Translational efficiency across healthy and tumor tissues is proliferation‐related

Background Different tissues express genes with particular codon usage and anticodon tRNA repertoires. However, the codon-anticodon co-adaptation in humans is not completely understood, as well as its effect on tissue-specific protein levels. Results We first validated the accuracy of small RNA-seq for tRNA quantification across five human cell lines. We then analyzed tRNA expression in more than 8000 tumor samples from TCGA, together with their paired mRNA-seq and proteomics data, to determine the Relative Translation Efficiency. We thereby elucidate that the dynamic adaptation of the tRNA pool is largely related to the proliferative state across tissues, which determines tissue-specific translation efficiency. Furthermore, the aberrant translational efficiency of ProCCA and GlyGGT in cancer, among other codons, which is partly regulated by the tRNA gene copy numbers and their promoter DNA methylation, is associated with poor patient survival. Conclusions The distribution of tissue-specific tRNA pools over the whole cellular translatome affects the subsequent translational efficiency, which functionally determines a condition-specific expression program in tissues both in healthy and tumor states.

[1]  A. Guo,et al.  tRic: a user-friendly data portal to explore the expression landscape of tRNAs in human cancers , 2019, RNA biology.

[2]  Martin H. Schaefer,et al.  Proliferation specific codon usage facilitates oncogene translation , 2019, bioRxiv.

[3]  E. Fornasiero,et al.  Pathological changes are associated with shifts in the employment of synonymous codons at the transcriptome level , 2019, BMC Genomics.

[4]  Luis V. Santana-Quintero,et al.  Codon and Codon-Pair Usage Tables (CoCoPUTs): Facilitating Genetic Variation Analyses and Recombinant Gene Design. , 2019, Journal of molecular biology.

[5]  S. Dietmann,et al.  Codon usage optimization in pluripotent embryonic stem cells , 2019, Genome Biology.

[6]  Aristeidis G. Telonis,et al.  tRNA Fragments Show Intertwining with mRNAs of Specific Repeat Content and Have Links to Disparities. , 2019, Cancer research.

[7]  O. Reina,et al.  Differential expression of human tRNA genes drives the abundance of tRNA-derived fragments , 2019, Proceedings of the National Academy of Sciences.

[8]  Patrick O'Donoghue,et al.  Pathways to disease from natural variations in human cytoplasmic tRNAs , 2019, The Journal of Biological Chemistry.

[9]  J. Weinstein,et al.  Global analysis of tRNA and translation factor expression reveals a dynamic landscape of translational regulation in human cancers , 2018, Communications Biology.

[10]  Y. Pilpel,et al.  Repertoires of tRNAs: The Couplers of Genomics and Proteomics. , 2018, Annual review of cell and developmental biology.

[11]  Thomas A. Hopf,et al.  Quantification and discovery of sequence determinants of protein‐per‐mRNA amount in 29 human tissues , 2018, bioRxiv.

[12]  E. O’Shea,et al.  Translational control through differential ribosome pausing during amino acid limitation in mammalian cells , 2018, bioRxiv.

[13]  Christopher J. Gregg,et al.  Codon usage of highly expressed genes affects proteome-wide translation efficiency , 2018, Proceedings of the National Academy of Sciences.

[14]  Peter F. Stadler,et al.  Accurate mapping of tRNA reads , 2018, Bioinformatics.

[15]  Tao Pan,et al.  Modifications and functional genomics of human transfer RNA , 2018, Cell Research.

[16]  Hui Shen,et al.  DNA methylation loss in late-replicating domains is linked to mitotic cell division , 2018, Nature Genetics.

[17]  J. Coller,et al.  Codon optimality, bias and usage in translation and mRNA decay , 2017, Nature Reviews Molecular Cell Biology.

[18]  M. Hafner,et al.  LARP4 mRNA codon-tRNA match contributes to LARP4 activity for ribosomal protein mRNA poly(A) tail length protection , 2017, eLife.

[19]  Paul Schimmel,et al.  The emerging complexity of the tRNA world: mammalian tRNAs beyond protein synthesis , 2017, Nature Reviews Molecular Cell Biology.

[20]  Luis V. Santana-Quintero,et al.  A new and updated resource for codon usage tables , 2017, BMC Bioinformatics.

[21]  L. Duret,et al.  Recombination, meiotic expression and human codon usage , 2017, eLife.

[22]  Thomas Tuschl,et al.  Characterizing Expression and Processing of Precursor and Mature Human tRNAs by Hydro-tRNAseq and PAR-CLIP. , 2017, Cell reports.

[23]  T. Pan,et al.  Determination of tRNA aminoacylation levels by high-throughput sequencing , 2017, Nucleic acids research.

[24]  Yong Sun Lee,et al.  Epigenetic regulation of noncoding RNA transcription by mammalian RNA polymerase III. , 2017, Epigenomics.

[25]  Henrik Molina,et al.  Modulated Expression of Specific tRNAs Drives Gene Expression and Cancer Progression , 2016, Cell.

