SwePep, a database designed for endogenous peptides and mass spectrometry

Bioinformatics for Prohormone and Neuropeptide Discovery

Neuropeptides and peptide hormones are signaling molecules produced via complex posttranslational modifications of precursor proteins known as prohormones. Neuropeptides activate specific receptors and are associated with the regulation of physiological systems and behaviors. The identification of prohormones-and the neuropeptides created by these prohormones-from genomic assemblies has become essential to support the annotation and use of the rapidly growing number of sequenced genomes. Here we describe a well-validated methodology for identifying the prohormone complement from genomic assemblies that employs widely available public toolsets and databases. The uncovered prohormone sequences can then be screened for putative neuropeptides to enable accurate proteomic discovery and validation.

Advances in neuroproteomics for neurotrauma: unraveling insights for personalized medicine and future prospects

Neuroproteomics, an emerging field at the intersection of neuroscience and proteomics, has garnered significant attention in the context of neurotrauma research. Neuroproteomics involves the quantitative and qualitative analysis of nervous system components, essential for understanding the dynamic events involved in the vast areas of neuroscience, including, but not limited to, neuropsychiatric disorders, neurodegenerative disorders, mental illness, traumatic brain injury, chronic traumatic encephalopathy, and other neurodegenerative diseases. With advancements in mass spectrometry coupled with bioinformatics and systems biology, neuroproteomics has led to the development of innovative techniques such as microproteomics, single-cell proteomics, and imaging mass spectrometry, which have significantly impacted neuronal biomarker research. By analyzing the complex protein interactions and alterations that occur in the injured brain, neuroproteomics provides valuable insights into the pathophysiological mechanisms underlying neurotrauma. This review explores how such insights can be harnessed to advance personalized medicine (PM) approaches, tailoring treatments based on individual patient profiles. Additionally, we highlight the potential future prospects of neuroproteomics, such as identifying novel biomarkers and developing targeted therapies by employing artificial intelligence (AI) and machine learning (ML). By shedding light on neurotrauma's current state and future directions, this review aims to stimulate further research and collaboration in this promising and transformative field.

Mass spectrometry in the discovery of peptides involved in intercellular communication: From targeted to untargeted peptidomics approaches

Endogenous peptide hormones represent an essential class of biomolecules, which regulate cell-cell communications in diverse physiological processes of organisms. Mass spectrometry (MS) has been developed to be a powerful technology for identifying and quantifying peptides in a highly efficient manner. However, it is difficult to directly identify these peptide hormones due to their diverse characteristics, dynamic regulations, low abundance, and existence in a complicated biological matrix. Here, we summarize and discuss the roles of targeted and untargeted MS in discovering peptide hormones using bioassay-guided purification, bioinformatics screening, or the peptidomics-based approach. Although the peptidomics approach is expected to discover novel peptide hormones unbiasedly, only a limited number of successful cases have been reported. The critical challenges and corresponding measures for peptidomics from the steps of sample preparation, peptide extraction, and separation to the MS data acquisition and analysis are also discussed. We also identify emerging technologies and methods that can be integrated into the discovery platform toward the comprehensive study of endogenous peptide hormones.

Cold exposure-induced alterations in the brain peptidome and gut microbiome are linked to energy homeostasis in mice

Energy homeostasis of mammals during cold exposure involves complicated neural regulation and is affected by gut microbiota. However, the regulatory mechanism remains unclear partially due to a lack of comprehensive knowledge of the signaling molecules involved. Herein, we performed region-resolvable quantitative profiling of the brain peptidome using cold-exposed mouse models and interrogated the interaction between gut microbes and brain peptides in response to cold. Region-specific alterations in the brain peptidome were observed during chronic cold exposure and were correlated with gut microbiome composition. Several proSAAS-derived peptides exhibited a positive correlation with Lactobacillus. The hypothalamus-pituitary axis exhibited a sensitive response to cold exposure. We obtained a candidate pool of bioactive peptides that potentially participate in the regulation of cold-induced energy homeostasis. Intervention with cold-adapted microbiota in mice decreased the abundance of hypothalamic neurokinin B and subsequently contributed to shifting the fuel source for energy consumption from lipids to glucose. Collectively, this study demonstrated that gut microbes modulate brain peptides contributing to energy metabolism, providing a data resource for understanding the regulatory mechanism of energy homeostasis upon cold exposure.

NeuroPpred-SVM: A New Model for Predicting Neuropeptides Based on Embeddings of BERT

Neuropeptides play pivotal roles in different physiological processes and are related to different kinds of diseases. Identification of neuropeptides is of great benefit for studying the mechanism of these physiological processes and the treatment of neurological disorders. Several state-of-the-art neuropeptide predictors have been developed by using a two-layer stacking ensemble algorithm. Although the two-layer stacking ensemble algorithm can improve the feature representability, these models are complex, which are not as efficient as the models based on one classifier. In this study, we proposed a new model, NeuroPpred-SVM, to predict neuropeptides based on the embeddings of Bidirectional Encoder Representations from Transformers and other sequential features by using a support vector machine (SVM). The experimental results indicate that our model achieved a cross-validation area under the receiver operating characteristic (AUROC) curve of 0.969 on the training data set and an AUROC of 0.966 on the independent test set. By comparing our model with the other four state-of-the-art models including NeuroPIpred, PredNeuroP, NeuroPpred-Fuse, and NeuroPpred-FRL on the independent test set, our model achieved the highest AUROC, Matthews correlation coefficient, accuracy, and specificity, which indicate that our model outperforms the existing models. We believed that NeuroPpred-SVM could be a useful tool for identifying neuropeptides with high accuracy and low cost. The data sets and Python code are available at https://github.com/liuyf-a/NeuroPpred-SVM.

