Fast and comprehensive N- and O-glycoproteomics analysis with MSFragger-Glyco

Proceedings of the EuBIC-MS developers meeting 2023

The 2023 European Bioinformatics Community for Mass Spectrometry (EuBIC-MS) Developers Meeting was held from January 15th to January 20th, 2023, in Congressi Stefano Franscin at Monte Verità in Ticino, Switzerland. The participants were scientists and developers working in computational mass spectrometry (MS), metabolomics, and proteomics. The 5-day program was split between introductory keynote lectures and parallel hackathon sessions focusing on "Artificial Intelligence in proteomics" to stimulate future directions in the MS-driven omics areas. During the latter, the participants developed bioinformatics tools and resources addressing outstanding needs in the community. The hackathons allowed less experienced participants to learn from more advanced computational MS experts and actively contribute to highly relevant research projects. We successfully produced several new tools applicable to the proteomics community by improving data analysis and facilitating future research.

Variation of Site-Specific Glycosylation Profiles of Recombinant Influenza Glycoproteins

This work presents a detailed determination of site-specific N-glycan distributions of the recombinant influenza glycoproteins hemagglutinin and neuraminidase. Variation in glycosylation among recombinant glycoproteins is not predictable and can depend on details of the biomanufacturing process as well as details of protein structure. In this study, recombinant influenza proteins were analyzed from eight strains of four different suppliers. These include five hemagglutinin and three neuraminidase proteins, each produced from a HEK293 cell line. Digestion was conducted using a series of complex multi-enzymatic methods designed to isolate glycopeptides containing single N-glycosylated sites. Site-specific glycosylation profiles of intact glycopeptides were produced using a recently developed method and comparisons were made using spectral similarity scores. Variation in glycan abundances and distribution was most pronounced between different strains of virus (similarity score = 383 out of 999), whereas digestion replicates and injection replicates showed relatively little variation (similarity score = 957). Notably, glycan distributions for homologous regions of influenza glycoprotein variants showed low variability. Due to the multiple possible sources of variation and inherent analytical difficulties in site-specific glycan determinations, variations were individually examined for multiple factors, including differences in supplier, production batch, protease digestion and replicate measurement. After comparing all glycosylation distributions, four distinguishable classes could be identified for the majority of sites. Finally, attempts to identify glycosylation distributions on adjacent potential N-glycosylated sites of one HA variant were made. Only the second site (NnST) was found to be occupied using two rarely used proteases in proteomics, subtilisin and esperase, both of which did selectively cleave these adjacent sites.

Quantitative Comparison of Deparaffinization, Rehydration, and Extraction Methods for FFPE Tissue Proteomics and Phosphoproteomics

Formalin-fixed paraffin-embedded (FFPE) tissues are suitable for proteomic and phosphoproteomic biomarker studies by data-independent acquisition mass spectrometry. The choice of the sample preparation method influences the number, intensity, and reproducibility of identifications. By comparing four deparaffinization and rehydration methods, including heptane, histolene, SubX, and xylene, we found that heptane and methanol produced the lowest coefficients of variation (CVs). Using this, five extraction methods from the literature were modified and evaluated for their performance using kidney, leg muscle, lung, and testicular rat organs. All methods performed well, except for SP3 due to insufficient tissue lysis. Heat n' Beat was the fastest and most reproducible method with the highest digestion efficiency and lowest CVs. S-Trap produced the highest peptide yield, while TFE produced the best phosphopeptide enrichment efficiency. The quantitation of FFPE-derived peptides remains an ongoing challenge with bias in UV and fluorescence assays across methods, most notably in SPEED. Functional enrichment analysis demonstrated that each method favored extracting some gene ontology cellular components over others including chromosome, cytoplasmic, cytoskeleton, endoplasmic reticulum, membrane, mitochondrion, and nucleoplasm protein groups. The outcome is a set of recommendations for choosing the most appropriate method for different settings.

In vivo Mapping of the Mouse Galnt3-specific O-Glycoproteome

The UDP-N-acetylgalactosamine polypeptide:N-acetylgalactosaminyltransferase (GalNAc-T) family of enzymes initiates O-linked glycosylation by catalyzing the addition of the first GalNAc sugar to serine or threonine on proteins destined to be membrane-bound or secreted. Defects in individual isoforms of the GalNAc-T family can lead to certain congenital disorders of glycosylation (CDG). The GALNT3-CDG, is caused by mutations in GALNT3, resulting in hyperphosphatemic familial tumoral calcinosis (HFTC) due to impaired glycosylation of the phosphate-regulating hormone FGF23 within osteocytes of the bone. Patients with hyperphosphatemia present altered bone density, abnormal tooth structure and calcified masses throughout the body. It is therefore important to identify all potential substrates of GalNAc-T3 throughout the body to understand the complex disease phenotypes. Here, we compared the Galnt3-/- mouse model, which partially phenocopies GALNT3-CDG, with wild-type mice and employed a multi-component approach utilizing chemoenzymatic conditions, a product-dependent method constructed using EThcD triggered scans in a mass spectrometry workflow, quantitative O-glycoproteomics, and global proteomics to identify 663 Galnt3-specific O-glycosites from 269 glycoproteins across multiple tissues. Consistent with the mouse and human phenotypes, functional networks of glycoproteins that contain GalNAc-T3-specific O-glycosites involved in skeletal morphology, mineral level maintenance and hemostasis were identified. This library of in vivo GalNAc-T3-specific substrate proteins and O-glycosites will serve as a valuable resource to understand the functional implications of O-glycosylation and to unravel the underlying causes of complex human GALNT3-CDG phenotypes.

Glycan-Tailored Glycoproteomic Analysis Reveals Serine is the Sole Residue Subjected to O-Linked Glycosylation in Acinetobacter baumannii

Protein glycosylation is a ubiquitous process observed across all domains of life. Within the human pathogen Acinetobacter baumannii, O-linked glycosylation is required for virulence; however, the targets and conservation of glycosylation events remain poorly defined. In this work, we expand our understanding of the breadth and site specificity of glycosylation within A. baumannii by demonstrating the value of strain specific glycan electron-transfer/higher-energy collision dissociation (EThcD) triggering for bacterial glycoproteomics. By coupling tailored EThcD-triggering regimes to complementary glycopeptide enrichment approaches, we assessed the observable glycoproteome of three A. baumannii strains (ATCC19606, BAL062, and D1279779). Combining glycopeptide enrichment techniques including ion mobility (FAIMS), metal oxide affinity chromatography (titanium dioxide), and hydrophilic interaction liquid chromatography (ZIC-HILIC), as well as the use of multiple proteases (trypsin, GluC, pepsin, and thermolysis), we expand the known A. baumannii glycoproteome to 33 unique glycoproteins containing 42 glycosylation sites. We demonstrate that serine is the sole residue subjected to glycosylation with the substitution of serine for threonine abolishing glycosylation in model glycoproteins. An A. baumannii pan-genome built from 576 reference genomes identified that serine glycosylation sites are highly conserved. Combined this work expands our knowledge of the conservation and site specificity of A. baumannii O-linked glycosylation.

Experimentally Determined Diagnostic Ions for Identification of Peptide Glycotopes

The analysis of the structures of glycans present on glycoproteins is an essential component for determining glycoprotein function; however, detailed glycan structural assignment on glycopeptides from proteomics mass spectrometric data remains challenging. Glycoproteomic analysis by mass spectrometry currently can provide significant, yet incomplete, information about the glycans present, including the glycan monosaccharide composition and in some circumstances the site(s) of glycosylation. Advancements in mass spectrometric resolution, using high-mass accuracy instrumentation and tailored MS/MS fragmentation parameters, coupled with a dedicated definition of diagnostic fragmentation ions have enabled the determination of some glycan structural features, or glycotopes, expressed on glycopeptides. Here we present a collation of diagnostic glycan fragments produced by traditional positive-ion-mode reversed-phase LC-ESI MS/MS proteomic workflows and describe the specific fragmentation energy settings required to identify specific glycotopes presented on N- or O-linked glycopeptides in a typical proteomics MS/MS experiment.

