Sequential Precipitation and Delipidation Enables Efficient Enrichment of Low-Molecular Weight Proteins and Peptides from Human Plasma

Advanced functional materials as reliable tools for capturing food-derived peptides to optimize the peptidomics pre-treatment enrichment workflow

Peptidomics strategies with high throughput, sensitivity, and reproducibility are key tools for comprehensively analyzing peptide composition and potential functional activities in foods. Nevertheless, complex signal interference, limited ionization efficiency, and low abundance have impeded food-derived peptides' progress in food detection and analysis. As a result, novel functional materials have been born at the right moment that could eliminate interference and perform efficient enrichment. Of note, few studies have focused on developing peptide enrichment materials for food sample analysis. This work summarizes the development of endogenous peptide, phosphopeptide, and glycopeptide enrichment utilizing materials that have been employed extensively recently: organic framework materials, carbon-based nanomaterials, bio-based materials, magnetic materials, and molecularly imprinted polymers. It focuses on the limitations, potential solutions, and future prospects for application in food peptidomics of various advanced functional materials. The size-exclusion effect of adjustable aperture and the modification of magnetic material enhanced the sensitivity and selectivity of endogenous peptide enrichment and aided in streamlining the enrichment process and cutting down on enrichment time. Not only that, the immobilization of metal ions such as Ti4+ and Nb5+ enhanced the capture of phosphopeptides, and the introduction of hydrophilic groups such as arginine, L-cysteine, and glutathione into bio-based materials effectively optimized the hydrophilic enrichment of glycopeptides. Although a portion of the carefully constructed functional materials currently only exhibit promising applications in the field of peptide enrichment for analytical chemistry, there is reason to believe that they will further advance the field of food peptidomics through improved pre-treatment steps.

Bacterial peptidoglycan serves as a critical modulator of the gut-immune-brain axis in Drosophila

Metabolites and compounds derived from gut-associated bacteria can modulate numerous physiological processes in the host, including immunity and behavior. Using a model of oral bacterial infection, we previously demonstrated that gut-derived peptidoglycan (PGN), an essential constituent of the bacterial cell envelope, influences female fruit fly egg-laying behavior by activating the NF-κB cascade in a subset of brain neurons. These findings underscore PGN as a potential mediator of communication between gut bacteria and the brain in Drosophila, prompting further investigation into its impact on all brain cells. Through high-resolution mass spectrometry, we now show that PGN fragments produced by gut bacteria can rapidly reach the central nervous system. In Addition, by employing a combination of whole-genome transcriptome analyses, comprehensive genetic assays, and reporter gene systems, we reveal that gut bacterial infection triggers a PGN dose-dependent NF-κB immune response in perineurial glia, forming the continuous outer cell layer of the blood-brain barrier. Furthermore, we demonstrate that persistent PGN-dependent NF-κB activation in perineurial glial cells correlates with a reduction in lifespan and early neurological decline. Overall, our findings establish gut-derived PGN as a critical mediator of the gut-immune-brain axis in Drosophila.

Optimizing and integrating depletion and precipitation methods for plasma proteomics through data-independent acquisition-mass spectrometry

The application of plasma proteomics is a reliable approach for the discovery of biomarkers. However, the utilization of mass spectrometry-based proteomics in plasma encounters limitations due to the presence of high-abundant proteins (HAPs) and the vast dynamic range. To address this issue, we conducted an optimization and integration of depletion and precipitation strategies eliminating interference from HAPs. The optimized procedure involved utilizing 40 µL of beads for the removal of 1 µL of plasma, and maintaining a ratio of 1:1:1 between plasma, urea, and trichloroacetic acid for the precipitation of 50 µL of plasma. To facilitate high-throughput processing, experimental procedures were carried out utilizing 96-well plates. The depletion method identified a total of 1510 proteins, whereas the precipitated method yielded a total of 802 proteins. The integration of these methods yielded a total of 1794 proteins, including a wide concentration range spanning over 8 orders of magnitude. Furthermore, these approaches exhibited a commendable level of reproducibility, as indicated by median coefficients of variation of 14.7 % and 21.1 % for protein intensities, respectively. The integrative method was found to be effective in precisely quantifying yeast proteins that were intentionally spiked in plasma at predetermined rations of 5, 2, 0.5, and 0.2 with a high genuine positive recovery with a range of 71 % to 91 % of all yeast proteins. The use of a complementary and finely tuned approach involving depletion and precipitation demonstrates tremendous potential in the field of discovering protein biomarkers from large-scale cohort studies.

