Optogenetic spatial patterning of cooperation in yeast populations.
Journal
Nature communications
ISSN: 2041-1723
Titre abrégé: Nat Commun
Pays: England
ID NLM: 101528555
Informations de publication
Date de publication:
02 Jan 2024
02 Jan 2024
Historique:
received:
17
05
2023
accepted:
11
12
2023
medline:
4
1
2024
pubmed:
4
1
2024
entrez:
3
1
2024
Statut:
epublish
Résumé
Microbial communities are shaped by complex metabolic interactions such as cooperation and competition for resources. Methods to control such interactions could lead to major advances in our ability to better engineer microbial consortia for synthetic biology applications. Here, we use optogenetics to control SUC2 invertase production in yeast, thereby shaping spatial assortment of cooperator and cheater cells. Yeast cells behave as cooperators (i.e., transform sucrose into hexose, a public good) upon blue light illumination or cheaters (i.e., consume hexose produced by cooperators to grow) in the dark. We show that cooperators benefit best from the hexoses they produce when their domain size is constrained between two cut-off length-scales. From an engineering point of view, the system behaves as a bandpass filter. The lower limit is the trace of cheaters' competition for hexoses, while the upper limit is defined by cooperators' competition for sucrose. Cooperation mostly occurs at the frontiers with cheater cells, which not only compete for hexoses but also cooperate passively by letting sucrose reach cooperators. We anticipate that this optogenetic method could be applied to shape metabolic interactions in a variety of microbial ecosystems.
Identifiants
pubmed: 38168087
doi: 10.1038/s41467-023-44379-5
pii: 10.1038/s41467-023-44379-5
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
75Subventions
Organisme : EC | EU Framework Programme for Research and Innovation H2020 | H2020 Priority Excellent Science | H2020 European Research Council (H2020 Excellent Science - European Research Council)
ID : 724813
Organisme : Agence Nationale de la Recherche (French National Research Agency)
ID : ANR ANR-16-CE33-0018
Organisme : Agence Nationale de la Recherche (French National Research Agency)
ID : ANR-11-LABX-0038
Organisme : Agence Nationale de la Recherche (French National Research Agency)
ID : ANR-10-IDEX-0001-02
Informations de copyright
© 2024. The Author(s).
Références
Foster, K. R. & Bell, T. Competition, not cooperation, dominates interactions among culturable microbial species. Curr. Biol. 22, 1845–1850 (2012).
pubmed: 22959348
doi: 10.1016/j.cub.2012.08.005
Coyte, K. Z., Schluter, J. & Foster, K. R. The ecology of the microbiome: networks, competition, and stability. Science 350, 663–666 (2015).
pubmed: 26542567
doi: 10.1126/science.aad2602
Nadell, C. D., Drescher, K. & Foster, K. R. Spatial structure, cooperation and competition in biofilms. Nat. Rev. Microbiol. 14, 589–600 (2016).
pubmed: 27452230
doi: 10.1038/nrmicro.2016.84
West, S. A., Griffin, A. S., Gardner, A. & Diggle, S. P. Social evolution theory for microorganisms. Nat. Rev. Microbiol 4, 597–607 (2006).
pubmed: 16845430
doi: 10.1038/nrmicro1461
Liu, J. et al. Metabolic co-dependence gives rise to collective oscillations within biofilms. Nature 523, 550–554 (2015).
pubmed: 26200335
pmcid: 4862617
doi: 10.1038/nature14660
Rosenthal, A. Z. et al. Metabolic interactions between dynamic bacterial subpopulations. eLife 7, e33099 (2018).
pubmed: 29809139
pmcid: 6025961
doi: 10.7554/eLife.33099
Blasche, S. et al. Metabolic cooperation and spatiotemporal niche partitioning in a kefir microbial community. Nat. Microbiol 6, 196–208 (2021).
pubmed: 33398099
pmcid: 7610452
doi: 10.1038/s41564-020-00816-5
Bronstein, J. L. The exploitation of mutualisms. Ecol. Lett. 4, 277–287 (2001).
doi: 10.1046/j.1461-0248.2001.00218.x
Popat, R. et al. Quorum-sensing and cheating in bacterial biofilms. Proc. Biol. Sci. 279, 4765–4771 (2012).