Recent advances in mass spectrometry analysis of neuropeptides

Due to their involvement in numerous biochemical pathways, neuropeptides have been the focus of many recent research studies. Unfortunately, classic analytical methods, such as western blots and enzyme-linked immunosorbent assays, are extremely limited in terms of global investigations, leading researchers to search for more advanced techniques capable of probing the entire neuropeptidome of an organism. With recent technological advances, mass spectrometry (MS) has provided methodology to gain global knowledge of a neuropeptidome on a spatial, temporal, and quantitative level. This review will cover key considerations for the analysis of neuropeptides by MS, including sample preparation strategies, instrumental advances for identification, structural characterization, and imaging; insightful functional studies; and newly developed absolute and relative quantitation strategies. While many discoveries have been made with MS, the methodology is still in its infancy. Many of the current challenges and areas that need development will also be highlighted in this review.

PepQSAR: a comprehensive data source and information platform for peptide quantitative structure-activity relationships

Peptide quantitative structure-activity relationships (pQSARs) have been widely applied to the statistical modeling and empirical prediction of peptide activity, property and feature. In the procedure, the peptide structure is characterized at sequence level using amino acid descriptors (AADs) and then correlated with observations by machine learning methods (MLMs), consequently resulting in a variety of quantitative regression models used to explain the structural factors that govern peptide activities, to generalize peptide properties of unknown from known samples, and to design new peptides with desired features. In this study, we developed a comprehensive platform, termed PepQSAR database, which is a systematic collection and decomposition of various data sources and abundant information regarding the pQSARs, including AADs, MLMs, data sets, peptide sequences, measured activities, model statistics, and literatures. The database also provides a comparison function for the various previously built pQSAR models reported by different groups via distinct approaches. The structured and searchable PepQSAR database is expected to provide a useful resource and powerful tool for the computational peptidology community, which is freely available at http://i.uestc.edu.cn/PQsarDB .

Combining mass spectrometry and machine learning to discover bioactive peptides

Peptides play important roles in regulating biological processes and form the basis of a multiplicity of therapeutic drugs. To date, only about 300 peptides in human have confirmed bioactivity, although tens of thousands have been reported in the literature. The majority of these are inactive degradation products of endogenous proteins and peptides, presenting a needle-in-a-haystack problem of identifying the most promising candidate peptides from large-scale peptidomics experiments to test for bioactivity. To address this challenge, we conducted a comprehensive analysis of the mammalian peptidome across seven tissues in four different mouse strains and used the data to train a machine learning model that predicts hundreds of peptide candidates based on patterns in the mass spectrometry data. We provide in silico validation examples and experimental confirmation of bioactivity for two peptides, demonstrating the utility of this resource for discovering lead peptides for further characterization and therapeutic development.

Well-Plate μFASP for Proteomic Analysis of Single Pancreatic Islets

Filter-aided sample preparation (FASP) is widely used in bottom-up proteomics for tryptic digestion. However, the sample recovery yield of this method is limited by the amount of the starting material. While ∼100 ng of digested protein is sufficient for thorough protein identification, proteomic information gets lost with a protein content <10 μg due to incomplete peptide recovery from the filter. We developed and optimized a flexible well-plate μFASP device and protocol that is suitable for an ∼1 μg protein sample. In 1 μg of HeLa digest, we identified 1295 ± 10 proteins with μFASP followed by analysis with liquid chromatography-mass spectrometry. In contrast, only 524 ± 5 proteins were identified with the standard FASP protocol, while 1395 ± 4 proteins were identified in 20 μg after standard FASP as a benchmark. Furthermore, we conducted a combined peptidomic and proteomic study of single pancreatic islets with well-plate μFASP. Here, we separated neuropeptides and digested the remaining on-filter proteins for bottom-up proteomic analysis. Our results indicate inter-islet heterogeneity for the expression of proteins involved in glucose catabolism, pancreatic hormone processing, and secreted peptide hormones. We consider our method to provide a useful tool for proteomic characterization of samples where the biological material is scarce. All proteomic data are available under DOI: 10.6019/PXD029039.

Peptidomics and Capillary Electrophoresis

Peptides play a crucial role in many vitally important functions of living organisms. The goal of peptidomics is the identification of the "peptidome," the whole peptide content of a cell, organ, tissue, body fluid, or organism. In peptidomic or proteomic studies, capillary electrophoresis (CE) is an alternative technique for liquid chromatography. It is a highly efficient and fast separation method requiring extremely low amounts of sample. In peptidomic approaches, CE is commonly combined with mass spectrometric (MS) detection. Most often, CE is coupled with electrospray ionization MS and less frequently with matrix-assisted laser desorption/ionization MS. CE-MS has been employed in numerous studies dealing with determination of peptide biomarkers in different body fluids for various diseases, or in food peptidomic research for the analysis and identification of peptides with special biological activities. In addition to the above topics, sample preparation techniques commonly applied in peptidomics before CE separation and possibilities for peptide identification and quantification by CE-MS or CE-MS/MS methods are discussed in this chapter.

Peptidomics: A Review of Clinical Applications and Methodologies

Improvements in both liquid chromatography (LC) and mass spectrometry (MS) instrumentation have greatly enhanced proteomic and small molecule metabolomic analysis in recent years. Less focus has been on the improved capability to detect and quantify small bioactive peptides, even though the exact sequences of the peptide species produced can have important biological consequences. Endogenous bioactive peptide hormones, for example, are generated by the targeted and regulated cleavage of peptides from their prohormone sequence. This process may include organ specific variants, as proglucagon is converted to glucagon in the pancreas but glucagon-like peptide-1 (GLP-1) in the small intestine, with glucagon raising, whereas GLP-1, as an incretin, lowering blood glucose. Therefore, peptidomics workflows must preserve the structure of the processed peptide products to prevent the misidentification of ambiguous peptide species. The poor in vivo and in vitro stability of peptides in biological matrices is a major factor that needs to be considered when developing methods to study them. The bioinformatic analysis of peptidomics data sets requires the inclusion of specific post-translational modifications, which are critical for the function of many bioactive peptides. This review aims to discuss and contrast the various extraction, analytical, and bioinformatics approaches used for human peptidomics studies in a multitude of matrices.

