Fitness
Environmental memory alters the fitness effects of adaptive mutations in fluctuating environments – Nature Ecology & Evolution
Woods, R., Schneider, D., Winkworth, C. L., Riley, M. A. & Lenski, R. E. Tests of parallel molecular evolution in a long-term experiment with Escherichia coli. Proc. Natl Acad. Sci. USA 103, 9107–9112 (2006).
Tenaillon, O. et al. The molecular diversity of adaptive convergence. Science 335, 457–461 (2012).
Venkataram, S. et al. Development of a comprehensive genotype-to-fitness map of adaptation-driving mutations in yeast. Cell 166, 1585–1596.e22 (2016).
Good, B. H., McDonald, M. J., Barrick, J. E., Lenski, R. E. & Desai, M. M. The dynamics of molecular evolution over 60,000 generations. Nature 551, 45–50 (2017).
Rudman, S. M. et al. Direct observation of adaptive tracking on ecological time scales in Drosophila. Science 375, eabj7484 (2022).
Kryazhimskiy, S., Rice, D. P., Jerison, E. R. & Desai, M. M. Global epistasis makes adaptation predictable despite sequence-level stochasticity. Science 344, 1519–1522 (2014).
Couce, A. & Tenaillon, O. A. The rule of declining adaptability in microbial evolution experiments. Front. Genet. 6, 99 (2015).
Kover, P. X. et al. Pleiotropic effects of environment-specific adaptation in Arabidopsis thaliana. New Phytol. 183, 816–825 (2009).
Schick, A., Bailey, S. F. & Kassen, R. Evolution of fitness trade-offs in locally adapted populations of Pseudomonas fluorescens. Am. Nat. 186, S48–S59 (2015).
Li, Y., Petrov, D. A. & Sherlock, G. Single nucleotide mapping of trait space reveals Pareto fronts that constrain adaptation. Nat. Ecol. Evol. 3, 1539–1551 (2019).
Jerison, E. R., Nguyen Ba, A. N., Desai, M. M. & Kryazhimskiy, S. Chance and necessity in the pleiotropic consequences of adaptation for budding yeast. Nat. Ecol. Evol. 4, 601–611 (2020).
Kinsler, G., Geiler-Samerotte, K. & Petrov, D. A. Fitness variation across subtle environmental perturbations reveals local modularity and global pleiotropy of adaptation. eLife 9, e61271 (2020).
Bono, L. M., Smith, L. B., Pfennig, D. W. & Burch, C. L. The emergence of performance trade-offs during local adaptation: insights from experimental evolution. Mol. Ecol. 26, 1720–1733 (2017).
Cooper, T. F. & Lenski, R. E. Experimental evolution with E. coli in diverse resource environments. I. Fluctuating environments promote divergence of replicate populations. BMC Evol. Biol. 10, 11 (2010).
Rodríguez-Verdugo, A. & Ackermann, M. Rapid evolution destabilizes species interactions in a fluctuating environment. ISME J. 15, 450–460 (2021).
Salignon, J., Richard, M., Fulcrand, E., Duplus-Bottin, H. & Yvert, G. Genomics of cellular proliferation in periodic environmental fluctuations. Mol. Syst. Biol. 14, e7823 (2018).
Boyer, S., Hérissant, L. & Sherlock, G. Adaptation is influenced by the complexity of environmental change during evolution in a dynamic environment. PLoS Genet. 17, e1009314 (2021).
Alto, B. W., Wasik, B. R., Morales, N. M. & Turner, P. E. Stochastic temperatures impede RNA virus adaptation. Evolution 67, 969–979 (2013).
Fasanello, V. J., Liu, P., Fay, J. C. & Botero, C. A. Fluctuating selection facilitates the discovery of broadly effective but difficult to reach adaptive outcomes in yeast. Evol. Lett. 8, 243–252 (2024).
Cvijović, I., Good, B. H., Jerison, E. R. & Desai, M. M. Fate of a mutation in a fluctuating environment. Proc. Natl Acad. Sci. USA 112, E5021–E5028 (2015).
Sæther, B.-E. & Engen, S. The concept of fitness in fluctuating environments. Trends Ecol. Evol. 30, 273–281 (2015).
