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

Relevance of mutation-derived neoantigens and non-classical antigens for anticancer therapies

Efficacy of cancer immunotherapies relies on correct recognition of tumor antigens by lymphocytes, eliciting thus functional responses capable of eliminating tumor cells. Therefore, important efforts have been carried out in antigen identification, with the aim of understanding mechanisms of response to immunotherapy and to design safer and more efficient strategies. In addition to classical tumor-associated antigens identified during the last decades, implementation of next-generation sequencing methodologies is enabling the identification of neoantigens (neoAgs) arising from mutations, leading to the development of new neoAg-directed therapies. Moreover, there are numerous non-classical tumor antigens originated from other sources and identified by new methodologies. Here, we review the relevance of neoAgs in different immunotherapies and the results obtained by applying neoAg-based strategies. In addition, the different types of non-classical tumor antigens and the best approaches for their identification are described. This will help to increase the spectrum of targetable molecules useful in cancer immunotherapies.

LncRNA-encoded peptides in cancer

Long non-coding RNAs (lncRNAs), once considered transcriptional noise, have emerged as critical regulators of gene expression and key players in cancer biology. Recent breakthroughs have revealed that certain lncRNAs can encode small open reading frame (sORF)-derived peptides, which are now understood to contribute to the pathogenesis of various cancers. This review synthesizes current knowledge on the detection, functional roles, and clinical implications of lncRNA-encoded peptides in cancer. We discuss technological advancements in the detection and validation of sORFs, including ribosome profiling and mass spectrometry, which have facilitated the discovery of these peptides. The functional roles of lncRNA-encoded peptides in cancer processes such as gene transcription, translation regulation, signal transduction, and metabolic reprogramming are explored in various types of cancer. The clinical potential of these peptides is highlighted, with a focus on their utility as diagnostic biomarkers, prognostic indicators, and therapeutic targets. The challenges and future directions in translating these findings into clinical practice are also discussed, including the need for large-scale validation, development of sensitive detection methods, and optimization of peptide stability and delivery.

Microproteins-Discovery, structure, and function

Advances in proteogenomic technologies have revealed hundreds to thousands of translated small open reading frames (sORFs) that encode microproteins in genomes across evolutionary space. While many microproteins have now been shown to play critical roles in biology and human disease, a majority of recently identified microproteins have little or no experimental evidence regarding their functionality. Computational tools have some limitations for analysis of short, poorly conserved microprotein sequences, so additional approaches are needed to determine the role of each member of this recently discovered polypeptide class. A currently underexplored avenue in the study of microproteins is structure prediction and determination, which delivers a depth of functional information. In this review, we provide a brief overview of microprotein discovery methods, then examine examples of microprotein structures (and, conversely, intrinsic disorder) that have been experimentally determined using crystallography, cryo-electron microscopy, and NMR, which provide insight into their molecular functions and mechanisms. Additionally, we discuss examples of predicted microprotein structures that have provided insight or context regarding their function. Analysis of microprotein structure at the angstrom level, and confirmation of predicted structures, therefore, has potential to identify translated microproteins that are of biological importance and to provide molecular mechanism for their in vivo roles.

Readthrough isoform of aquaporin-4 (AQP4) as a therapeutic target for Alzheimer's disease and other proteinopathies

The glymphatic system is a crucial component in preserving brain homeostasis by facilitating waste clearance from the central nervous system (CNS). Aquaporin-4 (AQP4) water channels facilitate the continuous interchange between cerebrospinal fluid and brain interstitial fluid by convective flow movement. This flow is responsible for guiding proteins and metabolites away from the CNS. Proteinopathies are neurological conditions characterized by the accumulation of aggregated proteins or peptides in the brain. In Alzheimer's disease (AD), the deposition of amyloid-β (Aβ) peptides causes the formation of senile plaques. This accumulation has been hypothesized to be a result of the imbalance between Aβ production and clearance. Recent studies have shown that an extended form of AQP4 increases Aβ clearance from the brain. In this mini-review, we present a summary of these findings and explore the potential for future therapeutic strategies aiming to boost waste clearance in AD.

What Can Ribo-Seq, Immunopeptidomics, and Proteomics Tell Us About the Noncanonical Proteome?

Ribosome profiling (Ribo-Seq) has proven transformative for our understanding of the human genome and proteome by illuminating thousands of noncanonical sites of ribosome translation outside the currently annotated coding sequences (CDSs). A conservative estimate suggests that at least 7000 noncanonical ORFs are translated, which, at first glance, has the potential to expand the number of human protein CDSs by 30%, from ∼19,500 annotated CDSs to over 26,000 annotated CDSs. Yet, additional scrutiny of these ORFs has raised numerous questions about what fraction of them truly produce a protein product and what fraction of those can be understood as proteins according to conventional understanding of the term. Adding further complication is the fact that published estimates of noncanonical ORFs vary widely by around 30-fold, from several thousand to several hundred thousand. The summation of this research has left the genomics and proteomics communities both excited by the prospect of new coding regions in the human genome but searching for guidance on how to proceed. Here, we discuss the current state of noncanonical ORF research, databases, and interpretation, focusing on how to assess whether a given ORF can be said to be "protein coding."

N-terminal proteoforms may engage in different protein complexes

Alternative translation initiation and alternative splicing may give rise to N-terminal proteoforms, proteins that differ at their N-terminus compared with their canonical counterparts. Such proteoforms can have altered localizations, stabilities, and functions. Although proteoforms generated from splice variants can be engaged in different protein complexes, it remained to be studied to what extent this applies to N-terminal proteoforms. To address this, we mapped the interactomes of several pairs of N-terminal proteoforms and their canonical counterparts. First, we generated a catalogue of N-terminal proteoforms found in the HEK293T cellular cytosol from which 22 pairs were selected for interactome profiling. In addition, we provide evidence for the expression of several N-terminal proteoforms, identified in our catalogue, across different human tissues, as well as tissue-specific expression, highlighting their biological relevance. Protein-protein interaction profiling revealed that the overlap of the interactomes for both proteoforms is generally high, showing their functional relation. We also showed that N-terminal proteoforms can be engaged in new interactions and/or lose several interactions compared with their canonical counterparts, thus further expanding the functional diversity of proteomes.

