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Kundu P, Beura S, Mondal S, Das AK, Ghosh A. Machine learning for the advancement of genome-scale metabolic modeling. Biotechnol Adv 2024; 74:108400. [PMID: 38944218 DOI: 10.1016/j.biotechadv.2024.108400] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2023] [Revised: 05/13/2024] [Accepted: 06/23/2024] [Indexed: 07/01/2024]
Abstract
Constraint-based modeling (CBM) has evolved as the core systems biology tool to map the interrelations between genotype, phenotype, and external environment. The recent advancement of high-throughput experimental approaches and multi-omics strategies has generated a plethora of new and precise information from wide-ranging biological domains. On the other hand, the continuously growing field of machine learning (ML) and its specialized branch of deep learning (DL) provide essential computational architectures for decoding complex and heterogeneous biological data. In recent years, both multi-omics and ML have assisted in the escalation of CBM. Condition-specific omics data, such as transcriptomics and proteomics, helped contextualize the model prediction while analyzing a particular phenotypic signature. At the same time, the advanced ML tools have eased the model reconstruction and analysis to increase the accuracy and prediction power. However, the development of these multi-disciplinary methodological frameworks mainly occurs independently, which limits the concatenation of biological knowledge from different domains. Hence, we have reviewed the potential of integrating multi-disciplinary tools and strategies from various fields, such as synthetic biology, CBM, omics, and ML, to explore the biochemical phenomenon beyond the conventional biological dogma. How the integrative knowledge of these intersected domains has improved bioengineering and biomedical applications has also been highlighted. We categorically explained the conventional genome-scale metabolic model (GEM) reconstruction tools and their improvement strategies through ML paradigms. Further, the crucial role of ML and DL in omics data restructuring for GEM development has also been briefly discussed. Finally, the case-study-based assessment of the state-of-the-art method for improving biomedical and metabolic engineering strategies has been elaborated. Therefore, this review demonstrates how integrating experimental and in silico strategies can help map the ever-expanding knowledge of biological systems driven by condition-specific cellular information. This multiview approach will elevate the application of ML-based CBM in the biomedical and bioengineering fields for the betterment of society and the environment.
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Affiliation(s)
- Pritam Kundu
- School School of Energy Science and Engineering, Indian Institute of Technology Kharagpur, West Bengal 721302, India
| | - Satyajit Beura
- Department of Bioscience and Biotechnology, Indian Institute of Technology, Kharagpur, West Bengal 721302, India
| | - Suman Mondal
- P.K. Sinha Centre for Bioenergy and Renewables, Indian Institute of Technology Kharagpur, West Bengal 721302, India
| | - Amit Kumar Das
- Department of Bioscience and Biotechnology, Indian Institute of Technology, Kharagpur, West Bengal 721302, India
| | - Amit Ghosh
- School School of Energy Science and Engineering, Indian Institute of Technology Kharagpur, West Bengal 721302, India; P.K. Sinha Centre for Bioenergy and Renewables, Indian Institute of Technology Kharagpur, West Bengal 721302, India.
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2
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Nanetti E, Scicchitano D, Palladino G, Interino N, Corlatti L, Pedrotti L, Zanetti F, Pagani E, Esposito E, Brambilla A, Grignolio S, Marotti I, Turroni S, Fiori J, Rampelli S, Candela M. The Alpine ibex (Capra ibex) gut microbiome, seasonal dynamics, and potential application in lignocellulose bioconversion. iScience 2024; 27:110194. [PMID: 38989465 PMCID: PMC11233967 DOI: 10.1016/j.isci.2024.110194] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2024] [Revised: 04/24/2024] [Accepted: 06/03/2024] [Indexed: 07/12/2024] Open
Abstract
Aiming to shed light on the biology of wild ruminants, we investigated the gut microbiome seasonal dynamics of the Alpine ibex (Capra ibex) from the Central Italian Alps. Feces were collected in spring, summer, and autumn during non-invasive sampling campaigns. Samples were analyzed by 16S rRNA amplicon sequencing, shotgun metagenomics, as well as targeted and untargeted metabolomics. Our findings revealed season-specific compositional and functional profiles of the ibex gut microbiome that may allow the host to adapt to seasonal changes in available forage, by fine-tuning the holobiont catabolic layout to fully exploit the available food. Besides confirming the importance of the host-associated microbiome in providing the phenotypic plasticity needed to buffer dietary changes, we obtained species-level genome bins and identified minimal gut microbiome community modules of 11-14 interacting strains as a possible microbiome-based solution for the bioconversion of lignocellulose to high-value compounds, such as volatile fatty acids.
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Affiliation(s)
- Enrico Nanetti
- Unit of Microbiome Science and Biotechnology, Department of Pharmacy and Biotechnology (FaBiT), Alma Mater Studiorum - University of Bologna, Via Belmeloro 6, 40126 Bologna, Italy
| | - Daniel Scicchitano
- Unit of Microbiome Science and Biotechnology, Department of Pharmacy and Biotechnology (FaBiT), Alma Mater Studiorum - University of Bologna, Via Belmeloro 6, 40126 Bologna, Italy
- Fano Marine Center, The Inter-Institute Center for Research on Marine Biodiversity, Resources and Biotechnologies, 61032 Fano, Italy
| | - Giorgia Palladino
- Unit of Microbiome Science and Biotechnology, Department of Pharmacy and Biotechnology (FaBiT), Alma Mater Studiorum - University of Bologna, Via Belmeloro 6, 40126 Bologna, Italy
- Fano Marine Center, The Inter-Institute Center for Research on Marine Biodiversity, Resources and Biotechnologies, 61032 Fano, Italy
| | - Nicolò Interino
- Department of Chemistry “G. Ciamician”, University of Bologna, Via Selmi 2, 40126 Bologna, Italy
| | - Luca Corlatti
- Stelvio National Park, 23032 Bormio, Italy
- University of Freiburg, 79098 Freiburg, Germany
| | | | - Federica Zanetti
- Department of Agricultural and Food Sciences, University of Bologna, Viale G. Fanin 44, 40127 Bologna, Italy
| | - Elena Pagani
- Department of Agricultural and Food Sciences, University of Bologna, Viale G. Fanin 44, 40127 Bologna, Italy
| | - Erika Esposito
- Department of Chemistry “G. Ciamician”, University of Bologna, Via Selmi 2, 40126 Bologna, Italy
| | - Alice Brambilla
- Department of Evolutionary Biology and Environmental Studies, University of Zurich, Winterthurerstrasse 190, 8057 Zurich (CH), Switzerland
- Centro Studi Fauna Alpina, Parco Nazionale Gran Paradiso, Loc. Degioz 11, 11010 Valsavarenche, Aosta, Italy
| | - Stefano Grignolio
- University of Ferrara, Department of Life Science and Biotechnology, via Borsari 46, I-44121 Ferrara, Italy
| | - Ilaria Marotti
- Department of Agricultural and Food Sciences, University of Bologna, Viale G. Fanin 44, 40127 Bologna, Italy
| | - Silvia Turroni
- Unit of Microbiome Science and Biotechnology, Department of Pharmacy and Biotechnology (FaBiT), Alma Mater Studiorum - University of Bologna, Via Belmeloro 6, 40126 Bologna, Italy
| | - Jessica Fiori
- Department of Chemistry “G. Ciamician”, University of Bologna, Via Selmi 2, 40126 Bologna, Italy
| | - Simone Rampelli
- Unit of Microbiome Science and Biotechnology, Department of Pharmacy and Biotechnology (FaBiT), Alma Mater Studiorum - University of Bologna, Via Belmeloro 6, 40126 Bologna, Italy
- Fano Marine Center, The Inter-Institute Center for Research on Marine Biodiversity, Resources and Biotechnologies, 61032 Fano, Italy
| | - Marco Candela
- Unit of Microbiome Science and Biotechnology, Department of Pharmacy and Biotechnology (FaBiT), Alma Mater Studiorum - University of Bologna, Via Belmeloro 6, 40126 Bologna, Italy
- Fano Marine Center, The Inter-Institute Center for Research on Marine Biodiversity, Resources and Biotechnologies, 61032 Fano, Italy
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3
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Sabater C, Sáez GD, Suárez N, Garro MS, Margolles A, Zárate G. Fermentation with Lactic Acid Bacteria for Bean Flour Improvement: Experimental Study and Molecular Modeling as Complementary Tools. Foods 2024; 13:2105. [PMID: 38998611 PMCID: PMC11241767 DOI: 10.3390/foods13132105] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2024] [Revised: 06/26/2024] [Accepted: 06/27/2024] [Indexed: 07/14/2024] Open
Abstract
Pulses are considered superfoods for the future world due to their properties, but they require processing to reduce antinutritional factors (ANFs) and increase bioactivity. In this study, bean flour (Phaseolus vulgaris L.) was fermented under different conditions (addition of Lactiplantibacillus plantarum CRL 2211 and/or Weissella paramesenteroides CRL 2182, temperature, time and dough yield) to improve its nutri-functional quality. Fermentation for 24 h at 37 °C with the mixed starter increased the lactic acid bacteria (LAB) population, acidity, polyphenol content (TPC) and ANF removal more than spontaneous fermentation. Statistical and rep-PCR analysis showed that fermentation was mainly conducted by Lp. plantarum CRL 2211. Metabolic modeling revealed potential cross-feeding between Lp. plantarum and W. paramesenteroides, while the molecular docking and dynamic simulation of LAB tannases and proteinases involved in ANF removal revealed their chemical affinity to gallocatechin and trypsin inhibitors. Fermentation was better than soaking, germination and cooking for enhancing bean flour properties: it increased the free amino acids content by 50% by releasing glutamine, glutamic acid, arginine, leucine and lysine and modified TPC by increasing gallic acid and decreasing caffeic, ferulic and vanillic acids and quercetin-3-glucoside. The combination of experimental and simulation data may help us to understand fermentation processes and to design products with desirable features.
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Affiliation(s)
- Carlos Sabater
- Department of Microbiology and Biochemistry of Dairy Products, Dairy Research Institute of Asturias (IPLA), Spanish National Research Council (CSIC), Paseo Río Linares S/N, 33300 Villaviciosa, Asturias, Spain
- Health Research Institute of Asturias (ISPA), 33011 Oviedo, Asturias, Spain
| | - Gabriel D Sáez
- Laboratory of Technological Ecophysiology, Reference Centre for Lactobacilli (CERELA-CONICET), Chacabuco 145, San Miguel de Tucumán 4000, Tucumán, Argentina
- Department of Food Microbiology, University of San Pablo Tucumán, Av. Solano Vera y Camino a Villa Nougués, San Pablo 4129, Tucumán, Argentina
| | - Nadia Suárez
- Laboratory of Technological Ecophysiology, Reference Centre for Lactobacilli (CERELA-CONICET), Chacabuco 145, San Miguel de Tucumán 4000, Tucumán, Argentina
| | - Marisa S Garro
- Laboratory of Technological Ecophysiology, Reference Centre for Lactobacilli (CERELA-CONICET), Chacabuco 145, San Miguel de Tucumán 4000, Tucumán, Argentina
| | - Abelardo Margolles
- Department of Microbiology and Biochemistry of Dairy Products, Dairy Research Institute of Asturias (IPLA), Spanish National Research Council (CSIC), Paseo Río Linares S/N, 33300 Villaviciosa, Asturias, Spain
- Health Research Institute of Asturias (ISPA), 33011 Oviedo, Asturias, Spain
| | - Gabriela Zárate
- Laboratory of Technological Ecophysiology, Reference Centre for Lactobacilli (CERELA-CONICET), Chacabuco 145, San Miguel de Tucumán 4000, Tucumán, Argentina
- Department of Food Microbiology, University of San Pablo Tucumán, Av. Solano Vera y Camino a Villa Nougués, San Pablo 4129, Tucumán, Argentina
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Chen C, Yang H, Zhang K, Ye G, Luo H, Zou W. Revealing microbiota characteristics and predicting flavor-producing sub-communities in Nongxiangxing baijiu pit mud through metagenomic analysis and metabolic modeling. Food Res Int 2024; 188:114507. [PMID: 38823882 DOI: 10.1016/j.foodres.2024.114507] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2024] [Revised: 04/29/2024] [Accepted: 05/07/2024] [Indexed: 06/03/2024]
Abstract
The microorganisms of the pit mud (PM) of Nongxiangxing baijiu (NXXB) have an important role in the synthesis of flavor substances, and they determine attributes and quality of baijiu. Herein, we utilize metagenomics and genome-scale metabolic models (GSMMs) to investigate the microbial composition, metabolic functions in PM microbiota, as well as to identify microorganisms and communities linked to flavor compounds. Metagenomic data revealed that the most prevalent assembly of bacteria and archaea was Proteiniphilum, Caproicibacterium, Petrimonas, Lactobacillus, Clostridium, Aminobacterium, Syntrophomonas, Methanobacterium, Methanoculleus, and Methanosarcina. The important enzymes ofPMwere in bothGH and GT familymetabolism. A total of 38 high-quality metagenome-assembled genomes (MAGs) were obtained, including those at the family level (n = 13), genus level (n = 17), and species level (n = 8). GSMMs of the 38 MAGs were then constructed. From the GSMMs, individual and community capabilities respectively were predicted to be able to produce 111 metabolites and 598 metabolites. Twenty-three predicted metabolites were consistent with the metabonomics detected flavors and served as targets. Twelve sub-community of were screened by cross-feeding of 38 GSMMs. Of them, Methanobacterium, Sphaerochaeta, Muricomes intestini, Methanobacteriaceae, Synergistaceae, and Caloramator were core microorganisms for targets in each sub-community. Overall, this study of metagenomic and target-community screening could help our understanding of the metabolite-microbiome association and further bioregulation of baijiu.
