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Phenotypic heterogeneity follows a growth-viability tradeoff in response to amino acid identity

Author

Listed:
  • Kiyan Shabestary

    (Imperial College London)

  • Cinzia Klemm

    (Imperial College London)

  • Benedict Carling

    (Imperial College London
    Imperial College Translation & Innovation Hub)

  • James Marshall

    (Imperial College London
    Imperial College Translation & Innovation Hub)

  • Juline Savigny

    (Imperial College London)

  • Marko Storch

    (Imperial College Translation & Innovation Hub
    Imperial College London)

  • Rodrigo Ledesma-Amaro

    (Imperial College London)

Abstract

In their natural environments, microorganisms mainly operate at suboptimal growth conditions with fluctuations in nutrient abundance. The resulting cellular adaptation is subject to conflicting tasks: growth or survival maximisation. Here, we study this adaptation by systematically measuring the impact of a nitrogen downshift to 24 nitrogen sources on cellular metabolism at the single-cell level. Saccharomyces lineages grown in rich media and exposed to a nitrogen downshift gradually differentiate to form two subpopulations of different cell sizes where one favours growth while the other favours viability with an extended chronological lifespan. This differentiation is asymmetrical with daughter cells representing the new differentiated state with increased viability. We characterise the metabolic response of the subpopulations using RNA sequencing, metabolic biosensors and a transcription factor-tagged GFP library coupled to high-throughput microscopy, imaging more than 800,000 cells. We find that the subpopulation with increased viability is associated with a dormant quiescent state displaying differences in MAPK signalling. Depending on the identity of the nitrogen source present, differentiation into the quiescent state can be actively maintained, attenuated, or aborted. These results establish amino acids as important signalling molecules for the formation of genetically identical subpopulations, involved in chronological lifespan and growth rate determination.

Suggested Citation

  • Kiyan Shabestary & Cinzia Klemm & Benedict Carling & James Marshall & Juline Savigny & Marko Storch & Rodrigo Ledesma-Amaro, 2024. "Phenotypic heterogeneity follows a growth-viability tradeoff in response to amino acid identity," Nature Communications, Nature, vol. 15(1), pages 1-16, December.
  • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-50602-8
    DOI: 10.1038/s41467-024-50602-8
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    References listed on IDEAS

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    1. Martin Lukačišin & Adriana Espinosa-Cantú & Tobias Bollenbach, 2022. "Intron-mediated induction of phenotypic heterogeneity," Nature, Nature, vol. 605(7908), pages 113-118, May.
    2. Jhonatan A. Hernandez-Valdes & Jordi van Gestel & Oscar P. Kuipers, 2020. "A riboswitch gives rise to multi-generational phenotypic heterogeneity in an auxotrophic bacterium," Nature Communications, Nature, vol. 11(1), pages 1-13, December.
    3. Nicola Dietler & Matthias Minder & Vojislav Gligorovski & Augoustina Maria Economou & Denis Alain Henri Lucien Joly & Ahmad Sadeghi & Chun Hei Michael Chan & Mateusz Koziński & Martin Weigert & Anne-F, 2020. "A convolutional neural network segments yeast microscopy images with high accuracy," Nature Communications, Nature, vol. 11(1), pages 1-8, December.
    4. Leisch, Friedrich, 2004. "FlexMix: A General Framework for Finite Mixture Models and Latent Class Regression in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 11(i08).
    5. Weilin Peng & Ruijie Song & Murat Acar, 2016. "Noise reduction facilitated by dosage compensation in gene networks," Nature Communications, Nature, vol. 7(1), pages 1-8, December.
    6. Guy Alexander Cooper & Ming Liu & Jorge Peña & Stuart Andrew West, 2022. "The evolution of mechanisms to produce phenotypic heterogeneity in microorganisms," Nature Communications, Nature, vol. 13(1), pages 1-13, December.
    7. Won-Ki Huh & James V. Falvo & Luke C. Gerke & Adam S. Carroll & Russell W. Howson & Jonathan S. Weissman & Erin K. O'Shea, 2003. "Global analysis of protein localization in budding yeast," Nature, Nature, vol. 425(6959), pages 686-691, October.
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