Ketola, T. & Kristensen, T. N. Experimental approaches for testing if tolerance curves are useful for predicting fitness in fluctuating environments. Front. Ecol. Evol. 5, 129 (2017).
Li, Y. et al. Hidden complexity of yeast adaptation under simple evolutionary conditions. Curr. Biol. 28, 515–525.e6 (2018).
Monod, J. The growth of bacterial cultures. Annu. Rev. Microbiol. 3, 371–394 (1949).
Siegal, M. L. Shifting sugars and shifting paradigms. PLoS Biol. 13, e1002068 (2015).
Escalante-Chong, R. et al. Galactose metabolic genes in yeast respond to a ratio of galactose and glucose. Proc. Natl Acad. Sci. USA 112, 1636–1641 (2015).
Basan, M. et al. A universal trade-off between growth and lag in fluctuating environments. Nature 584, 470–474 (2020).
Balakrishnan, R., de Silva, R. T., Hwa, T. & Cremer, J. Suboptimal resource allocation in changing environments constrains response and growth in bacteria. Mol. Syst. Biol. 17, e10597 (2021).
Acar, M., Mettetal, J. T. & van Oudenaarden, A. Stochastic switching as a survival strategy in fluctuating environments. Nat. Genet. 40, 471–475 (2008).
Lambert, G. & Kussell, E. Memory and fitness optimization of bacteria under fluctuating environments. PLoS Genet. 10, e1004556 (2014).
Kronholm, I. & Ketola, T. Effects of acclimation time and epigenetic mechanisms on growth of Neurospora in fluctuating environments. Heredity 121, 327–341 (2018).
Stajic, D., Bank, C. & Gordo, I. Adaptive potential of epigenetic switching during adaptation to fluctuating environments. Genome Biol. Evol. 14, evac065 (2022).
Torres-Garcia, S. et al. Epigenetic gene silencing by heterochromatin primes fungal resistance. Nature 585, 453–458 (2020).
Leung, C., Grulois, D., Quadrana, L. & Chevin, L.-M. Phenotypic plasticity evolves at multiple biological levels in response to environmental predictability in a long-term experiment with a halotolerant microalga. PLoS Biol. 21, e3001895 (2023).
Levy, S. F. et al. Quantitative evolutionary dynamics using high-resolution lineage tracking. Nature 519, 181–186 (2015).
New, A. M. et al. Different levels of catabolite repression optimize growth in stable and variable environments. PLoS Biol. 12, e1001764 (2014).
Zakrzewska, A. et al. Genome-wide analysis of yeast stress survival and tolerance acquisition to analyze the central trade-off between growth rate and cellular robustness. Mol. Biol. Cell 22, 4435–4446 (2011).
Dhar, R., Sägesser, R., Weikert, C. & Wagner, A. Yeast adapts to a changing stressful environment by evolving cross-protection and anticipatory gene regulation. Mol. Biol. Evol. 30, 573–588 (2013).
Ketola, T. & Saarinen, K. Experimental evolution in fluctuating environments: tolerance measurements at constant temperatures incorrectly predict the ability to tolerate fluctuating temperatures. J. Evol. Biol. 28, 800–806 (2015).
Vermeersch, L. et al. On the duration of the microbial lag phase. Curr. Genet. 65, 721–727 (2019).
McDaniel, E. A., Stuecker, T. N., Veluvolu, M., Gasch, A. P. & Lewis, J. A. Independent mechanisms for acquired salt tolerance versus growth resumption induced by mild ethanol pretreatment in Saccharomyces cerevisiae. mSphere 3, e00574-18 (2018).
Rivers, W. Characterizing Aft1/2-Grx3/4 Interaction and the Role of Bol2 During Iron Regulation in Saccharomyces cerevisiae. Senior thesis, Honors College (2019).
Martínez-Pastor, M. T., Perea-García, A. & Puig, S. Mechanisms of iron sensing and regulation in the yeast Saccharomyces cerevisiae. World J. Microbiol. Biotechnol. 33, 75 (2017).
