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Timely and effective use of antimicrobial drugs can improve patient outcomes, as well as help safeguard against resistance development. Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) is currently routinely used in clinical diagnostics for rapid species identification. Mining additional data from said spectra in the form of antimicrobial resistance (AMR) profiles is, therefore, highly promising. Such AMR profiles could serve as a drop-in solution for drastically improving treatment efficiency, effectiveness, and costs. This study endeavors to develop the first machine learning models capable of predicting AMR profiles for the whole repertoire of species and drugs encountered in clinical microbiology. The resulting models can be interpreted as drug recommender systems for infectious diseases. We find that our dual-branch method delivers considerably higher performance compared to previous approaches. In addition, experiments show that the models can be efficiently fine-tuned to data from other clinical laboratories. MALDI-TOF-based AMR recommender systems can, hence, greatly extend the value of MALDI-TOF MS for clinical diagnostics. All code supporting this study is distributed on PyPI and is packaged at https://github.com/gdewael/maldi-nn., Competing Interests: GD, WW, GM No competing interests declared, (© 2024, De Waele et al.)

A major scientific drive is to characterize the protein-coding genome as it provides the primary basis for the study of human health. But the fundamental question remains: what has been missed in prior genomic analyses? Over the past decade, the translation of non-canonical open reading frames (ncORFs) has been observed across human cell types and disease states, with major implications for proteomics, genomics, and clinical science. However, the impact of ncORFs has been limited by the absence of a large-scale understanding of their contribution to the human proteome. Here, we report the collaborative efforts of stakeholders in proteomics, immunopeptidomics, Ribo-seq ORF discovery, and gene annotation, to produce a consensus landscape of protein-level evidence for ncORFs. We show that at least 25% of a set of 7,264 ncORFs give rise to translated gene products, yielding over 3,000 peptides in a pan-proteome analysis encompassing 3.8 billion mass spectra from 95,520 experiments. With these data, we developed an annotation framework for ncORFs and created public tools for researchers through GENCODE and PeptideAtlas. This work will provide a platform to advance ncORF-derived proteins in biomedical discovery and, beyond humans, diverse animals and plants where ncORFs are similarly observed., Competing Interests: Declaration of interests J.R.P. has received research honoraria from Novartis Biosciences and is a paid consultant for ProFound Therapeutics. J.G.A. is a paid consultant for Enara Bio and Moderna. J.L.A. is an advisor to Microneedle Solutions. T.F.M. is a consultant for and holds equity in Velia Therapeutics. J.S.W. is an advisor and holds equity in Velia Therapeutics. G.M. is co-founder and CSO of OHMX.bio. S.A.C. is a member of the scientific advisory boards of Kymera, PTM BioLabs, Seer and PrognomIQ. N.T.I. hold equity in Velia Therapeutics and holds equity and serves as a scientific advisor to Tevard Biosciences. P.F. is a member of the scientific advisory board of Infinitopes. A.-R. C. is a member of the advisory board of ProFound Therapeutics.

The correct mapping of the proteome is an important step towards advancing our understanding of biological systems and cellular mechanisms. Methods that provide better mappings can fuel important processes such as drug discovery and disease understanding. Currently, true determination of translation initiation sites is primarily achieved by in vivo experiments. Here, we propose TIS Transformer, a deep learning model for the determination of translation start sites solely utilizing the information embedded in the transcript nucleotide sequence. The method is built upon deep learning techniques first designed for natural language processing. We prove this approach to be best suited for learning the semantics of translation, outperforming previous approaches by a large margin. We demonstrate that limitations in the model performance are primarily due to the presence of low-quality annotations against which the model is evaluated against. Advantages of the method are its ability to detect key features of the translation process and multiple coding sequences on a transcript. These include micropeptides encoded by short Open Reading Frames, either alongside a canonical coding sequence or within long non-coding RNAs. To demonstrate the use of our methods, we applied TIS Transformer to remap the full human proteome., (© The Author(s) 2023. Published by Oxford University Press on behalf of NAR Genomics and Bioinformatics.)

Motivation: The adoption of current single-cell DNA methylation sequencing protocols is hindered by incomplete coverage, outlining the need for effective imputation techniques. The task of imputing single-cell (methylation) data requires models to build an understanding of underlying biological processes., Results: We adapt the transformer neural network architecture to operate on methylation matrices through combining axial attention with sliding window self-attention. The obtained CpG Transformer displays state-of-the-art performances on a wide range of scBS-seq and scRRBS-seq datasets. Furthermore, we demonstrate the interpretability of CpG Transformer and illustrate its rapid transfer learning properties, allowing practitioners to train models on new datasets with a limited computational and time budget., Availability and Implementation: CpG Transformer is freely available at https://github.com/gdewael/cpg-transformer., Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author(s) 2021. Published by Oxford University Press.)