High-Throughput Proteomics and Phosphoproteomics of Rat Tissues Using Microflow Zeno SWATH

High-throughput tissue proteomics has great potential in the advancement of precision medicine. Here, we investigated the combined sensitivity of trap-elute microflow liquid chromatography with a ZenoTOF for DIA proteomics and phosphoproteomics. Method optimization was conducted on HEK293T cell lines to determine the optimal variable window size, MS2 accumulation time and gradient length. The ZenoTOF 7600 was then compared to the previous generation TripleTOF 6600 using eight rat organs, finding up to 23% more proteins using a fifth of the sample load and a third of the instrument time. Spectral reference libraries generated from Zeno SWATH data in FragPipe (MSFragger-DIA/DIA-NN) contained 4 times more fragment ions than the DIA-NN only library and quantified more proteins. Replicate single-shot phosphopeptide enrichments of 50-100 μg of rat tryptic peptide were analyzed by microflow HPLC using Zeno SWATH without fractionation. Using Spectronaut we quantified a shallow phosphoproteome containing 1000-3000 phosphoprecursors per organ. Promisingly, clear hierarchical clustering of organs was observed with high Pearson correlation coefficients >0.95 between replicate enrichments and median CV of 20%. The combined sensitivity of microflow HPLC with Zeno SWATH allows for the high-throughput quantitation of an extensive proteome and shallow phosphoproteome from small tissue samples.

Integrated serum proteomic and N-glycoproteomic characterization of dengue patients

Dengue fever is a mosquito-borne viral disease caused by the dengue virus (DENV). It poses a public health threat globally and, while most people with dengue have mild symptoms or are asymptomatic, approximately 5% of affected individuals develop severe disease and need hospital care. However, knowledge of the molecular mechanisms underlying dengue infection and the interaction between the virus and its host remains limited. In the present study, we performed a quantitative proteomic and N-glycoproteomic analysis of serum from 19 patients with dengue and 11 healthy people. The results revealed distinct proteomic and N-glycoproteomic landscapes between the two groups. Notably, we report for the first time the changes in the serum N glycosylation pattern following dengue infection and provide abundant information on glycoproteins, glycosylation sites, and intact N-glycopeptides using recently developed site-specific glycoproteomic approaches. Furthermore, a series of key functional pathways in proteomic and N-glycoproteomic were identified. Collectively, our findings significantly improve understanding of host and DENV interactions and the general pathogenesis and pathology of DENV, laying a foundation for functional studies of glycosylation and glycan structures in dengue infection.

Global View of Domain-Specific O-Linked Mannose Glycosylation in Glycoengineered Cells

Protein O-linked mannose (O-Man) glycosylation is an evolutionary conserved posttranslational modification that fulfills important biological roles during embryonic development. Three nonredundant enzyme families, POMT1/POMT2, TMTC1-4, and TMEM260, selectively coordinate the initiation of protein O-Man glycosylation on distinct classes of transmembrane proteins, including α-dystroglycan, cadherins, and plexin receptors. However, a systematic investigation of their substrate specificities is lacking, in part due to the ubiquitous expression of O-Man glycosyltransferases in cells, which precludes analysis of pathway-specific O-Man glycosylation on a proteome-wide scale. Here, we apply a targeted workflow for membrane glycoproteomics across five human cell lines to extensively map O-Man substrates and genetically deconstruct O-Man initiation by individual and combinatorial knockout of O-Man glycosyltransferase genes. We established a human cell library for the analysis of substrate specificities of individual O-Man initiation pathways by quantitative glycoproteomics. Our results identify 180 O-Man glycoproteins, demonstrate new protein targets for the POMT1/POMT2 pathway, and show that TMTC1-4 and TMEM260 pathways widely target distinct Ig-like protein domains of plasma membrane proteins involved in cell-cell and cell-extracellular matrix interactions. The identification of O-Man on Ig-like folds adds further knowledge on the emerging concept of domain-specific O-Man glycosylation which opens for functional studies of O-Man-glycosylated adhesion molecules and receptors.

MS-PyCloud: A Cloud Computing-Based Pipeline for Proteomic and Glycoproteomic Data Analyses

Rapid development and wide adoption of mass spectrometry-based glycoproteomic technologies have empowered scientists to study proteins and protein glycosylation in complex samples on a large scale. This progress has also created unprecedented challenges for individual laboratories to store, manage, and analyze proteomic and glycoproteomic data, both in the cost for proprietary software and high-performance computing and in the long processing time that discourages on-the-fly changes of data processing settings required in explorative and discovery analysis. We developed an open-source, cloud computing-based pipeline, MS-PyCloud, with graphical user interface (GUI), for proteomic and glycoproteomic data analysis. The major components of this pipeline include data file integrity validation, MS/MS database search for spectral assignments to peptide sequences, false discovery rate estimation, protein inference, quantitation of global protein levels, and specific glycan-modified glycopeptides as well as other modification-specific peptides such as phosphorylation, acetylation, and ubiquitination. To ensure the transparency and reproducibility of data analysis, MS-PyCloud includes open-source software tools with comprehensive testing and versioning for spectrum assignments. Leveraging public cloud computing infrastructure via Amazon Web Services (AWS), MS-PyCloud scales seamlessly based on analysis demand to achieve fast and efficient performance. Application of the pipeline to the analysis of large-scale LC-MS/MS data sets demonstrated the effectiveness and high performance of MS-PyCloud. The software can be downloaded at https://github.com/huizhanglab-jhu/ms-pycloud.

Recent advances in N-glycan biomarker discovery among human diseases

N-glycans play important roles in a variety of biological processes. In recent years, analytical technologies with high resolution and sensitivity have advanced exponentially, enabling analysts to investigate N-glycomic changes in different states. Specific glycan and glycosylation signatures have been identified in multiple diseases, including cancer, autoimmune diseases, nervous system disorders, and metabolic and cardiovascular diseases. These glycans demonstrate comparable or superior indicating capability in disease diagnosis and prognosis over routine biomarkers. Moreover, synchronous glycan alterations concurrent with disease initiation and progression provide novel insights into pathogenetic mechanisms and potential treatment targets. This review elucidates the biological significance of N-glycans, compares the existing glycomic technologies, and delineates the clinical performance of N-glycans across a range of diseases.

Quantitative proteome-wide O-glycoproteomics analysis with FragPipe

Identification of O-glycopeptides from tandem mass spectrometry data is complicated by the near complete dissociation of O-glycans from the peptide during collisional activation and by the combinatorial explosion of possible glycoforms when glycans are retained intact in electron-based activation. The recent O-Pair search method provides an elegant solution to these problems, using a collisional activation scan to identify the peptide sequence and total glycan mass, and a follow-up electron-based activation scan to localize the glycosite(s) using a graph-based algorithm in a reduced search space. Our previous O-glycoproteomics methods with MSFragger-Glyco allowed for extremely fast and sensitive identification of O-glycopeptides from collisional activation data but had limited support for site localization of glycans and quantification of glycopeptides. Here, we report an improved pipeline for O-glycoproteomics analysis that provides proteome-wide, site-specific, quantitative results by incorporating the O-Pair method as a module within FragPipe. In addition to improved search speed and sensitivity, we add flexible options for oxonium ion-based filtering of glycans and support for a variety of MS acquisition methods and provide a comparison between all software tools currently capable of O-glycosite localization in proteome-wide searches.