TP53BP1, a dual-coding gene, uses promoter switching and translational reinitiation to express a smORF protein

The complexity of the metazoan proteome is significantly increased by the expression of small proteins (<100 aa) derived from smORFs within lncRNAs, uORFs, 3' UTRs and, reading frames overlapping the CDS. These smORF encoded proteins (SEPs) have diverse roles, ranging from the regulation of cellular physiological to essential developmental functions. We report the characterization of a new member of this protein family, SEP53BP1, derived from a small internal ORF that overlaps the CDS encoding 53BP1. Its expression is coupled to the utilization of an alternative, cell-type specific promoter coupled to translational reinitiation events mediated by a uORF in the alternative 5' TL of the mRNA. This uORF-mediated reinitiation at an internal ORF is also observed in zebrafish. Interactome studies indicate that the human SEP53BP1 associates with components of the protein turnover pathway including the proteasome, and the TRiC/CCT chaperonin complex, suggesting that it may play a role in cellular proteostasis.

What can Ribo-seq and proteomics tell us about the non-canonical proteome?

Ribosome profiling (Ribo-seq) has proven transformative for our understanding of the human genome and proteome by illuminating thousands of non-canonical sites of ribosome translation outside of the currently annotated coding sequences (CDSs). A conservative estimate suggests that at least 7,000 non-canonical open reading frames (ORFs) are translated, which, at first glance, has the potential to expand the number of human protein-coding sequences by 30%, from ∼19,500 annotated CDSs to over 26,000. Yet, additional scrutiny of these ORFs has raised numerous questions about what fraction of them truly produce a protein product and what fraction of those can be understood as proteins according to conventional understanding of the term. Adding further complication is the fact that published estimates of non-canonical ORFs vary widely by around 30-fold, from several thousand to several hundred thousand. The summation of this research has left the genomics and proteomics communities both excited by the prospect of new coding regions in the human genome, but searching for guidance on how to proceed. Here, we discuss the current state of non-canonical ORF research, databases, and interpretation, focusing on how to assess whether a given ORF can be said to be "protein-coding".

In brief

The human genome encodes thousands of non-canonical open reading frames (ORFs) in addition to protein-coding genes. As a nascent field, many questions remain regarding non-canonical ORFs. How many exist? Do they encode proteins? What level of evidence is needed for their verification? Central to these debates has been the advent of ribosome profiling (Ribo-seq) as a method to discern genome-wide ribosome occupancy, and immunopeptidomics as a method to detect peptides that are processed and presented by MHC molecules and not observed in traditional proteomics experiments. This article provides a synthesis of the current state of non-canonical ORF research and proposes standards for their future investigation and reporting.

Highlights

Combined use of Ribo-seq and proteomics-based methods enables optimal confidence in detecting non-canonical ORFs and their protein products.Ribo-seq can provide more sensitive detection of non-canonical ORFs, but data quality and analytical pipelines will impact results.Non-canonical ORF catalogs are diverse and span both high-stringency and low-stringency ORF nominations.A framework for standardized non-canonical ORF evidence will advance the research field.

The alternative proteome in neurobiology

Translation involves the biosynthesis of a protein sequence following the decoding of the genetic information embedded in a messenger RNA (mRNA). Typically, the eukaryotic mRNA was considered to be inherently monocistronic, but this paradigm is not in agreement with the translational landscape of cells, tissues, and organs. Recent ribosome sequencing (Ribo-seq) and proteomics studies show that, in addition to currently annotated reference proteins (RefProt), other proteins termed alternative proteins (AltProts), and microproteins are encoded in regions of mRNAs thought to be untranslated or in transcripts annotated as non-coding. This experimental evidence expands the repertoire of functional proteins within a cell and potentially provides important information on biological processes. This review explores the hitherto overlooked alternative proteome in neurobiology and considers the role of AltProts in pathological and healthy neuromolecular processes.

Limited evidence for protein products 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. We show that, theoretically our strategy facilitates the detection of translation events of transcripts from non-coding regions, but experimentally only find 19 peptides that might originate from such translation events. Finally, Virotrap based interactome analysis of two N-terminal proteoforms originating from non-coding regions finally showed the functional potential of these novel proteins.

Small open reading frames in plant research: from prediction to functional characterization

Gene prediction is a laborious and time-consuming task. The advancement of sequencing technologies and bioinformatics tools, coupled with accelerated rate of ribosome profiling and mass spectrometry development, have made identification of small open reading frames (sORFs) (< 100 codons) in various plant genomes possible. The past 50 years have seen sORFs being isolated from many organisms. However, to date, a comprehensive sORF annotation pipeline is as yet unavailable, hence, addressed in our review. Here, we also provide current information on classification and functions of plant sORFs and their potential applications in crop improvement programs.

Unveiling a Ghost Proteome in the Glioblastoma Non-Coding RNAs

Glioblastoma is the most common brain cancer in adults. Nevertheless, the median survival time is 15 months, if treated with at least a near total resection and followed by radiotherapy in association with temozolomide. In glioblastoma (GBM), variations of non-coding ribonucleic acid (ncRNA) expression have been demonstrated in tumor processes, especially in the regulation of major signaling pathways. Moreover, many ncRNAs present in their sequences an Open Reading Frame (ORF) allowing their translations into proteins, so-called alternative proteins (AltProt) and constituting the “ghost proteome.” This neglected world in GBM has been shown to be implicated in protein–protein interaction (PPI) with reference proteins (RefProt) reflecting involvement in signaling pathways linked to cellular mobility and transfer RNA regulation. More recently, clinical studies have revealed that AltProt is also involved in the patient’s survival and bad prognosis. We thus propose to review the ncRNAs involved in GBM and highlight their function in the disease.

Ribosomal leaky scanning through a translated uORF requires eIF4G2

eIF4G2 (DAP5 or Nat1) is a homologue of the canonical translation initiation factor eIF4G1 in higher eukaryotes but its function remains poorly understood. Unlike eIF4G1, eIF4G2 does not interact with the cap-binding protein eIF4E and is believed to drive translation under stress when eIF4E activity is impaired. Here, we show that eIF4G2 operates under normal conditions as well and promotes scanning downstream of the eIF4G1-mediated 40S recruitment and cap-proximal scanning. Specifically, eIF4G2 facilitates leaky scanning for a subset of mRNAs. Apparently, eIF4G2 replaces eIF4G1 during scanning of 5′ UTR and the necessity for eIF4G2 only arises when eIF4G1 dissociates from the scanning complex. In particular, this event can occur when the leaky scanning complexes interfere with initiating or elongating 80S ribosomes within a translated uORF. This mechanism is therefore crucial for higher eukaryotes which are known to have long 5′ UTRs with highly frequent uORFs. We suggest that uORFs are not the only obstacle on the way of scanning complexes towards the main start codon, because certain eIF4G2 mRNA targets lack uORF(s). Thus, higher eukaryotes possess two distinct scanning complexes: the principal one that binds mRNA and initiates scanning, and the accessory one that rescues scanning when the former fails.