Transcriptome and ribosome sequencing have revealed the existence of many non-canonical transcripts, mainly containing splice variants, ncRNA, sORFs and altORFs. However, identification and characterization of products that may be translated out of these remains a challenge. Addressing this, we here report on 552 non-canonical proteins and splice variants in the model organism C. elegans using tandem mass spectrometry . Aided by sequencing-based prediction, we generated a custom proteome database tailored to search for non-canonical translation products of C. elegans . Using this database, we mined available mass spectrometric resources of C. elegans , from which 51 novel, non-canonical proteins could be identified . Furthermore, we utilized diverse proteomic and peptidomic strategies to detect 40 novel non-canonical proteins in C. elegans by LC-TIMS-MS/MS, of which 6 were common with our meta-analysis of existing resources. Together, this permits us to provide a resource with detailed annotation of 467 splice variants and 85 novel proteins mapped onto UTRs, non-coding regions and alternative open reading frames of the C. elegans genome., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Parmar, Peeters, Boonen, Clark, Baggerman, Menschaert and Temmerman.)

Bioactive peptides exhibit key roles in a wide variety of complex processes, such as regulation of body weight, learning, aging, and innate immune response. Next to the classical bioactive peptides, emerging from larger precursor proteins by specific proteolytic processing, a new class of peptides originating from small open reading frames (sORFs) have been recognized as important biological regulators. But their intrinsic properties, specific expression pattern and location on presumed non-coding regions have hindered the full characterization of the repertoire of bioactive peptides, despite their predominant role in various pathways. Although the development of peptidomics has offered the opportunity to study these peptides in vivo , it remains challenging to identify the full peptidome as the lack of cleavage enzyme specification and large search space complicates conventional database search approaches. In this study, we introduce a proteogenomics methodology using a new type of mass spectrometry instrument and the implementation of machine learning tools toward improved identification of potential bioactive peptides in the mouse brain. The application of trapped ion mobility spectrometry (tims) coupled to a time-of-flight mass analyzer (TOF) offers improved sensitivity, an enhanced peptide coverage, reduction in chemical noise and the reduced occurrence of chimeric spectra. Subsequent machine learning tools MS 2 PIP, predicting fragment ion intensities and DeepLC, predicting retention times, improve the database searching based on a large and comprehensive custom database containing both sORFs and alternative ORFs. Finally, the identification of peptides is further enhanced by applying the post-processing semi-supervised learning tool Percolator. Applying this workflow, the first peptidomics workflow combined with spectral intensity and retention time predictions, we identified a total of 167 predicted sORF-encoded peptides, of which 48 originating from presumed non-coding locations, next to 401 peptides from known neuropeptide precursors, linked to 66 annotated bioactive neuropeptides from within 22 different families. Additional PEAKS analysis expanded the pool of SEPs on presumed non-coding locations to 84, while an additional 204 peptides completed the list of peptides from neuropeptide precursors. Altogether, this study provides insights into a new robust pipeline that fuses technological advancements from different fields ensuring an improved coverage of the neuropeptidome in the mouse brain., Competing Interests: GM is a co-founder of OHMX.bio, Ghent, Belgium. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Peeters, Baggerman, Gabriels, Pepermans, Menschaert and Boonen.)

The effectiveness of deep learning methods can be largely attributed to the automated extraction of relevant features from raw data. In the field of functional genomics, this generally concerns the automatic selection of relevant nucleotide motifs from DNA sequences. To benefit from automated learning methods, new strategies are required that unveil the decision-making process of trained models. In this paper, we present a new approach that has been successful in gathering insights on the transcription process in Escherichia coli. This work builds upon a transformer-based neural network framework designed for prokaryotic genome annotation purposes. We find that the majority of subunits (attention heads) of the model are specialized towards identifying transcription factors and are able to successfully characterize both their binding sites and consensus sequences, uncovering both well-known and potentially novel elements involved in the initiation of the transcription process. With the specialization of the attention heads occurring automatically, we believe transformer models to be of high interest towards the creation of explainable neural networks in this field., (© The Author(s) 2021. Published by Oxford University Press.)

