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Anoxic Conditions Promote Species-Specific Mutualism between Gut Microbes In Silico
Open accessAbstract
The human gut is inhabited by thousands of microbial species, most of which are still uncharacterized. Gut microbes have adapted to each other's presence as well as to the host and engage in complex cross feeding. Constraint-based modeling has been successfully applied to predicting microbe-microbe interactions, such as commensalism, mutualism, and competition. Here, we apply a constraint-based approach to model pairwise interactions between 11 representative gut microbes. Microbe-microbe interactions were computationally modeled in conjunction with human small intestinal enterocytes, and the microbe pairs were subjected to three diets with various levels of carbohydrate, fat, and protein in normoxic or anoxic environments. Each microbe engaged in species-specific commensal, parasitic, mutualistic, or competitive interactions. For instance, Streptococcus thermophilus efficiently outcompeted microbes with which it was paired, in agreement with the domination of streptococci in the small intestinal microbiota. Under anoxic conditions, the probiotic organism Lactobacillus plantarum displayed mutualistic behavior toward six other species, which, surprisingly, were almost entirely abolished under normoxic conditions. This finding suggests that the anoxic conditions in the large intestine drive mutualistic cross feeding, leading to the evolvement of an ecosystem more complex than that of the small intestinal microbiota. Moreover, we predict that the presence of the small intestinal enterocyte induces competition over host-derived nutrients. The presented framework can readily be expanded to a larger gut microbial community. This modeling approach will be of great value for subsequent studies aiming to predict conditions favoring desirable microbes or suppressing pathogens.
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“In polymicrobial environments, Lactobacilli engage in advantageous, mutualistic interactions with other bacteria to survive under hypoxia and alter NO generation[217,413]”
From Enterosalivary nitrate metabolism and the microbiome: Intersection of microbial metabolism, nitric oxide and diet in cardiac and pulmonary vascular health (2017) · cites this paper as [413]
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“Models were thenmerged by integrating them into a common extracellular compartment (98, 99).”
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Ravcheev, D. A., & Thiele, I. (2017) · Frontiers in Genetics · doi:10.3389/fgene.2017.00111 · Cited by 139
“, 2014) as well as for whole HGM communities (Levy and Borenstein, 2013; Bauer et al., 2015; Heinken and Thiele, 2015; Noecker et al., 2016; Shashkova et al., 2016; Magnúsdóttir et al., 2017).”
BackgroundMetabolic control of nitrogen fixation in rhizobium-legume symbioses
Schulte, C. C. M., Borah, K., Wheatley, R. M., Terpolilli, J. J., Saalbach, G., Crang, N., de Groot, D. H., Ratcliffe, R. G., Kruger, N. J., Papachristodoulou, A., et al. (2021) · Science Advances · doi:10.1126/sciadv.abh2433 · Cited by 132
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Magnúsdóttir, S., & Thiele, I. (2018) · Current Opinion in Biotechnology · doi:10.1016/j.copbio.2017.12.005 · Cited by 127
Self-citationMetabolic modelling approaches for describing and engineering microbial communities
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“normoxic conditions [96]; to predict short chain fatty acids levels to use as treatment for Crohn’s”
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“Technically, this issue can be avoided by assuming equal growth rates [39], by setting lower limits on the growth rates, as in, for example, [55,99], or by coupling the respective transport fluxes to growth rate, as in [101].”
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“Depending on the effect that the bacteria could have on each other in terms of growth rates, there are six possible outcomes: 1) competition, 2) parasitism, 3) commensalism, 4) mutualism, 5) amensalism, and 6) neutralism (Heinken and Thiele, 2015).”
BackgroundSystems biology of host–microbe metabolomics
Heinken, A., & Thiele, I. (2015) · WIREs Systems Biology and Medicine · doi:10.1002/wsbm.1301 · Cited by 99
“72 The pairwise tradeoffs between microbes (Figure 1(b)) were investigated on 12 varying nutrient environments.”
BackgroundStandardizing biomass reactions and ensuring complete mass balance in genome-scale metabolic models
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“…even more critical when comparing multiple GSM models or modeling microbial communities that combine multiple organisms (Harcombe et al., 2014; Heinken and Thiele, 2015a,b; Khandelwal et al., 2013; Klitgord and Segrè, 2010; Magn usd ottir et al., 2016; Shoaie et al., 2013, 2015; Zhang et…”
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“A study based on 11 metabolic models (62) revealed a vivid exchange of fermentation”
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“Modeling studies suggest that anoxic conditions select for mutualism and enhanced cross-feeding among microbial community members (Heinken and Thiele, 2015).”
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“Microbes can also exhibit cooperative and mutualistic behaviours within the gut, such as the cross-feeding enzyme system between Bacteroides vulgatus and B. ovatus (Coyte & Rakoff-Nahoum, 2019) or between Lactobacillus plantarum and various gut microbes (Heinken & Thiele, 2015).”
