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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Cites
Dermyshi, E., Wang, Y., Yan, C., Hong, W., Qiu, G., Gong, X., & Zhang, T. (2017) · Neonatology · doi:10.1159/000454668 · Cited by 24,577 · see citing works
A human gut microbial gene catalogue established by metagenomic sequencing
MetaHIT Consortium, Qin, J., Li, R., Raes, J., Arumugam, M., Burgdorf, K. S., Manichanh, C., Nielsen, T., Pons, N., Levenez, F., et al. (2010) · Nature · doi:10.1038/nature08821 · Cited by 11,624 · see citing works
Diversity of the Human Intestinal Microbial Flora
Eckburg, P. B., Bik, E. M., Bernstein, C. N., Purdom, E., Dethlefsen, L., Sargent, M., Gill, S. R., Nelson, K. E., & Relman, D. A. (2005) · Science · doi:10.1126/science.1110591 · Cited by 7,913 · see citing works
Host-gut microbiota metabolic interactions
Nicholson, J. K., Holmes, E., Kinross, J., Burcelin, R., Gibson, G., Jia, W., & Pettersson, S. (2012) · Science · doi:10.1126/science.1223813 · Cited by 4,760 · see citing works
What is flux balance analysis?
Orth, J. D., Thiele, I., & Palsson, B. Ø. (2010) · Nature Biotechnology · doi:10.1038/nbt.1614 · Cited by 3,903 · see citing works
Self-citationQuantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox v2.0
Schellenberger, J., Que, R., Fleming, R. M. T., Thiele, I., Orth, J. D., Feist, A. M., Zielinski, D. C., Bordbar, A., Lewis, N. E., Rahmanian, S., et al. (2011) · Nature Protocols · doi:10.1038/nprot.2011.308 · Cited by 1,835 · see citing works
Self-citationH OW H OST -M ICROBIAL I NTERACTIONS S HAPE THE N UTRIENT E NVIRONMENT OF THE M AMMALIAN I NTESTINE
Hooper, L. V., Midtvedt, T., & Gordon, J. I. (2002) · Annual Review of Nutrition · doi:10.1146/annurev.nutr.22.011602.092259 · Cited by 1,623 · see citing works
The influence of diet on the gut microbiota
Scott, K. P., Gratz, S. W., Sheridan, P. O., Flint, H. J., & Duncan, S. H. (2013) · Pharmacological Research · doi:10.1016/j.phrs.2012.10.020 · Cited by 1,052 · see citing works
Bacteria, Colonic Fermentation, and Gastrointestinal Health
Macfarlane, G. T., & Macfarlane, S. (2012) · Journal of AOAC INTERNATIONAL · doi:10.5740/jaoacint.sge_macfarlane · Cited by 970 · see citing works
Belenguer, A., Duncan, S. H., Calder, A. G., Holtrop, G., Louis, P., Lobley, G. E., & Flint, H. J. (2006) · Applied and Environmental Microbiology · doi:10.1128/aem.72.5.3593-3599.2006 · Cited by 856 · see citing works
Recognition and Degradation of Plant Cell Wall Polysaccharides by Two Human Gut Symbionts
Martens, E. C., Lowe, E. C., Chiang, H. C., Pudlo, N. A., Wu, M., McNulty, N. P., Abbott, D. W., Henrissat, B., Gilbert, H. J., Bolam, D. N., et al. (2011) · PLoS Biology · doi:10.1371/journal.pbio.1001221 · Cited by 847 · see citing works
Duncan, S. H., Barcenilla, A., Stewart, C. S., Pryde, S. E., & Flint, H. J. (2002) · Applied and Environmental Microbiology · doi:10.1128/aem.68.10.5186-5190.2002 · Cited by 743 · see citing works
Characterizing a model human gut microbiota composed of members of its two dominant bacterial phyla
Mahowald, M. A., Rey, F. E., Seedorf, H., Turnbaugh, P. J., Fulton, R. S., Wollam, A., Shah, N., Wang, C., Magrini, V. J., Wilson, R. K., et al. (2009) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0901529106 · Cited by 737 · see citing works
Zoetendal, E. G., Raes, J., van den Bogert, B., Arumugam, M., Booijink, C. C. G. M., Troost, F. J., Bork, P., Wels, M., de Vos, W. M., & Kleerebezem, M. (2012) · The ISME Journal · doi:10.1038/ismej.2011.212 · Cited by 722 · see citing works
A humanized gnotobiotic mouse model of host-archaeal-bacterial mutualism
Samuel, B. S., & Gordon, J. I. (2006) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0602187103 · Cited by 674 · see citing works
Flint, H. J., Duncan, S. H., Scott, K. P., & Louis, P. (2007) · Environmental Microbiology · doi:10.1111/j.1462-2920.2007.01281.x · Cited by 632 · see citing works
Monostrain, multistrain and multispecies probiotics—A comparison of functionality and efficacy
Timmerman, H. M., Koning, C. J. M., Mulder, L., Rombouts, F. M., & Beynen, A. C. (2004) · International Journal of Food Microbiology · doi:10.1016/j.ijfoodmicro.2004.05.012 · Cited by 631 · see citing works
Synthetic microbial communities
Großkopf, T., & Soyer, O. S. (2014) · Current Opinion in Microbiology · doi:10.1016/j.mib.2014.02.002 · Cited by 559 · see citing works
Competitive and cooperative metabolic interactions in bacterial communities
Freilich, S., Zarecki, R., Eilam, O., Segal, E. S., Henry, C. S., Kupiec, M., Gophna, U., Sharan, R., & Ruppin, E. (2011) · Nature Communications · doi:10.1038/ncomms1597 · Cited by 557 · see citing works
Dysbiosis in inflammatory bowel diseases: the oxygen hypothesis
Rigottier‐Gois, L. (2013) · The ISME Journal · doi:10.1038/ismej.2013.80 · Cited by 437 · see citing works
Multidimensional Optimality of Microbial Metabolism
Schuetz, R., Zamboni, N., Zampieri, M., Heinemann, M., & Sauer, U. (2012) · Science · doi:10.1126/science.1216882 · Cited by 420 · cited 1× · see citing works
“Although the presented framework currently limits Pareto optimality analysis to two biomass functions, it is possible to compute the trade-offs for three or more competing objectives using constraint-based methods (48), which may lead to novel insights into multispecies interactions.”
