Mass spectrometry of short peptides reveals common features of metazoan peptidergic neurons.

The evolutionary origins of neurons remain unknown. Although recent genome data of extant early-branching animals have shown that neural genes existed in the common ancestor of animals, the physiological and genetic properties of neurons in the early evolutionary phase are still unclear. Here, we performed a mass spectrometry-based comprehensive survey of short peptides from early-branching lineages Cnidaria, Porifera and Ctenophora. We identified a number of mature ctenophore neuropeptides that are expressed in neurons associated with sensory, muscular and digestive systems. The ctenophore peptides are stored in vesicles in cell bodies and neurites, suggesting volume transmission similar to that of cnidarian and bilaterian peptidergic systems. A comparison of genetic characteristics revealed that the peptide-expressing cells of Cnidaria and Ctenophora express the vast majority of genes that have pivotal roles in maturation, secretion and degradation of neuropeptides in Bilateria. Functional analysis of neuropeptides and prediction of receptors with machine learning demonstrated peptide regulation of a wide range of target effector cells, including cells of muscular systems. The striking parallels between the peptidergic neuronal properties of Cnidaria and Bilateria and those of Ctenophora, the most basal neuron-bearing animals, suggest a common evolutionary origin of metazoan peptidergic nervous systems.

A combined strategy of neuropeptide prediction and tandem mass spectrometry identifies evolutionarily conserved ancient neuropeptides in the sea anemone Nematostella vectensis.

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.

Visualizing Energy Charge in Breast Carcinoma Tissues by MALDI Mass-spectrometry Imaging Profiles of Low-molecular-weight Metabolites.

BACKGROUND/AIM: Metabolomics is widely used for biomarker discovery, but conventional mass-spectrometry extraction procedures lose the spatial localization of metabolites. In this study, we directly analyzed breast carcinoma tissues embedded in frozen tissue microarrays (fTMAs) using MALDI mass-spectrometry imaging (MALDI-MSI). MATERIALS AND METHODS: A total of 119 breast tissues (84 carcinoma and 35 normal) were used. MSI data were extracted from each tissue. RESULTS: Overall, 185 of 1,915 peaks which were commonly detected in 60% of target areas were subjected to further analysis. One hundred and fifty-two peaks of carcinoma showed significantly higher intensity than normal. Comparing metabolite profiles from carcinoma and normal tissues, energy charge (EC) and the sum of adenosine phosphate compound (AXP) indicated significantly higher intensities in cancerous tissues than normal. But comparisons of EC and AXP among lymph node metastasis, tumor size and tumor subtypes indicated no significant differences. CONCLUSION: Breast carcinoma tissues had higher EC and AXP values than normal. MALDI-MSI could be a tool for characterizing breast carcinoma.

Analysis of spatiotemporal metabolomic dynamics for sensitively monitoring biological alterations in cisplatin-induced acute kidney injury.

Clinical application of the major anticancer drug, cisplatin, is limited by severe side effects, especially acute kidney injury (AKI) caused by nephrotoxicity. The detailed metabolic mechanism is still largely unknown. Here, we used an integrated technique combining mass spectrometry imaging (MSI) and liquid chromatography-mass spectrometry (LC-MS) to visualize the diverse spatiotemporal metabolic dynamics in the mouse kidney after cisplatin dosing. Biological responses to cisplatin was more sensitively detected within 24 h as a metabolic alteration, which is much earlier than possible with the conventional clinical chemistry method of blood urea nitrogen (BUN) measurement. Region-specific changes (e.g., medulla and cortex) in metabolites related to DNA damage and energy generation were observed over the 72-h exposure period. Therefore, this metabolomics approach may become a novel strategy for elucidating early renal responses to cisplatin, prior to the detection of kidney damage evaluated by conventional method.

A Chemometrics-driven Strategy for the Bioactivity Evaluation of Complex Multicomponent Systems and the Effective Selection of Bioactivity-predictive Chemical Combinations.