Techniques, perspectives, and challenges of bioactive peptide generation: A comprehensive systematic review

Due to the digestible refractory and absorbable structures of bioactive peptides (BPs), they could induce notable biological impacts on the living organism. In this regard, the current study was devoted to providing an overview regarding the available methods for BPs generation by the aid of a systematic review conducted on the published articles up to April 2019. In this context, the PubMed and Scopus databases were screened to retrieve the related publications. According to the results, although the characterization of BPs mainly has been performed using enzymatic and microbial in-vitro methods, they cannot be considered as suitable techniques for further stimulation of digestion in the gastrointestinal tract. Therefore, new approaches for both in-vivo and in-silico methods for BPs identification should be developed to overcome the obstacles that belonged to the current methods. The purpose of this review was to compile the recent analytical methods applied for studying various aspects of food-derived biopeptides, and emphasizing generation at in vitro, in vivo, and in silico.

Accurate detection of short and long active ORFs using Ribo-seq data

MOTIVATION

Ribo-seq, a technique for deep-sequencing ribosome-protected mRNA fragments, has enabled transcriptome-wide monitoring of translation in vivo. It has opened avenues for re-evaluating the coding potential of open reading frames (ORFs), including many short ORFs that were previously presumed to be non-translating. However, the detection of translating ORFs, specifically short ORFs, from Ribo-seq data, remains challenging due to its high heterogeneity and noise.

RESULTS

We present ribotricer, a method for detecting actively translating ORFs by directly leveraging the three-nucleotide periodicity of Ribo-seq data. Ribotricer demonstrates higher accuracy and robustness compared with other methods at detecting actively translating ORFs including short ORFs on multiple published datasets across species inclusive of Arabidopsis, Caenorhabditis elegans, Drosophila, human, mouse, rat, yeast and zebrafish.

AVAILABILITY AND IMPLEMENTATION

Ribotricer is available at https://github.com/smithlabcode/ribotricer. All analysis scripts and results are available at https://github.com/smithlabcode/ribotricer-results.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

A dynamic mouse peptidome landscape reveals probiotic modulation of the gut-brain axis

Certain probiotics have beneficial effects on the function of the central nervous system through modulation of the gut-brain axis. Here, we describe a dynamic landscape of the peptidome across multiple brain regions, modulated by oral administration of different probiotic species over various times. The spatiotemporal and strain-specific changes of the brain peptidome correlated with the composition of the gut microbiome. The hippocampus exhibited the most sensitive response to probiotic treatment. The administration of heat-killed probiotics altered the hippocampus peptidome but did not substantially change the gut microbiome. We developed a literature-mining algorithm to link the neuropeptides altered by probiotics with potential functional roles. We validated the probiotic-regulated role of corticotropin-releasing hormone by monitoring the hypothalamic-pituitary-adrenal axis, the prenatal stress–induced hyperactivity of which was attenuated by probiotics treatment. Our findings provide evidence for modulation of the brain peptidome by probiotics and provide a resource for further studies of the gut-brain axis and probiotic therapies.

The challenges of peptidomics in complementing proteomics in a clinical context

Naturally occurring peptides, including growth factors, hormones, and neurotransmitters, represent an important class of biomolecules and have crucial roles in human physiology. The study of these peptides in clinical samples is therefore as relevant as ever. Compared to more routine proteomics applications in clinical research, peptidomics research questions are more challenging and have special requirements with regard to sample handling, experimental design, and bioinformatics. In this review, we describe the issues that confront peptidomics in a clinical context. After these hurdles are (partially) overcome, peptidomics will be ready for a successful translation into medical practice.

Digging Deep into Peptidomics Applied to Body Fluids

Peptidomics techniques have allowed the identification of thousands of peptides that are derived from proteins in body fluids, despite the considerable challenges behind sample handling, MS-based identification, data analysis, and integration with bioinformatics tools. Body fluids' naturally occurring peptides are known to perform a variety of local and systemic functions; however, its knowledge is limited. Even so, the biological meaning that can be retrieved from peptidomics applied to the identification of disease markers and to the development of therapies using peptides has driven the progresses made in this field. In this review, a comparative analysis of body fluids' peptidome data retrieved from databases and from scientific papers is performed to identify the biological processes modulated by naturally occurring peptides. This integrative analysis highlights several interesting facts, such as the small overlap between blood-derived serum and plasma, which illustrates the impact of sample handling on these fluids peptidome. Urine is the body fluid with more naturally occurring peptides identified so far, most of which are derived from collagens. In saliva, the majority of peptides are originated from extracellular matrix proteins. Cerebrospinal fluid presents a high number of peptides derived from distinct proteins, mostly involved in the regulation of nervous system homeostasis. The lowest number of endogenous peptides was found in tears, most of which present antimicrobial activity. Collectively, data analysis highlights a peptidome signature for each body fluid, which comprehension will certainly help to improve disease management.