BackgroundDissecting the in Vivo Metabolic Potential of Two Human Gut Acetogens
Rey, F. E., Faith, J. J., Bain, J. R., Muehlbauer, M. J., Stevens, R. D., Newgard, C. B., & Gordon, J. I. (2010) · Journal of Biological Chemistry · doi:10.1074/jbc.m110.117713 · Cited by 417 · see citing works
Levy, R., & Borenstein, E. (2013) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1300926110 · Cited by 408 · see citing works
Zomorrodi, A. R., & Maranas, C. D. (2012) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002363 · Cited by 397 · see citing works
Espey, M. G. (2013) · Free Radical Biology and Medicine · doi:10.1016/j.freeradbiomed.2012.10.554 · Cited by 396 · see citing works
He, G., Shankar, R. A., Chzhan, M., Samouilov, A., Kuppusamy, P., & Zweier, J. L. (1999) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.96.8.4586 · Cited by 383 · see citing works
Faith, J. J., Ahern, P. P., Ridaura, V. K., Cheng, J., & Gordon, J. I. (2014) · Science Translational Medicine · doi:10.1126/scitranslmed.3008051 · Cited by 373 · see citing works
Computationally efficient flux variability analysis
Guðmundsson, S., & Thiele, I. (2010) · BMC Bioinformatics · doi:10.1186/1471-2105-11-489 · Cited by 360 · see citing works
Emergent cooperation in microbial metabolism
Wintermute, E. H., & Silver, P. A. (2010) · Molecular Systems Biology · doi:10.1038/msb.2010.66 · Cited by 349 · see citing works
Environments that Induce Synthetic Microbial Ecosystems
Klitgord, N., & Segrè, D. (2010) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1001002 · Cited by 331 · see citing works
Zhuang, K. H., Izallalen, M., Mouser, P., Richter, H., Risso, C., Mahadevan, R., & Lovley, D. R. (2010) · The ISME Journal · doi:10.1038/ismej.2010.117 · Cited by 321 · see citing works
Monk, J. M., Charusanti, P., Aziz, R. K., Lerman, J. A., Premyodhin, N., Orth, J. D., Feist, A. M., & Palsson, B. Ø. (2013) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1307797110 · Cited by 286 · see citing works
Teusink, B., Wiersma, A., Douwe Molenaar, B., Francke, C., de Vos, W. M., Siezen, R. J., & Smid, E. J. (2006) · Journal of Biological Chemistry · doi:10.1074/jbc.m606263200 · Cited by 283 · see citing works
Newell, P. D., & Douglas, A. E. (2013) · Applied and Environmental Microbiology · doi:10.1128/aem.02742-13 · Cited by 273 · see citing works
Large-scale reconstruction and phylogenetic analysis of metabolic environments
Borenstein, E., Kupiec, M., Feldman, M. W., & Ruppin, E. (2008) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0806162105 · Cited by 238 · see citing works
Functional Metabolic Map of Faecalibacterium prausnitzii, a Beneficial Human Gut Microbe
Heinken, A., Khan, M. T., Paglia, G., Rodionov, D. A., Harmsen, H. J. M., & Thiele, I. (2014) · Journal of Bacteriology · doi:10.1128/jb.01780-14 · Cited by 236 · see citing works
Thiele, I., Vo, T. D., Price, N. D., & Palsson, B. Ø. (2005) · Journal of Bacteriology · doi:10.1128/jb.187.16.5818-5830.2005 · Cited by 229 · see citing works
Systems-level characterization of a host-microbe metabolic symbiosis in the mammalian gut
Heinken, A., Sahoo, S., Fleming, R. M. T., & Thiele, I. (2013) · Gut Microbes · doi:10.4161/gmic.22370 · Cited by 219 · see citing works
Marzorati, M., Vanhoecke, B. W. A., Ryck, T. D., Sadabad, M. S., Pinheiro, I., Possemiers, S., den Abbeele, P. V., Derycke, L., Bracke, M., Pieters, J., et al. (2014) · BMC Microbiology · doi:10.1186/1471-2180-14-133 · Cited by 208 · see citing works
Ethanol metabolism and its effects on the intestinal epithelial barrier
Elamin, E. E., Masclee, A., Dekker, J. P., & Jonkers, D. M. A. E. (2013) · Nutrition Reviews · doi:10.1111/nure.12027 · Cited by 187 · see citing works
A road map for the development of community systems (CoSy) biology
Zengler, K., & Palsson, B. Ø. (2012) · Nature Reviews Microbiology · doi:10.1038/nrmicro2763 · Cited by 180 · see citing works
From meta-omics to causality: experimental models for human microbiome research
Fritz, J. V., Desai, M. S., Shah, P., Schneider, J. G., & Wilmes, P. (2013) · Microbiome · doi:10.1186/2049-2618-1-14 · Cited by 174 · see citing works
The gut microbiota and the metabolic health of the host
