Absolute Quantification of Protein and mRNA Abundances Demonstrate Variability in Gene-Specific Translation Efficiency in Yeast

Abstract

Protein synthesis is the most energy-consuming process in a proliferating cell, and understanding what controls protein abundances represents a key question in biology and biotechnology. We quantified absolute abundances of 5,354 mRNAs and 2,198 proteins in Saccharomyces cerevisiae under ten environmental conditions and protein turnover for 1,384 proteins under a reference condition. The overall correlation between mRNA and protein abundances across all conditions was low (0.46), but for differentially expressed proteins (n = 202), the median mRNA-protein correlation was 0.88. We used these data to model translation efficiencies and found that they vary more than 400-fold between genes. Non-linear regression analysis detected that mRNA abundance and translation elongation were the dominant factors controlling protein synthesis, explaining 61% and 15% of its variance. Metabolic flux balance analysis further showed that only mitochondrial fluxes were positively associated with changes at the transcript level. The present dataset represents a crucial expansion to the current resources for future studies on yeast physiology.

Translational regulation in response to stress in Saccharomyces cerevisiae

“…Studies in mammalian cells at ‘steady‐state’ demonstrate a role for translation that is at least as great as that of transcription in determining protein levels globally (Schwanhäusser et al, ; Vogel et al, ). However, recent work suggests a more prominent role for transcription in yeast, with the majority of protein abundance variation explained by differences in mRNA abundance (Lahtvee et al, ). Furthermore, the contribution of each process to the expression of individual genes and sets of genes varies widely.…”

Yeast Systems Biology: Model Organism and Cell Factory

“…Most older data were generated using DNA microarrays, whereas RNAseq has become the norm today, although good consistency has been proven between the two different approaches . One main advantage of RNAseq is that it is quantitative, and with standards it is now possible to measure >4000 mRNAs in yeast cells to absolute levels, with it being found that mRNA copy number per cell generally varies across two orders of magnitude, from 1 to 100 per cell. RNAseq is traditionally done using next‐generation sequencing, but recently it has been shown that is possible to use nanopore sequencing technology not only to obtain long DNA sequences required for genome assembly, but also for measuring mRNA levels …”

“…With state‐of‐the‐art proteomics it is now possible to measure >2500 proteins in yeast with a false discovery rate of <1% . Furthermore, using standards it is possible to measure absolute levels of >2000 proteins in yeast cells . Combining such data with quantitative mRNA data at ten different environmental conditions enabled the identification of strong linear relationships between protein and mRNA levels for 202 genes .…”

“…Furthermore, using standards it is possible to measure absolute levels of >2000 proteins in yeast cells . Combining such data with quantitative mRNA data at ten different environmental conditions enabled the identification of strong linear relationships between protein and mRNA levels for 202 genes . However, the slope of these relationships varied for individual proteins, pointing to large variations in translation rate (protein degradation was also quantified but found not to be an effector).…”

“…This “brake” is caused by trehalose‐6‐phosphate (T6P), formed from glucose‐6‐phosphate, and serving as an inhibitor of hexokinase. Using this simple kinetic model, Teusink and co‐workers could therefore capture the crucial role of this regulatory design . This important role of T6P can also explain why a yeast strain that lacks trehalose‐6‐phosphate synthase (Tps1) cannot grow when glucose is in excess .…”