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Wu S, Qu Z, Chen D, Wu H, Caiyin Q, Qiao J. Deciphering and designing microbial communities by genome-scale metabolic modelling. Comput Struct Biotechnol J 2024; 23:1990-2000. [PMID: 38765607 PMCID: PMC11098673 DOI: 10.1016/j.csbj.2024.04.055] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2024] [Revised: 04/21/2024] [Accepted: 04/21/2024] [Indexed: 05/22/2024] Open
Abstract
Microbial communities are shaped by the complex interactions among organisms and the environment. Genome-scale metabolic models (GEMs) can provide deeper insights into the complexity and ecological properties of various microbial communities, revealing their intricate interactions. Many researchers have modified GEMs for the microbial communities based on specific needs. Thus, GEMs need to be comprehensively summarized to better understand the trends in their development. In this review, we summarized the key developments in deciphering and designing microbial communities using different GEMs. A timeline of selected highlights in GEMs indicated that this area is evolving from the single-strain level to the microbial community level. Then, we outlined a framework for constructing GEMs of microbial communities. We also summarized the models and resources of static and dynamic community-level GEMs. We focused on the role of external environmental and intracellular resources in shaping the assembly of microbial communities. Finally, we discussed the key challenges and future directions of GEMs, focusing on the integration of GEMs with quorum sensing mechanisms, microbial ecology interactions, machine learning algorithms, and automatic modeling, all of which contribute to consortia-based applications in different fields.
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Affiliation(s)
- Shengbo Wu
- School of Chemical Engineering and Technology, Tianjin University, Tianjin 300072, China
- Zhejiang Shaoxing Research Institute of Tianjin University, Shaoxing 312300, China
| | - Zheping Qu
- School of Chemical Engineering and Technology, Tianjin University, Tianjin 300072, China
| | - Danlei Chen
- School of Chemical Engineering and Technology, Tianjin University, Tianjin 300072, China
- Zhejiang Shaoxing Research Institute of Tianjin University, Shaoxing 312300, China
| | - Hao Wu
- School of Chemical Engineering and Technology, Tianjin University, Tianjin 300072, China
- Zhejiang Shaoxing Research Institute of Tianjin University, Shaoxing 312300, China
| | - Qinggele Caiyin
- School of Chemical Engineering and Technology, Tianjin University, Tianjin 300072, China
- Zhejiang Shaoxing Research Institute of Tianjin University, Shaoxing 312300, China
- Key Laboratory of Systems Bioengineering, Ministry of Education (Tianjin University), Tianjin 300072, China
| | - Jianjun Qiao
- School of Chemical Engineering and Technology, Tianjin University, Tianjin 300072, China
- Zhejiang Shaoxing Research Institute of Tianjin University, Shaoxing 312300, China
- Key Laboratory of Systems Bioengineering, Ministry of Education (Tianjin University), Tianjin 300072, China
- Frontiers Science Center for Synthetic Biology (Ministry of Education), Tianjin University, Tianjin 300072, China
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2
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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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3
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Berruto CA, Demirer GS. Engineering agricultural soil microbiomes and predicting plant phenotypes. Trends Microbiol 2024:S0966-842X(24)00043-X. [PMID: 38429182 DOI: 10.1016/j.tim.2024.02.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2023] [Revised: 02/02/2024] [Accepted: 02/06/2024] [Indexed: 03/03/2024]
Abstract
Plant growth-promoting rhizobacteria (PGPR) can improve crop yields, nutrient use efficiency, plant tolerance to stressors, and confer benefits to future generations of crops grown in the same soil. Unlocking the potential of microbial communities in the rhizosphere and endosphere is therefore of great interest for sustainable agriculture advancements. Before plant microbiomes can be engineered to confer desirable phenotypic effects on their plant hosts, a deeper understanding of the interacting factors influencing rhizosphere community structure and function is needed. Dealing with this complexity is becoming more feasible using computational approaches. In this review, we discuss recent advances at the intersection of experimental and computational strategies for the investigation of plant-microbiome interactions and the engineering of desirable soil microbiomes.
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Affiliation(s)
- Chiara A Berruto
- Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA
| | - Gozde S Demirer
- Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, CA, USA.
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4
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Machado D. A benchmark of optimization solvers for genome-scale metabolic modeling of organisms and communities. mSystems 2024; 9:e0083323. [PMID: 38251879 PMCID: PMC10878033 DOI: 10.1128/msystems.00833-23] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2023] [Accepted: 12/11/2023] [Indexed: 01/23/2024] Open
Abstract
Genome-scale metabolic modeling is a powerful framework for predicting metabolic phenotypes of any organism with an annotated genome. For two decades, this framework has been used for the rational design of microbial cell factories. In the last decade, the range of applications has exploded, and new frontiers have emerged, including the study of the gut microbiome and its health implications and the role of microbial communities in global ecosystems. However, all the critical steps in this framework, from model construction to simulation, require the use of powerful linear optimization solvers, with the choice often relying on commercial solvers for their well-known computational efficiency. In this work, I benchmark a total of six solvers (two commercial and four open source) and measure their performance to solve linear and mixed-integer linear problems of increasing complexity. Although commercial solvers are still the fastest, at least two open-source solvers show comparable performance. These results show that genome-scale metabolic modeling does not need to be hindered by commercial licensing schemes and can become a truly open science framework for solving urgent societal challenges.IMPORTANCEModeling the metabolism of organisms and communities allows for computational exploration of their metabolic capabilities and testing their response to genetic and environmental perturbations. This holds the potential to address multiple societal issues related to human health and the environment. One of the current limitations is the use of commercial optimization solvers with restrictive licenses for academic and non-academic use. This work compares the performance of several commercial and open-source solvers to solve some of the most complex problems in the field. Benchmarking results show that, although commercial solvers are indeed faster, some of the open-source options can also efficiently tackle the hardest problems, showing great promise for the development of open science applications.
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Affiliation(s)
- Daniel Machado
- Department of Biotechnology and Food Science, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
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5
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Gelbach PE, Cetin H, Finley SD. Flux sampling in genome-scale metabolic modeling of microbial communities. BMC Bioinformatics 2024; 25:45. [PMID: 38287239 PMCID: PMC10826046 DOI: 10.1186/s12859-024-05655-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Accepted: 01/15/2024] [Indexed: 01/31/2024] Open
Abstract
BACKGROUND Microbial communities play a crucial role in ecosystem function through metabolic interactions. Genome-scale modeling is a promising method to understand these interactions and identify strategies to optimize the community. Flux balance analysis (FBA) is most often used to predict the flux through all reactions in a genome-scale model; however, the fluxes predicted by FBA depend on a user-defined cellular objective. Flux sampling is an alternative to FBA, as it provides the range of fluxes possible within a microbial community. Furthermore, flux sampling can capture additional heterogeneity across a population, especially when cells exhibit sub-maximal growth rates. RESULTS In this study, we simulate the metabolism of microbial communities and compare the metabolic characteristics found with FBA and flux sampling. With sampling, we find significant differences in the predicted metabolism, including an increase in cooperative interactions and pathway-specific changes in predicted flux. CONCLUSIONS Our results suggest the importance of sampling-based approaches to evaluate metabolic interactions. Furthermore, we emphasize the utility of flux sampling in quantitatively studying interactions between cells and organisms.
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Affiliation(s)
- Patrick E Gelbach
- Alfred E. Mann Department of Biomedical Engineering, University of Southern California, Los Angeles, CA, 90089, USA
| | - Handan Cetin
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA, 90089, USA
| | - Stacey D Finley
- Alfred E. Mann Department of Biomedical Engineering, University of Southern California, Los Angeles, CA, 90089, USA.
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA, 90089, USA.
- Mork Family Department of Chemical Engineering and Materials Science, University of Southern California, Los Angeles, CA, 90089, USA.
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6
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Zulfiqar M, Singh V, Steinbeck C, Sorokina M. Review on computer-assisted biosynthetic capacities elucidation to assess metabolic interactions and communication within microbial communities. Crit Rev Microbiol 2024:1-40. [PMID: 38270170 DOI: 10.1080/1040841x.2024.2306465] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2023] [Accepted: 01/12/2024] [Indexed: 01/26/2024]
Abstract
Microbial communities thrive through interactions and communication, which are challenging to study as most microorganisms are not cultivable. To address this challenge, researchers focus on the extracellular space where communication events occur. Exometabolomics and interactome analysis provide insights into the molecules involved in communication and the dynamics of their interactions. Advances in sequencing technologies and computational methods enable the reconstruction of taxonomic and functional profiles of microbial communities using high-throughput multi-omics data. Network-based approaches, including community flux balance analysis, aim to model molecular interactions within and between communities. Despite these advances, challenges remain in computer-assisted biosynthetic capacities elucidation, requiring continued innovation and collaboration among diverse scientists. This review provides insights into the current state and future directions of computer-assisted biosynthetic capacities elucidation in studying microbial communities.
