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Yang C, Xue B, Yuan Q, Wang S, Su H. Algorithm of spatial-temporal simulation for environment-strain interactions in strain-strain consortia based on resource competition mechanism. Comput Struct Biotechnol J 2024; 23:2861-2871. [PMID: 39100804 PMCID: PMC11296241 DOI: 10.1016/j.csbj.2024.06.033] [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: 01/25/2024] [Revised: 06/26/2024] [Accepted: 06/26/2024] [Indexed: 08/06/2024] Open
Abstract
Interaction simulation for co-culture systems is important for optimizing culture conditions and improving yields. For industrial production, the environment significantly affects the spatial-temporal microbial interactions. However, the current research on polymicrobial interactions mainly focuses on interaction patterns among strains, and neglects the environment influence. Based on the resource competition relationship between two strains, this research set up the modules of cellular physicochemical properties, nutrient uptake and metabolite release, cellular survival, cell swimming and substrate diffusion, and investigated the spatial-temporal strain-environment interactions through module coupling and data mining. Furthermore, in an Escherichia coli-Saccharomyces cerevisiae consortium, the total net reproduction rate decreased as glucose was consumed. E. coli gradually dominated favorable positions due to its higher glucose utilization capacity, reaching 100 % abundance with a competitive strength of 0.86 for glucose. Conversely, S. cerevisiae decreased to 0 % abundance with a competitive strength of 0.14. The simulation results of environment influence on strain competitiveness showed that inoculation ratio and dissolved oxygen strongly influenced strain competitiveness. Specifically, strain competitiveness increased with higher inoculation ratio, whereas E. coli competitiveness increased as dissolved oxygen increased, in contrast to S. cerevisiae. On the other hand, substrate diffusion condition, micronutrients and toxins had minimal influence on strain competitiveness. This method offers a straightforward procedure without featured downscaling and provides novel insights into polymicrobial interaction simulation.
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Affiliation(s)
- Chen Yang
- State Key Laboratory of Chemical Resource Engineering, Beijing Key Laboratory of Bioprocess, and Beijing Advanced Innovation Center for Soft Matter Science and Engineering, Beijing University of Chemical Technology, Beijing 100029, People’s Republic of China
| | - Boyuan Xue
- State Key Laboratory of Chemical Resource Engineering, Beijing Key Laboratory of Bioprocess, and Beijing Advanced Innovation Center for Soft Matter Science and Engineering, Beijing University of Chemical Technology, Beijing 100029, People’s Republic of China
| | - Qianqian Yuan
- State Key Laboratory of Chemical Resource Engineering, Beijing Key Laboratory of Bioprocess, and Beijing Advanced Innovation Center for Soft Matter Science and Engineering, Beijing University of Chemical Technology, Beijing 100029, People’s Republic of China
| | - Shaojie Wang
- State Key Laboratory of Chemical Resource Engineering, Beijing Key Laboratory of Bioprocess, and Beijing Advanced Innovation Center for Soft Matter Science and Engineering, Beijing University of Chemical Technology, Beijing 100029, People’s Republic of China
| | - Haijia Su
- State Key Laboratory of Chemical Resource Engineering, Beijing Key Laboratory of Bioprocess, and Beijing Advanced Innovation Center for Soft Matter Science and Engineering, Beijing University of Chemical Technology, Beijing 100029, People’s Republic of China
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2
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Kundu P, Beura S, Mondal S, Das AK, Ghosh A. Machine learning for the advancement of genome-scale metabolic modeling. Biotechnol Adv 2024; 74:108400. [PMID: 38944218 DOI: 10.1016/j.biotechadv.2024.108400] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2023] [Revised: 05/13/2024] [Accepted: 06/23/2024] [Indexed: 07/01/2024]
Abstract
Constraint-based modeling (CBM) has evolved as the core systems biology tool to map the interrelations between genotype, phenotype, and external environment. The recent advancement of high-throughput experimental approaches and multi-omics strategies has generated a plethora of new and precise information from wide-ranging biological domains. On the other hand, the continuously growing field of machine learning (ML) and its specialized branch of deep learning (DL) provide essential computational architectures for decoding complex and heterogeneous biological data. In recent years, both multi-omics and ML have assisted in the escalation of CBM. Condition-specific omics data, such as transcriptomics and proteomics, helped contextualize the model prediction while analyzing a particular phenotypic signature. At the same time, the advanced ML tools have eased the model reconstruction and analysis to increase the accuracy and prediction power. However, the development of these multi-disciplinary methodological frameworks mainly occurs independently, which limits the concatenation of biological knowledge from different domains. Hence, we have reviewed the potential of integrating multi-disciplinary tools and strategies from various fields, such as synthetic biology, CBM, omics, and ML, to explore the biochemical phenomenon beyond the conventional biological dogma. How the integrative knowledge of these intersected domains has improved bioengineering and biomedical applications has also been highlighted. We categorically explained the conventional genome-scale metabolic model (GEM) reconstruction tools and their improvement strategies through ML paradigms. Further, the crucial role of ML and DL in omics data restructuring for GEM development has also been briefly discussed. Finally, the case-study-based assessment of the state-of-the-art method for improving biomedical and metabolic engineering strategies has been elaborated. Therefore, this review demonstrates how integrating experimental and in silico strategies can help map the ever-expanding knowledge of biological systems driven by condition-specific cellular information. This multiview approach will elevate the application of ML-based CBM in the biomedical and bioengineering fields for the betterment of society and the environment.
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Affiliation(s)
- Pritam Kundu
- School School of Energy Science and Engineering, Indian Institute of Technology Kharagpur, West Bengal 721302, India
| | - Satyajit Beura
- Department of Bioscience and Biotechnology, Indian Institute of Technology, Kharagpur, West Bengal 721302, India
| | - Suman Mondal
- P.K. Sinha Centre for Bioenergy and Renewables, Indian Institute of Technology Kharagpur, West Bengal 721302, India
| | - Amit Kumar Das
- Department of Bioscience and Biotechnology, Indian Institute of Technology, Kharagpur, West Bengal 721302, India
| | - Amit Ghosh
- School School of Energy Science and Engineering, Indian Institute of Technology Kharagpur, West Bengal 721302, India; P.K. Sinha Centre for Bioenergy and Renewables, Indian Institute of Technology Kharagpur, West Bengal 721302, India.
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3
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Afarin M, Naeimpoor F. Effect of microbial interactions on performance of community metabolic modeling algorithms: flux balance analysis (FBA), community FBA (cFBA) and SteadyCom. Bioprocess Biosyst Eng 2024:10.1007/s00449-024-03072-7. [PMID: 39180547 DOI: 10.1007/s00449-024-03072-7] [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: 04/06/2024] [Accepted: 07/30/2024] [Indexed: 08/26/2024]
Abstract
To explore the impact of microbial interactions on outcomes from three prevalent algorithms (Flux Balance Analysis (FBA), community FBA (cFBA), and SteadyCom) analyzing microbial community metabolic networks, five toy community models representing common microbial interactions were designed. These include commensalism, mutualism, competition, mutualism-competition, and commensalism-competition. Various scenarios, considering different biomass yields and substrate constraints, were examined for each type. In commensal communities, all algorithms consistently produced similar results. However, changes in biomass yields and substrate constraints led to variable abundances (0.33-0.8) and community growth rates (2-5 1/h) within a broad range. For competitive communities, all algorithms predicted growth of fastest-growing member. To comply with the natural coexistence of members, suboptimal solutions over optimal point are recommended. FBA faced challenges in modeling mutualism, consistently predicting growth of only one member. Although cFBA and SteadyCom resulted in a lower community growth rate, coexistence of both members were satisfied. In toy models with dual interactions, more realistic outcomes were achieved contrary to purely competitive model as the dependency fosters the coexistence which was missing in the competitive only scenarios. These findings emphasize the importance of algorithm choice based on specific microbial interaction types for reliable community behavior predictions..
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Affiliation(s)
- Maryam Afarin
- Biotechnology Research Laboratory, School of Chemical, Petroleum and Gas Engineering, Iran University of Science and Technology, Tehran, Iran
| | - Fereshteh Naeimpoor
- Biotechnology Research Laboratory, School of Chemical, Petroleum and Gas Engineering, Iran University of Science and Technology, Tehran, Iran.
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4
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Raajaraam L, Raman K. Modeling Microbial Communities: Perspective and Challenges. ACS Synth Biol 2024; 13:2260-2270. [PMID: 39148432 DOI: 10.1021/acssynbio.4c00116] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/17/2024]
Abstract
Microbial communities are immensely important due to their widespread presence and profound impact on various facets of life. Understanding these complex systems necessitates mathematical modeling, a powerful tool for simulating and predicting microbial community behavior. This review offers a critical analysis of metabolic modeling and highlights key areas that would greatly benefit from broader discussion and collaboration. Moreover, we explore the challenges and opportunities linked to the intricate nature of these communities, spanning data generation, modeling, and validation. We are confident that ongoing advancements in modeling techniques, such as machine learning, coupled with interdisciplinary collaborations, will unlock the full potential of microbial communities across diverse applications.
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Affiliation(s)
- Lavanya Raajaraam
- Bhupat and Jyoti Mehta School of Biosciences, Department of Biotechnology, Indian Institute of Technology (IIT) Madras, Chennai 600 036, India
- Centre for Integrative Biology and Systems mEdicine, IIT Madras, Chennai 600 036, India
- Robert Bosch Centre for Data Science and Artificial Intelligence (RBCDSAI), IIT Madras, Chennai 600 036, India
| | - Karthik Raman
- Bhupat and Jyoti Mehta School of Biosciences, Department of Biotechnology, Indian Institute of Technology (IIT) Madras, Chennai 600 036, India
- Centre for Integrative Biology and Systems mEdicine, IIT Madras, Chennai 600 036, India
- Robert Bosch Centre for Data Science and Artificial Intelligence (RBCDSAI), IIT Madras, Chennai 600 036, India
- Department of Data Science and AI, Wadhwani School of Data Science and Artificial Intelligence, IIT Madras, Chennai 600 036, India
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5
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Beura S, Kundu P, Das AK, Ghosh A. Genome-scale community modelling elucidates the metabolic interaction in Indian type-2 diabetic gut microbiota. Sci Rep 2024; 14:17259. [PMID: 39060274 PMCID: PMC11282233 DOI: 10.1038/s41598-024-63718-0] [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: 12/04/2023] [Accepted: 05/31/2024] [Indexed: 07/28/2024] Open
Abstract
Type-2 diabetes (T2D) is a rapidly growing multifactorial metabolic disorder that induces the onset of various diseases in the human body. The compositional and metabolic shift of the gut microbiota is a crucial factor behind T2D. Hence, gaining insight into the metabolic profile of the gut microbiota is essential for revealing their role in regulating the metabolism of T2D patients. Here, we have focused on the genome-scale community metabolic model reconstruction of crucial T2D-associated gut microbes. The model-based analysis of biochemical flux in T2D and healthy gut conditions showed distinct biochemical signatures and diverse metabolic interactions in the microbial community. The metabolic interactions encompass cross-feeding of short-chain fatty acids, amino acids, and vitamins among individual microbes within the community. In T2D conditions, a reduction in the metabolic flux of acetate, butyrate, vitamin B5, and bicarbonate was observed in the microbial community model, which can impact carbohydrate metabolism. The decline in butyrate levels is correlated with both insulin resistance and diminished glucose metabolism in T2D patients. Compared to the healthy gut, an overall reduction in glucose consumption and SCFA production flux was estimated in the T2D gut environment. Moreover, the decreased consumption profiles of branch chain amino acids (BCAAs) and aromatic amino acids (AAAs) in the T2D gut microbiota can be a distinct biomarker for T2D. Hence, the flux-level analysis of the microbial community model can provide insights into the metabolic reprogramming in diabetic gut microbiomes, which may be helpful in personalized therapeutics and diet design against T2D.
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Affiliation(s)
- Satyajit Beura
- Department of Bioscience and Biotechnology, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal, 721302, India
| | - Pritam Kundu
- School of Energy Science and Engineering, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal, 721302, India
| | - Amit Kumar Das
- Department of Bioscience and Biotechnology, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal, 721302, India
| | - Amit Ghosh
- School of Energy Science and Engineering, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal, 721302, India.
- P.K. Sinha Centre for Bioenergy and Renewables, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal, 721302, India.
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6
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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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7
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Schroeder WL, Suthers PF, Willis TC, Mooney EJ, Maranas CD. Current State, Challenges, and Opportunities in Genome-Scale Resource Allocation Models: A Mathematical Perspective. Metabolites 2024; 14:365. [PMID: 39057688 PMCID: PMC11278519 DOI: 10.3390/metabo14070365] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2024] [Revised: 06/24/2024] [Accepted: 06/26/2024] [Indexed: 07/28/2024] Open
Abstract
Stoichiometric genome-scale metabolic models (generally abbreviated GSM, GSMM, or GEM) have had many applications in exploring phenotypes and guiding metabolic engineering interventions. Nevertheless, these models and predictions thereof can become limited as they do not directly account for protein cost, enzyme kinetics, and cell surface or volume proteome limitations. Lack of such mechanistic detail could lead to overly optimistic predictions and engineered strains. Initial efforts to correct these deficiencies were by the application of precursor tools for GSMs, such as flux balance analysis with molecular crowding. In the past decade, several frameworks have been introduced to incorporate proteome-related limitations using a genome-scale stoichiometric model as the reconstruction basis, which herein are called resource allocation models (RAMs). This review provides a broad overview of representative or commonly used existing RAM frameworks. This review discusses increasingly complex models, beginning with stoichiometric models to precursor to RAM frameworks to existing RAM frameworks. RAM frameworks are broadly divided into two categories: coarse-grained and fine-grained, with different strengths and challenges. Discussion includes pinpointing their utility, data needs, highlighting framework strengths and limitations, and appropriateness to various research endeavors, largely through contrasting their mathematical frameworks. Finally, promising future applications of RAMs are discussed.
