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Heinken A, El Kouche S, Guéant-Rodriguez RM, Guéant JL. Towards personalized genome-scale modeling of inborn errors of metabolism for systems medicine applications. Metabolism 2024; 150:155738. [PMID: 37981189 DOI: 10.1016/j.metabol.2023.155738] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Revised: 11/09/2023] [Accepted: 11/12/2023] [Indexed: 11/21/2023]
Abstract
Inborn errors of metabolism (IEMs) are a group of more than 1000 inherited diseases that are individually rare but have a cumulative global prevalence of 50 per 100,000 births. Recently, it has been recognized that like common diseases, patients with rare diseases can greatly vary in the manifestation and severity of symptoms. Here, we review omics-driven approaches that enable an integrated, holistic view of metabolic phenotypes in IEM patients. We focus on applications of Constraint-based Reconstruction and Analysis (COBRA), a widely used mechanistic systems biology approach, to model the effects of inherited diseases. Moreover, we review evidence that the gut microbiome is also altered in rare diseases. Finally, we outline an approach using personalized metabolic models of IEM patients for the prediction of biomarkers and tailored therapeutic or dietary interventions. Such applications could pave the way towards personalized medicine not just for common, but also for rare diseases.
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Affiliation(s)
- Almut Heinken
- Inserm UMRS 1256 NGERE - Nutrition, Genetics, and Environmental Risk Exposure, University of Lorraine, Nancy F-54000, France.
| | - Sandra El Kouche
- Inserm UMRS 1256 NGERE - Nutrition, Genetics, and Environmental Risk Exposure, University of Lorraine, Nancy F-54000, France
| | - Rosa-Maria Guéant-Rodriguez
- Inserm UMRS 1256 NGERE - Nutrition, Genetics, and Environmental Risk Exposure, University of Lorraine, Nancy F-54000, France; National Center of Inborn Errors of Metabolism, University Regional Hospital Center of Nancy, Nancy F-54000, France
| | - Jean-Louis Guéant
- Inserm UMRS 1256 NGERE - Nutrition, Genetics, and Environmental Risk Exposure, University of Lorraine, Nancy F-54000, France; National Center of Inborn Errors of Metabolism, University Regional Hospital Center of Nancy, Nancy F-54000, France
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2
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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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3
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Angarita-Rodríguez A, González-Giraldo Y, Rubio-Mesa JJ, Aristizábal AF, Pinzón A, González J. Control Theory and Systems Biology: Potential Applications in Neurodegeneration and Search for Therapeutic Targets. Int J Mol Sci 2023; 25:365. [PMID: 38203536 PMCID: PMC10778851 DOI: 10.3390/ijms25010365] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2023] [Revised: 12/01/2023] [Accepted: 12/19/2023] [Indexed: 01/12/2024] Open
Abstract
Control theory, a well-established discipline in engineering and mathematics, has found novel applications in systems biology. This interdisciplinary approach leverages the principles of feedback control and regulation to gain insights into the complex dynamics of cellular and molecular networks underlying chronic diseases, including neurodegeneration. By modeling and analyzing these intricate systems, control theory provides a framework to understand the pathophysiology and identify potential therapeutic targets. Therefore, this review examines the most widely used control methods in conjunction with genomic-scale metabolic models in the steady state of the multi-omics type. According to our research, this approach involves integrating experimental data, mathematical modeling, and computational analyses to simulate and control complex biological systems. In this review, we find that the most significant application of this methodology is associated with cancer, leaving a lack of knowledge in neurodegenerative models. However, this methodology, mainly associated with the Minimal Dominant Set (MDS), has provided a starting point for identifying therapeutic targets for drug development and personalized treatment strategies, paving the way for more effective therapies.
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Affiliation(s)
- Andrea Angarita-Rodríguez
- Departamento de Nutrición y Bioquímica, Facultad de Ciencias, Pontificia Universidad Javeriana, Edf. Carlos Ortiz, Oficina 107, Cra. 7 40-62, Bogotá 110231, Colombia; (A.A.-R.); (Y.G.-G.); (A.F.A.)
- Laboratorio de Bioinformática y Biología de Sistemas, Universidad Nacional de Colombia, Bogotá 111321, Colombia;
| | - Yeimy González-Giraldo
- Departamento de Nutrición y Bioquímica, Facultad de Ciencias, Pontificia Universidad Javeriana, Edf. Carlos Ortiz, Oficina 107, Cra. 7 40-62, Bogotá 110231, Colombia; (A.A.-R.); (Y.G.-G.); (A.F.A.)
| | - Juan J. Rubio-Mesa
- Departamento de Estadística, Facultad de Ciencias, Universidad Nacional de Colombia, Bogotá 111321, Colombia;
| | - Andrés Felipe Aristizábal
- Departamento de Nutrición y Bioquímica, Facultad de Ciencias, Pontificia Universidad Javeriana, Edf. Carlos Ortiz, Oficina 107, Cra. 7 40-62, Bogotá 110231, Colombia; (A.A.-R.); (Y.G.-G.); (A.F.A.)
| | - Andrés Pinzón
- Laboratorio de Bioinformática y Biología de Sistemas, Universidad Nacional de Colombia, Bogotá 111321, Colombia;
| | - Janneth González
- Departamento de Nutrición y Bioquímica, Facultad de Ciencias, Pontificia Universidad Javeriana, Edf. Carlos Ortiz, Oficina 107, Cra. 7 40-62, Bogotá 110231, Colombia; (A.A.-R.); (Y.G.-G.); (A.F.A.)
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Xu Q, Qin X, Zhang Y, Xu K, Li Y, Li Y, Qi B, Li Y, Yang X, Wang X. Plant miRNA bol-miR159 Regulates Gut Microbiota Composition in Mice: In Vivo Evidence of the Crosstalk between Plant miRNAs and Intestinal Microbes. JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY 2023; 71:16160-16173. [PMID: 37862127 DOI: 10.1021/acs.jafc.3c06104] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/22/2023]
Abstract
New evidence reveals that bol-miR159, an miRNA rich in fruits and vegetables, cross-kingdomly functions in mammalian bodies. However, whether the miRNA could regulate gut microbiota remains unclear. Here, the effect of miR159 on mouse intestinal microbes was comprehensively examined. The results showed that supplementation of miR159 to the chow diet significantly enhanced the diversity of mouse gut microbiota without causing pathological lesions or inflammatory responses on the intestines. At the phylum level, miR159 increased the abundance of Proteobacteria and decreased the Firmicute-to-Bacteroidetes (F/B) ratio. miR159 had prebiotic-like effects on mouse gut microbiota, as it promoted the growth of the bacteria that is beneficial for maintaining gut health. The miRNA can target bacteria genes and get into the bacteria cells. The data provide direct in vivo evidence on the crosstalk between plant miRNAs and intestinal microbes, highlighting the potential for miRNA-based strategies that modulate gut microbes to improve host health.
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Affiliation(s)
- Qin Xu
- Shaanxi Engineering Laboratory for Food Green Processing and Safety Control, and Shaanxi Key Laboratory for Hazard Factors Assessment in Processing and Storage of Agricultural Products, College of Food Engineering and Nutritional Science, Shaanxi Normal University, Xi'an, Shaanxi 710062, China
| | - Xinshu Qin
- Shaanxi Engineering Laboratory for Food Green Processing and Safety Control, and Shaanxi Key Laboratory for Hazard Factors Assessment in Processing and Storage of Agricultural Products, College of Food Engineering and Nutritional Science, Shaanxi Normal University, Xi'an, Shaanxi 710062, China
| | - Yi Zhang
- Department of Food Science, The Pennsylvania State University, University Park, Pennsylvania 16802, United States
| | - Ke Xu
- Department of Joint Surgery, Hong Hui Hospital, Xi'an Jiaotong University, Xi'an, Shaanxi 710054, China
| | - Ying Li
- Shaanxi Engineering Laboratory for Food Green Processing and Safety Control, and Shaanxi Key Laboratory for Hazard Factors Assessment in Processing and Storage of Agricultural Products, College of Food Engineering and Nutritional Science, Shaanxi Normal University, Xi'an, Shaanxi 710062, China
| | - Yinglei Li
- Shaanxi Engineering Laboratory for Food Green Processing and Safety Control, and Shaanxi Key Laboratory for Hazard Factors Assessment in Processing and Storage of Agricultural Products, College of Food Engineering and Nutritional Science, Shaanxi Normal University, Xi'an, Shaanxi 710062, China
| | - Bangran Qi
- Shaanxi Engineering Laboratory for Food Green Processing and Safety Control, and Shaanxi Key Laboratory for Hazard Factors Assessment in Processing and Storage of Agricultural Products, College of Food Engineering and Nutritional Science, Shaanxi Normal University, Xi'an, Shaanxi 710062, China
| | - Yan Li
- Shaanxi Engineering Laboratory for Food Green Processing and Safety Control, and Shaanxi Key Laboratory for Hazard Factors Assessment in Processing and Storage of Agricultural Products, College of Food Engineering and Nutritional Science, Shaanxi Normal University, Xi'an, Shaanxi 710062, China
| | - Xingbin Yang
- Shaanxi Engineering Laboratory for Food Green Processing and Safety Control, and Shaanxi Key Laboratory for Hazard Factors Assessment in Processing and Storage of Agricultural Products, College of Food Engineering and Nutritional Science, Shaanxi Normal University, Xi'an, Shaanxi 710062, China
| | - Xingyu Wang
- Shaanxi Engineering Laboratory for Food Green Processing and Safety Control, and Shaanxi Key Laboratory for Hazard Factors Assessment in Processing and Storage of Agricultural Products, College of Food Engineering and Nutritional Science, Shaanxi Normal University, Xi'an, Shaanxi 710062, China
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5
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Li L, Yan S, Liu S, Wang P, Li W, Yi Y, Qin S. In-depth insight into correlations between gut microbiota and dietary fiber elucidates a dietary causal relationship with host health. Food Res Int 2023; 172:113133. [PMID: 37689844 DOI: 10.1016/j.foodres.2023.113133] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2023] [Revised: 06/09/2023] [Accepted: 06/10/2023] [Indexed: 09/11/2023]
Abstract
Dietary fiber exerts a wide range of biological benefits on host health, which not only provides a powerful source of nutrition for gut microbiota but also supplies key microbial metabolites that directly affect host health. This review mainly focuses on the decomposition and metabolism of dietary fiber and the essential genera Bacteroides and Bifidobacterium in dietary fiber fermentation. Dietary fiber plays an essential role in host health by impacting outcomes related to obesity, enteritis, immune health, cancer and neurodegenerative diseases. Additionally, the gut microbiota-independent pathway of dietary fiber affecting host health is also discussed. Personalized dietary fiber intake combined with microbiome, genetics, epigenetics, lifestyle and other factors has been highlighted for development in the future. A higher level of evidence is needed to demonstrate which microbial phenotype benefits from which kind of dietary fiber. In-depth insights into the correlation between gut microbiota and dietary fiber provide strong theoretical support for the precise application of dietary fiber, which elucidates a dietary causal relationship with host health.
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Affiliation(s)
- Lili Li
- Yantai Institute of Coastal Zone Research, Chinese Academy of Sciences, Yantai 264003, China.
| | - Shuling Yan
- Yantai Institute of Coastal Zone Research, Chinese Academy of Sciences, Yantai 264003, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Shuangjiang Liu
- Shandong University, Qingdao 266237, China; Institute of Microbiology, Chinese Academy of Sciences, Beijing 100101, China.
| | - Ping Wang
- Yantai Institute of Coastal Zone Research, Chinese Academy of Sciences, Yantai 264003, China; Shandong University of Traditional Chinese Medicine, Jinan 250355, China.
| | - Wenjun Li
- Yantai Institute of Coastal Zone Research, Chinese Academy of Sciences, Yantai 264003, China.
| | - Yuetao Yi
- Yantai Institute of Coastal Zone Research, Chinese Academy of Sciences, Yantai 264003, China.
| | - Song Qin
- Yantai Institute of Coastal Zone Research, Chinese Academy of Sciences, Yantai 264003, China.
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6
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Jenior ML, Leslie JL, Kolling GL, Archbald-Pannone L, Powers DA, Petri WA, Papin JA. Systems-ecology designed bacterial consortium protects from severe Clostridioides difficile infection. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.08.08.552483. [PMID: 37609255 PMCID: PMC10441344 DOI: 10.1101/2023.08.08.552483] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/24/2023]
Abstract
Fecal Microbiota Transplant (FMT) is an emerging therapy that has had remarkable success in treatment and prevention of recurrent Clostridioides difficile infection (rCDI). FMT has recently been associated with adverse outcomes such as inadvertent transfer of antimicrobial resistance, necessitating development of more targeted bacteriotherapies. To address this challenge, we developed a novel systems biology pipeline to identify candidate probiotic strains that would be predicted to interrupt C. difficile pathogenesis. Utilizing metagenomic characterization of human FMT donor samples, we identified those metabolic pathways most associated with successful FMTs and reconstructed the metabolism of encoding species to simulate interactions with C. difficile . This analysis resulted in predictions of high levels of cross-feeding for amino acids in species most associated with FMT success. Guided by these in silico models, we assembled consortia of bacteria with increased amino acid cross-feeding which were then validated in vitro . We subsequently tested the consortia in a murine model of CDI, demonstrating total protection from severe CDI through decreased toxin levels, recovered gut microbiota, and increased intestinal eosinophils. These results support the novel framework that amino acid cross-feeding is likely a critical mechanism in the initial resolution of CDI by FMT. Importantly, we conclude that our predictive platform based on predicted and testable metabolic interactions between the microbiota and C. difficile led to a rationally designed biotherapeutic framework that may be extended to other enteric infections.