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Affiliation(s)
- Mahnoor Zulfiqar
- Institute for Inorganic and Analytical Chemistry, Friedrich Schiller University, Jena, Germany
- Cluster of Excellence Balance of the Microverse, Friedrich Schiller University Jena, Jena, Germany
| | - Vinay Singh
- Institute for Inorganic and Analytical Chemistry, Friedrich Schiller University, Jena, Germany
| | - Christoph Steinbeck
- Institute for Inorganic and Analytical Chemistry, Friedrich Schiller University, Jena, Germany
- Cluster of Excellence Balance of the Microverse, Friedrich Schiller University Jena, Jena, Germany
| | - Maria Sorokina
- Institute for Inorganic and Analytical Chemistry, Friedrich Schiller University, Jena, Germany
- Data Science and Artificial Intelligence, Research and Development, Pharmaceuticals, Bayer, Berlin, Germany
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7
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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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8
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Melkonian C, Zorrilla F, Kjærbølling I, Blasche S, Machado D, Junge M, Sørensen KI, Andersen LT, Patil KR, Zeidan AA. Microbial interactions shape cheese flavour formation. Nat Commun 2023; 14:8348. [PMID: 38129392 PMCID: PMC10739706 DOI: 10.1038/s41467-023-41059-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2023] [Accepted: 08/15/2023] [Indexed: 12/23/2023] Open
Abstract
Cheese fermentation and flavour formation are the result of complex biochemical reactions driven by the activity of multiple microorganisms. Here, we studied the roles of microbial interactions in flavour formation in a year-long Cheddar cheese making process, using a commercial starter culture containing Streptococcus thermophilus and Lactococcus strains. By using an experimental strategy whereby certain strains were left out from the starter culture, we show that S. thermophilus has a crucial role in boosting Lactococcus growth and shaping flavour compound profile. Controlled milk fermentations with systematic exclusion of single Lactococcus strains, combined with genomics, genome-scale metabolic modelling, and metatranscriptomics, indicated that S. thermophilus proteolytic activity relieves nitrogen limitation for Lactococcus and boosts de novo nucleotide biosynthesis. While S. thermophilus had large contribution to the flavour profile, Lactococcus cremoris also played a role by limiting diacetyl and acetoin formation, which otherwise results in an off-flavour when in excess. This off-flavour control could be attributed to the metabolic re-routing of citrate by L. cremoris from diacetyl and acetoin towards α-ketoglutarate. Further, closely related Lactococcus lactis strains exhibited different interaction patterns with S. thermophilus, highlighting the significance of strain specificity in cheese making. Our results highlight the crucial roles of competitive and cooperative microbial interactions in shaping cheese flavour profile.
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Affiliation(s)
- Chrats Melkonian
- Bioinformatics & Modeling, R&D Digital Innovation, Chr. Hansen A/S, 2970, Hørsholm, Denmark.
- Theoretical Biology and Bioinformatics, Science for Life, Utrecht University, Utrecht, the Netherlands.
- Bioinformatics Group, Wageningen University and Research, Wageningen, the Netherlands.
| | - Francisco Zorrilla
- Medical Research Council Toxicology Unit, University of Cambridge, Cambridge, UK
| | - Inge Kjærbølling
- Bioinformatics & Modeling, R&D Digital Innovation, Chr. Hansen A/S, 2970, Hørsholm, Denmark
| | - Sonja Blasche
- Medical Research Council Toxicology Unit, University of Cambridge, Cambridge, UK
- European Molecular Biology Laboratory (EMBL), Meyerhofstrasse 1, 69117, Heidelberg, Germany
| | - Daniel Machado
- European Molecular Biology Laboratory (EMBL), Meyerhofstrasse 1, 69117, Heidelberg, Germany
- Department of Biotechnology and Food Science, Norwegian University of Science and Technology, Trondheim, 7491, Norway
| | - Mette Junge
- Strain Improvement, R&D Food Microbiology, Chr. Hansen A/S, 2970, Hørsholm, Denmark
| | - Kim Ib Sørensen
- Strain Improvement, R&D Food Microbiology, Chr. Hansen A/S, 2970, Hørsholm, Denmark
| | | | - Kiran R Patil
- Medical Research Council Toxicology Unit, University of Cambridge, Cambridge, UK
- European Molecular Biology Laboratory (EMBL), Meyerhofstrasse 1, 69117, Heidelberg, Germany
| | - Ahmad A Zeidan
- Bioinformatics & Modeling, R&D Digital Innovation, Chr. Hansen A/S, 2970, Hørsholm, Denmark.
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9
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Sun X, Xie J, Zheng D, Xia R, Wang W, Xun W, Huang Q, Zhang R, Kovács ÁT, Xu Z, Shen Q. Metabolic interactions affect the biomass of synthetic bacterial biofilm communities. mSystems 2023; 8:e0104523. [PMID: 37971263 PMCID: PMC10734490 DOI: 10.1128/msystems.01045-23] [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: 10/04/2023] [Accepted: 10/05/2023] [Indexed: 11/19/2023] Open
Abstract
IMPORTANCE Co-occurrence network analysis is an effective tool for predicting complex networks of microbial interactions in the natural environment. Using isolates from a rhizosphere, we constructed multi-species biofilm communities and investigated co-occurrence patterns between microbial species in genome-scale metabolic models and in vitro experiments. According to our results, metabolic exchanges and resource competition may partially explain the co-occurrence network analysis results found in synthetic bacterial biofilm communities.
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Affiliation(s)
- Xinli Sun
- Key lab of organic-based fertilizers of China and Jiangsu provincial key lab for solid organic waste utilization, Nanjing Agricultural University, Nanjing, Jiangsu, China
- Bacterial Interactions and Evolution Group, DTU Bioengineering, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Jiyu Xie
- Key lab of organic-based fertilizers of China and Jiangsu provincial key lab for solid organic waste utilization, Nanjing Agricultural University, Nanjing, Jiangsu, China
- Bacterial Interactions and Evolution Group, DTU Bioengineering, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Daoyue Zheng
- Key lab of organic-based fertilizers of China and Jiangsu provincial key lab for solid organic waste utilization, Nanjing Agricultural University, Nanjing, Jiangsu, China
| | - Riyan Xia
- Key lab of organic-based fertilizers of China and Jiangsu provincial key lab for solid organic waste utilization, Nanjing Agricultural University, Nanjing, Jiangsu, China
| | - Wei Wang
- Key lab of organic-based fertilizers of China and Jiangsu provincial key lab for solid organic waste utilization, Nanjing Agricultural University, Nanjing, Jiangsu, China
| | - Weibing Xun
- Key lab of organic-based fertilizers of China and Jiangsu provincial key lab for solid organic waste utilization, Nanjing Agricultural University, Nanjing, Jiangsu, China
| | - Qiwei Huang
- Key lab of organic-based fertilizers of China and Jiangsu provincial key lab for solid organic waste utilization, Nanjing Agricultural University, Nanjing, Jiangsu, China
| | - Ruifu Zhang
- Key lab of organic-based fertilizers of China and Jiangsu provincial key lab for solid organic waste utilization, Nanjing Agricultural University, Nanjing, Jiangsu, China
| | - Ákos T. Kovács
- Bacterial Interactions and Evolution Group, DTU Bioengineering, Technical University of Denmark, Kongens Lyngby, Denmark
- Institute of Biology Leiden, Leiden University, Leiden, the Netherlands
| | - Zhihui Xu
- Key lab of organic-based fertilizers of China and Jiangsu provincial key lab for solid organic waste utilization, Nanjing Agricultural University, Nanjing, Jiangsu, China
| | - Qirong Shen
- Key lab of organic-based fertilizers of China and Jiangsu provincial key lab for solid organic waste utilization, Nanjing Agricultural University, Nanjing, Jiangsu, China
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10
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Carter EL, Constantinidou C, Alam MT. Applications of genome-scale metabolic models to investigate microbial metabolic adaptations in response to genetic or environmental perturbations. Brief Bioinform 2023; 25:bbad439. [PMID: 38048080 PMCID: PMC10694557 DOI: 10.1093/bib/bbad439] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2023] [Revised: 09/21/2023] [Accepted: 11/08/2023] [Indexed: 12/05/2023] Open
Abstract
Environmental perturbations are encountered by microorganisms regularly and will require metabolic adaptations to ensure an organism can survive in the newly presenting conditions. In order to study the mechanisms of metabolic adaptation in such conditions, various experimental and computational approaches have been used. Genome-scale metabolic models (GEMs) are one of the most powerful approaches to study metabolism, providing a platform to study the systems level adaptations of an organism to different environments which could otherwise be infeasible experimentally. In this review, we are describing the application of GEMs in understanding how microbes reprogram their metabolic system as a result of environmental variation. In particular, we provide the details of metabolic model reconstruction approaches, various algorithms and tools for model simulation, consequences of genetic perturbations, integration of '-omics' datasets for creating context-specific models and their application in studying metabolic adaptation due to the change in environmental conditions.