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Affiliation(s)
- Wheaton L. Schroeder
- Department of Chemical Engineering, The Pennsylvania State University, University Park, PA 16802, USA
- The Center for Bioenergy Innovation, Oak Ridge, TN 37830, USA
| | - Patrick F. Suthers
- Department of Chemical Engineering, The Pennsylvania State University, University Park, PA 16802, USA
- The Center for Bioenergy Innovation, Oak Ridge, TN 37830, USA
- DOE Center for Advanced Bioenergy and Bioproducts Innovation, The Pennsylvania State University, University Park, PA 16802, USA
| | - Thomas C. Willis
- Department of Chemical Engineering, The Pennsylvania State University, University Park, PA 16802, USA
- The Center for Bioenergy Innovation, Oak Ridge, TN 37830, USA
| | - Eric J. Mooney
- DOE Center for Advanced Bioenergy and Bioproducts Innovation, The Pennsylvania State University, University Park, PA 16802, USA
- Department of Biochemistry, Microbiology and Molecular Biology, The Pennsylvania State University, University Park, PA 16802, USA
| | - Costas D. Maranas
- Department of Chemical Engineering, The Pennsylvania State University, University Park, PA 16802, USA
- The Center for Bioenergy Innovation, Oak Ridge, TN 37830, USA
- DOE Center for Advanced Bioenergy and Bioproducts Innovation, The Pennsylvania State University, University Park, PA 16802, USA
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Tarzi C, Zampieri G, Sullivan N, Angione C. Emerging methods for genome-scale metabolic modeling of microbial communities. Trends Endocrinol Metab 2024; 35:533-548. [PMID: 38575441 DOI: 10.1016/j.tem.2024.02.018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Revised: 02/28/2024] [Accepted: 02/29/2024] [Indexed: 04/06/2024]
Abstract
Genome-scale metabolic models (GEMs) are consolidating as platforms for studying mixed microbial populations, by combining biological data and knowledge with mathematical rigor. However, deploying these models to answer research questions can be challenging due to the increasing number of available computational tools, the lack of universal standards, and their inherent limitations. Here, we present a comprehensive overview of foundational concepts for building and evaluating genome-scale models of microbial communities. We then compare tools in terms of requirements, capabilities, and applications. Next, we highlight the current pitfalls and open challenges to consider when adopting existing tools and developing new ones. Our compendium can be relevant for the expanding community of modelers, both at the entry and experienced levels.
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Affiliation(s)
- Chaimaa Tarzi
- School of Computing, Engineering and Digital Technologies, Teesside University, Southfield Rd, Middlesbrough, TS1 3BX, North Yorkshire, UK
| | - Guido Zampieri
- Department of Biology, University of Padova, Padova, 35122, Veneto, Italy
| | - Neil Sullivan
- Complement Genomics Ltd, Station Rd, Lanchester, Durham, DH7 0EX, County Durham, UK
| | - Claudio Angione
- School of Computing, Engineering and Digital Technologies, Teesside University, Southfield Rd, Middlesbrough, TS1 3BX, North Yorkshire, UK; Centre for Digital Innovation, Teesside University, Southfield Rd, Middlesbrough, TS1 3BX, North Yorkshire, UK; National Horizons Centre, Teesside University, 38 John Dixon Ln, Darlington, DL1 1HG, North Yorkshire, UK.
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9
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Mirzaei S, Tefagh M. GEM-based computational modeling for exploring metabolic interactions in a microbial community. PLoS Comput Biol 2024; 20:e1012233. [PMID: 38900842 PMCID: PMC11218945 DOI: 10.1371/journal.pcbi.1012233] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2023] [Revised: 07/02/2024] [Accepted: 06/03/2024] [Indexed: 06/22/2024] Open
Abstract
Microbial communities play fundamental roles in every complex ecosystem, such as soil, sea and the human body. The stability and diversity of the microbial community depend precisely on the composition of the microbiota. Any change in the composition of these communities affects microbial functions. An important goal of studying the interactions between species is to understand the behavior of microbes and their responses to perturbations. These interactions among species are mediated by the exchange of metabolites within microbial communities. We developed a computational model for the microbial community that has a separate compartment for exchanging metabolites. This model can predict possible metabolites that cause competition, commensalism, and mutual interactions between species within a microbial community. Our constraint-based community metabolic modeling approach provides insights to elucidate the pattern of metabolic interactions for each common metabolite between two microbes. To validate our approach, we used a toy model and a syntrophic co-culture of Desulfovibrio vulgaris and Methanococcus maripaludis, as well as another in co-culture between Geobacter sulfurreducens and Rhodoferax ferrireducens. For a more general evaluation, we applied our algorithm to the honeybee gut microbiome, composed of seven species, and the epiphyte strain Pantoea eucalypti 299R. The epiphyte strain Pe299R has been previously studied and cultured with six different phyllosphere bacteria. Our algorithm successfully predicts metabolites, which imply mutualistic, competitive, or commensal interactions. In contrast to OptCom, MRO, and MICOM algorithms, our COMMA algorithm shows that the potential for competitive interactions between an epiphytic species and Pe299R is not significant. These results are consistent with the experimental measurements of population density and reproductive success of the Pe299R strain.
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Affiliation(s)
- Soraya Mirzaei
- Department of Mathematical Sciences, Sharif University of Technology, Tehran, Iran
| | - Mojtaba Tefagh
- Department of Mathematical Sciences, Sharif University of Technology, Tehran, Iran
- Center for Information Systems & Data Science, Institute for Convergence Science & Technology, Sharif University of Technology, Tehran, Iran
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Raghu AK, Palanikumar I, Raman K. Designing function-specific minimal microbiomes from large microbial communities. NPJ Syst Biol Appl 2024; 10:46. [PMID: 38702322 PMCID: PMC11068740 DOI: 10.1038/s41540-024-00373-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2023] [Accepted: 04/17/2024] [Indexed: 05/06/2024] Open
Abstract
Microorganisms exist in large communities of diverse species, exhibiting various functionalities. The mammalian gut microbiome, for instance, has the functionality of digesting dietary fibre and producing different short-chain fatty acids. Not all microbes present in a community contribute to a given functionality; it is possible to find a minimal microbiome, which is a subset of the large microbiome, that is capable of performing the functionality while maintaining other community properties such as growth rate and metabolite production. Such a minimal microbiome will also contain keystone species for SCFA production in that community. In this work, we present a systematic constraint-based approach to identify a minimal microbiome from a large community for a user-proposed function. We employ a top-down approach with sequential deletion followed by solving a mixed-integer linear programming problem with the objective of minimising the L1-norm of the membership vector. Notably, we consider quantitative measures of community growth rate and metabolite production rates. We demonstrate the utility of our algorithm by identifying the minimal microbiomes corresponding to three model communities of the gut, and discuss their validity based on the presence of the keystone species in the community. Our approach is generic, flexible and finds application in studying a variety of microbial communities. The algorithm is available from https://github.com/RamanLab/minMicrobiome .
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Affiliation(s)
- Aswathy K Raghu
- Centre for Integrative Biology and Systems mEdicine (IBSE), Indian Institute of Technology (IIT) Madras, Chennai, 600 036, India
- Robert Bosch Centre for Data Science and Artificial Intelligence (RBCDSAI), IIT Madras, Chennai, 600 036, India
- Department of Chemical and Biological Engineering, Northwestern University, IL, 60208, USA
| | - Indumathi Palanikumar
- Centre for Integrative Biology and Systems mEdicine (IBSE), Indian Institute of Technology (IIT) Madras, Chennai, 600 036, India
- Robert Bosch Centre for Data Science and Artificial Intelligence (RBCDSAI), IIT Madras, Chennai, 600 036, India
- Department of Biotechnology, Bhupat Jyoti Mehta School of Biosciences, IIT Madras, Chennai, 600 036, India
| | - Karthik Raman
- Centre for Integrative Biology and Systems mEdicine (IBSE), Indian Institute of Technology (IIT) Madras, Chennai, 600 036, India.
- Robert Bosch Centre for Data Science and Artificial Intelligence (RBCDSAI), IIT Madras, Chennai, 600 036, India.
- Department of Biotechnology, Bhupat Jyoti Mehta School of Biosciences, IIT Madras, Chennai, 600 036, India.
- Department of Data Science and AI, Wadhwani School of Data Science and AI, IIT Madras, Chennai, 600 036, India.
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11
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Silva-Andrade C, Rodriguez-Fernández M, Garrido D, Martin AJM. Using metabolic networks to predict cross-feeding and competition interactions between microorganisms. Microbiol Spectr 2024; 12:e0228723. [PMID: 38506512 PMCID: PMC11064492 DOI: 10.1128/spectrum.02287-23] [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: 06/07/2023] [Accepted: 02/06/2024] [Indexed: 03/21/2024] Open
Abstract
Understanding the interactions between microorganisms and their impact on bacterial behavior at the community level is a key research topic in microbiology. Different methods, relying on experimental or mathematical approaches based on the diverse properties of bacteria, are currently employed to study these interactions. Recently, the use of metabolic networks to understand the interactions between bacterial pairs has increased, highlighting the relevance of this approach in characterizing bacteria. In this study, we leverage the representation of bacteria through their metabolic networks to build a predictive model aimed at reducing the number of experimental assays required for designing bacterial consortia with specific behaviors. Our novel method for predicting cross-feeding or competition interactions between pairs of microorganisms utilizes metabolic network features. Machine learning classifiers are employed to determine the type of interaction from automatically reconstructed metabolic networks. Several algorithms were assessed and selected based on comprehensive testing and careful separation of manually compiled data sets obtained from literature sources. We used different classification algorithms, including K Nearest Neighbors, XGBoost, Support Vector Machine, and Random Forest, tested different parameter values, and implemented several data curation approaches to reduce the biological bias associated with our data set, ultimately achieving an accuracy of over 0.9. Our method holds substantial potential to advance the understanding of community behavior and contribute to the development of more effective approaches for consortia design.IMPORTANCEUnderstanding bacterial interactions at the community level is critical for microbiology, and leveraging metabolic networks presents an efficient and effective approach. The introduction of this novel method for predicting interactions through machine learning classifiers has the potential to advance the field by reducing the number of experimental assays required and contributing to the development of more effective bacterial consortia.
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Affiliation(s)
- Claudia Silva-Andrade
- Programa de Doctorado en Genómica Integrativa, Vicerrectoría de Investigación, Universidad Mayor, Santiago, Chile
- Laboratorio de Redes Biológicas, Centro Científico y Tecnológico de Excelencia Ciencia & Vida, Fundación Ciencia & Vida, Santiago, Chile
| | - María Rodriguez-Fernández
- Institute for Biological and Medical Engineering, Schools of Engineering, Medicine and Biological Sciences, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Daniel Garrido
- Department of Chemical and Bioprocess Engineering, School of Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Alberto J. M. Martin
- Laboratorio de Redes Biológicas, Centro Científico y Tecnológico de Excelencia Ciencia & Vida, Fundación Ciencia & Vida, Santiago, Chile
- Escuela de Ingeniería, Facultad de Ingeniería, Arquitectura y Diseño, Universidad San Sebastián, Santiago, Chile
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12
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Liu Y, Xue B, Liu H, Wang S, Su H. Rational construction of synthetic consortia: Key considerations and model-based methods for guiding the development of a novel biosynthesis platform. Biotechnol Adv 2024; 72:108348. [PMID: 38531490 DOI: 10.1016/j.biotechadv.2024.108348] [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: 02/04/2024] [Revised: 03/07/2024] [Accepted: 03/13/2024] [Indexed: 03/28/2024]
Abstract
The rapid development of synthetic biology has significantly improved the capabilities of mono-culture systems in converting different substrates into various value-added bio-chemicals through metabolic engineering. However, overexpression of biosynthetic pathways in recombinant strains can impose a heavy metabolic burden on the host, resulting in imbalanced energy distribution and negatively affecting both cell growth and biosynthesis capacity. Synthetic consortia, consisting of two or more microbial species or strains with complementary functions, have emerged as a promising and efficient platform to alleviate the metabolic burden and increase product yield. However, research on synthetic consortia is still in its infancy, with numerous challenges regarding the design and construction of stable synthetic consortia. This review provides a comprehensive comparison of the advantages and disadvantages of mono-culture systems and synthetic consortia. Key considerations for engineering synthetic consortia based on recent advances are summarized, and simulation and computational tools for guiding the advancement of synthetic consortia are discussed. Moreover, further development of more efficient and cost-effective synthetic consortia with emerging technologies such as artificial intelligence and machine learning is highlighted.