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Affiliation(s)
- Cong Chen
- College of Bioengineering, Sichuan University of Science & Engineering, Yibin 644005, China
| | - Haiquan Yang
- The Key Laboratory of Industrial Biotechnology, Ministry of Education, School of Biotechnology, Jiangnan University, Wuxi 214122, China
| | - Kaizheng Zhang
- College of Bioengineering, Sichuan University of Science & Engineering, Yibin 644005, China
| | - Guangbin Ye
- College of Bioengineering, Sichuan University of Science & Engineering, Yibin 644005, China
| | - Huibo Luo
- Liquor Brewing Biotechnology and Application Key Laboratory of Sichuan Province, Sichuan University of Science and Engineering, Yibin, Sichuan 644005, China
| | - Wei Zou
- College of Bioengineering, Sichuan University of Science & Engineering, Yibin 644005, China; Liquor Brewing Biotechnology and Application Key Laboratory of Sichuan Province, Sichuan University of Science and Engineering, Yibin, Sichuan 644005, China.
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Raghu AK, Palanikumar I, Raman K. Designing function-specific minimal microbiomes from large microbial communities. NPJ Syst Biol Appl 2024; 10:46. [PMID: 38702322 PMCID: PMC11068740 DOI: 10.1038/s41540-024-00373-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2023] [Accepted: 04/17/2024] [Indexed: 05/06/2024] Open
Abstract
Microorganisms exist in large communities of diverse species, exhibiting various functionalities. The mammalian gut microbiome, for instance, has the functionality of digesting dietary fibre and producing different short-chain fatty acids. Not all microbes present in a community contribute to a given functionality; it is possible to find a minimal microbiome, which is a subset of the large microbiome, that is capable of performing the functionality while maintaining other community properties such as growth rate and metabolite production. Such a minimal microbiome will also contain keystone species for SCFA production in that community. In this work, we present a systematic constraint-based approach to identify a minimal microbiome from a large community for a user-proposed function. We employ a top-down approach with sequential deletion followed by solving a mixed-integer linear programming problem with the objective of minimising the L1-norm of the membership vector. Notably, we consider quantitative measures of community growth rate and metabolite production rates. We demonstrate the utility of our algorithm by identifying the minimal microbiomes corresponding to three model communities of the gut, and discuss their validity based on the presence of the keystone species in the community. Our approach is generic, flexible and finds application in studying a variety of microbial communities. The algorithm is available from https://github.com/RamanLab/minMicrobiome .
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Affiliation(s)
- Aswathy K Raghu
- Centre for Integrative Biology and Systems mEdicine (IBSE), Indian Institute of Technology (IIT) Madras, Chennai, 600 036, India
- Robert Bosch Centre for Data Science and Artificial Intelligence (RBCDSAI), IIT Madras, Chennai, 600 036, India
- Department of Chemical and Biological Engineering, Northwestern University, IL, 60208, USA
| | - Indumathi Palanikumar
- Centre for Integrative Biology and Systems mEdicine (IBSE), Indian Institute of Technology (IIT) Madras, Chennai, 600 036, India
- Robert Bosch Centre for Data Science and Artificial Intelligence (RBCDSAI), IIT Madras, Chennai, 600 036, India
- Department of Biotechnology, Bhupat Jyoti Mehta School of Biosciences, IIT Madras, Chennai, 600 036, India
| | - Karthik Raman
- Centre for Integrative Biology and Systems mEdicine (IBSE), Indian Institute of Technology (IIT) Madras, Chennai, 600 036, India.
- Robert Bosch Centre for Data Science and Artificial Intelligence (RBCDSAI), IIT Madras, Chennai, 600 036, India.
- Department of Biotechnology, Bhupat Jyoti Mehta School of Biosciences, IIT Madras, Chennai, 600 036, India.
- Department of Data Science and AI, Wadhwani School of Data Science and AI, IIT Madras, Chennai, 600 036, India.
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6
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Manrique P, Montero I, Fernandez-Gosende M, Martinez N, Cantabrana CH, Rios-Covian D. Past, present, and future of microbiome-based therapies. MICROBIOME RESEARCH REPORTS 2024; 3:23. [PMID: 38841413 PMCID: PMC11149097 DOI: 10.20517/mrr.2023.80] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/26/2023] [Revised: 03/07/2024] [Accepted: 03/12/2024] [Indexed: 06/07/2024]
Abstract
Technological advances in studying the human microbiome in depth have enabled the identification of microbial signatures associated with health and disease. This confirms the crucial role of microbiota in maintaining homeostasis and the host health status. Nowadays, there are several ways to modulate the microbiota composition to effectively improve host health; therefore, the development of therapeutic treatments based on the gut microbiota is experiencing rapid growth. In this review, we summarize the influence of the gut microbiota on the development of infectious disease and cancer, which are two of the main targets of microbiome-based therapies currently being developed. We analyze the two-way interaction between the gut microbiota and traditional drugs in order to emphasize the influence of gut microbial composition on drug effectivity and treatment response. We explore the different strategies currently available for modulating this ecosystem to our benefit, ranging from 1st generation intervention strategies to more complex 2nd generation microbiome-based therapies and their regulatory framework. Lastly, we finish with a quick overview of what we believe is the future of these strategies, that is 3rd generation microbiome-based therapies developed with the use of artificial intelligence (AI) algorithms.
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Rampelli S, Gallois S, D’Amico F, Turroni S, Fabbrini M, Scicchitano D, Candela M, Henry A. The gut microbiome of Baka forager-horticulturalists from Cameroon is optimized for wild plant foods. iScience 2024; 27:109211. [PMID: 38433907 PMCID: PMC10904984 DOI: 10.1016/j.isci.2024.109211] [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: 10/23/2023] [Revised: 12/21/2023] [Accepted: 02/07/2024] [Indexed: 03/05/2024] Open
Abstract
The human gut microbiome is losing biodiversity, due to the "microbiome modernization process" that occurs with urbanization. To keep track of it, here we applied shotgun metagenomics to the gut microbiome of the Baka, a group of forager-horticulturalists from Cameroon, who combine hunting and gathering with growing a few crops and working for neighboring Bantu-speaking farmers. We analyzed the gut microbiome of individuals with different access to and use of wild plant and processed foods, to explore the variation of their gut microbiome along the cline from hunter-gatherer to agricultural subsistence patterns. We found that 26 species-level genome bins from our cohort were pivotal for the degradation of the wild plant food substrates. These microbes include Old Friend species and are encoded for genes that are no longer present in industrialized gut microbiome. Our results highlight the potential relevance of these genes to human biology and health, in relation to lifestyle.
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Affiliation(s)
- Simone Rampelli
- Unit of Microbiome Science and Biotechnology, Department of Pharmacy and Biotechnology (FaBiT), Alma Mater Studiorum – University of Bologna, 40126 Bologna, Italy
| | - Sandrine Gallois
- Department of Archaeological Sciences, Faculty of Archaeology, Leiden University, 2311 Leiden, the Netherlands
- Institute of Environmental Science and Technology, ST, 08193 Bellaterra, Spain
| | - Federica D’Amico
- Microbiomics Unit, Department of Medical and Surgical Sciences (DiMeC), Alma Mater Studiorum – University of Bologna, 40138 Bologna, Italy
| | - Silvia Turroni
- Unit of Microbiome Science and Biotechnology, Department of Pharmacy and Biotechnology (FaBiT), Alma Mater Studiorum – University of Bologna, 40126 Bologna, Italy
| | - Marco Fabbrini
- Microbiomics Unit, Department of Medical and Surgical Sciences (DiMeC), Alma Mater Studiorum – University of Bologna, 40138 Bologna, Italy
| | - Daniel Scicchitano
- Unit of Microbiome Science and Biotechnology, Department of Pharmacy and Biotechnology (FaBiT), Alma Mater Studiorum – University of Bologna, 40126 Bologna, Italy
| | - Marco Candela
- Unit of Microbiome Science and Biotechnology, Department of Pharmacy and Biotechnology (FaBiT), Alma Mater Studiorum – University of Bologna, 40126 Bologna, Italy
| | - Amanda Henry
- Department of Archaeological Sciences, Faculty of Archaeology, Leiden University, 2311 Leiden, the Netherlands
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Vázquez X, Lumbreras-Iglesias P, Rodicio MR, Fernández J, Bernal T, Moreno AF, de Ugarriza PL, Fernández-Verdugo A, Margolles A, Sabater C. Study of the intestinal microbiota composition and the effect of treatment with intensive chemotherapy in patients recovered from acute leukemia. Sci Rep 2024; 14:5585. [PMID: 38454103 PMCID: PMC10920697 DOI: 10.1038/s41598-024-56054-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2023] [Accepted: 03/01/2024] [Indexed: 03/09/2024] Open
Abstract
A dataset comprising metagenomes of outpatients (n = 28) with acute leukemia (AL) and healthy controls (n = 14) was analysed to investigate the associations between gut microbiota composition and metabolic activity and AL. According to the results obtained, no significant differences in the microbial diversity between AL outpatients and healthy controls were found. However, significant differences in the abundance of specific microbial clades of healthy controls and AL outpatients were found. We found some differences at taxa level. The relative abundance of Enterobacteriaceae, Prevotellaceae and Rikenellaceae was increased in AL outpatients, while Bacteirodaceae, Bifidobacteriaceae and Lachnospiraceae was decreased. Interestingly, the abundances of several taxa including Bacteroides and Faecalibacterium species showed variations based on recovery time from the last cycle of chemotherapy. Functional annotation of metagenome-assembled genomes (MAGs) revealed the presence of functional domains corresponding to therapeutic enzymes including L-asparaginase in a wide range of genera including Prevotella, Ruminococcus, Faecalibacterium, Alistipes, Akkermansia. Metabolic network modelling revealed potential symbiotic relationships between Veillonella parvula and Levyella massiliensis and several species found in the microbiota of AL outpatients. These results may contribute to develop strategies for the recovery of microbiota composition profiles in the treatment of patients with AL.
