Life Science AllianceSystems BiologyPeer-reviewed journal article
Growth-Rate Dependent And Nutrient-Specific Gene Expression Resource Allocation In Fission Yeast
Istvan T. KleijniD, Amalia Martínez-SeguraiD, François BertauxiD, Malika SaintiD, Holger KrameriD, Vahid ShahrezaeiiD, Samuel MargueratiD
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.
- organism: yeast
- assay: proteomics
- assay: bulk RNA-seq
- research focus: differential expression
- research focus: gene regulatory network inference
- research focus: benchmarking
- approach: statistical model
- Citations
- 17
- FWCI
- 0.90
- Field percentile
- top 31%
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