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Affiliation(s)
- Elena Lucy Carter
- Warwick Medical School, University of Warwick, Coventry, CV4 7HL, UK
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11
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Herrick N, Walsh S. ILIAD: a suite of automated Snakemake workflows for processing genomic data for downstream applications. BMC Bioinformatics 2023; 24:424. [PMID: 37940870 PMCID: PMC10633908 DOI: 10.1186/s12859-023-05548-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2023] [Accepted: 10/27/2023] [Indexed: 11/10/2023] Open
Abstract
BACKGROUND Processing raw genomic data for downstream applications such as imputation, association studies, and modeling requires numerous third-party bioinformatics software tools. It is highly time-consuming and resource-intensive with computational demands and storage limitations that pose significant challenges that increase cost. The use of software tools independent of one another, in a disjointed stepwise fashion, increases the difficulty and sets forth higher error rates because of fragmented job executions in alignment, variant calling, and/or build conversion complications. As sequencing data availability grows, the ability for biologists to process it using stable, automated, and reproducible workflows is paramount as it significantly reduces the time to generate clean and reliable data. RESULTS The Iliad suite of genomic data workflows was developed to provide users with seamless file transitions from raw genomic data to a quality-controlled variant call format (VCF) file for downstream applications. Iliad benefits from the efficiency of the Snakemake best practices framework coupled with Singularity and Docker containers for repeatability, portability, and ease of installation. This feat is accomplished from the onset with download acquisitions of any raw data type (FASTQ, CRAM, IDAT) straight through to the generation of a clean merged data file that can combine any user-preferred datasets using robust programs such as BWA, Samtools, and BCFtools. Users can customize and direct their workflow with one straightforward configuration file. Iliad is compatible with Linux, MacOS, and Windows platforms and scalable from a local machine to a high-performance computing cluster. CONCLUSION Iliad offers automated workflows with optimized time and resource management that are comparable to other workflows available but generates analysis-ready VCF files from the most common datatypes using a single command. The storage footprint challenge of genomic data is overcome by utilizing temporary intermediate files before the final VCF is generated. This file is ready for use in imputation, genome-wide association study (GWAS) pipelines, high-throughput population genetics studies, select gene candidate studies, and more. Iliad was developed to be portable, compatible, scalable, robust, and repeatable with a simplistic setup, so biologists that are less familiar with programming can manage their own big data with this open-source suite of workflows.
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Affiliation(s)
- Noah Herrick
- Department of Biology, Indiana University Indianapolis, 723 W. Michigan Street, Indianapolis, IN, USA.
| | - Susan Walsh
- Department of Biology, Indiana University Indianapolis, 723 W. Michigan Street, Indianapolis, IN, USA
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12
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Marcelino VR, Welsh C, Diener C, Gulliver EL, Rutten EL, Young RB, Giles EM, Gibbons SM, Greening C, Forster SC. Disease-specific loss of microbial cross-feeding interactions in the human gut. Nat Commun 2023; 14:6546. [PMID: 37863966 PMCID: PMC10589287 DOI: 10.1038/s41467-023-42112-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Accepted: 09/27/2023] [Indexed: 10/22/2023] Open
Abstract
Many gut microorganisms critical to human health rely on nutrients produced by each other for survival; however, these cross-feeding interactions are still challenging to quantify and remain poorly characterized. Here, we introduce a Metabolite Exchange Score (MES) to quantify those interactions. Using metabolic models of prokaryotic metagenome-assembled genomes from over 1600 individuals, MES allows us to identify and rank metabolic interactions that are significantly affected by a loss of cross-feeding partners in 10 out of 11 diseases. When applied to a Crohn's disease case-control study, our approach identifies a lack of species with the ability to consume hydrogen sulfide as the main distinguishing microbiome feature of disease. We propose that our conceptual framework will help prioritize in-depth analyses, experiments and clinical targets, and that targeting the restoration of microbial cross-feeding interactions is a promising mechanism-informed strategy to reconstruct a healthy gut ecosystem.
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Affiliation(s)
- Vanessa R Marcelino
- Department of Molecular and Translational Sciences, Monash University, Clayton, VIC, 3168, Australia.
- Centre for Innate Immunity and Infectious Diseases, Hudson Institute of Medical Research, Clayton, VIC, 3168, Australia.
- Melbourne Integrative Genomics, School of BioSciences, University of Melbourne, Parkville, VIC, 3010, Australia.
- Department of Microbiology and Immunology at the Peter Doherty Institute for Infection and Immunity, University of Melbourne, Parkville, VIC, 3010, Australia.
| | - Caitlin Welsh
- Department of Microbiology, Biomedicine Discovery Institute, Clayton, VIC, 3800, Australia
| | | | - Emily L Gulliver
- Department of Molecular and Translational Sciences, Monash University, Clayton, VIC, 3168, Australia
- Centre for Innate Immunity and Infectious Diseases, Hudson Institute of Medical Research, Clayton, VIC, 3168, Australia
| | - Emily L Rutten
- Department of Molecular and Translational Sciences, Monash University, Clayton, VIC, 3168, Australia
- Centre for Innate Immunity and Infectious Diseases, Hudson Institute of Medical Research, Clayton, VIC, 3168, Australia
| | - Remy B Young
- Department of Molecular and Translational Sciences, Monash University, Clayton, VIC, 3168, Australia
- Centre for Innate Immunity and Infectious Diseases, Hudson Institute of Medical Research, Clayton, VIC, 3168, Australia
| | - Edward M Giles
- Centre for Innate Immunity and Infectious Diseases, Hudson Institute of Medical Research, Clayton, VIC, 3168, Australia
- Department of Paediatrics, Monash University, Clayton, VIC, 3168, Australia
| | - Sean M Gibbons
- Institute for Systems Biology, Seattle, WA, 98109, USA
- Department of Bioengineering, University of Washington, Seattle, WA, 98195, USA
- Department of Genome Sciences, University of Washington, Seattle, WA, 98195, USA
- eScience Institute, University of Washington, Seattle, WA, 98195, USA
| | - Chris Greening
- Department of Microbiology, Biomedicine Discovery Institute, Clayton, VIC, 3800, Australia
| | - Samuel C Forster
- Department of Molecular and Translational Sciences, Monash University, Clayton, VIC, 3168, Australia.
- Centre for Innate Immunity and Infectious Diseases, Hudson Institute of Medical Research, Clayton, VIC, 3168, Australia.
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13
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Tureţcaia AB, Garayburu-Caruso VA, Kaufman MH, Danczak RE, Stegen JC, Chu RK, Toyoda JG, Cardenas MB, Graham EB. Rethinking Aerobic Respiration in the Hyporheic Zone under Variation in Carbon and Nitrogen Stoichiometry. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2023; 57:15499-15510. [PMID: 37795960 PMCID: PMC10586321 DOI: 10.1021/acs.est.3c04765] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/19/2023] [Revised: 09/07/2023] [Accepted: 09/08/2023] [Indexed: 10/06/2023]
Abstract
Hyporheic zones (HZs)─zones of groundwater-surface water mixing─are hotspots for dissolved organic matter (DOM) and nutrient cycling that can disproportionately impact aquatic ecosystem functions. However, the mechanisms affecting DOM metabolism through space and time in HZs remain poorly understood. To resolve this gap, we investigate a recently proposed theory describing trade-offs between carbon (C) and nitrogen (N) limitations as a key regulator of HZ metabolism. We propose that throughout the extent of the HZ, a single process like aerobic respiration (AR) can be limited by both DOM thermodynamics and N content due to highly variable C/N ratios over short distances (centimeter scale). To investigate this theory, we used a large flume, continuous optode measurements of dissolved oxygen (DO), and spatially and temporally resolved molecular analysis of DOM. Carbon and N limitations were inferred from changes in the elemental stoichiometric ratio. We show sequential, depth-stratified relationships of DO with DOM thermodynamics and organic N that change across centimeter scales. In the shallow HZ with low C/N, DO was associated with the thermodynamics of DOM, while deeper in the HZ with higher C/N, DO was associated with inferred biochemical reactions involving organic N. Collectively, our results suggest that there are multiple competing processes that limit AR in the HZ. Resolving this spatiotemporal variation could improve predictions from mechanistic models, either via more highly resolved grid cells or by representing AR colimitation by DOM thermodynamics and organic N.
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Affiliation(s)
- Anna B Tureţcaia
- Department of Earth and Planetary Sciences, The University of Texas at Austin, Austin, Texas 78712, United States
| | | | - Matthew H Kaufman
- Pacific Northwest National Laboratory, Richland, Washington 99352, United States
- Department of Earth, Environment, and Physics, Worcester State University, Worcester, Massachusetts 01602, United States
| | - Robert E Danczak
- Pacific Northwest National Laboratory, Richland, Washington 99352, United States
| | - James C Stegen
- Pacific Northwest National Laboratory, Richland, Washington 99352, United States
- School of the Environment, Washington State University, Pullman, Washington 99164, United States
| | - Rosalie K Chu
- Environmental Molecular Sciences Laboratory, Richland, Washington 99352, United States
| | - Jason G Toyoda
- Environmental Molecular Sciences Laboratory, Richland, Washington 99352, United States
| | - M Bayani Cardenas
- Department of Earth and Planetary Sciences, The University of Texas at Austin, Austin, Texas 78712, United States
| | - Emily B Graham
- Pacific Northwest National Laboratory, Richland, Washington 99352, United States
- School of Biological Sciences, Washington State University, Pullman, Washington 99164, United States
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14
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Li P, Roos S, Luo H, Ji B, Nielsen J. Metabolic engineering of human gut microbiome: Recent developments and future perspectives. Metab Eng 2023; 79:1-13. [PMID: 37364774 DOI: 10.1016/j.ymben.2023.06.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2023] [Revised: 06/10/2023] [Accepted: 06/10/2023] [Indexed: 06/28/2023]
Abstract
Many studies have demonstrated that the gut microbiota is associated with human health and disease. Manipulation of the gut microbiota, e.g. supplementation of probiotics, has been suggested to be feasible, but subject to limited therapeutic efficacy. To develop efficient microbiota-targeted diagnostic and therapeutic strategies, metabolic engineering has been applied to construct genetically modified probiotics and synthetic microbial consortia. This review mainly discusses commonly adopted strategies for metabolic engineering in the human gut microbiome, including the use of in silico, in vitro, or in vivo approaches for iterative design and construction of engineered probiotics or microbial consortia. Especially, we highlight how genome-scale metabolic models can be applied to advance our understanding of the gut microbiota. Also, we review the recent applications of metabolic engineering in gut microbiome studies as well as discuss important challenges and opportunities.