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Grants
- FIS PI21/01590 Fondo de Investigación Sanitaria, Instituto de Salud Carlos III, Ministerio de Economía y Competitividad, Spain
- FIS PI21/01590 Fondo de Investigación Sanitaria, Instituto de Salud Carlos III, Ministerio de Economía y Competitividad, Spain
- FIS PI21/01590 Fondo de Investigación Sanitaria, Instituto de Salud Carlos III, Ministerio de Economía y Competitividad, Spain
- FIS PI21/01590 Fondo de Investigación Sanitaria, Instituto de Salud Carlos III, Ministerio de Economía y Competitividad, Spain
- FIS PI21/01590 Fondo de Investigación Sanitaria, Instituto de Salud Carlos III, Ministerio de Economía y Competitividad, Spain
- FIS PI21/01590 Fondo de Investigación Sanitaria, Instituto de Salud Carlos III, Ministerio de Economía y Competitividad, Spain
- FIS PI21/01590 Fondo de Investigación Sanitaria, Instituto de Salud Carlos III, Ministerio de Economía y Competitividad, Spain
- FIS PI21/01590 Fondo de Investigación Sanitaria, Instituto de Salud Carlos III, Ministerio de Economía y Competitividad, Spain
- FIS PI21/01590 Fondo de Investigación Sanitaria, Instituto de Salud Carlos III, Ministerio de Economía y Competitividad, Spain
- FIS PI21/01590 Fondo de Investigación Sanitaria, Instituto de Salud Carlos III, Ministerio de Economía y Competitividad, Spain
- GRUPIN IDI/2022/000033 Regional Ministry of Science of Asturias
- GRUPIN IDI/2022/000033 Regional Ministry of Science of Asturias
- GRUPIN IDI/2022/000033 Regional Ministry of Science of Asturias
- GRUPIN IDI/2022/000033 Regional Ministry of Science of Asturias
- GRUPIN IDI/2022/000033 Regional Ministry of Science of Asturias
- GRUPIN IDI/2022/000033 Regional Ministry of Science of Asturias
- GRUPIN IDI/2022/000033 Regional Ministry of Science of Asturias
- GRUPIN IDI/2022/000033 Regional Ministry of Science of Asturias
- GRUPIN IDI/2022/000033 Regional Ministry of Science of Asturias
- GRUPIN IDI/2022/000033 Regional Ministry of Science of Asturias
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Affiliation(s)
- Xenia Vázquez
- Dairy Research Institute of Asturias (IPLA), Spanish National Research Council, (CSIC), Villaviciosa, Asturias, Spain
- Instituto de Investigación Sanitaria del Principado de Asturias (ISPA), MicroHealth Group, Oviedo, Spain
| | - Pilar Lumbreras-Iglesias
- Traslational Microbiology Group, Instituto de Investigación Sanitaria del Principado de Asturias (ISPA), Oviedo, Spain
- Department of Clinical Microbiology, Hospital Universitario Central de Asturias (HUCA), Oviedo, Spain
- Department of Hematology Instituto de Investigación Sanitaria del Principado de Asturias (ISPA), Instituto de Oncología del Principado de Asturias (IUOPA), Hospital Universitario Central de Asturias (HUCA), 33011, Oviedo, Spain
| | - M Rosario Rodicio
- Traslational Microbiology Group, Instituto de Investigación Sanitaria del Principado de Asturias (ISPA), Oviedo, Spain
- Department of Functional Biology, Microbiology Area, University of Oviedo, Oviedo, Spain
| | - Javier Fernández
- Traslational Microbiology Group, Instituto de Investigación Sanitaria del Principado de Asturias (ISPA), Oviedo, Spain
- Department of Clinical Microbiology, Hospital Universitario Central de Asturias (HUCA), Oviedo, Spain
- Research & Innovation, Artificial Intelligence and Statistical Department, Pragmatech AI Solutions, Oviedo, Spain
- Centro de Investigación Biomédica en Red-Enfermedades Respiratorias, Madrid, Spain
| | - Teresa Bernal
- Department of Hematology Instituto de Investigación Sanitaria del Principado de Asturias (ISPA), Instituto de Oncología del Principado de Asturias (IUOPA), Hospital Universitario Central de Asturias (HUCA), 33011, Oviedo, Spain
| | - Ainhoa Fernández Moreno
- Department of Hematology Instituto de Investigación Sanitaria del Principado de Asturias (ISPA), Instituto de Oncología del Principado de Asturias (IUOPA), Hospital Universitario Central de Asturias (HUCA), 33011, Oviedo, Spain
| | - Paula López de Ugarriza
- Department of Hematology Instituto de Investigación Sanitaria del Principado de Asturias (ISPA), Instituto de Oncología del Principado de Asturias (IUOPA), Hospital Universitario Central de Asturias (HUCA), 33011, Oviedo, Spain
| | - Ana Fernández-Verdugo
- Traslational Microbiology Group, Instituto de Investigación Sanitaria del Principado de Asturias (ISPA), Oviedo, Spain
- Department of Clinical Microbiology, Hospital Universitario Central de Asturias (HUCA), Oviedo, Spain
| | - Abelardo Margolles
- Dairy Research Institute of Asturias (IPLA), Spanish National Research Council, (CSIC), Villaviciosa, Asturias, Spain
- Instituto de Investigación Sanitaria del Principado de Asturias (ISPA), MicroHealth Group, Oviedo, Spain
| | - Carlos Sabater
- Dairy Research Institute of Asturias (IPLA), Spanish National Research Council, (CSIC), Villaviciosa, Asturias, Spain.
- Instituto de Investigación Sanitaria del Principado de Asturias (ISPA), MicroHealth Group, Oviedo, Spain.
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9
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Cerk K, Ugalde‐Salas P, Nedjad CG, Lecomte M, Muller C, Sherman DJ, Hildebrand F, Labarthe S, Frioux C. Community-scale models of microbiomes: Articulating metabolic modelling and metagenome sequencing. Microb Biotechnol 2024; 17:e14396. [PMID: 38243750 PMCID: PMC10832553 DOI: 10.1111/1751-7915.14396] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Revised: 11/27/2023] [Accepted: 12/20/2023] [Indexed: 01/21/2024] Open
Abstract
Building models is essential for understanding the functions and dynamics of microbial communities. Metabolic models built on genome-scale metabolic network reconstructions (GENREs) are especially relevant as a means to decipher the complex interactions occurring among species. Model reconstruction increasingly relies on metagenomics, which permits direct characterisation of naturally occurring communities that may contain organisms that cannot be isolated or cultured. In this review, we provide an overview of the field of metabolic modelling and its increasing reliance on and synergy with metagenomics and bioinformatics. We survey the means of assigning functions and reconstructing metabolic networks from (meta-)genomes, and present the variety and mathematical fundamentals of metabolic models that foster the understanding of microbial dynamics. We emphasise the characterisation of interactions and the scaling of model construction to large communities, two important bottlenecks in the applicability of these models. We give an overview of the current state of the art in metagenome sequencing and bioinformatics analysis, focusing on the reconstruction of genomes in microbial communities. Metagenomics benefits tremendously from third-generation sequencing, and we discuss the opportunities of long-read sequencing, strain-level characterisation and eukaryotic metagenomics. We aim at providing algorithmic and mathematical support, together with tool and application resources, that permit bridging the gap between metagenomics and metabolic modelling.
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Affiliation(s)
- Klara Cerk
- Quadram Institute BioscienceNorwichUK
- Earlham InstituteNorwichUK
| | | | - Chabname Ghassemi Nedjad
- Inria, University of Bordeaux, INRAETalenceFrance
- University of Bordeaux, CNRS, Bordeaux INP, LaBRI, UMR 5800TalenceFrance
| | - Maxime Lecomte
- Inria, University of Bordeaux, INRAETalenceFrance
- INRAE STLO¸University of RennesRennesFrance
| | | | | | - Falk Hildebrand
- Quadram Institute BioscienceNorwichUK
- Earlham InstituteNorwichUK
| | - Simon Labarthe
- Inria, University of Bordeaux, INRAETalenceFrance
- INRAE, University of Bordeaux, BIOGECO, UMR 1202CestasFrance
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10
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Muñoz-Tamayo R, Davoudkhani M, Fakih I, Robles-Rodriguez CE, Rubino F, Creevey CJ, Forano E. Review: Towards the next-generation models of the rumen microbiome for enhancing predictive power and guiding sustainable production strategies. Animal 2023; 17 Suppl 5:100984. [PMID: 37821326 DOI: 10.1016/j.animal.2023.100984] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2022] [Revised: 09/01/2023] [Accepted: 09/07/2023] [Indexed: 10/13/2023] Open
Abstract
The rumen ecosystem harbours a galaxy of microbes working in syntrophy to carry out a metabolic cascade of hydrolytic and fermentative reactions. This fermentation process allows ruminants to harvest nutrients from a wide range of feedstuff otherwise inaccessible to the host. The interconnection between the ruminant and its rumen microbiota shapes key animal phenotypes such as feed efficiency and methane emissions and suggests the potential of reducing methane emissions and enhancing feed conversion into animal products by manipulating the rumen microbiota. Whilst significant technological progress in omics techniques has increased our knowledge of the rumen microbiota and its genome (microbiome), translating omics knowledge into effective microbial manipulation strategies remains a great challenge. This challenge can be addressed by modelling approaches integrating causality principles and thus going beyond current correlation-based approaches applied to analyse rumen microbial genomic data. However, existing rumen models are not yet adapted to capitalise on microbial genomic information. This gap between the rumen microbiota available omics data and the way microbial metabolism is represented in the existing rumen models needs to be filled to enhance rumen understanding and produce better predictive models with capabilities for guiding nutritional strategies. To fill this gap, the integration of computational biology tools and mathematical modelling frameworks is needed to translate the information of the metabolic potential of the rumen microbes (inferred from their genomes) into a mathematical object. In this paper, we aim to discuss the potential use of two modelling approaches for the integration of microbial genomic information into dynamic models. The first modelling approach explores the theory of state observers to integrate microbial time series data into rumen fermentation models. The second approach is based on the genome-scale network reconstructions of rumen microbes. For a given microorganism, the network reconstruction produces a stoichiometry matrix of the metabolism. This matrix is the core of the so-called genome-scale metabolic models which can be exploited by a plethora of methods comprised within the constraint-based reconstruction and analysis approaches. We will discuss how these methods can be used to produce the next-generation models of the rumen microbiome.
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Affiliation(s)
- R Muñoz-Tamayo
- Université Paris-Saclay, INRAE, AgroParisTech, UMR Modélisation Systémique Appliquée aux Ruminants, 91120 Palaiseau, France.
| | - M Davoudkhani
- Université Paris-Saclay, INRAE, AgroParisTech, UMR Modélisation Systémique Appliquée aux Ruminants, 91120 Palaiseau, France
| | - I Fakih
- Université Paris-Saclay, INRAE, AgroParisTech, UMR Modélisation Systémique Appliquée aux Ruminants, 91120 Palaiseau, France; Université Clermont Auvergne, INRAE, UMR 454 MEDIS, Clermont-Ferrand, France
| | | | - F Rubino
- Institute of Global Food Security, School of Biological Sciences, Queen's University Belfast, BT9 5DL Northern Ireland, UK
| | - C J Creevey
- Institute of Global Food Security, School of Biological Sciences, Queen's University Belfast, BT9 5DL Northern Ireland, UK
| | - E Forano
- Université Clermont Auvergne, INRAE, UMR 454 MEDIS, Clermont-Ferrand, France
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11
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Gonçalves OS, Creevey CJ, Santana MF. Designing a synthetic microbial community through genome metabolic modeling to enhance plant-microbe interaction. ENVIRONMENTAL MICROBIOME 2023; 18:81. [PMID: 37974247 PMCID: PMC10655421 DOI: 10.1186/s40793-023-00536-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/28/2023] [Accepted: 10/30/2023] [Indexed: 11/19/2023]
Abstract
BACKGROUND Manipulating the rhizosphere microbial community through beneficial microorganism inoculation has gained interest in improving crop productivity and stress resistance. Synthetic microbial communities, known as SynComs, mimic natural microbial compositions while reducing the number of components. However, achieving this goal requires a comprehensive understanding of natural microbial communities and carefully selecting compatible microorganisms with colonization traits, which still pose challenges. In this study, we employed multi-genome metabolic modeling of 270 previously described metagenome-assembled genomes from Campos rupestres to design a synthetic microbial community to improve the yield of important crop plants. RESULTS We used a targeted approach to select a minimal community (MinCom) encompassing essential compounds for microbial metabolism and compounds relevant to plant interactions. This resulted in a reduction of the initial community size by approximately 4.5-fold. Notably, the MinCom retained crucial genes associated with essential plant growth-promoting traits, such as iron acquisition, exopolysaccharide production, potassium solubilization, nitrogen fixation, GABA production, and IAA-related tryptophan metabolism. Furthermore, our in-silico selection for the SymComs, based on a comprehensive understanding of microbe-microbe-plant interactions, yielded a set of six hub species that displayed notable taxonomic novelty, including members of the Eremiobacterota and Verrucomicrobiota phyla. CONCLUSION Overall, the study contributes to the growing body of research on synthetic microbial communities and their potential to enhance agricultural practices. The insights gained from our in-silico approach and the selection of hub species pave the way for further investigations into the development of tailored microbial communities that can optimize crop productivity and improve stress resilience in agricultural systems.