Maximizing Glycoproteomics Results through an Integrated Parallel Accumulation Serial Fragmentation Workflow

Glycoproteins play important roles in numerous physiological processes and are often implicated in disease. Analysis of site-specific protein glycobiology through glycoproteomics has evolved rapidly in recent years thanks to hardware and software innovations. Particularly, the introduction of parallel accumulation serial fragmentation (PASEF) on hybrid trapped ion mobility time-of-flight mass spectrometry instruments combined deep proteome sequencing with separation of (near-)isobaric precursor ions or converging isotope envelopes through ion mobility separation. However, the reported use of PASEF in integrated glycoproteomics workflows to comprehensively capture the glycoproteome is still limited. To this end, we developed an integrated methodology using timsTOF Pro 2 to enhance N-glycopeptide identifications in complex mixtures. We systematically optimized the ion optics tuning, collision energies, mobility isolation width, and the use of dopant-enriched nitrogen gas (DEN). Thus, we obtained a marked increase in unique glycopeptide identification rates compared to standard proteomics settings, showcasing our results on a large set of glycopeptides. With short liquid chromatography gradients of 30 min, we increased the number of unique N-glycopeptide identifications in human plasma samples from around 100 identifications under standard proteomics conditions to up to 1500 with our optimized glycoproteomics approach, highlighting the need for tailored optimizations to obtain comprehensive data.

Glycoproteomic and proteomic analysis of Burkholderia cenocepacia reveals glycosylation events within FliF and MotB are dispensable for motility

Across the Burkholderia genus O-linked protein glycosylation is highly conserved. While the inhibition of glycosylation has been shown to be detrimental for virulence in Burkholderia cepacia complex species, such as Burkholderia cenocepacia, little is known about how specific glycosylation sites impact protein functionality. Within this study, we sought to improve our understanding of the breadth, dynamics, and requirement for glycosylation across the B. cenocepacia O-glycoproteome. Assessing the B. cenocepacia glycoproteome across different culture media using complementary glycoproteomic approaches, we increase the known glycoproteome to 141 glycoproteins. Leveraging this repertoire of glycoproteins, we quantitively assessed the glycoproteome of B. cenocepacia using Data-Independent Acquisition (DIA) revealing the B. cenocepacia glycoproteome is largely stable across conditions with most glycoproteins constitutively expressed. Examination of how the absence of glycosylation impacts the glycoproteome reveals that the protein abundance of only five glycoproteins (BCAL1086, BCAL2974, BCAL0525, BCAM0505, and BCAL0127) are altered by the loss of glycosylation. Assessing ΔfliF (ΔBCAL0525), ΔmotB (ΔBCAL0127), and ΔBCAM0505 strains, we demonstrate the loss of FliF, and to a lesser extent MotB, mirror the proteomic effects observed in the absence of glycosylation in ΔpglL. While both MotB and FliF are essential for motility, we find loss of glycosylation sites in MotB or FliF does not impact motility supporting these sites are dispensable for function. Combined this work broadens our understanding of the B. cenocepacia glycoproteome supporting that the loss of glycoproteins in the absence of glycosylation is not an indicator of the requirement for glycosylation for protein function.

IMPORTANCE

Burkholderia cenocepacia is an opportunistic pathogen of concern within the Cystic Fibrosis community. Despite a greater appreciation of the unique physiology of B. cenocepacia gained over the last 20 years a complete understanding of the proteome and especially the O-glycoproteome, is lacking. In this study, we utilize systems biology approaches to expand the known B. cenocepacia glycoproteome as well as track the dynamics of glycoproteins across growth phases, culturing media and in response to the loss of glycosylation. We show that the glycoproteome of B. cenocepacia is largely stable across conditions and that the loss of glycosylation only impacts five glycoproteins including the motility associated proteins FliF and MotB. Examination of MotB and FliF shows, while these proteins are essential for motility, glycosylation is dispensable. Combined this work supports that B. cenocepacia glycosylation can be dispensable for protein function and may influence protein properties beyond stability.

Advancing mass spectrometry-based glycoproteomic software tools for comprehensive site-specific glycoproteome analysis

Glycoproteome analysis at a site-specific level and proteome scale stands out as a highly promising approach for gaining insights into the intricate roles of glycans in biological systems. Recent years have witnessed an upsurge in the development of innovative methodologies tailored for precisely this purpose. Breakthroughs in mass spectrometry-based glycoproteomic techniques, enabling the identification, quantification, and systematic exploration of site-specific glycans, have significantly enhanced our capacity to comprehensively and thoroughly characterize glycoproteins. In this short review, we delve into novel tools in advancing site-specific glycoproteomic analysis and summarize pertinent studies published in the past two years. Lastly, we discuss the ongoing challenges and outline future prospects in the field, considering both the analytical strategies of mass spectrometry and the tools employed for data interpretation.

Comprehensive review of MS-based studies on N-glycoproteome and N-glycome of extracellular vesicles

Extracellular vesicles (EVs) are lipid bilayer-enclosed particles that can be released by all type of cells. Whereas, as one of the most common post-translational modifications, glycosylation plays a vital role in various biological functions of EVs, such as EV biogenesis, sorting, and cellular recognition. Nevertheless, compared with studies on RNAs or proteins, those investigating the glycoconjugates of EVs are limited. An in-depth investigation of N-glycosylation of EVs can improve the understanding of the biological functions of EVs and help to exploit EVs from different perspectives. The general focus of studies on glycosylation of EVs primarily includes isolation and characterization of EVs, preparation of glycoproteome/glycome samples and MS analysis. However, the low content of EVs and non-standard separation methods for downstream analysis are the main limitations of these studies. In this review, we highlight the importance of glycopeptide/glycan enrichment and derivatization owing to the low abundance of glycoproteins and the low ionization efficiency of glycans. Diverse fragmentation patterns and professional analytical software are indispensable for analysing glycosylation via MS. Altogether, this review summarises recent studies on glycosylation of EVs, revealing the role of EVs in disease progression and their remarkable potential as biomarkers.

Towards chemoenzymatic labeling strategies for profiling protein glycosylation

Protein glycosylation is one of the most common and important post-translational modifications of proteins involved in regulating glycoprotein functions. The chemoenzymatic glycan labeling strategy allows rapid, efficient, and selective interrogation of glycoproteins. Glycoproteomics identifies protein glycosylation events at a large scale, providing information such as peptide sequences, glycan structures, and glycosylated sites. This review discusses the recent development of chemoenzymatic labeling strategies for glycoprotein analysis, mainly including glycoprotein and glycosite profiling. Furthermore, we highlight the chemoenzymatic enrichment approaches in mass spectrometry analysis for three classes of glycan modifications, including N-glycosylation, O-GlcNAcylation, and mucin-type O-glycosylation. Finally, we highlight the emerging trends in new tools and cutting-edge technologies available for glycoproteomic research.

Analysis of N- and O-linked site-specific glycosylation by ion mobility mass spectrometry: State of the art and future directions

Glycosylation, the major post-translational modification of proteins, significantly increases the diversity of proteoforms. Glycans are involved in a variety of pivotal structural and functional roles of proteins, and changes in glycosylation are profoundly connected to the progression of numerous diseases. Mass spectrometry (MS) has emerged as the gold standard for glycan and glycopeptide analysis because of its high sensitivity and the wealth of fragmentation information that can be obtained. Various separation techniques have been employed to resolve glycan and glycopeptide isomers at the front end of the MS. However, differentiating structures of isobaric and isomeric glycopeptides constitutes a challenge in MS-based characterization. Many reports described the use of various ion mobility-mass spectrometry (IM-MS) techniques for glycomic analyses. Nevertheless, very few studies have focused on N- and O-linked site-specific glycopeptidomic analysis. Unlike glycomics, glycoproteomics presents a multitude of inherent challenges in microheterogeneity, which are further exacerbated by the lack of dedicated bioinformatics tools. In this review, we cover recent advances made towards the growing field of site-specific glycosylation analysis using IM-MS with a specific emphasis on the MS techniques and capabilities in resolving isomeric peptidoglycan structures. Furthermore, we discuss commonly used software that supports IM-MS data analysis of glycopeptides.

