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Metabolic heterogeneity and cross-feeding within isogenic yeast populations captured by DILAC

Metabolic heterogeneity and cross-feeding within isogenic yeast populations captured by DILAC

 


  • Saint, M. et al. Single-cell imaging and RNA sequencing reveal patterns of gene expression heterogeneity during fission yeast growth and adaptation. Nat. Microbiol. 4, 480–491 (2019).

    Article 
    CAS 

    Google Scholar
     

  • Nadal-Ribelles, M. et al. Sensitive high-throughput single-cell RNA-seq reveals within-clonal transcript correlations in yeast populations. Nat. Microbiol. 4, 683–692 (2019).

    Article 
    CAS 

    Google Scholar
     

  • Jackson, C. A., Castro, D. M., Saldi, G.-A., Bonneau, R. & Gresham, D. Gene regulatory network reconstruction using single-cell RNA sequencing of barcoded genotypes in diverse environments. eLife 9, e51254 (2020).

    Article 
    CAS 

    Google Scholar
     

  • Jariani, A. et al. A new protocol for single-cell RNA-seq reveals stochastic gene expression during lag phase in budding yeast. eLife 9, e55320 (2020).

    Article 

    Google Scholar
     

  • Rugbjerg, P. & Olsson, L. The future of self-selecting and stable fermentations. J. Ind. Microbiol. Biotechnol. 47, 993–1004 (2020).

    Article 
    CAS 

    Google Scholar
     

  • González-Cabaleiro, R., Mitchell, A. M., Smith, W., Wipat, A. & Ofiţeru, I. D. Heterogeneity in pure microbial systems: measurements and modeling. Front. Microbiol. 8, 1813 (2017).

    Article 

    Google Scholar
     

  • Campbell, K., Vowinckel, J. & Ralser, M. Cell-to-cell heterogeneity emerges as consequence of metabolic cooperation in a synthetic yeast community. Biotechnol. J. 11, 1169–1178 (2016).

    Article 
    CAS 

    Google Scholar
     

  • Morawska, L. P., Hernandez-Valdes, J. A. & Kuipers, O. P. Diversity of bet-hedging strategies in microbial communities–recent cases and insights. WIREs Mech. Dis. 14, e1544 (2022).


    Google Scholar
     

  • Rosenberg, A. et al. Antifungal tolerance is a subpopulation effect distinct from resistance and is associated with persistent candidemia. Nat. Commun. 9, 2470 (2018).

    Article 

    Google Scholar
     

  • Dewachter, L., Fauvart, M. & Michiels, J. Bacterial heterogeneity and antibiotic survival: understanding and combatting persistence and heteroresistance. Mol. Cell 76, 255–267 (2019).

    Article 
    CAS 

    Google Scholar
     

  • Bódi, Z. et al. Phenotypic heterogeneity promotes adaptive evolution. PLoS Biol. 15, e2000644 (2017).

    Article 

    Google Scholar
     

  • Levy, S. F., Ziv, N. & Siegal, M. L. Bet hedging in yeast by heterogeneous, age-correlated expression of a stress protectant. PLoS Biol. 10, e1001325 (2012).

    Article 
    CAS 

    Google Scholar
     

  • Balaban, N. Q., Merrin, J., Chait, R., Kowalik, L. & Leibler, S. Bacterial persistence as a phenotypic switch. Science 305, 1622–1625 (2004).

    Article 
    CAS 

    Google Scholar
     

  • Li, S., Giardina, D. M. & Siegal, M. L. Control of nongenetic heterogeneity in growth rate and stress tolerance of Saccharomyces cerevisiae by cyclic AMP-regulated transcription factors. PLoS Genet. 14, e1007744 (2018).

    Article 

    Google Scholar
     

  • Lukačišin, M., Espinosa-Cantú, A. & Bollenbach, T. Intron-mediated induction of phenotypic heterogeneity. Nature 605, 113–118 (2022).

    Article 

    Google Scholar
     

  • Avery, S. V. Microbial cell individuality and the underlying sources of heterogeneity. Nat. Rev. Microbiol. 4, 577–587 (2006).

