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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; 50:1053-1092. [PMID: 38270170 DOI: 10.1080/1040841x.2024.2306465] [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: 03/13/2023] [Revised: 11/17/2023] [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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Song HS, Lee NR, Kessell AK, McCullough HC, Park SY, Zhou K, Lee DY. Kinetics-based inference of environment-dependent microbial interactions and their dynamic variation. mSystems 2024; 9:e0130523. [PMID: 38682902 PMCID: PMC11097648 DOI: 10.1128/msystems.01305-23] [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: 12/05/2023] [Accepted: 03/28/2024] [Indexed: 05/01/2024] Open
Abstract
Microbial communities in nature are dynamically evolving as member species change their interactions subject to environmental variations. Accounting for such context-dependent dynamic variations in interspecies interactions is critical for predictive ecological modeling. In the absence of generalizable theoretical foundations, we lack a fundamental understanding of how microbial interactions are driven by environmental factors, significantly limiting our capability to predict and engineer community dynamics and function. To address this issue, we propose a novel theoretical framework that allows us to represent interspecies interactions as an explicit function of environmental variables (such as substrate concentrations) by combining growth kinetics and a generalized Lotka-Volterra model. A synergistic integration of these two complementary models leads to the prediction of alterations in interspecies interactions as the outcome of dynamic balances between positive and negative influences of microbial species in mixed relationships. The effectiveness of our method was experimentally demonstrated using a synthetic consortium of two Escherichia coli mutants that are metabolically dependent (due to an inability to synthesize essential amino acids) but competitively grow on a shared substrate. The analysis of the E. coli binary consortium using our model not only showed how interactions between the two amino acid auxotrophic mutants are controlled by the dynamic shifts in limiting substrates but also enabled quantifying previously uncharacterizable complex aspects of microbial interactions, such as asymmetry in interactions. Our approach can be extended to other ecological systems to model their environment-dependent interspecies interactions from growth kinetics.IMPORTANCEModeling environment-controlled interspecies interactions through separate identification of positive and negative influences of microbes in mixed relationships is a new capability that can significantly improve our ability to understand, predict, and engineer the complex dynamics of microbial communities. Moreover, the prediction of microbial interactions as a function of environmental variables can serve as valuable benchmark data to validate modeling and network inference tools in microbial ecology, the development of which has often been impeded due to the lack of ground truth information on interactions. While demonstrated against microbial data, the theory developed in this work is readily applicable to general community ecology to predict interactions among macroorganisms, such as plants and animals, as well as microorganisms.
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Affiliation(s)
- Hyun-Seob Song
- Department of Biological Systems Engineering, University of Nebraska-Lincoln, Lincoln, Nebraska, USA
- Department of Food Science and Technology, Nebraska Food for Health Center, University of Nebraska-Lincoln, Lincoln, Nebraska, USA
| | - Na-Rae Lee
- Research Institute for Bioactive-Metabolome Network, Konkuk University, Seoul, South Korea
| | - Aimee K. Kessell
- Department of Biological Systems Engineering, University of Nebraska-Lincoln, Lincoln, Nebraska, USA
| | - Hugh C. McCullough
- Department of Biological Systems Engineering, University of Nebraska-Lincoln, Lincoln, Nebraska, USA
| | - Seo-Young Park
- School of Chemical Engineering, Sungkyunkwan University, Suwon-si, Gyeonggi-do, South Korea
| | - Kang Zhou
- Department of Chemical and Biomolecular Engineering, National University of Singapore, Singapore, Singapore
| | - Dong-Yup Lee
- School of Chemical Engineering, Sungkyunkwan University, Suwon-si, Gyeonggi-do, South Korea
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Romdhane S, Huet S, Spor A, Bru D, Breuil MC, Philippot L. Manipulating the physical distance between cells during soil colonization reveals the importance of biotic interactions in microbial community assembly. ENVIRONMENTAL MICROBIOME 2024; 19:18. [PMID: 38504378 PMCID: PMC10953230 DOI: 10.1186/s40793-024-00559-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/09/2023] [Accepted: 03/03/2024] [Indexed: 03/21/2024]
Abstract
BACKGROUND Microbial communities are of tremendous importance for ecosystem functioning and yet we know little about the ecological processes driving the assembly of these communities in the environment. Here, we used an unprecedented experimental approach based on the manipulation of physical distance between neighboring cells during soil colonization to determine the role of bacterial interactions in soil community assembly. We hypothesized that experimentally manipulating the physical distance between bacterial cells will modify the interaction strengths leading to differences in microbial community composition, with increasing distance between neighbors favoring poor competitors. RESULTS We found significant differences in both bacterial community diversity, composition and co-occurrence networks after soil colonization that were related to physical distancing. We show that reducing distances between cells resulted in a loss of bacterial diversity, with at least 41% of the dominant OTUs being significantly affected by physical distancing. Our results suggest that physical distancing may differentially modulate competitiveness between neighboring species depending on the taxa present in the community. The mixing of communities that assembled at high and low cell densities did not reveal any "home field advantage" during coalescence. This confirms that the observed differences in competitiveness were due to biotic rather than abiotic filtering. CONCLUSIONS Our study demonstrates that the competitiveness of bacteria strongly depends on cell density and community membership, therefore highlighting the fundamental role of microbial interactions in the assembly of soil communities.