Post-transcriptional modifications of RNA constitute an emerging regulatory layer of gene expression. The demethylase fat mass- and obesity-associated protein (FTO), an eraser of N 6 -methyladenosine (m 6 A), has been shown to play a role in cancer, but its contribution to tumor progression and the underlying mechanisms remain unclear. Here, we report widespread FTO downregulation in epithelial cancers associated with increased invasion, metastasis and worse clinical outcome. Both in vitro and in vivo, FTO silencing promotes cancer growth, cell motility and invasion. In human-derived tumor xenografts (PDXs), FTO pharmacological inhibition favors tumorigenesis. Mechanistically, we demonstrate that FTO depletion elicits an epithelial-to-mesenchymal transition (EMT) program through increased m 6 A and altered 3'-end processing of key mRNAs along the Wnt signaling cascade. Accordingly, FTO knockdown acts via EMT to sensitize mouse xenografts to Wnt inhibition. We thus identify FTO as a key regulator, across epithelial cancers, of Wnt-triggered EMT and tumor progression and reveal a therapeutically exploitable vulnerability of FTO-low tumors., (© 2021. The Author(s), under exclusive licence to Springer Nature America, Inc.)

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 MS 2 PIP 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., Competing Interests: Conflict of interest G. M. is a founder of OHMX.bio, Ghent, Belgium. G. M., S. V., H. V. d. V., and A. M. are employees at OHMX.bio, Ghent, Belgium. M. W. and B. K. are founders and shareholders of MSAID GmbH, Garching near Munich, Germany, and OmicScouts GmbH, Freising, Germany. They have no operational role in both companies. S. G. is founder and shareholder of MSAID GmbH. S. G. is currently employed by MSAID GmbH but had no operational role in this company during the time of the study., (Copyright © 2021 The Authors. Published by Elsevier Inc. All rights reserved.)

Tet-enzyme-mediated 5-hydroxymethylation of cytosines in DNA plays a crucial role in mouse embryonic stem cells (ESCs). In RNA also, 5-hydroxymethylcytosine (5hmC) has recently been evidenced, but its physiological roles are still largely unknown. Here we show the contribution and function of this mark in mouse ESCs and differentiating embryoid bodies. Transcriptome-wide mapping in ESCs reveals hundreds of messenger RNAs marked by 5hmC at sites characterized by a defined unique consensus sequence and particular features. During differentiation a large number of transcripts, including many encoding key pluripotency-related factors (such as Eed and Jarid2), show decreased cytosine hydroxymethylation. Using Tet-knockout ESCs, we find Tet enzymes to be partly responsible for deposition of 5hmC in mRNA. A transcriptome-wide search further reveals mRNA targets to which Tet1 and Tet2 bind, at sites showing a topology similar to that of 5hmC sites. Tet-mediated RNA hydroxymethylation is found to reduce the stability of crucial pluripotency-promoting transcripts. We propose that RNA cytosine 5-hydroxymethylation by Tets is a mark of transcriptome flexibility, inextricably linked to the balance between pluripotency and lineage commitment.

Growing evidence illustrates the shortcomings on the current understanding of the full complexity of the proteome. Previously overlooked small open reading frames (sORFs) and their encoded microproteins have filled important gaps, exerting their function as biologically relevant regulators. The characterization of the full small proteome has potential applications in many fields. Continuous development of techniques and tools led to an improved sORF discovery, where these can originate from bioinformatics analyses, from sequencing routines or proteomics approaches. In this mini review, we discuss the ongoing trends in the three fields and suggest some strategies for further characterization of high potential candidates., (Copyright © 2020 Elsevier Inc. All rights reserved.)

The emergence of small open reading frame (sORF)-encoded peptides (SEPs) is rapidly expanding the known proteome at the lower end of the size distribution. Here, we show that the mitochondrial proteome, particularly the respiratory chain, is enriched for small proteins. Using a prediction and validation pipeline for SEPs, we report the discovery of 16 endogenous nuclear encoded, mitochondrial-localized SEPs (mito-SEPs). Through functional prediction, proteomics, metabolomics and metabolic flux modeling, we demonstrate that BRAWNIN, a 71 a.a. peptide encoded by C12orf73, is essential for respiratory chain complex III (CIII) assembly. In human cells, BRAWNIN is induced by the energy-sensing AMPK pathway, and its depletion impairs mitochondrial ATP production. In zebrafish, Brawnin deletion causes complete CIII loss, resulting in severe growth retardation, lactic acidosis and early death. Our findings demonstrate that BRAWNIN is essential for vertebrate oxidative phosphorylation. We propose that mito-SEPs are an untapped resource for essential regulators of oxidative metabolism.