Joyce, S. A., & Gahan, C. G. M. (2014) · Current Opinion in Gastroenterology · doi:10.1097/mog.0000000000000039 · Cited by 147 · see citing works
Thiele, I., Hyduke, D. R., Steeb, B., Fankam, G., Allen, D. K., Bazzani, S., Charusanti, P., Chen, F., Fleming, R. M. T., Hsiung, C. A., et al. (2011) · BMC Systems Biology · doi:10.1186/1752-0509-5-8 · Cited by 141 · see citing works
Chassard, C., & Bernalier‐Donadille, A. (2005) · FEMS Microbiology Letters · doi:10.1111/j.1574-6968.2005.00016.x · Cited by 131 · see citing works
Pastink, M. I., Teusink, B., Pascal Hols, A., Visser, S., de Vos, W. M., & Hugenholtz, J. (2009) · Applied and Environmental Microbiology · doi:10.1128/aem.00138-09 · Cited by 129 · see citing works
Flahaut, N. A. L., Wiersma, A., van de Bunt, B., Martens, D. E., Schaap, P. J., Sijtsma, L., dos Santos, V. A. P. M., & de Vos, W. M. (2013) · Applied Microbiology and Biotechnology · doi:10.1007/s00253-013-5140-2 · Cited by 128 · see citing works
Liao, Y.-C., Huang, T., Chen, F.-C., Charusanti, P., Hong, J. S.-J., Chang, H., Tsai, S., Palsson, B. Ø., & Hsiung, C. A. (2011) · Journal of Bacteriology · doi:10.1128/jb.01218-10 · Cited by 121 · see citing works
A systems biology approach to studying the role of microbes in human health
Thiele, I., Heinken, A., & Fleming, R. M. (2013) · Current Opinion in Biotechnology · doi:10.1016/j.copbio.2012.10.001 · Cited by 114 · see citing works
Heinken, A., & Thiele, I. (2015) · Gut Microbes · doi:10.1080/19490976.2015.1023494 · Cited by 105 · see citing works
Marteyn, B., Scorza, F. B., Sansonetti, P. J., & Tang, C. (2011) · Cellular Microbiology · doi:10.1111/j.1462-5822.2010.01549.x · Cited by 102 · see citing works
Manor, O., Levy, R., & Borenstein, E. (2014) · Cell Metabolism · doi:10.1016/j.cmet.2014.07.021 · Cited by 81 · see citing works
Colonization Resistance: Battle of the Bugs or Ménage à Trois with the Host?
Spees, A. M., Lopez, C. A., Kingsbury, D. D., Winter, S., & Bäumler, A. J. (2013) · PLoS Pathogens · doi:10.1371/journal.ppat.1003730 · Cited by 77 · see citing works
The evolution of metabolic networks of E. coli
Baumler, D. J., Peplinski, R. G., Reed, J. L., Glasner, J. D., & Perna, N. T. (2011) · BMC Systems Biology · doi:10.1186/1752-0509-5-182 · Cited by 71 · see citing works
Predicting the impact of diet and enzymopathies on human small intestinal epithelial cells
Sahoo, S., & Thiele, I. (2013) · Human Molecular Genetics · doi:10.1093/hmg/ddt119 · Cited by 67 · see citing works
The design of long‐term effective uranium bioremediation strategy using a community metabolic model
Zhuang, K. H., Ma, E., Lovley, D. R., & Mahadevan, R. (2012) · Biotechnology and Bioengineering · doi:10.1002/bit.24528 · Cited by 62 · see citing works
Thiele, I., Fleming, R. M. T., Bordbar, A., Schellenberger, J., & Palsson, B. Ø. (2010) · Biophysical Journal · doi:10.1016/j.bpj.2010.01.060 · Cited by 60 · see citing works
Diet alters probiotic L actobacillus persistence and function in the intestine
Tachon, S., Lee, B., & Marco, M. L. (2013) · Environmental Microbiology · doi:10.1111/1462-2920.12297 · Cited by 56 · see citing works
Thomas, M., Wrzosek, L., Ben-Yahia, L., Noordine, M.-L., Gitton, C., Chevret, D., Langella, P., Mayeur, C., Cherbuy, C., & Rul, F. (2011) · PLoS ONE · doi:10.1371/journal.pone.0028789 · Cited by 39 · see citing works
Metabolic Proximity in the Order of Colonization of a Microbial Community
Mazumdar, V., Amar, S., & Segrè, D. (2013) · PLoS ONE · doi:10.1371/journal.pone.0077617 · Cited by 35 · see citing works
Proteomic investigation of the adaptation of Lactococcus lactis to the mouse digestive tract
Roy, K., Meyrand, M., Corthier, G., Monnet, V., & Mistou, M. (2008) · PROTEOMICS · doi:10.1002/pmic.200700698 · Cited by 32 · see citing works
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Gut microbiota functions: metabolism of nutrients and other food components
Rowland, I., Gibson, G., Heinken, A., Scott, K., Swann, J., Thiele, I., & Tuohy, K. (2018) · European Journal of Nutrition · doi:10.1007/s00394-017-1445-8 · Cited by 2,878 · see citing works
“The model predicted that the need to regenerate reducing equivalents enforced mutualism in certain pairs under anoxic conditions [153].”
