1
|
Wu S, Qu Z, Chen D, Wu H, Caiyin Q, Qiao J. Deciphering and designing microbial communities by genome-scale metabolic modelling. Comput Struct Biotechnol J 2024; 23:1990-2000. [PMID: 38765607 PMCID: PMC11098673 DOI: 10.1016/j.csbj.2024.04.055] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2024] [Revised: 04/21/2024] [Accepted: 04/21/2024] [Indexed: 05/22/2024] Open
Abstract
Microbial communities are shaped by the complex interactions among organisms and the environment. Genome-scale metabolic models (GEMs) can provide deeper insights into the complexity and ecological properties of various microbial communities, revealing their intricate interactions. Many researchers have modified GEMs for the microbial communities based on specific needs. Thus, GEMs need to be comprehensively summarized to better understand the trends in their development. In this review, we summarized the key developments in deciphering and designing microbial communities using different GEMs. A timeline of selected highlights in GEMs indicated that this area is evolving from the single-strain level to the microbial community level. Then, we outlined a framework for constructing GEMs of microbial communities. We also summarized the models and resources of static and dynamic community-level GEMs. We focused on the role of external environmental and intracellular resources in shaping the assembly of microbial communities. Finally, we discussed the key challenges and future directions of GEMs, focusing on the integration of GEMs with quorum sensing mechanisms, microbial ecology interactions, machine learning algorithms, and automatic modeling, all of which contribute to consortia-based applications in different fields.
Collapse
Affiliation(s)
- Shengbo Wu
- School of Chemical Engineering and Technology, Tianjin University, Tianjin 300072, China
- Zhejiang Shaoxing Research Institute of Tianjin University, Shaoxing 312300, China
| | - Zheping Qu
- School of Chemical Engineering and Technology, Tianjin University, Tianjin 300072, China
| | - Danlei Chen
- School of Chemical Engineering and Technology, Tianjin University, Tianjin 300072, China
- Zhejiang Shaoxing Research Institute of Tianjin University, Shaoxing 312300, China
| | - Hao Wu
- School of Chemical Engineering and Technology, Tianjin University, Tianjin 300072, China
- Zhejiang Shaoxing Research Institute of Tianjin University, Shaoxing 312300, China
| | - Qinggele Caiyin
- School of Chemical Engineering and Technology, Tianjin University, Tianjin 300072, China
- Zhejiang Shaoxing Research Institute of Tianjin University, Shaoxing 312300, China
- Key Laboratory of Systems Bioengineering, Ministry of Education (Tianjin University), Tianjin 300072, China
| | - Jianjun Qiao
- School of Chemical Engineering and Technology, Tianjin University, Tianjin 300072, China
- Zhejiang Shaoxing Research Institute of Tianjin University, Shaoxing 312300, China
- Key Laboratory of Systems Bioengineering, Ministry of Education (Tianjin University), Tianjin 300072, China
- Frontiers Science Center for Synthetic Biology (Ministry of Education), Tianjin University, Tianjin 300072, China
| |
Collapse
|
2
|
Román L, Melis-Arcos F, Pröschle T, Saa PA, Garrido D. Genome-scale metabolic modeling of the human milk oligosaccharide utilization by Bifidobacterium longum subsp. infantis. mSystems 2024; 9:e0071523. [PMID: 38363147 PMCID: PMC10949479 DOI: 10.1128/msystems.00715-23] [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: 07/25/2023] [Accepted: 01/10/2024] [Indexed: 02/17/2024] Open
Abstract
Bifidobacterium longum subsp. infantis is a representative and dominant species in the infant gut and is considered a beneficial microbe. This organism displays multiple adaptations to thrive in the infant gut, regarded as a model for human milk oligosaccharides (HMOs) utilization. These carbohydrates are abundant in breast milk and include different molecules based on lactose. They contain fucose, sialic acid, and N-acetylglucosamine. Bifidobacterium metabolism is complex, and a systems view of relevant metabolic pathways and exchange metabolites during HMO consumption is missing. To address this limitation, a refined genome-scale network reconstruction of this bacterium is presented using a previous reconstruction of B. infantis ATCC 15967 as a template. The latter was expanded based on an extensive revision of genome annotations, current literature, and transcriptomic data integration. The metabolic reconstruction (iLR578) accounted for 578 genes, 1,047 reactions, and 924 metabolites. Starting from this reconstruction, we built context-specific genome-scale metabolic models using RNA-seq data from cultures growing