In-Depth Annotation of the Drosophila Bithorax-Complex Reveals the Presence of Several Alternative ORFs That Could Encode for Motif-Rich Peptides

It is recognized that a large proportion of eukaryotic RNAs and proteins is not produced from conventional genes but from short and alternative (alt) open reading frames (ORFs) that are not captured by gene prediction programs. Here we present an in silico prediction of altORFs by applying several selecting filters based on evolutionary conservation and annotations of previously characterized altORF peptides. Our work was performed in the Bithorax-complex (BX-C), which was one of the first genomic regions described to contain long non-coding RNAs in Drosophila. We showed that several altORFs could be predicted from coding and non-coding sequences of BX-C. In addition, the selected altORFs encode for proteins that contain several interesting molecular features, such as the presence of transmembrane helices or a general propensity to be rich in short interaction motifs. Of particular interest, one altORF encodes for a protein that contains a peptide sequence found in specific isoforms of two Drosophila Hox proteins. Our work thus suggests that several altORF proteins could be produced from a particular genomic region known for its critical role during Drosophila embryonic development. The molecular signatures of these altORF proteins further suggests that several of them could make numerous protein–protein interactions and be of functional importance in vivo.

How to Illuminate the Dark Proteome Using the Multi-omic OpenProt Resource

Ten of thousands of open reading frames (ORFs) are hidden within genomes. These alternative ORFs, or small ORFs, have eluded annotations because they are either small or within unsuspected locations. They are found in untranslated regions or overlap a known coding sequence in messenger RNA and anywhere in a "non-coding" RNA. Serendipitous discoveries have highlighted these ORFs' importance in biological functions and pathways. With their discovery came the need for deeper ORF annotation and large-scale mining of public repositories to gather supporting experimental evidence. OpenProt, accessible at https://openprot.org/, is the first proteogenomic resource enforcing a polycistronic model of annotation across an exhaustive transcriptome for 10 species. Moreover, OpenProt reports experimental evidence cumulated across a re-analysis of 114 mass spectrometry and 87 ribosome profiling datasets. The multi-omics OpenProt resource also includes the identification of predicted functional domains and evaluation of conservation for all predicted ORFs. The OpenProt web server provides two query interfaces and one genome browser. The query interfaces allow for exploration of the coding potential of genes or transcripts of interest as well as custom downloads of all information contained in OpenProt. © 2020 The Authors. Basic Protocol 1: Using the Search interface Basic Protocol 2: Using the Downloads interface.

Landscape of the Dark Transcriptome Revealed Through Re-mining Massive RNA-Seq Data

The "dark transcriptome" can be considered the multitude of sequences that are transcribed but not annotated as genes. We evaluated expression of 6,692 annotated genes and 29,354 unannotated open reading frames (ORFs) in the Saccharomyces cerevisiae genome across diverse environmental, genetic and developmental conditions (3,457 RNA-Seq samples). Over 30% of the highly transcribed ORFs have translation evidence. Phylostratigraphic analysis infers most of these transcribed ORFs would encode species-specific proteins ("orphan-ORFs"); hundreds have mean expression comparable to annotated genes. These data reveal unannotated ORFs most likely to be protein-coding genes. We partitioned a co-expression matrix by Markov Chain Clustering; the resultant clusters contain 2,468 orphan-ORFs. We provide the aggregated RNA-Seq yeast data with extensive metadata as a project in MetaOmGraph (MOG), a tool designed for interactive analysis and visualization. This approach enables reuse of public RNA-Seq data for exploratory discovery, providing a rich context for experimentalists to make novel, experimentally testable hypotheses about candidate genes.

Proteogenomic interrogation of cancer cell lines: an overview of the field

Introduction: Cancer cell lines (CCLs) have been a major resource for cancer research. Over the past couple of decades, they have been instrumental in omic profiling method development and as model systems to generate new knowledge in cell and cancer biology. More recently, with the increasing amount of genomic, transcriptomic and proteomic data being generated in hundreds of CCLs, there is growing potential for integrative proteogenomic data analyses to be performed.Areas covered: In this review, we first describe the most commonly used proteome profiling methods in CCLs. We then discuss how these proteomics data can be integrated with genomics data for proteogenomics analyses. Finally, we highlight some of the recent biological discoveries that have arisen from proteogenomics analyses of CCLs.Expert opinion: Protegeonomics analyses of CCLs have so far enabled the discovery of novel proteins and proteoforms. It has also improved our understanding of biological processes including post-transcriptional regulation of protein abundance and the presentation of antigens by major histocompatibility complex alleles. With proteomics data to be generated in hundreds to thousands of CCLs in coming years, there will be further potential for large-scale proteogenomics analyses and data integration with the phenotypically well-characterized CCLs.

Spectral Prediction Features as a Solution for the Search Space Size Problem in Proteogenomics

Proteogenomics approaches often struggle with the distinction between true and false peptide-to-spectrum matches as the database size enlarges. However, features extracted from tandem mass spectrometry intensity predictors can enhance the peptide identification rate and can provide extra confidence for peptide-to-spectrum matching in a proteogenomics context. To that end, features from the spectral intensity pattern predictors MS²PIP and Prosit were combined with the canonical scores from MaxQuant in the Percolator postprocessing tool for protein sequence databases constructed out of ribosome profiling and nanopore RNA-Seq analyses. The presented results provide evidence that this approach enhances both the identification rate as well as the validation stringency in a proteogenomic setting.

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First proteogenomics with PSM rescoring using machine learning–predicted spectra

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Demonstrated on both ribosome profiling and nanopore RNA-Seq–derived databases

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Rescoring leads to elevated stringency and increased identification rates

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Rescoring compensates for the search space size issues in proteogenomics

OpenProt 2021: deeper functional annotation of the coding potential of eukaryotic genomes

OpenProt (www.openprot.org) is the first proteogenomic resource supporting a polycistronic annotation model for eukaryotic genomes. It provides a deeper annotation of open reading frames (ORFs) while mining experimental data for supporting evidence using cutting-edge algorithms. This update presents the major improvements since the initial release of OpenProt. All species support recent NCBI RefSeq and Ensembl annotations, with changes in annotations being reported in OpenProt. Using the 131 ribosome profiling datasets re-analysed by OpenProt to date, non-AUG initiation starts are reported alongside a confidence score of the initiating codon. From the 177 mass spectrometry datasets re-analysed by OpenProt to date, the unicity of the detected peptides is controlled at each implementation. Furthermore, to guide the users, detectability statistics and protein relationships (isoforms) are now reported for each protein. Finally, to foster access to deeper ORF annotation independently of one's bioinformatics skills or computational resources, OpenProt now offers a data analysis platform. Users can submit their dataset for analysis and receive the results from the analysis by OpenProt. All data on OpenProt are freely available and downloadable for each species, the release-based format ensuring a continuous access to the data. Thus, OpenProt enables a more comprehensive annotation of eukaryotic genomes and fosters functional proteomic discoveries.