BackgroundMethodInfluentialIntestinal Short Chain Fatty Acids and their Link with Diet and Human Health
Ríos-Covián, D., Ruas‐Madiedo, P., Margollés, A., Gueimonde, M., Reyes‐Gavilán, C. G. D. L., & Salazar, N. (2016) · Frontiers in Microbiology · doi:10.3389/fmicb.2016.00185 · Cited by 2,215 · see citing works
“…Salazar nuriasg@ipla.csic.es Specialty section: This article was submitted to Microbial Physiology and Metabolism, a section of the journal Frontiers in Microbiology Received: 04 December 2015 Accepted: 02 February 2016 Published: 17 February 2016 Citation: Ríos-Covián D, Ruas-Madiedo P,”
BackgroundShort-chain fatty acids: Linking diet, the microbiome and immunity
Mann, E. R., Lam, Y. K., & Uhlig, H. H. (2024) · Nature reviews. Immunology · doi:10.1038/s41577-024-01014-8 · Cited by 1,595 · see citing works
“1), which is promoted by anoxic conditions in the colon 188 .”
Generation of genome-scale metabolic reconstructions for 773 members of the human gut microbiota
Magnúsdóttir, S., Heinken, A., Kutt, L., Ravcheev, D. A., Bauer, E., Noronha, A., Greenhalgh, K., Jäger, C., Baginska, J., Wilmes, P., et al. (2017) · Nature Biotechnology · doi:10.1038/nbt.3703 · Cited by 798 · see citing works
Impact of antibiotics on the human microbiome and consequences for host health
Patangia, D. V., Ryan, C. A., Dempsey, E. M., Ross, R. P., & Stanton, C. (2022) · MicrobiologyOpen · doi:10.1002/mbo3.1260 · Cited by 806 · see citing works
“This type of de- pendency is also seen in many other bacterial groups (Heinken & Thiele, 2015; Rowland et al., 2018).”
BackgroundA microfluidics-based in vitro model of the gastrointestinal human–microbe interface
Shah, P., Fritz, J. V., Glaab, E., Desai, M. S., Greenhalgh, K., Frachet, A., Niegowska, M., Estes, M., Jäger, C., Seguin-Devaux, C., et al. (2016) · Nature Communications · doi:10.1038/ncomms11535 · Cited by 660 · see citing works
Kawabata, K., Yoshioka, Y., & Terao, J. (2019) · Molecules · doi:10.3390/molecules24020370 · Cited by 563 · see citing works
“The gastrointestinal mucosa separates the highly vascularized and oxygen-rich epithelium from the anoxic gut lumen, and the colon, which is relatively anoxic compared with the small intestine, is helpful for intestinal bacteria to form the more complex community [74,75].”
BackgroundCockburn, D. W., & Koropatkin, N. M. (2016) · Journal of Molecular Biology · doi:10.1016/j.jmb.2016.06.021 · Cited by 542 · see citing works
Honey bees as models for gut microbiota research
Zheng, H., Steele, M. I., Leonard, S. P., Motta, E. V. S., & Moran, N. A. (2018) · Lab Animal · doi:10.1038/s41684-018-0173-x · Cited by 329 · see citing works
Costless metabolic secretions as drivers of interspecies interactions in microbial ecosystems
Pacheco, A. R., Moel, M., & Segrè, D. (2019) · Nature Communications · doi:10.1038/s41467-018-07946-9 · Cited by 315 · see citing works
Understanding Competition and Cooperation within the Mammalian Gut Microbiome
Coyte, K. Z., & Rakoff-Nahoum, S. (2019) · Current Biology · doi:10.1016/j.cub.2019.04.017 · Cited by 329 · see citing works
BacArena: Individual-based metabolic modeling of heterogeneous microbes in complex communities
Bauer, E., Zimmermann, J., Baldini, F., Thiele, I., & Kaleta, C. (2017) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1005544 · Cited by 308 · see citing works
Metabolic interactions in microbial communities: untangling the Gordian knot
Ponomarova, O., & Patil, K. R. (2015) · Current Opinion in Microbiology · doi:10.1016/j.mib.2015.06.014 · Cited by 274 · see citing works
Glycan Utilization and Cross-Feeding Activities by Bifidobacteria
Turroni, F., Milani, C., Duranti, S., Mahony, J., van Sinderen, D., & Ventura, M. (2018) · Trends in Microbiology · doi:10.1016/j.tim.2017.10.001 · Cited by 263 · see citing works
The chemistry of gut microbial metabolism of polyphenols
Stevens, J. F., & Maier, C. S. (2016) · Phytochemistry Reviews · doi:10.1007/s11101-016-9459-z · Cited by 244 · see citing works
“The gut epithelium separates the nearanoxic gut lumen from the highly vascularized and oxygen-rich sub-epithelium (Heinken and Thiele 2015; Taylor and Colgan 2007).”
