[en] Microbial consortia are an exciting alternative for increasing the performances of bioprocesses for the production of complex metabolic products. However, the functional properties of microbial communities remain challenging to control, considering the complex interaction mechanisms occurring between co-cultured microbial species. Indeed, microbial communities are highly dynamic and can adapt to changing environmental conditions through complex mechanisms, such as phenotypic diversification. We focused on stabilizing a co-culture of Saccharomyces cerevisiae and Escherichia coli in continuous cultures. Our preliminary data pointed out that transient diauxic shifts could lead to stable co-culture by providing periodic fitness advantages to the yeast. Based on a computational toolbox called MONCKS (for MONod-type Co-culture Kinetic Simulation), we were able to predict the dynamics of diauxic shift for both species based on a cybernetic approach. This toolbox was further used to predict the frequency of diauxic shift to be applied to reach co-culture stability. These simulations were successfully reproduced experimentally in continuous bioreactors with glucose pulsing. Finally, based on a bet-hedging reporter, we observed that the yeast population exhibited an increased phenotypic diversification process in co-culture compared with mono-culture, suggesting that this mechanism could be the basis of the metabolic fitness of the yeast.
Delvenne, Mathéo ; Université de Liège - ULiège > TERRA Research Centre
Henrion, Lucas ; Université de Liège - ULiège > TERRA Research Centre
Moreno, Fabian; TERRA Research and Teaching Centre, Microbial Processes and Interactions (MiPI), Gembloux Agro-Bio Tech, University of Liége, Gembloux, Belgium
Telek, Samuel ; Université de Liège - ULiège > Département GxABT > Microbial technologies
Dusny, Christian; Microscale Analysis and Engineering, Department of Solar Materials, Helmholtz-Centre for Environmental Research- UFZ Leipzig, Leipzig, Germany
Delvigne, Frank ; Université de Liège - ULiège > TERRA Research Centre > Microbial technologies
Language :
English
Title :
Controlling microbial co-culture based on substrate pulsing can lead to stability through differential fitness advantages.
EU - European Union [BE] Wallonia [BE] Saxon State Parliament [DE] F.R.S.-FNRS - Fund for Scientific Research [BE]
Funding text :
This project was supported through different research grants. First, we would like to thank the Era-Cobiotech established based on the H2020 European framework for providing funding (Contibio and ComRaDes projects). Wallonia is also gratefully acknowledged for financial support. This measure is co-financed with tax funds on the basis of the budget passed by the Saxon state parliament. JAM is supported by a post-doctoral grant (Contibio project). FM is supported by a postdoctoral grant (Sunup project, supported by Wallonia). LH is supported by a FRIA PhD grant provided by the Belgian Fund for Scientific Research (FNRS). MD is supported by a PhD grant provided by the Belgian Fund for Scientific Research (FNRS) in the context of an Era-Net Aquatic Pollutant project (ARENA). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Losoi PS, Santala VP, Santala SM. Enhanced Population Control in a Synthetic Bacterial Consortium by Interconnected Carbon Cross-Feeding. ACS Synthetic Biology. 2019; 8: 2642-2650. https://doi.org/ 10.1021/acssynbio.9b00316 PMID: 31751122
Escalante A, Elena Rodríguez M, Martínez A, López-Munguía A, Bolívar F, Gosset G. Characterization of bacterial diversity in Pulque, a traditional Mexican alcoholic fermented beverage, as determined by 16S rDNA analysis. FEMS Microbiology Letters. 2004. https://doi.org/10.1016/j.femsle.2004.04.045 PMID: 15183874
