Petra Van Damme

Deep Proteome Coverage Based on Ribosome Profiling Aids Mass Spectrometry-based Protein and Peptide Discovery and Provides Evidence of Alternative Translation Products and Near-cognate Translation Initiation Events*

An increasing number of studies involve integrative analysis of gene and protein expression data, taking advantage of new technologies such as next-generation transcriptome sequencing and highly sensitive mass spectrometry (MS) instrumentation. Recently, a strategy, termed ribosome profiling (or RIBO-seq), based on deep sequencing of ribosome-protected mRNA fragments, indirectly monitoring protein synthesis, has been described. We devised a proteogenomic approach constructing a custom protein sequence search space, built from both Swiss-Prot- and RIBO-seq-derived translation products, applicable for MS/MS spectrum identification. To record the impact of using the constructed deep proteome database, we performed two alternative MS-based proteomic strategies as follows: (i) a regular shotgun proteomic and (ii) an N-terminal combined fractional diagonal chromatography (COFRADIC) approach. Although the former technique gives an overall assessment on the protein and peptide level, the latter technique, specifically enabling the isolation of N-terminal peptides, is very appropriate in validating the RIBO-seq-derived (alternative) translation initiation site profile. We demonstrate that this proteogenomic approach increases the overall protein identification rate 2.5% (e.g. new protein products, new protein splice variants, single nucleotide polymorphism variant proteins, and N-terminally extended forms of known proteins) as compared with only searching UniProtKB-SwissProt. Furthermore, using this custom database, identification of N-terminal COFRADIC data resulted in detection of 16 alternative start sites giving rise to N-terminally extended protein variants besides the identification of four translated upstream ORFs. Notably, the characterization of these new translation products revealed the use of multiple near-cognate (non-AUG) start codons. As deep sequencing techniques are becoming more standard, less expensive, and widespread, we anticipate that mRNA sequencing and especially custom-tailored RIBO-seq will become indispensable in the MS-based protein or peptide identification process. The underlying mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium with the dataset identifier PXD000124.

PROTEOFORMER: deep proteome coverage through ribosome profiling and MS integration

An increasing amount of studies integrate mRNA sequencing data into MS-based proteomics to complement the translation product search space. However, several factors, including extensive regulation of mRNA translation and the need for three- or six-frame-translation, impede the use of mRNA-seq data for the construction of a protein sequence search database. With that in mind, we developed the PROTEOFORMER tool that automatically processes data of the recently developed ribosome profiling method (sequencing of ribosome-protected mRNA fragments), resulting in genome-wide visualization of ribosome occupancy. Our tool also includes a translation initiation site calling algorithm allowing the delineation of the open reading frames (ORFs) of all translation products. A complete protein synthesis-based sequence database can thus be compiled for mass spectrometry-based identification. This approach increases the overall protein identification rates with 3% and 11% (improved and new identifications) for human and mouse, respectively, and enables proteome-wide detection of 5′-extended proteoforms, upstream ORF translation and near-cognate translation start sites. The PROTEOFORMER tool is available as a stand-alone pipeline and has been implemented in the galaxy framework for ease of use.

N-terminal Proteomics and Ribosome Profiling Provide a Comprehensive View of the Alternative Translation Initiation Landscape in Mice and Men

Usage of presumed 5′UTR or downstream in-frame AUG codons, next to non-AUG codons as translation start codons contributes to the diversity of a proteome as protein isoforms harboring different N-terminal extensions or truncations can serve different functions. Recent ribosome profiling data revealed a highly underestimated occurrence of database nonannotated, and thus alternative translation initiation sites (aTIS), at the mRNA level. N-terminomics data in addition showed that in higher eukaryotes around 20% of all identified protein N termini point to such aTIS, to incorrect assignments of the translation start codon, translation initiation at near-cognate start codons, or to alternative splicing. We here report on more than 1700 unique alternative protein N termini identified at the proteome level in human and murine cellular proteomes. Customized databases, created using the translation initiation mapping obtained from ribosome profiling data, additionally demonstrate the use of initiator methionine decoded near-cognate start codons besides the existence of N-terminal extended protein variants at the level of the proteome. Various newly identified aTIS were confirmed by mutagenesis, and meta-analyses demonstrated that aTIS reside in strong Kozak-like motifs and are conserved among eukaryotes, hinting to a possible biological impact. Finally, TargetP analysis predicted that the usage of aTIS often results in altered subcellular localization patterns, providing a mechanism for functional diversification.