in lactose and three HMOs. The models revealed notable differences in HMO metabolism depending on the functional characteristics of the substrates. Particularly, fucosyl-lactose showed a divergent metabolism due to a fucose moiety. High yields of lactate and acetate were predicted under growth rate maximization in all conditions, whereas formate, ethanol, and 1,2-propanediol were substantially lower. Similar results were also obtained under near-optimal growth on each substrate when varying the empirically observed acetate-to-lactate production ratio. Model predictions displayed reasonable agreement between central carbon metabolism fluxes and expression data across all conditions. Flux coupling analysis revealed additional connections between succinate exchange and arginine and sulfate metabolism and a strong coupling between central carbon reactions and adenine metabolism. More importantly, specific networks of coupled reactions under each carbon source were derived and analyzed. Overall, the presented network reconstruction constitutes a valuable platform for probing the metabolism of this prominent infant gut bifidobacteria.IMPORTANCEThis work presents a detailed reconstruction of the metabolism of Bifidobacterium longum subsp. infantis, a prominent member of the infant gut microbiome, providing a systems view of its metabolism of human milk oligosaccharides.
Collapse
Affiliation(s)
- Loreto Román
- Department of Chemical and Bioprocess Engineering, School of Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Felipe Melis-Arcos
- Department of Chemical and Bioprocess Engineering, School of Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Tomás Pröschle
- Department of Chemical and Bioprocess Engineering, School of Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Pedro A. Saa
- Department of Chemical and Bioprocess Engineering, School of Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile
- Institute for Mathematical and Computational Engineering, Pontificia Universidad Católica de Chile, Vicuña Mackenna, Santiago, Chile
| | - Daniel Garrido
- Department of Chemical and Bioprocess Engineering, School of Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile
| |
Collapse
|
3
|
Vera-Siguenza E, Escribano-Gonzalez C, Serrano-Gonzalo I, Eskla KL, Spill F, Tennant D. Mathematical reconstruction of the metabolic network in an in-vitro multiple myeloma model. PLoS Comput Biol 2023; 19:e1011374. [PMID: 37713666 PMCID: PMC10503963 DOI: 10.1371/journal.pcbi.1011374] [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: 09/09/2022] [Accepted: 07/19/2023] [Indexed: 09/17/2023] Open
Abstract
It is increasingly apparent that cancer cells, in addition to remodelling their metabolism to survive and proliferate, adapt and manipulate the metabolism of other cells. This property may be a telling sign that pre-clinical tumour metabolism studies exclusively utilising in-vitro mono-culture models could prove to be limited for uncovering novel metabolic targets able to translate into clinical therapies. Although this is increasingly recognised, and work towards addressing the issue is becoming routinary much remains poorly understood. For instance, knowledge regarding the biochemical mechanisms through which cancer cells manipulate non-cancerous cell metabolism, and the subsequent impact on their survival and proliferation remains limited. Additionally, the variations in these processes across different cancer types and progression stages, and their implications for therapy, also remain largely unexplored. This study employs an interdisciplinary approach that leverages the predictive power of mathematical modelling to enrich experimental findings. We develop a functional multicellular in-silico model that facilitates the qualitative and quantitative analysis of the metabolic network spawned by an in-vitro co-culture model of bone marrow mesenchymal stem- and myeloma cell lines. To procure this model, we devised a bespoke human genome constraint-based reconstruction workflow that combines aspects from the legacy mCADRE & Metabotools algorithms, the novel redHuman algorithm, along with 13C-metabolic flux analysis. Our workflow transforms the latest human metabolic network matrix (Recon3D) into two cell-specific models coupled with a metabolic network spanning a shared growth medium. When cross-validating our in-silico model against the in-vitro model, we found that the in-silico model successfully reproduces vital metabolic behaviours of its in-vitro counterpart; results include cell growth predictions, respiration rates, as well as support for observations which suggest cross-shuttling of redox-active metabolites between cells.