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Affiliation(s)
- Peishun Li
- Department of Biology and Biological Engineering, Chalmers University of Technology, SE41296, Gothenburg, Sweden
| | - Stefan Roos
- Department of Molecular Sciences, Uppsala BioCenter, Swedish University of Agricultural Sciences, SE75007, Uppsala, Sweden
| | - Hao Luo
- Department of Biology and Biological Engineering, Chalmers University of Technology, SE41296, Gothenburg, Sweden
| | - Boyang Ji
- BioInnovation Institute, Ole Maaløes Vej 3, DK2200, Copenhagen, Denmark
| | - Jens Nielsen
- Department of Biology and Biological Engineering, Chalmers University of Technology, SE41296, Gothenburg, Sweden; BioInnovation Institute, Ole Maaløes Vej 3, DK2200, Copenhagen, Denmark.
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15
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Jadebeck JF, Wiechert W, Nöh K. Practical sampling of constraint-based models: Optimized thinning boosts CHRR performance. PLoS Comput Biol 2023; 19:e1011378. [PMID: 37566638 PMCID: PMC10446239 DOI: 10.1371/journal.pcbi.1011378] [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: 01/04/2023] [Revised: 08/23/2023] [Accepted: 07/21/2023] [Indexed: 08/13/2023] Open
Abstract
Thinning is a sub-sampling technique to reduce the memory footprint of Markov chain Monte Carlo. Despite being commonly used, thinning is rarely considered efficient. For sampling constraint-based models, a highly relevant use-case in systems biology, we here demonstrate that thinning boosts computational and, thereby, sampling efficiencies of the widely used Coordinate Hit-and-Run with Rounding (CHRR) algorithm. By benchmarking CHRR with thinning with simplices and genome-scale metabolic networks of up to thousands of dimensions, we find a substantial increase in computational efficiency compared to unthinned CHRR, in our examples by orders of magnitude, as measured by the effective sample size per time (ESS/t), with performance gains growing with polytope (effective network) dimension. Using a set of benchmark models we derive a ready-to-apply guideline for tuning thinning to efficient and effective use of compute resources without requiring additional coding effort. Our guideline is validated using three (out-of-sample) large-scale networks and we show that it allows sampling convex polytopes uniformly to convergence in a fraction of time, thereby unlocking the rigorous investigation of hitherto intractable models. The derivation of our guideline is explained in detail, allowing future researchers to update it as needed as new model classes and more training data becomes available. CHRR with deliberate utilization of thinning thereby paves the way to keep pace with progressing model sizes derived with the constraint-based reconstruction and analysis (COBRA) tool set. Sampling and evaluation pipelines are available at https://jugit.fz-juelich.de/IBG-1/ModSim/fluxomics/chrrt.
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Affiliation(s)
- Johann F. Jadebeck
- Institute of Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich, Jülich, Germany
- Computational Systems Biotechnology (AVT.CSB), RWTH Aachen University, Aachen, Germany
| | - Wolfgang Wiechert
- Institute of Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich, Jülich, Germany
- Computational Systems Biotechnology (AVT.CSB), RWTH Aachen University, Aachen, Germany
| | - Katharina Nöh
- Institute of Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich, Jülich, Germany
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16
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Gao X, Zhao J, Chen W, Zhai Q. Food and drug design for gut microbiota-directed regulation: Current experimental landscape and future innovation. Pharmacol Res 2023; 194:106867. [PMID: 37499703 DOI: 10.1016/j.phrs.2023.106867] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/29/2023] [Revised: 07/19/2023] [Accepted: 07/23/2023] [Indexed: 07/29/2023]
Abstract
Most diets and medications enhance host health via microbiota-dependent ways, but it is in the present situation of untargeted regulation. Non-targeted regulation may lead to the ineffectiveness of dietary supplements or drug treatment. Microbiota-directed food, aiming to improve diseases by targeting specific microbes without affecting other bacteria, have been proposed to deal with this problem. However, there is currently no universally applicable method to explore such foods or drugs. In this review, thirty studies on recent efforts in microbiota directed diets and medications are summarized from various databases. The methods used to find new foods and medications are primarily divided into four groups depending on the experimental models: in vivo and in vitro, as well as predictions based on bioinformatics. We also discuss their implementation, interpretation, and respective limitations, and describe the present situation. We further put forward a framework for microbiota-directed foods and medicine according to above methods and other microbiome manipulation, which will spur precision medicine.
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Affiliation(s)
- Xiaoxiang Gao
- State Key Laboratory of Food Science and Resources, Jiangnan University, Jiangnan University, Wuxi, Jiangsu 214122, China; School of Food Science and Technology, Jiangnan University, Wuxi, Jiangsu 214122, China
| | - Jianxin Zhao
- State Key Laboratory of Food Science and Resources, Jiangnan University, Jiangnan University, Wuxi, Jiangsu 214122, China; School of Food Science and Technology, Jiangnan University, Wuxi, Jiangsu 214122, China
| | - Wei Chen
- State Key Laboratory of Food Science and Resources, Jiangnan University, Jiangnan University, Wuxi, Jiangsu 214122, China; School of Food Science and Technology, Jiangnan University, Wuxi, Jiangsu 214122, China; National Engineering Research Center for Functional Food, Jiangnan University, Wuxi, Jiangsu 214122, China
| | - Qixiao Zhai
- State Key Laboratory of Food Science and Resources, Jiangnan University, Jiangnan University, Wuxi, Jiangsu 214122, China; School of Food Science and Technology, Jiangnan University, Wuxi, Jiangsu 214122, China.
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17
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Qiu S, Yang A, Zeng H. Flux balance analysis-based metabolic modeling of microbial secondary metabolism: Current status and outlook. PLoS Comput Biol 2023; 19:e1011391. [PMID: 37619239 PMCID: PMC10449171 DOI: 10.1371/journal.pcbi.1011391] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/26/2023] Open
Abstract
In microorganisms, different from primary metabolism for cellular growth, secondary metabolism is for ecological interactions and stress responses and an important source of natural products widely used in various areas such as pharmaceutics and food additives. With advancements of sequencing technologies and bioinformatics tools, a large number of biosynthetic gene clusters of secondary metabolites have been discovered from microbial genomes. However, due to challenges from the difficulty of genome-scale pathway reconstruction and the limitation of conventional flux balance analysis (FBA) on secondary metabolism, the quantitative modeling of secondary metabolism is poorly established, in contrast to that of primary metabolism. This review first discusses current efforts on the reconstruction of secondary metabolic pathways in genome-scale metabolic models (GSMMs), as well as related FBA-based modeling techniques. Additionally, potential extensions of FBA are suggested to improve the prediction accuracy of secondary metabolite production. As this review posits, biosynthetic pathway reconstruction for various secondary metabolites will become automated and a modeling framework capturing secondary metabolism onset will enhance the predictive power. Expectedly, an improved FBA-based modeling workflow will facilitate quantitative study of secondary metabolism and in silico design of engineering strategies for natural product production.
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Affiliation(s)
- Sizhe Qiu
- School of Food and Health, Beijing Technology and Business University, Bejing, China
- Department of Engineering Science, University of Oxford, Oxford, United Kingdom
| | - Aidong Yang
- Department of Engineering Science, University of Oxford, Oxford, United Kingdom
| | - Hong Zeng
- School of Food and Health, Beijing Technology and Business University, Bejing, China
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18
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Bartmanski BJ, Rocha M, Zimmermann-Kogadeeva M. Recent advances in data- and knowledge-driven approaches to explore primary microbial metabolism. Curr Opin Chem Biol 2023; 75:102324. [PMID: 37207402 PMCID: PMC10410306 DOI: 10.1016/j.cbpa.2023.102324] [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: 12/28/2022] [Revised: 04/15/2023] [Accepted: 04/18/2023] [Indexed: 05/21/2023]
Abstract
With the rapid progress in metabolomics and sequencing technologies, more data on the metabolome of single microbes and their communities become available, revealing the potential of microorganisms to metabolize a broad range of chemical compounds. The analysis of microbial metabolomics datasets remains challenging since it inherits the technical challenges of metabolomics analysis, such as compound identification and annotation, while harboring challenges in data interpretation, such as distinguishing metabolite sources in mixed samples. This review outlines the recent advances in computational methods to analyze primary microbial metabolism: knowledge-based approaches that take advantage of metabolic and molecular networks and data-driven approaches that employ machine/deep learning algorithms in combination with large-scale datasets. These methods aim at improving metabolite identification and disentangling reciprocal interactions between microbes and metabolites. We also discuss the perspective of combining these approaches and further developments required to advance the investigation of primary metabolism in mixed microbial samples.
