Neuropeptidomics: Mass Spectrometry-Based Identification and Quantitation of Neuropeptides

Origins, Technological Advancement, and Applications of Peptidomics

Peptidomics is the comprehensive characterization of peptides from biological sources instead of heading for a few single peptides in former peptide research. Mass spectrometry allows to detect a multitude of peptides in complex mixtures and thus enables new strategies leading to peptidomics. The term was established in the year 2001, and up to now, this new field has grown to over 3000 publications. Analytical techniques originally developed for fast and comprehensive analysis of peptides in proteomics were specifically adjusted for peptidomics. Although it is thus closely linked to proteomics, there are fundamental differences with conventional bottom-up proteomics. Fundamental technological advancements of peptidomics since have occurred in mass spectrometry and data processing, including quantification, and more slightly in separation technology. Different strategies and diverse sources of peptidomes are mentioned by numerous applications, such as discovery of neuropeptides and other bioactive peptides, including the use of biochemical assays. Furthermore, food and plant peptidomics are introduced similarly. Additionally, applications with a clinical focus are included, comprising biomarker discovery as well as immunopeptidomics. This overview extensively reviews recent methods, strategies, and applications including links to all other chapters of this book.

Mass Spectrometry Approaches Empowering Neuropeptide Discovery and Therapeutics

The discovery of insulin in the early 1900s ushered in the era of research related to peptides acting as hormones and neuromodulators, among other regulatory roles. These essential gene products are found in all organisms, from the most primitive to the most evolved, and carry important biologic information that coordinates complex physiology and behavior; their misregulation has been implicated in a variety of diseases. The evolutionary origins of at least 30 neuropeptide signaling systems have been traced to the common ancestor of protostomes and deuterostomes. With the use of relevant animal models and modern technologies, we can gain mechanistic insight into orthologous and paralogous endogenous peptides and translate that knowledge into medically relevant insights and new treatments. Groundbreaking advances in medicine and basic science influence how signaling peptides are defined today. The precise mechanistic pathways for over 100 endogenous peptides in mammals are now known and have laid the foundation for multiple drug development pipelines. Peptide biologics have become valuable drugs due to their unique specificity and biologic activity, lack of toxic metabolites, and minimal undesirable interactions. This review outlines modern technologies that enable neuropeptide discovery and characterization, and highlights lessons from nature made possible by neuropeptide research in relevant animal models that is being adopted by the pharmaceutical industry. We conclude with a brief overview of approaches/strategies for effective development of peptides as drugs. SIGNIFICANCE STATEMENT: Neuropeptides, an important class of cell-cell signaling molecules, are involved in maintaining a range of physiological functions. Since the discovery of insulin's activity, over 100 bioactive peptides and peptide analogs have been used as therapeutics. Because these are complex molecules not easily predicted from a genome and their activity can change with subtle chemical modifications, mass spectrometry (MS) has significantly empowered peptide discovery and characterization. This review highlights contributions of MS-based research towards the development of therapeutic peptides.

Developing mass spectrometry for the quantitative analysis of neuropeptides

INTRODUCTION

Neuropeptides are signaling molecules originating in the neuroendocrine system that can act as neurotransmitters and hormones in many biochemical processes. Their exact function is difficult to characterize, however, due to dependence on concentration, post-translational modifications, and the presence of other comodulating neuropeptides. Mass spectrometry enables sensitive, accurate, and global peptidomic analyses that can profile neuropeptide expression changes to understand their roles in many biological problems, such as neurodegenerative disorders and metabolic function.

AREAS COVERED

We provide a brief overview of the fundamentals of neuropeptidomic research, limitations of existing methods, and recent progress in the field. This review is focused on developments in mass spectrometry and encompasses labeling strategies, post-translational modification analysis, mass spectrometry imaging, and integrated multi-omic workflows, with discussion emphasizing quantitative advancements.

EXPERT OPINION

Neuropeptidomics is critical for future clinical research with impacts in biomarker discovery, receptor identification, and drug design. While advancements are being made to improve sensitivity and accuracy, there is still room for improvement. Better quantitative strategies are required for clinical analyses, and these methods also need to be amenable to mass spectrometry imaging, post-translational modification analysis, and multi-omics to facilitate understanding and future treatment of many diseases.

Neuropeptide signalling systems - An underexplored target for venom drug discovery

Neuropeptides are signalling molecules mainly secreted from neurons that act as neurotransmitters or peptide hormones to affect physiological processes and modulate behaviours. In humans, neuropeptides are implicated in numerous diseases and understanding their role in physiological processes and pathologies is important for therapeutic development. Teasing apart the (patho)physiology of neuropeptides remains difficult due to ligand and receptor promiscuity and the complexity of the signalling pathways. The current approach relies on a pharmacological toolbox of agonists and antagonists displaying high selectivity for independent receptor subtypes, with the caveat that only few selective ligands have been discovered or developed. Animal venoms represent an underexplored source for novel receptor subtype-selective ligands that could aid in dissecting human neuropeptide signalling systems. Multiple endogenous-like neuropeptides as well as peptides acting on neuropeptide receptors are present in venoms. In this review, we summarise current knowledge on neuropeptides and discuss venoms as a source for ligands targeting neuropeptide signalling systems.

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.

