metabolic environment such as the gut. Simulation of a set of bacterial species in a shared metabolic environment can enable the study of the effect of numerous perturbations, such as dietary changes or addition of a probiotic species in a personalized manner. This review aims to introduce to experimental biologists the possible applications of flux balance analysis in the host-microbiota interaction field and discusses its potential use to improve human health. Video abstract.
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Affiliation(s)
- Jack Jansma
- Host-Microbe metabolic Interactions, Groningen Biomolecular Sciences and Biotechnology Institute (GBB), University of Groningen, Groningen, Nijenborgh 7, 9747 AG Groningen, The Netherlands
| | - Sahar El Aidy
- Host-Microbe metabolic Interactions, Groningen Biomolecular Sciences and Biotechnology Institute (GBB), University of Groningen, Groningen, Nijenborgh 7, 9747 AG Groningen, The Netherlands
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34
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Lamichhane S, Sen P, Alves MA, Ribeiro HC, Raunioniemi P, Hyötyläinen T, Orešič M. Linking Gut Microbiome and Lipid Metabolism: Moving beyond Associations. Metabolites 2021; 11:55. [PMID: 33467644 PMCID: PMC7830997 DOI: 10.3390/metabo11010055] [Citation(s) in RCA: 47] [Impact Index Per Article: 15.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2020] [Revised: 01/09/2021] [Accepted: 01/12/2021] [Indexed: 02/08/2023] Open
Abstract
Various studies aiming to elucidate the role of the gut microbiome-metabolome co-axis in health and disease have primarily focused on water-soluble polar metabolites, whilst non-polar microbial lipids have received less attention. The concept of microbiota-dependent lipid biotransformation is over a century old. However, only recently, several studies have shown how microbial lipids alter intestinal and circulating lipid concentrations in the host, thus impacting human lipid homeostasis. There is emerging evidence that gut microbial communities play a particularly significant role in the regulation of host cholesterol and sphingolipid homeostasis. Here, we review and discuss recent research focusing on microbe-host-lipid co-metabolism. We also discuss the interplay of human gut microbiota and molecular lipids entering host systemic circulation, and its role in health and disease.
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Affiliation(s)
- Santosh Lamichhane
- Turku Bioscience Centre, University of Turku and Abo Akademi University, FI-20520 Turku, Finland; (P.S.); (M.A.A.); (H.C.R.); (P.R.); (M.O.)
| | - Partho Sen
- Turku Bioscience Centre, University of Turku and Abo Akademi University, FI-20520 Turku, Finland; (P.S.); (M.A.A.); (H.C.R.); (P.R.); (M.O.)
- School of Medical Sciences, Orebro University, 702 81 Orebro, Sweden
| | - Marina Amaral Alves
- Turku Bioscience Centre, University of Turku and Abo Akademi University, FI-20520 Turku, Finland; (P.S.); (M.A.A.); (H.C.R.); (P.R.); (M.O.)
| | - Henrique C. Ribeiro
- Turku Bioscience Centre, University of Turku and Abo Akademi University, FI-20520 Turku, Finland; (P.S.); (M.A.A.); (H.C.R.); (P.R.); (M.O.)
| | - Peppi Raunioniemi
- Turku Bioscience Centre, University of Turku and Abo Akademi University, FI-20520 Turku, Finland; (P.S.); (M.A.A.); (H.C.R.); (P.R.); (M.O.)
| | | | - Matej Orešič
- Turku Bioscience Centre, University of Turku and Abo Akademi University, FI-20520 Turku, Finland; (P.S.); (M.A.A.); (H.C.R.); (P.R.); (M.O.)
- School of Medical Sciences, Orebro University, 702 81 Orebro, Sweden
- Oil Crops Research Institute, Chinese Academy of Agricultural Sciences, Wuhan 430062, China
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35
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Dahal S, Zhao J, Yang L. Genome-scale Modeling of Metabolism and Macromolecular Expression and Their Applications. BIOTECHNOL BIOPROC E 2021. [DOI: 10.1007/s12257-020-0061-2] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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36
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Hajiagha MN, Taghizadeh S, Asgharzadeh M, Dao S, Ganbarov K, Köse Ş, Kafil HS. Gut microbiota And Human Body Interactions; Its Impact on Health: a review. Curr Pharm Biotechnol 2021; 23:4-14. [PMID: 33397232 DOI: 10.2174/1389201022666210104115836] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2020] [Revised: 11/17/2020] [Accepted: 11/19/2020] [Indexed: 12/27/2022]
Abstract
Gut microbiota (GM) as an organ of the human body has a particular and autonomous function that related to it. This review aimed to investigate human intestinal and gut microbiota interaction and its impact on health. As a creation referable database about this dynamic and complex organ, several comprehensive projects are implemented by using culture-dependent (culturomics), culture independent methods (e.g metagenomics, mathematics model), and Gnotobiological together. This study was done by searching PubMed, Scopus and Google scholar database in the gut, health microbiota and interaction keywords. The first acquired microbiota during pregnancy or childbirth is colonized in the gut by using specific and non-specific mechanisms. That`s structure and shape reach relative stability with selection pressure along with host development until adulthood and keep its resilience against external or internal variables depending on the host genetics and negative feedback. Due to several research individuals have 2 functional group microbiota including the core (common between vast majorities human) and flexible (transient population) microbiome. The most important role of the GM in the human body can be summarized in three basic landscapes: metabolic, immune system, and gut-brain axis interaction. So that loss of microbial population balance will lead to disorder and disease.