Wang L, York SW, Ingram LO, Shanmugam KT. Simultaneous fermentation of biomass-derived sugars to ethanol by a co-culture of an engineered Escherichia coli and Saccharomyces cerevisiae. Bioresource Technology. 2019; 273: 269-276. https://doi.org/10.1016/j.biortech.2018.11.016 PMID: 30448678
Ly S, Bajoul Kakahi F, Mith H, Phat C, Fifani B, Kenne T, et al. Engineering Synthetic Microbial Communities through a Selective Biofilm Cultivation Device for the Production of Fermented Beverages. Microorganisms. 2019; 7: 206. https://doi.org/10.3390/microorganisms7070206 PMID: 31330825
Fifani B, Steels S, Helmus C, Delacuvellerie A, Deracinois B, Phalip V, et al. Coculture of Trichoderma harzianum and Bacillus velezensis Based on Metabolic Cross-Feeding Modulates Lipopeptide Production. Microorganisms. 2022;10. https://doi.org/10.3390/microorganisms10051059 PMID: 35630500
Grandel NE, Reyes Gamas K, Bennett MR. Control of synthetic microbial consortia in time, space, and composition. Trends in Microbiology. 2021; 29: 1095-1105. https://doi.org/10.1016/j.tim.2021.04.001 PMID: 33966922
Rodríguez-Verdugo A, Vulin C, Ackermann M. The rate of environmental fluctuations shapes ecological dynamics in a two-species microbial system. Ecology Letters. 2019; 22: 838-846. https://doi.org/10. 1111/ele.13241 PMID: 30790416
Dal Co A, Ackermann M, van Vliet S. Metabolic activity affects the response of single cells to a nutrient switch in structured populations. Journal of The Royal Society Interface. 2019; 16: 20190182. https:// doi.org/10.1098/rsif.2019.0182 PMID: 31288652
Mancuso CP, Lee H, Abreu CI, Gore J, Khalil AS. Environmental fluctuations reshape an unexpected diversity-disturbance relationship in a microbial community. Shou W, Walczak AM, Shou W, editors. eLife. 2021; 10: e67175. https://doi.org/10.7554/eLife.67175 PMID: 34477107
Abreu CI, Andersen Woltz VL, Friedman J, Gore J. Microbial communities display alternative stable states in a fluctuating environment. PLOS Computational Biology. 2020; 16: 1-17. https://doi.org/10. 1371/journal.pcbi.1007934 PMID: 32453781
Kylilis N, Tuza ZA, Stan G-B, Polizzi KM. Tools for engineering coordinated system behaviour in synthetic microbial consortia. Nature Communications. 2018; 9: 2677. https://doi.org/10.1038/s41467-018-05046-2 PMID: 29992956
Liao MJ, Din MO, Tsimring L, Hasty J. Rock-paper-scissors: Engineered population dynamics increase genetic stability. Science. 2019; 365: 1045-1049. https://doi.org/10.1126/science.aaw0542 PMID: 31488693
Zengler K, Zaramela LS. The social network of microorganisms-how auxotrophies shape complex communities. Nature Reviews Microbiology. 2018; 16: 383-390. https://doi.org/10.1038/s41579-018-0004-5 PMID: 29599459
Burmeister A, Hilgers F, Langner A, Westerwalbesloh C, Kerkhoff Y, Tenhaef N, et al. A microfluidic cocultivation platform to investigate microbial interactions at defined microenvironments. Lab Chip. 2019; 19: 98-110. https://doi.org/10.1039/C8LC00977E PMID: 30488920
Lalwani MA, Kawabe H, Mays RL, Hoffman SM, Avalos JL. Optogenetic Control of Microbial Consortia Populations for Chemical Production. ACS Synthetic Biology. 2021;10. https://doi.org/10.1021/ acssynbio.1c00182 PMID: 34351122
Burmeister A, Akhtar Q, Hollmann L, Tenhaef N, Hilgers F, Hogenkamp F, et al. (Optochemical) Control of Synthetic Microbial Coculture Interactions on a Microcolony Level. ACS Synthetic Biology. 2021; 10: 1308-1319. https://doi.org/10.1021/acssynbio.0c00382 PMID: 34075749
Delvigne F, Baert J, Sassi H, Fickers P, Grünberger A, Dusny C. Taking control over microbial populations: Current approaches for exploiting biological noise in bioprocesses. Biotechnology Journal. 2017;12. https://doi.org/10.1002/biot.201600549 PMID: 28544731
Bloxham B, Lee H, Gore J. Diauxic lags explain unexpected coexistence in multi-resource environments. Molecular Systems Biology. 2022; 18: e10630. https://doi.org/10.15252/msb.202110630 PMID: 35507445