pubmed: 23034707
pmcid: 3497100
Ghoul, M., Griffin, A. S. & West, S. A. Toward an evolutionary definition of cheating. Evolution 68, 318–331 (2014).
pubmed: 24131102
doi: 10.1111/evo.12266
Smith, P. & Schuster, M. Public goods and cheating in microbes. Curr. Biol. 29, R442–R447 (2019).
pubmed: 31163154
doi: 10.1016/j.cub.2019.03.001
West, S. A., Pen, I. & Griffin, A. S. Cooperation and competition between relatives. Science 296, 72–75 (2002).
pubmed: 11935015
doi: 10.1126/science.1065507
Oliveira, N. M., Niehus, R. & Foster, K. R. Evolutionary limits to cooperation in microbial communities. Proc. Natl Acad. Sci. USA 111, 17941–17946 (2014).
pubmed: 25453102
pmcid: 4273359
doi: 10.1073/pnas.1412673111
Gore, J., Youk, H. & van Oudenaarden, A. Snowdrift game dynamics and facultative cheating in yeast. Nature 459, 253–256 (2009).
pubmed: 19349960
pmcid: 2888597
doi: 10.1038/nature07921
Greig, D. & Travisano, M. The prisoner’s dilemma and polymorphism in yeast SUC genes. Proc. R. Soc. Lond. Ser. B 271, S25–S26 (2004).
Koschwanez, J. H., Foster, K. R. & Murray, A. W. Sucrose utilization in budding yeast as a model for the origin of undifferentiated multicellularity. PLoS Biol. 9, e1001122 (2011).
pubmed: 21857801
doi: 10.1371/journal.pbio.1001122
Koschwanez, J. H., Foster, K. R. & Murray, A. W. Improved use of a public good selects for the evolution of undifferentiated multicellularity. eLife 2, e00367 (2013).
pubmed: 23577233
pmcid: 3614033
doi: 10.7554/eLife.00367
Maclean, R. C. & Brandon, C. Stable public goods cooperation and dynamic social interactions in yeast. J. Evolut. Biol. 21, 1836–1843 (2008).
doi: 10.1111/j.1420-9101.2008.01579.x
Marques, W. L. et al. Elimination of sucrose transport and hydrolysis in Saccharomyces cerevisiae: a platform strain for engineering sucrose metabolism. FEMS Yeast Res. 17, fox006 (2017).
Carlson, M. & Botstein, D. Two differentially regulated mRNAs with different 5′ ends encode secreted and intracellular forms of yeast invertase. Cell 28, 145–154 (1982).
pubmed: 7039847
doi: 10.1016/0092-8674(82)90384-1
Sutton, D. D. & Lampen, J. O. Localization of sucrose and maltose fermenting systems in Saccharomyces cerevisiae. Biochim. Biophys. Acta 56, 303–312 (1962).
pubmed: 13918560
doi: 10.1016/0006-3002(62)90567-X
Hol, F. J. H. et al. Spatial structure facilitates cooperation in a social dilemma: empirical evidence from a bacterial community. PLoS ONE 8, e77042 (2013).
pubmed: 24167557
pmcid: 3805552
doi: 10.1371/journal.pone.0077042
Mitri, S., Xavier, J. B. & Foster, K. R. Social evolution in multispecies biofilms. Proc. Natl Acad. Sci. USA 108, 10839–10846 (2011).
pubmed: 21690380
pmcid: 3131810
doi: 10.1073/pnas.1100292108
Yanni, D., Márquez-Zacarías, P., Yunker, P. J. & Ratcliff, W. C. Drivers of spatial structure in social microbial communities. Curr. Biol. 29, R545–R550 (2019).
pubmed: 31163168
doi: 10.1016/j.cub.2019.03.068
Estrela, S. & Brown, S. P. Metabolic and demographic feedbacks shape the emergent spatial structure and function of microbial communities. PLoS Comput. Biol. 9, e1003398 (2013).
pubmed: 24385891
pmcid: 3873226
doi: 10.1371/journal.pcbi.1003398
MacLean, R. C. & Gudelj, I. Resource competition and social conflict in experimental populations of yeast. Nature 441, 498–501 (2006).