Although understanding their chemical composition is vital for accurately predicting the bioactivity of multicomponent drugs, nutraceuticals, and foods, no analytical approach exists to easily predict the bioactivity of multicomponent systems from complex behaviors of multiple coexisting factors. We herein represent a metabolic profiling (MP) strategy for evaluating bioactivity in systems containing various small molecules. Composition profiles of diverse bioactive herbal samples from 21 green tea extract (GTE) panels were obtained by a high-throughput, non-targeted analytical procedure. This employed the matrix-assisted laser desorption ionization-mass spectrometry (MALDI-MS) technique, using 1,5-diaminonaphthalene (1,5-DAN) as the optical matrix for detecting GTE-derived components. Multivariate statistical analyses revealed differences among the GTEs in their antioxidant activity, oxygen radical absorbance capacity (ORAC). A reliable bioactivity-prediction model was constructed to predict the ORAC of diverse GTEs from their compositional balance. This chemometric procedure allowed the evaluation of GTE bioactivity by multicomponent rather than single-component information. The bioactivity could be easily evaluated by calculating the summed abundance of a few selected components that contributed most to constructing the prediction model. 1,5-DAN-MALDI-MS-MP, using diverse bioactive sample panels, represents a promising strategy for screening bioactivity-predictive multicomponent factors and selecting effective bioactivity-predictive chemical combinations for crude multicomponent systems.

Spatially resolved metabolic distribution for unraveling the physiological change and responses in tomato fruit using matrix-assisted laser desorption/ionization-mass spectrometry imaging (MALDI-MSI).

Information on spatiotemporal metabolic behavior is indispensable for a precise understanding of physiological changes and responses, including those of ripening processes and wounding stress, in fruit, but such information is still limited. Here, we visualized the spatial distribution of metabolites within tissue sections of tomato (Solanum lycopersicum L.) fruit using a matrix-assisted laser desorption/ionization-mass spectrometry imaging (MALDI-MSI) technique combined with a matrix sublimation/recrystallization method. This technique elucidated the unique distribution patterns of more than 30 metabolite-derived ions, including primary and secondary metabolites, simultaneously. To investigate spatiotemporal metabolic alterations during physiological changes at the whole-tissue level, MALDI-MSI was performed using the different ripening phenotypes of mature green and mature red tomato fruits. Although apparent alterations in the localization and intensity of many detected metabolites were not observed between the two tomatoes, the amounts of glutamate and adenosine monophosphate, umami compounds, increased in both mesocarp and locule regions during the ripening process. In contrast, malate, a sour compound, decreased in both regions. MALDI-MSI was also applied to evaluate more local metabolic responses to wounding stress. Accumulations of a glycoalkaloid, tomatine, and a low level of its glycosylated metabolite, esculeoside A, were found in the wound region where cell death had been induced. Their inverse levels were observed in non-wounded regions. Furthermore, the amounts of both compounds differed in the developmental stages. Thus, our MALDI-MSI technique increased the understanding of the physiological changes and responses of tomato fruit through the determination of spatiotemporally resolved metabolic alterations. Graphical abstract ᅟ.

MSIdV: a versatile tool to visualize biological indices from mass spectrometry imaging data.

Mass spectrometry imaging (MSI) visualizes the simultaneous lateral distribution of multiple compounds on sample surface. However, it is still difficult to visualize biological indices such as energy charge index from multiple compounds because of the lack of publicly available tools. Here we present MSIdV, a visualization tool for biological indices calculated from mass spectrometry imaging data, which can effectively scan a series of mass spectra and process, calculate and visualize user-defined index measures accurately with a number of signal processing features. AVAILABILITY AND IMPLEMENTATION: MSIdV is implemented in Python 2.7 and is freely available on the web at https://sourceforge.net/projects/msidv/ CONTACT: eisuke.hayakawa@gmail.comSupplementary information: Supplementary data are available at Bioinformatics online.

Improving the identification rate of data independent label-free quantitative proteomics experiments on non-model crops: a case study on apple fruit.

Complex peptide extracts from non-model crops are troublesome for proper identification and quantification. To increase the identification rate of label free DIA experiments of Braeburn apple a new workflow was developed where a DDA database was constructed and linked to the DIA data. At a first level, parent masses found in DIA were searched in the DDA database based on their mass to charge ratio and retention time; at a second level, masses of fragmentation ions were compared for each of the linked spectrum. Following this workflow, a tenfold increase of peptides was identified from a single DIA run. As proof of principle, the designed workflow was applied to determine the changes during a storage experiment, achieving a two-fold identification increase in the number of significant peptides. The corresponding protein families were divided into nine clusters, representing different time profiles of changes in abundances during storage. Up-regulated protein families already show a glimpse of important pathways affecting aging during long-term storage, such as ethylene synthesis, and responses to abiotic stresses and their influence on the central metabolism. BIOLOGICAL SIGNIFICANCE: Proteomics research on non-model crops causes additional difficulties in identifying the peptides present in, often complex, samples. This work proposes a new workflow to retrieve more identifications from a set of quantitative data, based on linking DIA and DDA data at two consecutive levels. As proof of principle, a storage experiment on Braeburn apple resulted in twice as much identified storage related peptides. Important proteins involved in central metabolism and stress are significantly up-regulated after long term storage. This article is part of a Special Issue entitled: Proteomics of non-model organisms.