A systematic assessment of current genome-scale metabolic reconstruction tools
Mendoza, S. N., Olivier, B. G., Molenaar, D., & Teusink, B. (2019) · Genome biology · doi:10.1186/s13059-019-1769-1 · Cited by 230 · see citing works
SteadyCom: Predicting microbial abundances while ensuring community stability
Chan, S. H. J., Simons, M., & Maranas, C. D. (2017) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1005539 · Cited by 233 · see citing works
Baldini, F., Hertel, J., Sandt, E., Thinnes, C. C., Neuberger-Castillo, L., Pavelka, L., Betsou, F., Krüger, R., Thiele, I., Aguayo, G., et al. (2020) · BMC Biology · doi:10.1186/s12915-020-00775-7 · Cited by 230 · see citing works
Synthetic Ecology of Microbes: Mathematical Models and Applications
Zomorrodi, A. R., & Segrè, D. (2015) · Journal of Molecular Biology · doi:10.1016/j.jmb.2015.10.019 · Cited by 212 · see citing works
“For example, Heinken and Thiele [185] constructed a metabolic model of pairwise interactions between 11 representative microorganisms in the gut, in conjunction with a metabolic model of human small intestinal enterocytes subject to three different diets.”
BackgroundSynthetic ecology of the human gut microbiota
Vrancken, G., Gregory, A., Huys, G. R. B., Faust, K., & Raes, J. (2019) · Nature Reviews Microbiology · doi:10.1038/s41579-019-0264-8 · Cited by 192 · see citing works
Modelling approaches for studying the microbiome
Kumar, M., Ji, B., Zengler, K., & Nielsen, J. (2019) · Nature Microbiology · doi:10.1038/s41564-019-0491-9 · Cited by 194 · see citing works
Short Chain Fatty Acid Metabolism in Relation to Gut Microbiota and Genetic Variability
Meyers, G. R., Samouda, H., & Bohn, T. (2022) · Nutrients · doi:10.3390/nu14245361 · Cited by 193 · see citing works
Koch, C. D., Gladwin, M. T., Freeman, B. A., Lundberg, J. O., Weitzberg, E., & Morris, A. (2017) · Free Radical Biology and Medicine · doi:10.1016/j.freeradbiomed.2016.12.015 · Cited by 186 · see citing works
Metabolic interaction models recapitulate leaf microbiota ecology
Schäfer, M., Pacheco, A. R., Künzler, R., Bortfeld‐Miller, M., Field, C. M., Vayena, E., Vassily Hatzimanikatis, A., & Vorholt, J. A. (2023) · Science · doi:10.1126/science.adf5121 · Cited by 179 · see citing works
“Models were thenmerged by integrating them into a common extracellular compartment (98, 99).”
MethodZhang, S., & Chen, D. (2019) · Chinese Medical Journal · doi:10.1097/cm9.0000000000000245 · Cited by 141 · see citing works
Ravcheev, D. A., & Thiele, I. (2017) · Frontiers in Genetics · doi:10.3389/fgene.2017.00111 · Cited by 136 · see citing works
“, 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 130 · see citing works
“Previous modeling studies of intermicrobial interactions found anoxic conditions to promote mutualistic interactions and increase the diversity of secreted metabolites (53, 54), and oxygen availability was determined to be a better indicator of secreted metabolites than species identity (54).”
BackgroundModeling metabolism of the human gut microbiome
Magnúsdóttir, S., & Thiele, I. (2018) · Current Opinion in Biotechnology · doi:10.1016/j.copbio.2017.12.005 · Cited by 127 · see citing works
Self-citationMetabolic modelling approaches for describing and engineering microbial communities
García-Jiménez, B., Torres‐Bacete, J., & Nogales, J. (2020) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2020.12.003 · Cited by 122 · see citing works
“normoxic conditions [96]; to predict short chain fatty acids levels to use as treatment for Crohn’s”
MethodConstraint-based stoichiometric modelling from single organisms to microbial communities
Gottstein, W., Olivier, B. G., Bruggeman, F. J., & Teusink, B. (2016) · Journal of The Royal Society Interface · doi:10.1098/rsif.2016.0627 · Cited by 109 · see citing works
“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].”
