Limited, but potentially functional translation of non-coding transcripts in the HEK293T cellular cytosol

Ribosome profiling has revealed translation outside of canonical coding sequences (CDSs) including translation of short upstream ORFs, long non-coding RNAs, overlapping ORFs, ORFs in UTRs or ORFs in alternative reading frames. Studies combining mass spectrometry, ribosome profiling and CRISPR-based screens showed that hundreds of ORFs derived from non-coding transcripts produce (micro)proteins, while other studies failed to find evidence for such types of non-canonical translation products. Here, we attempted to discover translation products from non-coding regions by strongly reducing the complexity of the sample prior to mass spectrometric analysis. We used an extended database as the search space and applied stringent filtering of the identified peptides to find evidence for novel translation events. Theoretically, we show that our strategy facilitates the detection of translation events of transcripts from non-coding regions, but experimentally only find 19 peptides (less than 1% of all identified peptides) that might originate from such translation events. Virotrap based interactome analysis of two N-terminal proteoforms originating from non-coding regions finally showed the functional potential of these novel proteins.

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Improved ribosome-footprint and mRNA measurements provide insights into dynamics and regulation of yeast translation

10.1101/021501 ◽

2015 ◽

Cited By ~ 2

Author(s):

David E Weinberg ◽

Premal Shah ◽

Stephen W Eichhorn ◽

Jeffrey A Hussmann ◽

Joshua B Plotkin ◽

...

Keyword(s):

Translational Control ◽

Nucleotide Composition ◽

Ribosome Profiling ◽

Open Reading Frames ◽

Untranslated Regions ◽

Coding Regions ◽

Genome Wide ◽

Upstream Open Reading Frames ◽

Improved Methods ◽

Reading Frames

Ribosome-footprint profiling provides genome-wide snapshots of translation, but technical challenges can confound its analysis. Here, we use improved methods to obtain ribosome-footprint profiles and mRNA abundances that more faithfully reflect gene expression in Saccharomyces cerevisiae. Our results support proposals that both the beginning of coding regions and codons matching rare tRNAs are more slowly translated. They also indicate that emergent polypeptides with as few as three basic residues within a 10-residue window tend to slow translation. With the improved mRNA measurements, the variation attributable to translational control in exponentially growing yeast was less than previously reported, and most of this variation could be predicted with a simple model that considered mRNA abundance, upstream open reading frames, cap-proximal structure and nucleotide composition, and lengths of the coding and 5′- untranslated regions. Collectively, our results reveal key features of translational control in yeast and provide a framework for executing and interpreting ribosome- profiling studies.

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Selection Pressure in Alternative Reading Frames

PLoS ONE ◽

10.1371/journal.pone.0108768 ◽

2014 ◽

Vol 9 (10) ◽

pp. e108768 ◽

Cited By ~ 14

Author(s):

Katharina Mir ◽

Steffen Schober

Keyword(s):

Selection Pressure ◽

Alternative Reading Frames ◽

Alternative Reading ◽

Reading Frames

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Influenza A Virus Infection Induces Viral and Cellular Defective Ribosomal Products Encoded by Alternative Reading Frames

The Journal of Immunology ◽

10.4049/jimmunol.1900070 ◽

2019 ◽

Vol 202 (12) ◽

pp. 3370-3380 ◽

Cited By ~ 8

Author(s):

Damien J. Zanker ◽

Sara Oveissi ◽

David C. Tscharke ◽

Mubing Duan ◽

Siyuan Wan ◽

...

Keyword(s):

Virus Infection ◽

Influenza A Virus ◽

Influenza A ◽

Influenza A Virus Infection ◽

Alternative Reading Frames ◽

Alternative Reading ◽

Reading Frames

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Ribosomal scanning past the primary initiation codon as a mechanism for expression of CTL epitopes encoded in alternative reading frames.

