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Inverted translational control of eukaryotic gene expression by ribosome collisions.

ABSTRACT: The canonical model of eukaryotic translation posits that efficient translation initiation increases protein expression and mRNA stability. Contrary to this model, we find that increasing initiation rate can decrease both protein expression and stability of certain mRNAs in the budding yeast Saccharomyces cerevisiae. These mRNAs encode a stretch of polybasic residues that cause ribosome stalling. Our computational modeling predicts that the observed decrease in gene expression at high initiation rates occurs when ribosome collisions at stalls stimulate abortive termination of the leading ribosome or cause endonucleolytic mRNA cleavage. Consistent with this prediction, the collision-associated quality-control factors Asc1 and Hel2 (orthologs of human RACK1 and ZNF598, respectively) decrease gene expression from stall-containing mRNAs only at high initiation rates. Remarkably, hundreds of S. cerevisiae mRNAs that contain ribosome stall sequences also exhibit lower translation efficiency. We propose that inefficient translation initiation allows these stall-containing endogenous mRNAs to escape collision-stimulated reduction in gene expression.

SUBMITTER: Park H

PROVIDER: S-EPMC6750593 | biostudies-literature | 2019 Sep

REPOSITORIES: biostudies-literature

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Inverted translational control of eukaryotic gene expression by ribosome collisions.

Park Heungwon H Subramaniam Arvind R AR

PLoS biology 20190918 9

The canonical model of eukaryotic translation posits that efficient translation initiation increases protein expression and mRNA stability. Contrary to this model, we find that increasing initiation rate can decrease both protein expression and stability of certain mRNAs in the budding yeast Saccharomyces cerevisiae. These mRNAs encode a stretch of polybasic residues that cause ribosome stalling. Our computational modeling predicts that the observed decrease in gene expression at high initiation ...[more]

PMID: 31532761

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Project description:The intrinsic stochasticity of gene expression leads to cell-to-cell variations, noise, in protein abundance. Several processes, including transcription, translation, and degradation of mRNA and proteins, can contribute to these variations. Recent single cell analyses of gene expression in yeast have uncovered a general trend where expression noise scales with protein abundance. This trend is consistent with a stochastic model of gene expression where mRNA copy number follows the random birth and death process. However, some deviations from this basic trend have also been observed, prompting questions about the contribution of gene-specific features to such deviations. For example, recent studies have pointed to the TATA box as a sequence feature that can influence expression noise by facilitating expression bursts. Transcription-originated noise can be potentially further amplified in translation. Therefore, we asked the question of to what extent sequence features known or postulated to accompany translation efficiency can also be associated with increase in noise strength and, on average, how such increase compares to the amplification associated with the TATA box. Untangling different components of expression noise is highly nontrivial, as they may be gene or gene-module specific. In particular, focusing on codon usage as one of the sequence features associated with efficient translation, we found that ribosomal genes display a different relationship between expression noise and codon usage as compared to other genes. Within nonribosomal genes we found that sequence high codon usage is correlated with increased noise relative to the average noise of proteins with the same abundance. Interestingly, by projecting the data on a theoretical model of gene expression, we found that the amplification of noise strength associated with codon usage is comparable to that of the TATA box, suggesting that the effect of translation on noise in eukaryotic gene expression might be more prominent than previously appreciated.

Dataset Information

Inverted translational control of eukaryotic gene expression by ribosome collisions.

Publications

Inverted translational control of eukaryotic gene expression by ribosome collisions.

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