bims-micpro 2021-03-14 papers

Issue of 2021‒03‒14
two papers selected by
Thomas Martinez
Salk Institute for Biological Studies

Mol Ther. 2021 Mar 04. pii: S1525-0016(21)00133-7. [Epub ahead of print]

The cardiac translational landscape reveals that micropeptides are new players involved in cardiomyocyte hypertrophy.

Youchen Yan, Rong Tang, Bin Li, Liangping Cheng, Shangmei Ye, Tiqun Yang, Yan-Chuang Han, Chen Liu, Yugang Dong, Liang-Hu Qu, Kathy O Lui, Jian-Hua Yang, Zhan-Peng Huang.

Hypertrophic growth of cardiomyocytes is one of the major compensatory responses in the heart after physiological or pathological stimulation. Protein synthesis enhancement, which is mediated by the translation of messenger RNAs, is one of the main features of cardiomyocyte hypertrophy. Although the transcriptome shift caused by cardiac hypertrophy induced by different stimuli has been extensively investigated, translatome dynamics in this cellular process has been less studied. Here, we generated a nucleotide-resolution translatome as well as transcriptome data from isolated primary cardiomyocytes undergoing hypertrophy. More than 10,000 open reading frames (ORFs) were detected from the deep sequencing of ribosome protected fragments (Ribo-seq), which orchestrated the shift of the translatome in hypertrophied cardiomyocytes. Our data suggests that rather than increase the translational rate of ribosomes, the increased efficiency of protein synthesis in cardiomyocyte hypertrophy was attributable to an increased quantity of ribosomes. In addition, more than 100 uncharacterized short open reading frames (sORFs) were detected in long noncoding RNA genes from Ribo-seq with potential of micropeptide coding. In a random test of 15 candidates, the coding potential of 11 sORFs were experimentally supported. Three micropeptides were identified to regulate cardiomyocyte hypertrophy by modulating the activities of Oxidative phosphorylation, the Calcium signaling pathway, and the MAPK pathway. Our study provides a genome-wide overview of the translational controls behind cardiomyocyte hypertrophy and demonstrate an unrecognized role of micropeptides in cardiomyocyte biology.

Keywords: Cardiac hypertrophy; Noncoding RNA; Ribo-seq; Short open reading frame; Translational landscape; micropeptide

DOI: https://doi.org/10.1016/j.ymthe.2021.03.004

Cell Rep. 2021 Mar 09. pii: S2211-1247(21)00129-7. [Epub ahead of print]34(10): 108815

Most non-canonical proteins uniquely populate the proteome or immunopeptidome.

Maria Virginia Ruiz Cuevas, Marie-Pierre Hardy, Jaroslav Hollý, Éric Bonneil, Chantal Durette, Mathieu Courcelles, Joël Lanoix, Caroline Côté, Louis M Staudt, Sébastien Lemieux, Pierre Thibault, Claude Perreault, Jonathan W Yewdell.

Combining RNA sequencing, ribosome profiling, and mass spectrometry, we elucidate the contribution of non-canonical translation to the proteome and major histocompatibility complex (MHC) class I immunopeptidome. Remarkably, of 14,498 proteins identified in three human B cell lymphomas, 2,503 are non-canonical proteins. Of these, 28% are novel isoforms and 72% are cryptic proteins encoded by ostensibly non-coding regions (60%) or frameshifted canonical genes (12%). Cryptic proteins are translated as efficiently as canonical proteins, have more predicted disordered residues and lower stability, and critically generate MHC-I peptides 5-fold more efficiently per translation event. Translating 5' "untranslated" regions hinders downstream translation of genes involved in transcription, translation, and antiviral responses. Novel protein isoforms show strong enrichment for signaling pathways deregulated in cancer. Only a small fraction of cryptic proteins detected in the proteome contribute to the MHC-I immunopeptidome, demonstrating the high preferential access of cryptic defective ribosomal products to the class I pathway.

Keywords: computational biology; defective ribosomal products; major histocompatibility complex; mass spectrometry; non-canonical translation; peptides; protein isoforms; proteomic methods; ribosome profiling

DOI: https://doi.org/10.1016/j.celrep.2021.108815