BackgroundKumar, M., Ji, B., Babaei, P., Das, P., Lappa, D., Ramakrishnan, G., Fox, T. E., Haque, R., Petri, W. A., Bäckhed, F., et al. (2018) · Metabolic Engineering · doi:10.1016/j.ymben.2018.07.018 · Cited by 103 · see citing works
“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 96 · see citing works
“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
Chan, S. H. J., Cai, J., Wang, L., Simons, M., & Maranas, C. D. (2017) · Bioinformatics · doi:10.1093/bioinformatics/btx453 · Cited by 97 · see citing works
“…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…”
BackgroundMethodInfluentialUsing metabolic networks to resolve ecological properties of microbiomes
Muller, E. E. L., Faust, K., Stefanie Widder, A., Herold, M., Arbas, S. M., & Wilmes, P. (2017) · Current Opinion in Systems Biology · doi:10.1016/j.coisb.2017.12.004 · Cited by 95 · see citing works
From Network Analysis to Functional Metabolic Modeling of the Human Gut Microbiota
Bauer, E., & Thiele, I. (2018) · mSystems · doi:10.1128/msystems.00209-17 · Cited by 90 · see citing works
“A study based on 11 metabolic models (62) revealed a vivid exchange of fermentation”
BackgroundBasile, A., Campanaro, S., Kovalovszki, A., Zampieri, G., Rossi, A., Angelidaki, İ., Valle, G., & Treu, L. (2020) · Metabolic Engineering · doi:10.1016/j.ymben.2020.08.013 · Cited by 85 · see citing works
A guide to deciphering microbial interactions and metabolic fluxes in microbiome communities
Antoniewicz, M. R. (2020) · Current Opinion in Biotechnology · doi:10.1016/j.copbio.2020.07.001 · Cited by 73 · see citing works
Advances in constraint-based modelling of microbial communities
Heinken, A., Basile, A., & Thiele, I. (2021) · Current Opinion in Systems Biology · doi:10.1016/j.coisb.2021.05.007 · Cited by 73 · see citing works
Polysaccharides influence human health via microbiota-dependent and -independent pathways
Gan, L., Wang, J., & Guo, Y. (2022) · Frontiers in Nutrition · doi:10.3389/fnut.2022.1030063 · Cited by 69 · see citing works
“Furthermore, microbiota that has limited metabolic capacities for processing complex polysaccharides must rely on other organisms that are capable of fermenting polysaccharides through microbe–microbe interactions, such as commensalism, mutualism, and competition (26, 33, 35, 36).”
BackgroundMendes‐Soares, H., Mundy, M. B., Soares, L. M., & Chia, N. (2016) · BMC Bioinformatics · doi:10.1186/s12859-016-1230-3 · Cited by 69 · see citing works
“Using previously calculated growth rates, this function quantifies the effect of pairwise interactions and assigns an interaction type to each pair, following Heinken and Thiele [33].”
BackgroundMethodNoecker, C. A., Chiu, H.-C., McNally, C. P., & Borenstein, E. (2019) · mSystems · doi:10.1128/msystems.00579-19 · Cited by 66 · see citing works
Microbial Systems Ecology to Understand Cross-Feeding in Microbiomes
Mataigne, A., Vannier, N., Vandenkoornhuyse, P., & Hacquard, S. (2021) · Frontiers in Microbiology · doi:10.3389/fmicb.2021.780469 · Cited by 63 · see citing works
“…environmental constraints are used to identify potential competition for nutrients and to predict cross-feeding or ecological niches, with applications in metabolic engineering (Heinken and Thiele, 2015; Magnúsdóttir et al., 2017; Frioux et al., 2018; Muller et al., 2018; Mendoza et al., 2019).”
Zhang, Y., Zhang, B., Dong, L., & Chang, P. (2018) · Advances in Nutrition · doi:10.1093/advances/nmy076 · Cited by 61 · see citing works
“Nevertheless, the proportions of anaerobes are overwhelmingly superior to the aerobes in a healthy gut (25) because the microenvironment in a heathy gut is anoxic, thus benefiting the growth of anaerobes (26), such as Bacteroides and Bifidobacterium (27).”
BackgroundGenomic Analysis of the Human Gut Microbiome Suggests Novel Enzymes Involved in Quinone Biosynthesis
Ravcheev, D. A., & Thiele, I. (2016) · Frontiers in Microbiology · doi:10.3389/fmicb.2016.00128 · Cited by 58 · see citing works
“…thereby, enabling the in silico study of HGM and their interactions with each other (Freilich et al., 2011; Zomorrodi and Maranas, 2012; Khandelwal et al., 2013; Heinken and Thiele, 2015a) as well as with the human host (Thiele et al., 2013a; Bauer et al., 2015; Heinken and Thiele, 2015b,c).”
BackgroundAgent Based Modeling of Human Gut Microbiome Interactions and Perturbations
Shashkova, T., Popenko, A., Tyakht, A., Peskov, K. V., Kosinsky, Y., Bogolubsky, L. I., Raigorodskii, A. M., Ischenko, D. S., Alexeev, D. G., & Govorun, V. M. (2016) · PLoS ONE · doi:10.1371/journal.pone.0148386 · Cited by 56 · see citing works
“Detection and study of such interaction types in microbial ecosystems is a subject of current scientific interest [21 – 22].”