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Affiliation(s)
- Yu Liu
- Beijing Key Laboratory of Bioprocess, Beijing Advanced Innovation Center for Soft Matter Science and Engineering, State Key Laboratory of Chemical Resource Engineering, Beijing University of Chemical Technology, Beijing 100029, People's Republic of China
| | - Boyuan Xue
- Beijing Key Laboratory of Bioprocess, Beijing Advanced Innovation Center for Soft Matter Science and Engineering, State Key Laboratory of Chemical Resource Engineering, Beijing University of Chemical Technology, Beijing 100029, People's Republic of China
| | - Hao Liu
- Beijing Key Laboratory of Bioprocess, Beijing Advanced Innovation Center for Soft Matter Science and Engineering, State Key Laboratory of Chemical Resource Engineering, Beijing University of Chemical Technology, Beijing 100029, People's Republic of China
| | - Shaojie Wang
- Beijing Key Laboratory of Bioprocess, Beijing Advanced Innovation Center for Soft Matter Science and Engineering, State Key Laboratory of Chemical Resource Engineering, Beijing University of Chemical Technology, Beijing 100029, People's Republic of China.
| | - Haijia Su
- Beijing Key Laboratory of Bioprocess, Beijing Advanced Innovation Center for Soft Matter Science and Engineering, State Key Laboratory of Chemical Resource Engineering, Beijing University of Chemical Technology, Beijing 100029, People's Republic of China.
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13
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Hashemi S, Laitinen R, Nikoloski Z. Models and molecular mechanisms for trade-offs in the context of metabolism. Mol Ecol 2024; 33:e16879. [PMID: 36773330 DOI: 10.1111/mec.16879] [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: 08/10/2022] [Revised: 01/19/2023] [Accepted: 02/07/2023] [Indexed: 02/13/2023]
Abstract
Accumulating evidence for trade-offs involving metabolic traits has demonstrated their importance in the evolution of organisms. Metabolic models with different levels of complexity have already been considered when investigating mechanisms that explain various metabolic trade-offs. Here we provide a systematic review of modelling approaches that have been used to study and explain trade-offs between: (i) the kinetic properties of individual enzymes, (ii) rates of metabolic reactions, (iii) the rate and yield of metabolic pathways and networks, (iv) different metabolic objectives in single organisms and in metabolic communities, and (v) metabolic concentrations. In providing insights into the mechanisms underlying these five types of metabolic trade-offs obtained from constraint-based metabolic modelling, we emphasize the relationship of metabolic trade-offs to the classical black box Y-model that provides a conceptual explanation for resource acquisition-allocation trade-offs. In addition, we identify several pressing concerns and offer a perspective for future research in the identification and manipulation of metabolic trade-offs by relying on the toolbox provided by constraint-based metabolic modelling for single organisms and microbial communities.
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Affiliation(s)
- Seirana Hashemi
- Bioinformatics, Institute of Biochemistry and Biology, University of Potsdam, Potsdam, Germany
- Systems Biology and Mathematical Modelling, Max Planck Institute of Molecular Plant Physiology, Potsdam, Germany
| | - Roosa Laitinen
- Faculty of Biological and Environmental Sciences, University of Helsinki, Helsinki, Finland
| | - Zoran Nikoloski
- Bioinformatics, Institute of Biochemistry and Biology, University of Potsdam, Potsdam, Germany
- Systems Biology and Mathematical Modelling, Max Planck Institute of Molecular Plant Physiology, Potsdam, Germany
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14
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Srinivasan S, Jnana A, Murali TS. Modeling Microbial Community Networks: Methods and Tools for Studying Microbial Interactions. MICROBIAL ECOLOGY 2024; 87:56. [PMID: 38587642 PMCID: PMC11001700 DOI: 10.1007/s00248-024-02370-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/01/2024] [Accepted: 03/28/2024] [Indexed: 04/09/2024]
Abstract
Microbial interactions function as a fundamental unit in complex ecosystems. By characterizing the type of interaction (positive, negative, neutral) occurring in these dynamic systems, one can begin to unravel the role played by the microbial species. Towards this, various methods have been developed to decipher the function of the microbial communities. The current review focuses on the various qualitative and quantitative methods that currently exist to study microbial interactions. Qualitative methods such as co-culturing experiments are visualized using microscopy-based techniques and are combined with data obtained from multi-omics technologies (metagenomics, metabolomics, metatranscriptomics). Quantitative methods include the construction of networks and network inference, computational models, and development of synthetic microbial consortia. These methods provide a valuable clue on various roles played by interacting partners, as well as possible solutions to overcome pathogenic microbes that can cause life-threatening infections in susceptible hosts. Studying the microbial interactions will further our understanding of complex less-studied ecosystems and enable design of effective frameworks for treatment of infectious diseases.
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Affiliation(s)
- Shanchana Srinivasan
- Department of Public Health Genomics, Manipal School of Life Sciences, Manipal Academy of Higher Education, Manipal, 576104, India
| | - Apoorva Jnana
- Department of Public Health Genomics, Manipal School of Life Sciences, Manipal Academy of Higher Education, Manipal, 576104, India
| | - Thokur Sreepathy Murali
- Department of Public Health Genomics, Manipal School of Life Sciences, Manipal Academy of Higher Education, Manipal, 576104, India.
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15
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Xue B, Liu Y, Yang C, Liu H, Yuan Q, Wang S, Su H. Co-Cultivated Enzyme Constraint Metabolic Network Model for Rational Guidance in Constructing Synthetic Consortia to Achieve Optimal Pathway Allocation Prediction. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024; 11:e2306662. [PMID: 38093511 PMCID: PMC10916542 DOI: 10.1002/advs.202306662] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Revised: 10/23/2023] [Indexed: 03/07/2024]
Abstract
Synthetic consortia have emerged as a promising biosynthetic platform that offers new opportunities for biosynthesis. Genome-scale metabolic network models (GEMs) with complex constraints are extensively utilized to guide the synthesis in monocultures. However, few methods are currently available to guide the rational construction of synthetic consortia for predicting the optimal allocation strategy of synthetic pathways aimed at enhancing product synthesis. A standardized method to construct the co-cultivated Enzyme Constraint metabolic network model (CulECpy) is proposed, which integrates enzyme constraints and modular interaction scale constraints based on the research concept of "independent + global". This method is applied to construct several synthetic consortia models, which encompassed different target products, strains, synthetic pathways, and compositional structures. Analyzing the model, the optimal pathway allocation and initial inoculum ratio that enhance the synthesis of target products by synthetic consortia are predicted and verified. When comparing with the constructed co-culture synthesis system, the normalized root mean square error of all optimal theoretical yield simulations is found to be less than or equal to 0.25. The analyses and verifications demonstrate that the method CulECpy can guide the rational construction of synthetic consortia systems to facilitate biochemical synthesis.
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Affiliation(s)
- Boyuan Xue
- Beijing Key Laboratory of Bioprocessand Beijing Advanced Innovation Center for Soft Matter Science and EngineeringBeijing University of Chemical TechnologyBeijing100029P. R. China
| | - Yu Liu
- Beijing Key Laboratory of Bioprocessand Beijing Advanced Innovation Center for Soft Matter Science and EngineeringBeijing University of Chemical TechnologyBeijing100029P. R. China
| | - Chen Yang
- Beijing Key Laboratory of Bioprocessand Beijing Advanced Innovation Center for Soft Matter Science and EngineeringBeijing University of Chemical TechnologyBeijing100029P. R. China
| | - Hao Liu
- Beijing Key Laboratory of Bioprocessand Beijing Advanced Innovation Center for Soft Matter Science and EngineeringBeijing University of Chemical TechnologyBeijing100029P. R. China
| | - Qianqian Yuan
- Biodesign CenterKey Laboratory of Engineering Biology for Low‐carbon ManufacturingTianjin Institute of Industrial BiotechnologyChinese Academy of SciencesTianjin300308P. R. China
| | - Shaojie Wang
- Beijing Key Laboratory of Bioprocessand Beijing Advanced Innovation Center for Soft Matter Science and EngineeringBeijing University of Chemical TechnologyBeijing100029P. R. China
| | - Haijia Su
- Beijing Key Laboratory of Bioprocessand Beijing Advanced Innovation Center for Soft Matter Science and EngineeringBeijing University of Chemical TechnologyBeijing100029P. R. China
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16
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Marbehan X, Roger M, Fournier F, Infossi P, Guedon E, Delecourt L, Lebrun R, Giudici-Orticoni MT, Delaunay S. Combining metabolic flux analysis with proteomics to shed light on the metabolic flexibility: the case of Desulfovibrio vulgaris Hildenborough. Front Microbiol 2024; 15:1336360. [PMID: 38463485 PMCID: PMC10920352 DOI: 10.3389/fmicb.2024.1336360] [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: 11/10/2023] [Accepted: 01/24/2024] [Indexed: 03/12/2024] Open
Abstract
Introduction Desulfovibrio vulgaris Hildenborough is a gram-negative anaerobic bacterium belonging to the sulfate-reducing bacteria that exhibits highly versatile metabolism. By switching from one energy mode to another depending on nutrients availability in the environments" it plays a central role in shaping ecosystems. Despite intensive efforts to study D. vulgaris energy metabolism at the genomic, biochemical and ecological level, bioenergetics in this microorganism remain far from being fully understood. Alternatively, metabolic modeling is a powerful tool to understand bioenergetics. However, all the current models for D. vulgaris appeared to be not easily adaptable to various environmental conditions. Methods To lift off these limitations, here we constructed a novel transparent and robust metabolic model to explain D. vulgaris bioenergetics by combining whole-cell proteomic analysis with modeling approaches (Flux Balance Analysis). Results The iDvu71 model showed over 0.95 correlation with experimental data. Further simulations allowed a detailed description of D. vulgaris metabolism in various conditions of growth. Altogether, the simulations run in this study highlighted the sulfate-to-lactate consumption ratio as a pivotal factor in D. vulgaris energy metabolism. Discussion In particular, the impact on the hydrogen/formate balance and biomass synthesis is discussed. Overall, this study provides a novel insight into D. vulgaris metabolic flexibility.
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Affiliation(s)
| | - Magali Roger
- BIP-UMR 7281, Laboratoire de Bioénergétique et Ingénierie des Protéines, Aix-Marseille Université, CNRS, Marseille, France
| | | | - Pascale Infossi
- BIP-UMR 7281, Laboratoire de Bioénergétique et Ingénierie des Protéines, Aix-Marseille Université, CNRS, Marseille, France
| | | | - Louis Delecourt
- BIP-UMR 7281, Laboratoire de Bioénergétique et Ingénierie des Protéines, Aix-Marseille Université, CNRS, Marseille, France
- LISM-UMR 7255, Laboratoire d’Ingénierie des Systèmes Macromoléculaires, Aix-Marseille Université, CNRS, Marseille, France
| | - Régine Lebrun
- IMM-FR3479, Marseille Protéomique, Aix-Marseille Université, CNRS, Marseille, France
| | - Marie-Thérèse Giudici-Orticoni
- BIP-UMR 7281, Laboratoire de Bioénergétique et Ingénierie des Protéines, Aix-Marseille Université, CNRS, Marseille, France
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17
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Brunner JD, Chia N. Metabolic model-based ecological modeling for probiotic design. eLife 2024; 13:e83690. [PMID: 38380900 PMCID: PMC10942782 DOI: 10.7554/elife.83690] [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: 09/24/2022] [Accepted: 02/19/2024] [Indexed: 02/22/2024] Open
Abstract
The microbial community composition in the human gut has a profound effect on human health. This observation has lead to extensive use of microbiome therapies, including over-the-counter 'probiotic' treatments intended to alter the composition of the microbiome. Despite so much promise and commercial interest, the factors that contribute to the success or failure of microbiome-targeted treatments remain unclear. We investigate the biotic interactions that lead to successful engraftment of a novel bacterial strain introduced to the microbiome as in probiotic treatments. We use pairwise genome-scale metabolic modeling with a generalized resource allocation constraint to build a network of interactions between taxa that appear in an experimental engraftment study. We create induced sub-graphs using the taxa present in individual samples and assess the likelihood of invader engraftment based on network structure. To do so, we use a generalized Lotka-Volterra model, which we show has strong ability to predict if a particular invader or probiotic will successfully engraft into an individual's microbiome. Furthermore, we show that the mechanistic nature of the model is useful for revealing which microbe-microbe interactions potentially drive engraftment.
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Affiliation(s)
- James D Brunner
- Biosciences Division, Los Alamos National LaboratoryLos AlamosUnited States
- Center for Nonlinear Studies, Los Alamos National LaboratoryLos AlamosUnited States
| | - Nicholas Chia
- Data Science and Learning, Argonne National LaboratoryLemontUnited States
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18
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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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19
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Joseph C, Zafeiropoulos H, Bernaerts K, Faust K. Predicting microbial interactions with approaches based on flux balance analysis: an evaluation. BMC Bioinformatics 2024; 25:36. [PMID: 38262921 PMCID: PMC10804772 DOI: 10.1186/s12859-024-05651-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2023] [Accepted: 01/11/2024] [Indexed: 01/25/2024] Open
Abstract
BACKGROUND Given a genome-scale metabolic model (GEM) of a microorganism and criteria for optimization, flux balance analysis (FBA) predicts the optimal growth rate and its corresponding flux distribution for a specific medium. FBA has been extended to microbial consortia and thus can be used to predict interactions by comparing in-silico growth rates for co- and monocultures. Although FBA-based methods for microbial interaction prediction are becoming popular, a systematic evaluation of their accuracy has not yet been performed. RESULTS Here, we evaluate the accuracy of FBA-based predictions of human and mouse gut bacterial interactions using growth data from the literature. For this, we collected 26 GEMs from the semi-curated AGORA database as well as four previously published curated GEMs. We tested the accuracy of three tools (COMETS, Microbiome Modeling Toolbox and MICOM) by comparing growth rates predicted in mono- and co-culture to growth rates extracted from the literature and also investigated the impact of different tool settings and media. We found that except for curated GEMs, predicted growth rates and their ratios (i.e. interaction strengths) do not correlate with growth rates and interaction strengths obtained from in vitro data. CONCLUSIONS Prediction of growth rates with FBA using semi-curated GEMs is currently not sufficiently accurate to predict interaction strengths reliably.