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Affiliation(s)
- Sana Romdhane
- Univ. Bourgogne Franche-Comté, INRAE, Institut Agro, Agroécologie, F-21000, Dijon, France.
| | - Sarah Huet
- Univ. Bourgogne Franche-Comté, INRAE, Institut Agro, Agroécologie, F-21000, Dijon, France
| | - Aymé Spor
- Univ. Bourgogne Franche-Comté, INRAE, Institut Agro, Agroécologie, F-21000, Dijon, France
| | - David Bru
- Univ. Bourgogne Franche-Comté, INRAE, Institut Agro, Agroécologie, F-21000, Dijon, France
| | - Marie-Christine Breuil
- Univ. Bourgogne Franche-Comté, INRAE, Institut Agro, Agroécologie, F-21000, Dijon, France
| | - Laurent Philippot
- Univ. Bourgogne Franche-Comté, INRAE, Institut Agro, Agroécologie, F-21000, Dijon, France
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Palacios OA, López BR, de-Bashan LE. Microalga Growth-Promoting Bacteria (MGPB): A formal term proposed for beneficial bacteria involved in microalgal–bacterial interactions. ALGAL RES 2022. [DOI: 10.1016/j.algal.2021.102585] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
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Smercina DN, Bailey VL, Hofmockel KS. Micro on a macroscale: relating microbial-scale soil processes to global ecosystem function. FEMS Microbiol Ecol 2021; 97:6315324. [PMID: 34223869 DOI: 10.1093/femsec/fiab091] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2021] [Accepted: 07/01/2021] [Indexed: 11/13/2022] Open
Abstract
Soil microorganisms play a key role in driving major biogeochemical cycles and in global responses to climate change. However, understanding and predicting the behavior and function of these microorganisms remains a grand challenge for soil ecology due in part to the microscale complexity of soils. It is becoming increasingly clear that understanding the microbial perspective is vital to accurately predicting global processes. Here, we discuss the microbial perspective including the microbial habitat as it relates to measurement and modeling of ecosystem processes. We argue that clearly defining and quantifying the size, distribution and sphere of influence of microhabitats is crucial to managing microbial activity at the ecosystem scale. This can be achieved using controlled and hierarchical sampling designs. Model microbial systems can provide key data needed to integrate microhabitats into ecosystem models, while adapting soil sampling schemes and statistical methods can allow us to collect microbially-focused data. Quantifying soil processes, like biogeochemical cycles, from a microbial perspective will allow us to more accurately predict soil functions and address long-standing unknowns in soil ecology.
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Affiliation(s)
- Darian N Smercina
- Biological Sciences Division, Earth and Biological Sciences Directorate, 3335 Innovation Blvd, Richland, WA, 99354, USA
| | - Vanessa L Bailey
- Biological Sciences Division, Earth and Biological Sciences Directorate, 3335 Innovation Blvd, Richland, WA, 99354, USA
| | - Kirsten S Hofmockel
- Biological Sciences Division, Earth and Biological Sciences Directorate, 3335 Innovation Blvd, Richland, WA, 99354, USA.,Department of Agronomy, Iowa State University, 716 Farm House Ln, Ames, IA 50011, USA
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Kessell AK, McCullough HC, Auchtung JM, Bernstein HC, Song HS. Predictive interactome modeling for precision microbiome engineering. Curr Opin Chem Eng 2020. [DOI: 10.1016/j.coche.2020.08.003] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/09/2023]
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Wainwright BJ, Zahn GL, Afiq-Rosli L, Tanzil JTI, Huang D. Host age is not a consistent predictor of microbial diversity in the coral Porites lutea. Sci Rep 2020; 10:14376. [PMID: 32873814 PMCID: PMC7463248 DOI: 10.1038/s41598-020-71117-4] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2020] [Accepted: 08/07/2020] [Indexed: 12/19/2022] Open
Abstract
Corals harbour diverse microbial communities that can change in composition as the host grows in age and size. Larger and older colonies have been shown to host a higher diversity of microbial taxa and this has been suggested to be a consequence of their more numerous, complex and varied micro-niches available. However, the effects of host age on community structure and diversity of microbial associates remain equivocal in the few studies performed to date. To test this relationship more robustly, we use established techniques to accurately determine coral host age by quantifying annual skeletal banding patterns, and utilise high-throughput sequencing to comprehensively characterise the microbiome of the common reef-building coral, Porites lutea. Our results indicate no clear link between coral age and microbial diversity or richness. Different sites display distinct age-dependent diversity patterns, with more anthropogenically impacted reefs appearing to show a winnowing of microbial diversity with host age, possibly a consequence of corals adapting to degraded environments. Less impacted sites do not show a signature of winnowing, and we observe increases in microbial richness and diversity as the host ages. Furthermore, we demonstrate that corals of a similar age from the same reef can show very different microbial richness and diversity.