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7
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Gao X, Zhao J, Chen W, Zhai Q. Food and drug design for gut microbiota-directed regulation: Current experimental landscape and future innovation. Pharmacol Res 2023; 194:106867. [PMID: 37499703 DOI: 10.1016/j.phrs.2023.106867] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/29/2023] [Revised: 07/19/2023] [Accepted: 07/23/2023] [Indexed: 07/29/2023]
Abstract
Most diets and medications enhance host health via microbiota-dependent ways, but it is in the present situation of untargeted regulation. Non-targeted regulation may lead to the ineffectiveness of dietary supplements or drug treatment. Microbiota-directed food, aiming to improve diseases by targeting specific microbes without affecting other bacteria, have been proposed to deal with this problem. However, there is currently no universally applicable method to explore such foods or drugs. In this review, thirty studies on recent efforts in microbiota directed diets and medications are summarized from various databases. The methods used to find new foods and medications are primarily divided into four groups depending on the experimental models: in vivo and in vitro, as well as predictions based on bioinformatics. We also discuss their implementation, interpretation, and respective limitations, and describe the present situation. We further put forward a framework for microbiota-directed foods and medicine according to above methods and other microbiome manipulation, which will spur precision medicine.
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Affiliation(s)
- Xiaoxiang Gao
- State Key Laboratory of Food Science and Resources, Jiangnan University, Jiangnan University, Wuxi, Jiangsu 214122, China; School of Food Science and Technology, Jiangnan University, Wuxi, Jiangsu 214122, China
| | - Jianxin Zhao
- State Key Laboratory of Food Science and Resources, Jiangnan University, Jiangnan University, Wuxi, Jiangsu 214122, China; School of Food Science and Technology, Jiangnan University, Wuxi, Jiangsu 214122, China
| | - Wei Chen
- State Key Laboratory of Food Science and Resources, Jiangnan University, Jiangnan University, Wuxi, Jiangsu 214122, China; School of Food Science and Technology, Jiangnan University, Wuxi, Jiangsu 214122, China; National Engineering Research Center for Functional Food, Jiangnan University, Wuxi, Jiangsu 214122, China
| | - Qixiao Zhai
- State Key Laboratory of Food Science and Resources, Jiangnan University, Jiangnan University, Wuxi, Jiangsu 214122, China; School of Food Science and Technology, Jiangnan University, Wuxi, Jiangsu 214122, China.
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8
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Wang W, Liu Y, Li Y, Luo B, Lin Z, Chen K, Liu Y. Dietary patterns and cardiometabolic health: Clinical evidence and mechanism. MedComm (Beijing) 2023; 4:e212. [PMID: 36776765 PMCID: PMC9899878 DOI: 10.1002/mco2.212] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Revised: 12/31/2022] [Accepted: 01/11/2023] [Indexed: 02/08/2023] Open
Abstract
For centuries, the search for nutritional interventions to underpin cardiovascular treatment and prevention guidelines has contributed to the rapid development of the field of dietary patterns and cardiometabolic disease (CMD). Numerous studies have demonstrated that healthy dietary patterns with emphasis on food-based recommendations are the gold standard for extending lifespan and reducing the risks of CMD and mortality. Healthy dietary patterns include various permutations of energy restriction, macronutrients, and food intake patterns such as calorie restriction, intermittent fasting, Mediterranean diet, plant-based diets, etc. Early implementation of healthy dietary patterns in patients with CMD is encouraged, but an understanding of the mechanisms by which these patterns trigger cardiometabolic benefits remains incomplete. Hence, this review examined several dietary patterns that may improve cardiometabolic health, including restrictive dietary patterns, regional dietary patterns, and diets based on controlled macronutrients and food groups, summarizing cutting-edge evidence and potential mechanisms for CMD prevention and treatment. Particularly, considering individual differences in responses to dietary composition and nutritional changes in organ tissue diversity, we highlighted the critical role of individual gut microbiota in the crosstalk between diet and CMD and recommend a more precise and dynamic nutritional strategy for CMD by developing dietary patterns based on individual gut microbiota profiles.
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Affiliation(s)
- Wenting Wang
- National Clinical Research Centre for Chinese Medicine Cardiology Xiyuan Hospital China Academy of Chinese Medical Sciences Beijing China
| | - Yanfei Liu
- National Clinical Research Centre for Chinese Medicine Cardiology Xiyuan Hospital China Academy of Chinese Medical Sciences Beijing China
| | - Yiwen Li
- National Clinical Research Centre for Chinese Medicine Cardiology Xiyuan Hospital China Academy of Chinese Medical Sciences Beijing China
| | - Binyu Luo
- National Clinical Research Centre for Chinese Medicine Cardiology Xiyuan Hospital China Academy of Chinese Medical Sciences Beijing China
| | - Zhixiu Lin
- Faculty of Medicine The Chinese University of Hong Kong Hong Kong
| | - Keji Chen
- National Clinical Research Centre for Chinese Medicine Cardiology Xiyuan Hospital China Academy of Chinese Medical Sciences Beijing China
| | - Yue Liu
- National Clinical Research Centre for Chinese Medicine Cardiology Xiyuan Hospital China Academy of Chinese Medical Sciences Beijing China
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9
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Beura S, Kundu P, Das AK, Ghosh A. Metagenome-scale community metabolic modelling for understanding the role of gut microbiota in human health. Comput Biol Med 2022; 149:105997. [DOI: 10.1016/j.compbiomed.2022.105997] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2022] [Revised: 07/03/2022] [Accepted: 08/14/2022] [Indexed: 11/03/2022]
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10
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Nursimulu N, Moses AM, Parkinson J. Architect: A tool for aiding the reconstruction of high-quality metabolic models through improved enzyme annotation. PLoS Comput Biol 2022; 18:e1010452. [PMID: 36074804 PMCID: PMC9488769 DOI: 10.1371/journal.pcbi.1010452] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2022] [Revised: 09/20/2022] [Accepted: 07/29/2022] [Indexed: 11/19/2022] Open
Abstract
Constraint-based modeling is a powerful framework for studying cellular metabolism, with applications ranging from predicting growth rates and optimizing production of high value metabolites to identifying enzymes in pathogens that may be targeted for therapeutic interventions. Results from modeling experiments can be affected at least in part by the quality of the metabolic models used. Reconstructing a metabolic network manually can produce a high-quality metabolic model but is a time-consuming task. At the same time, current methods for automating the process typically transfer metabolic function based on sequence similarity, a process known to produce many false positives. We created Architect, a pipeline for automatic metabolic model reconstruction from protein sequences. First, it performs enzyme annotation through an ensemble approach, whereby a likelihood score is computed for an EC prediction based on predictions from existing tools; for this step, our method shows both increased precision and recall compared to individual tools. Next, Architect uses these annotations to construct a high-quality metabolic network which is then gap-filled based on likelihood scores from the ensemble approach. The resulting metabolic model is output in SBML format, suitable for constraints-based analyses. Through comparisons of enzyme annotations and curated metabolic models, we demonstrate improved performance of Architect over other state-of-the-art tools, notably with higher precision and recall on the eukaryote C. elegans and when compared to UniProt annotations in two bacterial species. Code for Architect is available at https://github.com/ParkinsonLab/Architect. For ease-of-use, Architect can be readily set up and utilized using its Docker image, maintained on Docker Hub.
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Affiliation(s)
- Nirvana Nursimulu
- Department of Computer Science, University of Toronto, Toronto, Ontario, Canada
- Program in Molecular Medicine, The Hospital for Sick Children, Toronto, Ontario, Canada
| | - Alan M. Moses
- Department of Computer Science, University of Toronto, Toronto, Ontario, Canada
- Department of Cell & Systems Biology, University of Toronto, Toronto, Ontario, Canada
| | - John Parkinson
- Program in Molecular Medicine, The Hospital for Sick Children, Toronto, Ontario, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada
- Department of Biochemistry, University of Toronto, Toronto, Ontario, Canada
- * E-mail:
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11
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Ghaffari P, Shoaie S, Nielsen LK. Irritable bowel syndrome and microbiome; Switching from conventional diagnosis and therapies to personalized interventions. J Transl Med 2022; 20:173. [PMID: 35410233 PMCID: PMC9004034 DOI: 10.1186/s12967-022-03365-z] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2021] [Accepted: 03/26/2022] [Indexed: 02/08/2023] Open
Abstract
AbstractThe human microbiome has been linked to several diseases. Gastrointestinal diseases are still one of the most prominent area of study in host-microbiome interactions however the underlying microbial mechanisms in these disorders are not fully established. Irritable bowel syndrome (IBS) remains as one of the prominent disorders with significant changes in the gut microbiome composition and without definitive treatment. IBS has a severe impact on socio-economic and patient’s lifestyle. The association studies between the IBS and microbiome have shed a light on relevance of microbial composition, and hence microbiome-based trials were designed. However, there are no clear evidence of potential treatment for IBS. This review summarizes the epidemiology and socioeconomic impact of IBS and then focus on microbiome observational and clinical trials. At the end, we propose a new perspective on using data-driven approach and applying computational modelling and machine learning to design microbiome-aware personalized treatment for IBS.
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12
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He P, Yu L, Tian F, Zhang H, Chen W, Zhai Q. Dietary Patterns and Gut Microbiota: The Crucial Actors in Inflammatory Bowel Disease. Adv Nutr 2022; 13:1628-1651. [PMID: 35348593 PMCID: PMC9526834 DOI: 10.1093/advances/nmac029] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2021] [Revised: 02/25/2022] [Accepted: 03/22/2022] [Indexed: 02/06/2023] Open
Abstract
It is widely believed that diet and the gut microbiota are strongly related to the occurrence and progression of inflammatory bowel disease (IBD), but the effects of the interaction between dietary patterns and the gut microbiota on IBD have not been well elucidated. In this article, we aim to explore the complex relation between dietary patterns, gut microbiota, and IBD. We first comprehensively summarized the dietary patterns associated with IBD and found that dietary patterns can modulate the occurrence and progression of IBD through various signaling pathways, including mammalian target of rapamycin (mTOR), mitogen-activated protein kinases (MAPKs), signal transducer and activator of transcription 3 (STAT3), and NF-κB. Besides, the gut microbiota performs a vital role in the progression of IBD, which can affect the expression of IBD susceptibility genes, such as dual oxidase 2 (DUOX2) and APOA-1 , the intestinal barrier (in particular, the expression of tight junction proteins), immune function (especially the homeostasis between effector and regulatory T cells) and the physiological metabolism, in particular, SCFAs, bile acids (BAs), and tryptophan metabolism. Finally, we reviewed the current knowledge on the interaction between dietary patterns and the gut microbiota in IBD and found that dietary patterns modulate the onset and progression of IBD, which is partly attributed to the regulation of the gut microbiota (especially SCFAs-producing bacteria and Escherichia coli). Faecalibacteria as "microbiomarkers" of IBD could be used as a target for dietary interventions to alleviate IBD. A comprehensive understanding of the interplay between dietary intake, gut microbiota, and IBD will facilitate the development of personalized dietary strategies based on the regulation of the gut microbiota in IBD and expedite the era of precision nutritional interventions for IBD.
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Affiliation(s)
- Pandi He
- State Key Laboratory of Food Science and Technology, Jiangnan University, Wuxi, Jiangsu, China,School of Food Science and Technology, Jiangnan University, Wuxi, Jiangsu, China
| | - Leilei Yu
- State Key Laboratory of Food Science and Technology, Jiangnan University, Wuxi, Jiangsu, China,School of Food Science and Technology, Jiangnan University, Wuxi, Jiangsu, China
| | - Fengwei Tian
- State Key Laboratory of Food Science and Technology, Jiangnan University, Wuxi, Jiangsu, China,School of Food Science and Technology, Jiangnan University, Wuxi, Jiangsu, 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, Jiangsu, China
| | - Wei Chen
- 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
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13
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Can dietary patterns prevent cognitive impairment and reduce Alzheimer's disease risk: exploring the underlying mechanisms of effects. Neurosci Biobehav Rev 2022; 135:104556. [PMID: 35122783 DOI: 10.1016/j.neubiorev.2022.104556] [Citation(s) in RCA: 30] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2021] [Revised: 01/29/2022] [Accepted: 01/30/2022] [Indexed: 02/07/2023]
Abstract
Alzheimer's disease (AD) is one of the fastest growing cognitive decline-related neurological diseases. To date, effective curative strategies have remained elusive. A growing body of evidence indicates that dietary patterns have significant effects on cognitive function and the risk of developing AD. Previous studies on the association between diet and AD risk have mainly focused on individual food components and specific nutrients, and the mechanisms responsible for the beneficial effects of dietary patterns on AD are not well understood. This article provides a comprehensive overview of the effects of dietary patterns, including the Mediterranean diet (MedDiet), dietary approaches to stop hypertension (DASH) diet, Mediterranean-DASH diet intervention for neurological delay (MIND), ketogenic diet, caloric restriction, intermittent fasting, methionine restriction, and low-protein and high-carbohydrate diet, on cognitive impairment and summarizes the underlying mechanisms by which dietary patterns attenuate cognitive impairment, especially highlighting the modulation of dietary patterns on cognitive impairment through gut microbiota. Furthermore, considering the variability in individual metabolic responses to dietary intake, we put forward a framework to develop personalized dietary patterns for people with cognitive disorders or AD based on individual gut microbiome compositions.