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Affiliation(s)
| | - Sepehr Taghizadeh
- Research Center for Pharmaceutical Nanotechnology, Tabriz University of Medical Sciences, Tabriz. Iran
| | - Mohammad Asgharzadeh
- Biotechnology Research Center, Tabriz University of Medical Sciences, Tabriz. Iran
| | - Sounkalo Dao
- Faculté de Médecine, de Pharmacie et d'Odonto-Stomatologie (FMPOS), University of Bamako, Bamako. Mali
| | | | - Şükran Köse
- Department of Infectious Diseases and Clinical Microbiology, University of Health Sciences, Tepecik Training and Research Hospital, İzmir. Turkey
| | - Hossein Samadi Kafil
- Drug Applied Research Center, Faculty of Medicine, Tabriz University of Medical Sciences, Tabriz. Iran
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A network approach to elucidate and prioritize microbial dark matter in microbial communities. ISME JOURNAL 2020; 15:228-244. [PMID: 32963345 PMCID: PMC7852563 DOI: 10.1038/s41396-020-00777-x] [Citation(s) in RCA: 79] [Impact Index Per Article: 19.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/14/2020] [Revised: 08/18/2020] [Accepted: 09/10/2020] [Indexed: 01/13/2023]
Abstract
Microbes compose most of the biomass on the planet, yet the majority of taxa remain uncharacterized. These unknown microbes, often referred to as “microbial dark matter,” represent a major challenge for biology. To understand the ecological contributions of these Unknown taxa, it is essential to first understand the relationship between unknown species, neighboring microbes, and their respective environment. Here, we establish a method to study the ecological significance of “microbial dark matter” by building microbial co-occurrence networks from publicly available 16S rRNA gene sequencing data of four extreme aquatic habitats. For each environment, we constructed networks including and excluding unknown organisms at multiple taxonomic levels and used network centrality measures to quantitatively compare networks. When the Unknown taxa were excluded from the networks, a significant reduction in degree and betweenness was observed for all environments. Strikingly, Unknown taxa occurred as top hubs in all environments, suggesting that “microbial dark matter” play necessary ecological roles within their respective communities. In addition, novel adaptation-related genes were detected after using 16S rRNA gene sequences from top-scoring hub taxa as probes to blast metagenome databases. This work demonstrates the broad applicability of network metrics to identify and prioritize key Unknown taxa and improve understanding of ecosystem structure across diverse habitats.
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38
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Li C, Chen J, Li SC. Understanding Horizontal Gene Transfer network in human gut microbiota. Gut Pathog 2020; 12:33. [PMID: 32670414 PMCID: PMC7346641 DOI: 10.1186/s13099-020-00370-9] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/14/2020] [Accepted: 06/23/2020] [Indexed: 12/19/2022] Open
Abstract
Background Horizontal Gene Transfer (HGT) is the process of transferring genetic materials between species. Through sharing genetic materials, microorganisms in the human microbiota form a network. The network can provide insights into understanding the microbiota. Here, we constructed the HGT networks from the gut microbiota sequencing data and performed network analysis to characterize the HGT networks of gut microbiota. Results We constructed the HGT network and perform the network analysis to two typical gut microbiota datasets, a 283-sample dataset of Mother-to-Child and a 148-sample dataset of longitudinal inflammatory bowel disease (IBD) metagenome. The results indicated that (1) the HGT networks are scale-free. (2) The networks expand their complexities, sizes, and edge numbers, accompanying the early stage of lives; and microbiota established in children shared high similarity as their mother (p-value = 0.0138), supporting the transmission of microbiota from mother to child. (3) Groups harbor group-specific network edges, and network communities, which can potentially serve as biomarkers. For instances, IBD patient group harbors highly abundant communities of Proteobacteria (p-value = 0.0194) and Actinobacteria (p-value = 0.0316); children host highly abundant communities of Proteobacteria (p-value = 2.8785\documentclass[12pt]{minimal}
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\begin{document}$$e^{-7}$$\end{document}e-7). IBD patient networks contain more HGT edges in pathogenic genus, including Mycobacterium, Sutterella, and Pseudomonas. Children’s networks contain more edges from Bifidobacterium and Escherichia. Conclusion Hence, we proposed the HGT network constructions from the gut microbiota sequencing data. The HGT networks capture the host state and the response of microbiota to the environmental and host changes, and they are essential to understand the human microbiota.