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Affiliation(s)
- Osiel S Gonçalves
- Grupo de Genômica Eco-evolutiva Microbiana, Laboratório de Genética Molecular de Microrganismos, Departamento de Microbiologia, Instituto de Biotecnologia Aplicada à Agropecuária, Universidade Federal de Viçosa, Viçosa, Minas Gerais, Brazil
| | - Christopher J Creevey
- School of Biological Sciences, Institute for Global Food Security, Queen's University Belfast, Belfast, BT9 5DL, UK
| | - Mateus F Santana
- Grupo de Genômica Eco-evolutiva Microbiana, Laboratório de Genética Molecular de Microrganismos, Departamento de Microbiologia, Instituto de Biotecnologia Aplicada à Agropecuária, Universidade Federal de Viçosa, Viçosa, Minas Gerais, Brazil.
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12
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Karimi E, Dittami SM. Maintaining beneficial alga-associated bacterial communities under heat stress: insights from controlled co-culture experiments using antibiotic-resistant bacterial strains. FEMS Microbiol Ecol 2023; 99:fiad130. [PMID: 37833238 DOI: 10.1093/femsec/fiad130] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Revised: 09/29/2023] [Accepted: 10/12/2023] [Indexed: 10/15/2023] Open
Abstract
Brown algae, like many eukaryotes, possess diverse microbial communities. Ectocarpus-a model brown alga-relies on these communities for essential processes, such as growth development. Controlled laboratory systems are needed for functional studies of these algal-bacterial interactions. We selected bacterial strains based on their metabolic networks to provide optimal completion of the algal metabolism, rendered them resistant to two antibiotics, and inoculate them to establish controlled co-cultures with Ectocarpus under continuous antibiotic treatment. We then monitored the stability of the resulting associations under control conditions and heat stress using 16S metabarcoding. Antibiotics strongly reduced bacterial diversity both in terms of taxonomy and predicted metabolic functions. In the inoculated sample, 63%-69% of reads corresponded to the inoculated strains, and the communities remained stable during temperature stress. They also partially restored the predicted metabolic functions of the natural community. Overall, the development of antibiotic-resistant helper cultures offers a promising route to fully controlled laboratory experiments with algae and microbiota and thus represents an important step towards generating experimental evidence for specific host-microbe interactions in the systems studied. Further work will be required to achieve full control and progressively expand our repertoire of helper strains including those currently 'unculturable'.
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Affiliation(s)
- Elham Karimi
- Integrative Biology of Marine Models, Sorbonne Université/CNRS, UMR8227, Station Biologique de Roscoff, CS 90074, 29688 Roscoff Cedex, France
| | - Simon M Dittami
- Integrative Biology of Marine Models, Sorbonne Université/CNRS, UMR8227, Station Biologique de Roscoff, CS 90074, 29688 Roscoff Cedex, France
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13
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Treitli SC, Hanousková P, Beneš V, Brune A, Čepička I, Hampl V. Hydrogenotrophic methanogenesis is the key process in the obligately syntrophic consortium of the anaerobic ameba Pelomyxa schiedti. THE ISME JOURNAL 2023; 17:1884-1894. [PMID: 37634049 PMCID: PMC10579272 DOI: 10.1038/s41396-023-01499-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Revised: 08/15/2023] [Accepted: 08/17/2023] [Indexed: 08/28/2023]
Abstract
Pelomyxa is a genus of anaerobic amoebae that live in consortia with multiple prokaryotic endosymbionts. Although the symbionts represent a large fraction of the cellular biomass, their metabolic roles have not been investigated. Using single-cell genomics and transcriptomics, we have characterized the prokaryotic community associated with P. schiedti, which is composed of two bacteria, Candidatus Syntrophus pelomyxae (class Deltaproteobacteria) and Candidatus Vesiculincola pelomyxae (class Clostridia), and a methanogen, Candidatus Methanoregula pelomyxae. Fluorescence in situ hybridization and electron microscopy showed that Ca. Vesiculincola pelomyxae is localized inside vesicles, whereas the other endosymbionts occur freely in the cytosol, with Ca. Methanoregula pelomyxae enriched around the nucleus. Genome and transcriptome-based reconstructions of the metabolism suggests that the cellulolytic activity of P. schiedti produces simple sugars that fuel its own metabolism and the metabolism of a Ca. Vesiculincola pelomyxae, while Ca. Syntrophus pelomyxae energy metabolism relies on degradation of butyrate and isovalerate from the environment. Both species of bacteria and the ameba use hydrogenases to transfer the electrons from reduced equivalents to hydrogen, a process that requires a low hydrogen partial pressure. This is achieved by the third endosymbiont, Ca. Methanoregula pelomyxae, which consumes H2 and formate for methanogenesis. While the bacterial symbionts can be successfully eliminated by vancomycin treatment without affecting the viability of the amoebae, treatment with 2-bromoethanesulfonate, a specific inhibitor of methanogenesis, killed the amoebae, indicating the essentiality of the methanogenesis for this consortium.
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Affiliation(s)
- Sebastian C Treitli
- Department of Parasitology, Faculty of Science, Charles University, BIOCEV, Průmyslová 595, 252 42, Vestec, Czech Republic.
| | - Pavla Hanousková
- Department of Zoology, Faculty of Science, Charles University, Viničná 7, 128 00, Prague 2, Czech Republic
| | - Vladimír Beneš
- Genome Biology Unit, European Molecular Biology Laboratory (EMBL), Heidelberg, Germany
| | - Andreas Brune
- RG Insect Gut Microbiology and Symbiosis, Max Planck Institute for Terrestrial Microbiology, Marburg, Germany
| | - Ivan Čepička
- Department of Zoology, Faculty of Science, Charles University, Viničná 7, 128 00, Prague 2, Czech Republic
| | - Vladimír Hampl
- Department of Parasitology, Faculty of Science, Charles University, BIOCEV, Průmyslová 595, 252 42, Vestec, Czech Republic.
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14
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Walsh LH, Coakley M, Walsh AM, O'Toole PW, Cotter PD. Bioinformatic approaches for studying the microbiome of fermented food. Crit Rev Microbiol 2023; 49:693-725. [PMID: 36287644 DOI: 10.1080/1040841x.2022.2132850] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2022] [Revised: 08/11/2022] [Accepted: 09/28/2022] [Indexed: 11/03/2022]
Abstract
High-throughput DNA sequencing-based approaches continue to revolutionise our understanding of microbial ecosystems, including those associated with fermented foods. Metagenomic and metatranscriptomic approaches are state-of-the-art biological profiling methods and are employed to investigate a wide variety of characteristics of microbial communities, such as taxonomic membership, gene content and the range and level at which these genes are expressed. Individual groups and consortia of researchers are utilising these approaches to produce increasingly large and complex datasets, representing vast populations of microorganisms. There is a corresponding requirement for the development and application of appropriate bioinformatic tools and pipelines to interpret this data. This review critically analyses the tools and pipelines that have been used or that could be applied to the analysis of metagenomic and metatranscriptomic data from fermented foods. In addition, we critically analyse a number of studies of fermented foods in which these tools have previously been applied, to highlight the insights that these approaches can provide.
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Affiliation(s)
- Liam H Walsh
- Teagasc Food Research Centre, Moorepark, Fermoy, Cork, Ireland
- School of Microbiology, University College Cork, Ireland
| | - Mairéad Coakley
- Teagasc Food Research Centre, Moorepark, Fermoy, Cork, Ireland
| | - Aaron M Walsh
- Teagasc Food Research Centre, Moorepark, Fermoy, Cork, Ireland
| | - Paul W O'Toole
- School of Microbiology, University College Cork, Ireland
- APC Microbiome Ireland, University College Cork, Ireland
| | - Paul D Cotter
- Teagasc Food Research Centre, Moorepark, Fermoy, Cork, Ireland
- APC Microbiome Ireland, University College Cork, Ireland
- VistaMilk SFI Research Centre, Teagasc, Moorepark, Fermoy, Cork, Ireland
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15
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Jiménez NE, Acuña V, Cortés MP, Eveillard D, Maass AE. Unveiling abundance-dependent metabolic phenotypes of microbial communities. mSystems 2023; 8:e0049223. [PMID: 37668446 PMCID: PMC10654064 DOI: 10.1128/msystems.00492-23] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2023] [Accepted: 06/21/2023] [Indexed: 09/06/2023] Open
Abstract
IMPORTANCE In nature, organisms live in communities and not as isolated species, and their interactions provide a source of resilience to environmental disturbances. Despite their importance in ecology, human health, and industry, understanding how organisms interact in different environments remains an open question. In this work, we provide a novel approach that, only using genomic information, studies the metabolic phenotype exhibited by communities, where the exploration of suboptimal growth flux distributions and the composition of a community allows to unveil its capacity to respond to environmental changes, shedding light of the degrees of metabolic plasticity inherent to the community.
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Affiliation(s)
- Natalia E. Jiménez
- Center for Mathematical Modeling, University of Chile, Santiago, Chile
- Center for Genome Regulation, Millennium Institute, University of Chile, Santiago, Chile
| | - Vicente Acuña
- Center for Mathematical Modeling, University of Chile, Santiago, Chile
- Center for Genome Regulation, Millennium Institute, University of Chile, Santiago, Chile
| | - María Paz Cortés
- Center for Mathematical Modeling, University of Chile, Santiago, Chile
| | | | - Alejandro Eduardo Maass
- Center for Mathematical Modeling, University of Chile, Santiago, Chile
- Center for Genome Regulation, Millennium Institute, University of Chile, Santiago, Chile
- Department of Mathematical Engineering, University of Chile, Santiago, Chile
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16
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Nègre D, Larhlimi A, Bertrand S. Reconciliation and evolution of Penicillium rubens genome-scale metabolic networks-What about specialised metabolism? PLoS One 2023; 18:e0289757. [PMID: 37647283 PMCID: PMC10468094 DOI: 10.1371/journal.pone.0289757] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2023] [Accepted: 07/24/2023] [Indexed: 09/01/2023] Open
Abstract
In recent years, genome sequencing of filamentous fungi has revealed a high proportion of specialised metabolites with growing pharmaceutical interest. However, detecting such metabolites through in silico genome analysis does not necessarily guarantee their expression under laboratory conditions. However, one plausible strategy for enabling their production lies in modifying the growth conditions. Devising a comprehensive experimental design testing in different culture environments is time-consuming and expensive. Therefore, using in silico modelling as a preliminary step, such as Genome-Scale Metabolic Network (GSMN), represents a promising approach to predicting and understanding the observed specialised metabolite production in a given organism. To address these questions, we reconstructed a new high-quality GSMN for the Penicillium rubens Wisconsin 54-1255 strain, a commonly used model organism. Our reconstruction, iPrub22, adheres to current convention standards and quality criteria, incorporating updated functional annotations, orthology searches with different GSMN templates, data from previous reconstructions, and manual curation steps targeting primary and specialised metabolites. With a MEMOTE score of 74% and a metabolic coverage of 45%, iPrub22 includes 5,192 unique metabolites interconnected by 5,919 reactions, of which 5,033 are supported by at least one genomic sequence. Of the metabolites present in iPrub22, 13% are categorised as belonging to specialised metabolism. While our high-quality GSMN provides a valuable resource for investigating known phenotypes expressed in P. rubens, our analysis identifies bottlenecks related, in particular, to the definition of what is a specialised metabolite, which requires consensus within the scientific community. It also points out the necessity of accessible, standardised and exhaustive databases of specialised metabolites. These questions must be addressed to fully unlock the potential of natural product production in P. rubens and other filamentous fungi. Our work represents a foundational step towards the objective of rationalising the production of natural products through GSMN modelling.