    Article 
    CAS 

    Google Scholar
     

  • Holland, S. L., Reader, T., Dyer, P. S. & Avery, S. V. Phenotypic heterogeneity is a selected trait in natural yeast populations subject to environmental stress. Environ. Microbiol. 16, 1729–1740 (2014).

    Article 

    Google Scholar
     

  • Olin-Sandoval, V. et al. Lysine harvesting is an antioxidant strategy and triggers underground polyamine metabolism. Nature 572, 249–253 (2019).

    Article 
    CAS 

    Google Scholar
     

  • Alam, M. T. et al. The metabolic background is a global player in Saccharomyces gene expression epistasis. Nat. Microbiol 1, 15030 (2016).

    Article 
    CAS 

    Google Scholar
     

  • Yin, H., He, Y., Dong, J. & Lu, J. Transcriptional profiling of amino acid supplementation and impact on aroma production in a lager yeast fermentation. J. Inst. Brew. 124, 425–433 (2018).

    Article 
    CAS 

    Google Scholar
     

  • Boer, V. M. et al. Transcriptional responses of Saccharomyces cerevisiae to preferred and nonpreferred nitrogen sources in glucose-limited chemostat cultures. FEMS Yeast Res. 7, 604–620 (2007).

    Article 
    CAS 

    Google Scholar
     

  • Godard, P. et al. Effect of 21 different nitrogen sources on global gene expression in the yeast Saccharomyces cerevisiae. Mol. Cell. Biol. 27, 3065–3086 (2007).

    Article 
    CAS 

    Google Scholar
     

  • Costa, C. et al. New mechanisms of flucytosine resistance in C. glabrata unveiled by a chemogenomics analysis in S. cerevisiae. PLoS ONE 10, e0135110 (2015).

    Article 

    Google Scholar
     

  • Yu, J. S. L. et al. Microbial communities form rich extracellular metabolomes that foster metabolic interactions and promote drug tolerance. Nat. Microbiol. https://doi.org/10.1038/s41564-022-01072-5 (2022).

  • Campbell, K. et al. Self-establishing communities enable cooperative metabolite exchange in a eukaryote. eLife 4, e09943 (2015).

    Article 

    Google Scholar
     

  • Momeni, B., Brileya, K. A., Fields, M. W. & Shou, W. Strong inter-population cooperation leads to partner intermixing in microbial communities. eLife 2, e00230 (2013).

    Article 

    Google Scholar
     

  • Takhaveev, V. & Heinemann, M. Metabolic heterogeneity in clonal microbial populations. Curr. Opin. Microbiol. 45, 30–38 (2018).

    Article 
    CAS 

    Google Scholar
     

  • Fröhlich, F., Christiano, R. & Walther, T. C. Native SILAC: metabolic labeling of proteins in prototroph microorganisms based on lysine synthesis regulation. Mol. Cell. Proteomics 12, 1995–2005 (2013).

    Article 

    Google Scholar
     

  • Dannenmaier, S. et al. Complete native stable isotope labeling by amino acids of Saccharomyces cerevisiae for global proteomic analysis. Anal. Chem. 90, 10501–10509 (2018).

    Article 
    CAS 

    Google Scholar
     

  • Hammer, T., Bode, R., Schmidt, H. & Birnbaum, D. Distribution of three lysine-catabolizing enzymes in various yeast species. J. Basic Microbiol. 31, 43–49 (1991).

    Article 
    CAS 

    Google Scholar
     

  • Mülleder, M. et al. Functional metabolomics describes the yeast biosynthetic regulome. Cell 167, 553–565 (2016).

    Article 

    Google Scholar
     

  • Demichev, V., Messner, C. B., Vernardis, S. I., Lilley, K. S. & Ralser, M. DIA-NN: neural networks and interference correction enable deep proteome coverage in high throughput. Nat. Methods 17, 41–44 (2020).

    Article 
    CAS 

    Google Scholar
     

  • Vowinckel, J. et al. Cost-effective generation of precise label-free quantitative proteomes in high-throughput by microLC and data-independent acquisition. Sci. Rep. 8, 4346 (2018).