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Affiliation(s)
- Chen Li
- Department of Computer Science, City University of Hong Kong, Hong Kong, Hong Kong SAR, China
| | - Jiaxing Chen
- Department of Computer Science, City University of Hong Kong, Hong Kong, Hong Kong SAR, China
| | - Shuai Cheng Li
- Department of Computer Science, City University of Hong Kong, Hong Kong, Hong Kong SAR, China
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diCenzo GC, Tesi M, Pfau T, Mengoni A, Fondi M. Genome-scale metabolic reconstruction of the symbiosis between a leguminous plant and a nitrogen-fixing bacterium. Nat Commun 2020; 11:2574. [PMID: 32444627 PMCID: PMC7244743 DOI: 10.1038/s41467-020-16484-2] [Citation(s) in RCA: 36] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2019] [Accepted: 04/28/2020] [Indexed: 11/09/2022] Open
Abstract
The mutualistic association between leguminous plants and endosymbiotic rhizobial bacteria is a paradigmatic example of a symbiosis driven by metabolic exchanges. Here, we report the reconstruction and modelling of a genome-scale metabolic network of Medicago truncatula (plant) nodulated by Sinorhizobium meliloti (bacterium). The reconstructed nodule tissue contains five spatially distinct developmental zones and encompasses the metabolism of both the plant and the bacterium. Flux balance analysis (FBA) suggests that the metabolic costs associated with symbiotic nitrogen fixation are primarily related to supporting nitrogenase activity, and increasing N2-fixation efficiency is associated with diminishing returns in terms of plant growth. Our analyses support that differentiating bacteroids have access to sugars as major carbon sources, ammonium is the main nitrogen export product of N2-fixing bacteria, and N2 fixation depends on proton transfer from the plant cytoplasm to the bacteria through acidification of the peribacteroid space. We expect that our model, called 'Virtual Nodule Environment' (ViNE), will contribute to a better understanding of the functioning of legume nodules, and may guide experimental studies and engineering of symbiotic nitrogen fixation.
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Affiliation(s)
- George C diCenzo
- Department of Biology, University of Florence, Sesto Fiorentino, Italy
- Department of Biology, Queen's University, Kingston, ON, Canada
| | - Michelangelo Tesi
- Department of Biology, University of Florence, Sesto Fiorentino, Italy
| | - Thomas Pfau
- Life Sciences Research Unit, University of Luxembourg, Belvaux, Luxembourg
| | - Alessio Mengoni
- Department of Biology, University of Florence, Sesto Fiorentino, Italy.
| | - Marco Fondi
- Department of Biology, University of Florence, Sesto Fiorentino, Italy.
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40
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Zeisel SH. Precision (Personalized) Nutrition: Understanding Metabolic Heterogeneity. Annu Rev Food Sci Technol 2020; 11:71-92. [DOI: 10.1146/annurev-food-032519-051736] [Citation(s) in RCA: 34] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
People differ in their requirements for and responses to nutrients and bioactive molecules in the diet. Many inputs contribute to metabolic heterogeneity (including variations in genetics, epigenetics, microbiome, lifestyle, diet intake, and environmental exposure). Precision nutrition is not about developing unique prescriptions for individual people but rather about stratifying people into different subgroups of the population on the basis of biomarkers of the above-listed sources of metabolic variation and then using this stratification to better estimate the different subgroups’ dietary requirements, thereby enabling better dietary recommendations and interventions. The hope is that we will be able to subcategorize people into ever-smaller groups that can be targeted in terms of recommendations, but we will never achieve this at the individual level, thus, the choice of precision nutrition rather than personalized nutrition to designate this new field. This review focuses mainly on genetically related sources of metabolic heterogeneity and identifies challenges that need to be overcome to achieve a full understanding of the complex interactions between the many sources of metabolic heterogeneity that make people differ from one another in their requirements for and responses to foods. It also discusses the commercial applications of precision nutrition.