Assessing Single-Source Reproducibility of Human Head Hair Peptide Profiling from Different Regions of the Scalp

Neither microscopical hair comparisons nor mitochondrial DNA sequencing alone, or together, constitutes a basis for personal identification. Due to these limitations, a complementary technique to compare questioned and known hair shafts was investigated. Recently, scientists from Lawrence Livermore National Laboratory's Forensic Science Center and other collaborators developed a peptide profiling technique, which can infer non-synonymous single nucleotide polymorphisms (SNPs) preserved in hair shaft proteins as single amino acid polymorphisms (SAPs). In this study, peptide profiling was evaluated to determine if it can meet forensic expectations when samples are in limited quantities with the possibility that hair samples collected from different areas of a single donor's scalp (i.e., single source) might not exhibit the same SAP profile. The average dissimilarity, percent differences in SAP profiles within each source, ranged from 0% difference to 29%. This pilot study suggests that more work is needed before peptide profiling of hair can be considered for forensic comparisons.

Alternative proteins are functional regulators in cell reprogramming by PKA activation

It has been recently shown that many proteins are lacking from reference databases used in mass spectrometry analysis, due to their translation templated on alternative open reading frames. This questions our current understanding of gene annotation and drastically expands the theoretical proteome complexity. The functions of these alternative proteins (AltProts) still remain largely unknown. We have developed a large-scale and unsupervised approach based on cross-linking mass spectrometry (XL-MS) followed by shotgun proteomics to gather information on the functional role of AltProts by mapping them back into known signalling pathways through the identification of their reference protein (RefProt) interactors. We have identified and profiled AltProts in a cancer cell reprogramming system: NCH82 human glioma cells after 0, 16, 24 and 48 h Forskolin stimulation. Forskolin is a protein kinase A activator inducing cell differentiation and epithelial–mesenchymal transition. Our data show that AltMAP2, AltTRNAU1AP and AltEPHA5 interactions with tropomyosin 4 are downregulated under Forskolin treatment. In a wider perspective, Gene Ontology and pathway enrichment analysis (STRING) revealed that RefProts associated with AltProts are enriched in cellular mobility and transfer RNA regulation. This study strongly suggests novel roles of AltProts in multiple essential cellular functions and supports the importance of considering them in future biological studies.

Translation initiation downstream from annotated start codons in human mRNAs coevolves with the Kozak context

Eukaryotic translation initiation involves preinitiation ribosomal complex 5′-to-3′ directional probing of mRNA for codons suitable for starting protein synthesis. The recognition of codons as starts depends on the codon identity and on its immediate nucleotide context known as Kozak context. When the context is weak (i.e., nonoptimal), leaky scanning takes place during which a fraction of ribosomes continues the mRNA probing. We explored the relationship between the context of AUG codons annotated as starts of protein-coding sequences and the next AUG codon occurrence. We found that AUG codons downstream from weak starts occur in the same frame more frequently than downstream from strong starts. We suggest that evolutionary selection on in-frame AUGs downstream from weak start codons is driven by the advantage of the reduction of wasteful out-of-frame product synthesis and also by the advantage of producing multiple proteoforms from certain mRNAs. We confirmed translation initiation downstream from weak start codons using ribosome profiling data. We also tested translation of alternative start codons in 10 specific human genes using reporter constructs. In all tested cases, initiation at downstream start codons was more productive than at the annotated ones. In most cases, optimization of Kozak context did not completely abolish downstream initiation, and in the specific example of CMPK1 mRNA, the optimized start remained unproductive. Collectively, our work reveals previously uncharacterized forces shaping the evolution of protein-coding genes and points to the plurality of translation initiation and the existence of sequence features influencing start codon selection, other than Kozak context.

MinProtMaxVP: Generating a minimized number of protein variant sequences containing all possible variant peptides for proteogenomic analysis

Identifying single-amino-acid variants (SAVs) from mass spectrometry-based experiments is critical for validating single-nucleotide variants (SNVs) at the protein level to facilitate biomedical research. Currently, two approaches are usually applied to convert SNV annotations into SAV-harboring protein sequences. One approach generates one sequence containing exactly one SAV, and the other all SAVs. However, they may neglect the possibility of SAV combinations, e.g., haplotypes, existing in bio-samples. Therefore, it is necessary to consider all SAV combinations of a protein when generating SAV-harboring protein sequences. In this paper, we propose MinProtMaxVP, a novel approach which selects a minimized number of SAV-harboring protein sequences generated from the exhaustive approach, while still accommodating all possible variant peptides, by solving a classic set covering problem. Our study on known haplotype variations of TAS2R38 justifies the necessity for MinProtMaxVP to consider all combinations of SAVs. The performance of MinProtMaxVP is demonstrated by an in silico study on OR2T27 with five SAVs and real experimental data of the HEK293 cell line. Furthermore, assuming simulated somatic and germline variants of OR2T27 in tumor and normal tissues demonstrates that when adopting the appropriate somatic and germline SAV integration strategy, MinProtMaxVP is adaptable to labeling and label-free mass spectrometry-based experiments. SIGNIFICANCE: We present MinProtMaxVP, a novel approach to generate SAV-harboring protein sequences for constructing a customized protein sequence database, which is used in database searching for variant peptide identification. This approach outperforms the existing approaches in generating all possible variant peptides to be included in protein sequences and possibly leading to identification of more variant peptides in proteogenomic analysis.

