The Ribosome Profiling landscape of yeast reveals a high diversity in pervasive translation

Abstract

Pervasive translation is a widespread phenomenon that plays an important role in de novo gene birth; however, its underlying mechanisms remain unclear. Based on multiple Ribosome Profiling (Ribo-Seq) datasets, we investigated the RiboSeq landscape of coding and noncoding regions of yeast. Therefore, we developed a representation framework which allows the visual representation and rational classification of the entire diversity of Ribo-Seq signals that could be observed in yeast. We show that if coding regions are restricted to specific areas of the Ribo-Seq landscape, noncoding regions are associated with a wide diversity of translation signals and, conversely, populate the entire yeast Ribo-Seq landscape. Specifically, we reveal that noncoding regions are associated with canonical translation signals, but also with non-canonical ones absent from coding regions, and which appear to be a hallmark of pervasive translation. Notably, we report thousands of translated noncoding ORFs among which, 251 led to detectable products with Mass Spectrometry while being characterized by a wide range of translation specificities. Overall, we show that pervasive translation is not random with noncoding ORF translation signals being consistent across Ribo-Seq experiments. Finally, we show that the translation signal of noncoding ORFs is not explained by features related to the emergence of function, but rather determined by the translation start codon and the codon distribution in their two alternative frames. Overall, our results enable us to propose a topology of the pervasive Ribo-Seq landscape of a species, and open the way to future comparative analyses of this translation landscape under different conditions.

DE Novo emerged gene SEarch in Eukaryotes with DENSE

“…However, all novel transcripts, especially those resulting from pervasive expression, are not necessarily expected to last throughout evolution and can instead be considered as gene precursors (Grandchamp et al, 2023;Wacholder et al, 2023). As discussed in previous reports, transcription may predate gene emergence and, therefore, cannot be considered a sufficient criterion for a novel transcript to be classified as a gene (Cai et al, 2008;Carvunis et al, 2012;Chen et al, 2020;Papadopoulos et al, 2023;Reinhardt et al, 2013;Schlötterer, 2015). Furthermore, young de novo emerged genes are typically associated with stress response or adaptation, and are expected to be expressed under specific conditions (Carvunis et al, 2012;Colbourne et al, 2011;Donoghue et al, 2011;Schlötterer, 2015;Van Oss & Carvunis, 2019).…”

High Polymorphism Levels of De Novo ORFs in a Yoruba Human Population

“…In rice, the use of ribosome profiling has also recently led to the discovery of many unannotated de novo protein-coding genes ( Zhang et al 2019 ). Recently, the combination of Ribo-Seq data from multiple studies has led to the discovery of thousands of non-annotated translated ORFs in yeast ( Papadopoulos et al 2023 ; Wacholder et al 2023 ).…”