The Taub Faculty of Computer Science Events and Talks

Translation Efficiency (TE) is a basic process of favoring codons with higher levels of tRNAs. In this talk, I will survey three recent results that are related to studying TE from a computational/systems biological aspect:

a) TE in humans is efficient: It is believed that in many unicellular organisms codon bias has evolved to optimize TE. Previous studies, however, have left the question of TE in humans an intriguingly open one. We perform the first large scale tissue-specific analysis of TE in human tissues, using the tRNA Adaptation Index (tAI) as a measure for a gene's TE and tissue specific gene expression levels. We find that a gene's TE is correlated with its expression levels and with its functional importance (expression breadth across tissues, its evolutionary rate, degree in protein interaction network and its length). Optimization based analysis shows that the tRNA pool – codon bias co-adaptation is globally (rather than tissue- specific) driven. Taken together, our results indicate that codon bias has an important role in humans, making gene translation efficient.

b)P53 cancerous mutations exhibit selection for translation efficiency: The tumor suppressor P53 is known to be a key regulator in cancer, and more than half of human cancers exhibit mutations in this gene. Recent evidence shows that point mutations in P53 not only disrupt its function but also posses gain of function and dominant negative effects on wild type copies, thus making the mutated gene an oncogene. This hence brings about the possibility that P53 mutations may be under selection for increasing the overall TE of the P53 gene. Here we perform the first large scale analysis of TE in mutated P53 variants found in human cancer tissues, identifying a significant increase in TE that is correlated with the frequency of P53 mutations. Furthermore, mutations with a known oncogenic effect significantly increase their TE compared with the other P53 mutations. In addition, we find that P53 mutations have significantly higher TE increase in progressive vs. primary tumors. Analyzing P53 NCI-60 cell lines points to a co-adaptation between the mutations and the tRNA pool, increasing the overall P53 TE. These results indicate that TE may be a selection force shaping P53 cancerous mutations, thus encouraging further analysis in P53 and other cancer related genes.

c) A universal translation efficiency profile of proteins: We report a universally conserved profile of TE of proteins based on computing the adaptation between coding sequences and the tRNA pool. In this profile the first ~50 codons of genes show a strong preference towards rare tRNA, thus deduced to be translated with low efficiency, while in most species the last codons show highest efficiency. Whereas both tRNA pools and codon preferences change across species, their co-evolution appears to preserve the universal profile, indicating selection acting to preserve the profile. The profile enables to predict measured ribosomal density profile in yeast – areas of low TE correspond to high density, suggesting that ribosome density is encoded in genes’ sequences. Mathematical modeling suggests that the observed profile is optimal as it minimizes the consumption of free ribosomes and ribosomal traffic jams along transcript.

The talk is self-contained and requires no prior knowledge in Biology.

Host: Ron Pinter