Proteomic Detection and Validation of Translated Small Open Reading Frames

Small open reading frames (smORFs) encode previously unannotated polypeptides or short proteins that regulate translation in cis (eukaryotes) and/or are independently functional (prokaryotes and eukaryotes). Ongoing efforts for complete annotation and functional characterization of smORF-encoded proteins have yielded novel regulators and therapeutic targets. However, because they are excluded from protein databases, initiate at non-AUG start codons, and produce few unique tryptic peptides, unannotated small proteins cannot be detected with standard proteomic methods. Here,, we outline a procedure for mass spectrometry-based detection of translated smORFs in cultured human cells from protein extraction, digestion, and LC-MS/MS, to database preparation and data analysis. Following proteomic detection, translation from a unique smORF may be validated via siRNA-based silencing or overexpression and epitope tagging. This is necessary to unambiguously assign a peptide to a smORF within a specific transcript isoform or genomic locus. Provided that sufficient starting material is available, this workflow can be applied to any cell type/organism and adjusted to study specific (patho)physiological contexts including, but not limited to, development, stress, and disease. © 2019 by John Wiley & Sons, Inc. Basic Protocol 1: Protein extraction, size selection, and trypsin digestion Alternate Protocol 1: In-solution C8 column size selection Support Protocol 1: Chloroform/methanol precipitation Support Protocol 2: Reduction, alkylation, and in-solution protease digestion Support Protocol 3: Peptide de-salting Basic Protocol 2: Two-dimensional LC-MS/MS with ERLIC fractionation Basic Protocol 3: Transcriptomic database construction Alternate Protocol 2: Transcriptomics database generation with gffread Basic Protocol 4: Non-annotated peptide identification from LC-MS/MS data Basic Protocol 5: Validation using isotopically labeled synthetic peptide standards and siRNA Basic Protocol 6: Transcript validation using transient overexpression.

Modulation of Shoot Phosphate Level and Growth by PHOSPHATE1 Upstream Open Reading Frame

Inorganic orthophosphate (Pi) is an essential nutrient for plant growth, and its availability strongly impacts crop yield. PHOSPHATE1 (PHO1) transfers Pi from root to shoot via Pi export into root xylem vessels. In this work, we demonstrate that an upstream open reading frame (uORF) present in the 5' untranslated region of the Arabidopsis (Arabidopsis thaliana) PHO1 inhibits its translation and influences Pi homeostasis. The presence of the uORF strongly inhibited the translation of a PHO1 5'UTR-luciferase construct in protoplasts. A point mutation removing the PHO1 uORF (ΔuORF) in transgenic Arabidopsis resulted in increased association of its mRNA with polysomes and led to higher PHO1 protein levels, independent of Pi availability. Interestingly, deletion of the uORF led to higher shoot Pi content and was associated with improved shoot growth under low external Pi supply and no deleterious effects under Pi-sufficient conditions. We further show that natural accessions lacking the PHO1 uORF exhibit higher PHO1 protein levels and shoot Pi content. Increased shoot Pi content was linked to the absence of the PHO1 uORF in a population of F2 segregants. We identified the PHO1 uORF in genomes of crops such as rice (Oryza sativa), maize (Zea mays), barley (Hordeum vulgare), and wheat (Triticum aesativum), and we verified the inhibitory effect of the rice PHO1 uORF on translation in protoplasts. Our work suggests that regulation of PHO1 expression via its uORF might be a genetic resource useful-both in natural populations and in the context of genome editing-toward improving plant growth under Pi-deficient conditions.

Reconsidering proteomic diversity with functional investigation of small ORFs and alternative ORFs

The discovery of functional yet non-annotated open reading frames (ORFs) throughout the genome of several species presents an unprecedented challenge in current genome annotation. These novel ORFs are shorter than annotated ones and many can be found on the same RNA, in opposition to current assumptions in annotation methodologies. Whilst the literature lacks consensus, these novel ORFs are commonly referred to as small ORFs (sORFs) or alternative ORFs (alt-ORFs). Unannotated ORFs represent an overlooked layer of complexity in the coding potential of genomes and are transforming our current vision of the nature of coding genes. In this review, we outline what constitutes a sORF or an alt-ORF and emphasize differences between both nomenclatures. We then describe complementary large-scale methods to accurately discover novel ORFs as well as yield functional insights on the novel proteins they encode. While serendipitous discoveries highlighted the functional importance of some novel ORFs, omics methods facilitate and improve their characterization to better understand physiological and pathological pathways. Functional annotation of sORFs, alt-ORFs and their corresponding microproteins will likely help fundamental and clinical research.

Computational methods for ribosome profiling data analysis

Since the introduction of the ribosome profiling technique in 2009 its popularity has greatly increased. It is widely used for the comprehensive assessment of gene expression and for studying the mechanisms of regulation at the translational level. As the number of ribosome profiling datasets being produced continues to grow, so too does the need for reliable software that can provide answers to the biological questions it can address. This review describes the computational methods and tools that have been developed to analyze ribosome profiling data at the different stages of the process. It starts with initial routine processing of raw data and follows with more specific tasks such as the identification of translated open reading frames, differential gene expression analysis, or evaluation of local or global codon decoding rates. The review pinpoints challenges associated with each step and explains the ways in which they are currently addressed. In addition it provides a comprehensive, albeit incomplete, list of publicly available software applicable to each step, which may be a beneficial starting point to those unexposed to ribosome profiling analysis. The outline of current challenges in ribosome profiling data analysis may inspire computational biologists to search for novel, potentially superior, solutions that will improve and expand the bioinformatician's toolbox for ribosome profiling data analysis. This article is characterized under: Translation > Ribosome Structure/Function RNA Evolution and Genomics > Computational Analyses of RNA Translation > Translation Mechanisms Translation > Translation Regulation.

Non-AUG start codons: Expanding and regulating the small and alternative ORFeome

Recent ribosome profiling and proteomic studies have revealed the presence of thousands of novel coding sequences, referred to as small open reading frames (sORFs), in prokaryotic and eukaryotic genomes. These genes have defied discovery via traditional genomic tools not only because they tend to be shorter than standard gene annotation length cutoffs, but also because they are, as a class, enriched in sequence properties previously assumed to be unusual, including non-AUG start codons. In this review, we summarize what is currently known about the incidence, efficiency, and mechanism of non-AUG start codon usage in prokaryotes and eukaryotes, and provide examples of regulatory and functional sORFs that initiate at non-AUG codons. While only a handful of non-AUG-initiated novel genes have been characterized in detail to date, their participation in important biological processes suggests that an improved understanding of this class of genes is needed.