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14
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Begum N, Harzandi A, Lee S, Uhlen M, Moyes DL, Shoaie S. Host-mycobiome metabolic interactions in health and disease. Gut Microbes 2022; 14:2121576. [PMID: 36151873 PMCID: PMC9519009 DOI: 10.1080/19490976.2022.2121576] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/04/2022] [Revised: 08/31/2022] [Accepted: 08/31/2022] [Indexed: 02/04/2023] Open
Abstract
Fungal communities (mycobiome) have an important role in sustaining the resilience of complex microbial communities and maintenance of homeostasis. The mycobiome remains relatively unexplored compared to the bacteriome despite increasing evidence highlighting their contribution to host-microbiome interactions in health and disease. Despite being a small proportion of the total species, fungi constitute a large proportion of the biomass within the human microbiome and thus serve as a potential target for metabolic reprogramming in pathogenesis and disease mechanism. Metabolites produced by fungi shape host niches, induce immune tolerance and changes in their levels prelude changes associated with metabolic diseases and cancer. Given the complexity of microbial interactions, studying the metabolic interplay of the mycobiome with both host and microbiome is a demanding but crucial task. However, genome-scale modelling and synthetic biology can provide an integrative platform that allows elucidation of the multifaceted interactions between mycobiome, microbiome and host. The inferences gained from understanding mycobiome interplay with other organisms can delineate the key role of the mycobiome in pathophysiology and reveal its role in human disease.
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Affiliation(s)
- Neelu Begum
- Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King’s College London, London, UK
| | - Azadeh Harzandi
- Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King’s College London, London, UK
| | - Sunjae Lee
- Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King’s College London, London, UK
| | - Mathias Uhlen
- Science for Life Laboratory, KTH–Royal Institute of Technology, Stockholm, Sweden
| | - David L. Moyes
- Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King’s College London, London, UK
| | - Saeed Shoaie
- Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King’s College London, London, UK
- Science for Life Laboratory, KTH–Royal Institute of Technology, Stockholm, Sweden
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15
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Curry KD, Nute MG, Treangen TJ. It takes guts to learn: machine learning techniques for disease detection from the gut microbiome. Emerg Top Life Sci 2021; 5:815-827. [PMID: 34779841 PMCID: PMC8786294 DOI: 10.1042/etls20210213] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2021] [Revised: 09/29/2021] [Accepted: 10/06/2021] [Indexed: 02/01/2023]
Abstract
Associations between the human gut microbiome and expression of host illness have been noted in a variety of conditions ranging from gastrointestinal dysfunctions to neurological deficits. Machine learning (ML) methods have generated promising results for disease prediction from gut metagenomic information for diseases including liver cirrhosis and irritable bowel disease, but have lacked efficacy when predicting other illnesses. Here, we review current ML methods designed for disease classification from microbiome data. We highlight the computational challenges these methods have effectively overcome and discuss the biological components that have been overlooked to offer perspectives on future work in this area.
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Affiliation(s)
- Kristen D. Curry
- Department of Computer Science, Rice University, Houston, TX 77005, USA
| | - Michael G. Nute
- Department of Computer Science, Rice University, Houston, TX 77005, USA
| | - Todd J. Treangen
- Department of Computer Science, Rice University, Houston, TX 77005, USA
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16
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Ibrahim M, Raman K. Two-species community design of lactic acid bacteria for optimal production of lactate. Comput Struct Biotechnol J 2021; 19:6039-6049. [PMID: 34849207 PMCID: PMC8605394 DOI: 10.1016/j.csbj.2021.11.009] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2021] [Revised: 11/01/2021] [Accepted: 11/07/2021] [Indexed: 01/03/2023] Open
Abstract
Microbial communities that metabolise pentose and hexose sugars are useful in producing high-value chemicals, resulting in the effective conversion of raw materials to the product, a reduction in the production cost, and increased yield. Here, we present a computational analysis approach called CAMP (Co-culture/Community Analyses for Metabolite Production) that simulates and identifies appropriate communities to produce a metabolite of interest. To demonstrate this approach, we focus on the optimal production of lactate from various Lactic Acid Bacteria. We used genome-scale metabolic models (GSMMs) belonging to Lactobacillus, Leuconostoc, and Pediococcus species from the Virtual Metabolic Human (VMH; https://vmh.life/) resource and well-curated GSMMs of L. plantarum WCSF1 and L. reuteri JCM 1112. We analysed 1176 two-species communities using a constraint-based modelling method for steady-state flux-balance analysis of communities. Flux variability analysis was used to detect the maximum lactate flux in the communities. Using glucose or xylose as substrates separately or in combination resulted in either parasitism, amensalism, or mutualism being the dominant interaction behaviour in the communities. Interaction behaviour between members of the community was deduced based on variations in the predicted growth rates of monocultures and co-cultures. Acetaldehyde, ethanol, acetate, among other metabolites, were found to be cross-fed between community members. L. plantarum WCSF1 was found to be a member of communities with high lactate yields. In silico community optimisation strategies to predict reaction knock-outs for improving lactate flux were implemented. Reaction knock-outs of acetate kinase, phosphate acetyltransferase, and fumarate reductase in the communities were found to enhance lactate production.
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Affiliation(s)
- Maziya Ibrahim
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, IIT Madras, India
- Centre for Integrative Biology and Systems mEdicine (IBSE), IIT Madras, India
- Robert Bosch Centre for Data Science and Artificial Intelligence (RBC-DSAI), IIT Madras, India
| | - Karthik Raman
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, IIT Madras, India
- Centre for Integrative Biology and Systems mEdicine (IBSE), IIT Madras, India
- Robert Bosch Centre for Data Science and Artificial Intelligence (RBC-DSAI), IIT Madras, India
- Corresponding author at: Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, IIT Madras, India.
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17
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Esvap E, Ulgen KO. Advances in Genome-Scale Metabolic Modeling toward Microbial Community Analysis of the Human Microbiome. ACS Synth Biol 2021; 10:2121-2137. [PMID: 34402617 DOI: 10.1021/acssynbio.1c00140] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
A genome-scale metabolic model (GEM) represents metabolic pathways of an organism in a mathematical form and can be built using biochemistry and genome annotation data. GEMs are invaluable for understanding organisms since they analyze the metabolic capabilities and behaviors quantitatively and can predict phenotypes. The development of high-throughput data collection techniques led to an immense increase in omics data such as metagenomics, which expand our knowledge on the human microbiome, but this also created a need for systematic analysis of these data. In recent years, GEMs have also been reconstructed for microbial species, including human gut microbiota, and methods for the analysis of microbial communities have been developed to examine the interaction between the organisms or the host. The purpose of this review is to provide a comprehensive guide for the applications of GEMs in microbial community analysis. Starting with GEM repositories, automatic GEM reconstruction tools, and quality control of models, this review will give insights into microbe-microbe and microbe-host interaction predictions and optimization of microbial community models. Recent studies that utilize microbial GEMs and personalized models to infer the influence of microbiota on human diseases such as inflammatory bowel diseases (IBD) or Parkinson's disease are exemplified. Being powerful system biology tools for both species-level and community-level analysis of microbes, GEMs integrated with omics data and machine learning techniques will be indispensable for studying the microbiome and their effects on human physiology as well as for deciphering the mechanisms behind human diseases.
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Affiliation(s)
- Elif Esvap
- Department of Chemical Engineering, Bogazici University, 34342 Istanbul, Turkey
| | - Kutlu O. Ulgen
- Department of Chemical Engineering, Bogazici University, 34342 Istanbul, Turkey
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18
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Frades I, Foguet C, Cascante M, Araúzo-Bravo MJ. Genome Scale Modeling to Study the Metabolic Competition between Cells in the Tumor Microenvironment. Cancers (Basel) 2021; 13:4609. [PMID: 34572839 PMCID: PMC8470216 DOI: 10.3390/cancers13184609] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2021] [Revised: 09/06/2021] [Accepted: 09/09/2021] [Indexed: 12/31/2022] Open
Abstract
The tumor's physiology emerges from the dynamic interplay of numerous cell types, such as cancer cells, immune cells and stromal cells, within the tumor microenvironment. Immune and cancer cells compete for nutrients within the tumor microenvironment, leading to a metabolic battle between these cell populations. Tumor cells can reprogram their metabolism to meet the high demand of building blocks and ATP for proliferation, and to gain an advantage over the action of immune cells. The study of the metabolic reprogramming mechanisms underlying cancer requires the quantification of metabolic fluxes which can be estimated at the genome-scale with constraint-based or kinetic modeling. Constraint-based models use a set of linear constraints to simulate steady-state metabolic fluxes, whereas kinetic models can simulate both the transient behavior and steady-state values of cellular fluxes and concentrations. The integration of cell- or tissue-specific data enables the construction of context-specific models that reflect cell-type- or tissue-specific metabolic properties. While the available modeling frameworks enable limited modeling of the metabolic crosstalk between tumor and immune cells in the tumor stroma, future developments will likely involve new hybrid kinetic/stoichiometric formulations.
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Affiliation(s)
- Itziar Frades
- Computational Biology and Systems Biomedicine Group, Biodonostia Health Research Institute, 20009 San Sebastian, Spain;
| | - Carles Foguet
- Department of Biochemistry and Molecular Biomedicine, Institute of Biomedicine of University of Barcelona, Faculty of Biology, Universitat de Barcelona, Av. Diagonal 643, 08028 Barcelona, Spain; (C.F.); (M.C.)
- Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBEREHD) (CB17/04/00023) and Metabolomics Node at Spanish National Bioinformatics Institute (INB-ISCIII-ES-ELIXIR), Instituto de Salud Carlos III (ISCIII), 28020 Madrid, Spain
| | - Marta Cascante
- Department of Biochemistry and Molecular Biomedicine, Institute of Biomedicine of University of Barcelona, Faculty of Biology, Universitat de Barcelona, Av. Diagonal 643, 08028 Barcelona, Spain; (C.F.); (M.C.)
- Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBEREHD) (CB17/04/00023) and Metabolomics Node at Spanish National Bioinformatics Institute (INB-ISCIII-ES-ELIXIR), Instituto de Salud Carlos III (ISCIII), 28020 Madrid, Spain
| | - Marcos J. Araúzo-Bravo
- Computational Biology and Systems Biomedicine Group, Biodonostia Health Research Institute, 20009 San Sebastian, Spain;
- Max Planck Institute of Molecular Biomedicine, 48167 Münster, Germany
- Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (CIBERfes), 28015 Madrid, Spain
- Translational Bioinformatics Network (TransBioNet), 8001 Barcelona, Spain
- Ikerbasque, Basque Foundation for Science, 48012 Bilbao, Spain
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19
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Ezzamouri B, Shoaie S, Ledesma-Amaro R. Synergies of Systems Biology and Synthetic Biology in Human Microbiome Studies. Front Microbiol 2021; 12:681982. [PMID: 34531833 PMCID: PMC8438329 DOI: 10.3389/fmicb.2021.681982] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2021] [Accepted: 05/31/2021] [Indexed: 12/26/2022] Open
Abstract
A number of studies have shown that the microbial communities of the human body are integral for the maintenance of human health. Advances in next-generation sequencing have enabled rapid and large-scale quantification of the composition of microbial communities in health and disease. Microorganisms mediate diverse host responses including metabolic pathways and immune responses. Using a system biology approach to further understand the underlying alterations of the microbiota in physiological and pathological states can help reveal potential novel therapeutic and diagnostic interventions within the field of synthetic biology. Tools such as biosensors, memory arrays, and engineered bacteria can rewire the microbiome environment. In this article, we review the computational tools used to study microbiome communities and the current limitations of these methods. We evaluate how genome-scale metabolic models (GEMs) can advance our understanding of the microbe-microbe and microbe-host interactions. Moreover, we present how synergies between these system biology approaches and synthetic biology can be harnessed in human microbiome studies to improve future therapeutics and diagnostics and highlight important knowledge gaps for future research in these rapidly evolving fields.