    Article 

    Google Scholar
     

  • Demichev, V. et al. dia-PASEF data analysis using FragPipe and DIA-NN for deep proteomics of low sample amounts. Nat. Commun. 13, 3944 (2022).

    Article 
    CAS 

    Google Scholar
     

  • Meier, F. et al. diaPASEF: parallel accumulation-serial fragmentation combined with data-independent acquisition. Nat. Methods 17, 1229–1236 (2020).

    Article 
    CAS 

    Google Scholar
     

  • Feller, A., Dubois, E., Ramos, F. & Piérard, A. Repression of the genes for lysine biosynthesis in Saccharomyces cerevisiae is caused by limitation of Lys14-dependent transcriptional activation. Mol. Cell. Biol. 14, 6411–6418 (1994).

    CAS 

    Google Scholar
     

  • Kamrad, S. et al. Pyruvate kinase variant of fission yeast tunes carbon metabolism, cell regulation, growth and stress resistance. Mol. Syst. Biol. 16, e9270 (2020).

    Article 
    CAS 

    Google Scholar
     

  • Murphy, J. P., Stepanova, E., Everley, R. A., Paulo, J. A. & Gygi, S. P. Comprehensive temporal protein dynamics during the diauxic shift in Saccharomyces cerevisiae. Mol. Cell. Proteomics 14, 2454–2465 (2015).

    Article 
    CAS 

    Google Scholar
     

  • Brauer, M. J., Saldanha, A. J., Dolinski, K. & Botstein, D. Homeostatic adjustment and metabolic remodeling in glucose-limited yeast cultures. Mol. Biol. Cell 16, 2503–2517 (2005).

    Article 
    CAS 

    Google Scholar
     

  • Björkeroth, J. et al. Proteome reallocation from amino acid biosynthesis to ribosomes enables yeast to grow faster in rich media. Proc. Natl Acad. Sci. USA 117, 21804–21812 (2020).

    Article 

    Google Scholar
     

  • Hiesinger, M., Wagner, C. & Schüller, H. J. The acetyl-CoA synthetase gene ACS2 of the yeast Saccharomyces cerevisiae is coregulated with structural genes of fatty acid biosynthesis by the transcriptional activators Ino2p and Ino4p. FEBS Lett. 415, 16–20 (1997).

    Article 
    CAS 

    Google Scholar
     

  • Kornberg, H. L. The role and control of the glyoxylate cycle in Escherichia coli. Biochem. J. 99, 1–11 (1966).

    Article 
    CAS 

    Google Scholar
     

  • Duntze, W., Neumann, D., Gancedo, J. M., Atzpodien, W. & Holzer, H. Studies on the regulation and localization of the glyoxylate cycle enzymes in Saccharomyces cerevisiae. Eur. J. Biochem. 10, 83–89 (1969).

    Article 
    CAS 

    Google Scholar
     

  • Xiao, T., Khan, A., Shen, Y., Chen, L. & Rabinowitz, J. D. Glucose feeds the tricarboxylic acid cycle via excreted ethanol in fermenting yeast. Nat. Chem. Biol. https://doi.org/10.1038/s41589-022-01091-7 (2022).

  • Cole, J. A., Kohler, L., Hedhli, J. & Luthey-Schulten, Z. Spatially-resolved metabolic cooperativity within dense bacterial colonies. BMC Syst. Biol. 9, 15 (2015).

    Article 

    Google Scholar
     

  • Wolfsberg, E., Long, C. P. & Antoniewicz, M. R. Metabolism in dense microbial colonies: C metabolic flux analysis of E. coli grown on agar identifies two distinct cell populations with acetate cross-feeding. Metab. Eng. 49, 242–247 (2018).

    Article 
    CAS 

    Google Scholar
     

  • Dal Co, A., van Vliet, S. & Ackermann, M. Emergent microscale gradients give rise to metabolic cross-feeding and antibiotic tolerance in clonal bacterial populations. Philos. Trans. R. Soc. Lond. B Biol. Sci. 374, 20190080 (2019).

    Article 
    CAS 

    Google Scholar
     

  • Plocek, V., Váchová, L., Šťovíček, V. & Palková, Z. Cell distribution within yeast colonies and colony biofilms: how structure develops. Int. J. Mol. Sci. 21, 3873 (2020).