Improving the identification rate of endogenous peptides using electron transfer dissociation and collision-induced dissociation.

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.

Combining in silico prediction and ribosome profiling in a genome-wide search for novel putatively coding sORFs.

BACKGROUND: It was long assumed that proteins are at least 100 amino acids (AAs) long. Moreover, the detection of short translation products (e.g. coded from small Open Reading Frames, sORFs) is very difficult as the short length makes it hard to distinguish true coding ORFs from ORFs occurring by chance. Nevertheless, over the past few years many such non-canonical genes (with ORFs < 100 AAs) have been discovered in different organisms like Arabidopsis thaliana, Saccharomyces cerevisiae, and Drosophila melanogaster. Thanks to advances in sequencing, bioinformatics and computing power, it is now possible to scan the genome in unprecedented scrutiny, for example in a search of this type of small ORFs. RESULTS: Using bioinformatics methods, we performed a systematic search for putatively functional sORFs in the Mus musculus genome. A genome-wide scan detected all sORFs which were subsequently analyzed for their coding potential, based on evolutionary conservation at the AA level, and ranked using a Support Vector Machine (SVM) learning model. The ranked sORFs are finally overlapped with ribosome profiling data, hinting to sORF translation. All candidates are visually inspected using an in-house developed genome browser. In this way dozens of highly conserved sORFs, targeted by ribosomes were identified in the mouse genome, putatively encoding micropeptides. CONCLUSION: Our combined genome-wide approach leads to the prediction of a comprehensive but manageable set of putatively coding sORFs, a very important first step towards the identification of a new class of bioactive peptides, called micropeptides.

Peptidomic analysis of human reflex tear fluid.

Tear fluid is a complex mixture of biological compounds, including carbohydrates, lipids, electrolytes, proteins, and peptides. Despite the physiological importance of tear fluid, little is known about the identity of its endogenous peptides. In this study, we analyzed and identified naturally occurring peptide molecules in human reflex tear fluid by means of LC-MALDI-TOF-TOF. Tandem MS analyses revealed 30 peptides, most of which have not been identified before. Twenty-six peptides are derived from the proline-rich protein 4 and 4 peptides are derived from the polymeric immunoglobulin receptor. Based on their structural characteristics, we suggest that the identified tear fluid peptides contribute to the protective environment of the ocular surface.

Peptide signaling in Hydra.

Peptides play a number of crucial roles as signaling molecules in metazoans. In order to elaborate a more complete picture of the roles played by peptides in a single organism, we launched the "Hydra Peptide Project". For this project, we used Hydra magnipapillata, a species belonging to Cnidaria, one of the most basal metazoan phyla, and using a peptidomic approach, we systematically identified a number of peptide signaling molecules, their encoding genes and their functions. In this article, we report the peptides isolated from Hydra and other cnidarians, as well as their synthesis, processing and release from the cells to the target. Possible peptide signaling pathways are overviewed and finally we discuss the evolution of the peptide signaling system.

Spectral clustering in peptidomics studies allows homology searching and modification profiling: HomClus, a versatile tool.

Many genomes of nonmodel organisms are yet to be annotated. Peptidomics research on those organisms therefore cannot adopt the commonly used database-driven identification strategy, leaving the more difficult de novo sequencing approach as the only alternative. The reported tool uses the growing resources of publicly or in-house available fragmentation spectra and sequences of (model) organisms to elucidate the identity of peptides of experimental spectra of nonannotated species. Clustering algorithms are implemented to infer the identity of unknown peak lists based on their publicly or in-house available counterparts. The reported tool, which we call the HomClus-tool, can cope with post-translational modifications and amino acid substitutions. We applied this tool on two locusts (Schistocerca gregaria and Locusta migratoria) LC-MALDI-TOF/TOF datasets. Compared to a Mascot database search (using the available UniProt-KB proteins of these species), we were able to double the amount of peptide identifications for both spectral sets. Known bioactive peptides from Drosophila melanogaster (i.e., fragmentations spectra generated in silico thereof) were used as a starting point for clustering, trying to reveal their experimental homologues' counterparts.

Comparison of extraction methods for peptidomics analysis of mouse brain tissue.