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Affiliation(s)
| | - Miguel Rocha
- Centre of Biological Engineering, University of Minho, Campus of Gualtar, Braga, Portugal
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19
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Nagpal S, Mande SS. Environmental insults and compensative responses: when microbiome meets cancer. Discov Oncol 2023; 14:130. [PMID: 37453005 DOI: 10.1007/s12672-023-00745-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/12/2023] [Accepted: 07/04/2023] [Indexed: 07/18/2023] Open
Abstract
Tumor microenvironment has recently been ascribed a new hallmark-the polymorphic microbiome. Accumulating evidence regarding the tissue specific territories of tumor-microbiome have opened new and interesting avenues. A pertinent question is regarding the functional consequence of the interface between host-microbiome and cancer. Given microbial communities have predominantly been explored through an ecological perspective, it is important that the foundational aspects of ecological stress and the fight to 'survive and thrive' are accounted for tumor-micro(b)environment as well. Building on existing evidence and classical microbial ecology, here we attempt to characterize the ecological stresses and the compensative responses of the microorganisms inside the tumor microenvironment. What insults would microbes experience inside the cancer jungle? How would they respond to these insults? How the interplay of stress and microbial quest for survival would influence the fate of tumor? This work asks these questions and tries to describe this underdiscussed ecological interface of the tumor and its microbiota. It is hoped that a larger scientific thought on the importance of microbial competition sensing vis-à-vis tumor-microenvironment would be stimulated.
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Affiliation(s)
- Sunil Nagpal
- TCS Research, Tata Consultancy Services Ltd, Pune, 411013, India.
- CSIR-Institute of Genomics and Integrative Biology (CSIR-IGIB), New Delhi, 110025, India.
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, 201002, India.
| | - Sharmila S Mande
- TCS Research, Tata Consultancy Services Ltd, Pune, 411013, India.
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20
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Correia GD, Marchesi JR, MacIntyre DA. Moving beyond DNA: towards functional analysis of the vaginal microbiome by non-sequencing-based methods. Curr Opin Microbiol 2023; 73:102292. [PMID: 36931094 DOI: 10.1016/j.mib.2023.102292] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Revised: 02/10/2023] [Accepted: 02/14/2023] [Indexed: 03/17/2023]
Abstract
Over the last two decades, sequencing-based methods have revolutionised our understanding of niche-specific microbial complexity. In the lower female reproductive tract, these approaches have enabled identification of bacterial compositional structures associated with health and disease. Application of metagenomics and metatranscriptomics strategies have provided insight into the putative function of these communities but it is increasingly clear that direct measures of microbial and host cell function are required to understand the contribution of microbe-host interactions to pathophysiology. Here we explore and discuss current methods and approaches, many of which rely upon mass-spectrometry, being used to capture functional insight into the vaginal mucosal interface. In addition to improving mechanistic understanding, these methods offer innovative solutions for the development of diagnostic and therapeutic strategies designed to improve women's health.
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Affiliation(s)
- Gonçalo Ds Correia
- Institute of Reproductive and Developmental Biology, Department of Metabolism, Digestion and Reproduction, Imperial College London, London W12 0NN, UK; March of Dimes Prematurity Research Centre at Imperial College London, London, UK
| | - Julian R Marchesi
- March of Dimes Prematurity Research Centre at Imperial College London, London, UK; Centre for Digestive Diseases, Department of Metabolism, Digestion and Reproduction, Imperial College London, Imperial College London, London W2 1NY, UK
| | - David A MacIntyre
- Institute of Reproductive and Developmental Biology, Department of Metabolism, Digestion and Reproduction, Imperial College London, London W12 0NN, UK; March of Dimes Prematurity Research Centre at Imperial College London, London, UK.
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21
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Veseli I, Chen YT, Schechter MS, Vanni C, Fogarty EC, Watson AR, Jabri B, Blekhman R, Willis AD, Yu MK, Fernàndez-Guerra A, Füssel J, Eren AM. Microbes with higher metabolic independence are enriched in human gut microbiomes under stress. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.05.10.540289. [PMID: 37293035 PMCID: PMC10245760 DOI: 10.1101/2023.05.10.540289] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
A wide variety of human diseases are associated with loss of microbial diversity in the human gut, inspiring a great interest in the diagnostic or therapeutic potential of the microbiota. However, the ecological forces that drive diversity reduction in disease states remain unclear, rendering it difficult to ascertain the role of the microbiota in disease emergence or severity. One hypothesis to explain this phenomenon is that microbial diversity is diminished as disease states select for microbial populations that are more fit to survive environmental stress caused by inflammation or other host factors. Here, we tested this hypothesis on a large scale, by developing a software framework to quantify the enrichment of microbial metabolisms in complex metagenomes as a function of microbial diversity. We applied this framework to over 400 gut metagenomes from individuals who are healthy or diagnosed with inflammatory bowel disease (IBD). We found that high metabolic independence (HMI) is a distinguishing characteristic of microbial communities associated with individuals diagnosed with IBD. A classifier we trained using the normalized copy numbers of 33 HMI-associated metabolic modules not only distinguished states of health versus IBD, but also tracked the recovery of the gut microbiome following antibiotic treatment, suggesting that HMI is a hallmark of microbial communities in stressed gut environments.
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Affiliation(s)
- Iva Veseli
- Biophysical Sciences Program, The University of Chicago, Chicago, IL 60637, USA
- Department of Medicine, The University of Chicago, Chicago, IL 60637, USA
| | - Yiqun T. Chen
- Data Science Institute and Department of Biomedical Data Science, Stanford University, Stanford, CA, 94305, USA
| | - Matthew S. Schechter
- Department of Medicine, The University of Chicago, Chicago, IL 60637, USA
- Committee on Microbiology, The University of Chicago, Chicago, IL 60637, USA
| | - Chiara Vanni
- MARUM Center for Marine Environmental Sciences, University of Bremen, Bremen, Germany
| | - Emily C. Fogarty
- Department of Medicine, The University of Chicago, Chicago, IL 60637, USA
- Committee on Microbiology, The University of Chicago, Chicago, IL 60637, USA
| | - Andrea R. Watson
- Department of Medicine, The University of Chicago, Chicago, IL 60637, USA
- Committee on Microbiology, The University of Chicago, Chicago, IL 60637, USA
| | - Bana Jabri
- Department of Medicine, The University of Chicago, Chicago, IL 60637, USA
| | - Ran Blekhman
- Department of Medicine, The University of Chicago, Chicago, IL 60637, USA
| | - Amy D. Willis
- Department of Biostatistics, University of Washington, Seattle, WA, 98195, USA
| | - Michael K. Yu
- Toyota Technological Institute at Chicago, Chicago, IL 60605, USA
| | - Antonio Fernàndez-Guerra
- Lundbeck Foundation GeoGenetics Centre, GLOBE Institute, University of Copenhagen, Copenhagen, Denmark
| | - Jessika Füssel
- Department of Medicine, The University of Chicago, Chicago, IL 60637, USA
- Institute for Chemistry and Biology of the Marine Environment, University of Oldenburg, Oldenburg, Germany
| | - A. Murat Eren
- Department of Medicine, The University of Chicago, Chicago, IL 60637, USA
- Institute for Chemistry and Biology of the Marine Environment, University of Oldenburg, Oldenburg, Germany
- Marine ‘Omics Bridging Group, Max Planck Institute for Marine Microbiology, 28359 Bremen, Germany
- Alfred Wegener Institute for Polar and Marine Research, Bremerhaven, Germany
- Helmholtz Institute for Functional Marine Biodiversity, 26129, Oldenburg, Germany
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22
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Gelbach PE, Finley SD. Flux Sampling in Genome-scale Metabolic Modeling of Microbial Communities. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.04.18.537368. [PMID: 37197028 PMCID: PMC10173371 DOI: 10.1101/2023.04.18.537368] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 05/19/2023]
Abstract
Microbial communities play a crucial role in ecosystem function through metabolic interactions. Genome-scale modeling is a promising method to understand these interactions. Flux balance analysis (FBA) is most often used to predict the flux through all reactions in a genome-scale model. However, the fluxes predicted by FBA depend on a user-defined cellular objective. Flux sampling is an alternative to FBA, as it provides the range of fluxes possible within a microbial community. Furthermore, flux sampling may capture additional heterogeneity across cells, especially when cells exhibit sub-maximal growth rates. In this study, we simulate the metabolism of microbial communities and compare the metabolic characteristics found with FBA and flux sampling. We find significant differences in the predicted metabolism with sampling, including increased cooperative interactions and pathway-specific changes in predicted flux. Our results suggest the importance of sampling-based and objective function-independent approaches to evaluate metabolic interactions and emphasize their utility in quantitatively studying interactions between cells and organisms.
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Affiliation(s)
- Patrick E. Gelbach
- Alfred E. Mann Department of Biomedical Engineering, University of Southern California, Los Angeles, CA 90089, USA
| | - Stacey D. Finley
- Alfred E. Mann Department of Biomedical Engineering, University of Southern California, Los Angeles, CA 90089, USA
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA 90089, USA
- Mork Family Department of Chemical Engineering and Materials Science, University of Southern California, Los Angeles, CA 90089, USA
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23
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Borer B, Magnúsdóttir S. The media composition as a crucial element in high-throughput metabolic network reconstruction. Interface Focus 2023; 13:20220070. [PMID: 36789238 PMCID: PMC9912011 DOI: 10.1098/rsfs.2022.0070] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Accepted: 01/11/2023] [Indexed: 02/12/2023] Open
Abstract
In recent years, metagenome-assembled genomes (MAGs) have provided glimpses into the intra- and interspecies genetic diversity and interactions that form the bases of complex microbial communities. High-throughput reconstruction of genome-scale metabolic networks (GEMs) from MAGs is a promising avenue to disentangle the myriad trophic interactions stabilizing these communities. However, high-throughput reconstruction of GEMs relies on accurate gap filling of metabolic pathways using automated algorithms. Here, we systematically explore how the composition of the media (specification of the available nutrients and metabolites) during gap filling influences the resulting GEMs concerning predicted auxotrophies for fully sequenced model organisms and environmental isolates. We expand this analysis by using 106 MAGs from the same species with differing quality. We find that although the completeness of MAGs influences the fraction of gap-filled reactions, the composition of the media plays the dominant role in the accurate prediction of auxotrophies that form the basis of myriad community interactions. We propose that constraining the media composition for gap filling through both experimental approaches and computational approaches will increase the reliability of high-throughput reconstruction of genome-scale metabolic models from MAGs and paves the way for culture independent prediction of trophic interactions in complex microbial communities.