    Article 
    CAS 

    Google Scholar
     

  • Yuan, D. S. Zinc-regulated genes in Saccharomyces cerevisiae revealed by transposon tagging. Genetics 156, 45–58 (2000).

    Article 
    CAS 

    Google Scholar
     

  • Ghosh, A. et al. A peptide-based method for 13C metabolic flux analysis in microbial communities. PLoS Comput. Biol. 10, e1003827 (2014).

    Article 

    Google Scholar
     

  • Kleiner, M. Metaproteomics: much more than measuring gene expression in microbial communities. mSystems 4, e00115–19 (2019).

    Article 
    CAS 

    Google Scholar
     

  • Jehmlich, N., Vogt, C., Lünsmann, V., Richnow, H. H. & von Bergen, M. Protein-SIP in environmental studies. Curr. Opin. Biotechnol. 41, 26–33 (2016).

    Article 
    CAS 

    Google Scholar
     

  • Taubert, M. SIP-metaproteomics: linking microbial taxonomy, function, and activity. Methods Mol. Biol. 2046, 57–69 (2019).

    Article 
    CAS 

    Google Scholar
     

  • DeGennaro, C. M., Savir, Y. & Springer, M. Identifying metabolic subpopulations from population level mass spectrometry. PLoS ONE 11, e0151659 (2016).

    Article 

    Google Scholar
     

  • Sachsenberg, T. et al. MetaProSIP: automated inference of stable isotope incorporation rates in proteins for functional metaproteomics. J. Proteome Res. 14, 619–627 (2015).

    Article 
    CAS 

    Google Scholar
     

  • Zeng, X. et al. Gut bacterial nutrient preferences quantified in vivo. Cell 185, 3441–3456 (2022).

    Article 
    CAS 

    Google Scholar
     

  • Urban, P. L. et al. Carbon-13 labelling strategy for studying the ATP metabolism in individual yeast cells by micro-arrays for mass spectrometry. Mol. Biosyst. 7, 2837–2840 (2011).

    Article 
    CAS 

    Google Scholar
     

  • Cooper, A. L., Dean, A. C. & Hinshelwood, C. Factors affecting the growth of bacterial colonies on agar plates. Proc. R. Soc. Lond. B Biol. Sci. 171, 175–199 (1968).

    Article 
    CAS 

    Google Scholar
     

  • Pirt, S. J. A kinetic study of the mode of growth of surface colonies of bacteria and fungi. J. Gen. Microbiol. 47, 181–197 (1967).

    Article 
    CAS 

    Google Scholar
     

  • Vulin, C. et al. Growing yeast into cylindrical colonies. Biophys. J. 106, 2214–2221 (2014).

    Article 
    CAS 

    Google Scholar
     

  • Pipe, L. Z. & Grimson, M. J. Spatial-temporal modelling of bacterial colony growth on solid media. Mol. Biosyst. 4, 192–198 (2008).

    Article 
    CAS 

    Google Scholar
     

  • Díaz-Pascual, F. et al. Spatial alanine metabolism determines local growth dynamics of colonies. eLife 10, e70794 (2021).

    Article 

    Google Scholar
     

  • Mülleder, M. et al. A prototrophic deletion mutant collection for yeast metabolomics and systems biology. Nat. Biotechnol. 30, 1176–1178 (2012).

    Article 

    Google Scholar
     

  • Opalek, M. & Wloch-Salamon, D. Aspects of multicellularity in yeast: a review of evolutionary and physiological mechanisms. Genes (BASEL) 11, 690 (2020).

    Article 
    CAS 

    Google Scholar
     

  • Cáp, M., Stěpánek, L., Harant, K., Váchová, L. & Palková, Z. Cell differentiation within a yeast colony: metabolic and regulatory parallels with a tumor-affected organism. Mol. Cell 46, 436–448 (2012).

    Article 

    Google Scholar
     

  • Palková, Z. & Váchová, L. Spatially structured yeast communities: understanding structure formation and regulation with omics tools. Comput. Struct. Biotechnol. J. 19, 5613–5621 (2021).