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Affiliation(s)
- Steven H. Zeisel
- Nutrition Research Institute, Department of Nutrition, University of North Carolina, Kannapolis, North Carolina 28081, USA
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41
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Chen Z, Xie Y, Zhou F, Zhang B, Wu J, Yang L, Xu S, Stedtfeld R, Chen Q, Liu J, Zhang X, Xu H, Ren J. Featured Gut Microbiomes Associated With the Progression of Chronic Hepatitis B Disease. Front Microbiol 2020; 11:383. [PMID: 32265857 PMCID: PMC7098974 DOI: 10.3389/fmicb.2020.00383] [Citation(s) in RCA: 47] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2019] [Accepted: 02/20/2020] [Indexed: 12/11/2022] Open
Abstract
Dysbiosis of gut microbiota during the progression of HBV-related liver disease is not well understood, as there are very few reports that discuss the featured bacterial taxa in different stages. The aim of this study was to reveal the featured bacterial species whose abundances are directly associated with HBV disease progression, that is, progression from healthy subjects to, chronic HBV infection, chronic hepatitis B to liver cirrhosis. Approximately 400 fecal samples were collected, and 97 samples were subjected to 16S rRNA gene sequencing after age and BMI matching. Compared with the healthy individuals, significant gut microbiota alterations were associated with the progression of liver disease. LEfSe results showed that the HBV infected patients had higher Fusobacteria, Veillonella, and Haemophilus abundance while the healthy individuals had higher levels of Prevotella and Phascolarctobacterium. Indicator analysis revealed that 57 OTUs changed as the disease progressed, and their combination produced an AUC value of 90% (95% CI: 86-94%) between the LC and non-LC groups. In addition, the abundances of OTU51 (Dialister succinatiphilus) and OTU50 (Alistipes onderdonkii) decreased as the disease progressed, and these results were further verified by qPCR. The LC patients had the higher bacterial network complexity, which was accompanied with a lower abundance of potential beneficial bacterial taxa, such as Dialister and Alistipes, while they had a higher abundance of pathogenic species within Actinobacteria. The compositional and network changes in the gut microbiota in varied CHB stages, suggest the potential contributions of gut microbiota in CHB disease progression.
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Affiliation(s)
- Zhangran Chen
- Department of Gastroenterology, Zhongshan Hospital Xiamen University, Xiamen, China
- Institute for Microbial Ecology, School of Medicine, Xiamen University, Xiamen, China
- College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, China
| | - Yurou Xie
- Department of Gastroenterology, Zhongshan Hospital Xiamen University, Xiamen, China
- Institute for Microbial Ecology, School of Medicine, Xiamen University, Xiamen, China
| | - Fei Zhou
- Institute for Microbial Ecology, School of Medicine, Xiamen University, Xiamen, China
| | - Bangzhou Zhang
- Department of Gastroenterology, Zhongshan Hospital Xiamen University, Xiamen, China
- Institute for Microbial Ecology, School of Medicine, Xiamen University, Xiamen, China
| | - Jingtong Wu
- Department of Gastroenterology, Zhongshan Hospital Xiamen University, Xiamen, China
- Institute for Microbial Ecology, School of Medicine, Xiamen University, Xiamen, China
| | - Luxi Yang
- School of Life Sciences, Xiamen University, Xiamen, China
| | - Shuangbin Xu
- Fisheries College, Jimei University, Xiamen, China
| | - Robert Stedtfeld
- Civil and Environmental Engineering, Michigan State University, East Lansing, MI, United States
| | - Qiongyun Chen
- Department of Gastroenterology, Zhongshan Hospital Xiamen University, Xiamen, China
- Institute for Microbial Ecology, School of Medicine, Xiamen University, Xiamen, China
| | - Jingjing Liu
- Institute for Microbial Ecology, School of Medicine, Xiamen University, Xiamen, China
| | - Xiang Zhang
- Department of Gastroenterology, Zhongshan Hospital Xiamen University, Xiamen, China
| | - Hongzhi Xu
- Department of Gastroenterology, Zhongshan Hospital Xiamen University, Xiamen, China
- Institute for Microbial Ecology, School of Medicine, Xiamen University, Xiamen, China
| | - Jianlin Ren
- Department of Gastroenterology, Zhongshan Hospital Xiamen University, Xiamen, China
- Institute for Microbial Ecology, School of Medicine, Xiamen University, Xiamen, China
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42
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Chowdhury S, Fong SS. Computational Modeling of the Human Microbiome. Microorganisms 2020; 8:microorganisms8020197. [PMID: 32023941 PMCID: PMC7074762 DOI: 10.3390/microorganisms8020197] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2019] [Revised: 01/23/2020] [Accepted: 01/27/2020] [Indexed: 12/20/2022] Open
Abstract
The impact of microorganisms on human health has long been acknowledged and studied, but recent advances in research methodologies have enabled a new systems-level perspective on the collections of microorganisms associated with humans, the human microbiome. Large-scale collaborative efforts such as the NIH Human Microbiome Project have sought to kick-start research on the human microbiome by providing foundational information on microbial composition based upon specific sites across the human body. Here, we focus on the four main anatomical sites of the human microbiome: gut, oral, skin, and vaginal, and provide information on site-specific background, experimental data, and computational modeling. Each of the site-specific microbiomes has unique organisms and phenomena associated with them; there are also high-level commonalities. By providing an overview of different human microbiome sites, we hope to provide a perspective where detailed, site-specific research is needed to understand causal phenomena that impact human health, but there is equally a need for more generalized methodology improvements that would benefit all human microbiome research.