Probing the endogenous peptidomes of cancer for biomarkers: A new endeavor

The life span of cancer patients can be prolonged with appropriate therapies if detected early. Mass screening for early detection of cancer, however, requires sensitive and specific biomarkers obtainable from body fluids such as blood or urine. To date, most biomarker discovery programs focus on the proteome rather than the endogenous peptidome. It has been long-established that tumor cells and stromal cells produce tumor resident proteases (TRPs) to remodel the surrounding tumor microenvironment in support of tumor progression. In fact, proteolytic products of TRPs have been shown to correlate with malignant behavior. Being of low molecular weight, these unique peptides can pass through the endothelial barrier of the vasculature into the bloodstream. As such, the cancer peptidome has increasingly become a focus for biomarker discovery. In this review, we discuss on the various aspects of the peptidome in cancer biomarker research.

Molecular imaging mass spectrometry for probing protein dynamics in neurodegenerative disease pathology

Recent advances in the understanding of basic pathological mechanisms in various neurological diseases depend directly on the development of novel bioanalytical technologies that allow sensitive and specific chemical imaging at high resolution in cells and tissues. Mass spectrometry-based molecular imaging (IMS) has gained increasing popularity in biomedical research for mapping the spatial distribution of molecular species in situ. The technology allows for comprehensive, untargeted delineation of in situ distribution profiles of metabolites, lipids, peptides and proteins. A major advantage of IMS over conventional histochemical techniques is its superior molecular specificity. Imaging mass spectrometry has therefore great potential for probing molecular regulations in CNS-derived tissues and cells for understanding neurodegenerative disease mechanism. The goal of this review is to familiarize the reader with the experimental workflow, instrumental developments and methodological challenges as well as to give a concise overview of the major advances and recent developments and applications of IMS-based protein and peptide profiling with particular focus on neurodegenerative diseases. This article is part of the Special Issue "Proteomics".

Functional Peptidomics: Stimulus- and Time-of-Day-Specific Peptide Release in the Mammalian Circadian Clock

Daily oscillations of brain and body states are under complex temporal modulation by environmental light and the hypothalamic suprachiasmatic nucleus (SCN), the master circadian clock. To better understand mediators of differential temporal modulation, we characterize neuropeptide releasate profiles by nonselective capture of secreted neuropeptides in an optic nerve horizontal SCN brain slice model. Releasates are collected following electrophysiological stimulation of the optic nerve/retinohypothalamic tract under conditions that alter the phase of the SCN activity state. Secreted neuropeptides are identified by intact mass via matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS). We found time-of-day-specific suites of peptides released downstream of optic nerve stimulation. Peptide release was modified differentially with respect to time-of-day by stimulus parameters and by inhibitors of glutamatergic or PACAPergic neurotransmission. The results suggest that SCN physiology is modulated by differential peptide release of both known and unexpected peptides that communicate time-of-day-specific photic signals via previously unreported neuropeptide signatures.

Neuropeptidome of the Hypothalamus and Pituitary Gland of Indicine × Taurine Heifers: Evidence of Differential Neuropeptide Processing in the Pituitary Gland before and after Puberty

Puberty in cattle is regulated by an endocrine axis, which includes a complex milieu of neuropeptides in the hypothalamus and pituitary gland. The neuropeptidome of hypothalamic-pituitary gland tissue of pre- (PRE) and postpubertal (POST) Bos indicus-influenced heifers was characterized, followed by quantitative analysis of 51 fertility-related neuropeptides in these tissues. Comparison of peptide abundances with gene expression levels allowed assessment of post-transcriptional peptide processing. On the basis of classical cleavage, 124 mature neuropeptides from 35 precursor proteins were detected in hypothalamus and pituitary gland tissues of three PRE and three POST Brangus heifers. An additional 19 peptides (cerebellins, PEN peptides) previously reported as neuropeptides that did not follow classical cleavage were also identified. In the pre-pubertal hypothalamus, a greater diversity of neuropeptides (25.8%) was identified relative to post-pubertal heifers, while in the pituitary gland, 38.6% more neuropeptides were detected in the post-pubertal heifers. Neuro-tissues of PRE and POST heifers revealed abundance differences ( p < 0.05) in peptides from protein precursors involved in packaging and processing (e.g., the granin family and ProSAAS) or neuron stimulation (PENK, CART, POMC, cerebellins). On their own, the transcriptome data of the precursor genes could not predict the neuropeptide profile in the exact same tissues in several cases. This provides further evidence of the importance of differential processing of the neuropeptide precursors in the pituitary before and after puberty.

BioPepDB: an integrated data platform for food-derived bioactive peptides

Food-derived bioactive peptides play critical roles in regulating most biological processes and have considerable biological, medical and industrial importance. However, a large number of active peptides data, including sequence, function, source, commercial product information, references and other information are poorly integrated. BioPepDB is a searchable database of food-derived bioactive peptides and their related articles, including more than four thousand bioactive peptide entries. Moreover, BioPepDB provides modules of prediction and hydrolysis-simulation for discovering novel peptides. It can serve as a reference database to investigate the function of different bioactive peptides. BioPepDB is available at http://bis.zju.edu.cn/biopepdbr/ . The web page utilises Apache, PHP5 and MySQL to provide the user interface for accessing the database and predict novel peptides. The database itself is operated on a specialised server.