Brain derived peptides function as signaling molecules in the brain and regulate various physiological and behavioral processes. The low abundance and atypical fragmentation of these brain derived peptides makes detection using traditional proteomic methods challenging. In this study, we introduce and validate a new methodology for the discovery of novel peptides derived from mammalian brain. This methodology combines ribosome profiling and mass spectrometry-based peptidomics. Using this framework, we have identified a novel peptide in mouse whole brain whose expression is highest in the basal ganglia, hypothalamus and amygdala. Although its functional role is unknown, it has been previously detected in peripheral tissue as a component of the mRNA decapping complex. Continued discovery and studies of novel regulating peptides in mammalian brain may also provide insight into brain disorders., (Copyright © 2019. Published by Elsevier B.V.)

Background and Objective: Ribosome profiling is a recent next generation sequencing technique enabling the genome-wide study of gene expression in biomedical research at the translation level. Too often, researchers precipitously start trying to test their hypotheses after alignment of their data, without checking the quality and the general features of their mapped data. Despite the fact that these checks are essential to prevent errors and ensure valid conclusions afterwards, easy-to-use tools for visualizing the quality and overall outlook of mapped ribosome profiling data are lacking., Methods: We present mQC, a modular tool implemented as a Bioconda package and also available in the Galaxy tool shed. Herewith both bio-informaticians as well as non-experts can easily perform the indispensable visualization of both the quality and the general features of their mapped P-site corrected ribosome profiling reads. The user manual, the raw code and more information can be found on its GitHub repository (https://github.com/Biobix/mQC)., Results: mQC was tested on multiple datasets to assess its general applicability and was compared to other tools that partly perform similar tasks., Conclusions: Our results demonstrate that mQC can accomplish an unfilled but essential position in the ribosome profiling data analysis procedure by performing a thorough RIBO-Seq-specific exploration of aligned and P-site corrected ribosome profiling data., (Copyright © 2018 The Authors. Published by Elsevier B.V. All rights reserved.)

Neuropeptides are a class of bioactive peptides shown to be involved in various physiological processes, including metabolism, development, and reproduction. Although neuropeptide candidates have been predicted from genomic and transcriptomic data, comprehensive characterization of neuropeptide repertoires remains a challenge owing to their small size and variable sequences. De novo prediction of neuropeptides from genome or transcriptome data is difficult and usually only efficient for those peptides that have identified orthologs in other animal species. Recent peptidomics technology has enabled systematic structural identification of neuropeptides by using the combination of liquid chromatography and tandem mass spectrometry. However, reliable identification of naturally occurring peptides using a conventional tandem mass spectrometry approach, scanning spectra against a protein database, remains difficult because a large search space must be scanned due to the absence of a cleavage enzyme specification. We developed a pipeline consisting of in silico prediction of candidate neuropeptides followed by peptide-spectrum matching. This approach enables highly sensitive and reliable neuropeptide identification, as the search space for peptide-spectrum matching is highly reduced. Nematostella vectensis is a basal eumetazoan with one of the most ancient nervous systems. We scanned the Nematostella protein database for sequences displaying structural hallmarks typical of eumetazoan neuropeptide precursors, including amino- and carboxyterminal motifs and associated modifications. Peptide-spectrum matching was performed against a dataset of peptides that are cleaved in silico from these putative peptide precursors. The dozens of newly identified neuropeptides display structural similarities to bilaterian neuropeptides including tachykinin, myoinhibitory peptide, and neuromedin-U/pyrokinin, suggesting these neuropeptides occurred in the eumetazoan ancestor of all animal species., Competing Interests: The authors have declared that no competing interests exist.

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.