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Affiliation(s)
- Shomeek Chowdhury
- Integrative Life Sciences, Virginia Commonwealth University, 1000 West Cary Street, Richmond, VA 23284 USA;
| | - Stephen S. Fong
- Chemical and Life Science Engineering, Virginia Commonwealth University, 601 West Main Street, Richmond, VA 23284, USA
- Correspondence:
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43
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Fountain-Jones NM, Clark NJ, Kinsley AC, Carstensen M, Forester J, Johnson TJ, Miller EA, Moore S, Wolf TM, Craft ME. Microbial associations and spatial proximity predict North American moose (Alces alces) gastrointestinal community composition. J Anim Ecol 2020; 89:817-828. [PMID: 31782152 DOI: 10.1111/1365-2656.13154] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2019] [Accepted: 11/04/2019] [Indexed: 01/04/2023]
Abstract
Microbial communities are increasingly recognized as crucial for animal health. However, our understanding of how microbial communities are structured across wildlife populations is poor. Mechanisms such as interspecific associations are important in structuring free-living communities, but we still lack an understanding of how important interspecific associations are in structuring gut microbial communities in comparison with other factors such as host characteristics or spatial proximity of hosts. Here, we ask how gut microbial communities are structured in a population of North American moose Alces alces. We identify key microbial interspecific associations within the moose gut and quantify how important they are relative to key host characteristics, such as body condition, for predicting microbial community composition. We sampled gut microbial communities from 55 moose in a population experiencing decline due to a myriad of factors, including pathogens and malnutrition. We examined microbial community dynamics in this population utilizing novel graphical network models that can explicitly incorporate spatial information. We found that interspecific associations were the most important mechanism structuring gut microbial communities in moose and detected both positive and negative associations. Models only accounting for associations between microbes had higher predictive value compared to models including moose sex, evidence of previous pathogen exposure or body condition. Adding spatial information on moose location further strengthened our model and allowed us to predict microbe occurrences with ~90% accuracy. Collectively, our results suggest that microbial interspecific associations coupled with host spatial proximity are vital in shaping gut microbial communities in a large herbivore. In this case, previous pathogen exposure and moose body condition were not as important in predicting gut microbial community composition. The approach applied here can be used to quantify interspecific associations and gain a more nuanced understanding of the spatial and host factors shaping microbial communities in non-model hosts.
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Affiliation(s)
| | - Nicholas J Clark
- UQ Spatial Epidemiology Laboratory, School of Veterinary Science, The University of Queensland, Gatton, Qld, Australia
| | - Amy C Kinsley
- Department of Veterinary Population Medicine, University of Minnesota, St Paul, MN, USA.,Center for Animal Health and Food Safety, University of Minnesota, St Paul, MN, USA
| | - Michelle Carstensen
- Minnesota Department of Natural Resources, Wildlife Health Program, Forest Lake, MN, USA
| | - James Forester
- Department of Fisheries, Wildlife and Conservation Biology, University of Minnesota, St Paul, MN, USA
| | - Timothy J Johnson
- Center for Animal Health and Food Safety, University of Minnesota, St Paul, MN, USA
| | - Elizabeth A Miller
- Center for Animal Health and Food Safety, University of Minnesota, St Paul, MN, USA
| | - Seth Moore
- Department of Biology and Environment, Grand Portage Band of Chippewa, Grand Portage, MN, USA
| | - Tiffany M Wolf
- Department of Veterinary Population Medicine, University of Minnesota, St Paul, MN, USA
| | - Meggan E Craft
- Department of Veterinary Population Medicine, University of Minnesota, St Paul, MN, USA
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Abstract
The bacterial communities that live within the human gut have been linked to health and disease. However, we are still just beginning to understand how those bacteria interact and what potential interventions to our gut microbiome can make us healthier. Here, we present a mathematical modeling framework (named MICOM) that can recapitulate the growth rates of diverse bacterial species in the gut and can simulate metabolic interactions within microbial communities. We show that MICOM can unravel the ecological rules that shape the microbial landscape in our gut and that a given dietary or probiotic intervention can have widely different effects in different people. Compositional changes in the gut microbiota have been associated with a variety of medical conditions such as obesity, Crohn’s disease, and diabetes. However, connecting microbial community composition to ecosystem function remains a