Translational efficiency across healthy and tumor tissues is proliferation-related.

Xavier Hernandez‐Alias,Luis Serrano,Martin H Schaefer,Hannah Benisty

doi:10.15252/msb.202010097

Abstract

Translational efficiency across healthy and tumor tissues is proliferation-related.

Highlights

In the light of the genetic code, multiple 3-letter combinations of nucleotides in the mRNA can give rise to the same amino acid, which are known as synonymous codons
In order to test how accurately we can extract tRNA abundance information contained in small RNA sequencing data, we re-analyze four publicly available datasets of the cell line HEK293 (Flores et al, 2014a; Data ref: Flores et al, 2014b; Mefferd et al, 2015a; Data ref: Mefferd et al, 2015b; Torres et al, 2015a; Data ref: Torres et al, 2015b,c)
We cannot exclude the presence of tRNA-derived fragments in small RNA-seq datasets (Torres et al, 2019), we found that no differences between reads with or without mismatches are found when compared to tRNAseq protocols in which tRFs are removed before sequencing

Summary

Introduction

In the light of the genetic code, multiple 3-letter combinations of nucleotides in the mRNA can give rise to the same amino acid, which are known as synonymous codons. The non-uniform abundance of synonymous codons across different tissues and among distinct functional gene sets has been proposed as an adaptive mechanism of gene expression regulation (Najafabadi et al, 2009), linked to the proliferative state (Gingold et al, 2014). The 61 amino-acid-coding codons need to be recognized by 46 different tRNA isoacceptors distributed across 428 Pol-III-transcribed tRNA genes (Chan & Lowe, 2016), requiring wobble interactions (non-Watson-Crick base pairing). This complexity of the tRNA repertoire is further enhanced by an average of 11–13 base modifications per tRNA and all possible combinations thereof (Schimmel, 2018). It has been established that different conditions and tissues showcase distinct tRNA abundances (Dittmar et al, 2006; Gingold et al, 2014) and codon usages (Najafabadi et al, 2009; Waldman et al, 2010)

Methods

Results

Conclusion