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Affiliation(s)
- Benedict Borer
- Earth, Atmospheric and Planetary Sciences Department, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Stefanía Magnúsdóttir
- Department of Environmental Microbiology, Helmholtz Centre for Environmental Research-UFZ, Leipzig 04318, Germany
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24
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Zampieri G, Campanaro S, Angione C, Treu L. Metatranscriptomics-guided genome-scale metabolic modeling of microbial communities. CELL REPORTS METHODS 2023; 3:100383. [PMID: 36814842 PMCID: PMC9939383 DOI: 10.1016/j.crmeth.2022.100383] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/28/2022] [Revised: 10/07/2022] [Accepted: 12/12/2022] [Indexed: 01/09/2023]
Abstract
Multi-omics data integration via mechanistic models of metabolism is a scalable and flexible framework for exploring biological hypotheses in microbial systems. However, although most microorganisms are unculturable, such multi-omics modeling is limited to isolate microbes or simple synthetic communities. Here, we developed an approach for modeling microbial activity and interactions that leverages the reconstruction of metagenome-assembled genomes and associated genome-centric metatranscriptomes. At its core, we designed a method for condition-specific metabolic modeling of microbial communities through the integration of metatranscriptomic data. Using this approach, we explored the behavior of anaerobic digestion consortia driven by hydrogen availability and human gut microbiota dysbiosis associated with Crohn's disease, identifying condition-dependent amino acid requirements in archaeal species and a reduced short-chain fatty acid exchange network associated with disease, respectively. Our approach can be applied to complex microbial communities, allowing a mechanistic contextualization of multi-omics data on a metagenome scale.
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Affiliation(s)
- Guido Zampieri
- Department of Biology, University of Padova, Padova 35121, Italy
| | - Stefano Campanaro
- Department of Biology, University of Padova, Padova 35121, Italy
- CRIBI Biotechnology Center, University of Padova, Padova 35121, Italy
| | - Claudio Angione
- School of Computing, Engineering and Digital Technologies, Teesside University, Middlesbrough TS1 3BX, UK
- National Horizons Centre, Teesside University, Darlington DL1 1HG, UK
- Centre for Digital Innovation, Teesside University, Middlesbrough TS1 3BX, UK
| | - Laura Treu
- Department of Biology, University of Padova, Padova 35121, Italy
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25
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Devika NT, Katneni VK, Jangam AK, Suganya PN, Shekhar MS, Jithendran KP. In silico prediction of potential indigenous microbial biomarkers in Penaeus vannamei identified through meta-analysis and genome-scale metabolic modelling. ENVIRONMENTAL MICROBIOME 2023; 18:2. [PMID: 36631881 PMCID: PMC9835370 DOI: 10.1186/s40793-022-00458-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/02/2022] [Accepted: 12/25/2022] [Indexed: 06/17/2023]
Abstract
BACKGROUND Understanding the microbiome is crucial as it contributes to the metabolic health of the host and, upon dysbiosis, may influence disease development. With the recent surge in high-throughput sequencing technology, the availability of microbial genomic data has increased dramatically. Amplicon sequence-based analyses majorly profile microbial abundance and determine taxonomic markers. Furthermore, the availability of genome sequences for various microbial organisms has prompted the integration of genome-scale metabolic modelling that provides insights into the metabolic interactions influencing host health. However, the analysis from a single study may not be consistent, necessitating a meta-analysis. RESULTS We conducted a meta-analysis and integrated with constraint-based metabolic modelling approach, focusing on the microbiome of pacific white shrimp Penaeus vannamei, an extensively cultured marine candidate species. Meta-analysis revealed that Acinetobacter and Alteromonas are significant indicators of "health" and "disease" specific taxonomic biomarkers, respectively. Further, we enumerated metabolic interactions among the taxonomic biomarkers by applying a constraint-based approach to the community metabolic models (4416 pairs). Under different nutrient environments, a constraint-based flux simulation identified five beneficial species: Acinetobacter spWCHA55, Acinetobacter tandoii SE63, Bifidobacterium pseudolongum 49 D6, Brevundimonas pondensis LVF1, and Lutibacter profundi LP1 mediating parasitic interactions majorly under sucrose environment in the pairwise community. The study also reports the healthy biomarkers that can co-exist and have functionally dependent relationships to maintain a healthy state in the host. CONCLUSIONS Toward this, we collected and re-analysed the amplicon sequence data of P. vannamei (encompassing 117 healthy and 142 disease datasets). By capturing the taxonomic biomarkers and modelling the metabolic interaction between them, our study provides a valuable resource, a first-of-its-kind analysis in aquaculture scenario toward a sustainable shrimp farming.
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Affiliation(s)
- Neelakantan Thulasi Devika
- Nutrition Genetics and Biotechnology Division, Indian Council of Agricultural Research - Central Institute of Brackishwater Aquaculture, Chennai, India
| | - Vinaya Kumar Katneni
- Nutrition Genetics and Biotechnology Division, Indian Council of Agricultural Research - Central Institute of Brackishwater Aquaculture, Chennai, India.
| | - Ashok Kumar Jangam
- Nutrition Genetics and Biotechnology Division, Indian Council of Agricultural Research - Central Institute of Brackishwater Aquaculture, Chennai, India
| | - Panjan Nathamuni Suganya
- Nutrition Genetics and Biotechnology Division, Indian Council of Agricultural Research - Central Institute of Brackishwater Aquaculture, Chennai, India
| | - Mudagandur Shashi Shekhar
- Nutrition Genetics and Biotechnology Division, Indian Council of Agricultural Research - Central Institute of Brackishwater Aquaculture, Chennai, India
| | - Karingalakkandy Poochirian Jithendran
- Aquatic Animal Health and Environment Division, Indian Council of Agricultural Research - Central Institute of Brackishwater Aquaculture, Chennai, India
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Ruan Z, Xu M, Xing Y, Jiang Q, Yang B, Jiang J, Xu X. Interspecies Metabolic Interactions in a Synergistic Consortium Drive Efficient Degradation of the Herbicide Bromoxynil Octanoate. JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY 2022; 70:11613-11622. [PMID: 36089742 DOI: 10.1021/acs.jafc.2c03057] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Microbial communities play vital roles in biogeochemical cycles, allowing biodegradation of a wide range of pollutants. Although many studies have shown the importance of interspecies interactions on activities of communities, fully elucidating the complex interactions in microbial communities is still challenging. Here, we isolated a consortium containing two bacterial strains (Acinetobacter sp. AG3 and Bacillus sp. R45), which could mineralize bromoxynil octanoate (BO) with higher efficiency than either strain individually. The BO degradation pathway by the synergistic consortium was elucidated, and interspecies interactions in the consortium were explored using genome-scale metabolic models (GSMMs). Modeling showed that growth and degradation enhancements were driven by metabolic interactions, such as syntrophic exchanges of small metabolites in the consortium. Besides, nutritional enhancers were predicted to improve BO degradation, which were tested experimentally. Overall, our results will enhance our understanding of microbial mineralization of BO by consortia and promote the application of microbial communities for bioremediation.