[26]  Bianca M. Schmitt,et al.  Codon-Driven Translational Efficiency Is Stable across Diverse Mammalian Cell States , 2016, PLoS genetics.

[27]  Michael L. Gatza,et al.  Proteogenomics connects somatic mutations to signaling in breast cancer , 2016, Nature.

[28]  Reuven Agami,et al.  Tumour-specific proline vulnerability uncovered by differential ribosome codon reading , 2016, Nature.

[29]  Yan Guo,et al.  A micro-RNA expression signature for human NAFLD progression , 2016, Journal of Gastroenterology.

[30]  J. Mesirov,et al.  The Molecular Signatures Database Hallmark Gene Set Collection , 2015 .

[31]  Patricia P. Chan,et al.  GtRNAdb 2.0: an expanded database of transfer RNA genes identified in complete and draft genomes , 2015, Nucleic Acids Res..

[32]  Annette S. Kim,et al.  Transfer RNA detection by small RNA deep sequencing and disease association with myelodysplastic syndromes , 2015, BMC Genomics.

[33]  B. Cullen,et al.  Expression of CRISPR/Cas single guide RNAs using small tRNA promoters , 2015, RNA.

[34]  Steven J. M. Jones,et al.  Large-scale profiling of microRNAs for The Cancer Genome Atlas , 2015, Nucleic acids research.

[35]  Chengqi Yi,et al.  Efficient and quantitative high-throughput transfer RNA sequencing , 2015, Nature Methods.

[36]  J. Gorodkin,et al.  Differential and coherent processing patterns from small RNAs , 2015, Scientific Reports.

[37]  David L. Tabb,et al.  Proteomic analysis of colon and rectal carcinoma using standard and customized databases , 2015, Scientific Data.

[38]  O. Reina,et al.  Inosine modifications in human tRNAs are incorporated at the precursor tRNA level , 2015, Nucleic acids research.

[39]  Sebastian M. Waszak,et al.  A Dual Program for Translation Regulation in Cellular Proliferation and Differentiation , 2014, Cell.

[40]  B. Cullen,et al.  Differential RISC association of endogenous human microRNAs predicts their inhibitory potential , 2014, Nucleic acids research.

[41]  Judith Frydman,et al.  Evolutionary conservation of codon optimality reveals hidden signatures of co-translational folding , 2012, Nature Structural &Molecular Biology.

[42]  Y. Pilpel,et al.  Dynamic changes in translational efficiency are deduced from codon usage of the transcriptome , 2012, Nucleic acids research.

[43]  K. Gunderson,et al.  High density DNA methylation array with single CpG site resolution. , 2011, Genomics.

[44]  Y. Pilpel,et al.  Determinants of translation efficiency and accuracy , 2011, Molecular systems biology.

[45]  M. DePristo,et al.  The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. , 2010, Genome research.

[46]  Reza Salavati,et al.  Universal function-specificity of codon usage , 2009, Nucleic acids research.

[47]  Peter F. Stadler,et al.  Fast Mapping of Short Sequences with Mismatches, Insertions and Deletions Using Index Structures , 2009, PLoS Comput. Biol..

[48]  Gonçalo R. Abecasis,et al.  The Sequence Alignment/Map format and SAMtools , 2009, Bioinform..

[49]  Nicholas T. Ingolia,et al.  Genome-Wide Analysis in Vivo of Translation with Nucleotide Resolution Using Ribosome Profiling , 2009, Science.

[50]  Peter F. Stadler,et al.  tRNAdb 2009: compilation of tRNA sequences and tRNA genes , 2008, Nucleic Acids Res..

[51]  Tao Pan,et al.  Tissue-Specific Differences in Human Transfer RNA Expression , 2006, PLoS genetics.

[52]  J. Mesirov,et al.  From the Cover: Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles , 2005 .

[53]  L. Wernisch,et al.  Unexpected correlations between gene expression and codon usage bias from microarray data for the whole Escherichia coli K-12 genome. , 2003, Nucleic acids research.

[54]  Johannes Gerdes,et al.  The Ki‐67 protein: From the known and the unknown , 2000, Journal of cellular physiology.

[55]  E. Myers,et al.  Basic local alignment search tool. , 1990, Journal of molecular biology.

[56]  D. Besser,et al.  DNA methylation inhibits transcription by RNA polymerase III of a tRNA gene, but not of a 5S rRNA gene , 1990, FEBS letters.

[57]  Patricia P. Chan,et al.  tRNAscan-SE: Searching for tRNA Genes in Genomic Sequences. , 2019, Methods in molecular biology.

[58]  F. Supek.  The Code of Silence: Widespread Associations Between Synonymous Codon Biases and Gene Function , 2015, Journal of Molecular Evolution.

[59]  Yedael Y. Waldman,et al.  Nucleic Acids Research Advance Access published January 21, 2010 , 2009 .

[60]  L. Wernisch,et al.  Solving the riddle of codon usage preferences: a test for translational selection. , 2004, Nucleic acids research.

[61]  C. Lottaz,et al.  BIOINFORMATICS APPLICATIONS NOTE , 2001 .

[62]  the original work is properly cited. , 2022 .