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Differentially Expressed Proteins Deleted versus non-deleted clones

Differentially expressed genes in Xenograted T-ALL shSYNCRIP-SNHG5 versus ShCtrl

Differentially translated transcripts - translatome

Supp Fig. S1-6, Supp Tables S1-S3, Supp Methods and References

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

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 byin vivoexperiments. 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 is 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.

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.

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 .

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

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 comprises 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 several methods that can be used to gather insights on biological processes that drive any genome annotation task. This work builds upon a transformer-based neural network framework designed for prokaryotic genome annotation purposes. We find that the majority of sub-units (attention heads) of the model are specialized towards identifying DNA binding sites. Working with a neural network trained to detect transcription start sites in E. coli, we successfully characterize both locations and consensus sequences of transcription factor binding sites, including both well-known and potentially novel elements involved in the initiation of the transcription process.

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.

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.

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 .

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 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., Small open reading frame-encoded peptides (SEPs), shorter than 100 amino acids, are involved in many cell biological processes. Here the authors identify 16 nuclear-encoded mitochondrial SEPs, including BRAWNIN, an essential regulator of respiratory chain complex III assembly and ATP production.

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

Neuropeptides are a class of bioactive peptides and are responsible for various physiological processes including metabolism, development and reproduction. Although accumulated genome and transcriptome data have reported a number of neuropeptide candidates, it still remains difficult to obtain a comprehensive view of neuropeptide repertoires due to their small and variable nature. Neuropeptide prediction tools usually work only for those peptides for which sequentially related homologs have previously been identified. Recent peptidomics technology has enabled systematic structural identification of neuropeptides by using the combination of liquid chromatography and tandem mass spectrometry. However, obtaining reliable identifications of endogenous peptides is still difficult using a conventional tandem mass spectrometry-based peptide identification approach using protein database because a large search space has to be scanned due to the absence of a cleavage enzyme specification. We developed a pipeline consisting of the prediction ofin silicocleaved endogenous neuropeptides followed by peptide-spectrum matching enabling highly sensitive and reliable neuropeptide identification. This approach effectively reduces the search space of peptide-spectrum matching, and thus increases search sensitivity. To identify neuropeptides inNematostella vectensis,a basal eumetazoan having one of the most primitive nervous systems, we scanned theNematostellaprotein database for sequences displaying structural hallmarks of metazoan neuropeptides, including C/N-terminal structures and modifications. Peptide-spectrum matching was performed against thein silicocleaved peptides and successfully identified dozens of neuropeptides at high confidence. The identification of Nematostella neuropeptides structurally related the tachykinin, GnRH/AKH, neuromedin-U/pyrokinin peptide families indicate that these peptides already originated in the eumetazoan ancestor of all animal species, most likely concomitantly with the development of a nervous system.

The two-spotted spider mite Tetranychus urticae is an extremely polyphagous crop pest. Alongside an unparalleled detoxification potential for plant secondary metabolites, it has recently been shown that spider mites can attenuate or even suppress plant defenses. Salivary constituents, notably effectors, have been proposed to play an important role in manipulating plant defenses and might determine the outcome of plant-mite interactions. Here, the proteomic composition of saliva from T. urticae lines adapted to various host plants—bean, maize, soy, and tomato—was analyzed using a custom-developed feeding assay coupled with nano-LC tandem mass spectrometry. About 90 putative T. urticae salivary proteins were identified. Many are of unknown function, and in numerous cases belonging to multimembered gene families. RNAseq expression analysis revealed that many genes coding for these salivary proteins were highly expressed in the proterosoma, the mite body region that includes the salivary glands. A subset of genes encoding putative salivary proteins was selected for whole-mount in situ hybridization, and were found to be expressed in the anterior and dorsal podocephalic glands. Strikingly, host plant dependent expression was evident for putative salivary proteins, and was further studied in detail by micro-array based genome-wide expression profiling. This meta-analysis revealed for the first time the salivary protein repertoire of a phytophagous chelicerate. The availability of this salivary proteome will assist in unraveling the molecular interface between phytophagous mites and their host plants, and may ultimately facilitate the development of mite-resistant crops. Furthermore, the technique used in this study is a time- and resource-efficient method to examine the salivary protein composition of other small arthropods for which saliva or salivary glands cannot be isolated easily.

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.

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.

In this article, the following sentences and reference were erroneously published: “In their technical brief, Khitun and Slavoff describe their workflow to detect and validate non-annotated proteins with non-AUG start sites. This proteomic detection method is also exemplified with the characterization of the non-AUG initiated small protein (alt-RPL36) co-encoded with human ribosomal protein L36.” [11] S. A. K. Slavoff, Proteomics 2018 The Editorial Office apologizes for this error.