pubmed: 16724064
doi: 10.1038/nature04624
Marinkovic, Z. S. et al. A microfluidic device for inferring metabolic landscapes in yeast monolayer colonies. eLife 8, e47951 (2019).
pubmed: 31259688
pmcid: 6624017
doi: 10.7554/eLife.47951
Wang, M., Huang, Y. & Wu, Z. Simulation of yeast cooperation in 2D. Bull. Math. Biol. 78, 531–555 (2016).
pubmed: 26988702
doi: 10.1007/s11538-016-0153-5
Moreno Morales, N., Patel, M. T., Stewart, C. J., Sweeney, K. & McClean, M. N. Optogenetic tools for control of public goods in Saccharomyces cerevisiae. mSphere 6, e0058121 (2021).
pubmed: 34431694
doi: 10.1128/mSphere.00581-21
Gupta, S. et al. Investigating the dynamics of microbial consortia in spatially structured environments. Nat. Commun. 11, 2418 (2020).
pubmed: 32415107
pmcid: 7228966
doi: 10.1038/s41467-020-16200-0
Pirhanov, A. et al. Optogenetics in Sinorhizobium meliloti enables spatial control of exopolysaccharide production and biofilm structure. ACS Synth. Biol. 10, 345–356 (2021).
pubmed: 33465305
doi: 10.1021/acssynbio.0c00498
Jin, X. & Riedel-Kruse, I. H. Biofilm lithography enables high-resolution cell patterning via optogenetic adhesin expression. Proc. Natl Acad. Sci. USA 115, 3698–3703 (2018).
pubmed: 29555779
pmcid: 5889658
doi: 10.1073/pnas.1720676115
Motta-Mena, L. B. et al. An optogenetic gene expression system with rapid activation and deactivation kinetics. Nat. Chem. Biol. 10, 196–202 (2014).
pubmed: 24413462
pmcid: 3944926
doi: 10.1038/nchembio.1430
Liu, Z. et al. Systematic comparison of 2A peptides for cloning multi-genes in a polycistronic vector. Sci. Rep. 7, 2193 (2017).
pubmed: 28526819
pmcid: 5438344
doi: 10.1038/s41598-017-02460-2
Rothwell, D. G. et al. Functional expression of secreted proteins from a bicistronic retroviral cassette based on foot-and-mouth disease virus 2A can be position dependent. Hum. Gene Ther. 21, 1631–1637 (2010).
pubmed: 20528679
doi: 10.1089/hum.2009.197
Kunze, I. et al. The green fluorescent protein targets secretory proteins to the yeast vacuole. Biochim. Biophys. Acta 1410, 287–298 (1999).
pubmed: 10082794
doi: 10.1016/S0005-2728(99)00006-7
Torres, A. P., Oliveira, F. A. R., Silva, C. L. M. & Fortuna, S. P. The influence of ph on the kinetics of acid hydrolysis of sucrose. J. Food Process. Eng. 17, 191–208 (1994).
doi: 10.1111/j.1745-4530.1994.tb00335.x
Schülke, N. & Schmid, F. X. The stability of yeast invertase is not significantly influenced by glycosylation. J. Biol. Chem. 263, 8827–8831 (1988).
pubmed: 3288623
doi: 10.1016/S0021-9258(18)68381-4
Benzinger, D. & Khammash, M. Pulsatile inputs achieve tunable attenuation of gene expression variability and graded multi-gene regulation. Nat. Commun. 9, 3521 (2018).
A synthetic library of RNA control modules for predictable tuning of gene expression in yeast. Mol. Syst. Biol. 7, 471 (2011).
Lavrentovich, M. O., Koschwanez, J. H. & Nelson, D. R. Nutrient shielding in clusters of cells. Phys. Rev. E 87, 062703 (2013).
doi: 10.1103/PhysRevE.87.062703
Berthels, N., Corderootero, R., Bauer, F., Thevelein, J. & Pretorius, I. Discrepancy in glucose and fructose utilisation during fermentation by wine yeast strains. FEMS Yeast Res. 4, 683–689 (2004).
pubmed: 15093771
doi: 10.1016/j.femsyr.2004.02.005
Grandel, N. E., Gamas, K. R. & Bennett, M. R. Control of synthetic microbial consortia in time, space, and composition. Trends Microbiol. 29, 1095–1105 (2021).