O-GlcNAcylation Facilitates the Interaction between Keratin 18 and Isocitrate Dehydrogenases and Potentially Influencing Cholangiocarcinoma Progression

Glycosylation plays a pivotal role in the intricate landscape of human cholangiocarcinoma (CCA), actively participating in key pathophysiological processes driving tumor progression. Among the various glycosylation modifications, O-linked β-N-acetyl-glucosamine modification (O-GlcNAcylation) emerges as a dynamic regulator influencing diverse tumor-associated biological activities. In this study, we employed a state-of-the-art chemical proteomic approach to analyze intact glycopeptides, unveiling the critical role of O-GlcNAcylation in orchestrating Keratin 18 (K18) and its interplay with tricarboxylic acid (TCA) cycle enzymes, specifically isocitrate dehydrogenases (IDHs), to propel CCA progression. Our findings shed light on the mechanistic intricacies of O-GlcNAcylation, revealing that site-specific modification of K18 on Ser 30 serves as a stabilizing factor, amplifying the expression of cell cycle checkpoints. This molecular event intricately fosters cell cycle progression and augments cellular growth in CCA. Notably, the interaction between O-GlcNAcylated K18 and IDHs orchestrates metabolic reprogramming by down-regulating citrate and isocitrate levels while elevating α-ketoglutarate (α-KG). These metabolic shifts further contribute to the overall tumorigenic potential of CCA. Our study thus expands the current understanding of protein O-GlcNAcylation and introduces a new layer of complexity to post-translational control over metabolism and tumorigenesis.

Comprehensive proteogenomic characterization of rare kidney tumors

Non-clear cell renal cell carcinomas (non-ccRCCs) encompass diverse malignant and benign tumors. Refinement of differential diagnosis biomarkers, markers for early prognosis of aggressive disease, and therapeutic targets to complement immunotherapy are current clinical needs. Multi-omics analyses of 48 non-ccRCCs compared with 103 ccRCCs reveal proteogenomic, phosphorylation, glycosylation, and metabolic aberrations in RCC subtypes. RCCs with high genome instability display overexpression of IGF2BP3 and PYCR1. Integration of single-cell and bulk transcriptome data predicts diverse cell-of-origin and clarifies RCC subtype-specific proteogenomic signatures. Expression of biomarkers MAPRE3, ADGRF5, and GPNMB differentiates renal oncocytoma from chromophobe RCC, and PIGR and SOSTDC1 distinguish papillary RCC from MTSCC. This study expands our knowledge of proteogenomic signatures, biomarkers, and potential therapeutic targets in non-ccRCC.

O-linked glycosylations in human milk casein and major whey proteins during lactation

As glycosylations are difficult to analyze, their roles and effects are poorly understood. Glycosylations in human milk (HM) differ across lactation. Glycosylations can be involved in antimicrobial activities and may serve as food for beneficial microorganisms. This study aimed to identify and analyze O-linked glycans in HM by high-throughput mass spectrometry. 184 longitudinal HM samples from 66 donors from day 3 and months 1, 2, and 3 postpartum were subjected to a post-translational modification specific enrichment-based strategy using TiO2 and ZrO2 beads for O-linked glycopeptide enrichment. β-CN was found to be a major O-linked glycoprotein, additionally, αS1-CN, κ-CN, lactotransferrin, and albumin also contained O-linked glycans. As glycosyltransferases and glycosidases are involved in assembling the glycans including O-linked glycosylations, these were further investigated. Some glycosyltransferases and glycosidases were found to be significantly decreasing through lactation, including two O-linked glycan initiator enzymes (GLNT1 and GLNT2). Despite their decrease, the overall level of O-linked glycans remained stable in HM over lactation. Three different motifs for O-linked glycosylation were enriched in HM proteins: Gly-Xxx-Xxx-Gly-Ser/Thr, Arg-Ser/Thr and Lys-Ser/Thr. Further O-linked glycan motifs on β-CN were observed to differ between intact proteins and endogenous peptides in HM.

The glycosylation landscape of prostate cancer tissues and biofluids

An overview of the role of glycosylation in prostate cancer (PCa) development and progression is presented, focusing on recent advancements in defining the N-glycome through glycomic profiling and glycoproteomic methodologies. Glycosylation is a common post-translational modification typified by oligosaccharides attached N-linked to asparagine or O-linked to serine or threonine on carrier proteins. These attached sugars have crucial roles in protein folding and cellular recognition processes, such that altered glycosylation is a hallmark of cancer pathogenesis and progression. In the past decade, advancements in N-glycan profiling workflows using Matrix Assisted Laser Desorption/Ionization Mass Spectrometry Imaging (MALDI-MSI) technology have been applied to define the spatial distribution of glycans in PCa tissues. Multiple studies applying N-glycan MALDI-MSI to pathology-defined PCa tissues have identified significant alterations in N-glycan profiles associated with PCa progression. N-glycan compositions progressively increase in number, and structural complexity due to increased fucosylation and sialylation. Additionally, significant progress has been made in defining the glycan and glycopeptide compositions of prostatic-derived glycoproteins like prostate-specific antigen in tissues and biofluids. The glycosyltransferases involved in these changes are potential drug targets for PCa, and new approaches in this area are summarized. These advancements will be discussed in the context of the further development of clinical diagnostics and therapeutics targeting glycans and glycoproteins associated with PCa progression. Integration of large scale spatial glycomic data for PCa with other spatial-omic methodologies is now feasible at the tissue and single-cell levels.

Comparison of N-Glycopeptide to Released N-Glycan Abundances and the Influence of Glycopeptide Mass and Charge States on N-Linked Glycosylation of IgG Antibodies

We report the comparison of mass-spectral-based abundances of tryptic glycopeptides to fluorescence abundances of released labeled glycans and the effects of mass and charge state and in-source fragmentation on glycopeptide abundances. The primary glycoforms derived from Rituximab, NISTmAb, Evolocumab, and Infliximab were high-mannose and biantennary complex galactosylated and fucosylated N-glycans. Except for Evolocumab, in-source ions derived from the loss of HexNAc or HexNAc-Hex sugars are prominent for other therapeutic IgGs. After excluding in-source fragmentation of glycopeptide ions from the results, a linear correlation was observed between fluorescently labeled N-glycan and glycopeptide abundances over a dynamic range of 500. Different charge states of human IgG-derived glycopeptides containing a wider variety of abundant attached glycans were also investigated to examine the effects of the charge state on ion abundances. These revealed a linear dependence of glycopeptide abundance on the mass of the glycan with higher charge states favoring higher-mass glycans. Findings indicate that the mass spectrometry-based bottom-up approach can provide results as accurate as those of glycan release studies while revealing the origin of each attached glycan. These site-specific relative abundances are conveniently displayed and compared using previously described glycopeptide abundance distribution spectra "GADS" representations. Mass spectrometry data are available from the MAssIVE repository (MSV000093562).

Prediction of glycopeptide fragment mass spectra by deep learning

Deep learning has achieved a notable success in mass spectrometry-based proteomics and is now emerging in glycoproteomics. While various deep learning models can predict fragment mass spectra of peptides with good accuracy, they cannot cope with the non-linear glycan structure in an intact glycopeptide. Herein, we present DeepGlyco, a deep learning-based approach for the prediction of fragment spectra of intact glycopeptides. Our model adopts tree-structured long-short term memory networks to process the glycan moiety and a graph neural network architecture to incorporate potential fragmentation pathways of a specific glycan structure. This feature is beneficial to model explainability and differentiation ability of glycan structural isomers. We further demonstrate that predicted spectral libraries can be used for data-independent acquisition glycoproteomics as a supplement for library completeness. We expect that this work will provide a valuable deep learning resource for glycoproteomics.