New techniques, applications and perspectives in neuropeptide research

Neuropeptides are one of the most diverse classes of signaling molecules and have attracted great interest over the years owing to their roles in regulation of a wide range of physiological processes. However, there are unique challenges associated with neuropeptide studies stemming from the highly variable molecular sizes of the peptides, low in vivo concentrations, high degree of structural diversity and large number of isoforms. As a result, much effort has been focused on developing new techniques for studying neuropeptides, as well as novel applications directed towards learning more about these endogenous peptides. The areas of importance for neuropeptide studies include structure, localization within tissues, interaction with their receptors, including ion channels, and physiological function. Here, we discuss these aspects and the associated techniques, focusing on technologies that have demonstrated potential in advancing the field in recent years. Most identification and structural information has been gained by mass spectrometry, either alone or with confirmations from other techniques, such as nuclear magnetic resonance spectroscopy and other spectroscopic tools. While mass spectrometry and bioinformatic tools have proven to be the most powerful for large-scale analyses, they still rely heavily on complementary methods for confirmation. Localization within tissues, for example, can be probed by mass spectrometry imaging, immunohistochemistry and radioimmunoassays. Functional information has been gained primarily from behavioral studies coupled with tissue-specific assays, electrophysiology, mass spectrometry and optogenetic tools. Concerning the receptors for neuropeptides, the discovery of ion channels that are directly gated by neuropeptides opens up the possibility of developing a new generation of tools for neuroscience, which could be used to monitor neuropeptide release or to specifically change the membrane potential of neurons. It is expected that future neuropeptide research will involve the integration of complementary bioanalytical technologies and functional assays.

Mapping Molecular Datasets Back to the Brain Regions They are Extracted from: Remembering the Native Countries of Hypothalamic Expatriates and Refugees

This article focuses on approaches to link transcriptomic, proteomic, and peptidomic datasets mined from brain tissue to the original locations within the brain that they are derived from using digital atlas mapping techniques. We use, as an example, the transcriptomic, proteomic and peptidomic analyses conducted in the mammalian hypothalamus. Following a brief historical overview, we highlight studies that have mined biochemical and molecular information from the hypothalamus and then lay out a strategy for how these data can be linked spatially to the mapped locations in a canonical brain atlas where the data come from, thereby allowing researchers to integrate these data with other datasets across multiple scales. A key methodology that enables atlas-based mapping of extracted datasets-laser-capture microdissection-is discussed in detail, with a view of how this technology is a bridge between systems biology and systems neuroscience.

Bioinformatics for Prohormone and Neuropeptide Discovery

Neuropeptides and peptide hormones are signaling molecules produced via complex post-translational modifications of precursor proteins known as prohormones. Neuropeptides activate specific receptors and are associated with the regulation of physiological systems and behaviors. The identification of prohormones-and the neuropeptides created by these prohormones-from genomic assemblies has become essential to support the annotation and use of the rapidly growing number of sequenced genomes. Here we describe a methodology for identifying the prohormone complement from genomic assemblies that employs widely available public toolsets and databases. The uncovered prohormone sequences can then be screened for putative neuropeptides to enable accurate proteomic discovery and validation.

The Bright Future of Peptidomics

In this final chapter I project my personal perspective on the future of peptidomics. A bird's eye view is shed on the discipline and a bid is made to frame it in the broader arena of the life sciences of tomorrow. Inferring from its present state-of-the-art and from the general direction of some evolutionary trends which are to be discerned, a case is made that peptidomics enjoys full ripeness as a young branch of science today, from which a bright future for the discipline can be predicted.

The Peptidome Comes of Age: Mass Spectrometry-Based Characterization of the Circulating Cancer Peptidome

Peptides play a seminal role in most physiological processes acting as neurotransmitters, hormones, antibiotics, and immune regulation. In the context of tumor biology, it is hypothesized that endogenous peptides, hormones, cytokines, growth factors, and aberrant degradation of select protein networks (e.g., enzymatic activities, protein shedding, and extracellular matrix remodeling) are fundamental in mediating cancer progression. Analysis of peptides in biological fluids by mass spectrometry holds promise of providing sensitive and specific diagnostic and prognostic information for cancer and other diseases. The identification of circulating peptides in the context of disease constitutes a hitherto source of new clinical biomarkers. The field of peptidomics can be defined as the identification and comprehensive analysis of physiological and pathological peptides. Like proteomics, peptidomics has been advanced by the development of new separation strategies, analytical detection methods such as mass spectrometry, and bioinformatic technologies. Unlike proteomics, peptidomics is targeted toward identifying endogenous protein and peptide fragments, defining proteolytic enzyme substrate specificity, as well as protease cleavage recognition (degradome). Peptidomics employs "top-down proteomics" strategies where mass spectrometry is applied at the proteoform level to analyze intact proteins and large endogenous peptide fragments. With recent advances in prefractionation workflows for separating peptides, mass spectrometry instrumentation, and informatics, peptidomics is an important field that promises to impact on translational medicine. This review covers the current advances in peptidomics, including top-down and imaging mass spectrometry, comprehensive quantitative peptidome analyses (developments in reproducibility and coverage), peptide prefractionation and enrichment workflows, peptidomic data analyses, and informatic tools. The application of peptidomics in cancer biomarker discovery will be discussed.

Multiplexed Temporal Quantification of the Exercise-regulated Plasma Peptidome

Exercise is extremely beneficial to whole body health reducing the risk of a number of chronic human diseases. Some of these physiological benefits appear to be mediated via the secretion of peptide/protein hormones into the blood stream. The plasma peptidome contains the entire complement of low molecular weight endogenous peptides derived from secretion, protease activity and PTMs, and is a rich source of hormones. In the current study we have quantified the effects of intense exercise on the plasma peptidome to identify novel exercise regulated secretory factors in humans. We developed an optimized 2D-LC-MS/MS method and used multiple fragmentation methods including HCD and EThcD to analyze endogenous peptides. This resulted in quantification of 5,548 unique peptides during a time course of exercise and recovery. The plasma peptidome underwent dynamic and large changes during exercise on a time-scale of minutes with many rapidly reversible following exercise cessation. Among acutely regulated peptides, many were known hormones including insulin, glucagon, ghrelin, bradykinin, cholecystokinin and secretogranins validating the method. Prediction of bioactive peptides regulated with exercise identified C-terminal peptides from Transgelins, which were increased in plasma during exercise. In vitro experiments using synthetic peptides identified a role for transgelin peptides on the regulation of cell-cycle, extracellular matrix remodeling and cell migration. We investigated the effects of exercise on the regulation of PTMs and proteolytic processing by building a site-specific network of protease/substrate activity. Collectively, our deep peptidomic analysis of plasma revealed that exercise rapidly modulates the circulation of hundreds of bioactive peptides through a network of proteases and PTMs. These findings illustrate that peptidomics is an ideal method for quantifying changes in circulating factors on a global scale in response to physiological perturbations such as exercise. This will likely be a key method for pinpointing exercise regulated factors that generate health benefits.