Collapse
Affiliation(s)
- Elias Vera-Siguenza
- Institute of Metabolism and Systems Research, College of Medical and Dental Sciences, University of Birmingham, Birmingham, United Kingdom
- Watson School of Mathematics, University of Birmingham, Birmingham, United Kingdom
| | - Cristina Escribano-Gonzalez
- Institute of Metabolism and Systems Research, College of Medical and Dental Sciences, University of Birmingham, Birmingham, United Kingdom
| | - Irene Serrano-Gonzalo
- Instituto de Investigación Sanitaria Aragón, Fundación Española para el Estudio y Terapéutica de la enfermedad de Gaucher y otras Lisosomales, Zaragoza, España
| | - Kattri-Liis Eskla
- Institute of Metabolism and Systems Research, College of Medical and Dental Sciences, University of Birmingham, Birmingham, United Kingdom
- Department of Physiology, Institute of Biomedicine and Translational Medicine, University of Tartu, Tartu, Estonia
| | - Fabian Spill
- Watson School of Mathematics, University of Birmingham, Birmingham, United Kingdom
| | - Daniel Tennant
- Institute of Metabolism and Systems Research, College of Medical and Dental Sciences, University of Birmingham, Birmingham, United Kingdom
| |
Collapse
|
4
|
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.
Collapse
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.
| |
Collapse
|
5
|
Metabolic Modeling and Bidirectional Culturing of Two Gut Microbes Reveal Cross-Feeding Interactions and Protective Effects on Intestinal Cells. mSystems 2022; 7:e0064622. [PMID: 36005398 PMCID: PMC9600892 DOI: 10.1128/msystems.00646-22] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022] Open
Abstract
The gut microbiota is constituted by thousands of microbial interactions, some of which correspond to the exchange of metabolic by-products or cross-feeding. Inulin and xylan are two major dietary polysaccharides that are fermented by members of the human gut microbiota, resulting in different metabolic profiles. Here, we integrated community modeling and bidirectional culturing assays to study the metabolic interactions between two gut microbes, Phocaeicola dorei and Lachnoclostridium symbiosum, growing in inulin or xylan, and how they provide a protective effect in cultured cells. P. dorei (previously belonging to the Bacteroides genus) was able to consume inulin and xylan, while L. symposium only used certain inulin fractions to produce butyrate as a major end product. Constrained-based flux simulations of refined genome-scale metabolic models of both microbes predicted high lactate and succinate cross-feeding fluxes between P. dorei and L. symbiosum when growing in each fiber. Bidirectional culture assays in both substrates revealed that L. symbiosum growth increased in the presence of P. dorei. Carbohydrate consumption analyses showed a faster carbohydrate consumption in cocultures compared to monocultures. Lactate and succinate concentrations in bidirectional cocultures were lower than in monocultures, pointing to cross-feeding as initially suggested by the model. Butyrate concentrations were similar across all conditions. Finally, supernatants from both bacteria cultured in xylan in bioreactors significantly reduced tumor necrosis factor-α-induced inflammation in HT-29 cells and exerted a protective effect against the TcdB toxin in Caco-2 epithelial cells. Surprisingly, this effect was not observed in inulin cocultures. Overall, these results highlight the predictive value of metabolic models integrated with microbial culture assays for probing microbial interactions in the gut microbiota. They also provide an example of how metabolic exchange could lead to potential beneficial effects in the host. IMPORTANCE Microbial interactions represent the inner connections in the gut microbiome. By integrating mathematical modeling tools and microbial bidirectional culturing, we determined how two gut commensals engage in the exchange of cross-feeding metabolites, lactate and succinate, for increased growth in two fibers. These interactions underpinned butyrate production in cocultures, resulting in a significant reduction in cellular inflammation and protection against microbial toxins when applied to cellular models.