Oxonium ion scanning mass spectrometry for large-scale plasma glycoproteomics

Protein glycosylation, a complex and heterogeneous post-translational modification that is frequently dysregulated in disease, has been difficult to analyse at scale. Here we report a data-independent acquisition technique for the large-scale mass-spectrometric quantification of glycopeptides in plasma samples. The technique, which we named 'OxoScan-MS', identifies oxonium ions as glycopeptide fragments and exploits a sliding-quadrupole dimension to generate comprehensive and untargeted oxonium ion maps of precursor masses assigned to fragment ions from non-enriched plasma samples. By applying OxoScan-MS to quantify 1,002 glycopeptide features in the plasma glycoproteomes from patients with COVID-19 and healthy controls, we found that severe COVID-19 induces differential glycosylation in IgA, haptoglobin, transferrin and other disease-relevant plasma glycoproteins. OxoScan-MS may allow for the quantitative mapping of glycoproteomes at the scale of hundreds to thousands of samples.

Integrative proteomics and n-glycoproteomics reveal the synergistic anti-tumor effects of aspirin- and gemcitabine-based chemotherapy on pancreatic cancer cells

OBJECTIVE AND DESIGN

Pancreatic cancer is a highly malignant tumor that is well known for its poor prognosis. Based on glycosylation, we performed integrated quantitative N-glycoproteomics to investigate the synergistic anti-tumor effects of aspirin and gemcitabine on pancreatic cancer cells and explore the potential molecular mechanisms of chemotherapy in pancreatic cancer.

METHODS AND RESULTS

Two pancreatic cancer cell lines (PANC-1 and BxPC-3) were treated with gemcitabine, aspirin, and a combination (gemcitabine + aspirin). We found that the addition of aspirin enhanced the inhibitory effect of gemcitabine on the activity of PANC-1 and BxPC-3 cells. Quantitative N-glycoproteome, proteome, phosphorylation, and transcriptome data were obtained from integrated multi-omics analysis to evaluate the anti-tumor effects of aspirin and gemcitabine on pancreatic cancer cells. Mfuzz analysis of intact N-glycopeptide profiles revealed two consistent trends associated with the addition of aspirin, which showed a strong relationship between N-glycosylation and the synergistic effect of aspirin. Further analysis demonstrated that the dynamic regulation of sialylation and high-mannose glycoforms on ECM-related proteins (LAMP1, LAMP2, ITGA3, etc.) was a significant factor for the ability of aspirin to promote the anti-tumor activity of gemcitabine and the drug resistance of pancreatic cancer cells.

CONCLUSIONS

In-depth analysis of N-glycosylation-related processes and pathways in pancreatic cancer cells can provide new insight for future studies regarding pancreatic cancer therapeutic targets and drug resistance mechanisms.

Cu-Catalyzed Azide-Alkyne-Thiol Reaction Forms Ubiquitous Background in Chemical Proteomic Studies

We report here a Cu-catalyzed azide-alkyne-thiol reaction forming thiotriazoles as the major byproduct under widely used bio-orthogonal protein labeling "click" conditions. The development of Cu(I)-catalyzed azide-alkyne cycloaddition (CuAAC) had a tremendous impact on many biological discoveries. However, the considered chemoselectivity of CuAAC is hampered by the high reactivity of cysteine free thiols, yielding thiotriazole protein conjugates. The reaction byproducts generate false-positive protein hits in functional proteomic studies. The reported detail investigation of conjugates between chemical probes containing terminal alkynes, azide tags, and cell lysates reveals the formation of thiotriazoles, which can be readily detected by in-gel fluorescence scanning or after peptide and protein enrichment by mass spectrometry-based proteomics. In protein level identification and quantification experiments, the produced fluorescent bands or enriched proteins may not result from the important enzymatically driven reaction and can be falsely assigned as hits. This study provides a complete list of the most common background proteins. The knowledge of this previously overlooked reactivity now leads to the introduction of modified CuAAC conditions, which avoids the undesired product formation, diminishes the background, and hence improves the signal-to-noise ratio.

Mass spectrometry-based methods for investigating the dynamics and organization of the surfaceome: exploring potential clinical implications

INTRODUCTION

Cell-surface proteins are extremely important for many cellular events, such as regulating cell-cell communication and cell-matrix interactions. Aberrant alterations in surface protein expression, modification (especially glycosylation), and interactions are directly related to human diseases. Systematic investigation of surface proteins advances our understanding of protein functions, cellular activities, and disease mechanisms, which will lead to identifying surface proteins as disease biomarkers and drug targets.

AREAS COVERED

In this review, we summarize mass spectrometry (MS)-based proteomics methods for global analysis of cell-surface proteins. Then, investigations of the dynamics of surface proteins are discussed. Furthermore, we summarize the studies for the surfaceome interaction networks. Additionally, biological applications of MS-based surfaceome analysis are included, particularly highlighting the significance in biomarker identification, drug development, and immunotherapies.

EXPERT OPINION

Modern MS-based proteomics provides an opportunity to systematically characterize proteins. However, due to the complexity of cell-surface proteins, the labor-intensive workflow, and the limit of clinical samples, comprehensive characterization of the surfaceome remains extraordinarily challenging, especially in clinical studies. Developing and optimizing surfaceome enrichment methods and utilizing automated sample preparation workflow can expand the applications of surfaceome analysis and deepen our understanding of the functions of cell-surface proteins.

Glycosort: A Computational Solution to Post-process Quantitative Large-Scale Intact Glycopeptide Analyses

Protein glycosylation is a post-translational modification involving the addition of carbohydrates to proteins and plays a crucial role in protein folding and various biological processes such as cell recognition, differentiation, and immune response. The vast array of natural sugars available allows the generation of plenty of unique glycan structures in proteins, adding complexity to the regulation and biological functions of glycans. The diversity is further increased by enzymatic site preferences and stereochemical conjugation, leading to an immense amount of different glycan structures. Understanding glycosylation heterogeneity is vital for unraveling the impact of glycans on different biological functions. Evaluating site occupancies and structural heterogeneity aids in comprehending glycan-related alterations in biological processes. Several software tools are available for large-scale glycoproteomics studies; however, integrating identification and quantitative data to assess heterogeneity complexity often requires extensive manual data processing. To address this challenge, we present a python script that automates the integration of Byonic and MaxQuant outputs for glycoproteomic data analysis. The script enables the calculation of site occupancy percentages by glycans and facilitates the comparison of glycan structures and site occupancies between two groups. This automated tool offers researchers a means to organize and interpret their high-throughput quantitative glycoproteomic data effectively.

Efficient Analysis of Proteome-Wide FPOP Data by FragPipe

Monitoring protein structure before and after environmental alterations (e.g., different cell states) can give insights into the role and function of proteins. Fast photochemical oxidation of proteins (FPOP) coupled with mass spectrometry (MS) allows for monitoring of structural rearrangements by exposing proteins to OH radicals that oxidize solvent-accessible residues, indicating protein regions undergoing movement. Some of the benefits of FPOP include high throughput and a lack of scrambling due to label irreversibility. However, the challenges of processing FPOP data have thus far limited its proteome-scale uses. Here, we present a computational workflow for fast and sensitive analysis of FPOP data sets. Our workflow, implemented as part of the FragPipe computational platform, combines the speed of the MSFragger search with a unique hybrid search method to restrict the large search space of FPOP modifications. Together, these features enable more than 10-fold faster FPOP searches that identify 150% more modified peptide spectra than previous methods. We hope this new workflow will increase the accessibility of FPOP to enable more protein structure and function relationships to be explored.

