Stress-induced expression is enriched for evolutionarily young genes in diverse budding yeasts

Abstract

The Saccharomycotina subphylum (budding yeasts) spans 400 million years of evolution and includes species that thrive in diverse environments. To study niche-adaptation, we identify changes in gene expression in three divergent yeasts grown in the presence of various stressors. Duplicated and non-conserved genes are significantly more likely to respond to stress than genes that are conserved as single-copy orthologs. Next, we develop a sorting method that considers evolutionary origin and duplication timing to assign an evolutionary age to each gene. Subsequent analysis reveals that genes that emerged in recent evolutionary time are enriched amongst stress-responsive genes for each species. This gene expression pattern suggests that budding yeasts share a stress adaptation mechanism, whereby selective pressure leads to functionalization of young genes to improve growth in adverse conditions. Further characterization of young genes from species that thrive in harsh environments can inform the design of more robust strains for biotechnology.

Reconstruction of a catalogue of genome-scale metabolic models with enzymatic constraints using GECKO 2.0

“…Principal component analysis revealed that variance on absolute enzyme usages and abundance profiles for these core proteins is mostly explained by differences in the metabolic networks of the different species rather than by environmental conditions (Fig. S3 B-C), reinforcing previous results suggesting that, despite being phylogenetically related, their long-term stress responses at the molecular level have evolved independently after their divergence in evolutionary history 64 .…”

“…cerevisiae, Y. lipolytica and K. marxianus, grown in chemostats at 0.1 h -1 dilution rate and subject to several experimental conditions (high temperature, low pH and osmotic stress with KCl) 64 A further mapping of all enzymes in these ecModels to a list of 2,959 single copy protein-coding gene orthologs across the three yeast species 64 found 310 core proteins across these ecModels. Principal component analysis revealed that variance on absolute enzyme usages and abundance profiles for these core proteins is mostly explained by differences in the metabolic networks of the different species rather than by environmental conditions (Fig.…”

A Single Chromosome Strain ofS. cerevisiaeExhibits Diminished Ethanol Metabolism and Tolerance

“…RNAseq was mapped with STAR and reads were assigned with featureCounts. Differential expression results were generated using scripts from the OrthOmics pipeline (https://github.com/SysBioChalmers/OrthOmics) from Doughty et al 2020 33 , which is based on the limma and edgeR R packages. Raw datasets were uploaded to SRA under the accession number PRJNA594518 and differential expression results are reported in Supplemental Table I.…”

A novel yeast hybrid modeling framework integrating Boolean and enzyme-constrained networks enables exploration of the interplay between signaling and metabolism

“…To verify the ecModel performance, the predictions of exchange reaction fluxes at increasing dilution rates were compared against experimental data (Van Hoek et al, 1998) ( Figure S3), predictions showed a median relative error of 9.82% in the whole range of dilution rates from 0 to 0.4 h -1 , spanning both respiratory and fermentative metabolic regimes. The hybrid model, including regulation, was further compared with the ecModel in their ability to predict protein abundances by comparing the predicted abundances to proteomics data from the literature in both respiratory and fermentative conditions (Doughty et al, 2020;Paulo et al, 2016). By adding the regulation layer, the prediction accuracy of individual protein abundances was drastically improved, reducing the mean absolute log 10 -transformed ratio between predicted and measured values (r) from 2.62 to 1.55 for respiration and from 3.56 to 2.32 for fermentation ( Figure 3), which represents an average improvement in protein predictions by more than one order of magnitude for both conditions.…”

“…Protein abundance data on respiratory and fermentative conditions were compared to protein usage predictions by the hybrid model in order to assess its performance. For the respiration phase, absolute protein abundances were taken from a study of yeast growing under glucose-limited chemostat conditions at 30°C on minimal mineral medium with a dilution rate of 0.1 h −1 (Doughty et al, 2020).…”