Post-transcriptional Expression Regulation in the Yeast Saccharomyces cerevisiae on a Genomic Scale

Beyer, Andreas; Hollunder, Jens; Nasheuer, Heinz-Peter; Wilhelm, Thomas

doi:10.1074/mcp.m400099-mcp200

Cited by 216 publications

(246 citation statements)

References 32 publications

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“…S2 and S3). Because we do not have ribosome occupancy datasets to estimate protein production rates for most organisms, we estimated Δt ij and μ i /μ j using mRNA abundances (5,30) as proxies for protein production rate ϕ. We found a very high correlation between parameters estimated using mRNA abundances and protein production rates (ρ > 0.97; Figs.…”

Section: Model Fit Vs Model Predictionsmentioning

confidence: 97%

Explaining complex codon usage patterns with selection for translational efficiency, mutation bias, and genetic drift

Shah

Gilchrist

2011

Proc. Natl. Acad. Sci. U.S.A.

151

180

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The genetic code is redundant with most amino acids using multiple codons. In many organisms, codon usage is biased toward particular codons. Understanding the adaptive and nonadaptive forces driving the evolution of codon usage bias (CUB) has been an area of intense focus and debate in the fields of molecular and evolutionary biology. However, their relative importance in shaping genomic patterns of CUB remains unsolved. Using a nested model of protein translation and population genetics, we show that observed gene level variation of CUB in Saccharomyces cerevisiae can be explained almost entirely by selection for efficient ribosomal usage, genetic drift, and biased mutation. The correlation between observed codon counts within individual genes and our model predictions is 0.96. Although a variety of factors shape patterns of CUB at the level of individual sites within genes, our results suggest that selection for efficient ribosome usage is a central force in shaping codon usage at the genomic scale. In addition, our model allows direct estimation of codon-specific mutation rates and elongation times and can be readily applied to any organism with highthroughput expression datasets. More generally, we have developed a natural framework for integrating models of molecular processes to population genetics models to quantitatively estimate parameters underlying fundamental biological processes, such a protein translation.ribosome overhead cost | protein production rate F or many organisms, the preferential usage of certain codons, commonly referred to as codon usage bias (CUB), is strongly correlated with corresponding tRNA abundances and expression levels (1, 2). Explanations for these correlations abound; the most favored ones include selection against translational errors (3-5), selection for translational efficiency (6-8), effects on protein folding (9), and stability of mRNA secondary structures (10, 11). Because different combinations of these factors could lead to very similar patterns of codon usage, their relative importance in shaping the evolution of CUB is unknown (10, 12, 13). We believe that this uncertainty over their relative importance is, in large part, attributable to a lack of mechanistic models of processes hypothesized to give rise to these patterns (exceptions are found in refs. 5, 6, 13, and 14). Although most theories of codon usage predict that the degree of bias in codon usage should increase with gene expression (1, 4, 15), they lack any specific quantitative predictions about the rate and nature of these changes. This is because most commonly used indices of CUB, such as frequency of optimal codons (F op ) (1), codon adaptation index (CAI) (16), and codon bias index (CBI) (17), are both heuristic and aggregate measures of CUB and fail to define explicitly the factors responsible for the evolution of CUB. In contrast, we show that a mechanistic model of protein translation that explicitly includes the effects of biased mutation, genetic drift, and selection for efficient ribosome usage...

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Section: Model Fit Vs Model Predictionsmentioning

confidence: 97%

Explaining complex codon usage patterns with selection for translational efficiency, mutation bias, and genetic drift

Shah

Gilchrist

2011

Proc. Natl. Acad. Sci. U.S.A.

151

180

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“…(1) mRNA abundance at rich media, which was averaged over multiple studies (Beyer et al 2004) and log 2 -transformed. (2) Transcriptional plasticity, which was estimated as the average of the squared log 2 expression ratio from a large number of microarray experiments (Ihmels et al 2002).…”

Section: Gene Expressionmentioning

confidence: 99%

Two strategies for gene regulation by promoter nucleosomes

Tirosh

Barkai²

2008

Genome Res.

368

481

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Chromatin structure is central for the regulation of gene expression, but its genome-wide organization is only beginning to be understood. Here, we examine the connection between patterns of nucleosome occupancy and the capacity to modulate gene expression upon changing conditions, i.e., transcriptional plasticity. By analyzing genome-wide data of nucleosome positioning in yeast, we find that the presence of nucleosomes close to the transcription start site is associated with high transcriptional plasticity, while nucleosomes at more distant upstream positions are negatively correlated with transcriptional plasticity. Based on this, we identify two typical promoter structures associated with low or high plasticity, respectively. The first class is characterized by a relatively large nucleosome-free region close to the start site coupled with well-positioned nucleosomes further upstream, whereas the second class displays a more evenly distributed and dynamic nucleosome positioning, with high occupancy close to the start site. The two classes are further distinguished by multiple promoter features, including histone turnover, binding site locations, H2A.Z occupancy, expression noise, and expression diversity. Analysis of nucleosome positioning in human promoters reproduces the main observations. Our results suggest two distinct strategies for gene regulation by chromatin, which are selectively employed by different genes.

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“…translational regulation and differences in protein in vivo half-lives (Greenbaum et al, 2003;Beyer et al, 2004). Furthermore changes on phosphorylation cascades as well as posttranslational modifications and metabolites can not be detected.…”

Section: Nephrotoxicitymentioning

confidence: 99%

Identification of genes involved in gentamicin-induced nephrotoxicity in rats – A toxicogenomic investigation

Matheis

Gamber

et al. 2010

Experimental and Toxicologic Pathology

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To cite this version:N. Ozaki, K.A. Matheis, M. Gamber, T. Feidl, T. Nolte, et al.. Identification of genes involved in gentamicin induced nephrotoxicity in rats-a toxicogenomic investigation. Experimental and Toxicologic Pathology, Elsevier, 2010, 62 (5) This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting galley proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. A c c e p t e d m a n u s c r i p t

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Post-transcriptional Expression Regulation in the Yeast Saccharomyces cerevisiae on a Genomic Scale

Cited by 216 publications

References 32 publications

Explaining complex codon usage patterns with selection for translational efficiency, mutation bias, and genetic drift

Explaining complex codon usage patterns with selection for translational efficiency, mutation bias, and genetic drift

Two strategies for gene regulation by promoter nucleosomes

Identification of genes involved in gentamicin-induced nephrotoxicity in rats – A toxicogenomic investigation

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