challenge. Here, we introduce MICOM, a customizable metabolic model of the human gut microbiome. By using a heuristic optimization approach based on L2 regularization, we were able to obtain a unique set of realistic growth rates that corresponded well with observed replication rates. We integrated adjustable dietary and taxon abundance constraints to generate personalized metabolic models for individual metagenomic samples. We applied MICOM to a balanced cohort of metagenomes from 186 people, including a metabolically healthy population and individuals with type 1 and type 2 diabetes. Model results showed that individual bacterial genera maintained conserved niche structures across humans, while the community-level production of short-chain fatty acids (SCFAs) was heterogeneous and highly individual specific. Model output revealed complex cross-feeding interactions that would be difficult to measure in vivo. Metabolic interaction networks differed somewhat consistently between healthy and diabetic subjects. In particular, MICOM predicted reduced butyrate and propionate production in a diabetic cohort, with restoration of SCFA production profiles found in healthy subjects following metformin treatment. Overall, we found that changes in diet or taxon abundances have highly personalized effects. We believe MICOM can serve as a useful tool for generating mechanistic hypotheses for how diet and microbiome composition influence community function. All methods are implemented in an open-source Python package, which is available at https://github.com/micom-dev/micom. IMPORTANCE The bacterial communities that live within the human gut have been linked to health and disease. However, we are still just beginning to understand how those bacteria interact and what potential interventions to our gut microbiome can make us healthier. Here, we present a mathematical modeling framework (named MICOM) that can recapitulate the growth rates of diverse bacterial species in the gut and can simulate metabolic interactions within microbial communities. We show that MICOM can unravel the ecological rules that shape the microbial landscape in our gut and that a given dietary or probiotic intervention can have widely different effects in different people.
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Ansari AF, Acharya NIS, Kumaran S, Ravindra K, Reddy YBS, Dixit NM, Raut J. 110th Anniversary: High-Order Interactions Can Eclipse Pairwise Interactions in Shaping the Structure of Microbial Communities. Ind Eng Chem Res 2019. [DOI: 10.1021/acs.iecr.9b03190] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Aamir Faisal Ansari
- Department of Chemical Engineering, Indian Institute of Science, Bangalore 560012, India
| | | | | | | | | | - Narendra M. Dixit
- Department of Chemical Engineering, Indian Institute of Science, Bangalore 560012, India
- Centre for Biosystems Science and Engineering, Indian Institute of Science, Bangalore 560012, India
| | - Janhavi Raut
- Unilever R&D India Pvt, Ltd., Bangalore 560066, India
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Piperata BA, Lee S, Mayta Apaza AC, Cary A, Vilchez S, Oruganti P, Garabed R, Wilson W, Lee J. Characterization of the gut microbiota of Nicaraguan children in a water insecure context. Am J Hum Biol 2019; 32:e23371. [PMID: 31859435 DOI: 10.1002/ajhb.23371] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2019] [Revised: 11/26/2019] [Accepted: 11/27/2019] [Indexed: 12/14/2022] Open
Abstract
OBJECTIVES The gut microbiota varies across human populations. The first years of life are a critical period in its development. While delivery mode and diet contribute to observed variation, the additional contribution of specific environmental factors remains poorly understood. One factor is waterborne enteric pathogen exposure. In this pilot study, we explore the relationship between household water security and the gut microbiota of children. METHODS From Nicaraguan households (n = 39), we collected drinking water samples, as well as fecal samples from children aged one month to 5.99 years (n = 53). We tested water samples for total coliforms (CFU/mL) and the presence of common enteric pathogens. Composition and diversity of the gut microbiota were characterized by 16S rRNA sequencing. Households were classified as having drinking water that was "low" (<29 CFU/mL) or "high" (≥29 CFU/mL) in coliforms. We used permutational analyses of variance and Mann-Whitney U-tests to identify differences in the composition and diversity of the gut microbiota of children living in these two home types. RESULTS Insecure access led households to store drinking water and 85% tested positive for coliforms. High concentrations of Salmonella and Campylobacter were found in water and fecal samples. Controlling for age, the gut microbiota of children from high coliform homes were compositionally different and less diverse than those from low coliform homes. CONCLUSIONS Results indicate that research exploring the ways water insecurity affects human biology should consider the gut microbiome and that investigations of inter-population variation in the gut microbial community of children should consider pathogen exposure and infection.
