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Renewing Lost Genetic Variability with a Classical Yeast Genetics Approach.

ABSTRACT: Due to their long domestication time course, many industrial Saccharomyces cerevisiae strains are adopted in numerous processes mostly for historical reasons instead of scientific and technological needs. As such, there is still significant room for improvement for industrial yeast strains relying on yeast biodiversity. This paper strives to regenerate biodiversity with the innovative application of classic genetic methods to already available yeast strains. Extensive sporulation was indeed applied to three different yeast strains, specifically selected for their different origins as well as backgrounds, with the aim of clarifying how new variability was generated. A novel and easy method to obtain mono-spore colonies was specifically developed, and, to reveal the extent of the generated variability, no selection after sporulation was introduced. The obtained progenies were then tested for their growth in defined mediums with high stressor levels. A considerable and strain-specific increase in both phenotypic and metabolomic variability was assessed, and a few mono-spore colonies were found to be of great interest for their future exploitation in selected industrial processes.

SUBMITTER: Gupte AP

PROVIDER: S-EPMC9958831 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature

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Renewing Lost Genetic Variability with a Classical Yeast Genetics Approach.

Gupte Ameya Pankaj AP Pierantoni Debora Casagrande DC Conti Angela A Donati Leonardo L Basaglia Marina M Casella Sergio S Favaro Lorenzo L Corte Laura L Cardinali Gianluigi G

Journal of fungi (Basel, Switzerland) 20230216 2

Due to their long domestication time course, many industrial <i>Saccharomyces cerevisiae</i> strains are adopted in numerous processes mostly for historical reasons instead of scientific and technological needs. As such, there is still significant room for improvement for industrial yeast strains relying on yeast biodiversity. This paper strives to regenerate biodiversity with the innovative application of classic genetic methods to already available yeast strains. Extensive sporulation was inde ...[more]

PMID: 36836378

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Project description:BackgroundNew polymorphism datasets from heterochroneous data have arisen thanks to recent advances in experimental and microbial molecular evolution, and the sequencing of ancient DNA (aDNA). However, classical tools for population genetics analyses do not take into account heterochrony between subsets, despite potential bias on neutrality and population structure tests. Here, we characterize the extent of such possible biases using serial coalescent simulations.Methodology/principal findingsWe first use a coalescent framework to generate datasets assuming no or different levels of heterochrony and contrast most classical population genetic statistics. We show that even weak levels of heterochrony ( approximately 10% of the average depth of a standard population tree) affect the distribution of polymorphism substantially, leading to overestimate the level of polymorphism theta, to star like trees, with an excess of rare mutations and a deficit of linkage disequilibrium, which are the hallmark of e.g. population expansion (possibly after a drastic bottleneck). Substantial departures of the tests are detected in the opposite direction for more heterochroneous and equilibrated datasets, with balanced trees mimicking in particular population contraction, balancing selection, and population differentiation. We therefore introduce simple corrections to classical estimators of polymorphism and of the genetic distance between populations, in order to remove heterochrony-driven bias. Finally, we show that these effects do occur on real aDNA datasets, taking advantage of the currently available sequence data for Cave Bears (Ursus spelaeus), for which large mtDNA haplotypes have been reported over a substantial time period (22-130 thousand years ago (KYA)).Conclusions/significanceConsidering serial sampling changed the conclusion of several tests, indicating that neglecting heterochrony could provide significant support for false past history of populations and inappropriate conservation decisions. We therefore argue for systematically considering heterochroneous models when analyzing heterochroneous samples covering a large time scale.

Project description:The last decade has seen a rapid development of experimental techniques that allow data collection from individual cells. These techniques have enabled the discovery and characterization of variability within a population of genetically identical cells. Nonlinear mixed effects (NLME) modeling is an established framework for studying variability between individuals in a population, frequently used in pharmacokinetics and pharmacodynamics, but its potential for studies of cell-to-cell variability in molecular cell biology is yet to be exploited. Here we take advantage of this novel application of NLME modeling to study cell-to-cell variability in the dynamic behavior of the yeast transcription repressor Mig1. In particular, we investigate a recently discovered phenomenon where Mig1 during a short and transient period exits the nucleus when cells experience a shift from high to intermediate levels of extracellular glucose. A phenomenological model based on ordinary differential equations describing the transient dynamics of nuclear Mig1 is introduced, and according to the NLME methodology the parameters of this model are in turn modeled by a multivariate probability distribution. Using time-lapse microscopy data from nearly 200 cells, we estimate this parameter distribution according to the approach of maximizing the population likelihood. Based on the estimated distribution, parameter values for individual cells are furthermore characterized and the resulting Mig1 dynamics are compared to the single cell times-series data. The proposed NLME framework is also compared to the intuitive but limited standard two-stage (STS) approach. We demonstrate that the latter may overestimate variabilities by up to almost five fold. Finally, Monte Carlo simulations of the inferred population model are used to predict the distribution of key characteristics of the Mig1 transient response. We find that with decreasing levels of post-shift glucose, the transient response of Mig1 tend to be faster, more extended, and displays an increased cell-to-cell variability.

Dataset Information

Renewing Lost Genetic Variability with a Classical Yeast Genetics Approach.

Publications

Renewing Lost Genetic Variability with a Classical Yeast Genetics Approach.

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