Composition characterization of various viperidae snake venoms using MS-based proteomics N-glycoproteomics and N-glycomics

Viperidae snake species is widely abundant and responsible for most envenomation cases in Turkey. The structural and compositional profiles of snake venom have been investigated to study the venom component variation across different species and to profile the venom biological activity variation against prey. In this context, we used proteomics, glycoproteomics and glycomics strategies to characterize the protein, glycoproteins and glycan structural and compositional profiles of various snake venoms in the Viperidae family. Moreover, we compared these profiles using the downstream bioinformatics and machine learning classification modules. The overall mass spectrometry profiles identified 144 different proteins, 36 glycoproteins and 78 distinct N-glycan structures varying in composition across the five venoms. A high amount of the characterized proteins belongs to the glycosylated protein family Trypsin-like serine protease (Tryp_SPc), Disintegrin (DISIN), and ADAM Cysteine-Rich (ACR). Most identified N-glycans have a complex chain carrying galactosylated N-glycans abundantly. The glycan composition data obtained from glycoproteomics aligns consistently with the findings from glycomics. The clustering and principal component analyses (PCA) illustrated the composition-based similarities and differences between each snake venom species' proteome, glycoproteome and glycan profiles. Specifically, the N-glycan profiles of M. xanthina (Mx) and V. a. ammodytes (Vaa) venoms were identical and difficult to differentiate; in contrast, their proteome profiles were distinct. Interestingly, the variety of the proteins across the species highlighted the impact of glycosylation on the diversity of the glycosylated protein families. This proposed high throughput approach provides accurate and comprehensive profiles of the composition and function of various Viperidae snake venoms.

Quantitative mapping of the in vivo O-GalNAc glycoproteome in mouse tissues identifies GalNAc-T2 O-glycosites in metabolic disorder

The family of GalNAc-Ts (GalNAcpolypeptide:N-Acetylgalactosaminyl transferases) catalyzes the first committed step in the synthesis of O-glycans, which is an abundant and biologically important protein modification. Abnormalities in the activity of individual GalNAc-Ts can result in congenital disorders of O-glycosylation (CDG) and influence a broad array of biological functions. How site-specific O-glycans regulate biology is unclear. Compiling in vivo O-glycosites would be an invaluable step in determining the function of site-specific O-glycans. We integrated chemical and enzymatic conditions that cleave O-glycosites, a higher-energy dissociation product ions-triggered electron-transfer/higher-energy collision dissociation mass spectrometry (MS) workflow and software to study nine mouse tissues and whole blood. We identified 2,154 O-glycosites from 595 glycoproteins. The O-glycosites and glycoproteins displayed consensus motifs and shared functions as classified by Gene Ontology terms. Limited overlap of O-glycosites was observed with protein O-GlcNAcylation and phosphorylation sites. Quantitative glycoproteomics and proteomics revealed a tissue-specific regulation of O-glycosites that the differential expression of Galnt isoenzymes in tissues partly contributes to. We examined the Galnt2-null mouse model, which phenocopies congenital disorder of glycosylation involving GALNT2 and revealed a network of glycoproteins that lack GalNAc-T2-specific O-glycans. The known direct and indirect functions of these glycoproteins appear consistent with the complex metabolic phenotypes observed in the Galnt2-null animals. Through this study and interrogation of databases and the literature, we have compiled an atlas of experimentally identified mouse O-glycosites consisting of 2,925 O-glycosites from 758 glycoproteins.

Integrated Immunopeptidomics and Proteomics Study of SARS-CoV-2-Infected Calu-3 Cells Reveals Dynamic Changes in Allele-specific HLA Abundance and Antigen Presentation

We present an integrated immunopeptidomics and proteomics study of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection to comprehensively decipher the changes in host cells in response to viral infection. Immunopeptidomics analysis identified viral antigens presented by host cells through both class I and class II MHC system for recognition by the adaptive immune system. The host proteome changes were characterized by quantitative proteomics and glycoproteomics and from these data, the activation of toll-like receptor 3-interferon pathway was identified. Glycosylation analysis of human leukocyte antigen (HLA) proteins from the elution and flow-through of immunoprecipitation revealed that SARS-CoV-2 infection changed the glycosylation pattern of certain HLA alleles with different HLA alleles, showing distinct dynamic changes in relative abundance. The difference in the glycosylation and abundance of HLA alleles changed the number of strong binding antigens each allele presented, suggesting the impact of SARS-CoV-2 infection on antigen presentation is allele-specific. These results could be further exploited to explain the imbalanced response from innate and adaptive immune system in coronavirus disease 2019 cases, which would be helpful for the development of therapeutics and vaccine for coronavirus disease 2019 and preparation for future pandemic.

Click-iG: Simultaneous Enrichment and Profiling of Intact N-linked, O-GalNAc, and O-GlcNAcylated Glycopeptides

Proteins are ubiquitously modified with glycans of varied chemical structures through distinct glycosidic linkages, making the landscape of protein glycosylation challenging to map. Profiling of intact glycopeptides with mass spectrometry (MS) has recently emerged as a powerful tool for revealing matched information of the glycosylation sites and attached glycans (i.e., intact glycosites), but is largely limited to individual glycosylation types. Herein, we describe Click-iG, which integrates metabolic labeling of glycans with clickable unnatural sugars, an optimized MS method, and a tailored version of pGlyco3 software to enable simultaneous enrichment and profiling of three types of intact glycopeptides: N-linked, mucin-type O-linked, and O-GlcNAcylated glycopeptides. We demonstrate the utility of Click-iG by the identification of thousands of intact glycosites in cell lines and living mice. From the mouse lung, heart, and spleen, a total of 2053 intact N-glycosites, 262 intact O-GalNAc glycosites, and 1947 O-GlcNAcylation sites were identified. Click-iG-enabled comprehensive coverage of the protein glycosylation landscape lays the foundation for interrogating crosstalk between different glycosylation pathways.

Improved analysis ZIC-HILIC-HCD-Orbitrap method for mapping the glycopeptide by mass spectrometry

Glycosylation is one of the most common post-translational modifications (PTMs). Protein glycosylation analysis is the bottleneck to deeply understand their functions. At present, the LC-MS analysis of glycosylated post-translational modification is mainly focused on the analysis of glycopeptides. However, the factors affecting the identification of glycopeptides were not fully elucidated. In the paper, we have carefully studied the factors, e.g., HILIC materials, search engines, protein amount, gradient duration, extraction solution, etc. According to the results, HILIC materials were the most important factors affecting the glycopeptides identification, and the amphoteric sulfoalkyl betaine stationary phase enriched glycopeptides 6-fold more compared to the amphiphilic ion-bonded fully porous spherical silica stationary phase. We explored the influence of the extraction solutions on glycan identification. Comparing sodium dodecyl sulfate (SDS) and urea (UA), the results showed that N-glycolylneuraminic acid (NeuGc) type of glycan content was found to be increased 1.4-fold in the SDS compared to UA. Besides, we explored the influence of the search engine on glycopeptide identification. Comparing pGlyco3.0 and MSFragger-Glyco, it was observed that pGlyco3.0 outperformed MSFragger-Glyco in identifying glycopeptides. Then, using our optimized method we found that there was a significant difference in the distribution of monosaccharide types in plasma and brain tissue, e.g., the content of NeuAc in brain was 5-fold higher than that in plasma. To importantly, two glycoproteins (Neurexin-2 and SUN domain-containing protein 2) were also found for the first time by our method. In summary, we have comprehensively studied the factors influencing glycopeptide identification than any previous research, and the optimized method could be widely used for identifying the glycoproteins or glycolpeptides biomarkers for disease detection and therapeutic targets.

Sensitive N-Glycopeptide Profiling of Single and Rare Cells Using an Isobaric Labeling Strategy without Enrichment

Single-cell omics is critical in revealing population heterogeneity, discovering unique features of individual cells, and identifying minority subpopulations of interest. As one of the major post-translational modifications, protein N-glycosylation plays crucial roles in various important biological processes. Elucidation of the variation in N-glycosylation patterns at single-cell resolution may largely facilitate the understanding of their key roles in the tumor microenvironment and immune therapy. However, comprehensive N-glycoproteome profiling for single cells has not been achieved due to the extremely limited sample amount and incompatibility with the available enrichment strategies. Here, we have developed an isobaric labeling-based carrier strategy for highly sensitive intact N-glycopeptide profiling for single cells or a small number of rare cells without enrichment. Isobaric labeling has unique multiplexing properties, by which the "total" signal from all channels triggers MS/MS fragmentation for N-glycopeptide identification, while the reporter ions provide quantitative information. In our strategy, a carrier channel using N-glycopeptides obtained from bulk-cell samples significantly improved the "total" signal of N-glycopeptides and, therefore, promoted the first quantitative analysis of averagely 260 N-glycopeptides from single HeLa cells. We further applied this strategy to study the regional heterogeneity of N-glycosylation of microglia in mouse brain and discovered region-specific N-glycoproteome patterns and cell subtypes. In conclusion, the glycocarrier strategy provides an attractive solution for sensitive and quantitative N-glycopeptide profiling of single/rare cells that cannot be enriched by traditional workflows.