Microdialysis Coupled with LC-MS/MS for In Vivo Neurochemical Monitoring

Microdialysis is a powerful sampling technique used to monitor small molecules in vivo. Despite the many applications of microdialysis sampling, it is limited by the method of analyzing the resulting samples. An emerging technique for analysis of microdialysis samples is liquid chromatography-tandem mass spectrometry (LC-MS/MS). This technique is highly versatile, allowing multiplexed analysis of neurotransmitters, metabolites, and neuropeptides. Using LC-MS/MS for polar neurotransmitters is hampered by weak retention reverse phase LC columns. Several derivatization reagents have been utilized to enhance separation and resolution of neurochemicals in dialysate samples including benzoyl chloride (BzCl), dansyl chloride, formaldehyde, ethylchloroformate, and propionic anhydride. BzCl reacts with amine and phenol groups so that many neurotransmitters can be labeled. Besides improving separation on reverse phase columns, this reagent also increases sensitivity. It is available in a heavy form so that it can be used to make stable-isotope labeled internal standard for improved quantification. Using BzCl with LC-MS/MS has allowed for measuring as many as 70 neurochemicals in a single assay. With slightly different conditions, LC-MS/MS has also been used for monitoring endocannabinoids. LC-MS/MS is also useful for neuropeptide assay because it allows for highly sensitive, sequence specific measurement of most peptides. These advances have allowed for multiplexed neurotransmitter measurements in behavioral, circuit analysis, and drug effect studies.

CancerPDF: A repository of cancer-associated peptidome found in human biofluids

CancerPDF (Cancer Peptidome Database of bioFluids) is a comprehensive database of endogenous peptides detected in the human biofluids. The peptidome patterns reflect the synthesis, processing and degradation of proteins in the tissue environment and therefore can act as a gold mine to probe the peptide-based cancer biomarkers. Although an extensive data on cancer peptidome has been generated in the recent years, lack of a comprehensive resource restrains the facility to query the growing community knowledge. We have developed the cancer peptidome resource named CancerPDF, to collect and compile all the endogenous peptides isolated from human biofluids in various cancer profiling studies. CancerPDF has 14,367 entries with 9,692 unique peptide sequences corresponding to 2,230 unique precursor proteins from 56 high-throughput studies for ~27 cancer conditions. We have provided an interactive interface to query the endogenous peptides along with the primary information such as m/z, precursor protein, the type of cancer and its regulation status in cancer. To add-on, many web-based tools have been incorporated, which comprise of search, browse and similarity identification modules. We consider that the CancerPDF will be an invaluable resource to unwind the potential of peptidome-based cancer biomarkers. The CancerPDF is available at the web address http://crdd.osdd.net/raghava/cancerpdf/.

MS imaging and mass spectrometric synaptosome profiling identify PEP-19/pcp4 as a synaptic molecule involved in spatial learning in mice

The Morris water maze (MWM) spatial learning task has been demonstrated to involve a cognitive switch of action control to serve the transition from an early towards a late learning phase. However, the molecular mechanisms governing this switch are largely unknown. We employed MALDI MS imaging (MSI) to screen for changes in expression of small proteins in brain structures implicated in the different learning phases. We compared mice trained for 3days and 30days in the MWM, reflecting an early and a late learning phase in relation to the acquisition of a spatial learning task. An ion with m/z of 6724, identified as PEP-19/pcp4 by top-down tandem MS, was detected at higher intensity in the dorsal striatum of the late learning phase group compared with the early learning phase group. In addition, mass spectrometric analysis of synaptosomes confirmed the presence of PEP-19/pcp4 at the synapse. PEP-19/pcp4 has previously been identified as a critical determinant of synaptic plasticity in locomotor learning. Our findings extend PEP-19/pcp4 function to spatial learning in the forebrain and put MSI forward as a valid and unbiased research strategy for the discovery and identification of the molecular machinery involved in learning, memory and synaptic plasticity. This article is part of a Special Issue entitled: MALDI Imaging, edited by Dr. Corinna Henkel and Prof. Peter Hoffmann.

Ultra-deep sequencing of ribosome-associated poly-adenylated RNA in early Drosophila embryos reveals hundreds of conserved translated sORFs

There is growing recognition that small open reading frames (sORFs) encoding peptides shorter than 100 amino acids are an important class of functional elements in the eukaryotic genome, with several already identified to play critical roles in growth, development, and disease. However, our understanding of their biological importance has been hindered owing to the significant technical challenges limiting their annotation. Here we combined ultra-deep sequencing of ribosome-associated poly-adenylated RNAs with rigorous conservation analysis to identify a comprehensive population of translated sORFs during early Drosophila embryogenesis. In total, we identify 399 sORFs, including those previously annotated but without evidence of translational capacity, those found within transcripts previously classified as non-coding, and those not previously known to be transcribed. Further, we find, for the first time, evidence for translation of many sORFs with different isoforms, suggesting their regulation is as complex as longer ORFs. Furthermore, many sORFs are found not associated with ribosomes in late-stage Drosophila S2 cells, suggesting that many of the translated sORFs may have stage-specific functions during embryogenesis. These results thus provide the first comprehensive annotation of the sORFs present during early Drosophila embryogenesis, a necessary basis for a detailed delineation of their function in embryogenesis and other biological processes.