Kussell E, Leibler S. Phenotypic Diversity, Population Growth, and Information in Fluctuating Environments. Science. 2005; 309: 2075-2078. https://doi.org/10.1126/science.1114383 PMID: 16123265
Yurtsev EA, Conwill A, Gore J. Oscillatory dynamics in a bacterial cross-protection mutualism. Proceedings of the National Academy of Sciences. 2016; 113: 6236-6241. https://doi.org/10.1073/pnas. 1523317113 PMID: 27194723
Winkle JJ, Karamched BR, Bennett MR, Ott W, Josić K. Emergent spatiotemporal population dynamics with cell-length control of synthetic microbial consortia. PLOS Computational Biology. 2021; 17: 1-23. https://doi.org/10.1371/journal.pcbi.1009381 PMID: 34550968
Ramkrishna D, Song H-S. Dynamic Models of Metabolism: Review of the Cybernetic Approach. Bioengineering, Food, and Natural Products. 2012; 58: 986-997. https://doi.org/10.1002/aic
Schreiber F, Littmann S, Lavik G, Escrig S, Meibom A, Kuypers MMM, et al. Phenotypic heterogeneity driven by nutrient limitation promotes growth in fluctuating environments. Nature Microbiology. 2016; 1: 16055. https://doi.org/10.1038/nmicrobiol.2016.55 PMID: 27572840
Delvigne F, Zune Q, Lara AR, Al-Soud W, Sørensen SJ. Metabolic variability in bioprocessing: Implications of microbial phenotypic heterogeneity. Trends in Biotechnology. 2014; 32: 608-616. https://doi. org/10.1016/j.tibtech.2014.10.002 PMID: 25457387
Thattai M, van Oudenaarden A. Stochastic Gene Expression in Fluctuating Environments. Genetics. 2004; 167: 523-530. https://doi.org/10.1534/genetics.167.1.523 PMID: 15166174
Kompala DS, Ramkrishna D, Jansen NB, Tsao GT. Investigation of bacterial growth on mixed substrates: Experimental evaluation of cybernetic models. Biotechnology and Bioengineering. 1986; 28: 1044-1055. https://doi.org/10.1002/bit.260280715 PMID: 18555426
Varner J, Ramkrishna D. Metabolic Engineering from a Cybernetic Perspective Aspartate Familiy of Amino Acids. Metabolic engineering. 1999; 1: 88-116. https://doi.org/1096-7176/99
Song H-S, Ramkrishna D, Pinchuk GE, Beliaev AS, Konopka AE, Fredrickson JK. Dynamic modeling of aerobic growth of Shewanella oneidensis. Predicting triauxic growth, flux distributions, and energy requirement for growth. Metabolic Engineering. 2013; 15: 25-33. https://doi.org/10.1016/j.ymben.2012. 08.004 PMID: 23022551
Aboulmouna L, Raja R, Khanum S, Gupta S, Maurya MR, Grama A, et al. Cybernetic modeling of biological processes in mammalian systems. Current Opinion in Chemical Engineering. 2020; 30: 120-127. https://doi.org/10.1016/j.coche.2020.100660
García-Timermans C, Props R, Zacchetti B, Sakarika M, Delvigne F, Boon N, et al. Raman Spectroscopy- Based Measurements of Single-Cell Phenotypic Diversity in Microbial Populations. mSphere. 2020; 5: e00806-20. https://doi.org/10.1128/mSphere.00806-20 PMID: 33115836
Zid BM, O'Shea EK. Promoter sequences direct cytoplasmic localization and translation of mRNAs during starvation in yeast. Nature. 2014; 514: 117-121. https://doi.org/10.1038/nature13578 PMID: 25119046
Verduyn C, Postma E, Scheffers WA, Van Dijken JP. Effect of benzoic acid on metabolic fluxes in yeasts: A continuous-culture study on the regulation of respiration and alcoholic fermentation. Yeast. 1992; 8: 501-517. https://doi.org/10.1002/yea.320080703 PMID: 1523884
Delvigne F, Goffin P. Microbial heterogeneity affects bioprocess robustness: Dynamic single-cell analysis contributes to understanding of microbial populations. Biotechnology Journal. 2014; 9: 61-72. https://doi.org/10.1002/biot.201300119 PMID: 24408611
Sassi H, Nguyen TM, Telek S, Gosset G, Grünberger A, Delvigne F. Segregostat: a novel concept to control phenotypic diversification dynamics on the example of Gram-negative bacteria. Microbial Biotechnology. 2019; 12: 1064-1075. https://doi.org/10.1111/1751-7915.13442 PMID: 31141840