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Affiliation(s)
| | - Geoffrey L Zahn
- Biology Department, Utah Valley University, 800 W. University Parkway, Orem, UT, 84058, USA
| | - Lutfi Afiq-Rosli
- Department of Biological Sciences, National University of Singapore, 16 Science Drive 4, Singapore, 117558, Singapore
- Tropical Marine Science Institute, National University of Singapore, 18 Kent Ridge Road, Singapore, 119227, Singapore
| | - Jani T I Tanzil
- Tropical Marine Science Institute, National University of Singapore, 18 Kent Ridge Road, Singapore, 119227, Singapore
| | - Danwei Huang
- Department of Biological Sciences, National University of Singapore, 16 Science Drive 4, Singapore, 117558, Singapore
- Tropical Marine Science Institute, National University of Singapore, 18 Kent Ridge Road, Singapore, 119227, Singapore
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Lee JY, Sadler NC, Egbert RG, Anderton CR, Hofmockel KS, Jansson JK, Song HS. Deep learning predicts microbial interactions from self-organized spatiotemporal patterns. Comput Struct Biotechnol J 2020; 18:1259-1269. [PMID: 32612750 PMCID: PMC7298420 DOI: 10.1016/j.csbj.2020.05.023] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2019] [Revised: 05/16/2020] [Accepted: 05/17/2020] [Indexed: 12/27/2022] Open
Abstract
Microbial communities organize into spatial patterns that are largely governed by interspecies interactions. This phenomenon is an important metric for understanding community functional dynamics, yet the use of spatial patterns for predicting microbial interactions is currently lacking. Here we propose supervised deep learning as a new tool for network inference. An agent-based model was used to simulate the spatiotemporal evolution of two interacting organisms under diverse growth and interaction scenarios, the data of which was subsequently used to train deep neural networks. For small-size domains (100 µm × 100 µm) over which interaction coefficients are assumed to be invariant, we obtained fairly accurate predictions, as indicated by an average R2 value of 0.84. In application to relatively larger domains (450 µm × 450 µm) where interaction coefficients are varying in space, deep learning models correctly predicted spatial distributions of interaction coefficients without any additional training. Lastly, we evaluated our model against real biological data obtained using Pseudomonas fluorescens and Escherichia coli co-cultures treated with polymeric chitin or N-acetylglucosamine, the hydrolysis product of chitin. While P. fluorescens can utilize both substrates for growth, E. coli lacked the ability to degrade chitin. Consistent with our expectations, our model predicted context-dependent interactions across two substrates, i.e., degrader-cheater relationship on chitin polymers and competition on monomers. The combined use of the agent-based model and machine learning algorithm successfully demonstrates how to infer microbial interactions from spatially distributed data, presenting itself as a useful tool for the analysis of more complex microbial community interactions.
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Affiliation(s)
- Joon-Yong Lee
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA
| | - Natalie C. Sadler
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA
| | - Robert G. Egbert
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA
| | - Christopher R. Anderton
- Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, WA, USA
| | - Kirsten S. Hofmockel
- Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, WA, USA
- Department of Ecology, Evolution and Organismal Biology, Iowa State University, Ames, IA, USA
| | - Janet K. Jansson
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA
| | - Hyun-Seob Song
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA
- Department of Biological Systems Engineering, University of Nebraska-Lincoln, Lincoln, NE, USA
- Nebraska Food for Health Center, Department of Food Science and Technology, University of Nebraska-Lincoln, Lincoln, NE, USA
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