IPCC. in Climate Change 2013—The Physical Science Basis: Working Group I Contribution to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change Ch. 14, 1217–1308 (Cambridge University Press, 2014).
Khan, A. I., Dinh, D. M., Schneider, D., Lenski, R. E. & Cooper, T. F. Negative epistasis between beneficial mutations in an evolving bacterial population. Science 332, 1193–1196 (2011).
Chou, H.-H., Chiu, H.-C., Delaney, N. F., Segrè, D. & Marx, C. J. Diminishing returns epistasis among beneficial mutations decelerates adaptation. Science 332, 1190–1192 (2011).
Reddy, G. & Desai, M. M. Global epistasis emerges from a generic model of a complex trait. eLife 10, e64740 (2021).
Diaz-Colunga, J., Skwara, A., Vila, J. C. C., Bajic, D. & Sanchez, A. Global epistasis and the emergence of function in microbial consortia. Cell 187, 3108–3119.e30 (2024).
Violle, C., Pu, Z. & Jiang, L. Experimental demonstration of the importance of competition under disturbance. Proc. Natl Acad. Sci. USA 107, 12925–12929 (2010).
Abreu, C. I., Woltz, V. L. A., Friedman, J. & Gore, J. Microbial communities display alternative stable states in a fluctuating environment. PLoS Comput. Biol. 16, e1007934 (2020).
Yi, X. & Dean, A. M. Bounded population sizes, fluctuating selection and the tempo and mode of coexistence. Proc. Natl Acad. Sci. USA 110, 16945–16950 (2013).
Letten, A. D., Dhami, M. K., Ke, P.-J. & Fukami, T. Species coexistence through simultaneous fluctuation-dependent mechanisms. Proc. Natl Acad. Sci. USA 115, 6745–6750 (2018).
Mukherjee, A. et al. Coexisting ecotypes in long-term evolution emerged from interacting trade-offs. Nat. Commun. 14, 3805 (2023).
Chesson, P. & Huntly, N. The roles of harsh and fluctuating conditions in the dynamics of ecological communities. Am. Nat. 150, 519–553 (1997).
Gilchrist, C. & Stelkens, R. Aneuploidy in yeast: segregation error or adaptation mechanism? Yeast 36, 525–539 (2019).
Beaumont, H. J. E., Gallie, J., Kost, C., Ferguson, G. C. & Rainey, P. B. Experimental evolution of bet hedging. Nature 462, 90–93 (2009).
Bruijning, M., Metcalf, C. J. E., Jongejans, E. & Ayroles, J. F. The evolution of variance control. Trends Ecol. Evol. 35, 22–33 (2020).
Kinsler, G. et al. Extreme sensitivity of fitness to environmental conditions: lessons from #1BigBatch. J. Mol. Evol. 91, 293–310 (2023).
Venkataram, S. BarcodeCounter2. GitHub https://github.com/sandeepvenkataram/BarcodeCounter2 (2021).
Zhao, L., Liu, Z., Levy, S. F. & Wu, S. Bartender: a fast and accurate clustering algorithm to count barcode reads. Bioinformatics 34, 739–747 (2018).
Li, F., Mahadevan, A. & Sherlock, G. An improved algorithm for inferring mutational parameters from bar-seq evolution experiments. BMC Genom. 24, 246 (2023).
Kinsler, G., Li, Y., Sherlock, G. J. & Petrov, D. A shift from pleiotropic to modular adaptation revealed by a high-resolution two-step adaptive walk. Preprint at bioRxiv https://doi.org/10.1101/2024.04.17.589938 (2024).
Frank Sinatra fans celebrate 109 years since the iconic crooner was born at party in Grasmere dining spot | Inside Out
U.S. Continues To Dominate In World Cup Men’s Freeski Halfpipe
See the latest class of individuals joining the Greater Lansing Sports Hall of Fame
Luminous Pluck: The Story of a World War II Heroine
How Unequal Pay And Unpredictable Earnings Cause Stress For Women In Sports
The best unconventional Christmas movies as shared by Yahoo readers
Jameis Winston inactive, David Njoku active for Browns on Sunday
Apple might be working on a smart doorbell | TechCrunch
Eagles inactives: Sydney Brown will play vs. Commanders