Journal of Experimental Medicine ◽

10.1084/jem.184.4.1319 ◽

1996 ◽

Vol 184 (4) ◽

pp. 1319-1329 ◽

Cited By ~ 64

Author(s):

T N Bullock ◽

L C Eisenlohr

Keyword(s):

Mhc Class I ◽

Mutational Analysis ◽

Class I ◽

Initiation Codon ◽

Initiation Of Translation ◽

Cryptic Epitope ◽

Alternative Reading Frames ◽

Alternative Reading ◽

Reading Frames ◽

Mhc Class I Molecules

An increasing amount of evidence has shown that epitopes restricted to MHC class I molecules and recognized by CTL need not be encoded in a primary open reading frame (ORF). Such epitopes have been demonstrated after stop codons, in alternative reading frames (RF) and within introns. We have used a series of frameshifts (FS) introduced into the Influenza A/PR/8 /34 nucleoprotein (NP) gene to confirm the previous in vitro observations of cryptic epitope expression, and show that they are sufficiently expressed to prime immune responses in vivo. This presentation is not due to sub-dominant epitopes, transcription from cryptic promoters beyond the point of the FS, or internal initiation of translation. By introducing additional mutations to the construct exhibiting the most potent presentation, we have identified initiation codon readthrough (termed scanthrough here, where the scanning ribosome bypasses the conventional initiation codon, initiating translation further downstream) as the likely mechanism of epitope production. Further mutational analysis demonstrated that, while it should operate during the expression of wild-type (WT) protein, scanthrough does not provide a major source of processing substrate in our system. These findings suggest (i) that the full array of self- and pathogen-derived epitopes available during thymic selection and infection has not been fully appreciated and (ii) that cryptic epitope expression should be considered when the specificity of a CTL response cannot be identified or in therapeutic situations when conventional CTL targets are limited, as may be the case with latent viral infections and transformed cells. Finally, initiation codon readthrough provides a plausible explanation for the presentation of exocytic proteins by MHC class I molecules.

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Thousands of novel unannotated proteins expand the MHC I immunopeptidome in cancer

10.1101/2020.02.12.945840 ◽

2020 ◽

Cited By ~ 6

Author(s):

Tamara Ouspenskaia ◽

Travis Law ◽

Karl R. Clauser ◽

Susan Klaeger ◽

Siranush Sarkizova ◽

...

Keyword(s):

Somatic Mutations ◽

Tumor Antigens ◽

Ribosome Profiling ◽

Lymphocytic Leukemia ◽

Open Reading Frames ◽

Specific Expression ◽

Protein Coding ◽

Mhc I ◽

Coding Regions ◽

Reading Frames

AbstractTumor epitopes – peptides that are presented on surface-bound MHC I proteins - provide targets for cancer immunotherapy and have been identified extensively in the annotated protein-coding regions of the genome. Motivated by the recent discovery of translated novel unannotated open reading frames (nuORFs) using ribosome profiling (Ribo-seq), we hypothesized that cancer-associated processes could generate nuORFs that can serve as a new source of tumor antigens that harbor somatic mutations or show tumor-specific expression. To identify cancer-specific nuORFs, we generated Ribo-seq profiles for 29 malignant and healthy samples, developed a sensitive analytic approach for hierarchical ORF prediction, and constructed a high-confidence database of translated nuORFs across tissues. Peptides from 3,555 unique translated nuORFs were presented on MHC I, based on analysis of an extensive dataset of MHC I-bound peptides detected by mass spectrometry, with >20-fold more nuORF peptides detected in the MHC I immunopeptidomes compared to whole proteomes. We further detected somatic mutations in nuORFs of cancer samples and identified nuORFs with tumor-specific translation in melanoma, chronic lymphocytic leukemia and glioblastoma. NuORFs thus expand the pool of MHC I-presented, tumor-specific peptides, targetable by immunotherapies.

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Comprehensive Annotations of Human Herpesvirus 6A and 6B Genomes Reveal Novel and Conserved Genomic Features

10.1101/730028 ◽

2019 ◽

Author(s):

Yaara Finkel ◽

Dominik Schmiedel ◽

Julie Tai-Schmiedel ◽

Aharon Nachshon ◽

Michal Schwartz ◽

...