From Genes to Ecosystems in Microbiology: Modeling Approaches and the Importance of Individuality
Kreft, J., Plugge, C. M., Prats, C., Leveau, J. H. J., Zhang, W., & Hellweger, F. L. (2017) · Frontiers in Microbiology · doi:10.3389/fmicb.2017.02299 · Cited by 47 · see citing works
“…in Microbiology | www.frontiersin.org 6 November 2017 | Volume 8 | Article 2299 Bordbar et al. (2010), Freilich et al. (2011), Khandelwal et al. (2013), Nagarajan et al. (2013), Shoaie et al. (2013), Ye et al. (2014), El-Semman et al. (2014), Merino et al. (2015) and Heinken and Thiele (2015).”
Metabolic Modeling of Cystic Fibrosis Airway Communities Predicts Mechanisms of Pathogen Dominance
Henson, M. A., Orazi, G., Phalak, P., & O’Toole, G. A. (2019) · mSystems · doi:10.1128/msystems.00026-19 · Cited by 46 · see citing works
“Due to challenges 77 in developing manually curated reconstructions of poorly studied species, including those present in the 78 CF lung, most in silico community models have been restricted to ~5 microbial species (15-17) and fail to 79 adequately cover the diversity of in vivo communities.”
BackgroundMetabolic modeling of the International Space Station microbiome reveals key microbial interactions
Kumar, R. K., Singh, N. K., Balakrishnan, S., Parker, C. W., Raman, K., & Venkateswaran, K. J. (2022) · Microbiome · doi:10.1186/s40168-022-01279-y · Cited by 37 · see citing works
“A 10% or higher increase or decrease in growth rate, when in a community, was taken to be a significant effect [39].”
BackgroundHeinken, A., Acharya, G., Ravcheev, D. A., Hertel, J., Nyga, M., Okpala, O. E., Hogan, M., Magnúsdóttir, S., Martinelli, F., Preciat, G., et al. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.11.09.375451 · Cited by 38 · see citing works
Path to improving the life cycle and quality of genome-scale models of metabolism
Seif, Y., & Palsson, B. Ø. (2021) · Cell Systems · doi:10.1016/j.cels.2021.06.005 · Cited by 26 · see citing works
Community‐scale models of microbiomes: Articulating metabolic modelling and metagenome sequencing
Cerk, K., Ugalde‐Salas, P., Nedjad, C. G., Lecomte, M., Muller, C., Sherman, D. J., Hildebrand, F., Labarthe, S., & Frioux, C. (2024) · Microbial Biotechnology · doi:10.1111/1751-7915.14396 · Cited by 29 · see citing works
Systems biology perspective for studying the gut microbiota in human physiology and liver diseases
Altay, Ö., Nielsen, J., Uhlén, M., Borén, J., & Mardinoğlu, A. (2019) · EBioMedicine · doi:10.1016/j.ebiom.2019.09.057 · Cited by 31 · see citing works
Predicting microbial interactions with approaches based on flux balance analysis: an evaluation
Joseph, C., Zafeiropoulos, H., Bernaerts, K., & Faust, K. (2024) · BMC Bioinformatics · doi:10.1186/s12859-024-05651-7 · Cited by 30 · see citing works
“…of FBA-based microbial interaction prediction could include additional tools and approaches not tested here, such as Pareto optimality [53, 54] as well as topological approaches that ignore reaction stoichiometries [26, 55, 56], and FBA approaches integrating further biologically driven…”
MethodFlux sampling in genome-scale metabolic modeling of microbial communities
Gelbach, P. E., Çetin, H., & Finley, S. D. (2024) · BMC Bioinformatics · doi:10.1186/s12859-024-05655-3 · Cited by 30 · see citing works
“…model , wherein two metabolic models are merged into a single stoichiometric matrix with a shared compartment representing the extracellular space [22], (2) lumped model (also called “enzyme soup”) approach [23–25], where all metabolites and reactions are pooled into a single model in proportion…”
BackgroundMurakami, T., Segawa, T., Takeuchi, N., Sepúlveda, G. B., Labarca, P. P., Kohshima, S., & Hongoh, Y. (2018) · Environmental Microbiology · doi:10.1111/1462-2920.14420 · Cited by 28 · see citing works
Karimian, E., & Motamedian, E. (2020) · Scientific Reports · doi:10.1038/s41598-020-65659-w · Cited by 27 · see citing works
Eukaryotic Parasites Are Integral to a Productive Microbial Food Web in Oxygen-Depleted Waters
Suter, E. A., Pachiadaki, M., Taylor, G. T., & Edgcomb, V. P. (2022) · Frontiers in Microbiology · doi:10.3389/fmicb.2021.764605 · Cited by 26 · see citing works
“Modeling studies suggest that anoxic conditions select for mutualism and enhanced cross-feeding among microbial community members (Heinken and Thiele, 2015).”