Analytic framework for peptidomics applied to large-scale neuropeptide identification

Large-scale mass spectrometry-based peptidomics for drug discovery is relatively unexplored because of challenges in peptide degradation and identification following tissue extraction. Here we present a streamlined analytical pipeline for large-scale peptidomics. We developed an optimized sample preparation protocol to achieve fast, reproducible and effective extraction of endogenous peptides from sub-dissected organs such as the brain, while diminishing unspecific protease activity. Each peptidome sample was analysed by high-resolution tandem mass spectrometry and the resulting data set was integrated with publically available databases. We developed and applied an algorithm that reduces the peptide complexity for identification of biologically relevant peptides. The developed pipeline was applied to rat hypothalamus and identifies thousands of neuropeptides and their post-translational modifications, which is combined in a resource format for visualization, qualitative and quantitative analyses.

Neuropeptide research is challenged by technical difficulties in identifying new bioactive peptides. Here the authors present an analytical pipeline for large-scale peptidomics applied to the rat hypothalamus, identifying thousands of endogenous neuropeptides and their post-translational modifications.

Neuropeptidomics: Mass Spectrometry-Based Identification and Quantitation of Neuropeptides

Neuropeptides produced from prohormones by selective action of endopeptidases are vital signaling molecules, playing a critical role in a variety of physiological processes, such as addiction, depression, pain, and circadian rhythms. Neuropeptides bind to post-synaptic receptors and elicit cellular effects like classical neurotransmitters. While each neuropeptide could have its own biological function, mass spectrometry (MS) allows for the identification of the precise molecular forms of each peptide without a priori knowledge of the peptide identity and for the quantitation of neuropeptides in different conditions of the samples. MS-based neuropeptidomics approaches have been applied to various animal models and conditions to characterize and quantify novel neuropeptides, as well as known neuropeptides, advancing our understanding of nervous system function over the past decade. Here, we will present an overview of neuropeptides and MS-based neuropeptidomic strategies for the identification and quantitation of neuropeptides.

Computational Methods in Mass Spectrometry-Based Proteomics

This chapter introduces computational methods used in mass spectrometry-based proteomics, including those for addressing the critical problems such as peptide identification and protein inference, peptide and protein quantification, characterization of posttranslational modifications (PTMs), and data-independent acquisitions (DIA). The chapter concludes with emerging applications of proteomic techniques, such as metaproteomics, glycoproteomics, and proteogenomics.

Identification of best indicators of peptide-spectrum match using a permutation resampling approach

Various indicators of observed-theoretical spectrum matches were compared and the resulting statistical significance was characterized using permutation resampling. Novel decoy databases built by resampling the terminal positions of peptide sequences were evaluated to identify the conditions for accurate computation of peptide match significance levels. The methodology was tested on real and manually curated tandem mass spectra from peptides across a wide range of sizes. Spectra match indicators from complementary database search programs were profiled and optimal indicators were identified. The combination of the optimal indicator and permuted decoy databases improved the calculation of the peptide match significance compared to the approaches currently implemented in the database search programs that rely on distributional assumptions. Permutation tests using p-values obtained from software-dependent matching scores and E-values outperformed permutation tests using all other indicators. The higher overlap in matches between the database search programs when using end permutation compared to existing approaches confirmed the superiority of the end permutation method to identify peptides. The combination of effective match indicators and the end permutation method is recommended for accurate detection of peptides.

Advances in Mass Spectrometric Tools for Probing Neuropeptides

Neuropeptides are important mediators in the functionality of the brain and other neurological organs. Because neuropeptides exist in a wide range of concentrations, appropriate characterization methods are needed to provide dynamic, chemical, and spatial information. Mass spectrometry and compatible tools have been a popular choice in analyzing neuropeptides. There have been several advances and challenges, both of which are the focus of this review. Discussions range from sample collection to bioinformatic tools, although avenues such as quantitation and imaging are included. Further development of the presented methods for neuropeptidomic mass spectrometric analysis is inevitable, which will lead to a further understanding of the complex interplay of neuropeptides and other signaling molecules in the nervous system.

NeuroPep: a comprehensive resource of neuropeptides

Neuropeptides play a variety of roles in many physiological processes and serve as potential therapeutic targets for the treatment of some nervous-system disorders. In recent years, there has been a tremendous increase in the number of identified neuropeptides. Therefore, we have developed NeuroPep, a comprehensive resource of neuropeptides, which holds 5949 non-redundant neuropeptide entries originating from 493 organisms belonging to 65 neuropeptide families. In NeuroPep, the number of neuropeptides in invertebrates and vertebrates is 3455 and 2406, respectively. It is currently the most complete neuropeptide database. We extracted entries deposited in UniProt, the database (www.neuropeptides.nl) and NeuroPedia, and used text mining methods to retrieve entries from the MEDLINE abstracts and full text articles. All the entries in NeuroPep have been manually checked. 2069 of the 5949 (35%) neuropeptide sequences were collected from the scientific literature. Moreover, NeuroPep contains detailed annotations for each entry, including source organisms, tissue specificity, families, names, post-translational modifications, 3D structures (if available) and literature references. Information derived from these peptide sequences such as amino acid compositions, isoelectric points, molecular weight and other physicochemical properties of peptides are also provided. A quick search feature allows users to search the database with keywords such as sequence, name, family, etc., and an advanced search page helps users to combine queries with logical operators like AND/OR. In addition, user-friendly web tools like browsing, sequence alignment and mapping are also integrated into the NeuroPep database. Database URL: http://isyslab.info/NeuroPep

