Abstract
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.
Highlights
The stochastic nature of gene expression promotes cell-to-cell differences in protein level, usually referred to as noise [1,2,3]
The sources of variation in gene expression in an isogenic cell population are typically divided into two basic groups: (i) the intrinsic noise attributed to the inherent stochasticity of expression processes, and (ii) the extrinsic noise resulting from variation in cell state related to cell-cycle progression, cell size, subtle environmental differences, and other stochastic events that are external to the system – in this case external to the process of expression of an individual gene [1,23,24,25,26,27,28]
The stochastic nature of gene expression leads to cell-tocell differences in protein level referred to as noise
Summary
The stochastic nature of gene expression promotes cell-to-cell differences in protein level, usually referred to as noise [1,2,3]. Recent studies, both experimental and computational, have revealed that such cell-to-cell variability can be both disadvantageous [4,5,6,7,8], as variations in protein level might negatively affect the precision of signaling and regulation, and advantageous [9,10,11,12,13,14], by enabling heterogeneous stress-response programs to environmental changes [10]. Basic factors can be gleaned from correlations between noise level
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