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.

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.

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, co-chairs, 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. Further, 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 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.

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.

The human genome is pervasively transcribed, producing thousands of non-coding RNA transcripts. The majority of these transcripts are long non-coding RNAs (lncRNAs) and novel lncRNA genes are being identified at rapid pace. To streamline these efforts, we created LNCipedia, an online repository of lncRNA transcripts and annotation. Here, we present LNCipedia 3.0 (http://www.lncipedia.org), the latest version of the publicly available human lncRNA database. Compared to the previous version of LNCipedia, the database grew over five times in size, gaining over 90 000 new lncRNA transcripts. Assessment of the protein-coding potential of LNCipedia entries is improved with state-of-the art methods that include large-scale reprocessing of publicly available proteomics data. As a result, a high-confidence set of lncRNA transcripts with low coding potential is defined and made available for download. In addition, a tool to assess lncRNA gene conservation between human, mouse and zebrafish has been implemented.

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

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

In the search for new methods of pest control, the potential of RNA interference (RNAi) is being explored. Because the gut is the first barrier for the uptake of double-stranded (ds)RNA, pyrosequencing of the gut transcriptome is a powerful tool for obtaining the necessary sequences for specific dsRNA-mediated pest control. In the present study, a dataset representing the gut transcriptome of the Colorado potato beetle (CPB; Leptinotarsa decemlineata) was generated and analysed for the presence of RNAi-related genes. Almost all selected genes that were implicated in silencing efficiency at different levels in the RNAi pathway (core machinery, associated intracellular factors, dsRNA uptake, antiviral RNAi, nucleases), which uses different types of small RNA (small interfering RNA, microRNA and piwi-RNA), were expressed in the CPB gut. Although the database is of lower quality, the majority of the RNAi genes are also found to be present in the gut transcriptome of the tobacco hornworm [TH; Manduca sexta (19 out of 35 genes analysed)]. The high quality of the CPB transcriptome database will lay the foundation for future gene expression and functional studies regarding the gut and RNAi.

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

The identification of small proteins and peptides has consistently proven to be challenging. However, technological advances as well as multi-omics endeavors facilitate the identification of novel small coding sequences, leading to new insights. Specifically, the application of next generation sequencing technologies (NGS), providing accurate and sample specific transcriptome / translatome information, into the proteomics field led to more comprehensive results and new discoveries. This book chapter focuses on the inclusion of RNA-Seq and RIBO-Seq also known as ribosome profiling, an RNA-Seq based technique sequencing the +/- 30 bp long fragments captured by translating ribosomes. We emphasize the identification of micropeptides and neo-antigens, two distinct classes of small translation products, triggering our current understanding of biology. RNA-Seq is capable of capturing sample specific genomic variations, enabling focused neo-antigen identification. RIBO-Seq can identify translation events in small open reading frames which are considered to be non-coding, leading to the discovery of micropeptides. The identification of small translation products requires the integration of multi-omics data, stressing the importance of proteogenomics in this novel research area.

N-terminal acetylation (Nt-acetylation) by N-terminal acetyltransferases (NATs) is one of the most common protein modifications in eukaryotes. The NatC complex represents one of three major NATs of which the substrate profile remains largely unexplored. Here, we defined the in vivo human NatC Nt-acetylome on a proteome-wide scale by combining knockdown of its catalytic subunit Naa30 with positional proteomics. We identified 46 human NatC substrates, expanding our current knowledge on the substrate repertoire of NatC which now includes proteins harboring Met-Leu, Met-Ile, Met-Phe, Met-Trp, Met-Val, Met-Met, Met-His and Met-Lys N termini. Upon Naa30 depletion the expression levels of several organellar proteins were found reduced, in particular mitochondrial proteins, some of which were found to be NatC substrates. Interestingly, knockdown of Naa30 induced the loss of mitochondrial membrane potential and fragmentation of mitochondria. In conclusion, NatC N-tacetylates a large variety of proteins and is essential for mitochondrial integrity and function.