Resources and tools for the high-throughput, multi-omic study of intestinal microbiota
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Competition and cooperation: The plasticity of bacterial interactions across environments
Solowiej-Wedderburn, J., Pentz, J. T., Lizana, L., Schroeder, B. O., Lind, P. A., & Libby, E. (2025) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1013213 · Cited by 14
“In the context of the human gut, these models have been used to explore the effects of different diets on interactions between the microbiota [17,47] and, more broadly, identify environments which promote, e.g., obligate interactions between bacteria [48].”
Liu, J., Guo, X., Xu, S., Song, Y., & Ding, K. (2023) · Journal of Informetrics · doi:10.1016/j.joi.2022.101372 · Cited by 13
Mendes‐Soares, H., & Chia, N. (2016) · Free Radical Biology and Medicine · doi:10.1016/j.freeradbiomed.2016.12.017 · Cited by 18
“These are particularly important when researchers want to understand the role of the host in determining community dynamics [70].”
InfluentialMataragas, M., Alessandria, V., Ferrocino, I., Rantsiou, K., & Cocolin, L. (2018) · Food Microbiology · doi:10.1016/j.fm.2018.05.009 · Cited by 14
Meta‐Omics‐ and Metabolic Modeling‐Assisted Deciphering of Human Microbiota Metabolism
Sieow, B. F., Nurminen, T. J., Ling, H., & Chang, M. W. (2019) · Biotechnology Journal · doi:10.1002/biot.201800445 · Cited by 12
Timmis, K. N., Verstraete, W., Regina, V. R., & Hallsworth, J. E. (2023) · Environmental Microbiology · doi:10.1111/1462-2920.16438 · Cited by 12
Popovic, M. E., Šekularac, G. M., & Stevanović, M. (2023) · Journal of Biotechnology · doi:10.1016/j.jbiotec.2023.11.001 · Cited by 13
Orellana, E., Zampieri, G., Bernardini, N., Guerrero, L. D., Erijman, L., Campanaro, S., & Treu, L. (2025) · Environmental Science & Technology · doi:10.1021/acs.est.4c11180 · Cited by 11
Gut bacteria affect the tumoral immune milieu: distorting the efficacy of immunotherapy or not?
Pu, X., Chao, G., Dong, L., & Chang, P. (2020) · Gut Microbes · doi:10.1080/19490976.2020.1739794 · Cited by 11
“A healthy intestine provides an environment that favors the growth of anaerobes.(31,32) However, significant differences characterize bacterial diversity among individuals.”
BackgroundCommunity metabolic modeling of host-microbiota interactions through multi-objective optimization
Lambert, A., Budinich, M., Mahé, M. M., Chaffron, S., & Eveillard, D. (2024) · iScience · doi:10.1016/j.isci.2024.110092 · Cited by 8
GEM-based computational modeling for exploring metabolic interactions in a microbial community
Mirzaei, S., & Tefagh, M. (2024) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1012233 · Cited by 8
“By analyzing the increase or decrease in the growth rate of microbes, predictions about the type of interaction that both microorganisms have with each other were determined [21–25].”
BackgroundOn the mechanism of antibiotic resistance and fecal microbiota transplantation
Kang, X., Yan, J., Huang, F., & Yang, L. (2019) · Mathematical Biosciences & Engineering · doi:10.3934/mbe.2019354 · Cited by 8
García-Jiménez, B., Carrasco, J., Medina, J., & Wilkinson, M. D. (2020) · doi:10.21203/rs.2.24431/v1 · Cited by 7
“There are few studies combining microbiome analyses and metabolic models [28, 29, 30], and these are primarily static analyses.”
BackgroundMethodDevika, N. T., Katneni, V. K., Jangam, A. K., Nathamuni, S. P., Shekhar, M. S., & Jithendran, K. P. (2023) · Environmental Microbiome · doi:10.1186/s40793-022-00458-6 · Cited by 7
“The in silico growth rate of single and paired species are compared and observed for a 10 percent increase or decrease in growth of healthy/ disease species in the presence of another [24].”
BackgroundCai, J., Tan, T., & Chan, S. H. J. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/623173 · Cited by 7
Computational Modeling of Microbial Communities
Chan, S. H. J., Simons, M., & Maranas, C. D. (2017) · Systems Biology · doi:10.1002/9783527696130.ch6 · Cited by 6
Unraveling microbial interactions in the gut microbiomePreprint
Ravikrishnan, A., & Raman, K. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.05.17.444446 · Cited by 6
“This observation is in agreement with the results previously reported [58].”
ResultTaherkhani, H., KavianFar, A., Aminnezhad, S., Lanjanian, H., Ahmadi, A., Jamalkandi, S. A., & Masoudi‐Nejad, A. (2024) · Heliyon · doi:10.1016/j.heliyon.2024.e24775 · Cited by 6
Badr, K., He, Q. P., & Wang, J. (2024) · Microbiome Research Reports · doi:10.20517/mrr.2023.70 · Cited by 6
Afarin, M., & Naeimpoor, F. (2024) · Bioprocess and Biosystems Engineering · doi:10.1007/s00449-024-03072-7 · Cited by 5
“2 Frameworks for the generation of community metabolic models and the simulation algorithms used for their analysis including investigations of gut microbiome [23], the exploration of interactions between different tissues in humans and plants [24] and the optimization of chemical production [25].”