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Affiliation(s)
- Clémence Joseph
- Department of Microbiology, Immunology and Transplantation, Rega Institute for Medical Research, Laboratory of Molecular Bacteriology, KU Leuven, 3000, Leuven, Belgium
| | - Haris Zafeiropoulos
- Department of Microbiology, Immunology and Transplantation, Rega Institute for Medical Research, Laboratory of Molecular Bacteriology, KU Leuven, 3000, Leuven, Belgium
| | - Kristel Bernaerts
- Department of Chemical Engineering, Chemical and Biochemical Reactor Engineering and Safety (CREaS), KU Leuven, 3001, Leuven, Belgium
| | - Karoline Faust
- Department of Microbiology, Immunology and Transplantation, Rega Institute for Medical Research, Laboratory of Molecular Bacteriology, KU Leuven, 3000, Leuven, Belgium.
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20
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Lyu X, Nuhu M, Candry P, Wolfanger J, Betenbaugh M, Saldivar A, Zuniga C, Wang Y, Shrestha S. Top-down and bottom-up microbiome engineering approaches to enable biomanufacturing from waste biomass. J Ind Microbiol Biotechnol 2024; 51:kuae025. [PMID: 39003244 PMCID: PMC11287213 DOI: 10.1093/jimb/kuae025] [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: 04/11/2024] [Accepted: 07/12/2024] [Indexed: 07/15/2024]
Abstract
Growing environmental concerns and the need to adopt a circular economy have highlighted the importance of waste valorization for resource recovery. Microbial consortia-enabled biotechnologies have made significant developments in the biomanufacturing of valuable resources from waste biomass that serve as suitable alternatives to petrochemical-derived products. These microbial consortia-based processes are designed following a top-down or bottom-up engineering approach. The top-down approach is a classical method that uses environmental variables to selectively steer an existing microbial consortium to achieve a target function. While high-throughput sequencing has enabled microbial community characterization, the major challenge is to disentangle complex microbial interactions and manipulate the structure and function accordingly. The bottom-up approach uses prior knowledge of the metabolic pathway and possible interactions among consortium partners to design and engineer synthetic microbial consortia. This strategy offers some control over the composition and function of the consortium for targeted bioprocesses, but challenges remain in optimal assembly methods and long-term stability. In this review, we present the recent advancements, challenges, and opportunities for further improvement using top-down and bottom-up approaches for microbiome engineering. As the bottom-up approach is relatively a new concept for waste valorization, this review explores the assembly and design of synthetic microbial consortia, ecological engineering principles to optimize microbial consortia, and metabolic engineering approaches for efficient conversion. Integration of top-down and bottom-up approaches along with developments in metabolic modeling to predict and optimize consortia function are also highlighted. ONE-SENTENCE SUMMARY This review highlights the microbial consortia-driven waste valorization for biomanufacturing through top-down and bottom-up design approaches and describes strategies, tools, and unexplored opportunities to optimize the design and stability of such consortia.
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Affiliation(s)
- Xuejiao Lyu
- Department of Environmental Health and Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Mujaheed Nuhu
- Department of Environmental Health and Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Pieter Candry
- Laboratory of Systems and Synthetic Biology, Wageningen University & Research, 6708 WE Wageningen, The Netherlands
| | - Jenna Wolfanger
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Michael Betenbaugh
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Alexis Saldivar
- Department of Biology, San Diego State University, San Diego, CA 92182-4614, USA
| | - Cristal Zuniga
- Department of Biology, San Diego State University, San Diego, CA 92182-4614, USA
| | - Ying Wang
- Department of Soil and Crop Sciences, Texas A&M University, TX 77843, USA
| | - Shilva Shrestha
- Department of Environmental Health and Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
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21
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Wutkowska M, Tláskal V, Bordel S, Stein LY, Nweze JA, Daebeler A. Leveraging genome-scale metabolic models to understand aerobic methanotrophs. THE ISME JOURNAL 2024; 18:wrae102. [PMID: 38861460 PMCID: PMC11195481 DOI: 10.1093/ismejo/wrae102] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/04/2024] [Revised: 05/20/2024] [Accepted: 06/10/2024] [Indexed: 06/13/2024]
Abstract
Genome-scale metabolic models (GEMs) are valuable tools serving systems biology and metabolic engineering. However, GEMs are still an underestimated tool in informing microbial ecology. Since their first application for aerobic gammaproteobacterial methane oxidizers less than a decade ago, GEMs have substantially increased our understanding of the metabolism of methanotrophs, a microbial guild of high relevance for the natural and biotechnological mitigation of methane efflux to the atmosphere. Particularly, GEMs helped to elucidate critical metabolic and regulatory pathways of several methanotrophic strains, predicted microbial responses to environmental perturbations, and were used to model metabolic interactions in cocultures. Here, we conducted a systematic review of GEMs exploring aerobic methanotrophy, summarizing recent advances, pointing out weaknesses, and drawing out probable future uses of GEMs to improve our understanding of the ecology of methane oxidizers. We also focus on their potential to unravel causes and consequences when studying interactions of methane-oxidizing bacteria with other methanotrophs or members of microbial communities in general. This review aims to bridge the gap between applied sciences and microbial ecology research on methane oxidizers as model organisms and to provide an outlook for future studies.
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Affiliation(s)
- Magdalena Wutkowska
- Institute of Soil Biology and Biogeochemistry, Biology Centre CAS, 370 05 České Budějovice, Czech Republic
| | - Vojtěch Tláskal
- Institute of Soil Biology and Biogeochemistry, Biology Centre CAS, 370 05 České Budějovice, Czech Republic
| | - Sergio Bordel
- Department of Chemical Engineering and Environmental Technology, School of Industrial Engineering, University of Valladolid, Valladolid 47011, Spain
- Institute of Sustainable Processes, Valladolid 47011, Spain
| | - Lisa Y Stein
- Department of Biological Sciences, Faculty of Science, University of Alberta, Edmonton, AB T6G 2E9, Canada
| | - Justus Amuche Nweze
- Institute of Soil Biology and Biogeochemistry, Biology Centre CAS, 370 05 České Budějovice, Czech Republic
- Department of Ecosystem Biology, Faculty of Science, University of South Bohemia, 370 05 České Budějovice, Czech Republic
- Department of Science Laboratory Technology, Faculty of Physical Sciences, University of Nigeria, Nsukka 410001, Nigeria
| | - Anne Daebeler
- Institute of Soil Biology and Biogeochemistry, Biology Centre CAS, 370 05 České Budějovice, Czech Republic
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22
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Ghadermazi P, Chan SHJ. Microbial interactions from a new perspective: reinforcement learning reveals new insights into microbiome evolution. Bioinformatics 2024; 40:btae003. [PMID: 38212999 PMCID: PMC10799744 DOI: 10.1093/bioinformatics/btae003] [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: 08/04/2023] [Revised: 12/24/2023] [Accepted: 01/10/2024] [Indexed: 01/13/2024] Open
Abstract
MOTIVATION Microbes are essential part of all ecosystems, influencing material flow and shaping their surroundings. Metabolic modeling has been a useful tool and provided tremendous insights into microbial community metabolism. However, current methods based on flux balance analysis (FBA) usually fail to predict metabolic and regulatory strategies that lead to long-term survival and stability especially in heterogenous communities. RESULTS Here, we introduce a novel reinforcement learning algorithm, Self-Playing Microbes in Dynamic FBA, which treats microbial metabolism as a decision-making process, allowing individual microbial agents to evolve by learning and adapting metabolic strategies for enhanced long-term fitness. This algorithm predicts what microbial flux regulation policies will stabilize in the dynamic ecosystem of interest in the presence of other microbes with minimal reliance on predefined strategies. Throughout this article, we present several scenarios wherein our algorithm outperforms existing methods in reproducing outcomes, and we explore the biological significance of these predictions. AVAILABILITY AND IMPLEMENTATION The source code for this article is available at: https://github.com/chan-csu/SPAM-DFBA.
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Affiliation(s)
- Parsa Ghadermazi
- Department of Chemical and Biological Engineering, Colorado State University, Fort Collins, CO 80521, United States
| | - Siu Hung Joshua Chan
- Department of Chemical and Biological Engineering, Colorado State University, Fort Collins, CO 80521, United States
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23
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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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Burz SD, Causevic S, Dal Co A, Dmitrijeva M, Engel P, Garrido-Sanz D, Greub G, Hapfelmeier S, Hardt WD, Hatzimanikatis V, Heiman CM, Herzog MKM, Hockenberry A, Keel C, Keppler A, Lee SJ, Luneau J, Malfertheiner L, Mitri S, Ngyuen B, Oftadeh O, Pacheco AR, Peaudecerf F, Resch G, Ruscheweyh HJ, Sahin A, Sanders IR, Slack E, Sunagawa S, Tackmann J, Tecon R, Ugolini GS, Vacheron J, van der Meer JR, Vayena E, Vonaesch P, Vorholt JA. From microbiome composition to functional engineering, one step at a time. Microbiol Mol Biol Rev 2023; 87:e0006323. [PMID: 37947420 PMCID: PMC10732080 DOI: 10.1128/mmbr.00063-23] [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] [Indexed: 11/12/2023] Open
Abstract
SUMMARYCommunities of microorganisms (microbiota) are present in all habitats on Earth and are relevant for agriculture, health, and climate. Deciphering the mechanisms that determine microbiota dynamics and functioning within the context of their respective environments or hosts (the microbiomes) is crucially important. However, the sheer taxonomic, metabolic, functional, and spatial complexity of most microbiomes poses substantial challenges to advancing our knowledge of these mechanisms. While nucleic acid sequencing technologies can chart microbiota composition with high precision, we mostly lack information about the functional roles and interactions of each strain present in a given microbiome. This limits our ability to predict microbiome function in natural habitats and, in the case of dysfunction or dysbiosis, to redirect microbiomes onto stable paths. Here, we will discuss a systematic approach (dubbed the N+1/N-1 concept) to enable step-by-step dissection of microbiome assembly and functioning, as well as intervention procedures to introduce or eliminate one particular microbial strain at a time. The N+1/N-1 concept is informed by natural invasion events and selects culturable, genetically accessible microbes with well-annotated genomes to chart their proliferation or decline within defined synthetic and/or complex natural microbiota. This approach enables harnessing classical microbiological and diversity approaches, as well as omics tools and mathematical modeling to decipher the mechanisms underlying N+1/N-1 microbiota outcomes. Application of this concept further provides stepping stones and benchmarks for microbiome structure and function analyses and more complex microbiome intervention strategies.