MALDI mass spectrometry imaging (MSI) is a promising technique in the field of molecular (immuno)histology but is confronted with the problematic large-scale identification of peptides from thin tissue sections. In this study we present a workflow that significantly increased the number of identified peptides in a given MALDI-MSI data set and we evaluated its power concerning relative peptide quantifications. Fourier transform mass spectrometry (FTMS) profiling on matrix-coated thin tissue sections allowed us to align spectra of different MS sources, matching identical peaks in the process, thus linking MSI data to tandem mass spectrometry (MS/MS) on one hand and semiquantitative liquid chromatography (LC)/MS data on the other. Bonanza clustering was applied in order to group MS/MS spectra of structurally related peptides, making it possible to infer the identity of MSI-detected compounds based on identified members within the same cluster, effectively increasing the number of identifications in a single MSI data set. Out of 136 detected peptides with MALDI-MSI, we were able to identify 46 peptides. For 31 of these, a LC/quadrupole time-of-flight (QTOF) counterpart was detected, and we observed similar obese (ob/ob) to wild-type (wt) peak intensity ratios for 18 peptides. This workflow significantly increased the number of identifications of peptide masses detected with MALDI-MSI and evaluated the power of this imaging method for relative quantification of peptide levels between experimental conditions.

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.

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 4-10kDa), 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).

Glycosylation is a co- and/or post-translational protein modification that generates enormous structural diversity among glycoproteins. In this study, immobilized lectins were used to capture glycoproteins with different glycan profiles from Drosophila melanogaster extracts. On the basis of previous results from glycan array analyses, the snowdrop (Galanthus nivalis) agglutinin (GNA), the tobacco (Nicotiana tabacum) lectin (Nictaba) and the Rhizoctoni solani agglutinin (RSA) were used to select for a broad range of N- and O-glycan structures. After different lectin affinity chromatographies, the glycoproteome of Drosophila was analyzed using LC-MS/MS and glycoprotein abundances were calculated by different label-free methods. Bioinformatics tools were used to annotate the identified glycoproteins and the glycoproteins were classified according to their molecular function or their involvement in a biological process. Subsequent enrichment analysis (using the DAVID database) was employed to find biological processes or molecular functions in Drosophila in which a particular glycan signature is overrepresented. The results presented here clearly demonstrate that next to the presence of high-mannose and pauci-mannose N-glycans, Drosophila is capable of synthesizing glycoproteins carrying extended hybrid and complex N-linked glycans. Furthermore, it was demonstrated that a specific glycosylation signature can be associated with a functionally related group of glycoproteins in Drosophila, both in terms of biological process and molecular function.

When studying the set of biologically active peptides (the so-called peptidome) of a cell type, organ, or entire organism, the identification of peptides is mostly attempted by MS. However, identification rates are often dismally unsatisfactory. A great deal of failed or missed identifications may be attributable to the wealth of modifications on peptides, some of which may originate from in vivo post-translational processes to activate the molecule, whereas others could be introduced during the tissue preparation procedures. Preliminary knowledge of the modification profile of specific peptidome samples would greatly improve identification rates. To this end we developed an approach that performs clustering of mass spectra in a way that allows us to group spectra having similar peak patterns over significant segments. Comparing members of one spectral group enables us to assess the modifications (expressed as mass shifts in Dalton) present in a peptidome sample. The clustering algorithm in this study is called Bonanza, and it was applied to MALDI-TOF/TOF MS spectra from the mouse. Peptide identification rates went up from 17 to 36% for 278 spectra obtained from the pancreatic islets and from 21 to 43% for 163 pituitary spectra. Spectral clustering with subsequent advanced database search may result in the discovery of new biologically active peptides and modifications thereof, as shown by this report indeed.

The use of stable isotope tags in quantitative peptidomics offers many advantages, but the laborious identification of matching sets of labeled peptide peaks is still a major bottleneck. Here we present labelpepmatch, an R package for fast and straightforward analysis of LC MS spectra of labeled peptides. This open-source tool offers fast and accurate identification of peak pairs alongside an appropriate framework for statistical inference on quantitative peptidomics data, based on techniques from other-omics disciplines. A relevant case study on the desert locust Schistocerca gregaria proves our pipeline to be a reliable tool for quick but thorough explorative analyses.

The identification of small proteins and peptides has consistently proven to be challenging. However, technological advances as well as multi-omics endeavors facilitate the identification of novel small coding sequences, leading to new insights. Specifically, the application of next generation sequencing technologies (NGS), providing accurate and sample specific transcriptome / translatome information, into the proteomics field led to more comprehensive results and new discoveries. This book chapter focuses on the inclusion of RNA-Seq and RIBO-Seq also known as ribosome profiling, an RNA-Seq based technique sequencing the +/- 30 bp long fragments captured by translating ribosomes. We emphasize the identification of micropeptides and neo-antigens, two distinct classes of small translation products, triggering our current understanding of biology. RNA-Seq is capable of capturing sample specific genomic variations, enabling focused neo-antigen identification. RIBO-Seq can identify translation events in small open reading frames which are considered to be non-coding, leading to the discovery of micropeptides. The identification of small translation products requires the integration of multi-omics data, stressing the importance of proteogenomics in this novel research area.