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Affiliation(s)
- Bouchra Ezzamouri
- Unit for Population-Based Dermatology Research, St John’s Institute of Dermatology, Guy’s and St Thomas’ NHS Foundation Trust and King’s College London, London, United Kindom
- Faculty of Dentistry, Centre for Host-Microbiome Interactions, Oral and Craniofacial Sciences, King’s College London, London, United Kingdom
- Department of Bioengineering and Imperial College Centre for Synthetic Biology, Imperial College London, London, United Kingdom
| | - Saeed Shoaie
- Faculty of Dentistry, Centre for Host-Microbiome Interactions, Oral and Craniofacial Sciences, King’s College London, London, United Kingdom
- Science for Life Laboratory, KTH—Royal Institute of Technology, Stockholm, Sweden
| | - Rodrigo Ledesma-Amaro
- Department of Bioengineering and Imperial College Centre for Synthetic Biology, Imperial College London, London, United Kingdom
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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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Heinken A, Basile A, Hertel J, Thinnes C, Thiele I. Genome-Scale Metabolic Modeling of the Human Microbiome in the Era of Personalized Medicine. Annu Rev Microbiol 2021; 75:199-222. [PMID: 34314593 DOI: 10.1146/annurev-micro-060221-012134] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
The human microbiome plays an important role in human health and disease. Meta-omics analyses provide indispensable data for linking changes in microbiome composition and function to disease etiology. Yet, the lack of a mechanistic understanding of, e.g., microbiome-metabolome links hampers the translation of these findings into effective, novel therapeutics. Here, we propose metabolic modeling of microbial communities through constraint-based reconstruction and analysis (COBRA) as a complementary approach to meta-omics analyses. First, we highlight the importance of microbial metabolism in cardiometabolic diseases, inflammatory bowel disease, colorectal cancer, Alzheimer disease, and Parkinson disease. Next, we demonstrate that microbial community modeling can stratify patients and controls, mechanistically link microbes with fecal metabolites altered in disease, and identify host pathways affected by the microbiome. Finally, we outline our vision for COBRA modeling combined with meta-omics analyses and multivariate statistical analyses to inform and guide clinical trials, yield testable hypotheses, and ultimately propose novel dietary and therapeutic interventions. Expected final online publication date for the Annual Review of Microbiology, Volume 75 is October 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.
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Affiliation(s)
- Almut Heinken
- School of Medicine, National University of Ireland, Galway, H91 TK33, Ireland;
| | - Arianna Basile
- Department of Biology, University of Padua, Padua 35121, Italy
| | - Johannes Hertel
- School of Medicine, National University of Ireland, Galway, H91 TK33, Ireland; .,Department of Psychiatry and Psychotherapy, University of Greifswald, 17489 Greifswald, Germany
| | - Cyrille Thinnes
- School of Medicine, National University of Ireland, Galway, H91 TK33, Ireland;
| | - Ines Thiele
- School of Medicine, National University of Ireland, Galway, H91 TK33, Ireland; .,Division of Microbiology, National University of Ireland, Galway, H91 TK33, Ireland.,APC Microbiome Ireland, University College Cork, Cork, T12 K8AF, Ireland
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22
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Josephs-Spaulding J, Krogh TJ, Rettig HC, Lyng M, Chkonia M, Waschina S, Graspeuntner S, Rupp J, Møller-Jensen J, Kaleta C. Recurrent Urinary Tract Infections: Unraveling the Complicated Environment of Uncomplicated rUTIs. Front Cell Infect Microbiol 2021; 11:562525. [PMID: 34368008 PMCID: PMC8340884 DOI: 10.3389/fcimb.2021.562525] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2020] [Accepted: 05/18/2021] [Indexed: 12/14/2022] Open
Abstract
Urinary tract infections (UTIs) are frequent in humans, affecting the upper and lower urinary tract. Present diagnosis relies on the positive culture of uropathogenic bacteria from urine and clinical markers of inflammation of the urinary tract. The bladder is constantly challenged by adverse environmental stimuli which influence urinary tract physiology, contributing to a dysbiotic environment. Simultaneously, pathogens are primed by environmental stressors such as antibiotics, favoring recurrent UTIs (rUTIs), resulting in chronic illness. Due to different confounders for UTI onset, a greater understanding of the fundamental environmental mechanisms and microbial ecology of the human urinary tract is required. Such advancements could promote the tandem translation of bench and computational studies for precision treatments and clinical management of UTIs. Therefore, there is an urgent need to understand the ecological interactions of the human urogenital microbial communities which precede rUTIs. This review aims to outline the mechanistic aspects of rUTI ecology underlying dysbiosis between both the human microbiome and host physiology which predisposes humans to rUTIs. By assessing the applications of next generation and systems level methods, we also recommend novel approaches to elucidate the systemic consequences of rUTIs which requires an integrated approach for successful treatment. To this end, we will provide an outlook towards the so-called 'uncomplicated environment of UTIs', a holistic and systems view that applies ecological principles to define patient-specific UTIs. This perspective illustrates the need to withdraw from traditional reductionist perspectives in infection biology and instead, a move towards a systems-view revolving around patient-specific pathophysiology during UTIs.
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Affiliation(s)
- Jonathan Josephs-Spaulding
- Research Group Medical Systems Biology, Institute of Experimental Medicine, Christian-Albrechts-Universität, Kiel, Germany
| | - Thøger Jensen Krogh
- Department of Biochemistry and Molecular Biology, University of Southern Denmark, Odense, Denmark
| | - Hannah Clara Rettig
- Department of Infectious Diseases and Microbiology, University of Lübeck, Lübeck, Germany
| | - Mark Lyng
- Department of Biochemistry and Molecular Biology, University of Southern Denmark, Odense, Denmark
| | - Mariam Chkonia
- Department of Infectious Diseases and Microbiology, University of Lübeck, Lübeck, Germany
| | - Silvio Waschina
- Research Group Nutriinformatics, Institute of Human Nutrition and Food Science, Christian-Albrechts-Universität, Kiel, Germany
| | - Simon Graspeuntner
- Department of Infectious Diseases and Microbiology, University of Lübeck, Lübeck, Germany
| | - Jan Rupp
- Department of Infectious Diseases and Microbiology, University of Lübeck, Lübeck, Germany
- German Center for Infection Research (DZIF), Partner site Hamburg-Lübeck-Borstel-Riems, Lübeck, Germany
| | - Jakob Møller-Jensen
- Department of Biochemistry and Molecular Biology, University of Southern Denmark, Odense, Denmark
| | - Christoph Kaleta
- Research Group Medical Systems Biology, Institute of Experimental Medicine, Christian-Albrechts-Universität, Kiel, Germany
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23
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Heinken A, Hertel J, Thiele I. Metabolic modelling reveals broad changes in gut microbial metabolism in inflammatory bowel disease patients with dysbiosis. NPJ Syst Biol Appl 2021; 7:19. [PMID: 33958598 PMCID: PMC8102608 DOI: 10.1038/s41540-021-00178-6] [Citation(s) in RCA: 33] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2019] [Accepted: 04/07/2021] [Indexed: 12/26/2022] Open
Abstract
Inflammatory bowel diseases, such as Crohn's Disease, are characterised by an altered blood and faecal metabolome, and changes in gut microbiome composition. Here, we present an efficient, scalable, tractable systems biology framework to mechanistically link microbial strains and faecal metabolites. We retrieve strain-level relative abundances from metagenomics data from a cohort of paediatric Crohn's Disease patients with and without dysbiosis and healthy control children and construct and interrogate a personalised microbiome model for each sample. Predicted faecal secretion profiles and strain-level contributions to each metabolite vary broadly between healthy, dysbiotic, and non-dysbiotic microbiomes. The reduced microbial diversity in IBD results in reduced numbers of secreted metabolites, especially in sulfur metabolism. We demonstrate that increased potential to synthesise amino acids is linked to Proteobacteria contributions, in agreement with experimental observations. The established modelling framework yields testable hypotheses that may result in novel therapeutic and dietary interventions targeting the host-gut microbiome-diet axis.
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Affiliation(s)
- Almut Heinken
- School of Medicine, National University of Ireland, Galway, Ireland
- Ryan Institute, National University of Ireland, Galway, Ireland
| | - Johannes Hertel
- School of Medicine, National University of Ireland, Galway, Ireland
- Ryan Institute, National University of Ireland, Galway, Ireland
- Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Greifswald, Germany
| | - Ines Thiele
- School of Medicine, National University of Ireland, Galway, Ireland.
- Ryan Institute, National University of Ireland, Galway, Ireland.
- Division of Microbiology, National University of Galway, Galway, Ireland.
- APC Microbiome Ireland, Cork, Ireland.
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Hui C, Li Y, Zhang W, Yang G, Wang H, Gao Y, Niu L, Wang L, Zhang H. Coupling Genomics and Hydraulic Information to Predict the Nitrogen Dynamics in a Channel Confluence. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2021; 55:4616-4628. [PMID: 33760605 DOI: 10.1021/acs.est.0c04018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
The simulation of nitrogen dynamics in urban channel confluences is essential for the evaluation and improvement of water quality. The omics-based modeling approaches that have been rapidly developed have been increasingly applied to characterize metabolisms of the microbial community and transformation of the associated materials. However, the transport of microorganisms and chemicals within and among different phases, which could be the rate-limiting step for the nitrogen dynamics, are always neglected or oversimplified in omics-based models. Therefore, this study proposes a novel simulation system coupling genomic and hydraulic information to simulate transport and transformation processes and provide predictions of nitrogen dynamics in a confluence. The proposed model was able to capture multiphase mass transport, microbial population dynamics, and nitrogen transformation and accurately predict gene abundances and nitrogen concentrations in both water and sediment; the mean relative errors were all lower than 40%. The model emphasized the importance of transport processes, which contributed more than 90% to gene abundances and chemical concentrations. Moreover, the simulation of reaction rates exhibited the specific nitrogen transformation processes in the confluence. The sulfide oxidation and the nitrate reduction and anaerobic ammonium oxidation, with the participation of the genes nap and hzo, respectively, were promoted as the main processes of nitrate and nitrite reduction.
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Affiliation(s)
- Cizhang Hui
- Key Laboratory of Integrated Regulation and Resource Development on Shallow Lakes, Ministry of Education, College of Environment, Hohai University, Nanjing 210098, People's Republic of China
| | - Yi Li
- Key Laboratory of Integrated Regulation and Resource Development on Shallow Lakes, Ministry of Education, College of Environment, Hohai University, Nanjing 210098, People's Republic of China
| | - Wenlong Zhang
- Key Laboratory of Integrated Regulation and Resource Development on Shallow Lakes, Ministry of Education, College of Environment, Hohai University, Nanjing 210098, People's Republic of China
| | - Gang Yang
- Key Laboratory of Integrated Regulation and Resource Development on Shallow Lakes, Ministry of Education, College of Environment, Hohai University, Nanjing 210098, People's Republic of China
| | - Haolan Wang
- Key Laboratory of Integrated Regulation and Resource Development on Shallow Lakes, Ministry of Education, College of Environment, Hohai University, Nanjing 210098, People's Republic of China
| | - Yu Gao
- Key Laboratory of Integrated Regulation and Resource Development on Shallow Lakes, Ministry of Education, College of Environment, Hohai University, Nanjing 210098, People's Republic of China
| | - Lihua Niu
- Key Laboratory of Integrated Regulation and Resource Development on Shallow Lakes, Ministry of Education, College of Environment, Hohai University, Nanjing 210098, People's Republic of China
| | - Longfei Wang
- Key Laboratory of Integrated Regulation and Resource Development on Shallow Lakes, Ministry of Education, College of Environment, Hohai University, Nanjing 210098, People's Republic of China
| | - Huanjun Zhang
- Key Laboratory of Integrated Regulation and Resource Development on Shallow Lakes, Ministry of Education, College of Environment, Hohai University, Nanjing 210098, People's Republic of China
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25
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Zimmermann J, Kaleta C, Waschina S. gapseq: informed prediction of bacterial metabolic pathways and reconstruction of accurate metabolic models. Genome Biol 2021; 22:81. [PMID: 33691770 PMCID: PMC7949252 DOI: 10.1186/s13059-021-02295-1] [Citation(s) in RCA: 97] [Impact Index Per Article: 32.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2020] [Accepted: 02/10/2021] [Indexed: 12/21/2022] Open
Abstract
Genome-scale metabolic models of microorganisms are powerful frameworks to predict phenotypes from an organism's genotype. While manual reconstructions are laborious, automated reconstructions often fail to recapitulate known metabolic processes. Here we present gapseq ( https://github.com/jotech/gapseq ), a new tool to predict metabolic pathways and automatically reconstruct microbial metabolic models using a curated reaction database and a novel gap-filling algorithm. On the basis of scientific literature and experimental data for 14,931 bacterial phenotypes, we demonstrate that gapseq outperforms state-of-the-art tools in predicting enzyme activity, carbon source utilisation, fermentation products, and metabolic interactions within microbial communities.