Collapse
|
6
|
Balaguer-Trias J, Deepika D, Schuhmacher M, Kumar V. Impact of Contaminants on Microbiota: Linking the Gut-Brain Axis with Neurotoxicity. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph19031368. [PMID: 35162390 PMCID: PMC8835190 DOI: 10.3390/ijerph19031368] [Citation(s) in RCA: 27] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/28/2021] [Revised: 01/19/2022] [Accepted: 01/21/2022] [Indexed: 02/06/2023]
Abstract
Over the last years, research has focused on microbiota to establish a missing link between neuronal health and intestine imbalance. Many studies have considered microbiota as critical regulators of the gut–brain axis. The crosstalk between microbiota and the central nervous system is mainly explained through three different pathways: the neural, endocrine, and immune pathways, intricately interconnected with each other. In day-to-day life, human beings are exposed to a wide variety of contaminants that affect our intestinal microbiota and alter the bidirectional communication between the gut and brain, causing neuronal disorders. The interplay between xenobiotics, microbiota and neurotoxicity is still not fully explored, especially for susceptible populations such as pregnant women, neonates, and developing children. Precisely, early exposure to contaminants can trigger neurodevelopmental toxicity and long-term diseases. There is growing but limited research on the specific mechanisms of the microbiota–gut–brain axis (MGBA), making it challenging to understand the effect of environmental pollutants. In this review, we discuss the biological interplay between microbiota–gut–brain and analyse the role of endocrine-disrupting chemicals: Bisphenol A (BPA), Chlorpyrifos (CPF), Diethylhexyl phthalate (DEHP), and Per- and polyfluoroalkyl substances (PFAS) in MGBA perturbations and subsequent neurotoxicity. The complexity of the MGBA and the changing nature of the gut microbiota pose significant challenges for future research. However, emerging in-silico models able to analyse and interpret meta-omics data are a promising option for understanding the processes in this axis and can help prevent neurotoxicity.
Collapse
Affiliation(s)
- Jordina Balaguer-Trias
- Environmental Engineering Laboratory, Department of Chemical Engineering, Universitat Rovira i Virgili, 43007 Tarragona, Spain; (J.B.-T.); (D.D.); (M.S.)
| | - Deepika Deepika
- Environmental Engineering Laboratory, Department of Chemical Engineering, Universitat Rovira i Virgili, 43007 Tarragona, Spain; (J.B.-T.); (D.D.); (M.S.)
| | - Marta Schuhmacher
- Environmental Engineering Laboratory, Department of Chemical Engineering, Universitat Rovira i Virgili, 43007 Tarragona, Spain; (J.B.-T.); (D.D.); (M.S.)
| | - Vikas Kumar
- Environmental Engineering Laboratory, Department of Chemical Engineering, Universitat Rovira i Virgili, 43007 Tarragona, Spain; (J.B.-T.); (D.D.); (M.S.)