Täuber S, Golze C, Ho P, von Lieres E, Grünberger A. dMSCC: a microfluidic platform for microbial single- cell cultivation of Corynebacterium glutamicum under dynamic environmental medium conditions. Lab Chip. 2020; 20: 4442-4455. https://doi.org/10.1039/d0lc00711k PMID: 33095214
Dietler N, Minder M, Gligorovski V, Economou AM, Joly DAHL, Sadeghi A, et al. A convolutional neural network segments yeast microscopy images with high accuracy. Nature Communications. 2020; 11: 5723. https://doi.org/10.1038/s41467-020-19557-4 PMID: 33184262
Diderich JA, Schepper M, Hoek P van, Luttik MAH, Dijken JP van, Pronk JT, et al. Glucose Uptake Kinetics and Transcription of HXTGenes in Chemostat Cultures of Saccharomyces cerevisiae. Journal of Biological Chemistry. 1999; 274: 15350-15359. https://doi.org/10.1074/jbc.274.22.15350 PMID: 10336421
Jawed K, Yazdani SS, Koffas MA. Advances in the development and application of microbial consortia for metabolic engineering. Metabolic Engineering Communications. 2019; 9: e00095. https://doi.org/10. 1016/j.mec.2019.e00095 PMID: 31720211
Nguyen TM, Telek S, Zicler A, Martinez JA, Zacchetti B, Kopp J, et al. Reducing phenotypic instabilities of a microbial population during continuous cultivation based on cell switching dynamics. Biotechnology and Bioengineering. 2021; 118: 3847-3859. https://doi.org/10.1002/bit.27860 PMID: 34129251
Namjoshi AA, Ramkrishna D. Multiplicity and stability of steady states in continuous bioreactors: Dissection of cybernetic models. Chemical Engineering Science. 2001; 56: 5593-5607. https://doi.org/10. 1016/S0009-2509(01)00166-X
Stephens K, Pozo M, Tsao C-Y, Hauk P, Bentley WE. Bacterial co-culture with cell signaling translator and growth controller modules for autonomously regulated culture composition. Nature Communications. 2019; 10: 4129. https://doi.org/10.1038/s41467-019-12027-6 PMID: 31511505
Bader J, Mast-Gerlach E, Popović MK, Bajpai R, Stahl U. Relevance of microbial coculture fermentations in biotechnology. Journal of Applied Microbiology. 2010; 109: 371-387. https://doi.org/10.1111/j. 1365-2672.2009.04659.x PMID: 20070440
Ronda C, Wang HH. Engineering temporal dynamics in microbial communities. Current Opinion in Microbiology. 2022; 65: 47-55. https://doi.org/10.1016/j.mib.2021.10.009 PMID: 34739926
Klitgord N, Segrè D. Environments that Induce Synthetic Microbial Ecosystems. PLOS Computational Biology. 2010; 6: 1-17. https://doi.org/10.1371/journal.pcbi.1001002 PMID: 21124952
Dolinsek J, Goldschmidt F, Johnson DR. Synthetic microbial ecology and the dynamic interplay between microbial genotypes. FEMS Microbiology Reviews. 2016; 40: 961-979. https://doi.org/10. 1093/femsre/fuw024 PMID: 28201744
Mee MT, Wang HH. Engineering ecosystems and synthetic ecologies. Mol BioSyst. 2012; 8: 2470-2483. https://doi.org/10.1039/c2mb25133g PMID: 22722235
Tan C, Reza F, You L. Noise-Limited Frequency Signal Transmission in Gene Circuits. Biophysical Journal. 2007; 93: 3753-3761. https://doi.org/10.1529/biophysj.107.110403 PMID: 17704155
Tostevin F, ten Wolde PR. Mutual Information between Input and Output Trajectories of Biochemical Networks. Phys Rev Lett. 2009; 102: 218101. https://doi.org/10.1103/PhysRevLett.102.218101 PMID: 19519137
Marchal M, Goldschmidt F, Derksen-Müller SN, Panke S, Ackermann M, Johnson DR. A passive mutualistic interaction promotes the evolution of spatial structure within microbial populations. BMC Evolutionary Biology. 2017; 17: 106. https://doi.org/10.1186/s12862-017-0950-y PMID: 28438135
Cerulus B, Jariani A, Perez-Samper G, Vermeersch L, Pietsch JM, Crane MM, et al. Transition between fermentation and respiration determines history-dependent behavior in fluctuating carbon sources. Barkai N, editor. eLife. 2018; 7: e39234. https://doi.org/10.7554/eLife.39234 PMID: 30299256