Mass Spectrometry Advances and Perspectives for the Characterization of Emerging Adoptive Cell Therapies

Adoptive cell therapy is an emerging anti-cancer modality, whereby the patient's own immune cells are engineered to express T-cell receptor (TCR) or chimeric antigen receptor (CAR). CAR-T cell therapies have advanced the furthest, with recent approvals of two treatments by the Food and Drug Administration of Kymriah (trisagenlecleucel) and Yescarta (axicabtagene ciloleucel). Recent developments in proteomic analysis by mass spectrometry (MS) make this technology uniquely suited to enable the comprehensive identification and quantification of the relevant biochemical architecture of CAR-T cell therapies and fulfill current unmet needs for CAR-T product knowledge. These advances include improved sample preparation methods, enhanced separation technologies, and extension of MS-based proteomic to single cells. Innovative technologies such as proteomic analysis of raw material quality attributes (MQA) and final product quality attributes (PQA) may provide insights that could ultimately fuel development strategies and lead to broad implementation.

Full-Length Transcript-Based Proteogenomics of Rice Improves Its Genome and Proteome Annotation

Rice (Oryza sativa) molecular breeding has gained considerable attention in recent years, but inaccurate genome annotation hampers its progress and functional studies of the rice genome. In this study, we applied single-molecule long-read RNA sequencing (lrRNA_seq)-based proteogenomics to reveal the complexity of the rice transcriptome and its coding abilities. Surprisingly, approximately 60% of loci identified by lrRNA_seq are associated with natural antisense transcripts (NATs). The high-density genomic arrangement of NAT genes suggests their potential roles in the multifaceted control of gene expression. In addition, a large number of fusion and intergenic transcripts have been observed. Furthermore, 906,456 transcript isoforms were identified, and 72.9% of the genes can generate splicing isoforms. A total of 706,075 posttranscriptional events were subsequently categorized into 10 subtypes, demonstrating the interdependence of posttranscriptional mechanisms that contribute to transcriptome diversity. Parallel short-read RNA sequencing indicated that lrRNA_seq has a superior capacity for the identification of longer transcripts. In addition, over 190,000 unique peptides belonging to 9,706 proteoforms/protein groups were identified, expanding the diversity of the rice proteome. Our findings indicate that the genome organization, transcriptome diversity, and coding potential of the rice transcriptome are far more complex than previously anticipated.

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.

Not lost in host translation: The new roles of long noncoding RNAs in infectious diseases

Long non-coding RNAs (lncRNAs) play a central role in the regulation of gene expression. Although they were initially described as mRNA-like transcripts not encoding proteins, global approaches such as ribosome profiling have shown that they frequently associate with ribosomes, opening the possibility that lncRNAs are a source of cryptic translation events with functional roles. Recent studies have shed more light on small ORFs borne by lncRNAs and encoding short peptides potentially involved in infectious immunity. This review outlines the main strategies used to determine the coding potential of lncRNAs and discusses our emerging understanding of the implication of the encoded peptides in infectious diseases.

AssessORF: combining evolutionary conservation and proteomics to assess prokaryotic gene predictions

MOTIVATION

A core task of genomics is to identify the boundaries of protein coding genes, which may cover over 90% of a prokaryote's genome. Several programs are available for gene finding, yet it is currently unclear how well these programs perform and whether any offers superior accuracy. This is in part because there is no universal benchmark for gene finding and, therefore, most developers select their own benchmarking strategy.

RESULTS

Here, we introduce AssessORF, a new approach for benchmarking prokaryotic gene predictions based on evidence from proteomics data and the evolutionary conservation of start and stop codons. We applied AssessORF to compare gene predictions offered by GenBank, GeneMarkS-2, Glimmer and Prodigal on genomes spanning the prokaryotic tree of life. Gene predictions were 88-95% in agreement with the available evidence, with Glimmer performing the worst but no clear winner. All programs were biased towards selecting start codons that were upstream of the actual start. Given these findings, there remains considerable room for improvement, especially in the detection of correct start sites.

AVAILABILITY AND IMPLEMENTATION

AssessORF is available as an R package via the Bioconductor package repository.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Beyond Genes: Re-Identifiability of Proteomic Data and Its Implications for Personalized Medicine

The increasing availability of high throughput proteomics data provides us with opportunities as well as posing new ethical challenges regarding data privacy and re-identifiability of participants. Moreover, the fact that proteomics represents a level between the genotype and the phenotype further exacerbates the situation, introducing dilemmas related to publicly available data, anonymization, ownership of information and incidental findings. In this paper, we try to differentiate proteomics from genomics data and cover the ethical challenges related to proteomics data sharing. Finally, we give an overview of the proposed solutions and the outlook for future studies.

The Tomato Translational Landscape Revealed by Transcriptome Assembly and Ribosome Profiling

Recent applications of translational control in Arabidopsis (Arabidopsis thaliana) highlight the potential power of manipulating mRNA translation for crop improvement. However, to what extent translational regulation is conserved between Arabidopsis and other species is largely unknown, and the translatome of most crops remains poorly studied. Here, we combined de novo transcriptome assembly and ribosome profiling to study global mRNA translation in tomato (Solanum lycopersicum) roots. Exploiting features corresponding to active translation, we discovered widespread unannotated translation events, including 1,329 upstream open reading frames (uORFs) within the 5' untranslated regions of annotated coding genes and 354 small ORFs (sORFs) among unannotated transcripts. uORFs may repress translation of their downstream main ORFs, whereas sORFs may encode signaling peptides. Besides evolutionarily conserved sORFs, we uncovered 96 Solanaceae-specific sORFs, revealing the importance of studying translatomes directly in crops. Proteomic analysis confirmed that some of the unannotated ORFs generate stable proteins in planta. In addition to defining the translatome, our results reveal the global regulation by uORFs and microRNAs. Despite diverging over 100 million years ago, many translational features are well conserved between Arabidopsis and tomato. Thus, our approach provides a high-throughput method to discover unannotated ORFs, elucidates evolutionarily conserved and unique translational features, and identifies regulatory mechanisms hidden in a crop genome.

Harnessing the tissue and plasma lncRNA-peptidome to discover peptide-based cancer biomarkers

Proteome-centric studies, although have identified numerous lncRNA-encoded polypeptides, lack differential expression analysis of lncRNA-peptidome across primary tissues, cell lines and cancer states. We established a computational-proteogenomic workflow involving re-processing of publicly available LC-MS/MS data, which facilitated the identification of tissue-specific and universally expressed (UExp) lncRNA-polypeptides across 14 primary human tissues and 11 cell lines. The utility of lncRNA-peptidome as cancer-biomarkers was investigated by re-processing LC-MS/MS data from 92 colon-adenocarcinoma (COAD) and 30 normal colon-epithelium tissues. Intriguingly, a significant upregulation of five lncRNA UExp-polypeptides in COAD tissues was observed. Furthermore, clustering of the UExp-polypeptides led to the classification of COAD patients that coincided with the clinical stratification, underlining the prognostic potential of the UExp-polypeptides. Lastly, we identified differential abundance of the UExp-polypeptides in the plasma of prostate-cancer patients highlighting their potential as plasma-biomarker. The analysis of lncRNA-peptidome may pave the way to identify effective tissue/plasma biomarkers for different cancer types.