- IISPV (Pere Virgili Institute for Health Research), Sant Joan University Hospital, Universitat Rovira i Virgili, 43204 Reus, Spain
- Correspondence: ; Tel.: +34977558576
| |
Collapse
|
7
|
Mahapatra S, Mohanty S, Mishra R, Prasad P. An overview of cancer and the human microbiome. PROGRESS IN MOLECULAR BIOLOGY AND TRANSLATIONAL SCIENCE 2022; 191:83-139. [DOI: 10.1016/bs.pmbts.2022.07.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
|
8
|
Saa P, Urrutia A, Silva-Andrade C, Martín AJ, Garrido D. Modeling approaches for probing cross-feeding interactions in the human gut microbiome. Comput Struct Biotechnol J 2021; 20:79-89. [PMID: 34976313 PMCID: PMC8685919 DOI: 10.1016/j.csbj.2021.12.006] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2021] [Revised: 12/03/2021] [Accepted: 12/04/2021] [Indexed: 12/16/2022] Open
Abstract
Microbial communities perform emergent activities that are essentially different from those carried by their individual members. The gut microbiome and its metabolites have a significant impact on the host, contributing to homeostasis or disease. Food molecules shape this community, being fermented through cross-feeding interactions of metabolites such as lactate, acetate, and amino acids, or products derived from macromolecule degradation. Mathematical and experimental approaches have been applied to understand and predict the interactions between microorganisms in complex communities such as the gut microbiota. Rational and mechanistic understanding of microbial interactions is essential to exploit their metabolic activities and identify keystone taxa and metabolites. The latter could be used in turn to modulate or replicate the metabolic behavior of the community in different contexts. This review aims to highlight recent experimental and modeling approaches for studying cross-feeding interactions within the gut microbiome. We focus on short-chain fatty acid production and fiber fermentation, which are fundamental processes in human health and disease. Special attention is paid to modeling approaches, particularly kinetic and genome-scale stoichiometric models of metabolism, to integrate experimental data under different diet and health conditions. Finally, we discuss limitations and challenges for the broad application of these modeling approaches and their experimental verification for improving our understanding of the mechanisms of microbial interactions.
Collapse
Affiliation(s)
- Pedro Saa
- Department of Chemical and Bioprocess Engineering, School of Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile
- Institute for Mathematical and Computational Engineering, Pontificia Universidad Católica de Chile, Vicuña Mackenna, 4860 Santiago, Chile
| | - Arles Urrutia
- Department of Chemical and Bioprocess Engineering, School of Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Claudia Silva-Andrade
- Laboratorio de Biología de Redes, Centro de Genómica y Bioinformática, Facultad de Ciencias, Universidad Mayor, Santiago, Chile
| | - Alberto J. Martín
- Laboratorio de Biología de Redes, Centro de Genómica y Bioinformática, Facultad de Ciencias, Universidad Mayor, Santiago, Chile
| | - Daniel Garrido
- Department of Chemical and Bioprocess Engineering, School of Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile
| |
Collapse
|
9
|
Toward modeling metabolic state from single-cell transcriptomics. Mol Metab 2021; 57:101396. [PMID: 34785394 PMCID: PMC8829761 DOI: 10.1016/j.molmet.2021.101396] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/05/2021] [Revised: 10/21/2021] [Accepted: 11/09/2021] [Indexed: 12/31/2022] Open
Abstract
Background Single-cell metabolic studies bring new insights into cellular function, which can often not be captured on other omics layers. Metabolic information has wide applicability, such as for the study of cellular heterogeneity or for the understanding of drug mechanisms and biomarker development. However, metabolic measurements on single-cell level are limited by insufficient scalability and sensitivity, as well as resource intensiveness, and are currently not possible in parallel with measuring transcript state, commonly used to identify cell types. Nevertheless, because omics layers are strongly intertwined, it is possible to make metabolic predictions based on measured data of more easily measurable omics layers together with prior metabolic network knowledge. Scope of Review We summarize the current state of single-cell metabolic measurement and modeling approaches, motivating the use of computational techniques. We review three main classes of computational methods used for prediction of single-cell metabolism: pathway-level analysis, constraint-based modeling, and kinetic modeling. We describe the unique challenges arising when transitioning from bulk to single-cell modeling. Finally, we propose potential model extensions and computational methods that could be leveraged to achieve these goals. Major Conclusions Single-cell metabolic modeling is a rising field that provides a new perspective for understanding cellular functions. The presented modeling approaches vary in terms of input requirements and assumptions, scalability, modeled metabolic layers, and newly gained insights. We believe that the use of prior metabolic knowledge will lead to more robust predictions and will pave the way for mechanistic and interpretable machine-learning models. Single-cell RNA sequencing and prior metabolic knowledge enable metabolic predictions. When compared to bulk, single-cell modeling is linked to unique insights and challenges. Computational modelling approaches differ in applicability and newly provided insights. The use of prior metabolic knowledge paves the way for mechanistic machine-learning.