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Affiliation(s)
- Delphine Nègre
- Nantes Université, Institut des Substances et Organismes de la Mer, ISOMer, Nantes, France
- Nantes Université, École Centrale Nantes, CNRS, Nantes, France
| | | | - Samuel Bertrand
- Nantes Université, Institut des Substances et Organismes de la Mer, ISOMer, Nantes, France
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17
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Belcour A, Got J, Aite M, Delage L, Collén J, Frioux C, Leblanc C, Dittami SM, Blanquart S, Markov GV, Siegel A. Inferring and comparing metabolism across heterogeneous sets of annotated genomes using AuCoMe. Genome Res 2023; 33:972-987. [PMID: 37468308 PMCID: PMC10629481 DOI: 10.1101/gr.277056.122] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Accepted: 05/23/2023] [Indexed: 07/21/2023]
Abstract
Comparative analysis of genome-scale metabolic networks (GSMNs) may yield important information on the biology, evolution, and adaptation of species. However, it is impeded by the high heterogeneity of the quality and completeness of structural and functional genome annotations, which may bias the results of such comparisons. To address this issue, we developed AuCoMe, a pipeline to automatically reconstruct homogeneous GSMNs from a heterogeneous set of annotated genomes without discarding available manual annotations. We tested AuCoMe with three data sets, one bacterial, one fungal, and one algal, and showed that it successfully reduces technical biases while capturing the metabolic specificities of each organism. Our results also point out shared and divergent metabolic traits among evolutionarily distant algae, underlining the potential of AuCoMe to accelerate the broad exploration of metabolic evolution across the tree of life.
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Affiliation(s)
- Arnaud Belcour
- Univ Rennes, Inria, CNRS, IRISA, F-35000 Rennes, France;
| | - Jeanne Got
- Univ Rennes, Inria, CNRS, IRISA, F-35000 Rennes, France
| | - Méziane Aite
- Univ Rennes, Inria, CNRS, IRISA, F-35000 Rennes, France
| | - Ludovic Delage
- Sorbonne Université, CNRS, Integrative Biology of Marine Models (LBI2M), Station Biologique de Roscoff (SBR), 29680 Roscoff, France
| | - Jonas Collén
- Sorbonne Université, CNRS, Integrative Biology of Marine Models (LBI2M), Station Biologique de Roscoff (SBR), 29680 Roscoff, France
| | | | - Catherine Leblanc
- Sorbonne Université, CNRS, Integrative Biology of Marine Models (LBI2M), Station Biologique de Roscoff (SBR), 29680 Roscoff, France
| | - Simon M Dittami
- Sorbonne Université, CNRS, Integrative Biology of Marine Models (LBI2M), Station Biologique de Roscoff (SBR), 29680 Roscoff, France
| | | | - Gabriel V Markov
- Sorbonne Université, CNRS, Integrative Biology of Marine Models (LBI2M), Station Biologique de Roscoff (SBR), 29680 Roscoff, France
| | - Anne Siegel
- Univ Rennes, Inria, CNRS, IRISA, F-35000 Rennes, France;
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18
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Frioux C, Ansorge R, Özkurt E, Ghassemi Nedjad C, Fritscher J, Quince C, Waszak SM, Hildebrand F. Enterosignatures define common bacterial guilds in the human gut microbiome. Cell Host Microbe 2023; 31:1111-1125.e6. [PMID: 37339626 DOI: 10.1016/j.chom.2023.05.024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2023] [Revised: 04/03/2023] [Accepted: 05/23/2023] [Indexed: 06/22/2023]
Abstract
The human gut microbiome composition is generally in a stable dynamic equilibrium, but it can deteriorate into dysbiotic states detrimental to host health. To disentangle the inherent complexity and capture the ecological spectrum of microbiome variability, we used 5,230 gut metagenomes to characterize signatures of bacteria commonly co-occurring, termed enterosignatures (ESs). We find five generalizable ESs dominated by either Bacteroides, Firmicutes, Prevotella, Bifidobacterium, or Escherichia. This model confirms key ecological characteristics known from previous enterotype concepts, while enabling the detection of gradual shifts in community structures. Temporal analysis implies that the Bacteroides-associated ES is "core" in the resilience of westernized gut microbiomes, while combinations with other ESs often complement the functional spectrum. The model reliably detects atypical gut microbiomes correlated with adverse host health conditions and/or the presence of pathobionts. ESs provide an interpretable and generic model that enables an intuitive characterization of gut microbiome composition in health and disease.
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Affiliation(s)
- Clémence Frioux
- Food, Microbiome, and Health Institute Strategic Programme, Quadram Institute Bioscience, Norwich Research Park, NR4 7UQ Norwich, Norfolk, UK; Digital Biology, Earlham Institute NR4 7UZ Norwich, Norfolk, UK; Inria, University of Bordeaux, INRAE, 33400 Talence, France.
| | - Rebecca Ansorge
- Food, Microbiome, and Health Institute Strategic Programme, Quadram Institute Bioscience, Norwich Research Park, NR4 7UQ Norwich, Norfolk, UK; Digital Biology, Earlham Institute NR4 7UZ Norwich, Norfolk, UK
| | - Ezgi Özkurt
- Food, Microbiome, and Health Institute Strategic Programme, Quadram Institute Bioscience, Norwich Research Park, NR4 7UQ Norwich, Norfolk, UK; Digital Biology, Earlham Institute NR4 7UZ Norwich, Norfolk, UK
| | | | - Joachim Fritscher
- Food, Microbiome, and Health Institute Strategic Programme, Quadram Institute Bioscience, Norwich Research Park, NR4 7UQ Norwich, Norfolk, UK; Digital Biology, Earlham Institute NR4 7UZ Norwich, Norfolk, UK
| | - Christopher Quince
- Food, Microbiome, and Health Institute Strategic Programme, Quadram Institute Bioscience, Norwich Research Park, NR4 7UQ Norwich, Norfolk, UK; Digital Biology, Earlham Institute NR4 7UZ Norwich, Norfolk, UK
| | - Sebastian M Waszak
- Centre for Molecular Medicine Norway (NCMM), Nordic EMBL Partnership, University of Oslo and Oslo University Hospital, Oslo 0318, Norway; Department of Neurology, University of California, San Francisco, San Francisco, CA 94148, USA; Genome Biology Unit, European Molecular Biology Laboratory, Heidelberg 69117, Germany
| | - Falk Hildebrand
- Food, Microbiome, and Health Institute Strategic Programme, Quadram Institute Bioscience, Norwich Research Park, NR4 7UQ Norwich, Norfolk, UK; Digital Biology, Earlham Institute NR4 7UZ Norwich, Norfolk, UK.
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19
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Fakih I, Got J, Robles-Rodriguez CE, Siegel A, Forano E, Muñoz-Tamayo R. Dynamic genome-based metabolic modeling of the predominant cellulolytic rumen bacterium Fibrobacter succinogenes S85. mSystems 2023; 8:e0102722. [PMID: 37289026 PMCID: PMC10308913 DOI: 10.1128/msystems.01027-22] [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: 10/24/2022] [Accepted: 03/14/2023] [Indexed: 06/09/2023] Open
Abstract
Fibrobacter succinogenes is a cellulolytic bacterium that plays an essential role in the degradation of plant fibers in the rumen ecosystem. It converts cellulose polymers into intracellular glycogen and the fermentation metabolites succinate, acetate, and formate. We developed dynamic models of F. succinogenes S85 metabolism on glucose, cellobiose, and cellulose on the basis of a network reconstruction done with the automatic reconstruction of metabolic model workspace. The reconstruction was based on genome annotation, five template-based orthology methods, gap filling, and manual curation. The metabolic network of F. succinogenes S85 comprises 1,565 reactions with 77% linked to 1,317 genes, 1,586 unique metabolites, and 931 pathways. The network was reduced using the NetRed algorithm and analyzed for the computation of elementary flux modes. A yield analysis was further performed to select a minimal set of macroscopic reactions for each substrate. The accuracy of the models was acceptable in simulating F. succinogenes carbohydrate metabolism with an average coefficient of variation of the root mean squared error of 19%. The resulting models are useful resources for investigating the metabolic capabilities of F. succinogenes S85, including the dynamics of metabolite production. Such an approach is a key step toward the integration of omics microbial information into predictive models of rumen metabolism. IMPORTANCE F. succinogenes S85 is a cellulose-degrading and succinate-producing bacterium. Such functions are central for the rumen ecosystem and are of special interest for several industrial applications. This work illustrates how information of the genome of F. succinogenes can be translated to develop predictive dynamic models of rumen fermentation processes. We expect this approach can be applied to other rumen microbes for producing a model of rumen microbiome that can be used for studying microbial manipulation strategies aimed at enhancing feed utilization and mitigating enteric emissions.
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Affiliation(s)
- Ibrahim Fakih
- Université Clermont Auvergne, INRAE, UMR454 Microbiologie Environnement Digestif et Santé, 63000 Clermont-Ferrand, France
- Université Paris-Saclay, INRAE, AgroParisTech, UMR Modélisation Systémique Appliquée aux Ruminants, 91120 Palaiseau, France
| | - Jeanne Got
- Université Rennes, Inria, CNRS, IRISA, Dyliss team, 35042 Rennes, France
| | | | - Anne Siegel
- Université Rennes, Inria, CNRS, IRISA, Dyliss team, 35042 Rennes, France
| | - Evelyne Forano
- Université Clermont Auvergne, INRAE, UMR454 Microbiologie Environnement Digestif et Santé, 63000 Clermont-Ferrand, France
| | - Rafael Muñoz-Tamayo
- Université Paris-Saclay, INRAE, AgroParisTech, UMR Modélisation Systémique Appliquée aux Ruminants, 91120 Palaiseau, France
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20
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Eisenhofer R, Odriozola I, Alberdi A. Impact of microbial genome completeness on metagenomic functional inference. ISME COMMUNICATIONS 2023; 3:12. [PMID: 36797336 PMCID: PMC9935889 DOI: 10.1038/s43705-023-00221-z] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Revised: 01/30/2023] [Accepted: 02/09/2023] [Indexed: 02/18/2023]
Abstract
Inferring the functional capabilities of bacteria from metagenome-assembled genomes (MAGs) is becoming a central process in microbiology. Here we show that the completeness of genomes has a significant impact on the recovered functional signal, spanning all domains of metabolic functions. We identify factors that affect this relationship between genome completeness and function fullness, and provide baseline knowledge to guide efforts to correct for this overlooked bias in metagenomic functional inference.
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Affiliation(s)
- Raphael Eisenhofer
- Center for Evolutionary Hologenomics, Globe Institute, University of Copenhagen, Copenhagen, Denmark
| | - Iñaki Odriozola
- Center for Evolutionary Hologenomics, Globe Institute, University of Copenhagen, Copenhagen, Denmark
| | - Antton Alberdi
- Center for Evolutionary Hologenomics, Globe Institute, University of Copenhagen, Copenhagen, Denmark.
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21
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Rios Garza D, Gonze D, Zafeiropoulos H, Liu B, Faust K. Metabolic models of human gut microbiota: Advances and challenges. Cell Syst 2023; 14:109-121. [PMID: 36796330 DOI: 10.1016/j.cels.2022.11.002] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2022] [Revised: 08/24/2022] [Accepted: 11/04/2022] [Indexed: 02/17/2023]
Abstract
The human gut is a complex ecosystem consisting of hundreds of microbial species interacting with each other and with the human host. Mathematical models of the gut microbiome integrate our knowledge of this system and help to formulate hypotheses to explain observations. The generalized Lotka-Volterra model has been widely used for this purpose, but it does not describe interaction mechanisms and thus does not account for metabolic flexibility. Recently, models that explicitly describe gut microbial metabolite production and consumption have become popular. These models have been used to investigate the factors that shape gut microbial composition and to link specific gut microorganisms to changes in metabolite concentrations found in diseases. Here, we review how such models are built and what we have learned so far from their application to human gut microbiome data. In addition, we discuss current challenges of these models and how these can be addressed in the future.