PROTEOFORMER 2.0: Further Developments in the Ribosome Profiling-assisted Proteogenomic Hunt for New Proteoforms

PROTEOFORMER is a pipeline that enables the automated processing of data derived from ribosome profiling (RIBO-seq, i.e. the sequencing of ribosome-protected mRNA fragments). As such, genome-wide ribosome occupancies lead to the delineation of data-specific translation product candidates and these can improve the mass spectrometry-based identification. Since its first publication, different upgrades, new features and extensions have been added to the PROTEOFORMER pipeline. Some of the most important upgrades include P-site offset calculation during mapping, comprehensive data pre-exploration, the introduction of two alternative proteoform calling strategies and extended pipeline output features. These novelties are illustrated by analyzing ribosome profiling data of human HCT116 and Jurkat data. The different proteoform calling strategies are used alongside one another and in the end combined together with reference sequences from UniProt. Matching mass spectrometry data are searched against this extended search space with MaxQuant. Overall, besides annotated proteoforms, this pipeline leads to the identification and validation of different categories of new proteoforms, including translation products of up- and downstream open reading frames, 5' and 3' extended and truncated proteoforms, single amino acid variants, splice variants and translation products of so-called noncoding regions. Further, proof-of-concept is reported for the improvement of spectrum matching by including Prosit, a deep neural network strategy that adds extra fragmentation spectrum intensity features to the analysis. In the light of ribosome profiling-driven proteogenomics, it is shown that this allows validating the spectrum matches of newly identified proteoforms with elevated stringency. These updates and novel conclusions provide new insights and lessons for the ribosome profiling-based proteogenomic research field. More practical information on the pipeline, raw code, the user manual (README) and explanations on the different modes of availability can be found at the GitHub repository of PROTEOFORMER: https://github.com/Biobix/proteoformer.

De novo annotation and characterization of the translatome with ribosome profiling data

By capturing and sequencing the RNA fragments protected by translating ribosomes, ribosome profiling provides snapshots of translation at subcodon resolution. The growing needs for comprehensive annotation and characterization of the context-dependent translatomes are calling for an efficient and unbiased method to accurately recover the signal of active translation from the ribosome profiling data. Here we present our new method, RiboCode, for such purpose. Being tested with simulated and real ribosome profiling data, and validated with cell type-specific QTI-seq and mass spectrometry data, RiboCode exhibits superior efficiency, sensitivity, and accuracy for de novo annotation of the translatome, which covers various types of ORFs in the previously annotated coding and non-coding regions. As an example, RiboCode was applied to assemble the context-specific translatomes of yeast under normal and stress conditions. Comparisons among these translatomes revealed stress-activated novel upstream and downstream ORFs, some of which are associated with translational dysregulations of the annotated main ORFs under the stress conditions.

A comprehensive catalog of predicted functional upstream open reading frames in humans

Upstream open reading frames (uORFs) latent in mRNA transcripts are thought to modify translation of coding sequences by altering ribosome activity. Not all uORFs are thought to be active in such a process. To estimate the impact of uORFs on the regulation of translation in humans, we first circumscribed the universe of all possible uORFs based on coding gene sequence motifs and identified 1.3 million unique uORFs. To determine which of these are likely to be biologically relevant, we built a simple Bayesian classifier using 89 attributes of uORFs labeled as active in ribosome profiling experiments. This allowed us to extrapolate to a comprehensive catalog of likely functional uORFs. We validated our predictions using in vivo protein levels and ribosome occupancy from 46 individuals. This is a substantially larger catalog of functional uORFs than has previously been reported. Our ranked list of likely active uORFs allows researchers to test their hypotheses regarding the role of uORFs in health and disease. We demonstrate several examples of biological interest through the application of our catalog to somatic mutations in cancer and disease-associated germline variants in humans.

Ribosome Profiling: Global Views of Translation

The translation of messenger RNA (mRNA) into protein and the folding of the resulting protein into an active form are prerequisites for virtually every cellular process and represent the single largest investment of energy by cells. Ribosome profiling-based approaches have revolutionized our ability to monitor every step of protein synthesis in vivo, allowing one to measure the rate of protein synthesis across the proteome, annotate the protein coding capacity of genomes, monitor localized protein synthesis, and explore cotranslational folding and targeting. The rich and quantitative nature of ribosome profiling data provides an unprecedented opportunity to explore and model complex cellular processes. New analytical techniques and improved experimental protocols will provide a deeper understanding of the factors controlling translation speed and its impact on protein function and cell physiology as well as the role of ribosomal RNA and mRNA modifications in regulating translation.

A protein identification algorithm for tandem mass spectrometry by incorporating the abundance of mRNA into a binomial probability scoring model

Peptide-spectrum matches (PSM) scoring between the experimental and theoretical spectrum is a key step in the identification of proteins using mass spectrometry (MS)-based proteomics analyses. Efficient protein identification using MS/MS data remains a challenge. The strategy of using RNA-seq data increases the number of proteins identified by re-constructing the custom search database and integrating mRNA abundance into the false discovery rate of post-PSM. However, this process lacks an algorithm that can allow the incorporation of mRNA abundance into the key scoring model of PSM. Therefore, we developed a novel PSM scoring model, which incorporates mRNA abundance for improved peptide and protein identification. In the new algorithm, abundance information of mRNA was transformed to the prior probability of protein identification and integrated to re-score in PSM using the binomial probability distribution model. Compared with other algorithms using five MS/MS datasets, the results showed that the least improvement ratios of peptide and protein groups were 3.39%-9.79% and 0.48%-8.16% in different datasets (human, rat, zebrafish, yeast, and Arabidopsis thaliana). The new strategy offers an effective solution for MS-based identification of peptides and proteins. SIGNIFICANCE: The new algorithm identifies proteins by quantifying mRNA abundance (FPKM) and incorporating it into a scoring model for peptide-spectrum matches. It is important to improve peptide and protein identification from MS/MS datasets in proteomics research.