A proteogenomics approach integrating proteomics and ribosome profiling increases the efficiency of protein identification and enables the discovery of alternative translation start sites

Next‐generation transcriptome sequencing is increasingly integrated with MS to enhance MS‐based protein and peptide identification. Recently, a breakthrough in transcriptome analysis was achieved with the development of ribosome profiling (ribo‐seq). This technology is based on the deep sequencing of ribosome‐protected mRNA fragments, thereby enabling the direct observation of in vivo protein synthesis at the transcript level. In order to explore the impact of a ribo‐seq‐derived protein sequence search space on MS/MS spectrum identification, we performed a comprehensive proteome study on a human cancer cell line, using both shotgun and N‐terminal proteomics, next to ribosome profiling, which was used to delineate (alternative) translational reading frames. By including protein‐level evidence of sample‐specific genetic variation and alternative translation, this strategy improved the identification score of 69 proteins and identified 22 new proteins in the shotgun experiment. Furthermore, we discovered 18 new alternative translation start sites in the N‐terminal proteomics data and observed a correlation between the quantitative measures of ribo‐seq and shotgun proteomics with a Pearson correlation coefficient ranging from 0.483 to 0.664. Overall, this study demonstrated the benefits of ribosome profiling for MS‐based protein and peptide identification and we believe this approach could develop into a common practice for next‐generation proteomics.

The proteome under translational control.

A single eukaryotic gene can give rise to a variety of protein forms (proteoforms) as a result of genetic variation and multilevel regulation of gene expression. In addition to alternative splicing, an increasing line of evidence shows that alternative translation contributes to the overall complexity of proteomes. Identifying the repertoire of proteins and micropeptides expressed by alternative selection of (near‐)cognate translation initiation sites and different reading frames however remains challenging with contemporary proteomics. MS‐enabled identification of proteoforms is expected to benefit from transcriptome and translatome data by the creation of customized and sample‐specific protein sequence databases. Here, we focus on contemporary integrative omics approaches that complement proteomics with DNA‐ and/or RNA‐oriented technologies to elucidate the mechanisms of translational control. Together, these technologies enable to map the translation (initiation) landscape and more comprehensively define the inventory of proteoforms raised upon alternative translation, thus assisting in the (re‐)annotation of genomes.

Positional proteomics reveals differences in N-terminal proteoform stability

To understand the impact of alternative translation initiation on a proteome, we performed a proteome‐wide study on protein turnover using positional proteomics and ribosome profiling to distinguish between N‐terminal proteoforms of individual genes. By combining pulsed SILAC with N‐terminal COFRADIC, we monitored the stability of 1,941 human N‐terminal proteoforms, including 147 N‐terminal proteoform pairs that originate from alternative translation initiation, alternative splicing or incomplete processing of the initiator methionine. N‐terminally truncated proteoforms were less abundant than canonical proteoforms and often displayed altered stabilities, likely attributed to individual protein characteristics, including intrinsic disorder, but independent of N‐terminal amino acid identity or truncation length. We discovered that the removal of initiator methionine by methionine aminopeptidases reduced the stability of processed proteoforms, while susceptibility for N‐terminal acetylation did not seem to influence protein turnover rates. Taken together, our findings reveal differences in protein stability between N‐terminal proteoforms and point to a role for alternative translation initiation and co‐translational initiator methionine removal, next to alternative splicing, in the overall regulation of proteome homeostasis.