On average a human cell type expresses around 10,000 different protein coding genes synthesizing all the different molecular forms of the protein product (proteoforms) found in a cell. In a typical shotgun bottom up proteomic approach, the proteins are enzymatically cleaved, producing several 100,000 s of different peptides that are analyzed with liquid chromatography-tandem mass spectrometry (LC-MSMS). One of the major consequences of this high sample complexity is that coelution of peptides cannot be avoided. Moreover, low abundant peptides are difficult to identify as they have a lower chance of being selected for fragmentation due to ion-suppression effects and the semi-stochastic nature of the precursor selection in data-dependent shotgun proteomic analysis where peptides are selected for fragmentation analysis one-by-one as they elute from the column. In the current study we explore a simple novel approach that has the potential to counter some of the effect of coelution of peptides and improves the number of peptide identifications in a bottom-up proteomic analysis. In this method, peptides from a HeLa cell digest were eluted from the reverse phase column using three different elution solvents (acetonitrile, methanol and acetone) in three replicate reversed phase LC-MS/MS shotgun proteomic analysis. Results were compared with three technical replicates using the same solvent, which is common practice in proteomic analysis. In total, we see an increase of up to 10% in unique protein and up to 30% in unique peptide identifications from the combined analysis using different elution solvents when compared to the combined identifications from the three replicates of the same solvent. In addition, the overlap of unique peptide identifications common in all three LC-MS analyses in our approach is only 23% compared to 50% in the replicates using the same solvent. The method presented here thus provides an easy to implement method to significantly reduce the effects of coelution and ion suppression of peptides and improve protein coverage in shotgun proteomics. Data are available via ProteomeXchange with identifier PXD011908., (Copyright © 2019. Published by Elsevier B.V.)

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., (© 2019 Verbruggen et al.)

Mass-spectrometry-based proteomics enables the high-throughput identification and quantification of proteins, including sequence variants and post-translational modifications (PTMs) in biological samples. However, most workflows require that such variations be included in the search space used to analyze the data, and doing so remains challenging with most analysis tools. In order to facilitate the search for known sequence variants and PTMs, the Proteomics Standards Initiative (PSI) has designed and implemented the PSI extended FASTA format (PEFF). PEFF is based on the very popular FASTA format but adds a uniform mechanism for encoding substantially more metadata about the sequence collection as well as individual entries, including support for encoding known sequence variants, PTMs, and proteoforms. The format is very nearly backward compatible, and as such, existing FASTA parsers will require little or no changes to be able to read PEFF files as FASTA files, although without supporting any of the extra capabilities of PEFF. PEFF is defined by a full specification document, controlled vocabulary terms, a set of example files, software libraries, and a file validator. Popular software and resources are starting to support PEFF, including the sequence search engine Comet and the knowledge bases neXtProt and UniProtKB. Widespread implementation of PEFF is expected to further enable proteogenomics and top-down proteomics applications by providing a standardized mechanism for encoding protein sequences and their known variations. All the related documentation, including the detailed file format specification and example files, are available at http://www.psidev.info/peff .

Annotation of gene expression in prokaryotes often finds itself corrected due to small variations of the annotated gene regions observed between different (sub)-species. It has become apparent that traditional sequence alignment algorithms, used for the curation of genomes, are not able to map the full complexity of the genomic landscape. We present DeepRibo, a novel neural network utilizing features extracted from ribosome profiling information and binding site sequence patterns that shows to be a precise tool for the delineation and annotation of expressed genes in prokaryotes. The neural network combines recurrent memory cells and convolutional layers, adapting the information gained from both the high-throughput ribosome profiling data and ribosome binding translation initiation sequence region into one model. DeepRibo is designed as a single model trained on a variety of ribosome profiling experiments, used for the identification of open reading frames in prokaryotes without a priori knowledge of the translational landscape. Through extensive validation of the model trained on various sets of data, multiple species sequence similarity, mass spectrometry and Edman degradation verified proteins, the effectiveness of DeepRibo is highlighted., (© The Author(s) 2019. Published by Oxford University Press on behalf of Nucleic Acids Research.)

Ribosome profiling involves sequencing of approximately 30-base-long stretches of ribosome-protected mRNA. The technique enables genome-wide mapping of RNA undergoing active translation. Numerous small open reading frames have been identified by using ribosome profiling, leading researchers to question the assumed non-functional character of sORFs and to the identification of various important sORF translation products. sORFs.org (https://www.sorfs.org) is a public repository of small open reading frames identified by ribosome profiling in a database of over 3 million sORFs across 78 datasets from six species. sORFs.org is a multi-omics endeavor providing tools and metrics to assess the coding potential of the delineated sORFs. A pipeline is also in place to systematically rescan public mass spectrometry datasets to acquire new experimental evidence for sORF-encoded polypeptides. sORFs.org provides two distinct query interfaces, export functionality, and various visualization tools to enable inspection of the available information. © 2018 by John Wiley & Sons, Inc., (© 2018 John Wiley & Sons, Inc.)