Identification and characterization of sORF-encoded polypeptides

Molecular biology, genomics and proteomics methods have been utilized to reveal a non-annotated class of endogenous polypeptides (small proteins and peptides) encoded by short open reading frames (sORFs), or small open reading frames (smORFs). We refer to these polypeptides as s(m)ORF-encoded polypeptides or SEPs. The early SEPs were identified via genetic screens, and many of the RNAs that contain s(m)ORFs were originally considered to be non-coding; however, elegant work in bacteria and flies demonstrated that these s(m)ORFs code for functional polypeptides as small as 11-amino acids in length. The discovery of these initial SEPs led to search for these molecules using methods such as ribosome profiling and proteomics, which have revealed the existence of many SEPs, including novel human SEPs. Unlike screens, omics methods do not necessarily link a SEP to a cellular or biological function, but functional genomic and proteomic strategies have demonstrated that at least some of these newly discovered SEPs have biochemical and cellular functions. Here, we provide an overview of these results and discuss the future directions in this emerging field.

Current peptidomics: applications, purification, identification, quantification, and functional analysis

Peptidomics is an emerging field branching from proteomics that targets endogenously produced protein fragments. Endogenous peptides are often functional within the body-and can be both beneficial and detrimental. This review covers the use of peptidomics in understanding digestion, and identifying functional peptides and biomarkers. Various techniques for peptide and glycopeptide extraction, both at analytical and preparative scales, and available options for peptide detection with MS are discussed. Current algorithms for peptide sequence determination, and both analytical and computational techniques for quantification are compared. Techniques for statistical analysis, sequence mapping, enzyme prediction, and peptide function, and structure prediction are explored.

Spatial neuroproteomics using imaging mass spectrometry

The nervous system constitutes arguably the most complicated and least understood cellular network in the human body. This consequently manifests itself in the fact that the molecular bases of neurodegenerative diseases remain unknown. The limited understanding of neurobiological mechanisms relates directly to the lack of appropriate bioanalytical technologies that allow highly resolved, sensitive, specific and comprehensive molecular imaging in complex biological matrices. Imaging mass spectrometry (IMS) is an emerging technique for molecular imaging. The technique is characterized by its high chemical specificity allowing comprehensive, spatial protein and peptide profiling in situ. Imaging MS represents therefore a powerful approach for investigation of spatio-temporal protein and peptide regulations in CNS derived tissue and cells. This review aims to provide a concise overview of major developments and applications concerning imaging mass spectrometry based protein and peptide profiling in neurobiological and biomedical research. This article is part of a Special Issue entitled: Neuroproteomics: Applications in Neuroscience and Neurology.

Functional neuropeptidomics in invertebrates

Neuropeptides are key messengers in almost all physiological processes. They originate from larger precursors and are extensively processed to become bioactive. Neuropeptidomics aims to comprehensively identify the collection of neuropeptides in an organism, organ, tissue or cell. The neuropeptidome of several invertebrates is thoroughly explored since they are important model organisms (and models for human diseases), disease vectors and pest species. The charting of the neuropeptidome is the first step towards understanding peptidergic signaling. This review will first discuss the latest developments in exploring the neuropeptidome. The physiological roles and modes of action of neuropeptides can be explored in two ways, which are largely orthogonal and therefore complementary. The first way consists of inferring the functions of neuropeptides by a forward approach where neuropeptide profiles are compared under different physiological conditions. Second is the reverse approach were neuropeptide collections are used to screen for receptor-binding. This is followed by localization studies and functional tests. This review will focus on how these different functional screening methods contributed to the field of invertebrate neuropeptidomics and expanded our knowledge of peptidergic signaling. This article is part of a Special Issue entitled: Neuroproteomics: Applications in Neuroscience and Neurology.

Rhythmic control of activity and sleep by class B1 GPCRs

Members of the class B1 family of G-protein coupled receptors (GPCRs) whose ligands are neuropeptides have been implicated in regulation of circadian rhythms and sleep in diverse metazoan clades. This review discusses the cellular and molecular mechanisms by which class B1 GPCRs, especially the mammalian VPAC2 receptor and its functional homologue PDFR in Drosophila and C. elegans, regulate arousal and daily rhythms of sleep and wake. There are remarkable parallels in the cellular and molecular roles played by class B1 intercellular signaling pathways in coordinating arousal and circadian timekeeping across multiple cells and tissues in these very different genetic model organisms.

Accurate assignment of significance to neuropeptide identifications using Monte Carlo k-permuted decoy databases

In support of accurate neuropeptide identification in mass spectrometry experiments, novel Monte Carlo permutation testing was used to compute significance values. Testing was based on k-permuted decoy databases, where k denotes the number of permutations. These databases were integrated with a range of peptide identification indicators from three popular open-source database search software (OMSSA, Crux, and X! Tandem) to assess the statistical significance of neuropeptide spectra matches. Significance p-values were computed as the fraction of the sequences in the database with match indicator value better than or equal to the true target spectra. When applied to a test-bed of all known manually annotated mouse neuropeptides, permutation tests with k-permuted decoy databases identified up to 100% of the neuropeptides at p-value < 10(-5). The permutation test p-values using hyperscore (X! Tandem), E-value (OMSSA) and Sp score (Crux) match indicators outperformed all other match indicators. The robust performance to detect peptides of the intuitive indicator "number of matched ions between the experimental and theoretical spectra" highlights the importance of considering this indicator when the p-value was borderline significant. Our findings suggest permutation decoy databases of size 1×105 are adequate to accurately detect neuropeptides and this can be exploited to increase the speed of the search. The straightforward Monte Carlo permutation testing (comparable to a zero order Markov model) can be easily combined with existing peptide identification software to enable accurate and effective neuropeptide detection. The source code is available at http://stagbeetle.animal.uiuc.edu/pepshop/MSMSpermutationtesting.