Perez-Samper G, Cerulus B, Jariani A, Vermeersch L, Simancas NB, Bisschops MMM, et al. The Crabtree Effect Shapes the Saccharomyces cerevisiae Lag Phase during the Switch between Different Carbon Sources. mBio. 2018; 9: e01331-18. https://doi.org/10.1128/mBio.01331-18 PMID: 30377274
Bagamery LE, Justman QA, Garner EC, Murray AW. A Putative Bet-Hedging Strategy Buffers Budding Yeast against Environmental Instability. Current Biology. 2020; 30: 4563. https://doi.org/10.1016/j.cub. 2020.08.092 PMID: 32976801
Levy SF, Ziv N, Siegal ML. Bet Hedging in Yeast by Heterogeneous, Age-Correlated Expression of a Stress Protectant. PLOS Biology. 2012; 10: 1-16. https://doi.org/10.1371/journal.pbio.1001325 PMID: 22589700
Ziv N, Siegal ML, Gresham D. Genetic and Nongenetic Determinants of Cell Growth Variation Assessed by High-Throughput Microscopy. Molecular Biology and Evolution. 2013; 30: 2568-2578. https://doi.org/10.1093/molbev/mst138 PMID: 23938868
Kerner A, Park J, Williams A, Lin XN. A Programmable Escherichia coli Consortium via Tunable Symbiosis. PLOS ONE. 2012; 7: 1-10. https://doi.org/10.1371/journal.pone.0034032 PMID: 22479509
Balagaddé FK, Song H, Ozaki J, Collins CH, Barnet M, Arnold FH, et al. A synthetic Escherichia coli predator-prey ecosystem. Molecular Systems Biology. 2008; 4: 187. https://doi.org/10.1038/msb.2008. 24 PMID: 18414488
Abisado RG, Benomar S, Klaus JR, Dandekar AA, Chandler JR, Garsin DA. Bacterial Quorum Sensing and Microbial Community Interactions. mBio. 2018; 9: e02331-17. https://doi.org/10.1128/mBio.02331-17 PMID: 29789364
Smith NW, Shorten PR, Altermann E, Roy NC, McNabb WC. The Classification and Evolution of Bacterial Cross-Feeding. Frontiers in Ecology and Evolution. 2019;7. https://doi.org/10.3389/fevo.2019. 00153
Wang Z, Goyal A, Dubinkina V, George AB, Wang T, Fridman Y, et al. Complementary resource preferences spontaneously emerge in diauxic microbial communities. Nature Communications. 2021; 12: 6661. https://doi.org/10.1038/s41467-021-27023-y PMID: 34795267
Pacciani-Mori L, Giometto A, Suweis S, Maritan A. Dynamic metabolic adaptation can promote species coexistence in competitive microbial communities. PLOS Computational Biology. 2020; 16: 1-18. https://doi.org/10.1371/journal.pcbi.1007896 PMID: 32379752
Stephens ML, Lyberatos G. Effect of cycling on final mixed culture fate. Biotechnology and Bioengineering. 1987; 29: 672-678. https://doi.org/10.1002/bit.260290603 PMID: 18576501
Lenas P, Pavlou S. Coexistence of three competing microbial populations in a chemostat with periodically varying dilution rate. Mathematical Biosciences. 1995; 129: 111-142. https://doi.org/10.1016/ 0025-5564(94)00056-6 PMID: 7549217
Mondragón-Palomino O, Danino T, Selimkhanov J, Tsimring L, Hasty J. Entrainment of a Population of Synthetic Genetic Oscillators. Science. 2011; 333: 1315-1319. https://doi.org/10.1126/science. 1205369 PMID: 21885786
Ackermann M. A functional perspective on phenotypic heterogeneity in microorganisms. Nature Reviews Microbiology. 2015; 13: 497. https://doi.org/10.1038/nrmicro3491 PMID: 26145732
Dal Co A, van Vliet S, Kiviet DJ, Schlegel S, Ackermann M. Short-range interactions govern the dynamics and functions of microbial communities. Nature Ecology & Evolution. 2020; 4: 366-375. https://doi. org/10.1038/s41559-019-1080-2 PMID: 32042125
Martinez JA, Bulte DB, Contreras MA, Palomares LA, Ramirez OT. Dynamic Modeling of CHO cell Metabolism Using the Hybrid Cybernetic Approach With a Novel Elementary Mode Analysis Strategy. Frontiers in Bioengineering and Biotechnology. 2020; 279. https://doi.org/10.3389/fbioe.2020.00279 PMID: 32351947
Song H-S, Morgan JA, Ramkrishna D. Systematic development of hybrid cybernetic models: Application to recombinant yeast co-consuming glucose and xylose. Biotechnology and Bioengineering. 2009; 103: 984-1002. https://doi.org/10.1002/bit.22332 PMID: 19449391