Deletion of chromosome 6q is a well-recognized abnormality found in poor-prognosis T-cell acute lymphoblastic leukemia (T-ALL). Using integrated genomic approaches, we identified two candidate haploinsufficient genes contiguous at 6q14, SYNCRIP (encoding hnRNP-Q) and SNHG5 (that hosts snoRNAs), both involved in regulating RNA maturation and translation. Combined silencing of both genes, but not of either gene alone, accelerated leukemogeneis in a Tal1/Lmo1/Notch1 -driven mouse model, demonstrating the tumor-suppressive nature of the two-gene region. Proteomic and translational profiling of cells in which we engineered a short 6q deletion by CRISPR/Cas9 genome editing indicated decreased ribosome and mitochondrial activities, suggesting that the resulting metabolic changes may regulate tumor progression. Indeed, xenograft experiments showed an increased leukemia-initiating cell activity of primary human leukemic cells upon coextinction of SYNCRIP and SNHG5. Our findings not only elucidate the nature of 6q deletion but also highlight the role of ribosomes and mitochondria in T-ALL tumor progression. SIGNIFICANCE: The oncogenic role of 6q deletion in T-ALL has remained elusive since this chromosomal abnormality was first identified more than 40 years ago. We combined genomic analysis and functional models to show that the codeletion of two contiguous genes at 6q14 enhances malignancy through deregulation of a ribosome-mitochondria axis, suggesting the potential for therapeutic intervention. This article is highlighted in the In This Issue feature, p. 1494 ., (©2018 American Association for Cancer Research.)

Bio-active peptides are involved in the regulation of most physiological processes in the body. Classical bio-active peptides (CBAPs) are cleaved from a larger precursor protein and stored in secretion vesicles from which they are released in the extracellular space. Recently, another non-classical type of bio-active peptides (NCBAPs) has gained interest. These typically are not secreted but instead appear to be translated from short open reading frames (sORF) and released directly into the cytoplasm. In contrast to CBAPs, these peptides are involved in the regulation of intra-cellular processes such as transcriptional control, calcium handling and DNA repair. However, bio-chemical evidence for the translation of sORFs remains elusive. Comprehensive analysis of sORF-encoded polypeptides (SEPs) is hampered by a number of methodological and biological challenges: the low molecular mass (many 4-10 kDa), the low abundance, transient expression and complications in data analysis. We developed a strategy to address a number of these issues. Our strategy is to exclude false positive identifications. In total sample, we identified 926 peptides originated from 37 known (neuro)peptide precursors in mouse striatum. In addition, four SEPs were identified including NoBody, a SEP that was previously discovered in humans and three novel SEPS from 5' untranslated transcript regions (UTRs)., (© 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)

Neuropeptides constitute a vast and functionally diverse family of neurochemical signaling molecules and are widely involved in the regulation of various physiological processes. The nematode Caenorhabditis elegans is well-suited for the study of neuropeptide biochemistry and function, as neuropeptide biosynthesis enzymes are not essential for C. elegans viability. This permits the study of neuropeptide biosynthesis in mutants lacking certain neuropeptide-processing enzymes. Mass spectrometry has been used to study the effects of proprotein convertase and carboxypeptidase mutations on proteolytic processing of neuropeptide precursors and on the peptidome in C. elegans However, the enzymes required for the last step in the production of many bioactive peptides, the carboxyl-terminal amidation reaction, have not been characterized in this manner. Here, we describe three genes that encode homologs of neuropeptide amidation enzymes in C. elegans and used tandem LC-MS to compare neuropeptides in WT animals with those in newly generated mutants for these putative amidation enzymes. We report that mutants lacking both a functional peptidylglycine α-hydroxylating monooxygenase and a peptidylglycine α-amidating monooxygenase had a severely altered neuropeptide profile and also a decreased number of offspring. Interestingly, single mutants of the amidation enzymes still expressed some fully processed amidated neuropeptides, indicating the existence of a redundant amidation mechanism in C. elegans All MS data are available via ProteomeXchange with the identifier PXD008942. In summary, the key steps in neuropeptide processing in C. elegans seem to be executed by redundant enzymes, and loss of these enzymes severely affects brood size, supporting the need of amidated peptides for C. elegans reproduction., (© 2018 by The American Society for Biochemistry and Molecular Biology, Inc.)