Concerted expression of a cell cycle regulator and a metabolic enzyme from a bicistronic transcript in plants

Eukaryotic mRNAs frequently contain upstream open reading frames (uORFs), encoding small peptides that may control translation of the main ORF (mORF). Here, we report the characterization of a distinct bicistronic transcript in Arabidopsis. We analysed loss-of-function phenotypes of the inorganic polyphosphatase TRIPHOSPHATE TUNNEL METALLOENZYME 3 (AtTTM3), and found that catalytically inactive versions of the enzyme could fully complement embryo and growth-related phenotypes. We could rationalize these puzzling findings by characterizing a uORF in the AtTTM3 locus encoding CELL DIVISION CYCLE PROTEIN 26 (CDC26), an orthologue of the cell cycle regulator. We demonstrate that AtCDC26 is part of the plant anaphase promoting complex/cyclosome (APC/C), regulates accumulation of APC/C target proteins and controls cell division, growth and embryo development. AtCDC26 and AtTTM3 are translated from a single transcript conserved across the plant lineage. While there is no apparent biochemical connection between the two gene products, AtTTM3 coordinates AtCDC26 translation by recruiting the transcript into polysomes. Our work highlights that uORFs may encode functional proteins in plant genomes.

Cell-Type-Specific Profiling of Alternative Translation Identifies Regulated Protein Isoform Variation in the Mouse Brain

Alternative translation initiation and stop codon readthrough in a few well-studied cases have been shown to allow the same transcript to generate multiple protein variants. Because the brain shows a particularly abundant use of alternative splicing, we sought to study alternative translation in CNS cells. We show that alternative translation is widespread and regulated across brain transcripts. In neural cultures, we identify alternative initiation on hundreds of transcripts, confirm several N-terminal protein variants, and show the modulation of the phenomenon by KCl stimulation. We also detect readthrough in cultures and show differential levels of normal and readthrough versions of AQP4 in gliotic diseases. Finally, we couple translating ribosome affinity purification to ribosome footprinting (TRAP-RF) for cell-type-specific analysis of neuronal and astrocytic translational readthrough in the mouse brain. We demonstrate that this unappreciated mechanism generates numerous and diverse protein isoforms in a cell-type-specific manner in the brain.

A recap of RNA recapping

The N7-methylguanosine cap is a hallmark of the 5' end of eukaryotic mRNAs and is required for gene expression. Loss of the cap was believed to lead irreversibly to decay. However, nearly a decade ago, it was discovered that mammalian cells contain enzymes in the cytoplasm that are capable of restoring caps onto uncapped RNAs. In this review, we summarize recent advances in our understanding of cytoplasmic RNA recapping and discuss the biochemistry of this process and its impact on regulating and diversifying the transcriptome. Although most studies focus on mammalian RNA recapping, we also highlight new observations for recapping in disparate eukaryotic organisms, with the trypanosome recapping system appearing to be a fascinating example of convergent evolution. We conclude with emerging insights into the biological significance of RNA recapping and prospects for the future of this evolving area of study. This article is categorized under: RNA Processing > RNA Editing and Modification Translation > Translation Regulation RNA Processing > Capping and 5' End Modifications RNA Turnover and Surveillance > Regulation of RNA Stability.

Cell-Type-Specific Proteomics: A Neuroscience Perspective

Cell-type-specific analysis has become a major focus for many investigators in the field of neuroscience, particularly because of the large number of different cell populations found in brain tissue that play roles in a variety of developmental and behavioral disorders. However, isolation of these specific cell types can be challenging due to their nonuniformity and complex projections to different brain regions. Moreover, many analytical techniques used for protein detection and quantitation remain insensitive to the low amounts of protein extracted from specific cell populations. Despite these challenges, methods to improve proteomic yield and increase resolution continue to develop at a rapid rate. In this review, we highlight the importance of cell-type-specific proteomics in neuroscience and the technical difficulties associated. Furthermore, current progress and technological advancements in cell-type-specific proteomics research are discussed with an emphasis in neuroscience.

ProteomeGenerator: A Framework for Comprehensive Proteomics Based on de Novo Transcriptome Assembly and High-Accuracy Peptide Mass Spectral Matching

Modern mass spectrometry now permits genome-scale and quantitative measurements of biological proteomes. However, analysis of specific specimens is currently hindered by the incomplete representation of biological variability of protein sequences in canonical reference proteomes and the technical demands for their construction. Here, we report ProteomeGenerator, a framework for de novo and reference-assisted proteogenomic database construction and analysis based on sample-specific transcriptome sequencing and high-accuracy mass spectrometry proteomics. This enables the assembly of proteomes encoded by actively transcribed genes, including sample-specific protein isoforms resulting from non-canonical mRNA transcription, splicing, or editing. To improve the accuracy of protein isoform identification in non-canonical proteomes, ProteomeGenerator relies on statistical target-decoy database matching calibrated using sample-specific controls. Its current implementation includes automatic integration with MaxQuant mass spectrometry proteomics algorithms. We applied this method for the proteogenomic analysis of splicing factor SRSF2 mutant leukemia cells, demonstrating high-confidence identification of non-canonical protein isoforms arising from alternative transcriptional start sites, intron retention, and cryptic exon splicing as well as improved accuracy of genome-scale proteome discovery. Additionally, we report proteogenomic performance metrics for current state-of-the-art implementations of SEQUEST HT, MaxQuant, Byonic, and PEAKS mass spectral analysis algorithms. Finally, ProteomeGenerator is implemented as a Snakemake workflow within a Singularity container for one-step installation in diverse computing environments, thereby enabling open, scalable, and facile discovery of sample-specific, non-canonical, and neomorphic biological proteomes.

The Protein Coded by a Short Open Reading Frame, Not by the Annotated Coding Sequence, Is the Main Gene Product of the Dual-Coding Gene MIEF1

Proteogenomics and ribosome profiling concurrently show that genes may code for both a large and one or more small proteins translated from annotated coding sequences (CDSs) and unannotated alternative open reading frames (named alternative ORFs or altORFs), respectively, but the stoichiometry between large and small proteins translated from a same gene is unknown. MIEF1, a gene recently identified as a dual-coding gene, harbors a CDS and a newly annotated and actively translated altORF located in the 5′UTR. Here, we use absolute quantification with stable isotope-labeled peptides and parallel reaction monitoring to determine levels of both proteins in two human cells lines and in human colon. We report that the main MIEF1 translational product is not the canonical 463 amino acid MiD51 protein but the small 70 amino acid alternative MiD51 protein (altMiD51). These results demonstrate the inadequacy of the single CDS concept and provide a strong argument for incorporating altORFs and small proteins in functional annotations.