Exploring the Potential of Bioactive Peptides: From Natural Sources to Therapeutics

Bioactive peptides, specific protein fragments with positive health effects, are gaining traction in drug development for advantages like enhanced penetration, low toxicity, and rapid clearance. This comprehensive review navigates the intricate landscape of peptide science, covering discovery to functional characterization. Beginning with a peptidomic exploration of natural sources, the review emphasizes the search for novel peptides. Extraction approaches, including enzymatic hydrolysis, microbial fermentation, and specialized methods for disulfide-linked peptides, are extensively covered. Mass spectrometric analysis techniques for data acquisition and identification, such as liquid chromatography, capillary electrophoresis, untargeted peptide analysis, and bioinformatics, are thoroughly outlined. The exploration of peptide bioactivity incorporates various methodologies, from in vitro assays to in silico techniques, including advanced approaches like phage display and cell-based assays. The review also discusses the structure-activity relationship in the context of antimicrobial peptides (AMPs), ACE-inhibitory peptides (ACEs), and antioxidative peptides (AOPs). Concluding with key findings and future research directions, this interdisciplinary review serves as a comprehensive reference, offering a holistic understanding of peptides and their potential therapeutic applications.

Identified Small Open Reading Frame-Encoded Peptides in Human Serum with Nanoparticle Protein Coronas

The low-molecular-weight proteins (LMWP) in serum and plasma are related to various human diseases and can be valuable biomarkers. A small open reading frame-encoded peptide (SEP) is one kind of LMWP, which has been found to function in many bioprocesses and has also been found in human blood, making it a potential biomarker. The detection of LMWP by a mass spectrometry (MS)-based proteomic assay is often inhibited by the wide dynamic range of serum/plasma protein abundance. Nanoparticle protein coronas are a newly emerging protein enrichment method. To analyze SEPs in human serum, we have developed a protocol integrated with nanoparticle protein coronas and liquid chromatography (LC)/MS/MS. With three nanoparticles, TiO2, Fe3O4@SiO2, and Fe3O4@SiO2@TiO2, we identified 164 new SEPs in the human serum sample. Fe3O4@SiO2 and a nanoparticle mixture obtained the maximum number and the largest proportion of identified SEPs, respectively. Compared with acetonitrile-based extraction, nanoparticle protein coronas can cover more small proteins and SEPs. The magnetic nanoparticle is also fit for high-throughput parallel protein separation before LC/MS. This method is fast, efficient, reproducible, and easy to operate in 96-well plates and centrifuge tubes, which will benefit the research on SEPs and biomarkers.

Exploring novel ANGICon-EIPs through ameliorated peptidomics techniques: Can deep learning strategies as a core breakthrough in peptide structure and function prediction?

Dairy-derived angiotensin-I-converting enzyme inhibitory peptides (ANGICon-EIPs) have been regarded as a relatively safe supplementary diet-therapy strategy for individuals with hypertension, and short-chain peptides may have more relevant antihypertensive benefits due to their direct intestinal absorption. Our previous explorations have confirmed that endogenous goat milk short-chain peptides are also an essential source of ANGICon-EIPs. Nonetheless, there are limited explorations on endogenous ANGICon-EIPs owing to the limitations of the extraction and enrichment of endogenous peptides, currently. This review outlined ameliorated pre-treatment strategies, data acquisition methods, and tools for the prediction of peptide structure and function, aiming to provide creative ideas for discovering novel ANGICon-EIPs. Currently, deep learning-based peptide structure and function prediction algorithms have achieved significant advancements. The convolutional neural network (CNN) and peptide sequence-based multi-label deep learning approach for determining the multi-functionalities of bioactive peptides (MLBP) can predict multiple peptide functions with absolute true value and accuracy of 0.699 and 0.708, respectively. Utilizing peptide sequence input, torsion angles, and inter-residue distance to train neural networks, APPTEST predicted the average backbone root mean square deviation (RMSD) value of peptide (5-40 aa) structures as low as 1.96 Å. Overall, with the exploration of more neural network architectures, deep learning could be considered a critical research tool to reduce the cost and improve the efficiency of identifying novel endogenous ANGICon-EIPs.

Microprotein Dysregulation in the Serum of Patients with Atrial Fibrillation

The incidence rate of atrial fibrillation (AF) has stayed at a high level in recent years. Despite the intensive efforts to study the pathologic changes of AF, the molecular mechanism of disease development remains unclarified. Microproteins are ribosomally translated gene products from small open reading frames (sORFs) and are found to play crucial biological functions, while remain rare attention and indistinct in AF study. In this work, we recruited 65 AF patients and 65 healthy subjects for microproteomic profiling. By differential analysis and cross-validation between independent datasets, a total of 4 microproteins were identified as significantly different, including 3 annotated ones and 1 novel one. Additionally, we established a diagnostic model with either microproteins or global proteins by machine learning methods and found the model with microproteins achieved comparable and excellent performance as that with global proteins. Our results confirmed the abnormal expression of microproteins in AF and may provide new perspectives on the mechanism study of AF.