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Affiliation(s)
- Johannes Zimmermann
- Christian-Albrechts-University Kiel, Institute of Experimental Medicine, Research Group Medical Systems Biology, Michaelis-Str. 5, Kiel, 24105 Germany
| | - Christoph Kaleta
- Christian-Albrechts-University Kiel, Institute of Experimental Medicine, Research Group Medical Systems Biology, Michaelis-Str. 5, Kiel, 24105 Germany
| | - Silvio Waschina
- Christian-Albrechts-University Kiel, Institute of Experimental Medicine, Research Group Medical Systems Biology, Michaelis-Str. 5, Kiel, 24105 Germany
- Christian-Albrechts-University Kiel, Institute of Human Nutrition and Food Science, Nutriinformatics, Heinrich-Hecht-Platz 10, Kiel, 24118 Germany
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26
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Iablokov SN, Klimenko NS, Efimova DA, Shashkova T, Novichkov PS, Rodionov DA, Tyakht AV. Metabolic Phenotypes as Potential Biomarkers for Linking Gut Microbiome With Inflammatory Bowel Diseases. Front Mol Biosci 2021; 7:603740. [PMID: 33537340 PMCID: PMC7848230 DOI: 10.3389/fmolb.2020.603740] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2020] [Accepted: 12/09/2020] [Indexed: 12/13/2022] Open
Abstract
The gut microbiome is of utmost importance to human health. While a healthy microbiome can be represented by a variety of structures, its functional capacity appears to be more important. Gene content of the community can be assessed by “shotgun” metagenomics, but this approach is still too expensive. High-throughput amplicon-based surveys are a method of choice for large-scale surveys of links between microbiome, diseases, and diet, but the algorithms for predicting functional composition need to be improved to achieve good precision. Here we show how feature engineering based on microbial phenotypes, an advanced method for functional prediction from 16S rRNA sequencing data, improves identification of alterations of the gut microbiome linked to the disease. We processed a large collection of published gut microbial datasets of inflammatory bowel disease (IBD) patients to derive their community phenotype indices (CPI)—high-precision semiquantitative profiles aggregating metabolic potential of the community members based on genome-wide metabolic reconstructions. The list of selected metabolic functions included metabolism of short-chain fatty acids, vitamins, and carbohydrates. The machine-learning approach based on microbial phenotypes allows us to distinguish the microbiome profiles of healthy controls from patients with Crohn's disease and from ones with ulcerative colitis. The classifiers were comparable in quality to conventional taxonomy-based classifiers but provided new findings giving insights into possible mechanisms of pathogenesis. Feature-wise partial dependence plot (PDP) analysis of contribution to the classification result revealed a diversity of patterns. These observations suggest a constructive basis for defining functional homeostasis of the healthy human gut microbiome. The developed features are promising interpretable candidate biomarkers for assessing microbiome contribution to disease risk for the purposes of personalized medicine and clinical trials.
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Affiliation(s)
- Stanislav N Iablokov
- A.A. Kharkevich Institute for Information Transmission Problems, Russian Academy of Sciences, Moscow, Russia.,P.G. Demidov Yaroslavl State University, Yaroslavl, Russia
| | - Natalia S Klimenko
- Atlas Biomed Group-Knomics LLC, London, United Kingdom.,Center for Precision Genome Editing and Genetic Technologies for Biomedicine, Institute of Gene Biology, Russian Academy of Sciences, Moscow, Russia
| | | | - Tatiana Shashkova
- Atlas Biomed Group-Knomics LLC, London, United Kingdom.,Moscow Institute of Physics and Technology, Moscow, Russia
| | - Pavel S Novichkov
- PhenoBiome Inc., San Francisco, CA, United States.,Lawrence Berkeley National Lab, Berkeley, CA, United States
| | - Dmitry A Rodionov
- A.A. Kharkevich Institute for Information Transmission Problems, Russian Academy of Sciences, Moscow, Russia.,Sanford-Burnham-Prebys Medical Discovery Institute, La Jolla, CA, United States
| | - Alexander V Tyakht
- Atlas Biomed Group-Knomics LLC, London, United Kingdom.,Center for Precision Genome Editing and Genetic Technologies for Biomedicine, Institute of Gene Biology, Russian Academy of Sciences, Moscow, Russia
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27
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Morales Fénero C, Amaral MA, Xavier IK, Padovani BN, Paredes LC, Takiishi T, Lopes-Ferreira M, Lima C, Colombo A, Saraiva Câmara NO. Short chain fatty acids (SCFAs) improves TNBS-induced colitis in zebrafish. CURRENT RESEARCH IN IMMUNOLOGY 2021; 2:142-154. [PMID: 35492385 PMCID: PMC9040093 DOI: 10.1016/j.crimmu.2021.08.003] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2021] [Revised: 08/25/2021] [Accepted: 08/25/2021] [Indexed: 12/20/2022] Open
Affiliation(s)
- Camila Morales Fénero
- Department of Immunology, Institute of Biomedical Sciences, University of São Paulo, São Paulo, Brazil
- Corresponding author.
| | | | - Izabella Karina Xavier
- Department of Immunology, Institute of Biomedical Sciences, University of São Paulo, São Paulo, Brazil
| | - Barbara Nunes Padovani
- Department of Immunology, Institute of Biomedical Sciences, University of São Paulo, São Paulo, Brazil
| | - Lais Cavalieri Paredes
- Department of Immunology, Institute of Biomedical Sciences, University of São Paulo, São Paulo, Brazil
| | - Tatiana Takiishi
- ULB Center for Diabetes Research, Medical Faculty, Université Libre de Bruxelles, Brussels, Belgium
| | - Mônica Lopes-Ferreira
- Center of Toxins, Immune Response and Cellular Signalling (CeTICS), Instituto Butantan, São Paulo, Brazil
| | - Carla Lima
- Center of Toxins, Immune Response and Cellular Signalling (CeTICS), Instituto Butantan, São Paulo, Brazil
| | - Alicia Colombo
- Department of Pathologic Anatomy, Faculty of Medicine, University of Chile, Santiago, Chile
| | - Niels Olsen Saraiva Câmara
- Department of Immunology, Institute of Biomedical Sciences, University of São Paulo, São Paulo, Brazil
- Department of Medicine, Nephrology Division, Federal University of São Paulo, Brazil
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28
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Hosseinkhani F, Heinken A, Thiele I, Lindenburg PW, Harms AC, Hankemeier T. The contribution of gut bacterial metabolites in the human immune signaling pathway of non-communicable diseases. Gut Microbes 2021; 13:1-22. [PMID: 33590776 PMCID: PMC7899087 DOI: 10.1080/19490976.2021.1882927] [Citation(s) in RCA: 101] [Impact Index Per Article: 33.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/23/2020] [Revised: 01/07/2021] [Accepted: 01/14/2021] [Indexed: 02/04/2023] Open
Abstract
The interaction disorder between gut microbiota and its host has been documented in different non-communicable diseases (NCDs) such as metabolic syndrome, neurodegenerative disease, and autoimmune disease. The majority of these altered interactions arise through metabolic cross-talk between gut microbiota and host immune system, inducing a low-grade chronic inflammation that characterizes all NCDs. In this review, we discuss the contribution of bacterial metabolites to immune signaling pathways involved in NCDs. We then review recent advances that aid to rationally design microbial therapeutics. A deeper understanding of these intersections between host and gut microbiota metabolism using metabolomics-based system biology platform promises to reveal the fundamental mechanisms that drive metabolic predispositions to disease and suggest new avenues to use microbial therapeutic opportunities for NCDs treatment and prevention. Abbreviations: NCDs: non-communicable disease, IBD: inflammatory bowel disease, IL: interleukin, T2D: type 2 diabetes, SCFAs: short-chain fatty acids, HDAC: histone deacetylases, GPCR: G-protein coupled receptors, 5-HT: 5-hydroxytryptamine receptor signaling, DCs: dendritic cells, IECs: intestinal epithelial cells, T-reg: T regulatory cell, NF-κB: nuclear factor κB, TNF-α: tumor necrosis factor alpha, Th: T helper cell, CNS: central nervous system, ECs: enterochromaffin cells, NSAIDs: non-steroidal anti-inflammatory drugs, AhR: aryl hydrocarbon receptor, IDO: indoleamine 2,3-dioxygenase, QUIN: quinolinic acid, PC: phosphatidylcholine, TMA: trimethylamine, TMAO: trimethylamine N-oxide, CVD: cardiovascular disease, NASH: nonalcoholic steatohepatitis, BAs: bile acids, FXR: farnesoid X receptor, CDCA: chenodeoxycholic acid, DCA: deoxycholic acid, LCA: lithocholic acid, UDCA: ursodeoxycholic acid, CB: cannabinoid receptor, COBRA: constraint-based reconstruction and analysis.
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Affiliation(s)
- F. Hosseinkhani
- Division of Systems Biomedicine and Pharmacology, Leiden Academic Centre for Drug Research, Leiden University, Leiden, Netherlands
| | - A. Heinken
- Division of System Biomedicine, College of Medicine, Nursing and Health Sciences, National University of Ireland, Galway, Ireland
| | - I. Thiele
- Division of System Biomedicine, College of Medicine, Nursing and Health Sciences, National University of Ireland, Galway, Ireland
| | - P. W. Lindenburg
- Division of Systems Biomedicine and Pharmacology, Leiden Academic Centre for Drug Research, Leiden University, Leiden, Netherlands
- Research Group Metabolomics, Faculty Science & Technology, Leiden Centre for Applied Bioscience, University of Applied Sciences, Leiden, Netherlands
| | - A. C. Harms
- Division of Systems Biomedicine and Pharmacology, Leiden Academic Centre for Drug Research, Leiden University, Leiden, Netherlands
| | - T. Hankemeier
- Division of Systems Biomedicine and Pharmacology, Leiden Academic Centre for Drug Research, Leiden University, Leiden, Netherlands
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García-Jiménez B, Torres-Bacete J, Nogales J. Metabolic modelling approaches for describing and engineering microbial communities. Comput Struct Biotechnol J 2020; 19:226-246. [PMID: 33425254 PMCID: PMC7773532 DOI: 10.1016/j.csbj.2020.12.003] [Citation(s) in RCA: 40] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2020] [Revised: 12/02/2020] [Accepted: 12/05/2020] [Indexed: 12/17/2022] Open
Abstract
Microbes do not live in isolation but in microbial communities. The relevance of microbial communities is increasing due to growing awareness of their influence on a huge number of environmental, health and industrial processes. Hence, being able to control and engineer the output of both natural and synthetic communities would be of great interest. However, most of the available methods and biotechnological applications involving microorganisms, both in vivo and in silico, have been developed in the context of isolated microbes. In vivo microbial consortia development is extremely difficult and costly because it implies replicating suitable environments in the wet-lab. Computational approaches are thus a good, cost-effective alternative to study microbial communities, mainly via descriptive modelling, but also via engineering modelling. In this review we provide a detailed compilation of examples of engineered microbial communities and a comprehensive, historical revision of available computational metabolic modelling methods to better understand, and rationally engineer wild and synthetic microbial communities.
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Affiliation(s)
- Beatriz García-Jiménez
- Department of Systems Biology, Centro Nacional de Biotecnología (CSIC), 28049 Madrid, Spain
- Centro de Biotecnología y Genómica de Plantas (CBGP, UPM-INIA), Universidad Politécnica de Madrid (UPM), Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA), Campus de Montegancedo-UPM, 28223-Pozuelo de Alarcón, Madrid, Spain
| | - Jesús Torres-Bacete
- Department of Systems Biology, Centro Nacional de Biotecnología (CSIC), 28049 Madrid, Spain
| | - Juan Nogales
- Department of Systems Biology, Centro Nacional de Biotecnología (CSIC), 28049 Madrid, Spain
- Interdisciplinary Platform for Sustainable Plastics towards a Circular Economy‐Spanish National Research Council (SusPlast‐CSIC), Madrid, Spain
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30
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Chowdhury S, Fong SS. Leveraging genome-scale metabolic models for human health applications. Curr Opin Biotechnol 2020; 66:267-276. [PMID: 33120253 DOI: 10.1016/j.copbio.2020.08.017] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2020] [Revised: 08/27/2020] [Accepted: 08/31/2020] [Indexed: 02/07/2023]
Abstract
Genome-scale metabolic modeling is a scalable and extensible computational method for analyzing and predicting biological function. With the ongoing improvements in computational methods and experimental capabilities, genome-scale metabolic models (GEMs) are demonstrating utility in addressing human health applications. The initial areas of highest impact are likely to be health applications where disease states involve metabolic changes. In this review, we focus on recent application of GEMs to studying cancer and the human microbiome by describing the enabling methodologies and outcomes of these studies. We conclude with proposing some areas of research that are likely to arise as a result of recent methodological advances.
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Affiliation(s)
- Shomeek Chowdhury
- Integrative Life Sciences, Virginia Commonwealth University, 1000 West Main Street, Richmond, 23284, VA, USA
| | - Stephen S Fong
- Integrative Life Sciences, Virginia Commonwealth University, 1000 West Main Street, Richmond, 23284, VA, USA; Chemical and Life Science Engineering, Virginia Commonwealth University, 601 West Main Street, Richmond, 23284, VA, USA.
