Phenotypic heterogeneity in a batch culture of Chlamydomonas reinhardtii with different light tolerances
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Abstract
Genetic diversity of populations is essential for generating phenotypic variation to allow a flexible response to a shift in environmental conditions. Therefore, in populations of genetically identical individuals grown in the lab, you would predict that phenotypic heterogeneity would be small. However, we isolated two subpopulations of genetically identical individuals from an exponentially growing batch culture of the microalga Chlamydomonas reinhardtii using Percoll step-gradients. The culture fractionated into a low-density, Top fraction and a high-density, Bottom fraction. These subpopulations displayed several phenotypic differences, including size, protein content, the amount of chlorophyll per cell, and photosynthetic performance. Because of the variation in pigment content and photosynthetic performance, we tested the hypothesis that there are differences in their tolerance to light stress. Following high-light stress, the Bottom subpopulation was more resistant to photodamage, had a greater capacity for light dissipation, and had a minimal photoacclimation response to high light, compared to the Top subpopulation. The Bottom population also had a greater resistance to exogenously induced singlet oxygen stress mediated by rose bengal. We hypothesize that these subpopulations are derived from stochastic mechanism where the Bottom subpopulation has activated a general high-light stress response pathway as part of a “bet-hedging” strategy that could give it a fitness advantage with a shift towards a light-stress environment.
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Would be interesting to see if the source of phenotypic heterogeneity in isogenic lines can be teased apart by high dimensional phenotyping. For example one might expect isogenic lines that vary by phenotype in phototaxis to also maintain segregation among correlated phenotypes like sensitivity to high light stress. If subpopulations continue to segregate in these circumstances, the cause may be stochastic factors but if segregated populations maintain separate phenotypes for assays where the phenotype is correlated, it may be epigenetic or other heritable but primarily genetic sequence-independent factors.
In short, high dimensional phenotyping incorporating assays with varying degrees of mutual information may have promise in understanding micro-environmental, epigenetic, and stochastic factors of variation.
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