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.

Recent advances in top-down proteome sample processing ahead of MS analysis

Top-down proteomics is emerging as a preferred approach to investigate biological systems, with objectives ranging from the detailed assessment of a single protein therapeutic, to the complete characterization of every possible protein including their modifications, which define the human proteoform. Given the controlling influence of protein modifications on their biological function, understanding how gene products manifest or respond to disease is most precisely achieved by characterization at the intact protein level. Top-down mass spectrometry (MS) analysis of proteins entails unique challenges associated with processing whole proteins while maintaining their integrity throughout the processes of extraction, enrichment, purification, and fractionation. Recent advances in each of these critical front-end preparation processes, including minimalistic workflows, have greatly expanded the capacity of MS for top-down proteome analysis. Acknowledging the many contributions in MS technology and sample processing, the present review aims to highlight the diverse strategies that have forged a pathway for top-down proteomics. We comprehensively discuss the evolution of front-end workflows that today facilitate optimal characterization of proteoform-driven biology, including a brief description of the clinical applications that have motivated these impactful contributions.

Simultaneous Quantification of Apolipoprotein C-III O-Glycoforms by Protein-MRM

Apolipoprotein C-III (APOC-III) regulates triglyceride levels, associated with a risk of cardiovascular disease. One gene generates several proteoforms, each with a different molecular mass and a unique function. Unlike peptide multiple reaction monitoring (MRM), protein-MRM without digestion is required to analyze clinically relevant individual proteoforms. We developed a protein-MRM method without digestion to individually quantify APOC-III proteoforms in human serum. We optimized the protein-MRM method following 60% acetonitrile extraction with C18 filtration. Bovine serum and myoglobin served as supporting cushions and the internal standard during sample preparation, respectively. Furthermore, we evaluated the LOD, lower limit of quantification, linearity, accuracy, and precision. Good correlation compared with turbidimetric immunoassay (TIA) and peptide-MRM was observed using 30 clinical sera. Individual APOC-III O-glycoforms were identified by top-down proteomics and simultaneously quantified using the protein-MRM method. The sum abundance of APOC-III proteoforms was significantly correlated with TIA and peptide-MRM. Our protein-MRM method provides an affordable and rapid quantification of potential disease-specific proteoforms. Precise quantification of each proteoform allows investigators to identify novel biological roles potentially related to cardiovascular disease or novel biomarkers. We expect our protein-oriented method to be more clinically useful than antibody-based immunoassays and peptide-oriented MRM analysis, especially for quantification of a biomarker proteoform with certain post-translational modifications.

An Optimized Proteomics Approach Reveals Novel Alternative Proteins in Mouse Liver Development

Alternative ORFs (AltORFs) are unannotated sequences in genome that encode novel peptides or proteins named alternative proteins (AltProts). Although ribosome profiling and bioinformatics predict a large number of AltProts, mass spectrometry as the only direct way of identification is hampered by the short lengths and relative low abundance of AltProts. There is an urgent need for improvement of mass spectrometry methodologies for AltProt identification. Here, we report an approach based on size-exclusion chromatography for simultaneous enrichment and fractionation of AltProts from complex proteome. This method greatly simplifies the variance of AltProts discovery by enriching small proteins smaller than 40 kDa. In a systematic comparison between 10 methods, the approach we reported enabled the discovery of more AltProts with overall higher intensities, with less cost of time and effort compared to other workflows. We applied this approach to identify 89 novel AltProts from mouse liver, 39 of which were differentially expressed between embryonic and adult mice. During embryonic development, the upregulated AltProts were mainly involved in biological pathways on RNA splicing and processing, whereas the AltProts involved in metabolisms were more active in adult livers. Our study not only provides an effective approach for identifying AltProts but also novel AltProts that are potentially important in developmental biology.