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Affiliation(s)
- Daniel Rios Garza
- KU Leuven, Department of Microbiology, Immunology and Transplantation, Rega Institute for Medical Research, Laboratory of Molecular Bacteriology, 3000 Leuven, Belgium
| | - Didier Gonze
- Unité de Chronobiologie Théorique, Faculté des Sciences, CP 231, Université Libre de Bruxelles, Bvd du Triomphe, 1050 Bruxelles, Belgium
| | - Haris Zafeiropoulos
- Biology Department, University of Crete, Heraklion 700 13, Greece; Institute of Marine Biology, Biotechnology and Aquaculture (IMBBC), Hellenic Centre for Marine Research (HCMR), Former U.S. Base of Gournes P.O. Box 2214, 71003, Heraklion, Crete, Greece
| | - Bin Liu
- KU Leuven, Department of Microbiology, Immunology and Transplantation, Rega Institute for Medical Research, Laboratory of Molecular Bacteriology, 3000 Leuven, Belgium
| | - Karoline Faust
- KU Leuven, Department of Microbiology, Immunology and Transplantation, Rega Institute for Medical Research, Laboratory of Molecular Bacteriology, 3000 Leuven, Belgium.
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22
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KleinJan H, Frioux C, Califano G, Aite M, Fremy E, Karimi E, Corre E, Wichard T, Siegel A, Boyen C, Dittami SM. Insights into the potential for mutualistic and harmful host-microbe interactions affecting brown alga freshwater acclimation. Mol Ecol 2023; 32:703-723. [PMID: 36326449 PMCID: PMC10099861 DOI: 10.1111/mec.16766] [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: 11/09/2021] [Revised: 10/25/2022] [Accepted: 10/27/2022] [Indexed: 11/05/2022]
Abstract
Microbes can modify their hosts' stress tolerance, thus potentially enhancing their ecological range. An example of such interactions is Ectocarpus subulatus, one of the few freshwater-tolerant brown algae. This tolerance is partially due to its (un)cultivated microbiome. We investigated this phenomenon by modifying the microbiome of laboratory-grown E. subulatus using mild antibiotic treatments, which affected its ability to grow in low salinity. Low salinity acclimation of these algal-bacterial associations was then compared. Salinity significantly impacted bacterial and viral gene expression, albeit in different ways across algal-bacterial communities. In contrast, gene expression of the host and metabolite profiles were affected almost exclusively in the freshwater-intolerant algal-bacterial communities. We found no evidence of bacterial protein production that would directly improve algal stress tolerance. However, vitamin K synthesis is one possible bacterial service missing specifically in freshwater-intolerant cultures in low salinity. In this condition, we also observed a relative increase in bacterial transcriptomic activity and the induction of microbial genes involved in the biosynthesis of the autoinducer AI-1, a quorum-sensing regulator. This could have resulted in dysbiosis by causing a shift in bacterial behaviour in the intolerant algal-bacterial community. Together, these results provide two promising hypotheses to be examined by future targeted experiments. Although they apply only to the specific study system, they offer an example of how bacteria may impact their host's stress response.
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Affiliation(s)
- Hetty KleinJan
- Station Biologique de Roscoff, Laboratory of Integrative Biology of Marine ModelsSorbonne University, CNRSRoscoffFrance
- CEBEDEAU, Research and Expertise Centre for WaterQuartier Polytech 1LiègeBelgium
| | - Clémence Frioux
- Inria, CNRS, IRISAUniversity of RennesRennesFrance
- InriaUniversity of Bordeaux, INRAETalenceFrance
| | - Gianmaria Califano
- Institute for Inorganic and Analytical ChemistryFriedrich Schiller University JenaJenaGermany
| | - Méziane Aite
- Inria, CNRS, IRISAUniversity of RennesRennesFrance
| | - Enora Fremy
- Inria, CNRS, IRISAUniversity of RennesRennesFrance
| | - Elham Karimi
- Station Biologique de Roscoff, Laboratory of Integrative Biology of Marine ModelsSorbonne University, CNRSRoscoffFrance
| | - Erwan Corre
- Station BiologiqueFR2424, ABiMS, Sorbonne Université, CNRSRoscoffFrance
| | - Thomas Wichard
- Institute for Inorganic and Analytical ChemistryFriedrich Schiller University JenaJenaGermany
| | - Anne Siegel
- Inria, CNRS, IRISAUniversity of RennesRennesFrance
| | - Catherine Boyen
- Station Biologique de Roscoff, Laboratory of Integrative Biology of Marine ModelsSorbonne University, CNRSRoscoffFrance
| | - Simon M. Dittami
- Station Biologique de Roscoff, Laboratory of Integrative Biology of Marine ModelsSorbonne University, CNRSRoscoffFrance
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Mataigne V, Vannier N, Vandenkoornhuyse P, Hacquard S. Multi-genome metabolic modeling predicts functional inter-dependencies in the Arabidopsis root microbiome. MICROBIOME 2022; 10:217. [PMID: 36482420 PMCID: PMC9733318 DOI: 10.1186/s40168-022-01383-z] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/23/2022] [Accepted: 09/23/2022] [Indexed: 05/28/2023]
Abstract
BACKGROUND From a theoretical ecology point of view, microbiomes are far more complex than expected. Besides competition and competitive exclusion, cooperative microbe-microbe interactions have to be carefully considered. Metabolic dependencies among microbes likely explain co-existence in microbiota. METHODOLOGY In this in silico study, we explored genome-scale metabolic models (GEMs) of 193 bacteria isolated from Arabidopsis thaliana roots. We analyzed their predicted producible metabolites under simulated nutritional constraints including "root exudate-mimicking growth media" and assessed the potential of putative metabolic exchanges of by- and end-products to avoid those constraints. RESULTS We found that the genome-encoded metabolic potential is quantitatively and qualitatively clustered by phylogeny, highlighting metabolic differentiation between taxonomic groups. Random, synthetic combinations of increasing numbers of strains (SynComs) indicated that the number of producible compounds by GEMs increased with average phylogenetic distance, but that most SynComs were centered around an optimal phylogenetic distance. Moreover, relatively small SynComs could reflect the capacity of the whole community due to metabolic redundancy. Inspection of 30 specific end-product metabolites (i.e., target metabolites: amino acids, vitamins, phytohormones) indicated that the majority of the strains had the genetic potential to produce almost all the targeted compounds. Their production was predicted (1) to depend on external nutritional constraints and (2) to be facilitated by nutritional constraints mimicking root exudates, suggesting nutrient availability and root exudates play a key role in determining the number of producible metabolites. An answer set programming solver enabled the identification of numerous combinations of strains predicted to depend on each other to produce these targeted compounds under severe nutritional constraints thus indicating a putative sub-community level of functional redundancy. CONCLUSIONS This study predicts metabolic restrictions caused by available nutrients in the environment. By extension, it highlights the importance of the environment for niche potential, realization, partitioning, and overlap. Our results also suggest that metabolic dependencies and cooperation among root microbiota members compensate for environmental constraints and help maintain co-existence in complex microbial communities. Video Abstract.
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Affiliation(s)
- Victor Mataigne
- Université de Rennes 1, CNRS, UMR6553 ECOBIO, Campus Beaulieu, 35000, Rennes, France
- Max Planck Institute for Plant Breeding Research, Department of Plant Microbe Interactions, 50829, Cologne, Germany
| | - Nathan Vannier
- Max Planck Institute for Plant Breeding Research, Department of Plant Microbe Interactions, 50829, Cologne, Germany
| | | | - Stéphane Hacquard
- Max Planck Institute for Plant Breeding Research, Department of Plant Microbe Interactions, 50829, Cologne, Germany.
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24
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Combination of Whole Genome Sequencing and Metagenomics for Microbiological Diagnostics. Int J Mol Sci 2022; 23:ijms23179834. [PMID: 36077231 PMCID: PMC9456280 DOI: 10.3390/ijms23179834] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2022] [Revised: 08/24/2022] [Accepted: 08/26/2022] [Indexed: 12/21/2022] Open
Abstract
Whole genome sequencing (WGS) provides the highest resolution for genome-based species identification and can provide insight into the antimicrobial resistance and virulence potential of a single microbiological isolate during the diagnostic process. In contrast, metagenomic sequencing allows the analysis of DNA segments from multiple microorganisms within a community, either using an amplicon- or shotgun-based approach. However, WGS and shotgun metagenomic data are rarely combined, although such an approach may generate additive or synergistic information, critical for, e.g., patient management, infection control, and pathogen surveillance. To produce a combined workflow with actionable outputs, we need to understand the pre-to-post analytical process of both technologies. This will require specific databases storing interlinked sequencing and metadata, and also involves customized bioinformatic analytical pipelines. This review article will provide an overview of the critical steps and potential clinical application of combining WGS and metagenomics together for microbiological diagnosis.
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25
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Selection of Anabaena sp. PCC 7938 as a Cyanobacterium Model for Biological ISRU on Mars. Appl Environ Microbiol 2022; 88:e0059422. [PMID: 35862672 PMCID: PMC9361815 DOI: 10.1128/aem.00594-22] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2023] Open
Abstract
Crewed missions to Mars are expected to take place in the coming decades. After short-term stays, a permanent presence will be desirable to enable a wealth of scientific discoveries. This will require providing crews with life-support consumables in amounts that are too large to be imported from Earth. Part of these consumables could be produced on site with bioprocesses, but the feedstock should not have to be imported. A solution under consideration lies in using diazotrophic, rock-weathering cyanobacteria as primary producers: fed with materials naturally available on site, they would provide the nutrients required by other organisms. This concept has recently gained momentum but progress is slowed by a lack of consistency across contributing teams, and notably of a shared model organism. With the hope to address this issue, we present the work performed to select our current model. We started with preselected strains from the Nostocaceae family. After sequencing the genome of Anabaena sp. PCC 7938-the only one not yet available-we compared the strains' genomic data to determine their relatedness and provide insights into their physiology. We then assessed and compared relevant features: chiefly, their abilities to utilize nutrients from Martian regolith, their resistance to perchlorates (toxic compounds present in the regolith), and their suitability as feedstock for secondary producers (here a heterotrophic bacterium and a higher plant). This led to the selection of Anabaena sp. PCC 7938, which we propose as a model cyanobacterium for the development of bioprocesses based on Mars's natural resources. IMPORTANCE The sustainability of crewed missions to Mars could be increased by biotechnologies which are connected to resources available on site via primary producers: diazotrophic, rock-leaching cyanobacteria. Indeed, this could greatly reduce the mass of payloads to be imported from Earth. The concept is gaining momentum but progress is hindered by a lack of consistency across research teams. We consequently describe the selection process that led to the choice of our model strain, demonstrate its relevance to the field, and propose it as a shared model organism. We expect this contribution to support the development of cyanobacterium-based biotechnologies on Mars.
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26
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San León D, Nogales J. Toward merging bottom-up and top-down model-based designing of synthetic microbial communities. Curr Opin Microbiol 2022; 69:102169. [PMID: 35763963 DOI: 10.1016/j.mib.2022.102169] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2022] [Revised: 04/25/2022] [Accepted: 05/11/2022] [Indexed: 11/16/2022]
Abstract
The increasing interest of microbial communities as promising biocatalyst is leading an intense effort into the development of computational frameworks assisting the analysis and rational engineering of such complex ecosystems. Here, we critically review the recent computational and model-guided advances in the system-level engineering of microbiome, including both the rational bottom-up and the evolutionary top-down approaches. Furthermore, we highlight modeling and computational methods supporting both engineering paradigms. Finally, we discuss the advantages of combining both strategies into a hybrid top-down/bottom-up (middle-out) strategy to engineer synthetic microbial communities with improved performance and scope.