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Affiliation(s)
- Sebastian Dan Burz
- Department of Fundamental Microbiology, University of Lausanne, Lausanne, Switzerland
| | - Senka Causevic
- Department of Fundamental Microbiology, University of Lausanne, Lausanne, Switzerland
| | - Alma Dal Co
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
| | - Marija Dmitrijeva
- Department of Molecular Life Sciences, University of Zurich, Zurich, Switzerland
| | - Philipp Engel
- Department of Fundamental Microbiology, University of Lausanne, Lausanne, Switzerland
| | - Daniel Garrido-Sanz
- Department of Fundamental Microbiology, University of Lausanne, Lausanne, Switzerland
| | - Gilbert Greub
- Institut de microbiologie, CHUV University Hospital Lausanne, Lausanne, Switzerland
| | | | | | | | - Clara Margot Heiman
- Department of Fundamental Microbiology, University of Lausanne, Lausanne, Switzerland
| | | | | | - Christoph Keel
- Department of Fundamental Microbiology, University of Lausanne, Lausanne, Switzerland
| | | | - Soon-Jae Lee
- Department of Ecology and Evolution, University of Lausanne, Lausanne, Switzerland
| | - Julien Luneau
- Department of Fundamental Microbiology, University of Lausanne, Lausanne, Switzerland
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
| | - Lukas Malfertheiner
- Department of Molecular Life Sciences, University of Zurich, Zurich, Switzerland
| | - Sara Mitri
- Department of Fundamental Microbiology, University of Lausanne, Lausanne, Switzerland
| | - Bidong Ngyuen
- Institute of Microbiology, ETH Zürich, Zürich, Switzerland
| | - Omid Oftadeh
- Laboratory of Computational Systems Biotechnology, EPF Lausanne, Lausanne, Switzerland
| | | | | | - Grégory Resch
- Center for Research and Innovation in Clinical Pharmaceutical Sciences, CHUV University Hospital Lausanne, Lausanne, Switzerland
| | | | - Asli Sahin
- Laboratory of Computational Systems Biotechnology, EPF Lausanne, Lausanne, Switzerland
| | - Ian R. Sanders
- Department of Ecology and Evolution, University of Lausanne, Lausanne, Switzerland
| | - Emma Slack
- Department of Health Sciences and Technology, ETH Zürich, Zürich, Switzerland
| | | | - Janko Tackmann
- Department of Molecular Life Sciences, University of Zurich, Zurich, Switzerland
| | - Robin Tecon
- Department of Fundamental Microbiology, University of Lausanne, Lausanne, Switzerland
| | | | - Jordan Vacheron
- Department of Fundamental Microbiology, University of Lausanne, Lausanne, Switzerland
| | | | - Evangelia Vayena
- Laboratory of Computational Systems Biotechnology, EPF Lausanne, Lausanne, Switzerland
| | - Pascale Vonaesch
- Department of Fundamental Microbiology, University of Lausanne, Lausanne, Switzerland
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25
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McDaniel EA, Scarborough M, Mulat DG, Lin X, Sampara PS, Olson HM, Young RP, Eder EK, Attah IK, Markillie LM, Hoyt DW, Lipton MS, Hallam SJ, Ziels RM. Diverse electron carriers drive syntrophic interactions in an enriched anaerobic acetate-oxidizing consortium. THE ISME JOURNAL 2023; 17:2326-2339. [PMID: 37880541 PMCID: PMC10689502 DOI: 10.1038/s41396-023-01542-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Revised: 10/09/2023] [Accepted: 10/11/2023] [Indexed: 10/27/2023]
Abstract
In many anoxic environments, syntrophic acetate oxidation (SAO) is a key pathway mediating the conversion of acetate into methane through obligate cross-feeding interactions between SAO bacteria (SAOB) and methanogenic archaea. The SAO pathway is particularly important in engineered environments such as anaerobic digestion (AD) systems operating at thermophilic temperatures and/or with high ammonia. Despite the widespread importance of SAOB to the stability of the AD process, little is known about their in situ physiologies due to typically low biomass yields and resistance to isolation. Here, we performed a long-term (300-day) continuous enrichment of a thermophilic (55 °C) SAO community from a municipal AD system using acetate as the sole carbon source. Over 80% of the enriched bioreactor metagenome belonged to a three-member consortium, including an acetate-oxidizing bacterium affiliated with DTU068 encoding for carbon dioxide, hydrogen, and formate production, along with two methanogenic archaea affiliated with Methanothermobacter_A. Stable isotope probing was coupled with metaproteogenomics to quantify carbon flux into each community member during acetate conversion and inform metabolic reconstruction and genome-scale modeling. This effort revealed that the two Methanothermobacter_A species differed in their preferred electron donors, with one possessing the ability to grow on formate and the other only consuming hydrogen. A thermodynamic analysis suggested that the presence of the formate-consuming methanogen broadened the environmental conditions where ATP production from SAO was favorable. Collectively, these results highlight how flexibility in electron partitioning during SAO likely governs community structure and fitness through thermodynamic-driven mutualism, shedding valuable insights into the metabolic underpinnings of this key functional group within methanogenic ecosystems.
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Affiliation(s)
- Elizabeth A McDaniel
- Department of Civil Engineering, The University of British Columbia, Vancouver, BC, Canada
- Department of Microbiology and Immunology, The University of British Columbia, Vancouver, BC, Canada
| | - Matthew Scarborough
- Department of Civil and Environmental Engineering, University of Vermont, Burlington, VT, USA
| | - Daniel Girma Mulat
- Department of Civil Engineering, The University of British Columbia, Vancouver, BC, Canada
| | - Xuan Lin
- Department of Civil Engineering, The University of British Columbia, Vancouver, BC, Canada
| | - Pranav S Sampara
- Department of Civil Engineering, The University of British Columbia, Vancouver, BC, Canada
| | - Heather M Olson
- Environmental and Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland, WA, USA
| | - Robert P Young
- Environmental and Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland, WA, USA
| | - Elizabeth K Eder
- Environmental and Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland, WA, USA
| | - Isaac K Attah
- Environmental and Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland, WA, USA
| | - Lye Meng Markillie
- Environmental and Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland, WA, USA
| | - David W Hoyt
- Environmental and Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland, WA, USA
| | - Mary S Lipton
- Environmental and Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland, WA, USA
| | - Steven J Hallam
- Department of Microbiology and Immunology, The University of British Columbia, Vancouver, BC, Canada
- ECOSCOPE Training Program, The University of British Columbia, Vancouver, BC, Canada
- Graduate Program in Bioinformatics, The University of British Columbia, Vancouver, BC, Canada
- Genome Science and Technology Program, The University of British Columbia, Vancouver, BC, Canada
- Life Sciences Institute, The University of British Columbia, Vancouver, BC, Canada
| | - Ryan M Ziels
- Department of Civil Engineering, The University of British Columbia, Vancouver, BC, Canada.
- Genome Science and Technology Program, The University of British Columbia, Vancouver, BC, Canada.
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26
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Brunner JD, Gallegos-Graves LA, Kroeger ME. Inferring microbial interactions with their environment from genomic and metagenomic data. PLoS Comput Biol 2023; 19:e1011661. [PMID: 37956203 PMCID: PMC10681327 DOI: 10.1371/journal.pcbi.1011661] [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: 07/05/2023] [Revised: 11/27/2023] [Accepted: 11/04/2023] [Indexed: 11/15/2023] Open
Abstract
Microbial communities assemble through a complex set of interactions between microbes and their environment, and the resulting metabolic impact on the host ecosystem can be profound. Microbial activity is known to impact human health, plant growth, water quality, and soil carbon storage which has lead to the development of many approaches and products meant to manipulate the microbiome. In order to understand, predict, and improve microbial community engineering, genome-scale modeling techniques have been developed to translate genomic data into inferred microbial dynamics. However, these techniques rely heavily on simulation to draw conclusions which may vary with unknown parameters or initial conditions, rather than more robust qualitative analysis. To better understand microbial community dynamics using genome-scale modeling, we provide a tool to investigate the network of interactions between microbes and environmental metabolites over time. Using our previously developed algorithm for simulating microbial communities from genome-scale metabolic models (GSMs), we infer the set of microbe-metabolite interactions within a microbial community in a particular environment. Because these interactions depend on the available environmental metabolites, we refer to the networks that we infer as metabolically contextualized, and so name our tool MetConSIN: Metabolically Contextualized Species Interaction Networks.
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Affiliation(s)
- James D. Brunner
- Biosciences Division, Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America
- Center for Nonlinear Studies, Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America
| | | | - Marie E. Kroeger
- Biosciences Division, Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America
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27
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Walsh LH, Coakley M, Walsh AM, O'Toole PW, Cotter PD. Bioinformatic approaches for studying the microbiome of fermented food. Crit Rev Microbiol 2023; 49:693-725. [PMID: 36287644 DOI: 10.1080/1040841x.2022.2132850] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2022] [Revised: 08/11/2022] [Accepted: 09/28/2022] [Indexed: 11/03/2022]
Abstract
High-throughput DNA sequencing-based approaches continue to revolutionise our understanding of microbial ecosystems, including those associated with fermented foods. Metagenomic and metatranscriptomic approaches are state-of-the-art biological profiling methods and are employed to investigate a wide variety of characteristics of microbial communities, such as taxonomic membership, gene content and the range and level at which these genes are expressed. Individual groups and consortia of researchers are utilising these approaches to produce increasingly large and complex datasets, representing vast populations of microorganisms. There is a corresponding requirement for the development and application of appropriate bioinformatic tools and pipelines to interpret this data. This review critically analyses the tools and pipelines that have been used or that could be applied to the analysis of metagenomic and metatranscriptomic data from fermented foods. In addition, we critically analyse a number of studies of fermented foods in which these tools have previously been applied, to highlight the insights that these approaches can provide.
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Affiliation(s)
- Liam H Walsh
- Teagasc Food Research Centre, Moorepark, Fermoy, Cork, Ireland
- School of Microbiology, University College Cork, Ireland
| | - Mairéad Coakley
- Teagasc Food Research Centre, Moorepark, Fermoy, Cork, Ireland
| | - Aaron M Walsh
- Teagasc Food Research Centre, Moorepark, Fermoy, Cork, Ireland
| | - Paul W O'Toole
- School of Microbiology, University College Cork, Ireland
- APC Microbiome Ireland, University College Cork, Ireland
| | - Paul D Cotter
- Teagasc Food Research Centre, Moorepark, Fermoy, Cork, Ireland
- APC Microbiome Ireland, University College Cork, Ireland
- VistaMilk SFI Research Centre, Teagasc, Moorepark, Fermoy, Cork, Ireland
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28
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Jiménez NE, Acuña V, Cortés MP, Eveillard D, Maass AE. Unveiling abundance-dependent metabolic phenotypes of microbial communities. mSystems 2023; 8:e0049223. [PMID: 37668446 PMCID: PMC10654064 DOI: 10.1128/msystems.00492-23] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2023] [Accepted: 06/21/2023] [Indexed: 09/06/2023] Open
Abstract
IMPORTANCE In nature, organisms live in communities and not as isolated species, and their interactions provide a source of resilience to environmental disturbances. Despite their importance in ecology, human health, and industry, understanding how organisms interact in different environments remains an open question. In this work, we provide a novel approach that, only using genomic information, studies the metabolic phenotype exhibited by communities, where the exploration of suboptimal growth flux distributions and the composition of a community allows to unveil its capacity to respond to environmental changes, shedding light of the degrees of metabolic plasticity inherent to the community.
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Affiliation(s)
- Natalia E. Jiménez
- Center for Mathematical Modeling, University of Chile, Santiago, Chile
- Center for Genome Regulation, Millennium Institute, University of Chile, Santiago, Chile
| | - Vicente Acuña
- Center for Mathematical Modeling, University of Chile, Santiago, Chile
- Center for Genome Regulation, Millennium Institute, University of Chile, Santiago, Chile
| | - María Paz Cortés
- Center for Mathematical Modeling, University of Chile, Santiago, Chile
| | | | - Alejandro Eduardo Maass
- Center for Mathematical Modeling, University of Chile, Santiago, Chile
- Center for Genome Regulation, Millennium Institute, University of Chile, Santiago, Chile
- Department of Mathematical Engineering, University of Chile, Santiago, Chile
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29
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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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30
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Griesemer M, Navid A. Uses of Multi-Objective Flux Analysis for Optimization of Microbial Production of Secondary Metabolites. Microorganisms 2023; 11:2149. [PMID: 37763993 PMCID: PMC10536367 DOI: 10.3390/microorganisms11092149] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2023] [Revised: 08/07/2023] [Accepted: 08/16/2023] [Indexed: 09/29/2023] Open
Abstract
Secondary metabolites are not essential for the growth of microorganisms, but they play a critical role in how microbes interact with their surroundings. In addition to this important ecological role, secondary metabolites also have a variety of agricultural, medicinal, and industrial uses, and thus the examination of secondary metabolism of plants and microbes is a growing scientific field. While the chemical production of certain secondary metabolites is possible, industrial-scale microbial production is a green and economically attractive alternative. This is even more true, given the advances in bioengineering that allow us to alter the workings of microbes in order to increase their production of compounds of interest. This type of engineering requires detailed knowledge of the "chassis" organism's metabolism. Since the resources and the catalytic capacity of enzymes in microbes is finite, it is important to examine the tradeoffs between various bioprocesses in an engineered system and alter its working in a manner that minimally perturbs the robustness of the system while allowing for the maximum production of a product of interest. The in silico multi-objective analysis of metabolism using genome-scale models is an ideal method for such examinations.
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Affiliation(s)
| | - Ali Navid
- Lawrence Livermore National Laboratory, Biosciences & Biotechnology Division, Physical & Life Sciences Directorate, Livermore, CA 94550, USA
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31
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Theorell A, Stelling J. Assumptions on decision making and environment can yield multiple steady states in microbial community models. BMC Bioinformatics 2023; 24:262. [PMID: 37349675 DOI: 10.1186/s12859-023-05325-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Accepted: 05/05/2023] [Indexed: 06/24/2023] Open
Abstract
BACKGROUND Microbial community simulations using genome scale metabolic networks (GSMs) are relevant for many application areas, such as the analysis of the human microbiome. Such simulations rely on assumptions about the culturing environment, affecting if the culture may reach a metabolically stationary state with constant microbial concentrations. They also require assumptions on decision making by the microbes: metabolic strategies can be in the interest of individual community members or of the whole community. However, the impact of such common assumptions on community simulation results has not been investigated systematically. RESULTS Here, we investigate four combinations of assumptions, elucidate how they are applied in literature, provide novel mathematical formulations for their simulation, and show how the resulting predictions differ qualitatively. Our results stress that different assumption combinations give qualitatively different predictions on microbial coexistence by differential substrate utilization. This fundamental mechanism is critically under explored in the steady state GSM literature with its strong focus on coexistence states due to crossfeeding (division of labor). Furthermore, investigating a realistic synthetic community, where the two involved strains exhibit no growth in isolation, but grow as a community, we predict multiple modes of cooperation, even without an explicit cooperation mechanism. CONCLUSIONS Steady state GSM modelling of microbial communities relies both on assumed decision making principles and environmental assumptions. In principle, dynamic flux balance analysis addresses both. In practice, our methods that address the steady state directly may be preferable, especially if the community is expected to display multiple steady states.