pubmed: 33966922
doi: 10.1016/j.tim.2021.04.001
Giri, S., Shitut, S. & Kost, C. Harnessing ecological and evolutionary principles to guide the design of microbial production consortia. Curr. Opin. Biotechnol. 62, 228–238 (2020).
pubmed: 31954367
doi: 10.1016/j.copbio.2019.12.012
Cavaliere, M., Feng, S., Soyer, O. S. & Jiménez, J. I. Cooperation in microbial communities and their biotechnological applications. Environ. Microbiol. 19, 2949–2963 (2017).
pubmed: 28447371
pmcid: 5575505
doi: 10.1111/1462-2920.13767
Li, X. et al. Design of stable and self-regulated microbial consortia for chemical synthesis. Nat. Commun. 13, 1554 (2022).
pubmed: 35322005
pmcid: 8943006
doi: 10.1038/s41467-022-29215-6
Gilbert, C. et al. Living materials with programmable functionalities grown from engineered microbial co-cultures. Nat. Mater. 20, 691–700 (2021).
pubmed: 33432140
doi: 10.1038/s41563-020-00857-5
Stein, R. R. et al. Computer-guided design of optimal microbial consortia for immune system modulation. eLife 7, e30916 (2018).
pubmed: 29664397
pmcid: 5959721
doi: 10.7554/eLife.30916
van der Lelie, D. et al. Rationally designed bacterial consortia to treat chronic immune-mediated colitis and restore intestinal homeostasis. Nat. Commun. 12, 3105 (2021).
pubmed: 34050144
pmcid: 8163890
doi: 10.1038/s41467-021-23460-x
Molinari, S., Tesoriero, R. F. & Ajo-Franklin, C. M. Bottom-up approaches to engineered living materials: Challenges and future directions. Matter 4, 3095–3120 (2021).
doi: 10.1016/j.matt.2021.08.001
Rodrigo-Navarro, A., Sankaran, S., Dalby, M. J., del Campo, A. & Salmeron-Sanchez, M. Engineered living biomaterials. Nat. Rev. Mater. 6, 1175–1190 (2021).
doi: 10.1038/s41578-021-00350-8
Zaky, A., Glastras, S. J., Wong, M. Y. W., Pollock, C. A. & Saad, S. The role of the gut microbiome in diabetes and obesity-related kidney disease. Int. J. Mol. Sci. 22, 9641 (2021).
pubmed: 34502562
pmcid: 8431784
doi: 10.3390/ijms22179641
Villamil, M. B. et al. Microbial signatures in fertile soils under long-term N management. Front. Soil Sci. 1, 1–22 (2021).
Laughery, M. F. et al. New vectors for simple and streamlined CRISPR-Cas9 genome editing in Saccharomyces cerevisiae. Yeast 32, 711–720 (2015).
pubmed: 26305040
doi: 10.1002/yea.3098
Lee, M. E., DeLoache, W. C., Cervantes, B. & Dueber, J. E. A Highly Characterized Yeast Toolkit for Modular, Multipart Assembly . ACS Synth. Biol. 4, 975–986 (2015).
Gerhardt, K. P. et al. An open-hardware platform for optogenetics and photobiology. Sci. Rep. 6, 35363 (2016).
pubmed: 27805047
pmcid: 5096413
doi: 10.1038/srep35363
Lu, A. X., Zarin, T., Hsu, I. S. & Moses, A. M. YeastSpotter: accurate and parameter-free web segmentation for microscopy images of yeast cells. Bioinformatics 35, 4525–4527 (2019).
pubmed: 31095270
pmcid: 6821424
doi: 10.1093/bioinformatics/btz402
Cellier, N. & Ruyer-Quil, C. scikit-finite-diff, a new tool for PDE solving. J. Open Source Softw. 4, 1356 (2019).
doi: 10.21105/joss.01356
Matthias Le Bec, et al. Lab513/Optogenetic Spatial Patterning of Cooperation in Yeast Populations. https://doi.org/10.5281/zenodo.10260274 (2023).
Michaelis, L. & Menten, M. Die Kinetik der Invertinwirkung. Biochem. Z. 49, 333–369 (1913).