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Affiliation(s)
- David San León
- Department of Systems Biology, Centro Nacional de Biotecnología, CSIC, Madrid, Spain; Interdisciplinary Platform for Sustainable Plastics towards a Circular Economy-Spanish National Research Council (SusPlast-CSIC), Madrid, Spain.
| | - Juan Nogales
- Department of Systems Biology, Centro Nacional de Biotecnología, CSIC, Madrid, Spain; Interdisciplinary Platform for Sustainable Plastics towards a Circular Economy-Spanish National Research Council (SusPlast-CSIC), Madrid, Spain.
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27
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Arabinoxylan and Pectin Metabolism in Crohn’s Disease Microbiota: An In Silico Study. Int J Mol Sci 2022; 23:ijms23137093. [PMID: 35806099 PMCID: PMC9266297 DOI: 10.3390/ijms23137093] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2022] [Revised: 06/20/2022] [Accepted: 06/22/2022] [Indexed: 12/03/2022] Open
Abstract
Inflammatory bowel disease is a chronic disorder including ulcerative colitis and Crohn’s disease (CD). Gut dysbiosis is often associated with CD, and metagenomics allows a better understanding of the microbial communities involved. The objective of this study was to reconstruct in silico carbohydrate metabolic capabilities from metagenome-assembled genomes (MAGs) obtained from healthy and CD individuals. This computational method was developed as a mean to aid rationally designed prebiotic interventions to rebalance CD dysbiosis, with a focus on metabolism of emergent prebiotics derived from arabinoxylan and pectin. Up to 1196 and 1577 MAGs were recovered from CD and healthy people, respectively. MAGs of Akkermansia muciniphila, Barnesiella viscericola DSM 18177 and Paraprevotella xylaniphila YIT 11841 showed a wide range of unique and specific enzymes acting on arabinoxylan and pectin. These glycosidases were also found in MAGs recovered from CD patients. Interestingly, these arabinoxylan and pectin degraders are predicted to exhibit metabolic interactions with other gut microbes reduced in CD. Thus, administration of arabinoxylan and pectin may ameliorate dysbiosis in CD by promoting species with key metabolic functions, capable of cross-feeding other beneficial species. These computational methods may be of special interest for the rational design of prebiotic ingredients targeting at CD.
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Yan J, Liao C, Taylor BP, Fontana E, Amoretti LA, Wright RJ, Littmann ER, Dai A, Waters N, Peled JU, Taur Y, Perales MA, Siranosian BA, Bhatt AS, van den Brink MRM, Pamer EG, Schluter J, Xavier JB. A compilation of fecal microbiome shotgun metagenomics from hematopoietic cell transplantation patients. Sci Data 2022; 9:219. [PMID: 35585088 PMCID: PMC9117330 DOI: 10.1038/s41597-022-01302-9] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2021] [Accepted: 02/21/2022] [Indexed: 12/15/2022] Open
Abstract
Hospitalized patients receiving hematopoietic cell transplants provide a unique opportunity to study the human gut microbiome. We previously compiled a large-scale longitudinal dataset of fecal microbiota and associated metadata, but we had limited that analysis to taxonomic composition of bacteria from 16S rRNA gene sequencing. Here we augment those data with shotgun metagenomics. The compilation amounts to a nested subset of 395 samples compiled from different studies at Memorial Sloan Kettering. Shotgun metagenomics describes the microbiome at the functional level, particularly in antimicrobial resistances and virulence factors. We provide accession numbers that link each sample to the paired-end sequencing files deposited in a public repository, which can be directly accessed by the online services of PATRIC to be analyzed without the users having to download or transfer the files. Then, we show how shotgun sequencing enables the assembly of genomes from metagenomic data. The new data, combined with the metadata published previously, enables new functional studies of the microbiomes of patients with cancer receiving bone marrow transplantation.
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Affiliation(s)
- Jinyuan Yan
- Program for Computational and Systems Biology, Memorial Sloan-Kettering Cancer Center, New York, NY, USA.
| | - Chen Liao
- Program for Computational and Systems Biology, Memorial Sloan-Kettering Cancer Center, New York, NY, USA
| | - Bradford P Taylor
- Center for Communicable Disease Dynamics, Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Emily Fontana
- Infectious Disease Service, Department of Medicine, and Immunology Program, Sloan Kettering Institute, New York, NY, USA
| | - Luigi A Amoretti
- Infectious Disease Service, Department of Medicine, and Immunology Program, Sloan Kettering Institute, New York, NY, USA
| | - Roberta J Wright
- Infectious Disease Service, Department of Medicine, and Immunology Program, Sloan Kettering Institute, New York, NY, USA
| | - Eric R Littmann
- Duchossois Family Institute, University of Chicago, Chicago, IL, USA
| | - Anqi Dai
- Adult Bone Marrow Transplantation Service, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Nicholas Waters
- Adult Bone Marrow Transplantation Service, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Jonathan U Peled
- Adult Bone Marrow Transplantation Service, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Weill Cornell Medical College, New York, NY, USA
| | - Ying Taur
- Infectious Disease Service, Department of Medicine, and Immunology Program, Sloan Kettering Institute, New York, NY, USA
| | - Miguel-Angel Perales
- Adult Bone Marrow Transplantation Service, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Weill Cornell Medical College, New York, NY, USA
| | | | - Ami S Bhatt
- Department of Genetics, Stanford University, Stanford, CA, USA
- Department of Medicine, Division of Hematology, Stanford University, Stanford, CA, USA
- Department of Medicine, Division of Blood and Marrow Transplantation and Cellular Therapy, Stanford University School of Medicine, Stanford, CA, USA
| | - Marcel R M van den Brink
- Adult Bone Marrow Transplantation Service, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Weill Cornell Medical College, New York, NY, USA
| | - Eric G Pamer
- Duchossois Family Institute, University of Chicago, Chicago, IL, USA
| | - Jonas Schluter
- Institute for Computational Medicine, Department of Microbiology, New York University, New York, NY, USA
| | - Joao B Xavier
- Program for Computational and Systems Biology, Memorial Sloan-Kettering Cancer Center, New York, NY, USA.
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Zhou Z, Tran PQ, Breister AM, Liu Y, Kieft K, Cowley ES, Karaoz U, Anantharaman K. METABOLIC: high-throughput profiling of microbial genomes for functional traits, metabolism, biogeochemistry, and community-scale functional networks. MICROBIOME 2022; 10:33. [PMID: 35172890 PMCID: PMC8851854 DOI: 10.1186/s40168-021-01213-8] [Citation(s) in RCA: 137] [Impact Index Per Article: 68.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/11/2021] [Accepted: 12/09/2021] [Indexed: 05/22/2023]
Abstract
BACKGROUND Advances in microbiome science are being driven in large part due to our ability to study and infer microbial ecology from genomes reconstructed from mixed microbial communities using metagenomics and single-cell genomics. Such omics-based techniques allow us to read genomic blueprints of microorganisms, decipher their functional capacities and activities, and reconstruct their roles in biogeochemical processes. Currently available tools for analyses of genomic data can annotate and depict metabolic functions to some extent; however, no standardized approaches are currently available for the comprehensive characterization of metabolic predictions, metabolite exchanges, microbial interactions, and microbial contributions to biogeochemical cycling. RESULTS We present METABOLIC (METabolic And BiogeOchemistry anaLyses In miCrobes), a scalable software to advance microbial ecology and biogeochemistry studies using genomes at the resolution of individual organisms and/or microbial communities. The genome-scale workflow includes annotation of microbial genomes, motif validation of biochemically validated conserved protein residues, metabolic pathway analyses, and calculation of contributions to individual biogeochemical transformations and cycles. The community-scale workflow supplements genome-scale analyses with determination of genome abundance in the microbiome, potential microbial metabolic handoffs and metabolite exchange, reconstruction of functional networks, and determination of microbial contributions to biogeochemical cycles. METABOLIC can take input genomes from isolates, metagenome-assembled genomes, or single-cell genomes. Results are presented in the form of tables for metabolism and a variety of visualizations including biogeochemical cycling potential, representation of sequential metabolic transformations, community-scale microbial functional networks using a newly defined metric "MW-score" (metabolic weight score), and metabolic Sankey diagrams. METABOLIC takes ~ 3 h with 40 CPU threads to process ~ 100 genomes and corresponding metagenomic reads within which the most compute-demanding part of hmmsearch takes ~ 45 min, while it takes ~ 5 h to complete hmmsearch for ~ 3600 genomes. Tests of accuracy, robustness, and consistency suggest METABOLIC provides better performance compared to other software and online servers. To highlight the utility and versatility of METABOLIC, we demonstrate its capabilities on diverse metagenomic datasets from the marine subsurface, terrestrial subsurface, meadow soil, deep sea, freshwater lakes, wastewater, and the human gut. CONCLUSION METABOLIC enables the consistent and reproducible study of microbial community ecology and biogeochemistry using a foundation of genome-informed microbial metabolism, and will advance the integration of uncultivated organisms into metabolic and biogeochemical models. METABOLIC is written in Perl and R and is freely available under GPLv3 at https://github.com/AnantharamanLab/METABOLIC . Video abstract.
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Affiliation(s)
- Zhichao Zhou
- Department of Bacteriology, University of Wisconsin-Madison, Madison, WI, 53706, USA
| | - Patricia Q Tran
- Department of Bacteriology, University of Wisconsin-Madison, Madison, WI, 53706, USA
- Department of Integrative Biology, University of Wisconsin-Madison, Madison, WI, 53706, USA
| | - Adam M Breister
- Department of Bacteriology, University of Wisconsin-Madison, Madison, WI, 53706, USA
| | - Yang Liu
- Archaeal Biology Center, Institute for Advanced Study, Shenzhen University, Shenzhen, Guangdong, 518060, China
| | - Kristopher Kieft
- Department of Bacteriology, University of Wisconsin-Madison, Madison, WI, 53706, USA
- Microbiology Doctoral Training Program, University of Wisconsin-Madison, Madison, WI, 53706, USA
| | - Elise S Cowley
- Department of Bacteriology, University of Wisconsin-Madison, Madison, WI, 53706, USA
- Microbiology Doctoral Training Program, University of Wisconsin-Madison, Madison, WI, 53706, USA
| | - Ulas Karaoz
- Earth and Environmental Sciences, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
| | - Karthik Anantharaman
- Department of Bacteriology, University of Wisconsin-Madison, Madison, WI, 53706, USA.
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30
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Fournier P, Pellan L, Barroso-Bergadà D, Bohan DA, Candresse T, Delmotte F, Dufour MC, Lauvergeat V, Le Marrec C, Marais A, Martins G, Masneuf-Pomarède I, Rey P, Sherman D, This P, Frioux C, Labarthe S, Vacher C. The functional microbiome of grapevine throughout plant evolutionary history and lifetime. ADV ECOL RES 2022. [DOI: 10.1016/bs.aecr.2022.09.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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Saa P, Urrutia A, Silva-Andrade C, Martín AJ, Garrido D. Modeling approaches for probing cross-feeding interactions in the human gut microbiome. Comput Struct Biotechnol J 2021; 20:79-89. [PMID: 34976313 PMCID: PMC8685919 DOI: 10.1016/j.csbj.2021.12.006] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2021] [Revised: 12/03/2021] [Accepted: 12/04/2021] [Indexed: 12/16/2022] Open
Abstract
Microbial communities perform emergent activities that are essentially different from those carried by their individual members. The gut microbiome and its metabolites have a significant impact on the host, contributing to homeostasis or disease. Food molecules shape this community, being fermented through cross-feeding interactions of metabolites such as lactate, acetate, and amino acids, or products derived from macromolecule degradation. Mathematical and experimental approaches have been applied to understand and predict the interactions between microorganisms in complex communities such as the gut microbiota. Rational and mechanistic understanding of microbial interactions is essential to exploit their metabolic activities and identify keystone taxa and metabolites. The latter could be used in turn to modulate or replicate the metabolic behavior of the community in different contexts. This review aims to highlight recent experimental and modeling approaches for studying cross-feeding interactions within the gut microbiome. We focus on short-chain fatty acid production and fiber fermentation, which are fundamental processes in human health and disease. Special attention is paid to modeling approaches, particularly kinetic and genome-scale stoichiometric models of metabolism, to integrate experimental data under different diet and health conditions. Finally, we discuss limitations and challenges for the broad application of these modeling approaches and their experimental verification for improving our understanding of the mechanisms of microbial interactions.