Toward the Next Generation of In Silico Modeling of Dynamic Host-Microbiota Interactions in the Skin
Lee, J., Wu, H. L. A., Mannan, A. A., Nakamura, Y., Amagai, M., Irvine, A. D., & Tanaka, R. J. (2025) · JID Innovations · doi:10.1016/j.xjidi.2025.100385 · Cited by 4
Systems biology of bacteria‐host interactions
Heinken, A., Ravcheev, D. A., & Thiele, I. (2016) · Wiley eBooks · doi:10.1002/9781118982907.ch7 · Cited by 4
Dysbiosis and its Varied Impacts
Sharma, R., & Kumar, K. (2023) · Wiley eBooks · doi:10.1002/9781119904786.ch5 · Cited by 4
Taherkhani, H., KavianFar, A., Lanjanian, H., Hosseinkhan, N., Salimi, M., Nematzadeh, S., Naghizadeh, M. M., Jamalkandi, S. A., & Masoudi‐Nejad, A. (2025) · Heliyon · doi:10.1016/j.heliyon.2025.e42944 · Cited by 3
“On the other hand, negative interactions occur when at least one species suffers a loss and has a negative outcome as a result of the interaction (parasitism: + ; amensalism: 0 -; competition: -) [45] (Supplementary File 2).”
Badr, K., He, Q. P., & Wang, J. (2022) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2022.07.429 · Cited by 3
“It is worth mentioning that the metabolic exchanges predicted by the coculture GEM agree well with literatures, as existing research on different microbial communities suggests that metabolites in TCA cycle and amino acids are the main crossfed metabolites (Heinken and Thiele, 2015).”
Du, S., Wouters, A., Glorieux, M., van Lieshout, L., Horemans, B., & Springael, D. (2025) · Applied and Environmental Microbiology · doi:10.1128/aem.01149-25 · Cited by 2
Badr, K., He, Q. P., & Wang, J. (2022) · Proceedings of the ... American Control Conference/Proceedings of the American Control Conference · doi:10.23919/acc53348.2022.9867540 · Cited by 2
Metabolic constraints on nitrogen fixation by rhizobia in legume nodulesPreprint
Schulte, C. C. M., Borah, K., Wheatley, R. M., Terpolilli, J. J., Saalbach, G., Crang, N., de Groot, D. H., Ratcliffe, R. G., Kruger, N. J., Papachristodoulou, A., et al. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.02.16.431433 · Cited by 2
Srikanth, V., & Vellanki, B. P. (2026) · Environmental Science Water Research & Technology · doi:10.1039/d5ew00929d · Cited by 1
The beneficial role of healthy microbiome in metabolic syndrome and cardiovascular health
Arora, S., Bhambri, N., Puri, S., Anamika, A., Dasgupta, N., Khater, S., & Das, G. (2022) · Elsevier eBooks · doi:10.1016/b978-0-12-822238-6.00003-0 · Cited by 1
Foesel, B. U., Pfeiffer, S., Raj, A. C. D., Krauss‐Etschmann, S., & Schloter, M. (2019) · European Respiratory Society eBooks · doi:10.1183/2312508x.10015718 · Cited by 1
Metadata
- Authors
- Heinken, A., & Thiele, I.
- Affiliations
- University of Luxembourg (LU)
- Venue
- Applied and Environmental Microbiology
- Publisher
- American Society for Microbiology
- Published
- 2015-06-15
- Volume
- 81
- Issue
- 12
- Pages
- 4049-4061
- ISSN
- 0099-2240 (ISSN-L)
- 1098-5336
- PMC
- PMC4524141
- OpenAlex
- W1976253726
- Cited by
- 123
- References
- 61
- Citations by year
- 2026: 3 · 2025: 7 · 2024: 14 · 2023: 8 · 2022: 18 · 2021: 12 · 2020: 10 · 2019: 18 · 2018: 6 · 2017: 12 · 2016: 12 · 2015: 3
- FWCI
- 4.78
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Applied and Environmental Microbiology (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Open Repository and Bibliography (University of Luxembourg) (submittedVersion)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gut microbiota and health
- Life Sciences › Agricultural and Biological Sciences › Food Science › Probiotics and Fermented Foods
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Microbial Metabolic Engineering and Bioproduction
- Keywords
- gut microbiota, microbial mutualism, anoxia, cross-feeding, constraint-based modeling, Lactobacillus plantarum, Streptococcus thermophilus, enterocytes, diet composition, small intestinal microbiota, large intestine, commensalism, competition (biology), probiotics
- MeSH terms
- Anaerobiosis, Computer Simulation, Diet, Humans, Oxygen, Species Specificity, Symbiosis, Enterocytes, Gastrointestinal Tract, Streptococcus thermophilus, Microbial Interactions, Microbiota
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