Improved profiling of low molecular weight serum proteome for gastric carcinoma by data-independent acquisition

Low molecular weight proteins (LMWPs) in the bloodstream participate in various biological processes and are closely associated with disease status, whereas identification of serous LMWPs remains a great technical challenge due to the wide dynamic range of protein components. In this study, we constructed an integrated LMWP library by combining the LMWPs obtained by three enrichment methods (50% ACN, 20% ACN + 20 mM ABC, and 30 kDa) and their fractions identified by the data-dependent acquisition method. With this newly constructed library, we comprehensively profiled LMWPs in serum using data-independent acquisition and reliably achieved quantitative results for 75% serous LMWPs. When applying this strategy to quantify LMWPs in human serum samples, we could identify 405 proteins on average per sample, of which 136 proteins were with a MW less than 30 kDa and 293 proteins were with a MW less than 65 kDa. Of note, pre- and post-operative gastric carcinoma (GC) patients showed differentially expressed serous LWMPs, which was also different from the pattern of LWMP expression in healthy controls. In conclusion, our results showed that LMWPs could efficiently distinguish GC patients from healthy controls as well as between pre- and post-operative statuses, and more importantly, our newly developed LMWP profiling platform could be used to discover candidate LMWP biomarkers for disease diagnosis and status monitoring.

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.

LncRNA-encoded microproteins: A new form of cargo in cell culture-derived and circulating extracellular vesicles

Advancements in omics-based technologies over the past few years have led to the discovery of numerous biologically relevant peptides encoded by small open reading frames (smORFs) embedded in long noncoding RNA (lncRNA) transcripts (referred to as microproteins here) in a variety of species. However, the mechanisms and modes of action that underlie the roles of microproteins have yet to be fully characterized. Herein, we provide the first experimental evidence of abundant microproteins in extracellular vesicles (EVs) derived from glioma cancer cells, indicating that the EV-mediated transfer of microproteins may represent a novel mechanism for intercellular communication. Intriguingly, when examining human plasma, 48, 11 and 3 microproteins were identified from purified EVs, whole plasma and EV-free plasma, respectively, suggesting that circulating microproteins are primarily enriched in EVs. Most importantly, the preliminary data showed that the expression profile of EV microproteins in glioma patient diverged from the health donors, suggesting that the circulating microproteins in EVs might have potential diagnostic application in identifying patients with glioma.

Extending the Depth of Human Plasma Proteome Coverage Using Simple Fractionation Techniques

Human plasma is one of the most widely used tissues in clinical analysis, and plasma-based biomarkers are used for monitoring patient health status and/or response to medical treatment to avoid unnecessary invasive biopsy. Data-driven plasma proteomics has suffered from a lack of throughput and detection sensitivity, largely due to the complexity of the plasma proteome and in particular the enormous quantitative dynamic range, estimated to be between 9 and 13 orders of magnitude between the lowest and the highest abundance protein. A major challenge is to identify workflows that can achieve depth of plasma proteome coverage while minimizing the complexity of the sample workup and maximizing the sample throughput. In this study, we have performed intensive depletion of high-abundant plasma proteins or enrichment of low-abundant proteins using the Agilent multiple affinity removal liquid chromatography (LC) column-Human 6 (Hu6), the Agilent multiple affinity removal LC column-Human 14 (Hu14), and ProteoMiner followed by sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS PAGE) and C18 prefractionation techniques. We compared the performance of each of these fractionation approaches to identify the method that satisfies requirements for analysis of clinical samples and to include good plasma proteome coverage in combination with reasonable sample output. In this study, we report that one-dimensional (1D) gel-based prefractionation allows parallel sample processing and no loss of proteome coverage, compared with serial chromatographic separation, and significantly accelerates analysis time, particularly important for large clinical projects. Furthermore, we show that a variety of methodologies can achieve similarly high plasma proteome coverage, allowing flexibility in method selection based on project-specific needs. These considerations are important in the effort to accelerate plasma proteomics research so as to provide efficient, reliable, and accurate diagnoses, population-based health screening, clinical research studies, and other clinical work.

Recent advances in mass spectrometry-based peptidomics workflows to identify short-open-reading-frame-encoded peptides and explore their functions

Short open reading frame (sORF)-encoded polypeptides (SEPs) have recently emerged as key regulators of major cellular processes. Computational methods for the annotation of sORFs combined with transcriptomics and ribosome profiling approaches predicted the existence of tens of thousands of SEPs across the kingdom of life. Although, we still lack unambiguous evidence for most of them. The method of choice to validate the expression of SEPs is mass spectrometry (MS)-based peptidomics. Peptides are less abundant than proteins, which tends to hinder their detection. Therefore, optimization and enrichment methods are necessary to validate the existence of SEPs. In this article, we discuss the challenges for the detection of SEPs by MS and recent developments of biochemical approaches applied to the study of these peptides. We detail the advances made in the different key steps of a typical peptidomics workflow and highlight possible alternatives that have not been explored yet.