Capillary electrophoresis coupled to MALDI mass spectrometry imaging with large volume sample stacking injection for improved coverage of C. borealis neuropeptidome

Neuropeptides are important signaling molecules responsible for a wide range of functions within the nervous and neuroendocrine system. However, they are difficult to study due to numerous challenges, most notably their large degree of variability and low abundance in vivo. As a result, effective separation methods with sensitive detection capabilities are necessary for profiling neuropeptides in tissue samples, particularly those of simplified model organisms such as crustaceans. In order to address these challenges, this study utilized a capillary electrophoresis (CE)-matrix-assisted laser desorption/ionization (MALDI)-mass spectrometry imaging (MSI) platform, building upon our previous design for improved neuropeptidomic coverage. The capillary was coated with polyethylenimine (PEI) to reduce peptide adsorption and reverse the electroosmotic flow, and large volume sample stacking (LVSS) was used to load and pre-concentrate 1 μL of sample. The method demonstrated good reproducibility, with lower than 5% relative standard deviation for standards, and a limit of detection of approximately 100 pM for an allatostatin III peptide standard. The method was tested on brain and sinus gland (SG) tissue extracts and enabled detection of over 200 neuropeptides per run. When comparing the number detected in brain extracts in a direct spot, 60-second fractions, and 30-second fractions, the continuous trace collection afforded by the CE-MALDI-MSI platform yielded the largest number of detected neuropeptides. The method was compared to conventional LC-ESI-MS, and though the number of neuropeptides detected by LC-ESI-MS was slightly larger, the two methods were highly complementary, indicating the potential for the CE-MALDI-MSI method to uncover previously undetected neuropeptides in the crustacean nervous system. These results indicate the potential of CE-MALDI-MSI for routine use in neuropeptide research.

Absolute Quantitation of Oxidizable Peptides by Coulometric Mass Spectrometry

Quantitation methods for peptides using mass spectrometry have advanced rapidly. These methods rely on using standard and/or isotope-labeled peptides, which might be difficult or expensive to synthesize. To tackle this challenge, we present a new approach for absolute quantitation without the use of standards or calibration curves based on coulometry combined with mass spectrometry (MS). In this approach, which we call coulometric mass spectrometry (CMS), the mass spectrum of a target peptide containing one or more tyrosine residues is recorded before and after undergoing electrochemical oxidation. We record the total integrated oxidation current from the electrochemical measurement, which according to the Faraday's Law of coulometry, provides the number of moles of oxidized peptide. The ion intensity ratio of the target peptide before and after oxidation provides an excellent estimate of the fraction of the peptide that has been oxidized, from which the total amount of peptide is calculated. The striking strength of CMS is that it needs no standard peptide, but CMS does require the peptide to contain a known number of oxidizable groups. To illustrate the power of this method, we analyzed various tyrosine-containing peptides such as GGYR, DRVY, oxytocin, [Arg⁸]-vasotocin and angiotensinogen 1-14 with a quantification error ranging from - 7.5 to + 2.4%. This approach is also applicable to quantifying phosphopeptides and could be useful in proteomics research.

Origins, Technological Development, and Applications of Peptidomics

Peptidomics is the comprehensive characterization of peptides from biological sources mainly by HPLC and mass spectrometry. Mass spectrometry allows the detection of a multitude of single peptides in complex mixtures. The term first appeared in full papers in the year 2001, after over 100 years of peptide research with a main focus on one or a few specific peptides. Within the last 15 years, this new field has grown to over 1200 publications. Mass spectrometry techniques, in combination with other analytical methods, were developed for the fast and comprehensive analysis of peptides in proteomics and specifically adjusted to implement peptidomics technologies. Although peptidomics is closely linked to proteomics, there are fundamental differences with conventional bottom-up proteomics. The development of peptidomics is described, including the most important implementations for its technological basis. Different strategies are covered which are applied to several important applications, such as neuropeptidomics and discovery of bioactive peptides or biomarkers. This overview includes links to all other chapters in the book as well as recent developments of separation, mass spectrometric, and data processing technologies. Additionally, some new applications in food and plant peptidomics as well as immunopeptidomics are introduced.

Progress with peptide scanning to study structure-activity relationships: the implications for drug discovery

INTRODUCTION

Peptides have gained renewed interest as candidate therapeutics. However, to bring them to a broader clinical use, challenges such as the rational optimization of their pharmacological properties remain. Peptide scanning techniques offer a systematic framework to gain information on the functional role of individual amino acids of a peptide. Due to progress in mastering new chemical synthesis routes targeting amino acid backbone, they are currently diversified. Structure-activity relationship (SAR) analyses such as alanine- or enantioneric- scanning can now be supplemented by N-substitution, lactam cyclisation- or aza-amino scanning procedures addressing not only SAR considerations but also the peptide pharmacological properties.

AREAS COVERED

This review highlights the different scanning techniques currently available and illustrates how they can impact drug discovery.

EXPERT OPINION

Progress in peptide scanning techniques opens new perspectives for peptide drug development. It comes with the promise of a paradigm change in peptide drug design in which peptide drugs will be closer to the parent peptides. However, scanning still remains assimilable to a trial and error strategy that could benefit from being combined with specific in silico approaches that start reaching maturity.