Ribosome decision graphs for the representation of eukaryotic RNA translation complexity.

The application of ribosome profiling has revealed an unexpected abundance of translation in addition to that responsible for the synthesis of previously annotated protein-coding regions. Multiple short sequences have been found to be translated within single RNA molecules, within both annotated protein-coding and noncoding regions. The biological significance of this translation is a matter of intensive investigation. However, current schematic or annotation-based representations of mRNA translation generally do not account for the apparent multitude of translated regions within the same molecules. They also do not take into account the stochasticity of the process that allows alternative translations of the same RNA molecules by different ribosomes. There is a need for formal representations of mRNA complexity that would enable the analysis of quantitative information on translation and more accurate models for predicting the phenotypic effects of genetic variants affecting translation. To address this, we developed a conceptually novel abstraction that we term ribosome decision graphs (RDGs). RDGs represent translation as multiple ribosome paths through untranslated and translated mRNA segments. We termed the latter "translons." Nondeterministic events, such as initiation, reinitiation, selenocysteine insertion, or ribosomal frameshifting, are then represented as branching points. This representation allows for an adequate representation of eukaryotic translation complexity and focuses on locations critical for translation regulation. We show how RDGs can be used for depicting translated regions and for analyzing genetic variation and quantitative genome-wide data on translation for characterization of regulatory modulators of translation.

Stem-loop-induced ribosome queuing in the uORF2/ATF4 overlap fine-tunes stress-induced human ATF4 translational control.

Activating transcription factor 4 (ATF4) is a master transcriptional regulator of the integrated stress response, leading cells toward adaptation or death. ATF4's induction under stress was thought to be due to delayed translation reinitiation, where the reinitiation-permissive upstream open reading frame 1 (uORF1) plays a key role. Accumulating evidence challenging this mechanism as the sole source of ATF4 translation control prompted us to investigate additional regulatory routes. We identified a highly conserved stem-loop in the uORF2/ATF4 overlap, immediately preceded by a near-cognate CUG, which introduces another layer of regulation in the form of ribosome queuing. These elements explain how the inhibitory uORF2 can be translated under stress, confirming prior observations but contradicting the original regulatory model. We also identified two highly conserved, potentially modified adenines performing antagonistic roles. Finally, we demonstrated that the canonical ATF4 translation start site is substantially leaky scanned. Thus, ATF4's translational control is more complex than originally described, underpinning its key role in diverse biological processes.

Stem-loop induced ribosome queuing in the uORF2/ATF4 overlap fine-tunes stress-induced human ATF4 translational control.

ATF4 is a master transcriptional regulator of the integrated stress response leading cells towards adaptation or death. ATF4's induction under stress was thought to be mostly due to delayed translation reinitiation, where the reinitiation-permissive uORF1 plays a key role. Accumulating evidence challenging this mechanism as the sole source of ATF4 translation control prompted us to investigate additional regulatory routes. We identified a highly conserved stem-loop in the uORF2/ATF4 overlap, immediately preceded by a near-cognate CUG, which introduces another layer of regulation in the form of ribosome queuing. These elements explain how the inhibitory uORF2 can be translated under stress, confirming prior observations, but contradicting the original regulatory model. We also identified two highly conserved, potentially modified adenines performing antagonistic roles. Finally, we demonstrate that the canonical ATF4 translation start site is substantially leaky-scanned. Thus, ATF4's translational control is more complex than originally described underpinning its key role in diverse biological processes.

Ribosome Decision Graphs for the Representation of Eukaryotic RNA Translation Complexity.

The application of ribosome profiling has revealed an unexpected abundance of translation in addition to that responsible for the synthesis of previously annotated protein-coding regions. Multiple short sequences have been found to be translated within single RNA molecules, both within annotated protein-coding and non-coding regions. The biological significance of this translation is a matter of intensive investigation. However, current schematic or annotation-based representations of mRNA translation generally do not account for the apparent multitude of translated regions within the same molecules. They also do not take into account the stochasticity of the process that allows alternative translations of the same RNA molecules by different ribosomes. There is a need for formal representations of mRNA complexity that would enable the analysis of quantitative information on translation and more accurate models for predicting the phenotypic effects of genetic variants affecting translation. To address this, we developed a conceptually novel abstraction that we term Ribosome Decision Graphs (RDGs). RDGs represent translation as multiple ribosome paths through untranslated and translated mRNA segments. We termed the later 'translons'. Non-deterministic events, such as initiation, re-initiation, selenocysteine insertion or ribosomal frameshifting are then represented as branching points. This representation allows for an adequate representation of eukaryotic translation complexity and focuses on locations critical for translation regulation. We show how RDGs can be used for depicting translated regions, analysis of genetic variation and quantitative genome-wide data on translation for characterisation of regulatory modulators of translation.

ORFik: a comprehensive R toolkit for the analysis of translation.

BACKGROUND: With the rapid growth in the use of high-throughput methods for characterizing translation and the continued expansion of multi-omics, there is a need for back-end functions and streamlined tools for processing, analyzing, and characterizing data produced by these assays. RESULTS: Here, we introduce ORFik, a user-friendly R/Bioconductor API and toolbox for studying translation and its regulation. It extends GenomicRanges from the genome to the transcriptome and implements a framework that integrates data from several sources. ORFik streamlines the steps to process, analyze, and visualize the different steps of translation with a particular focus on initiation and elongation. It accepts high-throughput sequencing data from ribosome profiling to quantify ribosome elongation or RCP-seq/TCP-seq to also quantify ribosome scanning. In addition, ORFik can use CAGE data to accurately determine 5'UTRs and RNA-seq for determining translation relative to RNA abundance. ORFik supports and calculates over 30 different translation-related features and metrics from the literature and can annotate translated regions such as proteins or upstream open reading frames (uORFs). As a use-case, we demonstrate using ORFik to rapidly annotate the dynamics of 5' UTRs across different tissues, detect their uORFs, and characterize their scanning and translation in the downstream protein-coding regions. CONCLUSION: In summary, ORFik introduces hundreds of tested, documented and optimized methods. ORFik is designed to be easily customizable, enabling users to create complete workflows from raw data to publication-ready figures for several types of sequencing data. Finally, by improving speed and scope of many core Bioconductor functions, ORFik offers enhancement benefiting the entire Bioconductor environment. AVAILABILITY: http://bioconductor.org/packages/ORFik .

Non-canonical translation initiation in yeast generates a cryptic pool of mitochondrial proteins.

Utilization of non-AUG alternative translation start sites is most common in bacteria and viruses, but it has been also reported in other organisms. This phenomenon increases proteome complexity by allowing expression of multiple protein isoforms from a single gene. In Saccharomyces cerevisiae, a few described cases concern proteins that are translated from upstream near-cognate start codons as N-terminally extended variants that localize to mitochondria. Using bioinformatics tools, we provide compelling evidence that in yeast the potential for producing alternative protein isoforms by non-AUG translation initiation is much more prevalent than previously anticipated and may apply to as many as a few thousand proteins. Several hundreds of candidates are predicted to gain a mitochondrial targeting signal (MTS), generating an unrecognized pool of mitochondrial proteins. We confirmed mitochondrial localization of a subset of proteins previously not identified as mitochondrial, whose standard forms do not carry an MTS. Our data highlight the potential of non-canonical translation initiation in expanding the capacity of the mitochondrial proteome and possibly also other cellular features.