Codon usage variability determines the correlation between proteome and transcriptome fold changes
Artikel i vetenskaplig tidskrift, 2011

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.

eukaryotic translation





yeast proteome

translational selection


messenger-rna expression



Roberto Olivares Hernandez

Chalmers, Kemi- och bioteknik, Livsvetenskaper

Sergio Velasco

Chalmers, Kemi- och bioteknik, Livsvetenskaper

Jens B Nielsen

Chalmers, Kemi- och bioteknik, Livsvetenskaper

BMC Systems Biology

1752-0509 (eISSN)

Vol. 5 33


Biologiska vetenskaper


Livsvetenskaper och teknik (2010-2018)



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