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Affiliation(s)
- Zhepu Ruan
- Department of Microbiology, College of Life Sciences, Nanjing Agricultural University, Key Laboratory of Agricultural and Environmental Microbiology, Ministry of Agriculture and Rural Affairs, Nanjing 210095, China
| | - Mengjun Xu
- Department of Microbiology, College of Life Sciences, Nanjing Agricultural University, Key Laboratory of Agricultural and Environmental Microbiology, Ministry of Agriculture and Rural Affairs, Nanjing 210095, China
| | - Youwen Xing
- Department of Microbiology, College of Life Sciences, Nanjing Agricultural University, Key Laboratory of Agricultural and Environmental Microbiology, Ministry of Agriculture and Rural Affairs, Nanjing 210095, China
| | - Qi Jiang
- Department of Microbiology, College of Life Sciences, Nanjing Agricultural University, Key Laboratory of Agricultural and Environmental Microbiology, Ministry of Agriculture and Rural Affairs, Nanjing 210095, China
| | - Bingang Yang
- Department of Microbiology, College of Life Sciences, Nanjing Agricultural University, Key Laboratory of Agricultural and Environmental Microbiology, Ministry of Agriculture and Rural Affairs, Nanjing 210095, China
| | - Jiandong Jiang
- Department of Microbiology, College of Life Sciences, Nanjing Agricultural University, Key Laboratory of Agricultural and Environmental Microbiology, Ministry of Agriculture and Rural Affairs, Nanjing 210095, China
| | - Xihui Xu
- Department of Microbiology, College of Life Sciences, Nanjing Agricultural University, Key Laboratory of Agricultural and Environmental Microbiology, Ministry of Agriculture and Rural Affairs, Nanjing 210095, China
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Chiciudean I, Russo G, Bogdan DF, Levei EA, Faur L, Hillebrand-Voiculescu A, Moldovan OT, Banciu HL. Competition-cooperation in the chemoautotrophic ecosystem of Movile Cave: first metagenomic approach on sediments. ENVIRONMENTAL MICROBIOME 2022; 17:44. [PMID: 35978381 PMCID: PMC9386943 DOI: 10.1186/s40793-022-00438-w] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/27/2022] [Accepted: 08/05/2022] [Indexed: 05/30/2023]
Abstract
BACKGROUND Movile Cave (SE Romania) is a chemoautotrophically-based ecosystem fed by hydrogen sulfide-rich groundwater serving as a primary energy source analogous to the deep-sea hydrothermal ecosystems. Our current understanding of Movile Cave microbiology has been confined to the sulfidic water and its proximity, as most studies focused on the water-floating microbial mat and planktonic accumulations likely acting as the primary production powerhouse of this unique subterranean ecosystem. By employing comprehensive genomic-resolved metagenomics, we questioned the spatial variation, chemoautotrophic abilities, ecological interactions and trophic roles of Movile Cave's microbiome thriving beyond the sulfidic-rich water. RESULTS A customized bioinformatics pipeline led to the recovery of 106 high-quality metagenome-assembled genomes from 7 cave sediment metagenomes. Assemblies' taxonomy spanned 19 bacterial and three archaeal phyla with Acidobacteriota, Chloroflexota, Proteobacteria, Planctomycetota, Ca. Patescibacteria, Thermoproteota, Methylomirabilota, and Ca. Zixibacteria as prevalent phyla. Functional gene analyses predicted the presence of CO2 fixation, methanotrophy, sulfur and ammonia oxidation in the explored sediments. Species Metabolic Coupling Analysis of metagenome-scale metabolic models revealed the highest competition-cooperation interactions in the sediments collected away from the water. Simulated metabolic interactions indicated autotrophs and methanotrophs as major donors of metabolites in the sediment communities. Cross-feeding dependencies were assumed only towards 'currency' molecules and inorganic compounds (O2, PO43-, H+, Fe2+, Cu2+) in the water proximity sediment, whereas hydrogen sulfide and methanol were assumedly traded exclusively among distant gallery communities. CONCLUSIONS These findings suggest that the primary production potential of Movile Cave expands way beyond its hydrothermal waters, enhancing our understanding of the functioning and ecological interactions within chemolithoautotrophically-based subterranean ecosystems.
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Affiliation(s)
- Iulia Chiciudean
- Department of Molecular Biology and Biotechnology, Babeș-Bolyai University, Cluj-Napoca, Romania
| | - Giancarlo Russo
- EMBL Partner Institute for Genome Editing, Life Sciences Center–Vilnius University, Vilnius, Lithuania
| | - Diana Felicia Bogdan
- Department of Molecular Biology and Biotechnology, Babeș-Bolyai University, Cluj-Napoca, Romania
- Doctoral School of Integrative Biology, Babeș-Bolyai University, Cluj-Napoca, Romania
| | - Erika Andrea Levei
- National Institute for Research and Development for Optoelectronics, Research Institute for Analytical Instrumentation Subsidiary, Cluj-Napoca, Romania
| | - Luchiana Faur
- Emil Racovita Institute of Speleology, Geospeleology and Paleontology Department, Bucharest, Romania
- Romanian Institute of Science and Technology, Cluj-Napoca, Romania
| | - Alexandra Hillebrand-Voiculescu
- Romanian Institute of Science and Technology, Cluj-Napoca, Romania
- Biospeology and Edaphobiology Department, Emil Racovita Institute of Speleology, Bucharest, Romania
| | - Oana Teodora Moldovan
- Romanian Institute of Science and Technology, Cluj-Napoca, Romania
- Cluj-Napoca Department, Emil Racovita Institute of Speleology, Cluj-Napoca, Romania
| | - Horia Leonard Banciu
- Department of Molecular Biology and Biotechnology, Babeș-Bolyai University, Cluj-Napoca, Romania
- Centre for Systems Biology, Biodiversity and Bioresources, Babeș-Bolyai University, Cluj-Napoca, Romania
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van den Berg NI, Machado D, Santos S, Rocha I, Chacón J, Harcombe W, Mitri S, Patil KR. Ecological modelling approaches for predicting emergent properties in microbial communities. Nat Ecol Evol 2022; 6:855-865. [PMID: 35577982 PMCID: PMC7613029 DOI: 10.1038/s41559-022-01746-7] [Citation(s) in RCA: 50] [Impact Index Per Article: 25.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2021] [Accepted: 03/23/2022] [Indexed: 12/20/2022]
Abstract
Recent studies have brought forward the critical role of emergent properties in shaping microbial communities and the ecosystems of which they are a part. Emergent properties-patterns or functions that cannot be deduced linearly from the properties of the constituent parts-underlie important ecological characteristics such as resilience, niche expansion and spatial self-organization. While it is clear that emergent properties are a consequence of interactions within the community, their non-linear nature makes mathematical modelling imperative for establishing the quantitative link between community structure and function. As the need for conservation and rational modulation of microbial ecosystems is increasingly apparent, so is the consideration of the benefits and limitations of the approaches to model emergent properties. Here we review ecosystem modelling approaches from the viewpoint of emergent properties. We consider the scope, advantages and limitations of Lotka-Volterra, consumer-resource, trait-based, individual-based and genome-scale metabolic models. Future efforts in this research area would benefit from capitalizing on the complementarity between these approaches towards enabling rational modulation of complex microbial ecosystems.
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Affiliation(s)
| | - Daniel Machado
- Department of Biotechnology and Food Science, Norwegian University of Science and Technology, Trondheim, Norway
| | - Sophia Santos
- Centre of Biological Engineering, University of Minho, Braga, Portugal
| | - Isabel Rocha
- Instituto de Tecnologia Química e Biológica António Xavier, Universidade Nova de Lisboa, Oeiras, Portugal
| | - Jeremy Chacón
- Ecology, Evolution and Behavior, University of Minnesota, Minneapolis, MN, USA
| | - William Harcombe
- Ecology, Evolution and Behavior, University of Minnesota, Minneapolis, MN, USA
| | - Sara Mitri
- Département de Microbiologie Fondamentale, University of Lausanne, Lausanne, Switzerland
| | - Kiran R Patil
- Medical Research Council Toxicology Unit, University of Cambridge, Cambridge, UK.
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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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Sepich-Poore GD, Guccione C, Laplane L, Pradeu T, Curtius K, Knight R. Cancer's second genome: Microbial cancer diagnostics and redefining clonal evolution as a multispecies process: Humans and their tumors are not aseptic, and the multispecies nature of cancer modulates clinical care and clonal evolution: Humans and their tumors are not aseptic, and the multispecies nature of cancer modulates clinical care and clonal evolution. Bioessays 2022; 44:e2100252. [PMID: 35253252 PMCID: PMC10506734 DOI: 10.1002/bies.202100252] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2021] [Revised: 01/31/2022] [Accepted: 02/16/2022] [Indexed: 12/13/2022]
Abstract
The presence and role of microbes in human cancers has come full circle in the last century. Tumors are no longer considered aseptic, but implications for cancer biology and oncology remain underappreciated. Opportunities to identify and build translational diagnostics, prognostics, and therapeutics that exploit cancer's second genome-the metagenome-are manifold, but require careful consideration of microbial experimental idiosyncrasies that are distinct from host-centric methods. Furthermore, the discoveries of intracellular and intra-metastatic cancer bacteria necessitate fundamental changes in describing clonal evolution and selection, reflecting bidirectional interactions with non-human residents. Reconsidering cancer clonality as a multispecies process similarly holds key implications for understanding metastasis and prognosing therapeutic resistance while providing rational guidance for the next generation of bacterial cancer therapies. Guided by these new findings and challenges, this Review describes opportunities to exploit cancer's metagenome in oncology and proposes an evolutionary framework as a first step towards modeling multispecies cancer clonality. Also see the video abstract here: https://youtu.be/-WDtIRJYZSs.
