Proliferation specific codon usage facilitates oncogene translation

Benisty, Hannah; Weber, Marc; Hernandez‐Alias, Xavier; Schaefer, Martin H.; Serrano, Luís

doi:10.1101/695957

Cited by 2 publications

(7 citation statements)

References 54 publications

(69 reference statements)

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“…On the level of translational efficiency, in agreement with previous studies (preprint: Benisty et al , 2019a; Gingold et al , ), we detect that the proliferative state is the major determinant of SDA differences both across healthy tissues and in cancer. Moreover, in contrast to recent work challenging the tissue specificity of codon–anticodon co‐adaptation in human (Rudolph et al , ; Eraslan et al , ), our data here support the idea that tissue‐specific SDAw have functional implications on the tissue phenotype (e.g., in favoring neural differentiation in brain or abnormal proliferation in cancer).…”

Section: Discussionsupporting

confidence: 92%

“…In contrast to previous studies analyzing tRNA expression from small RNA‐seq data (Zhang et al , 2018, 2019), we use a computational pipeline specifically developed for the accurate mapping of tRNA reads (Hoffmann et al , 2018) in order to quantify all different isoacceptor species (Fig 1A, see Materials and Methods). To validate the accuracy of these small RNA‐seq quantifications, we retrieve four datasets of well‐established tRNA sequencing methods (Hydro‐tRNAseq and demethylase‐tRNAseq) applied to the same cell type (Zheng et al , 2015a; Data ref: Zheng et al , 2015b; Gogakos et al , 2017a; Data ref: Gogakos et al , 2017b; Mattijssen et al , 2017a; Data ref: Mattijssen et al , 2017b; preprint: Benisty et al , 2019a; Data ref: Benisty et al , 2019b), which autocorrelate in the range of 0.75–0.85 among themselves (Table EV1, Fig EV1A). In comparison, our four HEK293 small RNA‐seq quantifications show an average Spearman correlation against these four conventional datasets of 0.73.…”

Section: Resultsmentioning

confidence: 99%

“… The Spearman correlations between all HEK293 tRNA quantifications, including four small RNA‐seq datasets (Data ref: Flores et al , 2014b; Data ref: Mefferd et al , 2015b; Data ref: Torres et al , 2015b,c), four well‐established tRNAseq datasets using Hydro‐tRNAseq and DM‐tRNAseq (Data ref: Zheng et al , 2015b; Data ref: Gogakos et al , 2017b; Data ref: Mattijssen et al , 2017b; Data ref: Benisty et al , 2019b), and one previously published quantification from small RNA‐seq data by Zhang et al (2018). The scatter plots are square‐root-normalized for better visualization. Principal component analysis (PCA) and linear discriminant analysis (LDA) of all absolute tRNA quantifications of HCT116, HELA, HEK293, and BJ fibroblasts, both from small RNA‐seq and from Hydro‐tRNAseq data.…”

Section: Resultsmentioning

confidence: 99%

“…The resulting cDNA was purified using a QIAQuick PCR Purification Kit and sequenced on Illumina HiSeq 2500 platform in 50 bp paired‐end format. Hydro‐tRNAseq data of HCT116, MDA‐MB‐231, and fibroblast BJ/hTERT have been generated in this study, while sequencing data from HEK293 and HeLa had been previously published (Data ref: Benisty et al , 2019b).…”

Section: Methodsmentioning

confidence: 99%

“…The resulting cDNA was purified using a QIAQuick PCR Purification Kit and sequenced on Illumina HiSeq 2500 platform in 50 bp paired-end format. Hydro-tRNAseq data of HCT116, MDA-MB-231, and fibroblast BJ/hTERT have been generated in this study, while sequencing data from HEK293 and HeLa had been previously published (Data ref: Benisty et al, 2019b). From all five cell lines, the isoacceptor abundances of MDA-MB-231 yielded a median of 3-5 times higher standard deviation than the other Hydro-tRNAseq quantifications (Table EV2), thus suggesting some technical problem with this cell line.…”

Section: Rna Extractionmentioning

confidence: 99%

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Translational efficiency across healthy and tumor tissues is proliferation‐related