Epigenetics, and more specifically DNA methylation is a fast evolving research area. In almost every cancer type, each month new publications confirm the differentiated regulation of specific genes due to methylation and mention the discovery of novel methylation markers. Therefore, it would be extremely useful to have an annotated, reviewed, sorted and summarized overview of all available data. PubMeth is a cancer methylation database that includes genes that are reported to be methylated in various cancer types. A query can be based either on genes (to check in which cancer types the genes are reported as being methylated) or on cancer types (which genes are reported to be methylated in the cancer (sub) types of interest). The database is freely accessible at http://www.pubmeth.org. PubMeth is based on text-mining of Medline/PubMed abstracts, combined with manual reading and annotation of preselected abstracts. The text-mining approach results in increased speed and selectivity (as for instance many different aliases of a gene are searched at once), while the manual screening significantly raises the specificity and quality of the database. The summarized overview of the results is very useful in case more genes or cancer types are searched at the same time.

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.

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 effects of increasing levels of dietaryphospholipids (PL) on the reproductive performance,egg and larval quality, and lipid composition offemales of the freshwater prawn Macrobrachiumrosenbergii were investigated. Three isolipidic dietscontaining similar amounts of highly unsaturated fattyacids but varying levels of PL (0.8, 2.4 and 4.6%)were fed during 180 days to three groups of eightfemales originating from Thai ponds. No significantdifferences were observed for fecundity, egg size andhatchability, starved larvae size, and size, survivaland tolerance to stress of 8 day-old larvae.Similarly, no major differences in the lipidcomposition of the midgut gland, ovaries and muscletissue of females could be detected. Results indicatethe lack of need of dietary supplementation of PL forM. rosenbergii broodstock, as previouslyreported for earlier life stages. It is suggested thatthe basal level of 0.8% dietary PL was sufficient tomeet the dietary demands of the prawn broodstock. ThePL requirements of M. rosenbergii broodstock, ifany, may be satisfied in commercial feeds through theinclusion of ingredients containing some phospholipidsendogenously.

Tandem mass spectrometry (MS/MS) combined with bioinformatics tools have enabled fast and systematic protein identification based on peptide-to-spectrum matches. However, it remains challenging to obtain accurate identification of endogenous peptides, such as neuropeptides, peptide hormones, peptide pheromones, venom peptides, and antimicrobial peptides. Since these peptides are processed at sites that are difficult to predict reliably, the search of their MS/MS spectra in sequence databases needs to be done without any protease setting. In addition, many endogenous peptides carry various post-translational modifications, making it essential to take these into account in the database search. These characteristics of endogenous peptides result in a huge search space, frequently leading to poor confidence of the peptide characterizations in peptidomics studies. We have developed a new MS/MS spectrum search tool for highly accurate and confident identification of endogenous peptides by combining two different fragmentation methods. Our approach takes advantage of the combination of two independent fragmentation methods (collision-induced dissociation and electron transfer dissociation). Their peptide spectral matching is carried out separately in both methods, and the final score is built as a combination of the two separate scores. We demonstrate that this approach is very effective in discriminating correct peptide identifications from false hits. We applied this approach to a spectral data set of neuropeptides extracted from mouse pituitary tumor cells. Compared to conventional MS-based identification, i.e., using a single fragmentation method, our approach significantly increased the peptide identification rate. It proved also highly effective for scanning spectra against a very large search space, enabling more accurate genome-wide searches and searches including multiple potential post-translational modifications.

The term peptidomics for a new promising "omics" field was not introduced until the beginning of 2000. The approach has been proven successful in several domains such as neuroendocrine research and biomarker or drug discovery. This review reports on bioinformatics tools and methodologies within the peptidomics field and the application thereof. Obviously, a plethora of proteomics data analysis tools lends themselves to direct use in peptidomics because the latter is a subfield of the former, at least to a certain extent. Nevertheless, peptidomics-specific tool extensions, inventions, and validation procedures have emerged, and certain tools are more suitable for this subfield than others due to small but important differences in peptidomics sample analysis. This paper focuses on these topics. Furthermore, it gives a comprehensive overview of available online tools tailored to the peptidomics field. To conclude, an ideal pipeline for bioactive peptide identification is presented.

Noncanonical translation of prespliced mRNA provides physiological meaning to nuclear translation in generating antigenic peptide substrates for the endogenous major histocompatibility complex class I pathway.

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

ispartof: Communications in Agricultural and Applied Biological Sciences vol:74 issue:4 pages:67-73 ispartof: location:Belgium status: published