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Affiliation(s)
- Axel Theorell
- Department of Biosystems Science and Engineering (D-BSSE) and SIB Swiss Institute of Bioinformatics, ETH Zurich, 4058, Basel, Switzerland.
| | - Jörg Stelling
- Department of Biosystems Science and Engineering (D-BSSE) and SIB Swiss Institute of Bioinformatics, ETH Zurich, 4058, Basel, Switzerland.
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32
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Kundu P, Ghosh A. Genome-scale community modeling for deciphering the inter-microbial metabolic interactions in fungus-farming termite gut microbiome. Comput Biol Med 2023; 154:106600. [PMID: 36739820 DOI: 10.1016/j.compbiomed.2023.106600] [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: 09/27/2022] [Revised: 12/27/2022] [Accepted: 01/22/2023] [Indexed: 01/27/2023]
Abstract
Specialized microbial communities in the fungus-farming termite gut and fungal comb microbiome help maintain host nutrition through interactive biochemical activities of complex carbohydrate degradation. Numerous research studies have been focused on identifying the microbial species in the termite gut and fungal comb microbiota, but the community-wide metabolic interaction patterns remain obscure. The inter-microbial metabolic interactions in the community environment are essential for executing biochemical processes like complex carbohydrate degradation and maintaining the host's physicochemical homeostasis. Recent progress in high-throughput sequencing techniques and mathematical modeling provides suitable platforms for constructing multispecies genome-scale community metabolic models that can render sound knowledge about microbial metabolic interaction patterns. Here, we have implemented the genome-scale metabolic modeling strategy to map the relationship between genes, proteins, and reactions of 12 key bacterial species from fungal cultivating termite gut and fungal comb microbiota. The resulting individual genome-scale metabolic models (GEMs) have been analyzed using flux balance analysis (FBA) to optimize the metabolic flux distribution pattern. Further, these individual GEMs have been integrated into genome-scale community metabolic models where a heuristics-based computational procedure has been employed to track the inter-microbial metabolic interactions. Two separate genome-scale community metabolic models were reconstructed for the O. badius gut and fungal comb microbiome. Analysis of the community models showed up to ∼167% increased flux range in lignocellulose degradation, amino acid biosynthesis, and nucleotide metabolism pathways. The inter-microbial metabolic exchange of amino acids, SCFAs, and small sugars was also upregulated in the multispecies community for maximum biomass formation. The flux variability analysis (FVA) has also been performed to calculate the feasible flux range of metabolic reactions. Furthermore, based on the calculated metabolic flux values, newly defined parameters, i.e., pairwise metabolic assistance (PMA) and community metabolic assistance (CMA) showed that the microbial species are getting up to 15% higher metabolic benefits in the multispecies community compared to pairwise growth. Assessment of the inter-microbial metabolic interaction patterns through pairwise growth support index (PGSI) indicated an increased mutualistic interaction in the termite gut environment compared to the fungal comb. Thus, this genome-scale community modeling study provides a systematic methodology to understand the inter-microbial interaction patterns with several newly defined parameters like PMA, CMA, and PGSI. The microbial metabolic assistance and interaction patterns derived from this computational approach will enhance the understanding of combinatorial microbial activities and may help develop effective synergistic microcosms to utilize complex plant polymers.
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Affiliation(s)
- Pritam Kundu
- School of Energy Science and Engineering, Indian Institute of Technology Kharagpur, West Bengal, 721302, India
| | - Amit Ghosh
- School of Energy Science and Engineering, Indian Institute of Technology Kharagpur, West Bengal, 721302, India; P.K. Sinha Centre for Bioenergy and Renewables, Indian Institute of Technology Kharagpur, West Bengal, 721302, India.
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33
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Investigating the Unique Ability of Trichodesmium To Fix Carbon and Nitrogen Simultaneously Using MiMoSA. mSystems 2023; 8:e0060120. [PMID: 36598239 PMCID: PMC9948733 DOI: 10.1128/msystems.00601-20] [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] [Indexed: 01/05/2023] Open
Abstract
The open ocean is an extremely competitive environment, partially due to the dearth of nutrients. Trichodesmium erythraeum, a marine diazotrophic cyanobacterium, is a keystone species in the ocean due to its ability to fix nitrogen and leak 30 to 50% into the surrounding environment, providing a valuable source of a necessary macronutrient to other species. While there are other diazotrophic cyanobacteria that play an important role in the marine nitrogen cycle, Trichodesmium is unique in its ability to fix both carbon and nitrogen simultaneously during the day without the use of specialized cells called heterocysts to protect nitrogenase from oxygen. Here, we use the advanced modeling framework called multiscale multiobjective systems analysis (MiMoSA) to investigate how Trichodesmium erythraeum can reduce dimolecular nitrogen to ammonium in the presence of oxygen. Our simulations indicate that nitrogenase inhibition is best modeled as Michealis-Menten competitive inhibition and that cells along the filament maintain microaerobia using high flux through Mehler reactions in order to protect nitrogenase from oxygen. We also examined the effect of location on metabolic flux and found that cells at the end of filaments operate in distinctly different metabolic modes than internal cells despite both operating in a photoautotrophic mode. These results give us important insight into how this species is able to operate photosynthesis and nitrogen fixation simultaneously, giving it a distinct advantage over other diazotrophic cyanobacteria because they can harvest light directly to fuel the energy demand of nitrogen fixation. IMPORTANCE Trichodesmium erythraeum is a marine cyanobacterium responsible for approximately half of all biologically fixed nitrogen, making it an integral part of the global nitrogen cycle. Interestingly, unlike other nitrogen-fixing cyanobacteria, Trichodesmium does not use temporal or spatial separation to protect nitrogenase from oxygen poisoning; instead, it operates photosynthesis and nitrogen fixation reactions simultaneously during the day. Unfortunately, the exact mechanism the cells utilize to operate carbon and nitrogen fixation simultaneously is unknown. Here, we use an advanced metabolic modeling framework to investigate and identify the most likely mechanisms Trichodesmium uses to protect nitrogenase from oxygen. The model predicts that cells operate in a microaerobic mode, using both respiratory and Mehler reactions to dramatically reduce intracellular oxygen concentrations.
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34
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Oarga A, Bannerman BP, Júlvez J. CONTRABASS: exploiting flux constraints in genome-scale models for the detection of vulnerabilities. Bioinformatics 2023; 39:7000333. [PMID: 36692133 PMCID: PMC9907045 DOI: 10.1093/bioinformatics/btad053] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2022] [Revised: 11/16/2022] [Accepted: 01/23/2023] [Indexed: 01/25/2023] Open
Abstract
MOTIVATION Despite the fact that antimicrobial resistance is an increasing health concern, the pace of production of new drugs is slow due to the high cost and uncertain success of the process. The development of high-throughput technologies has allowed the integration of biological data into detailed genome-scale models of multiple organisms. Such models can be exploited by means of computational methods to identify system vulnerabilities such as chokepoint reactions and essential reactions. These vulnerabilities are appealing drug targets that can lead to novel drug developments. However, the current approach to compute these vulnerabilities is only based on topological data and ignores the dynamic information of the model. This can lead to misidentified drug targets. RESULTS This work computes flux constraints that are consistent with a certain growth rate of the modelled organism, and integrates the computed flux constraints into the model to improve the detection of vulnerabilities. By exploiting these flux constraints, we are able to obtain a directionality of the reactions of metabolism consistent with a given growth rate of the model, and consequently, a more realistic detection of vulnerabilities can be performed. Several sets of reactions that are system vulnerabilities are defined and the relationships among them are studied. The approach for the detection of these vulnerabilities has been implemented in the Python tool CONTRABASS. Such tool, for which an online web server has also been implemented, computes flux constraints and generates a report with the detected vulnerabilities. AVAILABILITY AND IMPLEMENTATION CONTRABASS is available as an open source Python package at https://github.com/openCONTRABASS/CONTRABASS under GPL-3.0 License. An online web server is available at http://contrabass.unizar.es. SUPPLEMENTARY INFORMATION A glossary of terms are available at Bioinformatics online.
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Affiliation(s)
- Alexandru Oarga
- Department of Computer Science and Systems Engineering, University of Zaragoza, Zaragoza 50018, Spain
| | - Bridget P Bannerman
- Lucy Cavendish College, Biological Sciences, University of Cambridge, Cambridge CB3 0BU, UK.,Science Resources Foundation, Health Unit, London EC1V 2NX, UK
| | - Jorge Júlvez
- Department of Computer Science and Systems Engineering, University of Zaragoza, Zaragoza 50018, Spain
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35
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Lo Prete C, Radhakrishnan A. Leader-follower equilibria to examine investment decisions in grid resilience-enhancing measures. RISK ANALYSIS : AN OFFICIAL PUBLICATION OF THE SOCIETY FOR RISK ANALYSIS 2023; 43:280-307. [PMID: 35445759 DOI: 10.1111/risa.13921] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Large-area, long-duration power outages are increasingly common in the United States, and cost the economy billions of dollars each year. Building a strategy to enhance grid resilience requires an understanding of the optimal mix of preventive and corrective actions, the inefficiencies that arise when self-interested parties make resilience investment decisions, and the conditions under which regulators may facilitate the realization of efficient market outcomes. We develop a bi-level model to examine the mix of preventive and corrective measures that enhances grid resilience to a severe storm. The model represents a Stackelberg game between a regulated utility (leader) that may harden distribution feeders before a long-duration outage and/or deploy restoration crews after the disruption, and utility customers with varying preferences for reliable power (followers) who may invest in backup generators. We show that the regulator's denial of cost recovery for the utility's preventive expenditures, coupled with the misalignment between private objectives and social welfare maximization, yields significant inefficiencies in the resilience investment mix. Allowing cost recovery for a higher share of the utility's capital expenditures in preventive measures, extending the time horizon associated with damage cost recovery, and adopting a storm restoration compensation mechanism shift the realized market outcome toward the efficient solution. If about one-fifth of preventive resilience investments is approved by regulators, requiring utilities to pay a compensation of $365 per customer for a 3-day outage (about seven times the level of compensation currently offered by US utilities) provides significant incentives toward more efficient preventive resilience investments.
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Affiliation(s)
- Chiara Lo Prete
- John and Willie Leone Family Department of Energy and Mineral Engineering, The Pennsylvania State University, Pennsylvania, USA
| | - Ashish Radhakrishnan
- John and Willie Leone Family Department of Energy and Mineral Engineering, The Pennsylvania State University, Pennsylvania, USA
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36
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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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37
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Fujita H, Ushio M, Suzuki K, Abe MS, Yamamichi M, Okazaki Y, Canarini A, Hayashi I, Fukushima K, Fukuda S, Kiers ET, Toju H. Facilitative interaction networks in experimental microbial community dynamics. Front Microbiol 2023; 14:1153952. [PMID: 37113242 PMCID: PMC10126487 DOI: 10.3389/fmicb.2023.1153952] [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: 01/30/2023] [Accepted: 03/09/2023] [Indexed: 04/29/2023] Open
Abstract
Facilitative interactions between microbial species are ubiquitous in various types of ecosystems on the Earth. Therefore, inferring how entangled webs of interspecific interactions shift through time in microbial ecosystems is an essential step for understanding ecological processes driving microbiome dynamics. By compiling shotgun metagenomic sequencing data of an experimental microbial community, we examined how the architectural features of facilitative interaction networks could change through time. A metabolic modeling approach for estimating dependence between microbial genomes (species) allowed us to infer the network structure of potential facilitative interactions at 13 time points through the 110-day monitoring of experimental microbiomes. We then found that positive feedback loops, which were theoretically predicted to promote cascade breakdown of ecological communities, existed within the inferred networks of metabolic interactions prior to the drastic community-compositional shift observed in the microbiome time-series. We further applied "directed-graph" analyses to pinpoint potential keystone species located at the "upper stream" positions of such feedback loops. These analyses on facilitative interactions will help us understand key mechanisms causing catastrophic shifts in microbial community structure.
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Affiliation(s)
- Hiroaki Fujita
- Center for Ecological Research, Kyoto University, Kyoto, Japan
- *Correspondence: Hiroaki Fujita,
| | - Masayuki Ushio
- Department of Ocean Science (OCES), The Hong Kong University of Science and Technology (HKUST), Kowloon, Hong Kong SAR, China
| | - Kenta Suzuki
- Integrated Bioresource Information Division, BioResource Research Center, RIKEN, Tsukuba, Japan
| | - Masato S. Abe
- Faculty of Culture and Information Science, Doshisha University, Kyoto, Japan
| | - Masato Yamamichi
- School of Biological Sciences, The University of Queensland, Brisbane, QLD, Australia
- Department of International Health and Medical Anthropology, Institute of Tropical Medicine, Nagasaki University, Nagasaki, Japan
| | - Yusuke Okazaki
- Institute for Chemical Research, Kyoto University, Kyoto, Japan
| | | | - Ibuki Hayashi
- Center for Ecological Research, Kyoto University, Kyoto, Japan
| | - Keitaro Fukushima
- Faculty of Food and Agricultural Sciences, Fukushima University, Fukushima, Japan
| | - Shinji Fukuda
- Institute for Advanced Biosciences, Keio University, Tsuruoka, Japan
- Gut Environmental Design Group, Kanagawa Institute of Industrial Science and Technology, Kawasaki, Japan
- Transborder Medical Research Center, University of Tsukuba, Tsukuba, Japan
- Laboratory for Regenerative Microbiology, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - E. Toby Kiers
- Amsterdam Institute for Life and Environment, Vrije Universiteit, Amsterdam, Netherlands
| | - Hirokazu Toju
- Center for Ecological Research, Kyoto University, Kyoto, Japan
- Hirokazu Toju,
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38
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Volk MJ, Tran VG, Tan SI, Mishra S, Fatma Z, Boob A, Li H, Xue P, Martin TA, Zhao H. Metabolic Engineering: Methodologies and Applications. Chem Rev 2022; 123:5521-5570. [PMID: 36584306 DOI: 10.1021/acs.chemrev.2c00403] [Citation(s) in RCA: 29] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
Metabolic engineering aims to improve the production of economically valuable molecules through the genetic manipulation of microbial metabolism. While the discipline is a little over 30 years old, advancements in metabolic engineering have given way to industrial-level molecule production benefitting multiple industries such as chemical, agriculture, food, pharmaceutical, and energy industries. This review describes the design, build, test, and learn steps necessary for leading a successful metabolic engineering campaign. Moreover, we highlight major applications of metabolic engineering, including synthesizing chemicals and fuels, broadening substrate utilization, and improving host robustness with a focus on specific case studies. Finally, we conclude with a discussion on perspectives and future challenges related to metabolic engineering.