sORFs.org (http://www.sorfs.org) is a public repository of small open reading frames (sORFs) identified by ribosome profiling (RIBO-seq). This update elaborates on the major improvements implemented since its initial release. sORFs.org now additionally supports three more species (zebrafish, rat and Caenorhabditis elegans) and currently includes 78 RIBO-seq datasets, a vast increase compared to the three that were processed in the initial release. Therefore, a novel pipeline was constructed that also enables sORF detection in RIBO-seq datasets comprising solely elongating RIBO-seq data while previously, matching initiating RIBO-seq data was necessary to delineate the sORFs. Furthermore, a novel noise filtering algorithm was designed, able to distinguish sORFs with true ribosomal activity from simulated noise, consequently reducing the false positive identification rate. The inclusion of other species also led to the development of an inner BLAST pipeline, assessing sequence similarity between sORFs in the repository. Building on the proof of concept model in the initial release of sORFs.org, a full PRIDE-ReSpin pipeline was now released, reprocessing publicly available MS-based proteomics PRIDE datasets, reporting on true translation events. Next to reporting those identified peptides, sORFs.org allows visual inspection of the annotated spectra within the Lorikeet MS/MS viewer, thus enabling detailed manual inspection and interpretation., (© The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research.)

Translation initiation generally occurs at AUG codons in eukaryotes, although it has been shown that non-AUG or noncanonical translation initiation can also occur. However, the evidence for noncanonical translation initiation sites (TISs) is largely indirect and based on ribosome profiling (Ribo-seq) studies. Here, using a strategy specifically designed to enrich N termini of proteins, we demonstrate that many human proteins are translated at noncanonical TISs. The large majority of TISs that mapped to 5' untranslated regions were noncanonical and led to N-terminal extension of annotated proteins or translation of upstream small open reading frames (uORF). It has been controversial whether the amino acid corresponding to the start codon is incorporated at the TIS or methionine is still incorporated. We found that methionine was incorporated at almost all noncanonical TISs identified in this study. Comparison of the TISs determined through mass spectrometry with ribosome profiling data revealed that about two-thirds of the novel annotations were indeed supported by the available ribosome profiling data. Sequence conservation across species and a higher abundance of noncanonical TISs than canonical ones in some cases suggests that the noncanonical TISs can have biological functions. Overall, this study provides evidence of protein translation initiation at noncanonical TISs and argues that further studies are required for elucidation of functional implications of such noncanonical translation initiation., (© 2018 Na et al.; Published by Cold Spring Harbor Laboratory Press.)

The salivary protein repertoire released by the herbivorous pest Tetranychus urticae is assumed to hold keys to its success on diverse crops. We report on a spider mite-specific protein family that is expanded in T. urticae. The encoding genes have an expression pattern restricted to the anterior podocephalic glands, while peptide fragments were found in the T. urticae secretome, supporting the salivary nature of these proteins. As peptide fragments were identified in a host-dependent manner, we designated this family as the SHOT (secreted host-responsive protein of Tetranychidae) family. The proteins were divided in three groups based on sequence similarity. Unlike TuSHOT3 genes, TuSHOT1 and TuSHOT2 genes were highly expressed when feeding on a subset of family Fabaceae, while expression was depleted on other hosts. TuSHOT1 and TuSHOT2 expression was induced within 24 h after certain host transfers, pointing toward transcriptional plasticity rather than selection as the cause. Transfer from an 'inducer' to a 'noninducer' plant was associated with slow yet strong downregulation of TuSHOT1 and TuSHOT2, occurring over generations rather than hours. This asymmetric on and off regulation points toward host-specific effects of SHOT proteins, which is further supported by the diversity of SHOT genes identified in Tetranychidae with a distinct host repertoire.

The Proteomics Standards Initiative (PSI) of the Human Proteome Organization (HUPO) has now been developing and promoting open community standards and software tools in the field of proteomics for 15 years. Under the guidance of the chair, cochairs, and other leadership positions, the PSI working groups are tasked with the development and maintenance of community standards via special workshops and ongoing work. Among the existing ratified standards, the PSI working groups continue to update PSI-MI XML, MITAB, mzML, mzIdentML, mzQuantML, mzTab, and the MIAPE (Minimum Information About a Proteomics Experiment) guidelines with the advance of new technologies and techniques. Furthermore, new standards are currently either in the final stages of completion (proBed and proBAM for proteogenomics results as well as PEFF) or in early stages of design (a spectral library standard format, a universal spectrum identifier, the qcML quality control format, and the Protein Expression Interface (PROXI) web services Application Programming Interface). In this work we review the current status of all of these aspects of the PSI, describe synergies with other efforts such as the ProteomeXchange Consortium, the Human Proteome Project, and the metabolomics community, and provide a look at future directions of the PSI.