MSBooster: improving peptide identification rates using deep learning-based features

Peptide identification in liquid chromatography-tandem mass spectrometry (LC-MS/MS) experiments relies on computational algorithms for matching acquired MS/MS spectra against sequences of candidate peptides using database search tools, such as MSFragger. Here, we present a new tool, MSBooster, for rescoring peptide-to-spectrum matches using additional features incorporating deep learning-based predictions of peptide properties, such as LC retention time, ion mobility, and MS/MS spectra. We demonstrate the utility of MSBooster, in tandem with MSFragger and Percolator, in several different workflows, including nonspecific searches (immunopeptidomics), direct identification of peptides from data independent acquisition data, single-cell proteomics, and data generated on an ion mobility separation-enabled timsTOF MS platform. MSBooster is fast, robust, and fully integrated into the widely used FragPipe computational platform.

Detecting diagnostic features in MS/MS spectra of post-translationally modified peptides

Post-translational modifications are an area of great interest in mass spectrometry-based proteomics, with a surge in methods to detect them in recent years. However, post-translational modifications can introduce complexity into proteomics searches by fragmenting in unexpected ways, ultimately hindering the detection of modified peptides. To address these deficiencies, we present a fully automated method to find diagnostic spectral features for any modification. The features can be incorporated into proteomics search engines to improve modified peptide recovery and localization. We show the utility of this approach by interrogating fragmentation patterns for a cysteine-reactive chemoproteomic probe, RNA-crosslinked peptides, sialic acid-containing glycopeptides, and ADP-ribosylated peptides. We also analyze the interactions between a diagnostic ion's intensity and its statistical properties. This method has been incorporated into the open-search annotation tool PTM-Shepherd and the FragPipe computational platform.

"Ghost" Fragment Ions in Structure and Site-Specific Glycoproteomics Analysis

Mass spectrometry (MS) can unlock crucial insights into the intricate world of glycosylation analysis. Despite its immense potential, the qualitative and quantitative analysis of isobaric glycopeptide structures remains one of the most daunting hurdles in the field of glycoproteomics. The ability to distinguish between these complex glycan structures poses a significant challenge, hindering our ability to accurately measure and understand the role of glycoproteins in biological systems. A few recent publications described the use of collision energy (CE) modulation to improve structural elucidation, especially for qualitative purposes. Different linkages of glycan units usually demonstrate different stabilities under CID/HCD fragmentation conditions. Fragmentation of the glycan moiety produces low molecular weight ions (oxonium ions) that can serve as a structure-specific signature for specific glycan moieties; however, the specificity of these fragments has never been examined closely. Here, we particularly focused on N-glycoproteomics analysis and investigated fragmentation specificity using synthetic stable isotope-labeled N-glycopeptide standards. These standards were isotopically labeled at the reducing terminal GlcNAc, which allowed us to resolve fragments produced by the oligomannose core moiety and fragments generated from outer antennary structures. Our research identified the potential for false-positive structure assignments due to the occurrence of "Ghost" fragments resulting from single glyco unit rearrangement or mannose core fragmentation within the collision cell. To mitigate this issue, we have established a minimal intensity threshold for these fragments to prevent misidentification of structure-specific fragments in glycoproteomics analysis. Our findings provide a crucial step forward in the quest for more accurate and reliable glycoproteomics measurements.

Glycopeptide database search and de novo sequencing with PEAKS GlycanFinder enable highly sensitive glycoproteomics

Here we present GlycanFinder, a database search and de novo sequencing tool for the analysis of intact glycopeptides from mass spectrometry data. GlycanFinder integrates peptide-based and glycan-based search strategies to address the challenge of complex fragmentation of glycopeptides. A deep learning model is designed to capture glycan tree structures and their fragment ions for de novo sequencing of glycans that do not exist in the database. We performed extensive analyses to validate the false discovery rates (FDRs) at both peptide and glycan levels and to evaluate GlycanFinder based on comprehensive benchmarks from previous community-based studies. Our results show that GlycanFinder achieved comparable performance to other leading glycoproteomics softwares in terms of both FDR control and the number of identifications. Moreover, GlycanFinder was also able to identify glycopeptides not found in existing databases. Finally, we conducted a mass spectrometry experiment for antibody N-linked glycosylation profiling that could distinguish isomeric peptides and glycans in four immunoglobulin G subclasses, which had been a challenging problem to previous studies.

Alternative Identification of Glycosides Using MS/MS Matching with an In Silico-Modified Aglycone Mass Spectra Library

Glycosylation of metabolites serves multiple purposes. Adding sugars makes metabolites more water soluble and improves their biodistribution, stability, and detoxification. In plants, the increase in melting points enables storing otherwise volatile compounds that are released by hydrolysis when needed. Classically, glycosylated metabolites were identified by mass spectrometry (MS/MS) using [M-sugar] neutral losses. Herein, we studied 71 pairs of glycosides with their respective aglycones, including hexose, pentose, and glucuronide moieties. Using liquid chromatography (LC) coupled to electrospray ionization high-resolution mass spectrometry, we detected the classic [M-sugar] product ions for only 68% of glycosides. Instead, we found that most aglycone MS/MS product ions were conserved in the MS/MS spectra of their corresponding glycosides, even when no [M-sugar] neutral losses were observed. We added pentose and hexose units to the precursor masses of an MS/MS library of 3057 aglycones to enable rapid identification of glycosylated natural products with standard MS/MS search algorithms. When searching unknown compounds in untargeted LC-MS/MS metabolomics data of chocolate and tea, we structurally annotated 108 novel glycosides in standard MS-DIAL data processing. We uploaded this new in silico-glycosylated product MS/MS library to GitHub to enable users to detect natural product glycosides without authentic chemical standards.

Unraveling the glycosylated immunopeptidome with HLA-Glyco

Recent interest in targeted therapies has been sparked by the study of MHC-associated peptides (MAPs) that undergo post-translational modifications (PTMs), particularly glycosylation. In this study, we introduce a fast computational workflow that merges the MSFragger-Glyco search algorithm with a false discovery rate control for glycopeptide analysis from mass spectrometry-based immunopeptidome data. By analyzing eight large-scale publicly available studies, we find that glycosylated MAPs are predominantly presented by MHC class II. Here, we present HLA-Glyco, a comprehensive resource containing over 3,400 human leukocyte antigen (HLA) class II N-glycopeptides from 1,049 distinct protein glycosylation sites. This resource provides valuable insights, including high levels of truncated glycans, conserved HLA-binding cores, and differences in glycosylation positional specificity between HLA allele groups. We integrate the workflow within the FragPipe computational platform and provide HLA-Glyco as a free web resource. Overall, our work provides a valuable tool and resource to aid the nascent field of glyco-immunopeptidomics.

Efficient Analysis of Proteome-wide FPOP Data by FragPipe

Monitoring protein structure before and after perturbations can give insights into the role and function of proteins. Fast photochemical oxidation of proteins (FPOP) coupled with mass spectrometry (MS) allows monitoring of structural rearrangements by exposing proteins to OH radicals that oxidize solvent accessible residues, indicating protein regions undergoing movement. Some of the benefits of FPOP include high throughput and lack of scrambling due to label irreversibility. However, the challenges of processing FPOP data have thus far limited its proteome-scale uses. Here, we present a computational workflow for fast and sensitive analysis of FPOP datasets. Our workflow combines the speed of MSFragger search with a unique hybrid search method to restrict the large search space of FPOP modifications. Together, these features enable more than 10-fold faster FPOP searches that identify 50% more modified peptide spectra than previous methods. We hope this new workflow will increase the accessibility of FPOP to enable more protein structure and function relationships to be explored.