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Affiliation(s)
- Michael J Volk
- Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,DOE Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
| | - Vinh G Tran
- Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,DOE Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
| | - Shih-I Tan
- Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,DOE Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,Department of Chemical Engineering, National Cheng Kung University, Tainan 70101, Taiwan
| | - Shekhar Mishra
- Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,DOE Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
| | - Zia Fatma
- Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,DOE Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
| | - Aashutosh Boob
- Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,DOE Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
| | - Hongxiang Li
- Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,Department of Chemistry, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
| | - Pu Xue
- Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,DOE Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
| | - Teresa A Martin
- Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,DOE Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
| | - Huimin Zhao
- Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,DOE Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,Department of Chemistry, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
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39
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Sen P. Flux balance analysis of metabolic networks for efficient engineering of microbial cell factories. Biotechnol Genet Eng Rev 2022:1-34. [PMID: 36476223 DOI: 10.1080/02648725.2022.2152631] [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: 05/31/2022] [Accepted: 11/16/2022] [Indexed: 12/14/2022]
Abstract
Metabolic engineering principles have long been applied to explore the metabolic networks of complex microbial cell factories under a variety of environmental constraints for effective deployment of the microorganisms in the optimal production of biochemicals like biofuels, polymers, amino acids, recombinant proteins. One of the methodologies used for analyzing microbial metabolic networks is the Flux Balance Analysis (FBA), which employs applications of optimization techniques for forecasting biomass growth and metabolic flux distribution of industrially important products under specified environmental conditions. The in silico flux simulations are instrumental for designing the production-specific microbial cell factories. However, FBA has some inherent limitations. The present review emphasizes how the incorporation of additional kinetic, thermodynamic, expression and regulatory constraints and integration of omics data into the classical FBA platform improve the prediction accuracy of FBA. A programmed comparison of the simulated data with the experimental observations is presented for supporting the claim. The review further accounts for the successful implementation of classical FBA in biotechnological applications and identifies areas in which classical FBA fails to make correct predictions. The analysis of the predictive capabilities of the different FBA strategies presented here is expected to help researchers in finding new avenues in engineering highly efficient microbial metabolic pathways and identify the key metabolic bottlenecks during the process. Based on the appropriate metabolic network design, fermentation engineers will be able to effectively design the bioreactors and optimize large-scale biochemical production through suitable pathway modifications.
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Affiliation(s)
- Pramita Sen
- Department of Chemical Engineering, Heritage Institute of Technology Kolkata, Kolkata, India
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40
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Mostolizadeh R, Glöckler M, Dräger A. Towards the human nasal microbiome: Simulating D. pigrum and S. aureus. Front Cell Infect Microbiol 2022; 12:925215. [PMID: 36605126 PMCID: PMC9810029 DOI: 10.3389/fcimb.2022.925215] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2022] [Accepted: 08/15/2022] [Indexed: 01/12/2023] Open
Abstract
The human nose harbors various microbes that decisively influence the wellbeing and health of their host. Among the most threatening pathogens in this habitat is Staphylococcus aureus. Multiple epidemiological studies identify Dolosigranulum pigrum as a likely beneficial bacterium based on its positive association with health, including negative associations with S. aureus. Carefully curated GEMs are available for both bacterial species that reliably simulate their growth behavior in isolation. To unravel the mutual effects among bacteria, building community models for simulating co-culture growth is necessary. However, modeling microbial communities remains challenging. This article illustrates how applying the NCMW fosters our understanding of two microbes' joint growth conditions in the nasal habitat and their intricate interplay from a metabolic modeling perspective. The resulting community model combines the latest available curated GEMs of D. pigrum and S. aureus. This uses case illustrates how to incorporate genuine GEM of participating microorganisms and creates a basic community model mimicking the human nasal environment. Our analysis supports the role of negative microbe-microbe interactions involving D. pigrum examined experimentally in the lab. By this, we identify and characterize metabolic exchange factors involved in a specific interaction between D. pigrum and S. aureus as an in silico candidate factor for a deep insight into the associated species. This method may serve as a blueprint for developing more complex microbial interaction models. Its direct application suggests new ways to prevent disease-causing infections by inhibiting the growth of pathogens such as S. aureus through microbe-microbe interactions.
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Affiliation(s)
- Reihaneh Mostolizadeh
- Computational Systems Biology of Infections and Antimicrobial-Resistant Pathogens, Institute for Bioinformatics and Medical Informatics (IBMI), University of Tübingen, Tübingen, Germany,Department of Computer Science, University of Tübingen, Tübingen, Germany,German Center for Infection Research (DZIF), Partner site, Tübingen, Germany,Cluster of Excellence ‘Controlling Microbes to Fight Infections’, University of Tübingen, Tübingen, Germany,*Correspondence: Reihaneh Mostolizadeh,
| | - Manuel Glöckler
- Department of Computer Science, University of Tübingen, Tübingen, Germany
| | - Andreas Dräger
- Computational Systems Biology of Infections and Antimicrobial-Resistant Pathogens, Institute for Bioinformatics and Medical Informatics (IBMI), University of Tübingen, Tübingen, Germany,Department of Computer Science, University of Tübingen, Tübingen, Germany,German Center for Infection Research (DZIF), Partner site, Tübingen, Germany,Cluster of Excellence ‘Controlling Microbes to Fight Infections’, University of Tübingen, Tübingen, Germany
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41
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Aminian-Dehkordi J, Valiei A, Mofrad MRK. Emerging computational paradigms to address the complex role of gut microbial metabolism in cardiovascular diseases. Front Cardiovasc Med 2022; 9:987104. [PMID: 36299869 PMCID: PMC9589059 DOI: 10.3389/fcvm.2022.987104] [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: 07/05/2022] [Accepted: 09/20/2022] [Indexed: 11/13/2022] Open
Abstract
The human gut microbiota and its associated perturbations are implicated in a variety of cardiovascular diseases (CVDs). There is evidence that the structure and metabolic composition of the gut microbiome and some of its metabolites have mechanistic associations with several CVDs. Nevertheless, there is a need to unravel metabolic behavior and underlying mechanisms of microbiome-host interactions. This need is even more highlighted when considering that microbiome-secreted metabolites contributing to CVDs are the subject of intensive research to develop new prevention and therapeutic techniques. In addition to the application of high-throughput data used in microbiome-related studies, advanced computational tools enable us to integrate omics into different mathematical models, including constraint-based models, dynamic models, agent-based models, and machine learning tools, to build a holistic picture of metabolic pathological mechanisms. In this article, we aim to review and introduce state-of-the-art mathematical models and computational approaches addressing the link between the microbiome and CVDs.
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Affiliation(s)
| | | | - Mohammad R. K. Mofrad
- Department of Bioengineering and Mechanical Engineering, University of California, Berkeley, Berkeley, CA, United States
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42
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Beura S, Kundu P, Das AK, Ghosh A. Metagenome-scale community metabolic modelling for understanding the role of gut microbiota in human health. Comput Biol Med 2022; 149:105997. [DOI: 10.1016/j.compbiomed.2022.105997] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2022] [Revised: 07/03/2022] [Accepted: 08/14/2022] [Indexed: 11/03/2022]
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43
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Martínez-López YE, Esquivel-Hernández DA, Sánchez-Castañeda JP, Neri-Rosario D, Guardado-Mendoza R, Resendis-Antonio O. Type 2 diabetes, gut microbiome, and systems biology: A novel perspective for a new era. Gut Microbes 2022; 14:2111952. [PMID: 36004400 PMCID: PMC9423831 DOI: 10.1080/19490976.2022.2111952] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/04/2023] Open
Abstract
The association between the physio-pathological variables of type 2 diabetes (T2D) and gut microbiota composition suggests a new avenue to track the disease and improve the outcomes of pharmacological and non-pharmacological treatments. This enterprise requires new strategies to elucidate the metabolic disturbances occurring in the gut microbiome as the disease progresses. To this end, physiological knowledge and systems biology pave the way for characterizing microbiota and identifying strategies in a move toward healthy compositions. Here, we dissect the recent associations between gut microbiota and T2D. In addition, we discuss recent advances in how drugs, diet, and exercise modulate the microbiome to favor healthy stages. Finally, we present computational approaches for disentangling the metabolic activity underlying host-microbiota codependence. Altogether, we envision that the combination of physiology and computational modeling of microbiota metabolism will drive us to optimize the diagnosis and treatment of T2D patients in a personalized way.
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Affiliation(s)
- Yoscelina Estrella Martínez-López
- Human Systems Biology Laboratory. Instituto Nacional de Medicina Genómica (INMEGEN). México City, México,Programa de Doctorado en Ciencias Médicas, Odontológicas y de la Salud, Universidad Nacional Autónoma de México (UNAM). Ciudad de México, México,Metabolic Research Laboratory, Department of Medicine and Nutrition. University of Guanajuato. León, Guanajuato, México
| | | | - Jean Paul Sánchez-Castañeda
- Human Systems Biology Laboratory. Instituto Nacional de Medicina Genómica (INMEGEN). México City, México,Programa de Maestría en Ciencias Bioquímicas, Universidad Nacional Autónoma de México (UNAM). Ciudad de México, México
| | - Daniel Neri-Rosario
- Human Systems Biology Laboratory. Instituto Nacional de Medicina Genómica (INMEGEN). México City, México,Programa de Maestría en Ciencias Bioquímicas, Universidad Nacional Autónoma de México (UNAM). Ciudad de México, México
| | - Rodolfo Guardado-Mendoza
- Metabolic Research Laboratory, Department of Medicine and Nutrition. University of Guanajuato. León, Guanajuato, México,Research Department, Hospital Regional de Alta Especialidad del Bajío. León, Guanajuato, México,Rodolfo Guardado-Mendoza Metabolic Research Laboratory, Department of Medicine and Nutrition. University of Guanajuato. León, Guanajuato, México
| | - Osbaldo Resendis-Antonio
- Human Systems Biology Laboratory. Instituto Nacional de Medicina Genómica (INMEGEN). México City, México,Coordinación de la Investigación Científica – Red de Apoyo a la Investigación, Universidad Nacional Autónoma de México (UNAM). Ciudad de México, México,CONTACT Osbaldo Resendis-Antonio Centro de Ciencias de la Complejidad, Universidad Nacional Autónoma de México (UNAM), Periferico Sur 4809, Arenal Tepepan, Tlalpan, 14610 Ciudad de México, CDMX
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44
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Liu Y, Xu P. Quantitative and analytical tools to analyze the spatiotemporal population dynamics of microbial consortia. Curr Opin Biotechnol 2022; 76:102754. [PMID: 35809433 DOI: 10.1016/j.copbio.2022.102754] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Revised: 05/30/2022] [Accepted: 06/06/2022] [Indexed: 12/27/2022]
Abstract
Microorganisms occupy almost every niche on earth. They play critical roles in maintaining ecological balance, atmospheric C/N cycle, and human health. Microbes live in consortia with metabolite exchange or signal communication. Quantitative and analytical tools are becoming increasingly important to study microbial consortia dynamics. We argue that a combined reductionist and holistic approach will be important to understanding the assembly rules and spatiotemporal population dynamics of the microbial community (MICOM). Reductionism allows us to reconstruct complex MICOM from isolated or simple synthetic consortia. Holism allows us to understand microbes as a community with cooperation and competition. Here we review the recent development of quantitative and analytical tools to uncover the underlying principles in microbial communities that govern their spatiotemporal change and interaction dynamics. Mathematical models and analytical tools will continue to provide essential knowledge and expand our capability to manipulate and control microbial consortia.
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Affiliation(s)
- Yugeng Liu
- Department of Chemical Engineering, Guangdong Technion-Israel Institute of Technology, Shantou, Guangdong 515063, China
| | - Peng Xu
- Department of Chemical Engineering, Guangdong Technion-Israel Institute of Technology, Shantou, Guangdong 515063, China; Guangdong Provincial Key Laboratory of Materials and Technologies for Energy Conversion, Guangdong Technion-Israel Institute of Technology, Shantou, Guangdong 515063, China.