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31
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Zhu Y, Xiong Y, Gu Y, Li Q, Liu Y. Chiropractic Therapy Modulated Gut Microbiota and Attenuated Allergic Airway Inflammation in an Immature Rat Model. Med Sci Monit 2020; 26:e926039. [PMID: 32990279 PMCID: PMC7532697 DOI: 10.12659/msm.926039] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022] Open
Abstract
Background As a type of traditional Chinese massage, chiropractic therapy is applied to prevent and treat children with asthma in China. However, its mechanism of action is unclear. Allergic airway inflammation plays a key role in the occurrence and development of asthma, in which changes in gut microbiota are involved. The present study investigated the influence of chiropractic therapy on allergic airway inflammation (AAI) and gut microbiota in an immature rat model. Material/Methods Three-week-old male Sprague-Dawley rats were divided randomly into control (CN), AAI, and chiropractic (CP) groups. AAI and CP groups were sensitized and challenged with ovalbumin (OVA) to induce AAI. The CP group received chiropractic therapy during AAI modelling. AAI was assessed by cell counts in bronchoalveolar lavage fluid and HE staining of lung tissues. Plasma OVA-sIgE, IFN-γ, IL-4, and IL-10 levels were detected by ELISA. DNA extraction from feces samples was used for 16S rRNA gene sequencing and analyzed for gut microbiota by Quantitative Insights Into Microbial Ecology (QIIME). Results AAI group had significantly lower richness and diversity of gut microbiota along with Th2 response and allergic airway inflammation. Moreover, the AAI group had lower abundance of butyrate-producing bacterial taxa with more Lactobacillus. Chiropractic therapy significantly increased the richness and diversity of gut microbiota and increased butyrate-producing bacterial taxa and decreased Lactobacillus, along with attenuating Th2 response and allergic airway inflammation during AAI modelling. Conclusions Chiropractic therapy attenuated allergic airway inflammation and optimized gut microbiota in an immature rat model, which might promote the development of adult-like butyrogenic milieu, immunotolerance, and inflammation attenuation.
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Affiliation(s)
- Yan Zhu
- Graduate College, Nanjing University of Chinese Medicine, Nanjing, Jiangsu, China (mainland)
| | - Ying Xiong
- Teaching and Research Section of Massage, Acupuncture and Massage College, Nanjing University of Chinese Medicine, Nanjing, Jiangsu, China (mainland)
| | - Yun Gu
- Key Laboratory of Acupuncture and Medicine Research of Ministry of Education, Acupuncture and Massage College, Nanjing University of Chinese Medicine, Nanjing, Jiangsu, China (mainland)
| | - Qain Li
- Key Laboratory of Acupuncture and Medicine Research of Ministry of Education, Acupuncture and Massage College, Nanjing University of Chinese Medicine, Nanjing, Jiangsu, China (mainland)
| | - Yu Liu
- Pediatric Massage Department, Affiliated Hospital of Nanjing University of Chinese Medicine/Jiangsu Province Hospital of Chinese Medicine, Nanjing, Jiangsu, China (mainland)
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32
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Janney A, Powrie F, Mann EH. Host–microbiota maladaptation in colorectal cancer. Nature 2020; 585:509-517. [DOI: 10.1038/s41586-020-2729-3] [Citation(s) in RCA: 127] [Impact Index Per Article: 31.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2020] [Accepted: 07/29/2020] [Indexed: 12/19/2022]
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Integrated Analyses of Microbiome and Longitudinal Metabolome Data Reveal Microbial-Host Interactions on Sulfur Metabolism in Parkinson's Disease. Cell Rep 2020; 29:1767-1777.e8. [PMID: 31722195 PMCID: PMC6856723 DOI: 10.1016/j.celrep.2019.10.035] [Citation(s) in RCA: 86] [Impact Index Per Article: 21.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2018] [Revised: 07/17/2019] [Accepted: 10/09/2019] [Indexed: 02/07/2023] Open
Abstract
Parkinson’s disease (PD) exhibits systemic effects on the human metabolism, with emerging roles for the gut microbiome. Here, we integrate longitudinal metabolome data from 30 drug-naive, de novo PD patients and 30 matched controls with constraint-based modeling of gut microbial communities derived from an independent, drug-naive PD cohort, and prospective data from the general population. Our key results are (1) longitudinal trajectory of metabolites associated with the interconversion of methionine and cysteine via cystathionine differed between PD patients and controls; (2) dopaminergic medication showed strong lipidomic signatures; (3) taurine-conjugated bile acids correlated with the severity of motor symptoms, while low levels of sulfated taurolithocholate were associated with PD incidence in the general population; and (4) computational modeling predicted changes in sulfur metabolism, driven by A. muciniphila and B. wadsworthia, which is consistent with the changed metabolome. The multi-omics integration reveals PD-specific patterns in microbial-host sulfur co-metabolism that may contribute to PD severity. Longitudinal metabolomics reveal disturbed transsulfuration in Parkinson’s disease Metabolic modeling of gut microbiomes show altered microbial sulfur metabolism Changed microbial sulfur metabolism is linked to B. wadsworthia and A. muciniphila Taurine-conjugated bile acids are associated with incident Parkinson’s disease
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Marinos G, Kaleta C, Waschina S. Defining the nutritional input for genome-scale metabolic models: A roadmap. PLoS One 2020; 15:e0236890. [PMID: 32797084 PMCID: PMC7428157 DOI: 10.1371/journal.pone.0236890] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2020] [Accepted: 07/15/2020] [Indexed: 12/13/2022] Open
Abstract
The reconstruction and application of genome-scale metabolic network models is a central topic in the field of systems biology with numerous applications in biotechnology, ecology, and medicine. However, there is no agreed upon standard for the definition of the nutritional environment for these models. The objective of this article is to provide a guideline and a clear paradigm on how to translate nutritional information into an in-silico representation of the chemical environment. Step-by-step procedures explain how to characterise and categorise the nutritional input and to successfully apply it to constraint-based metabolic models. In parallel, we illustrate the proposed procedure with a case study of the growth of Escherichia coli in a complex nutritional medium and show that an accurate representation of the medium is crucial for physiological predictions. The proposed framework will assist researchers to expand their existing metabolic models of their microbial systems of interest with detailed representations of the nutritional environment, which allows more accurate and reproducible predictions of microbial metabolic processes.
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Affiliation(s)
- Georgios Marinos
- Research Group Medical Systems Biology, Institute of Experimental Medicine, Kiel University, University Medical Center Schleswig-Holstein, Kiel, Schleswig-Holstein, Germany
| | - Christoph Kaleta
- Research Group Medical Systems Biology, Institute of Experimental Medicine, Kiel University, University Medical Center Schleswig-Holstein, Kiel, Schleswig-Holstein, Germany
| | - Silvio Waschina
- Division of Nutriinformatics, Institute for Human Nutrition and Food Sciences, Kiel University, Kiel, Schleswig-Holstein, Germany
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35
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Baldini F, Hertel J, Sandt E, Thinnes CC, Neuberger-Castillo L, Pavelka L, Betsou F, Krüger R, Thiele I. Parkinson's disease-associated alterations of the gut microbiome predict disease-relevant changes in metabolic functions. BMC Biol 2020; 18:62. [PMID: 32517799 PMCID: PMC7285525 DOI: 10.1186/s12915-020-00775-7] [Citation(s) in RCA: 108] [Impact Index Per Article: 27.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2019] [Accepted: 03/27/2020] [Indexed: 12/21/2022] Open
Abstract
BACKGROUND Parkinson's disease (PD) is a systemic disease clinically defined by the degeneration of dopaminergic neurons in the brain. While alterations in the gut microbiome composition have been reported in PD, their functional consequences remain unclear. Herein, we addressed this question by an analysis of stool samples from the Luxembourg Parkinson's Study (n = 147 typical PD cases, n = 162 controls). RESULTS All individuals underwent detailed clinical assessment, including neurological examinations and neuropsychological tests followed by self-reporting questionnaires. Stool samples from these individuals were first analysed by 16S rRNA gene sequencing. Second, we predicted the potential secretion for 129 microbial metabolites through personalised metabolic modelling using the microbiome data and genome-scale metabolic reconstructions of human gut microbes. Our key results include the following. Eight genera and seven species changed significantly in their relative abundances between PD patients and healthy controls. PD-associated microbial patterns statistically depended on sex, age, BMI, and constipation. Particularly, the relative abundances of Bilophila and Paraprevotella were significantly associated with the Hoehn and Yahr staging after controlling for the disease duration. Furthermore, personalised metabolic modelling of the gut microbiomes revealed PD-associated metabolic patterns in the predicted secretion potential of nine microbial metabolites in PD, including increased methionine and cysteinylglycine. The predicted microbial pantothenic acid production potential was linked to the presence of specific non-motor symptoms. CONCLUSION Our results suggest that PD-associated alterations of the gut microbiome can translate into substantial functional differences affecting host metabolism and disease phenotype.
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Affiliation(s)
- Federico Baldini
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Campus Belval, Esch-sur-Alzette, Luxembourg
| | - Johannes Hertel
- School of Medicine, National University of Ireland, Galway, Ireland
- Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Greifswald, Germany
| | - Estelle Sandt
- Integrated BioBank of Luxembourg, Dudelange, Luxembourg
| | | | | | - Lukas Pavelka
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Campus Belval, Esch-sur-Alzette, Luxembourg
- Parkinson Research Clinic, Centre Hospitalier de Luxembourg (CHL), Luxembourg City, Luxembourg
| | - Fay Betsou
- Integrated BioBank of Luxembourg, Dudelange, Luxembourg
| | - Rejko Krüger
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Campus Belval, Esch-sur-Alzette, Luxembourg
- Parkinson Research Clinic, Centre Hospitalier de Luxembourg (CHL), Luxembourg City, Luxembourg
- Transversal Translational Medicine, Luxembourg Institute of Health (LIH), Strassen, Luxembourg
| | - Ines Thiele
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Campus Belval, Esch-sur-Alzette, Luxembourg.
- School of Medicine, National University of Ireland, Galway, Ireland.
- Discipline of Microbiology, School of Natural Sciences, National University of Ireland, Galway, Ireland.
- APC Microbiome, Cork, Ireland.
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Wu Q, Chen T, El-Nezami H, Savidge TC. Food ingredients in human health: Ecological and metabolic perspectives implicating gut microbiota function. Trends Food Sci Technol 2020. [DOI: 10.1016/j.tifs.2020.04.007] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
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Daliri EBM, Ofosu FK, Chelliah R, Lee BH, An H, Elahi F, Barathikannan K, Kim JH, Oh DH. Influence of fermented soy protein consumption on hypertension and gut microbial modulation in spontaneous hypertensive rats. BIOSCIENCE OF MICROBIOTA FOOD AND HEALTH 2020; 39:199-208. [PMID: 33117618 PMCID: PMC7573110 DOI: 10.12938/bmfh.2020-001] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/06/2020] [Accepted: 04/19/2020] [Indexed: 12/12/2022]
Abstract
Plant proteins are known to possess important bioactive peptides and have a positive
impact on gut microbial modulation. In this study, we studied the ability of a single dose
of a fermented soy protein product (P-SPI) to reduce high blood pressure in spontaneous
hypertensive rats (SHR) and how it modulates the gut microbiota after six weeks of
feeding. SHRs were fed with P-SPI, Captopril or distilled water once, and their blood
pressures were monitored from the first to twelfth-hour post-administration. Consumption
of P-SPI significantly reduced systolic and diastolic blood pressures up to the sixth hour
by 25 ± 4 mmHg and 40 ± 5 mmHg respectively. P-SPI consumption inhibited serum ACE
activity, increased superoxide dismutase activity and nitric oxide levels and reduced
malondialdehyde levels in serum. Analysis of fecal microbial 16S rRNA of hypertensive rats
revealed a significant reduction in microbial richness and diversity in the gut, while
P-SPI consumption improved microbial richness and increased diversity. Also, P-SPI feeding
significantly reduced the Firmicutes/Bacteroidetes
ratio, increased propionate- and H2S-producing bacteria and reduced
Streptococcaceae and Erysipelotrichales levels. Our
results show that P-SPI is a potential antihypertensive functional food which could
remodel the altered gut microbiota of hypertensive patients.