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Affiliation(s)
- Pedro Saa
- Department of Chemical and Bioprocess Engineering, School of Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile
- Institute for Mathematical and Computational Engineering, Pontificia Universidad Católica de Chile, Vicuña Mackenna, 4860 Santiago, Chile
| | - Arles Urrutia
- Department of Chemical and Bioprocess Engineering, School of Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Claudia Silva-Andrade
- Laboratorio de Biología de Redes, Centro de Genómica y Bioinformática, Facultad de Ciencias, Universidad Mayor, Santiago, Chile
| | - Alberto J. Martín
- Laboratorio de Biología de Redes, Centro de Genómica y Bioinformática, Facultad de Ciencias, Universidad Mayor, Santiago, Chile
| | - Daniel Garrido
- Department of Chemical and Bioprocess Engineering, School of Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile
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Xiang B, Zhao L, Zhang M. Metagenome-Scale Metabolic Network Suggests Folate Produced by Bifidobacterium longum Might Contribute to High-Fiber-Diet-Induced Weight Loss in a Prader-Willi Syndrome Child. Microorganisms 2021; 9:microorganisms9122493. [PMID: 34946095 PMCID: PMC8705902 DOI: 10.3390/microorganisms9122493] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2021] [Revised: 11/13/2021] [Accepted: 11/29/2021] [Indexed: 01/14/2023] Open
Abstract
Gut-microbiota-targeted nutrition intervention has achieved success in the management of obesity, but its underlying mechanism still needs extended exploration. An obese Prader-Willi syndrome boy lost 25.8 kg after receiving a high-fiber dietary intervention for 105 days. The fecal microbiome sequencing data taken from the boy on intervention days 0, 15, 30, 45, 60, 75, and 105, along with clinical indexes, were used to construct a metagenome-scale metabolic network. Firstly, the abundances of the microbial strains were obtained by mapping the sequencing reads onto the assembly of gut organisms through use of reconstruction and analysis (AGORA) genomes. The nutritional components of the diet were obtained through the Virtual Metabolic Human database. Then, a community model was simulated using the Microbiome Modeling Toolbox. Finally, the significant Spearman correlations among the metabolites and the clinical indexes were screened and the strains that were producing these metabolites were identified. The high-fiber diet reduced the overall amount of metabolite secretions, but the secretions of folic acid derivatives by Bifidobacterium longum strains were increased and were significantly relevant to the observed weight loss. Reduced metabolites might also have directly contributed to the weight loss or indirectly contribute by enhancing leptin and decreasing adiponectin. Metagenome-scale metabolic network technology provides a cost-efficient solution for screening the functional microbial strains and metabolic pathways that are responding to nutrition therapy.
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Contribution of single-cell omics to microbial ecology. Trends Ecol Evol 2021; 37:67-78. [PMID: 34602304 DOI: 10.1016/j.tree.2021.09.002] [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] [Received: 05/15/2021] [Revised: 08/25/2021] [Accepted: 09/01/2021] [Indexed: 12/14/2022]
Abstract
Micro-organisms play key roles in various ecosystems, but many of their functions and interactions remain undefined. To investigate the ecological relevance of microbial communities, new molecular tools are being developed. Among them, single-cell omics assessing genetic diversity at the population and community levels and linking each individual cell to its functions is gaining interest in microbial ecology. By giving access to a wider range of ecological scales (from individual to community) than culture-based approaches and meta-omics, single-cell omics can contribute not only to micro-organisms' genomic and functional identification but also to the testing of concepts in ecology. Here, we discuss the contribution of single-cell omics to possible breakthroughs in concepts and knowledge on microbial ecosystems and ecoevolutionary processes.
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Karimi E, Geslain E, Belcour A, Frioux C, Aïte M, Siegel A, Corre E, Dittami SM. Robustness analysis of metabolic predictions in algal microbial communities based on different annotation pipelines. PeerJ 2021; 9:e11344. [PMID: 33996285 PMCID: PMC8106915 DOI: 10.7717/peerj.11344] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2020] [Accepted: 04/03/2021] [Indexed: 01/29/2023] Open
Abstract
Animals, plants, and algae rely on symbiotic microorganisms for their development and functioning. Genome sequencing and genomic analyses of these microorganisms provide opportunities to construct metabolic networks and to analyze the metabolism of the symbiotic communities they constitute. Genome-scale metabolic network reconstructions rest on information gained from genome annotation. As there are multiple annotation pipelines available, the question arises to what extent differences in annotation pipelines impact outcomes of these analyses. Here, we compare five commonly used pipelines (Prokka, MaGe, IMG, DFAST, RAST) from predicted annotation features (coding sequences, Enzyme Commission numbers, hypothetical proteins) to the metabolic network-based analysis of symbiotic communities (biochemical reactions, producible compounds, and selection of minimal complementary bacterial communities). While Prokka and IMG produced the most extensive networks, RAST and DFAST networks produced the fewest false positives and the most connected networks with the fewest dead-end metabolites. Our results underline differences between the outputs of the tested pipelines at all examined levels, with small differences in the draft metabolic networks resulting in the selection of different microbial consortia to expand the metabolic capabilities of the algal host. However, the consortia generated yielded similar predicted producible compounds and could therefore be considered functionally interchangeable. This contrast between selected communities and community functions depending on the annotation pipeline needs to be taken into consideration when interpreting the results of metabolic complementarity analyses. In the future, experimental validation of bioinformatic predictions will likely be crucial to both evaluate and refine the pipelines and needs to be coupled with increased efforts to expand and improve annotations in reference databases.
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Affiliation(s)
- Elham Karimi
- UMR8227, Integrative Biology of Marine Models, Sorbonne Université/CNRS, Station Biologique de Roscoff, Roscoff, France
| | - Enora Geslain
- UMR8227, Integrative Biology of Marine Models, Sorbonne Université/CNRS, Station Biologique de Roscoff, Roscoff, France.,FR2424, Sorbonne Université/CNRS, Station Biologique de Roscoff, Roscoff, France
| | - Arnaud Belcour
- Equipe Dyliss, Univ Rennes, Inria, CNRS, IRISA, Rennes, France
| | | | - Méziane Aïte
- Equipe Dyliss, Univ Rennes, Inria, CNRS, IRISA, Rennes, France
| | - Anne Siegel
- Equipe Dyliss, Univ Rennes, Inria, CNRS, IRISA, Rennes, France
| | - Erwan Corre
- FR2424, Sorbonne Université/CNRS, Station Biologique de Roscoff, Roscoff, France
| | - Simon M Dittami
- UMR8227, Integrative Biology of Marine Models, Sorbonne Université/CNRS, Station Biologique de Roscoff, Roscoff, France
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Ruuskanen MO, Åberg F, Männistö V, Havulinna AS, Méric G, Liu Y, Loomba R, Vázquez-Baeza Y, Tripathi A, Valsta LM, Inouye M, Jousilahti P, Salomaa V, Jain M, Knight R, Lahti L, Niiranen TJ. Links between gut microbiome composition and fatty liver disease in a large population sample. Gut Microbes 2021; 13:1-22. [PMID: 33651661 PMCID: PMC7928040 DOI: 10.1080/19490976.2021.1888673] [Citation(s) in RCA: 42] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/17/2020] [Revised: 01/14/2021] [Accepted: 01/28/2021] [Indexed: 02/08/2023] Open
Abstract
Fatty liver disease is the most common liver disease in the world. Its connection with the gut microbiome has been known for at least 80 y, but this association remains mostly unstudied in the general population because of underdiagnosis and small sample sizes. To address this knowledge gap, we studied the link between the Fatty Liver Index (FLI), a well-established proxy for fatty liver disease, and gut microbiome composition in a representative, ethnically homogeneous population sample of 6,269 Finnish participants. We based our models on biometric covariates and gut microbiome compositions from shallow metagenome sequencing. Our classification models could discriminate between individuals with a high FLI (≥60, indicates likely liver steatosis) and low FLI (<60) in internal cross-region validation, consisting of 30% of the data not used in model training, with an average AUC of 0.75 and AUPRC of 0.56 (baseline at 0.30). In addition to age and sex, our models included differences in 11 microbial groups from class Clostridia, mostly belonging to orders Lachnospirales and Oscillospirales. Our models were also predictive of the high FLI group in a different Finnish cohort, consisting of 258 participants, with an average AUC of 0.77 and AUPRC of 0.51 (baseline at 0.21). Pathway analysis of representative genomes of the positively FLI-associated taxa in (NCBI) Clostridium subclusters IV and XIVa indicated the presence of, e.g., ethanol fermentation pathways. These results support several findings from smaller case-control studies, such as the role of endogenous ethanol producers in the development of the fatty liver.
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Affiliation(s)
- Matti O. Ruuskanen
- Department of Internal Medicine, University of Turku, Turku, Finland
- Department of Public Health Solutions, Finnish Institute for Health and Welfare, Helsinki, Finland
| | - Fredrik Åberg
- Transplantation and Liver Surgery Clinic, Helsinki University Hospital, University of Helsinki, Helsinki, Finland
- Transplant Institute, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Ville Männistö
- Department of Medicine, Kuopio University Hospital, University of Eastern Finland, Kuopio, Finland
- Department of Experimental Vascular Medicine, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
| | - Aki S. Havulinna
- Department of Public Health Solutions, Finnish Institute for Health and Welfare, Helsinki, Finland
- Institute for Molecular Medicine Finland, FIMM - HiLIFE, Helsinki, Finland
| | - Guillaume Méric
- Cambridge Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia
- Department of Infectious Diseases, Central Clinical School, Monash University, Melbourne, Victoria, Australia
| | - Yang Liu
- Cambridge Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia
- Department of Clinical Pathology, The University of Melbourne, Melbourne, Victoria, Australia
| | - Rohit Loomba
- Department of Medicine, NAFLD Research Center, La Jolla, CA, USA
- Department of Medicine, University of California San Diego, La Jolla, CA, USA
| | - Yoshiki Vázquez-Baeza
- Center for Microbiome Innovation, Jacobs School of Engineering, University of California San Diego, La Jolla, CA, USA
| | - Anupriya Tripathi
- Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, USA
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, USA
- Division of Biological Sciences, University of California San Diego, La Jolla, CA, USA
| | - Liisa M. Valsta
- Department of Public Health Solutions, Finnish Institute for Health and Welfare, Helsinki, Finland
| | - Michael Inouye
- Cambridge Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia
- Department of Public Health and Primary Care, Cambridge University, Cambridge, UK
| | - Pekka Jousilahti
- Department of Public Health Solutions, Finnish Institute for Health and Welfare, Helsinki, Finland
| | - Veikko Salomaa
- Department of Public Health Solutions, Finnish Institute for Health and Welfare, Helsinki, Finland
| | - Mohit Jain
- Department of Medicine, University of California San Diego, La Jolla, CA, USA
- Department of Pharmacology, University of California San Diego, La Jolla, California, USA
| | - Rob Knight
- Center for Microbiome Innovation, Jacobs School of Engineering, University of California San Diego, La Jolla, CA, USA
- Department of Pediatrics, School of Medicine, University of California San Diego, La Jolla, California, USA
- Department of Computer Science & Engineering, University of California San Diego, La Jolla, California, USA
| | - Leo Lahti
- Deparment of Computing, University of Turku, Turku, Finland
| | - Teemu J. Niiranen
- Department of Internal Medicine, University of Turku, Turku, Finland
- Department of Public Health Solutions, Finnish Institute for Health and Welfare, Helsinki, Finland
- Division of Medicine, Turku University Hospital, Turku, Finland
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