Absence of N-terminal acetyltransferase diversification during evolution of eukaryotic organisms

Protein N-terminal acetylation is an ancient and ubiquitous co-translational modification catalyzed by a highly conserved family of N-terminal acetyltransferases (NATs). Prokaryotes have at least 3 NATs, whereas humans have six distinct but highly conserved NATs, suggesting an increase in regulatory complexity of this modification during eukaryotic evolution. Despite this, and against our initial expectations, we determined that NAT diversification did not occur in the eukaryotes, as all six major human NATs were most likely present in the Last Eukaryotic Common Ancestor (LECA). Furthermore, we also observed that some NATs were actually secondarily lost during evolution of major eukaryotic lineages; therefore, the increased complexity of the higher eukaryotic proteome occurred without a concomitant diversification of NAT complexes.

N-terminal Proteomics Assisted Profiling of the Unexplored Translation Initiation Landscape in Arabidopsis thaliana

Proteogenomics is an emerging research field yet lacking a uniform method of analysis. Proteogenomic studies in which N-terminal proteomics and ribosome profiling are combined, suggest that a high number of protein start sites are currently missing in genome annotations. We constructed a proteogenomic pipeline specific for the analysis of N-terminal proteomics data, with the aim of discovering novel translational start sites outside annotated protein coding regions. In summary, unidentified MS/MS spectra were matched to a specific N-terminal peptide library encompassing protein N termini encoded in the Arabidopsis thaliana genome. After a stringent false discovery rate filtering, 117 protein N termini compliant with N-terminal methionine excision specificity and indicative of translation initiation were found. These include N-terminal protein extensions and translation from transposable elements and pseudogenes. Gene prediction provided supporting protein-coding models for approximately half of the protein N termini. Besides the prediction of functional domains (partially) contained within the newly predicted ORFs, further supporting evidence of translation was found in the recently released Araport11 genome re-annotation of Arabidopsis and computational translations of sequences stored in public repositories. Most interestingly, complementary evidence by ribosome profiling was found for 23 protein N termini. Finally, by analyzing protein N-terminal peptides, an in silico analysis demonstrates the applicability of our N-terminal proteogenomics strategy in revealing protein-coding potential in species with well- and poorly-annotated genomes.

eIF1 modulates the recognition of suboptimal translation initiation sites and steers gene expression via uORFs

Abstract Alternative translation initiation mechanisms such as leaky scanning and reinitiation potentiate the polycistronic nature of human transcripts. By allowing for reprogrammed translation, these mechanisms can mediate biological responses to stimuli. We combined proteomics with ribosome profiling and mRNA sequencing to identify the biological targets of translation control triggered by the eukaryotic translation initiation factor 1 (eIF1), a protein implicated in the stringency of start codon selection. We quantified expression changes of over 4000 proteins and 10 000 actively translated transcripts, leading to the identification of 245 transcripts undergoing translational control mediated by upstream open reading frames (uORFs) upon eIF1 deprivation. Here, the stringency of start codon selection and preference for an optimal nucleotide context were largely diminished leading to translational upregulation of uORFs with suboptimal start. Interestingly, genes affected by eIF1 deprivation were implicated in energy production and sensing of metabolic stress.

Noncoding after All: Biases in Proteomics Data Do Not Explain Observed Absence of lncRNA Translation Products

Over the past decade, long noncoding RNAs (lncRNAs) have emerged as novel functional entities of the eukaryotic genome. However, the scientific community remains divided over the amount of true noncoding transcripts among the large number of unannotated transcripts identified by recent large scale and deep RNA-sequencing efforts. Here, we systematically exclude possible technical reasons underlying the absence of lncRNA-encoded proteins in mass spectrometry data sets, strongly suggesting that the large majority of lncRNAs is indeed not translated.