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Affiliation(s)
- Eric Banan-Mwine Daliri
- Department of Food Science and Biotechnology, Kangwon National University, Chuncheon 200-701, Korea
| | - Fred Kwame Ofosu
- Department of Food Science and Biotechnology, Kangwon National University, Chuncheon 200-701, Korea
| | - Ramachandran Chelliah
- Department of Food Science and Biotechnology, Kangwon National University, Chuncheon 200-701, Korea
| | - Byong H Lee
- Department of Microbiology/Immunology, McGill University, Montreal, QC, H3A 2B4, Canada.,SportBiomics, Inc., Sacramento, CA, USA
| | | | - Fazle Elahi
- Department of Food Science and Biotechnology, Kangwon National University, Chuncheon 200-701, Korea
| | - Kaliyan Barathikannan
- Department of Food Science and Biotechnology, Kangwon National University, Chuncheon 200-701, Korea
| | - Joong-Hark Kim
- R&D, Erom Company Limited, R&D Center, 111, Toegye Nonggong-ro, Chuncheon-si, Gangwon-do 24427, Korea.,Department of Medical Biotechnology, College of Biomedical Sciences, Kangwon National University, Chuncheon, Gangwon-do 24341, Korea
| | - Deog-Hwan Oh
- Department of Food Science and Biotechnology, Kangwon National University, Chuncheon 200-701, Korea
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Eetemadi A, Rai N, Pereira BMP, Kim M, Schmitz H, Tagkopoulos I. The Computational Diet: A Review of Computational Methods Across Diet, Microbiome, and Health. Front Microbiol 2020; 11:393. [PMID: 32318028 PMCID: PMC7146706 DOI: 10.3389/fmicb.2020.00393] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2019] [Accepted: 02/26/2020] [Indexed: 12/12/2022] Open
Abstract
Food and human health are inextricably linked. As such, revolutionary impacts on health have been derived from advances in the production and distribution of food relating to food safety and fortification with micronutrients. During the past two decades, it has become apparent that the human microbiome has the potential to modulate health, including in ways that may be related to diet and the composition of specific foods. Despite the excitement and potential surrounding this area, the complexity of the gut microbiome, the chemical composition of food, and their interplay in situ remains a daunting task to fully understand. However, recent advances in high-throughput sequencing, metabolomics profiling, compositional analysis of food, and the emergence of electronic health records provide new sources of data that can contribute to addressing this challenge. Computational science will play an essential role in this effort as it will provide the foundation to integrate these data layers and derive insights capable of revealing and understanding the complex interactions between diet, gut microbiome, and health. Here, we review the current knowledge on diet-health-gut microbiota, relevant data sources, bioinformatics tools, machine learning capabilities, as well as the intellectual property and legislative regulatory landscape. We provide guidance on employing machine learning and data analytics, identify gaps in current methods, and describe new scenarios to be unlocked in the next few years in the context of current knowledge.
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Affiliation(s)
- Ameen Eetemadi
- Department of Computer Science, University of California, Davis, Davis, CA, United States
- Genome Center, University of California, Davis, Davis, CA, United States
| | - Navneet Rai
- Genome Center, University of California, Davis, Davis, CA, United States
| | - Beatriz Merchel Piovesan Pereira
- Genome Center, University of California, Davis, Davis, CA, United States
- Department of Microbiology, University of California, Davis, Davis, CA, United States
| | - Minseung Kim
- Department of Computer Science, University of California, Davis, Davis, CA, United States
- Genome Center, University of California, Davis, Davis, CA, United States
- Process Integration and Predictive Analytics (PIPA LLC), Davis, CA, United States
| | - Harold Schmitz
- Graduate School of Management, University of California, Davis, Davis, CA, United States
| | - Ilias Tagkopoulos
- Department of Computer Science, University of California, Davis, Davis, CA, United States
- Genome Center, University of California, Davis, Davis, CA, United States
- Process Integration and Predictive Analytics (PIPA LLC), Davis, CA, United States
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39
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Ternes D, Karta J, Tsenkova M, Wilmes P, Haan S, Letellier E. Microbiome in Colorectal Cancer: How to Get from Meta-omics to Mechanism? Trends Microbiol 2020; 28:401-423. [PMID: 32298617 DOI: 10.1016/j.tim.2020.01.001] [Citation(s) in RCA: 127] [Impact Index Per Article: 31.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2019] [Revised: 12/20/2019] [Accepted: 01/10/2020] [Indexed: 02/07/2023]
Abstract
Mounting evidence from metagenomic analyses suggests that a state of pathological microbial imbalance or dysbiosis is prevalent in the gut of patients with colorectal cancer. Several bacterial taxa have been identified of which representative isolate cultures interact with human cancer cells in vitro and trigger disease pathways in animal models. However, how the complex interrelationships in dysbiotic communities may be involved in cancer pathogenesis remains a crucial question. Here, we provide a survey of current knowledge of the gut microbiome in colorectal cancer. Moving beyond observational studies, we outline new experimental approaches for gaining ecosystem-level mechanistic understanding of the gut microbiome's role in cancer pathogenesis.
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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, Esch-sur-Alzette, Luxembourg
| | - Jessica Karta
- Molecular Disease Mechanisms Group, Department of Life Sciences and Medicine, Faculty of Science, Technology and Medicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Mina Tsenkova
- Molecular Disease Mechanisms Group, Department of Life Sciences and Medicine, Faculty of Science, Technology and Medicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Paul Wilmes
- Eco-Systems Biology group, Luxembourg Center for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Serge Haan
- Molecular Disease Mechanisms Group, Department of Life Sciences and Medicine, Faculty of Science, Technology and Medicine, 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, Esch-sur-Alzette, Luxembourg.
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40
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Deciphering the metabolic capabilities of Bifidobacteria using genome-scale metabolic models. Sci Rep 2019; 9:18222. [PMID: 31796826 PMCID: PMC6890778 DOI: 10.1038/s41598-019-54696-9] [Citation(s) in RCA: 34] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2019] [Accepted: 11/13/2019] [Indexed: 12/17/2022] Open
Abstract
Bifidobacteria, the initial colonisers of breastfed infant guts, are considered as the key commensals that promote a healthy gastrointestinal tract. However, little is known about the key metabolic differences between different strains of these bifidobacteria, and consequently, their suitability for their varied commercial applications. In this context, the present study applies a constraint-based modelling approach to differentiate between 36 important bifidobacterial strains, enhancing their genome-scale metabolic models obtained from the AGORA (Assembly of Gut Organisms through Reconstruction and Analysis) resource. By studying various growth and metabolic capabilities in these enhanced genome-scale models across 30 different nutrient environments, we classified the bifidobacteria into three specific groups. We also studied the ability of the different strains to produce short-chain fatty acids, finding that acetate production is niche- and strain-specific, unlike lactate. Further, we captured the role of critical enzymes from the bifid shunt pathway, which was found to be essential for a subset of bifidobacterial strains. Our findings underline the significance of analysing metabolic capabilities as a powerful approach to explore distinct properties of the gut microbiome. Overall, our study presents several insights into the nutritional lifestyles of bifidobacteria and could potentially be leveraged to design species/strain-specific probiotics or prebiotics.
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41
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Aden K, Rehman A, Waschina S, Pan WH, Walker A, Lucio M, Nunez AM, Bharti R, Zimmerman J, Bethge J, Schulte B, Schulte D, Franke A, Nikolaus S, Schroeder JO, Vandeputte D, Raes J, Szymczak S, Waetzig GH, Zeuner R, Schmitt-Kopplin P, Kaleta C, Schreiber S, Rosenstiel P. Metabolic Functions of Gut Microbes Associate With Efficacy of Tumor Necrosis Factor Antagonists in Patients With Inflammatory Bowel Diseases. Gastroenterology 2019; 157:1279-1292.e11. [PMID: 31326413 DOI: 10.1053/j.gastro.2019.07.025] [Citation(s) in RCA: 154] [Impact Index Per Article: 30.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/28/2019] [Revised: 06/18/2019] [Accepted: 07/04/2019] [Indexed: 02/07/2023]
Abstract
BACKGROUND & AIMS Altered interactions between the mucosal immune system and intestinal microbiota contribute to pathogenesis of inflammatory bowel diseases (IBD). It is not clear how inhibitors of cytokines, such as antagonists of tumor necrosis factor (anti-TNF), affect the intestinal microbiome. We investigated the effects of anti-TNF agents on gut microbe community structure and function in a longitudinal 2-step study of patients with IBD. We correlated our findings with outcomes of treatment and investigated patterns of metabolites in fecal samples before and after anti-TNF therapy. METHODS We performed a prospective study of 2 cohorts of patients in Germany; the discovery cohort comprised 12 patients with IBD, 17 patients with rheumatic disease, and 19 healthy individuals (controls); fecal samples were collected at baseline and 2, 6, and 30 weeks after induction of anti-TNF therapy. The validation cohort comprised 23 patients with IBD treated with anti-TNF or vedolizumab (anti-α4β7 integrin) and 99 healthy controls; fecal samples were collected at baseline and at weeks 2, 6, and 14. Fecal microbiota were analyzed by V3-V4 16S ribosomal RNA gene amplicon sequencing. Clinical response and remission were determined by clinical disease activity scores. Metabolic network reconstruction and associated fecal metabolite level inference was performed in silico using the AGORA (Assembly of Gut Organisms through Reconstruction and Analysis) resource. Metabolomic analyses of fecal samples from a subset of patients were performed to validate metabolites associated with treatment outcomes. RESULTS Anti-TNF therapy shifted the diversity of fecal microbiota in patients with IBD, but not with rheumatic disease, toward that of controls. Across timepoints, diversity indices did not vary significantly between patients with IBD who did or did not achieve clinical remission after therapy. In contrast, in silico modeling of metabolic interactions between gut microbes found metabolite exchange to be significantly reduced at baseline in fecal samples from patients with IBD and to be associated with later clinical remission. Predicted levels of butyrate and substrates involved in butyrate synthesis (ethanol or acetaldehyde) were significantly associated with clinical remission following anti-TNF therapy, verified by fecal metabolomic analyses. CONCLUSIONS Metabolic network reconstruction and assessment of metabolic profiles of fecal samples might be used to identify patients with IBD likely to achieve clinical remission following anti-TNF therapy and increase our understanding of the heterogeneity of IBD.
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Affiliation(s)
- Konrad Aden
- Institute of Clinical Molecular Biology, Christian-Albrechts-University and University Hospital Schleswig-Holstein, Campus Kiel, Kiel, Germany; Department of Internal Medicine I., Christian-Albrechts-University and University Hospital Schleswig-Holstein, Campus Kiel, Kiel, Germany
| | - Ateequr Rehman
- Institute of Clinical Molecular Biology, Christian-Albrechts-University and University Hospital Schleswig-Holstein, Campus Kiel, Kiel, Germany
| | - Silvio Waschina
- Institute for Experimental Medicine, Christian-Albrechts-University and University Hospital Schleswig-Holstein, Campus Kiel, Kiel, Germany
| | - Wei-Hung Pan
- Institute of Clinical Molecular Biology, Christian-Albrechts-University and University Hospital Schleswig-Holstein, Campus Kiel, Kiel, Germany
| | - Alesia Walker
- Research Unit Analytical BioGeoChemistry, Helmholtz ZentrumMünchen, German Research Centre for Environmental Health (GmbH), Neuherberg, Germany
| | - Marianna Lucio
- Research Unit Analytical BioGeoChemistry, Helmholtz ZentrumMünchen, German Research Centre for Environmental Health (GmbH), Neuherberg, Germany
| | - Alejandro Mena Nunez
- Institute of Clinical Molecular Biology, Christian-Albrechts-University and University Hospital Schleswig-Holstein, Campus Kiel, Kiel, Germany
| | - Richa Bharti
- Institute of Clinical Molecular Biology, Christian-Albrechts-University and University Hospital Schleswig-Holstein, Campus Kiel, Kiel, Germany
| | - Johannes Zimmerman
- Institute for Experimental Medicine, Christian-Albrechts-University and University Hospital Schleswig-Holstein, Campus Kiel, Kiel, Germany
| | - Johannes Bethge
- Department of Internal Medicine I., Christian-Albrechts-University and University Hospital Schleswig-Holstein, Campus Kiel, Kiel, Germany
| | - Berenice Schulte
- Department of Internal Medicine I., Christian-Albrechts-University and University Hospital Schleswig-Holstein, Campus Kiel, Kiel, Germany
| | - Dominik Schulte
- Department of Internal Medicine I., Christian-Albrechts-University and University Hospital Schleswig-Holstein, Campus Kiel, Kiel, Germany
| | - Andre Franke
- Institute of Clinical Molecular Biology, Christian-Albrechts-University and University Hospital Schleswig-Holstein, Campus Kiel, Kiel, Germany
| | - Susanna Nikolaus
- Department of Internal Medicine I., Christian-Albrechts-University and University Hospital Schleswig-Holstein, Campus Kiel, Kiel, Germany
| | - Johann Oltmann Schroeder
- Department of Internal Medicine I., Christian-Albrechts-University and University Hospital Schleswig-Holstein, Campus Kiel, Kiel, Germany
| | - Doris Vandeputte
- VIB-KU Leuven Center for Microbiology, Campus Gasthuisberg, Leuven, Belgium
| | - Jeroen Raes
- VIB-KU Leuven Center for Microbiology, Campus Gasthuisberg, Leuven, Belgium
| | - Silke Szymczak
- Institute of Medical Informatics and Statistics, University of Kiel, Kiel, Germany
| | | | - Rainald Zeuner
- Department of Internal Medicine I., Christian-Albrechts-University and University Hospital Schleswig-Holstein, Campus Kiel, Kiel, Germany
| | - Philippe Schmitt-Kopplin
- Research Unit Analytical BioGeoChemistry, Helmholtz ZentrumMünchen, German Research Centre for Environmental Health (GmbH), Neuherberg, Germany
| | - Christoph Kaleta
- Institute for Experimental Medicine, Christian-Albrechts-University and University Hospital Schleswig-Holstein, Campus Kiel, Kiel, Germany
| | - Stefan Schreiber
- Institute of Clinical Molecular Biology, Christian-Albrechts-University and University Hospital Schleswig-Holstein, Campus Kiel, Kiel, Germany; Department of Internal Medicine I., Christian-Albrechts-University and University Hospital Schleswig-Holstein, Campus Kiel, Kiel, Germany.
| | - Philip Rosenstiel
- Institute of Clinical Molecular Biology, Christian-Albrechts-University and University Hospital Schleswig-Holstein, Campus Kiel, Kiel, Germany.