BackgroundAhmed, L. A., & Al‐Massri, K. F. (2022) · Current Molecular Pharmacology · doi:10.2174/1874467215666220222105004 · Cited by 23 · see citing works
Du, H., Li, M., & Liu, Y. (2023) · Quantitative Biology · doi:10.15302/j-qb-022-0313 · Cited by 23 · see citing works
Brandon-Mong, G.-J., Shaw, G. T.-W., Chen, W., Chen, C., & Wang, D. (2020) · BMC Microbiology · doi:10.1186/s12866-020-01981-7 · Cited by 24 · see citing works
Adolf, L. A., & Heilbronner, S. (2022) · Metabolites · doi:10.3390/metabo12060489 · Cited by 23 · see citing works
Chan, S. H. J., Friedman, E. S., Wu, G. D., & Maranas, C. D. (2019) · Processes · doi:10.3390/pr7070394 · Cited by 21 · see citing works
Yang, X., Yang, T., Zhang, Z., Zhang, Y., Mei, X., Gao, Y., Wang, N., Jiang, G., Xu, Y., Shen, Q., et al. (2025) · Nature Ecology & Evolution · doi:10.1038/s41559-025-02908-z · Cited by 20 · see citing works
“For the SynCom models, individual strain models were integrated into a common extracellular compartment 49,50 , with all metabolites, reactions and genes specifically labelled, except those in the extracellular compartment.”
Two-species community design of lactic acid bacteria for optimal production of lactate
Ibrahim, M., & Raman, K. (2021) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2021.11.009 · Cited by 19 · see citing works
Degregori, S., Wang, X., Kommala, A., Schulhof, N., Moradi, S., MacDonald, A., Eblen, K., Jukovich, S., Smith, E., Kelleher, E., et al. (2024) · Biological reviews/Biological reviews of the Cambridge Philosophical Society · doi:10.1111/brv.13161 · Cited by 19 · see citing works
“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
Blanco‐Míguez, A., Fdez‐Riverola, F., Sánchez, B., & Lourenço, A. M. G. (2017) · Briefings in Bioinformatics · doi:10.1093/bib/bbx156 · Cited by 16 · see citing works
“namely, different dietary regimes as well as anoxic and oxic conditions, in the human gut ecosystem [321].”
BackgroundDevika, N. T., Jangam, A. K., Katneni, V. K., Patil, P. K., Nathamuni, S. P., & Shekhar, M. S. (2021) · Frontiers in Cellular and Infection Microbiology · doi:10.3389/fcimb.2021.752477 · Cited by 17 · see citing works
Designing function-specific minimal microbiomes from large microbial communities
Raghu, A. K., Palanikumar, I., & Raman, K. (2024) · npj Systems Biology and Applications · doi:10.1038/s41540-024-00373-1 · Cited by 16 · see citing works
A Systematic Assessment Of Current Genome-Scale Metabolic Reconstruction ToolsPreprint
Mendoza, S. N., Olivier, B. G., Douwe Molenaar, B., & Teusink, B. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/558411 · Cited by 16 · see citing works
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 13 · see citing works
“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 · see citing works
Mendes‐Soares, H., & Chia, N. (2016) · Free Radical Biology and Medicine · doi:10.1016/j.freeradbiomed.2016.12.017 · Cited by 14 · see citing works
“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 13 · see citing works
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 · see citing works
Timmis, K. N., Verstraete, W., Regina, V. R., & Hallsworth, J. E. (2023) · Environmental Microbiology · doi:10.1111/1462-2920.16438 · Cited by 12 · see citing works
Popovic, M. E., Šekularac, G. M., & Stevanović, M. (2023) · Journal of Biotechnology · doi:10.1016/j.jbiotec.2023.11.001 · Cited by 13 · see citing works
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 · see citing works
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 10 · see citing works
“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 · see citing works
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 · see citing works
“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 · see citing works
García-Jiménez, B., Carrasco, J., Medina, J., & Wilkinson, M. D. (2020) · doi:10.21203/rs.2.24431/v1 · Cited by 8 · see citing works
“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 · see citing works
“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 · see citing works
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 · see citing works
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 · see citing works
“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 · see citing works
Badr, K., He, Q. P., & Wang, J. (2024) · Microbiome Research Reports · doi:10.20517/mrr.2023.70 · Cited by 5 · see citing works
Afarin, M., & Naeimpoor, F. (2024) · Bioprocess and Biosystems Engineering · doi:10.1007/s00449-024-03072-7 · Cited by 5 · see citing works
“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 · see citing works
Systems biology of bacteria‐host interactions
Heinken, A., Ravcheev, D. A., & Thiele, I. (2016) · Wiley eBooks · doi:10.1002/9781118982907.ch7 · Cited by 4 · see citing works
Dysbiosis and its Varied Impacts
Sharma, R., & Kumar, K. (2023) · doi:10.1002/9781119904786.ch5 · Cited by 4 · see citing works
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 · see citing works
“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 · see citing works
“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 · see citing works
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 · see citing works
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 · see citing works
Srikanth, V., & Vellanki, B. P. (2026) · Environmental Science Water Research & Technology · doi:10.1039/d5ew00929d · Cited by 1 · see citing works
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 · see citing works
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 · see citing works
- 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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