Determinants of Protein Abundance and Translation Efficiency in S. cerevisiae

The translation efficiency of most Saccharomyces cerevisiae genes remains fairly constant across poor and rich growth media. This observation has led us to revisit the available data and to examine the potential utility of a protein abundance predictor in reinterpreting existing mRNA expression data. Our predictor is based on large-scale data of mRNA levels, the tRNA adaptation index, and the evolutionary rate. It attains a correlation of 0.76 with experimentally determined protein abundance levels on unseen data and successfully cross-predicts protein abundance levels in another yeast species (Schizosaccharomyces pombe). The predicted abundance levels of proteins in known S. cerevisiae complexes, and of interacting proteins, are significantly more coherent than their corresponding mRNA expression levels. Analysis of gene expression measurement experiments using the predicted protein abundance levels yields new insights that are not readily discernable when clustering the corresponding mRNA expression levels. Comparing protein abundance levels across poor and rich media, we find a general trend for homeostatic regulation where transcription and translation change in a reciprocal manner. This phenomenon is more prominent near origins of replications. Our analysis shows that in parallel to the adaptation occurring at the tRNA level via the codon bias, proteins do undergo a complementary adaptation at the amino acid level to further increase their abundance

Regular Patterns for Proteome-Wide Distribution of Protein Abundance across Species

A proteome of the bio-entity, including cell, tissue, organ, and organism, consists of proteins of diverse abundance. The principle that determines the abundance of different proteins in a proteome is of fundamental significance for an understanding of the building blocks of the bio-entity. Here, we report three regular patterns in the proteome-wide distribution of protein abundance across species such as human, mouse, fly, worm, yeast, and bacteria: in most cases, protein abundance is positively correlated with the protein's origination time or sequence conservation during evolution; it is negatively correlated with the protein's domain number and positively correlated with domain coverage in protein structure, and the correlations became stronger during the course of evolution; protein abundance can be further stratified by the function of the protein, whereby proteins that act on material conversion and transportation (mass category) are more abundant than those that act on information modulation (information category). Thus, protein abundance is intrinsically related to the protein's inherent characters of evolution, structure, and function

Codon usage variability determines the correlation between proteome and transcriptome fold changes

Background: The availability of high throughput experimental methods has made possible to observe the relationships between proteome and transcirptome. The protein abundances show a positive but weak correlation with the concentrations of their cognate mRNAs. This weak correlation implies that there are other crucial effects involved in the regulation of protein translation, different from the sole availability of mRNA. It is well known that ribosome and tRNA concentrations are sources of variation in protein levels. Thus, by using integrated analysis of omics data, genomic information, transcriptome and proteome, we aim to unravel important variables affecting translation. Results: We identified how much of the variability in the correlation between protein and mRNA concentrations can be attributed to the gene codon frequencies. We propose the hypothesis that the influence of codon frequency is due to the competition of cognate and near-cognate tRNA binding; which in turn is a function of the tRNA concentrations. Transcriptome and proteome data were combined in two analytical steps; first, we used Self-Organizing Maps (SOM) to identify similarities among genes, based on their codon frequencies, grouping them into different clusters; and second, we calculated the variance in the protein mRNA correlation in the sampled genes from each cluster. This procedure is justified within a mathematical framework. Conclusions: With the proposed method we observed that in all the six studied cases most of the variability in the relation protein-transcript could be explained by the variation in codon composition

Analysis and Prediction of Translation Rate Based on Sequence and Functional Features of the mRNA

Protein concentrations depend not only on the mRNA level, but also on the translation rate and the degradation rate. Prediction of mRNA's translation rate would provide valuable information for in-depth understanding of the translation mechanism and dynamic proteome. In this study, we developed a new computational model to predict the translation rate, featured by (1) integrating various sequence-derived and functional features, (2) applying the maximum relevance & minimum redundancy method and incremental feature selection to select features to optimize the prediction model, and (3) being able to predict the translation rate of RNA into high or low translation rate category. The prediction accuracies under rich and starvation condition were 68.8% and 70.0%, respectively, evaluated by jackknife cross-validation. It was found that the following features were correlated with translation rate: codon usage frequency, some gene ontology enrichment scores, number of RNA binding proteins known to bind its mRNA product, coding sequence length, protein abundance and 5′UTR free energy. These findings might provide useful information for understanding the mechanisms of translation and dynamic proteome. Our translation rate prediction model might become a high throughput tool for annotating the translation rate of mRNAs in large-scale

Bicodon bias can determine the role of synonymous SNPs in human diseases

Background: For a long time synonymous single nucleotide polymorphisms were considered as silent mutations. However, nowadays it is well known that they can affect protein conformation and function, leading to altered disease susceptibilities, differential prognosis and/or drug responses, among other clinically relevant genetic traits. This occurs through different mechanisms: by disrupting the splicing signals of precursor mRNAs, affecting regulatory binding-sites of transcription factors and miRNAs, or by modifying the secondary structure of mRNAs. Results: In this paper we considered 22 human genetic diseases or traits, linked to 35 synonymous single nucleotide polymorphisms in 27 different genes. We performed a local sequence context analysis in terms of the ribosomal pause propensity affected by synonymous single nucleotide polymorphisms. We found that synonymous mutations related to the above mentioned mechanisms presented small pause propensity changes, whereas synonymous mutations that were not related to those mechanisms presented large pause propensity changes. On the other hand, we did not observe large variations in the codon usage of codons associated with these mutations. Furthermore, we showed that the changes in the pause propensity associated with benign sSNPs are significantly lower than the pause propensity changes related to sSNPs associated to diseases. Conclusions: These results suggest that the genetic diseases or traits related to synonymous mutations with large pause propensity changes, could be the consequence of another mechanism underlying non-silent synonymous mutations. Namely, alternative protein configuration related, in turn, to alterations in the ribosome-mediated translational attenuation program encoded by pairs of consecutive codons, not codons. These findings shed light on the latter mechanism based on the perturbation of the co-translational folding process.Facultad de Ciencias ExactasCentro Regional de Estudios Genómico