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Sieow BFL, Nurminen TJ, Ling H, Chang MW. Meta-Omics- and Metabolic Modeling-Assisted Deciphering of Human Microbiota Metabolism. Biotechnol J 2019; 14:e1800445. [PMID: 31144773 DOI: 10.1002/biot.201800445] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2019] [Revised: 04/24/2019] [Indexed: 12/15/2022]
Abstract
The human microbiota is a complex community of commensal, symbiotic, and pathogenic microbes that play a crucial role in maintaining the homeostasis of human health. Such a homeostasis is maintained through the collective functioning of enzymatic genes responsible for the production of metabolites, enabling the interaction and signaling within microbiota as well as between microbes and the human host. Understanding microbial genes, their associated chemistries and functions would be valuable for engineering systemic metabolic pathways within the microbiota to manage human health and diseases. Given that there are many unknown gene metabolic functions and interactions, increasing efforts have been made to gain insights into the underlying functions of microbiota metabolism. This can be achieved through culture-independent metagenomic approaches and metabolic modeling to simulate the microenvironment of human microbiota. In this article, the recent advances in metagenome mining and functional profiling for the discovery of the genetic and biochemical links in human microbiota metabolism as well as metabolic modeling for simulation and prediction of metabolic fluxes in the human microbiota are reviewed. This review provides useful insights into the understanding, reconstruction, and modulation of the human microbiota guided by the knowledge acquired from the basic understanding of the human microbiota metabolism.
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Affiliation(s)
- Brendan Fu-Long Sieow
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, 8 Medical Drive, Singapore, 117597, Singapore.,NUS Synthetic Biology for Clinical and Technological Innovation (SynCTI), Life Sciences Institute, National University of Singapore, Singapore, 117456, Singapore.,NUS Graduate School of Integrative Sciences and Engineering (NGS), University Hall, Tan Chin Tuan Wing, National University of Singapore, Singapore, 119077, Singapore
| | - Toni Juhani Nurminen
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, 8 Medical Drive, Singapore, 117597, Singapore.,NUS Synthetic Biology for Clinical and Technological Innovation (SynCTI), Life Sciences Institute, National University of Singapore, Singapore, 117456, Singapore.,NUS Graduate School of Integrative Sciences and Engineering (NGS), University Hall, Tan Chin Tuan Wing, National University of Singapore, Singapore, 119077, Singapore
| | - Hua Ling
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, 8 Medical Drive, Singapore, 117597, Singapore.,NUS Synthetic Biology for Clinical and Technological Innovation (SynCTI), Life Sciences Institute, National University of Singapore, Singapore, 117456, Singapore
| | - Matthew Wook Chang
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, 8 Medical Drive, Singapore, 117597, Singapore.,NUS Synthetic Biology for Clinical and Technological Innovation (SynCTI), Life Sciences Institute, National University of Singapore, Singapore, 117456, Singapore.,NUS Graduate School of Integrative Sciences and Engineering (NGS), University Hall, Tan Chin Tuan Wing, National University of Singapore, Singapore, 119077, Singapore
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43
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Heinken A, Ravcheev DA, Baldini F, Heirendt L, Fleming RMT, Thiele I. Systematic assessment of secondary bile acid metabolism in gut microbes reveals distinct metabolic capabilities in inflammatory bowel disease. MICROBIOME 2019; 7:75. [PMID: 31092280 PMCID: PMC6521386 DOI: 10.1186/s40168-019-0689-3] [Citation(s) in RCA: 179] [Impact Index Per Article: 35.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/07/2018] [Accepted: 04/26/2019] [Indexed: 05/10/2023]
Abstract
BACKGROUND The human gut microbiome performs important functions in human health and disease. A classic example for host-gut microbial co-metabolism is host biosynthesis of primary bile acids and their subsequent deconjugation and transformation by the gut microbiome. To understand these system-level host-microbe interactions, a mechanistic, multi-scale computational systems biology approach that integrates the different types of omic data is needed. Here, we use a systematic workflow to computationally model bile acid metabolism in gut microbes and microbial communities. RESULTS Therefore, we first performed a comparative genomic analysis of bile acid deconjugation and biotransformation pathways in 693 human gut microbial genomes and expanded 232 curated genome-scale microbial metabolic reconstructions with the corresponding reactions (available at https://vmh.life ). We then predicted the bile acid biotransformation potential of each microbe and in combination with other microbes. We found that each microbe could produce maximally six of the 13 secondary bile acids in silico, while microbial pairs could produce up to 12 bile acids, suggesting bile acid biotransformation being a microbial community task. To investigate the metabolic potential of a given microbiome, publicly available metagenomics data from healthy Western individuals, as well as inflammatory bowel disease patients and healthy controls, were mapped onto the genomes of the reconstructed strains. We constructed for each individual a large-scale personalized microbial community model that takes into account strain-level abundances. Using flux balance analysis, we found considerable variation in the potential to deconjugate and transform primary bile acids between the gut microbiomes of healthy individuals. Moreover, the microbiomes of pediatric inflammatory bowel disease patients were significantly depleted in their bile acid production potential compared with that of controls. The contributions of each strain to overall bile acid production potential across individuals were found to be distinct between inflammatory bowel disease patients and controls. Finally, bottlenecks limiting secondary bile acid production potential were identified in each microbiome model. CONCLUSIONS This large-scale modeling approach provides a novel way of analyzing metagenomics data to accelerate our understanding of the metabolic interactions between the host and gut microbiomes in health and diseases states. Our models and tools are freely available to the scientific community.
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Affiliation(s)
- Almut Heinken
- School of Medicine, National University of Ireland, Galway, University Road, Galway, Ireland
| | - Dmitry A Ravcheev
- School of Medicine, National University of Ireland, Galway, University Road, Galway, Ireland
| | - Federico Baldini
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux, Luxembourg
| | - Laurent Heirendt
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux, Luxembourg
| | - Ronan M T Fleming
- Division of Analytical Biosciences, Leiden Academic Centre for Drug Research, Faculty of Science, University of Leiden, Leiden, The Netherlands
| | - Ines Thiele
- School of Medicine, National University of Ireland, Galway, University Road, Galway, Ireland.
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux, Luxembourg.
- Discipline of Microbiology, School of Natural Sciences, National University of Ireland, Galway, University Road, Galway, Ireland.
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44
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Pinu FR, Beale DJ, Paten AM, Kouremenos K, Swarup S, Schirra HJ, Wishart D. Systems Biology and Multi-Omics Integration: Viewpoints from the Metabolomics Research Community. Metabolites 2019; 9:E76. [PMID: 31003499 PMCID: PMC6523452 DOI: 10.3390/metabo9040076] [Citation(s) in RCA: 306] [Impact Index Per Article: 61.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2019] [Revised: 04/15/2019] [Accepted: 04/16/2019] [Indexed: 02/07/2023] Open
Abstract
The use of multiple omics techniques (i.e., genomics, transcriptomics, proteomics, and metabolomics) is becoming increasingly popular in all facets of life science. Omics techniques provide a more holistic molecular perspective of studied biological systems compared to traditional approaches. However, due to their inherent data differences, integrating multiple omics platforms remains an ongoing challenge for many researchers. As metabolites represent the downstream products of multiple interactions between genes, transcripts, and proteins, metabolomics, the tools and approaches routinely used in this field could assist with the integration of these complex multi-omics data sets. The question is, how? Here we provide some answers (in terms of methods, software tools and databases) along with a variety of recommendations and a list of continuing challenges as identified during a peer session on multi-omics integration that was held at the recent 'Australian and New Zealand Metabolomics Conference' (ANZMET 2018) in Auckland, New Zealand (Sept. 2018). We envisage that this document will serve as a guide to metabolomics researchers and other members of the community wishing to perform multi-omics studies. We also believe that these ideas may allow the full promise of integrated multi-omics research and, ultimately, of systems biology to be realized.
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Affiliation(s)
- Farhana R Pinu
- The New Zealand Institute for Plant and Food Research Limited, Private Bag 92169, Auckland 1142, New Zealand.
| | - David J Beale
- Land and Water, Commonwealth Scientific and Industrial Research Organization (CSIRO), Ecosciences Precinct, Dutton Park, Dutton Park, QLD 4102, Australia.
| | - Amy M Paten
- Land and Water, Commonwealth Scientific and Industrial Research Organization (CSIRO), Research and Innovation Park, Acton, ACT 2601, Australia.
| | - Konstantinos Kouremenos
- Trajan Scientific and Medical, Ringwood, VIC 3134, Australia.
- Bio21 Institute, The University of Melbourne, Parkville, VIC 3010, Australia.
| | - Sanjay Swarup
- Department of Biological Sciences, National University of Singapore, Singapore 117411, Singapore.
| | - Horst J Schirra
- Centre for Advanced Imaging, The University of Queensland, St Lucia, QLD 4072, Australia.
| | - David Wishart
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E8, Canada.
- Department of Computing Science, University of Alberta, Edmonton, AB T6G 2E8, Canada.
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45
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Esser D, Lange J, Marinos G, Sieber M, Best L, Prasse D, Bathia J, Rühlemann MC, Boersch K, Jaspers C, Sommer F. Functions of the Microbiota for the Physiology of Animal Metaorganisms. J Innate Immun 2018; 11:393-404. [PMID: 30566939 PMCID: PMC6738199 DOI: 10.1159/000495115] [Citation(s) in RCA: 45] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2018] [Revised: 10/30/2018] [Accepted: 10/30/2018] [Indexed: 12/11/2022] Open
Abstract
Animals are usually regarded as independent entities within their respective environments. However, within an organism, eukaryotes and prokaryotes interact dynamically to form the so-called metaorganism or holobiont, where each partner fulfils its versatile and crucial role. This review focuses on the interplay between microorganisms and multicellular eukaryotes in the context of host physiology, in particular aging and mucus-associated crosstalk. In addition to the interactions between bacteria and the host, we highlight the importance of viruses and nonmodel organisms. Moreover, we discuss current culturing and computational methodologies that allow a deeper understanding of underlying mechanisms controlling the physiology of metaorganisms.
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Affiliation(s)
- Daniela Esser
- Institute of Experimental Medicine, Christian Albrecht University Kiel, Kiel, Germany
| | - Janina Lange
- Zoological Institute, Christian Albrecht University Kiel, Kiel, Germany
| | - Georgios Marinos
- Institute of Experimental Medicine, Christian Albrecht University Kiel, Kiel, Germany
| | - Michael Sieber
- Department of Evolutionary Theory, Max Planck Institute for Evolutionary Biology, Plön, Germany
| | - Lena Best
- Institute of Experimental Medicine, Christian Albrecht University Kiel, Kiel, Germany
| | - Daniela Prasse
- Institute of General Microbiology, Christian Albrecht University Kiel, Kiel, Germany
| | - Jay Bathia
- Zoological Institute, Christian Albrecht University Kiel, Kiel, Germany
| | - Malte C Rühlemann
- Institute of Clinical Molecular Biology, Christian Albrecht University Kiel, Kiel, Germany
| | - Kathrin Boersch
- Institute of Clinical Molecular Biology, Christian Albrecht University Kiel, Kiel, Germany
| | - Cornelia Jaspers
- Evolutionary Ecology of Marine Fishes, GEOMAR - Helmholtz Center for Ocean Research, Kiel, Germany
- National Institute of Aquatic Resources, Technical University of Denmark, Lyngby, Denmark
| | - Felix Sommer
- Institute of Clinical Molecular Biology, Christian Albrecht University Kiel, Kiel, Germany,
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46
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Abstract
An important hallmark of the human gut microbiota is its species diversity and complexity. Various diseases have been associated with a decreased diversity leading to reduced metabolic functionalities. Common approaches to investigate the human microbiota include high-throughput sequencing with subsequent correlative analyses. However, to understand the ecology of the human gut microbiota and consequently design novel treatments for diseases, it is important to represent the different interactions between microbes with their associated metabolites. Computational systems biology approaches can give further mechanistic insights by constructing data- or knowledge-driven networks that represent microbe interactions. In this minireview, we will discuss current approaches in systems biology to analyze the human gut microbiota, with a particular focus on constraint-based modeling. We will discuss various community modeling techniques with their advantages and differences, as well as their application to predict the metabolic mechanisms of intestinal microbial communities. Finally, we will discuss future perspectives and current challenges of simulating realistic and comprehensive models of the human gut microbiota.
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Affiliation(s)
- Eugen Bauer
- Luxembourg Centre for Systems Biomedicine, Universite du Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Ines Thiele
- Luxembourg Centre for Systems Biomedicine, Universite du Luxembourg, Esch-sur-Alzette, Luxembourg
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