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Affiliation(s)
| | - Seungjun Lee
- College of Public Health, Division of Environmental Health Sciences, The Ohio State University, Columbus, Ohio
| | - Alba C Mayta Apaza
- Department of Food Science and Technology, The Ohio State University, Columbus, Ohio
| | - Adelaide Cary
- Department of Anthropology, The Ohio State University, Columbus, Ohio
| | - Samuel Vilchez
- Department of Microbiology, National Autonomous University of Nicaragua, León, Nicaragua
| | - Pallavi Oruganti
- College of Veterinary Medicine, Department of Preventative Veterinary Medicine, The Ohio State University, Columbus, Ohio
| | - Rebecca Garabed
- College of Veterinary Medicine, Department of Preventative Veterinary Medicine, The Ohio State University, Columbus, Ohio
| | - Warren Wilson
- Department of Anthropology and Archaeology, University of Calgary, Calgary, Canada
| | - Jiyoung Lee
- College of Public Health, Division of Environmental Health Sciences, The Ohio State University, Columbus, Ohio.,Department of Food Science and Technology, The Ohio State University, Columbus, Ohio
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Wang T, Goyal A, Dubinkina V, Maslov S. Evidence for a multi-level trophic organization of the human gut microbiome. PLoS Comput Biol 2019; 15:e1007524. [PMID: 31856158 PMCID: PMC6922320 DOI: 10.1371/journal.pcbi.1007524] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2019] [Accepted: 11/01/2019] [Indexed: 11/30/2022] Open
Abstract
The human gut microbiome is a complex ecosystem, in which hundreds of microbial species and metabolites coexist, in part due to an extensive network of cross-feeding interactions. However, both the large-scale trophic organization of this ecosystem, and its effects on the underlying metabolic flow, remain unexplored. Here, using a simplified model, we provide quantitative support for a multi-level trophic organization of the human gut microbiome, where microbes consume and secrete metabolites in multiple iterative steps. Using a manually-curated set of metabolic interactions between microbes, our model suggests about four trophic levels, each characterized by a high level-to-level metabolic transfer of byproducts. It also quantitatively predicts the typical metabolic environment of the gut (fecal metabolome) in approximate agreement with the real data. To understand the consequences of this trophic organization, we quantify the metabolic flow and biomass distribution, and explore patterns of microbial and metabolic diversity in different levels. The hierarchical trophic organization suggested by our model can help mechanistically establish causal links between the abundances of microbes and metabolites in the human gut.
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Affiliation(s)
- Tong Wang
- Department of Physics, University of Illinois at Urbana-Champaign, Champaign, Illinois, United States of America
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Champaign, Illinois, United States of America
| | - Akshit Goyal
- Simons Centre for the Study of Living Machines, National Centre for Biological Sciences, Tata Institute of Fundamental Research, Bengaluru, India
| | - Veronika Dubinkina
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Champaign, Illinois, United States of America
- Department of Bioengineering, University of Illinois at Urbana-Champaign, Champaign, Illinois, United States of America
| | - Sergei Maslov
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Champaign, Illinois, United States of America
- Department of Bioengineering, University of Illinois at Urbana-Champaign, Champaign, Illinois, United States of America
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Phalak P, Henson MA. Metabolic modelling of chronic wound microbiota predicts mutualistic interactions that drive community composition. J Appl Microbiol 2019; 127:1576-1593. [PMID: 31436369 PMCID: PMC6790277 DOI: 10.1111/jam.14421] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2019] [Revised: 08/06/2019] [Accepted: 08/13/2019] [Indexed: 12/17/2022]
Abstract
AIMS To identify putative mutualistic interactions driving community composition in polymicrobial chronic wound infections using metabolic modelling. METHODS AND RESULTS We developed a 12 species metabolic model that covered 74% of 16S rDNA pyrosequencing reads of dominant genera from 2963 chronic wound patients. The community model was used to predict species abundances averaged across this large patient population. We found that substantially improved predictions were obtained when the model was constrained with genera prevalence data and predicted abundances were averaged over 5000 ensemble simulations with community participants randomly determined according to the experimentally determined prevalences. Staphylococcus and Pseudomonas were predicted to exhibit a strong mutualistic relationship that resulted in community growth rate and diversity simultaneously increasing, suggesting that these two common chronic wound pathogens establish dominance by cooperating with less harmful commensal species. In communities lacking one or both dominant pathogens, other mutualistic relationship including Staphylococcus/Acinetobacter, Pseudomonas/Serratia and Streptococcus/Enterococcus were predicted consistent with published experimental data. CONCLUSIONS Mutualistic interactions were predicted to be driven by crossfeeding of organic acids, alcohols and amino acids that could potentially be disrupted to slow chronic wound disease progression. SIGNIFICANCE AND IMPACT OF THE STUDY Approximately 2% of the US population suffers from nonhealing chronic wounds infected by a combination of commensal and pathogenic bacteria. These polymicrobial infections are often resilient to antibiotic treatment due to the nutrient-rich wound environment and species interactions that promote community stability and robustness. The simulation results from this study were used to identify putative mutualistic interactions between bacteria that could be targeted to enhance treatment efficacy.
