Growth-Rate Dependent And Nutrient-Specific Gene Expression Resource Allocation In Fission Yeast

Abstract

Cellular resources are limited and their relative allocation to gene expression programmes determines physiological states and global properties such as the growth rate. Quantitative studies using various growth conditions have singled out growth rate as a major physiological variable explaining relative protein abundances. Here, we used the simple eukaryote Schizosaccharomyces pombe to determine the importance of growth rate in explaining relative changes in protein and mRNA levels during growth on a series of non-limiting nitrogen sources. Although half of fission yeast genes were significantly correlated with the growth rate, this came alongside wide-spread nutrient-specific regulation. Proteome and transcriptome often showed coordinated regulation but with notable exceptions, such as metabolic enzymes. Genes positively correlated with growth rate participated in every level of protein production with the notable exception of RNA polymerase II, whereas those negatively correlated mainly belonged to the environmental stress response programme. Critically, metabolic enzymes, which represent ~55-70% of the proteome by mass, showed mainly condition-specific regulation. Specifically, many enzymes involved in glycolysis and NAD-dependent metabolism as well as the fermentative and respiratory pathways were condition-dependent and not consistently correlated with growth. In summary, we provide a rich account of resource allocation to gene expression in a simple eukaryote, advancing our basic understanding of the interplay between growth-rate dependent and nutrient-specific gene expression.

The Proteomic Landscape of Genome-Wide Genetic Perturbations

“…While this could be an underestimation given that such networks are still incomplete, this result highlights the role of general biological mechanisms that may guide differential protein expression. One known such factor is the growth rate, known to be correlated with changes in proteome and transcriptome (Airoldi et al, 2009; Fazio et al, 2008; Hughes et al, 2000a; Kemmeren et al, 2014; Kleijn et al, 2022; Slavov and Botstein, 2011; Wytock and Motter, 2019; Yu et al, 2021). Here, our large-scale dataset provides a differentiated picture about the role of growth-rate-dependent changes in differential protein expression.…”

A computational toolbox to investigate the metabolic potential and resource allocation in fission yeast

“…As growth rate continues to increase, the active constraints change (blue shaded region in Figure 3), and so does the predicted metabolic behavior. At very fast growth rates, instead of mitochondrial proteome capacity, the unspecified protein (UP) fraction (a proxy for the cytosolic proteome capacity), starts to limit growth (UP mass fraction in the proteome reaches the minimal value we estimated on the basis of proteomics data (Kleijn et al, 2022)). As a result, any increase in growth has to be accompanied by trading in mitochondrial proteins for cytosolic ones (Figure 3d, panel “Mito.…”

“…Next we assessed the peptide elongation rate of the cytosolic ribosomes and the fraction of proteome, occupied by “inactive” ribosomes Φ R,0 (following (Metzl-Raz et al, 2017)), other key parameters, as shown for the pcYeast model (Elsemman et al, 2022) (Figure 2c). We used quantitative proteomics data from turbidostat experiments in EMM2 media (2% glucose), supplemented with different single nitrogen sources (Kleijn et al, 2022). First, we computed the fraction of “inactive” ribosomes Φ R,0 ≈ 0.05 g ( g protein ) ‒1 from the linear regression of the experimental data points (Figure 2c, black dashed line).…”

“…Existing experimental datasets of S. pombe , unfortunately, are not as comprehensive. Although many of the datasets are of high-quality, they consider only one aspect of cell growth, for instance, exometabolite fluxes (de Jong-Gubbels et al, 1996), or proteome composition (Kleijn et al, 2022). For modelling purposes, systemic experiments which cover several layers of information at once (e.g.…”

Genome-Scale Reconstruction of Microbial Dynamic Phenotype: Successes and Challenges

“…It means that two options, 3 and 4, are justified, and s can be considered as the common independent factor affecting the SGR and rRNA expression (the SGR dependence on s was discovered 80 years ago [43] and is beyond any doubt). Finally, all the studied microorganisms, prokaryotic or eucaryotic, uniformly follow the so-called 'ribosomal growth law', which states that the ribosomal content in cells is the principal internal bottleneck [16,17,44,45]; the translation process is recognized as the slowest biosynthetic step; hence, growth acceleration can be achieved only through an increase of the ribosomal copy numbers. In other words, the SGR depends on the rRNA level, not the opposite!…”