Translating mRNAs strongly correlate to proteins in a multivariate manner and their translation ratios are phenotype specific

Abstract

As a well-known phenomenon, total mRNAs poorly correlate to proteins in their abundances as reported. Recent findings calculated with bivariate models suggested even poorer such correlation, whereas focusing on the translating mRNAs (ribosome nascent-chain complex-bound mRNAs, RNC-mRNAs) subset. In this study, we analysed the relative abundances of mRNAs, RNC-mRNAs and proteins on genome-wide scale, comparing human lung cancer A549 and H1299 cells with normal human bronchial epithelial (HBE) cells, respectively. As discovered, a strong correlation between RNC-mRNAs and proteins in their relative abundances could be established through a multivariate linear model by integrating the mRNA length as a key factor. The R2 reached 0.94 and 0.97 in A549 versus HBE and H1299 versus HBE comparisons, respectively. This correlation highlighted that the mRNA length significantly contributes to the translational modulation, especially to the translational initiation, favoured by its correlation with the mRNA translation ratio (TR) as observed. We found TR is highly phenotype specific, which was substantiated by both pathway analysis and biased TRs of the splice variants of BDP1 gene, which is a key transcription factor of transfer RNAs. These findings revealed, for the first time, the intrinsic and genome-wide translation modulations at translatomic level in human cells at steady-state, which are tightly correlated to the protein abundance and functionally relevant to cellular phenotypes.

Prediction of survival of diffuse large B‐cell lymphoma patients via the expression of three inflammatory genes

“…Previous studies demonstrated the abundance of translating mRNAs correlated positively with that of proteins, but negatively with the length of mRNAs in tumor cells 32. Since the mRNAs for these five genes were relatively short, we then determined the possibility of using these five proteins to develop a novel predictive survival model.…”

How to discover new proteins—translatome profiling

“…However, transcriptome sequencing still has crucial drawbacks even with substantially developed nextgeneration sequencing techniques (NGS). Numerous studies have found untranslated mRNA species in various human cell lines [15][16][17][18], and these species comprise approximately 5% of the transcriptome [2,14]. Transcriptome sequencing does not distinguish the translating and untranslated RNA.…”

“…To correct the possible annotation errors in genome and to discover new proteins, we proposed a new strategy that analyzes the translating mRNA (translatome sequencing) to find the translating evidence of known and unknown coding genes [14,19]. It is known that proteins are synthesized via translation, therefore the translating evidence of proteins, obtained by translatome sequencing and sequence analysis, can be a sensitive method to find new proteins.…”

“…This method purifies ribosome-nascent-chain complex (RNC) and sequences the mRNA in this fraction. The RNC purification is the key step that can be performed efficiently using sucrose gradient ultracentrifugation [14]. Under steady-state, protein species can exist only via translation, i.e., translating mRNAs most likely correspond to proteins.…”

“…The high sensitivity of NGS ensures high identification efficiency. Under normal sequencing throughput (10-20 million reads), it is possible to identify and quantify more than 14000 RNC-mRNA in RefSeq database from single human cell lines, by using highly accurate FANSe series mapping algorithm [14,19,20]. The power of quantification and identification exceeds the mass spectrometry considerably at low cost.…”