The SHDRA syndrome-associated gene TMEM260 encodes a protein-specific O-mannosyltransferase

Mutations in the TMEM260 gene cause structural heart defects and renal anomalies syndrome, but the function of the encoded protein remains unknown. We previously reported wide occurrence of O-mannose glycans on extracellular immunoglobulin, plexin, transcription factor (IPT) domains found in the hepatocyte growth factor receptor (cMET), macrophage-stimulating protein receptor (RON), and plexin receptors, and further demonstrated that two known protein O-mannosylation systems orchestrated by the POMT1/2 and transmembrane and tetratricopeptide repeat-containing proteins 1-4 gene families were not required for glycosylation of these IPT domains. Here, we report that the TMEM260 gene encodes an ER-located protein O-mannosyltransferase that selectively glycosylates IPT domains. We demonstrate that disease-causing TMEM260 mutations impair O-mannosylation of IPT domains and that TMEM260 knockout in cells results in receptor maturation defects and abnormal growth of 3D cell models. Thus, our study identifies the third protein-specific O-mannosylation pathway in mammals and demonstrates that O-mannosylation of IPT domains serves critical functions during epithelial morphogenesis. Our findings add a new glycosylation pathway and gene to a growing group of congenital disorders of glycosylation.

"Ghost" fragment ions in structure and site-specific glycoproteomics analysis

Mass spectrometry (MS) can unlock crucial insights into the intricate world of glycosylation analysis. Despite its immense potential, the qualitative and quantitative analysis of isobaric glycopeptide structures remains one of the most daunting hurdles in the field of glycoproteomics. The ability to distinguish between these complex glycan structures poses a significant challenge, hindering our ability to accurately measure and understand the role of glycoproteins in biological systems. A few recent publications described the use of collision energy (CE) modulation to improve structural elucidation, especially for qualitative purposes. Different linkages of glycan units usually demonstrate different stabilities under CID/HCD fragmentation conditions. Fragmentation of the glycan moiety produces low molecular weight ions (oxonium ions) that can serve as a structure-specific signature for specific glycan moieties, however, specificity of these fragments has never been examined closely. Here, we investigated fragmentation specificity using synthetic stable isotope-labelled glycopeptide standards. These standards were isotopically labelled at the reducing terminal GlcNAc, which allowed us to resolve fragments produced by oligomannose core moiety and fragments generated from outer antennary structures. Our research identified the potential for false positive structure assignments due to the occurrence of "Ghost" fragments resulting from single glyco unit rearrangement or mannose core fragmentation within the collision cell. To mitigate this issue, we have established a minimal intensity threshold for these fragments to prevent the misidentification of structure-specific fragments in glycoproteomics analysis. Our findings provide a crucial step forward in the quest for more accurate and reliable glycoproteomics measurements.

Graphical abstract

Identification, characterization, and engineering of glycosylation in thrombolyticsa

Cardiovascular diseases, such as myocardial infarction, ischemic stroke, and pulmonary embolism, are the most common causes of disability and death worldwide. Blood clot hydrolysis by thrombolytic enzymes and thrombectomy are key clinical interventions. The most widely used thrombolytic enzyme is alteplase, which has been used in clinical practice since 1986. Another clinically used thrombolytic protein is tenecteplase, which has modified epitopes and engineered glycosylation sites, suggesting that carbohydrate modification in thrombolytic enzymes is a viable strategy for their improvement. This comprehensive review summarizes current knowledge on computational and experimental identification of glycosylation sites and glycan identity, together with methods used for their reengineering. Practical examples from previous studies focus on modification of glycosylations in thrombolytics, e.g., alteplase, tenecteplase, reteplase, urokinase, saruplase, and desmoteplase. Collected clinical data on these glycoproteins demonstrate the great potential of this engineering strategy. Outstanding combinatorics originating from multiple glycosylation sites and the vast variety of covalently attached glycan species can be addressed by directed evolution or rational design. Directed evolution pipelines would benefit from more efficient cell-free expression and high-throughput screening assays, while rational design must employ structure prediction by machine learning and in silico characterization by supercomputing. Perspectives on challenges and opportunities for improvement of thrombolytic enzymes by engineering and evolution of protein glycosylation are provided.

Emerging roles of O-glycosylation in regulating protein aggregation, phase separation, and functions

Protein O-glycosylation is widely identified in various proteins involved in diverse biological processes. Recent studies have demonstrated that O-glycosylation plays crucial and multifaceted roles in modulating protein amyloid aggregation and liquid-liquid phase separation (LLPS) under physiological conditions. Dysregulation of these processes is closely associated with human diseases such as neurodegenerative diseases (NDs) and cancers. In this review, we first summarize the distinct roles of O-glycosylation in regulating pathological aggregation of different amyloid proteins related to NDs and elaborate the underlying mechanisms of how O-glycosylation modulates protein aggregation kinetics, induces new aggregated structures, and mediates the pathogenesis of amyloid aggregates under diseased conditions. Furthermore, we introduce recent discoveries on O-GlcNAc-mediated regulation of synaptic LLPS and phase separation potency of low-complexity domain-enriched proteins. Finally, we identify challenges in future research and highlight the potential for developing new therapeutic strategies of NDs by targeting protein O-glycosylation.

MSFragger-Labile: A Flexible Method to Improve Labile PTM Analysis in Proteomics

Posttranslational modifications of proteins play essential roles in defining and regulating the functions of the proteins they decorate, making identification of these modifications critical to understanding biology and disease. Methods for enriching and analyzing a wide variety of biological and chemical modifications of proteins have been developed using mass spectrometry-based proteomics, largely relying on traditional database search methods to identify the resulting mass spectra of modified peptides. These database search methods treat modifications as static attachments of a mass to particular position in the peptide sequence, but many modifications undergo fragmentation in tandem mass spectrometry experiments alongside, or instead of, the peptide backbone. While this fragmentation can confound traditional search methods, it also offers unique opportunities for improved searches that incorporate modification-specific fragment ions. Here, we present a new labile mode in the MSFragger search engine that provides the flexibility to tailor modification-centric searches to the fragmentation observed. We show that labile mode can dramatically improve spectrum identification rates of phosphopeptides, RNA-crosslinked peptides, and ADP-ribosylated peptides. Each of these modifications presents distinct fragmentation characteristics, showcasing the flexibility of MSFragger labile mode to improve search for a wide variety of biological and chemical modifications.

Integrative Proteomics and N-Glycoproteomics Analyses of Rheumatoid Arthritis Synovium Reveal Immune-Associated Glycopeptides

Rheumatoid arthritis (RA) is a typical autoimmune disease characterized by synovial inflammation, synovial tissue hyperplasia, and destruction of bone and cartilage. Protein glycosylation plays key roles in the pathogenesis of RA but in-depth glycoproteomics analysis of synovial tissues is still lacking. Here, by using a strategy to quantify intact N-glycopeptides, we identified 1260 intact N-glycopeptides from 481 N-glycosites on 334 glycoproteins in RA synovium. Bioinformatics analysis revealed that the hyper-glycosylated proteins in RA were closely linked to immune responses. By using DNASTAR software, we identified 20 N-glycopeptides whose prototype peptides were highly immunogenic. We next calculated the enrichment scores of nine types of immune cells using specific gene sets from public single-cell transcriptomics data of RA and revealed that the N-glycosylation levels at some sites, such as IGSF10_N2147, MOXD2P_N404, and PTCH2_N812, were significantly correlated with the enrichment scores of certain immune cell types. Furthermore, we showed that aberrant N-glycosylation in the RA synovium was related to increased expression of glycosylation enzymes. Collectively, this work presents, for the first time, the N-glycoproteome of RA synovium and describes immune-associated glycosylation, providing novel insights into RA pathogenesis.