Prokaryotic genome annotation is highly dependent on automated methods, as manual curation cannot keep up with the exponential growth of sequenced genomes. Current automated methods depend heavily on sequence composition and often underestimate the complexity of the proteome. We developed RibosomeE Profiling Assisted (re-)AnnotaTION (REPARATION), a de novo machine learning algorithm that takes advantage of experimental protein synthesis evidence from ribosome profiling (Ribo-seq) to delineate translated open reading frames (ORFs) in bacteria, independent of genome annotation (https://github.com/Biobix/REPARATION). REPARATION evaluates all possible ORFs in the genome and estimates minimum thresholds based on a growth curve model to screen for spurious ORFs. We applied REPARATION to three annotated bacterial species to obtain a more comprehensive mapping of their translation landscape in support of experimental data. In all cases, we identified hundreds of novel (small) ORFs including variants of previously annotated ORFs and >70% of all (variants of) annotated protein coding ORFs were predicted by REPARATION to be translated. Our predictions are supported by matching mass spectrometry proteomics data, sequence composition and conservation analysis. REPARATION is unique in that it makes use of experimental translation evidence to intrinsically perform a de novo ORF delineation in bacterial genomes irrespective of the sequence features linked to open reading frames., (© The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research.)

A large body of evidence indicates that genome annotation pipelines have biased our view of coding sequences because they generally undersample small proteins and peptides. The recent development of genome-wide translation profiling reveals the prevalence of small/short open reading frames (smORFs or sORFs), which are scattered over all classes of transcripts, including both mRNAs and presumptive long noncoding RNAs. Proteomic approaches further confirm an unexpected variety of smORF-encoded peptides (SEPs), representing an overlooked reservoir of bioactive molecules. Indeed, functional studies in a broad range of species from yeast to humans demonstrate that SEPs can harbor key activities for the control of development, differentiation, and physiology. Here we summarize recent advances in the discovery and functional characterization of smORF/SEPs and discuss why these small players can no longer be ignored with regard to genome function.

Proteogenomics is a research area that combines areas as proteomics and genomics in a multi-omics setup using both mass spectrometry and high-throughput sequencing technologies. Currently, the main goals of the field are to aid genome annotation or to unravel the proteome complexity. Mass spectrometry based identifications of matching or homologues peptides can further refine gene models. Also, the identification of novel proteoforms is also made possible based on detection of novel translation initiation sites (cognate or near-cognate), novel transcript isoforms, sequence variation or novel (small) open reading frames in intergenic or un-translated genic regions by analyzing high-throughput sequencing data from RNAseq or ribosome profiling experiments. Other proteogenomics studies using a combination of proteomics and genomics techniques focus on antibody sequencing, the identification of immunogenic peptides or venom peptides. Over the years, a growing amount of bioinformatics tools and databases became available to help streamlining these cross-omics studies. Some of these solutions only help in specific steps of the proteogenomics studies, e.g. building custom sequence databases (based on next generation sequencing output) for mass spectrometry fragmentation spectrum matching. Over the last few years a handful integrative tools also became available that can execute complete proteogenomics analyses. Some of these are presented as stand-alone solutions, whereas others are implemented in a web-based framework such as Galaxy. In this review we aimed at sketching a comprehensive overview of all the bioinformatics solutions that are available for this growing research area. © 2015 Wiley Periodicals, Inc. Mass Spec Rev 36:584-599, 2017., (© 2015 Wiley Periodicals, Inc.)

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., (© The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research.)

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.

The introduction of new standard formats, proBAM and proBed, improves the integration of genomics and proteomics information, thus aiding proteogenomics applications. These novel formats enable peptide spectrum matches (PSM) to be stored, inspected, and analyzed within the context of the genome. However, an easy-to-use and transparent tool to convert mass spectrometry identification files to these new formats is indispensable. proBAMconvert enables the conversion of common identification file formats (mzIdentML, mzTab, and pepXML) to proBAM/proBed using an intuitive interface. Furthermore, ProBAMconvert enables information to be output both at the PSM and peptide levels and has a command line interface next to the graphical user interface. Detailed documentation and a completely worked-out tutorial is available at http://probam.biobix.be .