Hernandez‐Alias

Benisty

Schaefer

et al. 2020

Molecular Systems Biology

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Different tissues express genes with particular codon usage and anticodon tRNA repertoires. However, the codon–anticodon co‐adaptation in humans is not completely understood, nor is its effect on tissue‐specific protein levels. Here, we first validated the accuracy of small RNA‐seq for tRNA quantification across five human cell lines. We then analyzed the tRNA abundance of more than 8,000 tumor samples from TCGA, together with their paired mRNA‐seq and proteomics data, to determine the Supply‐to‐Demand Adaptation. We thereby elucidate that the dynamic adaptation of the tRNA pool is largely related to the proliferative state across tissues. The distribution of such tRNA pools over the whole cellular translatome affects the subsequent translational efficiency, which functionally determines a condition‐specific expression program both in healthy and tumor states. Furthermore, the aberrant translational efficiency of some codons in cancer, exemplified by ProCCA and GlyGGT, is associated with poor patient survival. The regulation of these tRNA profiles is partly explained by the tRNA gene copy numbers and their promoter DNA methylation.

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Section: Discussionsupporting

confidence: 92%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Rna Extractionmentioning

confidence: 99%

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Translational efficiency across healthy and tumor tissues is proliferation‐related

Hernandez‐Alias

Benisty

Schaefer

et al. 2020

Molecular Systems Biology

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Translational efficiency across healthy and tumor tissues is proliferation-related

Hernandez‐Alias

Benisty

Schaefer

et al. 2019

Preprint

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11Background: Different tissues express genes with particular codon usage and anticodon 12 tRNA repertoires. However, the codon-anticodon co-adaptation in humans is not completely 13 understood, as well as its effect on tissue-specific protein levels. 14 Results:We first validated the accuracy of small RNA-seq for tRNA quantification across 15 five human cell lines. We then analyzed tRNA expression in more than 8000 tumor samples 16 from TCGA, together with their paired mRNA-seq and proteomics data, to determine the 17 Relative Translation Efficiency. We thereby elucidate that the dynamic adaptation of the 18 tRNA pool is largely related to the proliferative state across tissues, which determines tissue-19 specific translation efficiency. Furthermore, the aberrant translational efficiency of ProCCA 20 and GlyGGT in cancer, among other codons, which is partly regulated by the tRNA gene 21 copy numbers and their promoter DNA methylation, is associated with poor patient survival. 22Conclusions: The distribution of tissue-specific tRNA pools over the whole cellular 23 translatome affects the subsequent translational efficiency, which functionally determines a 24 condition-specific expression program in tissues both in healthy and tumor states. 25 (14)(15)(16)(17)(18). In this context, The Cancer Genome Atlas (TCGA) has been recently used to 52 investigate the alteration of tRNA gene expression and translational machinery in cancer, 53 which may play a role in driving aberrant translation (19,20). 54To validate the use of small RNA-seq for tRNA quantification, we first compare tRNA levels 55 determined in HEK293 by well-established tRNA sequencing methods (Hydro-tRNAseq and 56 demethylase-tRNA-seq) (12,13,21), with those obtained by small RNA-seq. Then we 57 quantify the tRNA repertoire of five cell lines using Hydro-tRNAseq and perform small RNA-58 seq in parallel. Comparison of the tRNA abundance obtained by both approaches shows that 59 it is possible to accurately estimate relative tRNA abundance of cells and tissues using small 60 RNA-seq. Furthermore, we show that both types of quantification are informative enough to 61 distinguish between the five analyzed human cell lines covering multiple tissue types. In 62 consequence, we apply a tRNA-specific computational pipeline to re-analyze 8,534 small 63 RNA-seq datasets from TCGA (22). We find that the tissue-specificity of tRNA expression is 64 largely proliferation-related, even within healthy tissues. The tRNA quantification of TCGA 65 samples enables their comparison with paired and publicly available mRNA-seq, proteomic, 66 DNA methylation and copy number data, which underscores the role of tRNAs in globally 67 controlling a condition-specific translational program. We discover multiple codons, including 68ProCCA and GlyGGT, whose translational efficiency is compromised and leads to poor 69 prognosis in cancer. Finally, promoter DNA methylation and tRNA gene copy number arise 70 as two regulatory mechanisms controlling tRNA gene expression in cancer. 71 RESULT...

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Proliferation specific codon usage facilitates oncogene translation

Cited by 2 publications

References 54 publications

Translational efficiency across healthy and tumor tissues is proliferation‐related

Translational efficiency across healthy and tumor tissues is proliferation‐related

Translational efficiency across healthy and tumor tissues is proliferation-related

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