    Article 

    Google Scholar
     

  • Wilkinson, D. et al. Transcriptome remodeling of differentiated cells during chronological ageing of yeast colonies: new insights into metabolic differentiation. Oxid. Med. Cell. Longev. 2018, 4932905 (2018).

    Article 

    Google Scholar
     

  • Traven, A. et al. Transcriptional profiling of a yeast colony provides new insight into the heterogeneity of multicellular fungal communities. PLoS ONE 7, e46243 (2012).

    Article 
    CAS 

    Google Scholar
     

  • Varahan, S., Walvekar, A., Sinha, V., Krishna, S. & Laxman, S. Metabolic constraints drive self-organization of specialized cell groups. eLife 8, e46735 (2019).

    Article 

    Google Scholar
     

  • Varahan, S., Sinha, V., Walvekar, A., Krishna, S. & Laxman, S. Resource plasticity-driven carbon-nitrogen budgeting enables specialization and division of labor in a clonal community. eLife 9, e57609 (2020).

    Article 
    CAS 

    Google Scholar
     

  • Smukalla, S. et al. FLO1 is a variable green beard gene that drives biofilm-like cooperation in budding yeast. Cell 135, 726–737 (2008).

    Article 
    CAS 

    Google Scholar
     

  • Bojsen, R., Regenberg, B. & Folkesson, A. Saccharomyces cerevisiae biofilm tolerance towards systemic antifungals depends on growth phase. BMC Microbiol. 14, 305 (2014).

    Article 

    Google Scholar
     

  • Mülleder, M., Bluemlein, K. & Ralser, M. A high-throughput method for the quantitative determination of free amino acids in by hydrophilic interaction chromatography-tandem mass spectrometry. Cold Spring Harb. Protoc. 2017, db.prot089094 (2017).

    Article 

    Google Scholar
     

  • Messner, C. B. et al. Ultra-fast proteomics with Scanning SWATH. Nat. Biotechnol. 39, 846–854 (2021).

    Article 
    CAS 

    Google Scholar
     

  • Messner, C. B. et al. The proteomic landscape of genome-wide genetic perturbations. Preprint at bioRxiv https://doi.org/10.1101/2022.05.17.492318 (2022).

  • Cox, J. et al. Accurate proteome-wide label-free quantification by delayed normalization and maximal peptide ratio extraction, termed MaxLFQ. Mol. Cell. Proteomics 13, 2513–2526 (2014).

    Article 
    CAS 

    Google Scholar
     

  • MacLean, B. et al. Skyline: an open source document editor for creating and analyzing targeted proteomics experiments. Bioinformatics 26, 966–968 (2010).

    Article 
    CAS 

    Google Scholar
     

  • Virtanen, P. et al. SciPy 1.0: fundamental algorithms for scientific computing in Python. Nat. Methods 17, 261–272 (2020).

    Article 
    CAS 

    Google Scholar
     

  • Raudvere, U. et al. g:Profiler: a web server for functional enrichment analysis and conversions of gene lists (2019 update). Nucleic Acids Res. 47, W191–W198 (2019).

    Article 
    CAS 

    Google Scholar
     

  • Darzi, Y., Letunic, I., Bork, P. & Yamada, T. iPath3.0: interactive pathways explorer v3. Nucleic Acids Res. 46, W510–W513 (2018).

    Article 
    CAS 

    Google Scholar
     

  • Deutsch, E. W. et al. The ProteomeXchange consortium in 2020: enabling “big data” approaches in proteomics. Nucleic Acids Res. 48, D1145–D1152 (2020).

    CAS 

    Google Scholar
     

  • Perez-Riverol, Y. et al. The PRIDE database and related tools and resources in 2019: improving support for quantification data. Nucleic Acids Res. 47, D442–D450 (2019).

    Article 
    CAS 

    Google Scholar
     

  • Sharma, V. et al. Panorama Public: a public repository for quantitative data sets processed in Skyline. Mol. Cell. Proteomics 17, 1239–1244 (2018).

    Article 
    CAS 

    Google Scholar
     

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    1/ https://Google.com/

    2/ https://www.nature.com/articles/s41564-022-01304-8

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