Keyword(s):

Human Herpesvirus ◽

Ribosome Profiling ◽

Open Reading Frames ◽

Temporal Expression ◽

Protein Coding ◽

Functional Studies ◽

Viral Genes ◽

Non Coding Rnas ◽

Coding Potential ◽

Reading Frames

AbstractHuman herpesvirus 6 (HHV-6) A and B are highly ubiquitous betaherpesviruses, infecting the majority of the human population. Like other herpesviruses, they encompass large genomes and our understanding of their protein coding potential is far from complete. Here we employ ribosome profiling and systematic transcript analysis to experimentally define the HHV-6 translation products and to follow their temporal expression. We identify hundreds of new open reading frames (ORFs), including many upstream ORFs (uORFs) and internal ORFs (iORFs), generating a complete unbiased atlas of HHV-6 proteome. Furthermore, by integrating systematic data from the prototypic betaherpesvirus, human cytomegalovirus, we uncover numerous uORFs and iORFs that are conserved across betaherpesviruses and we show that uORFs are specifically enriched in late viral genes. Using our transcriptome measurements, we identified three highly abundant HHV-6 encoded long non-coding RNAs (lncRNAs), one of which generates a non-polyadenylated stable intron that appears to be a conserved feature of betaherpesviruses. Overall, our work reveals the complexity of HHV-6 genomes and highlights novel features that are conserved between betaherpesviruses, providing a rich resource for future functional studies.

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A community-driven roadmap to advance research on translated open reading frames detected by Ribo-seq

10.1101/2021.06.10.447896 ◽

2021 ◽

Author(s):

Jonathan M Mudge ◽

Jorge Ruiz-Orera ◽

John R Prensner ◽

Marie A Brunet ◽

Jose Manuel Gonzalez ◽

...

Keyword(s):

Gene Annotation ◽

Ribosome Profiling ◽

Open Reading Frames ◽

Untranslated Regions ◽

Biological Databases ◽

Protein Coding ◽

Circular Problem ◽

Advance Research ◽

Non Coding Rnas ◽

Reading Frames

Ribosome profiling (Ribo-seq) has catalyzed a paradigm shift in our understanding of the translational vocabulary of the human genome, discovering thousands of translated open reading frames (ORFs) within long non-coding RNAs and presumed untranslated regions of protein-coding genes. However, reference gene annotation projects have been circumspect in their incorporation of these ORFs due to uncertainties about their experimental reproducibility and physiological roles. Yet, it is indisputable that certain Ribo-seq ORFs make stable proteins, others mediate gene regulation, and many have medical implications. Ultimately, the absence of standardized ORF annotation has created a circular problem: while Ribo-seq ORFs remain unannotated by reference biological databases, this lack of characterisation will thwart research efforts examining their roles. Here, we outline the initial stages of a community-led effort supported by GENCODE / Ensembl, HGNC and UniProt to produce a consolidated catalog of human Ribo-seq ORFs.

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orfipy: a fast and flexible tool for extracting ORFs

10.1101/2020.10.20.348052 ◽

2020 ◽

Author(s):

Urminder Singh ◽

Eve Syrkin Wurtele

Keyword(s):

Open Reading Frames ◽

Supplementary Information ◽

Rna Seq ◽

Flexible Tool ◽

Coding Regions ◽

Link Type ◽

Alternative Reading Frames ◽

Downstream Analysis ◽

Fine Tune ◽

Reading Frames

SummarySearching for ORFs in transcripts is a critical step prior to annotating coding regions in newly-sequenced genomes and to search for alternative reading frames within known genes. With the tremendous increase in RNA-Seq data, faster tools are needed to handle large input datasets. These tools should be versatile enough to fine-tune search criteria and allow efficient downstream analysis. Here we present a new python based tool, orfipy, which allows the user to flexibly search for open reading frames in fasta sequences. The search is rapid and is fully customizable, with a choice of Fasta and BED output formats.Availability and implementationorfipy is implemented in python and is compatible with python v3.6 and higher. Source code: https://github.com/urmi-21/orfipy. Installation: from the source, or via PyPi (https://pypi.org/project/orfipy) or bioconda (https://anaconda.org/bioconda/orfipy)[email protected], [email protected] informationSupplementary data are available at https://github.com/urmi-21/orfipy

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