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Affiliation(s)
- Poonam Phalak
- Department of Chemical Engineering and Institute for Applied Life Science, University of Massachusetts, Amherst MA 01003, USA
| | - Michael A. Henson
- Department of Chemical Engineering and Institute for Applied Life Science, University of Massachusetts, Amherst MA 01003, USA
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49
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Metabolic modeling for predicting VFA production from protein‐rich substrates by mixed‐culture fermentation. Biotechnol Bioeng 2019; 117:73-84. [DOI: 10.1002/bit.27177] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2019] [Revised: 08/27/2019] [Accepted: 09/17/2019] [Indexed: 11/07/2022]
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50
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Lawley B, Otal A, Moloney-Geany K, Diana A, Houghton L, Heath ALM, Taylor RW, Tannock GW. Fecal Microbiotas of Indonesian and New Zealand Children Differ in Complexity and Bifidobacterial Taxa during the First Year of Life. Appl Environ Microbiol 2019; 85:e01105-19. [PMID: 31375480 PMCID: PMC6752005 DOI: 10.1128/aem.01105-19] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2019] [Accepted: 07/22/2019] [Indexed: 02/07/2023] Open
Abstract
The biological succession that occurs during the first year of life in the gut of infants in Western countries is broadly predictable in terms of the increasing complexity of the composition of microbiotas. Less information is available about microbiotas in Asian countries, where environmental, nutritional, and cultural influences may differentially affect the composition and development of the microbial community. We compared the fecal microbiotas of Indonesian (n = 204) and New Zealand (NZ) (n = 74) infants 6 to 7 months and 12 months of age. Comparisons were made by analysis of 16S rRNA gene sequences and derivation of community diversity metrics, relative abundances of bacterial families, enterotypes, and cooccurrence correlation networks. Abundances of Bifidobacterium longum subsp. infantis and B. longum subsp. longum were determined by quantitative PCR. All observations supported the view that the Indonesian and NZ infant microbiotas developed in complexity over time, but the changes were much greater for NZ infants. B. longum subsp. infantis dominated the microbiotas of Indonesian children, whereas B. longum subsp. longum was dominant in NZ children. Network analysis showed that the niche model (in which trophic adaptation results in preferential colonization) of the assemblage of microbiotas was supported in Indonesian infants, whereas the neutral (stochastic) model was supported by the development of the microbiotas of NZ infants. The results of the study show that the development of the fecal microbiota is not the same for infants in all countries, and they point to the necessity of obtaining a better understanding of the factors that control the colonization of the gut in early life.IMPORTANCE This study addresses the microbiology of a natural ecosystem (the infant bowel) for children in a rural setting in Indonesia and in an urban environment in New Zealand. Analysis of DNA sequences generated from the microbial community (microbiota) in the feces of the infants during the first year of life showed marked differences in the composition and complexity of the bacterial collections. The differences were most likely due to differences in the prevalence and duration of breastfeeding of infants in the two countries. These kinds of studies are essential for developing concepts of microbial ecology related to the influence of nutrition and environment on the development of the gut microbiota and for determining the long-term effects of microbiological events in early life on human health and well-being.
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Affiliation(s)
- Blair Lawley
- Department of Microbiology and Immunology, University of Otago, Dunedin, New Zealand
| | - Anna Otal
- Department of Microbiology and Immunology, University of Otago, Dunedin, New Zealand
| | - Kit Moloney-Geany
- Department of Microbiology and Immunology, University of Otago, Dunedin, New Zealand
| | - Aly Diana
- Department of Human Nutrition, University of Otago, Dunedin, New Zealand
| | - Lisa Houghton
- Department of Human Nutrition, University of Otago, Dunedin, New Zealand
- Microbiome Otago, University of Otago, Dunedin, New Zealand
| | - Anne-Louise M Heath
- Department of Human Nutrition, University of Otago, Dunedin, New Zealand
- Microbiome Otago, University of Otago, Dunedin, New Zealand
| | - Rachael W Taylor
- Microbiome Otago, University of Otago, Dunedin, New Zealand
- Department of Medicine, University of Otago, Dunedin, New Zealand
| | - Gerald W Tannock
- Department of Microbiology and Immunology, University of Otago, Dunedin, New Zealand
- Microbiome Otago, University of Otago, Dunedin, New Zealand
- Riddet Centre of Research Excellence, Massey University, Palmerston North, New Zealand
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