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45
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Briones-Baez MF, Aguilera-Vazquez L, Rangel-Valdez N, Martinez-Salazar AL, Zuñiga C. Multi-Objective Optimization of Microalgae Metabolism: An Evolutive Algorithm Based on FBA. Metabolites 2022; 12:metabo12070603. [PMID: 35888727 PMCID: PMC9325016 DOI: 10.3390/metabo12070603] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2022] [Revised: 06/17/2022] [Accepted: 06/22/2022] [Indexed: 12/07/2022] Open
Abstract
Studies enabled by metabolic models of different species of microalgae have become significant since they allow us to understand changes in their metabolism and physiological stages. The most used method to study cell metabolism is FBA, which commonly focuses on optimizing a single objective function. However, recent studies have brought attention to the exploration of simultaneous optimization of multiple objectives. Such strategies have found application in optimizing biomass and several other bioproducts of interest; they usually use approaches such as multi-level models or enumerations schemes. This work proposes an alternative in silico multiobjective model based on an evolutionary algorithm that offers a broader approximation of the Pareto frontier, allowing a better angle for decision making in metabolic engineering. The proposed strategy is validated on a reduced metabolic network of the microalgae Chlamydomonas reinhardtii while optimizing for the production of protein, carbohydrates, and CO2 uptake. The results from the conducted experimental design show a favorable difference in the number of solutions achieved compared to a classic tool solving FBA.
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Affiliation(s)
- Monica Fabiola Briones-Baez
- TECNM/Instituto Tecnológico de Ciudad Madero, División de Estudios de Posgrado e Investigación, Los Mangos 89440, Mexico; (M.F.B.-B.); (L.A.-V.)
| | - Luciano Aguilera-Vazquez
- TECNM/Instituto Tecnológico de Ciudad Madero, División de Estudios de Posgrado e Investigación, Los Mangos 89440, Mexico; (M.F.B.-B.); (L.A.-V.)
| | - Nelson Rangel-Valdez
- CONACyT—TECNM/Instituto Tecnológico de Ciudad Madero, División de Estudios de Posgrado e Investigación, Los Mangos 89440, Mexico;
| | - Ana Lidia Martinez-Salazar
- TECNM/Instituto Tecnológico de Ciudad Madero, División de Estudios de Posgrado e Investigación, Los Mangos 89440, Mexico; (M.F.B.-B.); (L.A.-V.)
- Correspondence:
| | - Cristal Zuñiga
- Department of Biology, San Diego State University, 5500 Campanile Drive, San Diego, CA 92182, USA;
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46
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Proffitt C, Bidkhori G, Lee S, Tebani A, Mardinoglu A, Uhlen M, Moyes DL, Shoaie S. Genome-scale metabolic modelling of the human gut microbiome reveals changes of the glyoxylate and dicarboxylate metabolism in metabolic disorders. iScience 2022; 25:104513. [PMID: 35754734 PMCID: PMC9213702 DOI: 10.1016/j.isci.2022.104513] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2021] [Revised: 03/14/2022] [Accepted: 05/27/2022] [Indexed: 11/20/2022] Open
Abstract
The human gut microbiome has been associated with metabolic disorders including obesity, type 2 diabetes, and atherosclerosis. Understanding the contribution of microbiome metabolic changes is important for elucidating the role of gut bacteria in regulating metabolism. We used available metagenomics data from these metabolic disorders, together with genome-scale metabolic modeling of key bacteria in the individual and community-level to investigate the mechanistic role of the gut microbiome in metabolic diseases. Modeling predicted increased levels of glutamate consumption along with the production of ammonia, arginine, and proline in gut bacteria common across the disorders. Abundance profiles and network-dependent analysis identified the enrichment of tartrate dehydrogenase in the disorders. Moreover, independent plasma metabolite levels showed associations between metabolites including proline and tyrosine and an increased tartrate metabolism in healthy obese individuals. We, therefore, propose that an increased tartrate metabolism could be a significant mediator of the microbiome metabolic changes in metabolic disorders. Metagenomic analysis highlights key common bacterial species across metabolic diseases Metabolic models showed higher levels of acetate produced by disease enriched bacteria Reaction analysis revealed increases in the glyoxylate and dicarboxylate pathway Metabolomics and modeling analysis showed the potential role of tartrate metabolism
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Affiliation(s)
- Ceri Proffitt
- Centre for Host–Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King’s College London, London, SE1 9RT, UK
| | - Gholamreza Bidkhori
- Centre for Host–Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King’s College London, London, SE1 9RT, UK
| | - Sunjae Lee
- Centre for Host–Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King’s College London, London, SE1 9RT, UK
| | - Abdellah Tebani
- Science for Life Laboratory, KTH–Royal Institute of Technology, Stockholm, Sweden
| | - Adil Mardinoglu
- Centre for Host–Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King’s College London, London, SE1 9RT, UK
- Science for Life Laboratory, KTH–Royal Institute of Technology, Stockholm, Sweden
| | - Mathias Uhlen
- Science for Life Laboratory, KTH–Royal Institute of Technology, Stockholm, Sweden
| | - David L. Moyes
- Centre for Host–Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King’s College London, London, SE1 9RT, UK
| | - Saeed Shoaie
- Centre for Host–Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King’s College London, London, SE1 9RT, UK
- Science for Life Laboratory, KTH–Royal Institute of Technology, Stockholm, Sweden
- Corresponding author
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Sampaio M, Rocha M, Dias O. Exploring synergies between plant metabolic modelling and machine learning. Comput Struct Biotechnol J 2022; 20:1885-1900. [PMID: 35521559 PMCID: PMC9052043 DOI: 10.1016/j.csbj.2022.04.016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2022] [Revised: 04/08/2022] [Accepted: 04/11/2022] [Indexed: 11/03/2022] Open
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Glöckler M, Dräger A, Mostolizadeh R. NCMW: A Python Package to Analyze Metabolic Interactions in the Nasal Microbiome. FRONTIERS IN BIOINFORMATICS 2022; 2:827024. [PMID: 36304309 PMCID: PMC9580955 DOI: 10.3389/fbinf.2022.827024] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2021] [Accepted: 01/19/2022] [Indexed: 11/13/2022] Open
Abstract
The human upper respiratory tract is the reservoir of a diverse community of commensals and potential pathogens (pathobionts), including Streptococcus pneumoniae (pneumococcus), Haemophilus influenzae, Moraxella catarrhalis, and Staphylococcus aureus, which occasionally turn into pathogens causing infectious diseases, while the contribution of many nasal microorganisms to human health remains undiscovered. To better understand the composition of the nasal microbiome community, we create a workflow of the community model, which mimics the human nasal environment. To address this challenge, constraint-based reconstruction of biochemically accurate genome-scale metabolic models (GEMs) networks of microorganisms is mandatory. Our workflow applies constraint-based modeling (CBM), simulates the metabolism between species in a given microbiome, and facilitates generating novel hypotheses on microbial interactions. Utilizing this workflow, we hope to gain a better understanding of interactions from the metabolic modeling perspective. This article presents nasal community modeling workflow (NCMW)—a python package based on GEMs of species as a starting point for understanding the composition of the nasal microbiome community. The package is constructed as a step-by-step mathematical framework for metabolic modeling and analysis of the nasal microbial community. Using constraint-based models reduces the need for culturing species in vitro, a process that is not convenient in the environment of human noses.Availability: NCMW is freely available on the Python Package Index (PIP) via pip install NCMW. The source code, documentation, and usage examples (Jupyter Notebook and example files) are available at https://github.com/manuelgloeckler/ncmw.
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Affiliation(s)
- Manuel Glöckler
- Department of Computer Science, University of Tübingen, Tübingen, Germany
| | - Andreas Dräger
- Department of Computer Science, University of Tübingen, Tübingen, Germany
- Computational Systems Biology of Infections and Antimicrobial-Resistant Pathogens, Institute for Bioinformatics and Medical Informatics (IBMI), University of Tübingen, Tübingen, Germany
- German Center for Infection Research (DZIF), Partner Site Tübingen, Tübingen, Germany
- Cluster of Excellence “Controlling Microbes to Fight Infections”, University of Tübingen, Tübingen, Germany
| | - Reihaneh Mostolizadeh
- Department of Computer Science, University of Tübingen, Tübingen, Germany
- Computational Systems Biology of Infections and Antimicrobial-Resistant Pathogens, Institute for Bioinformatics and Medical Informatics (IBMI), University of Tübingen, Tübingen, Germany
- German Center for Infection Research (DZIF), Partner Site Tübingen, Tübingen, Germany
- Cluster of Excellence “Controlling Microbes to Fight Infections”, University of Tübingen, Tübingen, Germany
- *Correspondence: Reihaneh Mostolizadeh,
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Casey JR, Boiteau RM, Engqvist MKM, Finkel ZV, Li G, Liefer J, Müller CL, Muñoz N, Follows MJ. Basin-scale biogeography of marine phytoplankton reflects cellular-scale optimization of metabolism and physiology. SCIENCE ADVANCES 2022; 8:eabl4930. [PMID: 35061539 PMCID: PMC8782455 DOI: 10.1126/sciadv.abl4930] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/16/2021] [Accepted: 11/29/2021] [Indexed: 06/14/2023]
Abstract
Extensive microdiversity within Prochlorococcus, the most abundant marine cyanobacterium, occurs at scales from a single droplet of seawater to ocean basins. To interpret the structuring role of variations in genetic potential, as well as metabolic and physiological acclimation, we developed a mechanistic constraint-based modeling framework that incorporates the full suite of genes, proteins, metabolic reactions, pigments, and biochemical compositions of 69 sequenced isolates spanning the Prochlorococcus pangenome. Optimizing each strain to the local, observed physical and chemical environment along an Atlantic Ocean transect, we predicted variations in strain-specific patterns of growth rate, metabolic configuration, and physiological state, defining subtle niche subspaces directly attributable to differences in their encoded metabolic potential. Predicted growth rates covaried with observed ecotype abundances, affirming their significance as a measure of fitness and inferring a nonlinear density dependence of mortality. Our study demonstrates the potential to interpret global-scale ecosystem organization in terms of cellular-scale processes.
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Affiliation(s)
- John R. Casey
- Department of Earth, Atmospheric, and Planetary Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
- School of Ocean and Earth Science and Technology, University of Hawai‘i at Ma¯noa, Honolulu, HI, USA
| | - Rene M. Boiteau
- College of Earth, Ocean, and Atmospheric Sciences, Oregon State University, Corvallis, OR, USA
| | - Martin K. M. Engqvist
- Department of Biology and Biological Engineering, Chalmers University of Technology, Göteborg, Sweden
| | - Zoe V. Finkel
- Department of Oceanography, Dalhousie University, Halifax, NS, Canada
| | - Gang Li
- Department of Biology and Biological Engineering, Chalmers University of Technology, Göteborg, Sweden
| | - Justin Liefer
- Department of Biology, Mount Allison University, Sackville, NB, Canada
| | | | - Nathalie Muñoz
- Environmental Molecular Sciences Division, Pacific Northwest National Laboratories, Richland, WA, USA
| | - Michael J. Follows
- Department of Earth, Atmospheric, and Planetary Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
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Kim M, Sung J, Chia N. Resource-allocation constraint governs structure and function of microbial communities in metabolic modeling. Metab Eng 2022; 70:12-22. [PMID: 34990848 DOI: 10.1016/j.ymben.2021.12.011] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Revised: 12/01/2021] [Accepted: 12/29/2021] [Indexed: 10/19/2022]
Abstract
Predictive modeling tools for assessing microbial communities are important for realizing transformative capabilities of microbiomes in agriculture, ecology, and medicine. Constraint-based community-scale metabolic modeling is unique in its potential for making mechanistic predictions regarding both the structure and function of microbial communities. However, accessing this potential requires an understanding of key physicochemical constraints, which are typically considered on a per-species basis. What is needed is a means of incorporating global constraints relevant to microbial ecology into community models. Resource-allocation constraint, which describes how limited resources should be distributed to different cellular processes, sets limits on the efficiency of metabolic and ecological processes. In this study, we investigate the implications of resource-allocation constraints in community-scale metabolic modeling through a simple mechanism-agnostic implementation of resource-allocation constraints directly at the flux level. By systematically performing single-, two-, and multi-species growth simulations, we show that resource-allocation constraints are indispensable for predicting the structure and function of microbial communities. Our findings call for a scalable workflow for implementing a mechanistic version of resource-allocation constraints to ultimately harness the full potential of community-scale metabolic modeling tools.
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Affiliation(s)
- Minsuk Kim
- Microbiome Program, Center for Individualized Medicine, Mayo Clinic, Rochester, MN, 55905, USA; Division of Surgical Research, Department of Surgery, Mayo Clinic, Rochester, MN, 55905, USA
| | - Jaeyun Sung
- Microbiome Program, Center for Individualized Medicine, Mayo Clinic, Rochester, MN, 55905, USA; Division of Surgical Research, Department of Surgery, Mayo Clinic, Rochester, MN, 55905, USA; Division of Rheumatology, Department of Medicine, Mayo Clinic, Rochester, MN, 55905, USA
| | - Nicholas Chia
- Microbiome Program, Center for Individualized Medicine, Mayo Clinic, Rochester, MN, 55905, USA; Division of Surgical Research, Department of Surgery, Mayo Clinic, Rochester, MN, 55905, USA.
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