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Affiliation(s)
| | - Caitlin Guccione
- Division of Biomedical Informatics, Department of Medicine, University of California San Diego, La Jolla, CA 92093, USA
- Bioinformatics and Systems Biology Program, University of California San Diego, La Jolla, CA 92093, USA
- Department of Pediatrics, University of California San Diego, La Jolla, CA 92093, USA
| | - Lucie Laplane
- Institut d’histoire et de philosophie des sciences et des techniques (UMR8590), CNRS & Panthéon-Sorbonne University, 75006 Paris, France
- Hematopoietic stem cells and the development of myeloid malignancies (UMR1287), Gustave Roussy Cancer Campus, 94800 Villejuif, France
| | - Thomas Pradeu
- ImmunoConcept (UMR5164), CNRS & University of Bordeaux, 33076 Bordeaux Cedex, France
| | - Kit Curtius
- Division of Biomedical Informatics, Department of Medicine, University of California San Diego, La Jolla, CA 92093, USA
- Bioinformatics and Systems Biology Program, University of California San Diego, La Jolla, CA 92093, USA
| | - Rob Knight
- Department of Bioengineering, University of California San Diego, La Jolla, CA 92093, USA
- Department of Pediatrics, University of California San Diego, La Jolla, CA 92093, USA
- Department of Computer Science and Engineering, University of California San Diego, La Jolla, CA 92093, USA
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Küken A, Langary D, Nikoloski Z. The hidden simplicity of metabolic networks is revealed by multireaction dependencies. SCIENCE ADVANCES 2022; 8:eabl6962. [PMID: 35353565 PMCID: PMC8967225 DOI: 10.1126/sciadv.abl6962] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/29/2021] [Accepted: 02/08/2022] [Indexed: 06/14/2023]
Abstract
Understanding the complexity of metabolic networks has implications for manipulation of their functions. The complexity of metabolic networks can be characterized by identifying multireaction dependencies that are challenging to determine due to the sheer number of combinations to consider. Here, we propose the concept of concordant complexes that captures multireaction dependencies and can be efficiently determined from the algebraic structure and operational constraints of metabolic networks. The concordant complexes imply the existence of concordance modules based on which the apparent complexity of 12 metabolic networks of organisms from all kingdoms of life can be reduced by at least 78%. A comparative analysis against an ensemble of randomized metabolic networks shows that the metabolic network of Escherichia coli contains fewer concordance modules and is, therefore, more tightly coordinated than expected by chance. Together, our findings demonstrate that metabolic networks are considerably simpler than what can be perceived from their structure alone.
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Affiliation(s)
- Anika Küken
- Bioinformatics, Institute of Biochemistry and Biology, University of Potsdam, Potsdam, Germany
| | - Damoun Langary
- Bioinformatics, Institute of Biochemistry and Biology, University of Potsdam, Potsdam, Germany
- Systems Biology and Mathematical Modeling, Max Planck Institute of Molecular Plant Physiology, Potsdam, Germany
| | - Zoran Nikoloski
- Bioinformatics, Institute of Biochemistry and Biology, University of Potsdam, Potsdam, Germany
- Systems Biology and Mathematical Modeling, Max Planck Institute of Molecular Plant Physiology, Potsdam, Germany
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Koduru L, Lakshmanan M, Hoon S, Lee DY, Lee YK, Ow DSW. Systems Biology of Gut Microbiota-Human Receptor Interactions: Toward Anti-inflammatory Probiotics. Front Microbiol 2022; 13:846555. [PMID: 35308387 PMCID: PMC8928190 DOI: 10.3389/fmicb.2022.846555] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2021] [Accepted: 02/11/2022] [Indexed: 12/14/2022] Open
Abstract
The incidence and prevalence of inflammatory disorders have increased globally, and is projected to double in the next decade. Gut microbiome-based therapeutics have shown promise in ameliorating chronic inflammation. However, they are largely experimental, context- or strain-dependent and lack a clear mechanistic basis. This hinders precision probiotics and poses significant risk, especially to individuals with pre-existing conditions. Molecules secreted by gut microbiota act as ligands to several health-relevant receptors expressed in human gut, such as the G-protein coupled receptors (GPCRs), Toll-like receptor 4 (TLR4), pregnane X receptor (PXR), and aryl hydrocarbon receptor (AhR). Among these, the human AhR expressed in different tissues exhibits anti-inflammatory effects and shows activity against a wide range of ligands produced by gut bacteria. However, different AhR ligands induce varying host responses and signaling in a tissue/organ-specific manner, which remain mostly unknown. The emerging systems biology paradigm, with its powerful in silico tool repertoire, provides opportunities for comprehensive and high-throughput strain characterization. In particular, combining metabolic models with machine learning tools can be useful to delineate tissue and ligand-specific signaling and thus their causal mechanisms in disease and health. The knowledge of such a mechanistic basis is indispensable to account for strain heterogeneity and actualize precision probiotics.
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Affiliation(s)
- Lokanand Koduru
- Institute of Molecular and Cell Biology, Agency for Science, Technology and Research (ASTAR), Singapore, Singapore
| | - Meiyappan Lakshmanan
- Bioprocessing Technology Institute, Agency for Science, Technology and Research (ASTAR), Singapore, Singapore.,Bioinformatics Institute, Agency for Science, Technology and Research (ASTAR), Singapore, Singapore
| | - Shawn Hoon
- Institute of Molecular and Cell Biology, Agency for Science, Technology and Research (ASTAR), Singapore, Singapore
| | - Dong-Yup Lee
- School of Chemical Engineering, Sungkyunkwan University, Suwon, South Korea
| | - Yuan Kun Lee
- Department of Microbiology and Immunology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.,Department of Surgery, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Dave Siak-Wei Ow
- Bioprocessing Technology Institute, Agency for Science, Technology and Research (ASTAR), Singapore, Singapore
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Palù M, Basile A, Zampieri G, Treu L, Rossi A, Silvia Morlino M, Campanaro S. KEMET - a python tool for KEGG Module evaluation and microbial genome annotation expansion. Comput Struct Biotechnol J 2022; 20:1481-1486. [PMID: 35422973 PMCID: PMC8976094 DOI: 10.1016/j.csbj.2022.03.015] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2021] [Revised: 03/17/2022] [Accepted: 03/18/2022] [Indexed: 11/23/2022] Open
Abstract
KEMET allows evaluating and expanding microbial genome annotations. KEMET can identify unannotated genes having partial sequence truncations. Annotation expansion can improve contextual draft genome-scale metabolic model reconstruction.
Background The rapid accumulation of sequencing data from metagenomic studies is enabling the generation of huge collections of microbial genomes, with new challenges for mapping their functional potential. In particular, metagenome-assembled genomes are typically incomplete and harbor partial gene sequences that can limit their annotation from traditional tools. New scalable solutions are thus needed to facilitate the evaluation of functional potential in microbial genomes. Methods To resolve annotation gaps in microbial genomes, we developed KEMET, an open-source Python library devised for the analysis of Kyoto Encyclopedia of Genes and Genomes (KEGG) functional units. KEMET focuses on the in-depth analysis of metabolic reaction networks to identify missing orthologs through hidden Markov model profiles. Results We evaluate the potential of KEMET for expanding functional annotations by simulating the effect of assembly issues on real gene sequences and showing that our approach can identify missing KEGG orthologs. Additionally, we show that recovered gene annotations can sensibly increase the quality of draft genome-scale metabolic models obtained from metagenome-assembled genomes, in some cases reaching the accuracy of models generated from complete genomes. Conclusions KEMET therefore allows expanding genome annotations by targeted searches for orthologous sequences, enabling a better qualitative and quantitative assessment of metabolic capabilities in novel microbial organisms.
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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: 126] [Impact Index Per Article: 63.0] [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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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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Zrimec J, Kokina M, Jonasson S, Zorrilla F, Zelezniak A. Plastic-Degrading Potential across the Global Microbiome Correlates with Recent Pollution Trends. mBio 2021; 12:e0215521. [PMID: 34700384 PMCID: PMC8546865 DOI: 10.1128/mbio.02155-21] [Citation(s) in RCA: 34] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2021] [Accepted: 09/10/2021] [Indexed: 12/12/2022] Open
Abstract
Biodegradation is a plausible route toward sustainable management of the millions of tons of plastic waste that have accumulated in terrestrial and marine environments. However, the global diversity of plastic-degrading enzymes remains poorly understood. Taking advantage of global environmental DNA sampling projects, here we constructed hidden Markov models from experimentally verified enzymes and mined ocean and soil metagenomes to assess the global potential of microorganisms to degrade plastics. By controlling for false positives using gut microbiome data, we compiled a catalogue of over 30,000 nonredundant enzyme homologues with the potential to degrade 10 different plastic types. While differences between the ocean and soil microbiomes likely reflect the base compositions of these environments, we find that ocean enzyme abundance increases with depth as a response to plastic pollution and not merely taxonomic composition. By obtaining further pollution measurements, we observed that the abundance of the uncovered enzymes in both ocean and soil habitats significantly correlates with marine and country-specific plastic pollution trends. Our study thus uncovers the earth microbiome's potential to degrade plastics, providing evidence of a measurable effect of plastic pollution on the global microbial ecology as well as a useful resource for further applied research. IMPORTANCE Utilization of synthetic biology approaches to enhance current plastic degradation processes is of crucial importance, as natural plastic degradation processes are very slow. For instance, the predicted lifetime of a polyethylene terephthalate (PET) bottle under ambient conditions ranges from 16 to 48 years. Moreover, although there is still unexplored diversity in microbial communities, synergistic degradation of plastics by microorganisms holds great potential to revolutionize the management of global plastic waste. To this end, the methods and data on novel plastic-degrading enzymes presented here can help researchers by (i) providing further information about the taxonomic diversity of such enzymes as well as understanding of the mechanisms and steps involved in the biological breakdown of plastics, (ii) pointing toward the areas with increased availability of novel enzymes, and (iii) giving a basis for further application in industrial plastic waste biodegradation. Importantly, our findings provide evidence of a measurable effect of plastic pollution on the global microbial ecology.
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Affiliation(s)
- Jan Zrimec
- Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden
- Department of Biotechnology and Systems Biology, National Institute of Biology, Ljubljana, Slovenia
| | - Mariia Kokina
- Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Sara Jonasson
- Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden
| | - Francisco Zorrilla
- Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden
- MRC Toxicology Unit, Cambridge, United Kingdom
| | - Aleksej Zelezniak
- Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden
- Institute of Biotechnology, Life Sciences Center, Vilnius University, Vilnius, Lithuania
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