A Comprehensive, Quantitative, and Genome-Wide Model of Translation

Translation is still poorly characterised at the level of individual proteins and its role in regulation of gene expression has been constantly underestimated. To better understand the process of protein synthesis we developed a comprehensive and quantitative model of translation, characterising protein synthesis separately for individual genes. The main advantage of the model is that basing it on only a few datasets and general assumptions allows the calculation of many important translational parameters, which are extremely difficult to measure experimentally. In the model, each gene is attributed with a set of translational parameters, namely the absolute number of transcripts, ribosome density, mean codon translation time, total transcript translation time, total time required for translation initiation and elongation, translation initiation rate, mean mRNA lifetime, and absolute number of proteins produced by gene transcripts. Most parameters were calculated based on only one experimental dataset of genome-wide ribosome profiling. The model was implemented in Saccharomyces cerevisiae, and its results were compared with available data, yielding reasonably good correlations. The calculated coefficients were used to perform a global analysis of translation in yeast, revealing some interesting aspects of the process. We have shown that two commonly used measures of translation efficiency – ribosome density and number of protein molecules produced – are affected by two distinct factors. High values of both measures are caused, i.a., by very short times of translation initiation, however, the origins of initiation time reduction are completely different in both cases. The model is universal and can be applied to any organism, if the necessary input data are available. The model allows us to better integrate transcriptomic and proteomic data. A few other possibilities of the model utilisation are discussed concerning the example of the yeast system

Co-regulation of translation in protein complexes

Background Co-regulation of gene expression has been known for many years, and studied widely both globally and for individual genes. Nevertheless, most analyses concerned transcriptional control, which in case of physically interacting proteins and protein complex subunits may be of secondary importance. This research is the first quantitative analysis that provides global-scale evidence for translation co-regulation among associated proteins. Results By analyzing the results of our previous quantitative model of translation, we have demonstrated that protein production rates plus several other translational parameters, such as mRNA and protein abundance, or number of produced proteins from a gene, are well concerted between stable complex subunits and party hubs. This may be energetically favorable during synthesis of complex building blocks and ensure their accurate production in time. In contrast, for connections with regulatory particles and date hubs translational co-regulation is less visible, indicating that in these cases maintenance of accurate levels of interacting particles is not necessarily beneficial. Conclusions Similar results obtained for distantly related model organisms, Saccharomyces cerevisiae and Homo sapiens, suggest that the phenomenon of translational co-regulation applies to the variety of living organisms and concerns many complex constituents. This phenomenon was also observed among the set of functionally linked proteins from Escherichia coli operons. This leads to the conclusion that translational regulation of a protein should always be studied with respect to the expression of its primary interacting partners. Reviewers This article was reviewed by Sandor Pongor and Claus Wilke

Calculated protein abundance vs experimental studies.

<p>Correlations between protein abundances calculated in our model (as times ) and those obtained in experimental studies <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0073943#pone.0073943-Newman1" target="_blank">[8]</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0073943#pone.0073943-Lu1" target="_blank">[11]</a>–<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0073943#pone.0073943-Nagaraj1" target="_blank">[13]</a>; n – sample size, – Spearman correlation coefficient and its 95% confidence interval.</p

The summary of translational parameters calculated in the model.

<p>Column description: () transcript length; () number of gene transcripts; () number of proteins produced from one transcript; () ribosome density in number of ribosomes per 100 codons; () number of ribosomes on a transcript; () initiation time in s; () elongation time in s; () mean elongation time of one transcript codon in ms; and () mean transcript lifetime in min (bacteria, yeast), or in h (humans). For all parameters, except and , the rows 1–15 were calculated for 1738, 4470, and 7494 genes for bacteria, yeast, and humans, respectively. For parameter and , the rows were calculated for 1574, 3425, and 6205 genes, respectively.</p

Summary of data sets and variables used as an input of the model.

<p>Details on data parsing and calculations may be found in the main text. Cell lines and growth conditions (temperature and medium) denote those used in the ribosome profiling experiments. The numbers marked by an asterix were taken from the RNA Tools and Calculators section at the Invitrogen Website (<a href="http://www.invitrogen.com" target="_blank">www.invitrogen.com</a>, accessed April 2013). The coding sequences were downloaded from the following databases: NCBI (<a href="http://www.ncbi.nlm.nih.gov.ftp" target="_blank">www.ncbi.nlm.nih.gov.ftp</a>, accessed May 2012), SGD (<a href="http://www.yeastgenome.org" target="_blank">www.yeastgenome.org</a>, accessed June 2009), and UCSC (<a href="http://genome.ucsc.edu" target="_blank">http://genome.ucsc.edu</a>, accessed July 2012).</p

Translation speed plot generated by Transimulation.

<p>An example plot of translation speed (in aa/sec) in relation to the coding sequence of one of the <i>E.coli</i> genes. To facilitate analysis, the plot was smoothed by calculating translation speed over a 10-codon sliding window. Similar plots for window sizes of 1, 2, 5, 10, 20, 30, and 50 codons are generated for all analyzed genes and sequences uploaded by the user.</p

Transimulation - protein biosynthesis web service

Although translation is the key step during gene expression, it remains poorly characterized at the level of individual genes. For this reason, we developed Transimulation - a web service measuring translational activity of genes in three model organisms: Escherichia coli, Saccharomyces cerevisiae and Homo sapiens. The calculations are based on our previous computational model of translation and experimental data sets. Transimulation quantifies mean translation initiation and elongation time (expressed in SI units), and the number of proteins produced per transcript. It also approximates the number of ribosomes that typically occupy a transcript during translation, and simulates their propagation. The simulation of ribosomes' movement is interactive and allows modifying the coding sequence on the fly. It also enables uploading any coding sequence and simulating its translation in one of three model organisms. In such a case, ribosomes propagate according to mean codon elongation times of the host organism, which may prove useful for heterologous expression. Transimulation was used to examine evolutionary conservation of translational parameters of orthologous genes. Transimulation may be accessed at http://nexus.ibb.waw.pl/Transimulation (requires Java version 1.7 or higher). Its manual and source code, distributed under the GPL-2.0 license, is freely available at the website