Collapse
|
10
|
Jean-Pierre F, Henson MA, O'Toole GA. Metabolic Modeling to Interrogate Microbial Disease: A Tale for Experimentalists. Front Mol Biosci 2021; 8:634479. [PMID: 33681294 PMCID: PMC7930556 DOI: 10.3389/fmolb.2021.634479] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2020] [Accepted: 01/19/2021] [Indexed: 12/14/2022] Open
Abstract
The explosion of microbiome analyses has helped identify individual microorganisms and microbial communities driving human health and disease, but how these communities function is still an open question. For example, the role for the incredibly complex metabolic interactions among microbial species cannot easily be resolved by current experimental approaches such as 16S rRNA gene sequencing, metagenomics and/or metabolomics. Resolving such metabolic interactions is particularly challenging in the context of polymicrobial communities where metabolite exchange has been reported to impact key bacterial traits such as virulence and antibiotic treatment efficacy. As novel approaches are needed to pinpoint microbial determinants responsible for impacting community function in the context of human health and to facilitate the development of novel anti-infective and antimicrobial drugs, here we review, from the viewpoint of experimentalists, the latest advances in metabolic modeling, a computational method capable of predicting metabolic capabilities and interactions from individual microorganisms to complex ecological systems. We use selected examples from the literature to illustrate how metabolic modeling has been utilized, in combination with experiments, to better understand microbial community function. Finally, we propose how such combined, cross-disciplinary efforts can be utilized to drive laboratory work and drug discovery moving forward.
Collapse
Affiliation(s)
- Fabrice Jean-Pierre
- Department of Microbiology and Immunology, Geisel School of Medicine at Dartmouth, Hanover, NH, United States
| | - Michael A Henson
- Department of Chemical Engineering and Institute for Applied Life Sciences, University of Massachusetts, Amherst, MA, United States
| | - George A O'Toole
- Department of Microbiology and Immunology, Geisel School of Medicine at Dartmouth, Hanover, NH, United States
| |
Collapse
|
11
|
Borer B, Or D. Spatiotemporal metabolic modeling of bacterial life in complex habitats. Curr Opin Biotechnol 2021; 67:65-71. [PMID: 33493977 DOI: 10.1016/j.copbio.2021.01.004] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2020] [Revised: 12/21/2020] [Accepted: 01/07/2021] [Indexed: 01/04/2023]
Abstract
The combination of genome-scale metabolic networks with spatially explicit representation of microbial habitats (spatiotemporal metabolic network modeling) paves the way to predict complex metabolic landscapes to a hitherto unparalleled detail, thus providing new insights into trophic interactions occurring at different scales. Placing detailed bacterial metabolism in realistic physical environment highlights the roles of physical barriers and diffusional bottlenecks on bacterial community interactions, structure and stability. We review recent advances in spatiotemporal metabolic network modeling using a few illustrative examples that highlight the immense potential of these novel approaches to interpret and design metabolic mediated interactions in structures (natural and engineered) environments.
Collapse
Affiliation(s)
- Benedict Borer
- Department of Environmental Systems Science, ETH Zurich, Universitätstrasse 16, 8092 Zürich, Switzerland; The Department for Earth, Atmospheric and Planetary Science, MIT, Boston, MA, USA.
| | - Dani Or
- Department of Environmental Systems Science, ETH Zurich, Universitätstrasse 16, 8092 Zürich, Switzerland; Div. of Hydrologic Sciences, Desert Research Institute, Reno, NV, USA
| |
Collapse
|