The peptidome encompasses all the peptides present in a particular cell, tissue or organism at a particular point in time. Neuropeptidomics studies the peptidome of the nervous system and will become increasingly important in neuroscience research. Novel peptides can be discovered and, when applied to disease models, key players in pathophysiological mechanisms will be identified. That way, they can serve as drug targets or biomarkers. Presently, different extraction protocols are in use, but no consensus has been reached on what fixation and extraction protocol is best suited for brain tissue. Therefore, in this article we compare different methods for quenching of proteolytic activity (snap-freezing of whole mouse in liquid nitrogen immediately after cervical dislocation, freezing of the dissected brain in 2-methyl-butane and heat denaturation of the tissue by microwave treatment) in combination with different extraction methods. The protocol that combines submersion in liquid nitrogen with extraction in 0.25% acetic acid results in the highest number of unique identifications, a high conservation of posttranslational modifications, the best reproducibility between duplicate samples and the best comparison with former studies on mouse brain peptides. For these reasons, we recommend the use of this protocol in future neuropeptidomics studies.

The dynamic genome of Hydra.

The freshwater cnidarian Hydra was first described in 1702 and has been the object of study for 300 years. Experimental studies of Hydra between 1736 and 1744 culminated in the discovery of asexual reproduction of an animal by budding, the first description of regeneration in an animal, and successful transplantation of tissue between animals. Today, Hydra is an important model for studies of axial patterning, stem cell biology and regeneration. Here we report the genome of Hydra magnipapillata and compare it to the genomes of the anthozoan Nematostella vectensis and other animals. The Hydra genome has been shaped by bursts of transposable element expansion, horizontal gene transfer, trans-splicing, and simplification of gene structure and gene content that parallel simplification of the Hydra life cycle. We also report the sequence of the genome of a novel bacterium stably associated with H. magnipapillata. Comparisons of the Hydra genome to the genomes of other animals shed light on the evolution of epithelia, contractile tissues, developmentally regulated transcription factors, the Spemann-Mangold organizer, pluripotency genes and the neuromuscular junction.

Identification and relative quantification of neuropeptides from the endocrine tissues.

Endocrine tissues like the pituitary, hypothalamus and islets of Langerhans are rich in bioactive peptides. These are used for intercellular signalling and are involved in regulation of almost all physiological processes. Peptidomics is the comprehensive analysis of peptides in tissues, fluids and cells. Peptidomics applied to (neuro-)endocrine tissues aims therefore to identify as many bioactive peptides as possible. Peptidomics of (neuro-)endocrine tissues requires an integrated approach that consists of careful sample handling, peptide separation techniques, mass spectrometry and bioinformatics. Here we describe the methods for isolation and dissection of endocrine tissues, the extraction of bioactive peptides and further sample handling and identification of peptides by mass spectrometry and hyphenated techniques. We also present a straightforward method for the comparison of relative levels of bioactive peptides in these endocrine tissues under varying physiological conditions. The latter helps to elucidate functions of the bioactive peptides.

Bioinformatic approaches to the identification of novel neuropeptide precursors.

With the entire genome sequence of several animals now available, it is becoming possible to identify in silico all putative peptides and their precursors in an organism. In this chapter we describe a searching algorithm that can be used to scan the genome for predicted proteins with the structural hallmarks of (neuro)peptide precursors. We also describe how to use search strings such as the presence of a glycine residue as a putative amidation site, dibasic cleavage sites, the presence of a signal peptide, and specific peptide motifs to improve a standard BLAST search and make it suitable for searching (neuro)peptides in EST data. We briefly explain how bioinformatic tools and in silico predicted peptide precursor sequences can aid experimental peptide identification with mass spectrometry.

Spectral clustering in peptidomics studies helps to unravel modification profile of biologically active peptides and enhances peptide identification rate.

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.

Further characterization of the PW peptide family that inhibits neuron differentiation in Hydra.

From an evolutionary point of view, Hydra has one of the most primitive nervous systems among metazoans. Two different groups of peptides that affect neuron differentiation were identified in a systematic screening of peptide signaling molecules in Hydra. Within the first group of peptides, a neuropeptide, Hym-355, was previously shown to positively regulate neuron differentiation. The second group of peptides encompasses the PW family of peptides that negatively regulate neuron differentiation. In this study, we identified the gene encoding PW peptide preprohormone. Moreover, we made the antibody that specifically recognizes LPW. In situ hybridization and immunohistochemical analyses showed that the PW peptides and the gene encoding them were expressed in ectodermal epithelial cells throughout the body except for the basal disk. The PW peptides are produced by epithelial cells and are therefore termed "epitheliopeptides." Together with Hym-355, the PW family peptides mediate communication between neurons and epithelial cells and thereby maintain a specific density of neurons in Hydra.