N-GlycositeAtlas: a database resource for mass spectrometry-based human N-linked glycoprotein and glycosylation site mapping

A novel glycogene-related signature for prognostic prediction and immune microenvironment assessment in kidney renal clear cell carcinoma

BACKGROUND

Kidney Renal Clear Cell Carcinoma (KIRC) is a prevalent urinary malignancies worldwide. Glycosylation is a key post-translational modification that is essential in cancer progression. However, its relationship with prognosis, tumour microenvironment (TME), and treatment response in KIRC remains unclear.

METHOD

Expression profiles and clinical data were retrieved from The Cancer Genome Atlas and Gene Expression Omnibus databases. Consensus clustering, Cox regression, and LASSO regression analyses were conducted to develop an optimal glycogene-related signature. The prognostic relevance of this molecular signature was rigorously analyzed, along with its connections to tumour microenvironment (TME), tumour mutation burden, immune checkpoint activity, cancer-immunity cycle regulation, immunomodulatory gene expression patterns, and therapeutic response profiles. Validation was performed using real-world clinical specimens, quantitative PCR (qPCR), and immunohistochemistry (IHC), supported by cohort analyses from the Human Protein Atlas (HPA) database.

RESULTS

A glycogene-associated prognostic scoring system was established to categorize patients into risk-stratified subgroups. Patients in the high-risk cohort exhibited significantly poorer survival outcomes (p < 0.001). By incorporating clinicopathological variables into this framework, we established a predictive nomogram demonstrating strong calibration and a concordance index (C-index) of 0.78. The high-risk subgroup displayed elevated immune infiltration scores (p < 0.001), upregulated expression of immune checkpoint-related genes (p < 0.05), and an increased frequency of somatic mutations (p = 0.043). The risk score positively correlated with cancer-immunity cycle activation and immunotherapy-related signals. The high-risk groups also showed associations with T cell exhaustion, immune-activating genes, chemokines, and receptors. Drug sensitivity analysis revealed that low-risk patients were more sensitive to sorafenib, pazopanib, and erlotinib, whereas high-risk individuals responded better to temsirolimus (p < 0.01). qPCR and IHC analyses consistently revealed distinct expression patterns of MX2 and other key genes across the risk groups, further corroborated by the HPA findings.

CONCLUSION

This glycogene-based signature provides a robust tool for predicting prognosis, TME characteristics, and therapeutic responses in KIRC, offering potential clinical utility in patient management.

Comprehensive PTM profiling with SCASP-PTM uncovers mechanisms of p62 degradation and ALDOA-mediated tumor progression

Multiple post-translational modification (PTM) proteomics typically combines PTM enrichment with multiplex isobaric labeling and peptide fractionation. However, effective methods for sequentially enriching multiple PTMs from a single sample for data-independent acquisition mass spectrometry (DIA-MS) remain lacking. We present SDS-cyclodextrin-assisted sample preparation (SCASP)-PTM, an approach that enables desalting-free enrichment of diverse PTMs, including phosphopeptides, ubiquitinated peptides, acetylated peptides, glycopeptides, and biotinylated peptides. SCASP-PTM uses SDS for protein denaturation, which is sequestered by cyclodextrins before trypsin digestion, facilitating sequential PTM enrichment without additional purification steps. Combined with DIA-MS, SCASP-PTM quantifies the proteome, ubiquitinome, phosphoproteome, and glycoproteome in HeLa-S3 cell samples, identifying serine 28 phosphorylation as a key driver of poly(I:C)-induced p62 degradation. This method also quantifies PTMs in clinical tissue samples, revealing the critical role of ALDOA K330 ubiquitination/acetylation in tumor progression. SCASP-PTM offers a streamlined workflow for comprehensive PTM analysis in both basic research and clinical applications.

RNA-binding proteins and glycoRNAs form domains on the cell surface for cell-penetrating peptide entry

The composition and organization of the cell surface determine how cells interact with their environment. Traditionally, glycosylated transmembrane proteins were thought to be the major constituents of the external surface of the plasma membrane. Here, we provide evidence that a group of RNA-binding proteins (RBPs) is present on the surface of living cells. These cell-surface RBPs (csRBPs) precisely organize into well-defined nanoclusters enriched for multiple RBPs and glycoRNAs, and their clustering can be disrupted by extracellular RNase addition. These glycoRNA-csRBP clusters further serve as sites of cell-surface interaction for the cell-penetrating peptide trans-activator of transcription (TAT). Removal of RNA from the cell surface, or loss of RNA-binding activity by TAT, causes defects in TAT cell internalization. Together, we provide evidence of an expanded view of the cell surface by positioning glycoRNA-csRBP clusters as a regulator of communication between cells and the extracellular environment.

Inter-tissue glycan heterogeneity: site-specific glycoform analysis of mouse tissue N-glycoproteomes using MS1-based glycopeptide detection method assisted by lectin microarray

To understand the biological and pathological functions of protein glycosylation, the glycan heterogeneities for each glycosite in a single glycoprotein need to be elucidated depending on the type and status of cells. For this aim, a reliable strategy is needed to compare site-specific glycoforms of multiple glycoprotein samples in a comprehensive manner. To analyze this "inter-heterogeneity" of samples, we previously introduced an MS1-based glycopeptide detection method, "Glyco-RIDGE." Here, to elucidate inter-tissue glycan heterogeneities, this estimation method was applied to site-specific N-glycoforms of glycoproteins from six normal mouse tissues (liver, kidney, spleen, pancreas, stomach, and testis). The analyses of desialylated glycopeptides estimated 11,325 site-specific N-glycoforms with 239 glycan compositions at 1260 sites (1122 non-redundant core peptides) in 800 glycoproteins, revealing inter-tissue differences in macro-, micro-, and meta-glycan heterogeneities. To obtain detailed information on their glycan features and tissue distribution, the Glyco-RIDGE results were compared with laser microdissection-assisted lectin microarray (LMD-LMA)-based mouse tissue glycome mapping data deposited on LM-GlycomeAtlas, as well as MS-based mouse tissue glycome data deposited on GlycomeAtlas. This integrated approach supported the certainty of Glyco-RIDGE results and suggested that inter-tissue differences exist in glycan motifs, such as alpha-galactose and bisecting N-acetylglucosamine, in both whole tissue glycomes and specific glycoproteins, Anpep and Lamc1. In addition, the comparison with LMD-LMA-based tissue glycome mapping data suggested the possibility of estimating the tissue distribution of the assigned glycans and glycopeptides. Taken together, these findings demonstrate the utility of an integrated approach using MS assisted by LMA for large-scale analyses.

Functions of unique middle loop and C-terminal tail in GnT-III activity and secretion

BACKGROUND

N-Glycan branching modulates the diversity of protein functions. β1,4-N-acetylglucosaminyltransferase III (GnT-III or MGAT3) produces a unique GlcNAc branch, "bisecting GlcNAc", in N-glycans, and is involved in Alzheimer's disease and cancer. However, the 3D structure and catalytic mechanism of GnT-III are unclear. According to AlphaFold-based structure prediction, GnT-III likely contains two putative disordered segments, a long middle loop (Loop) and a C-terminal tail (Tail). We hypothesized that these segments play important roles in regulating the activity or intracellular behaviors of GnT-III.

METHODS

We expressed wild-type GnT-III (GnT-III-WT), GnT-III-Loop- and -Tail-deletion mutants in cells. Their in vitro catalytic activity and glycan biosynthesis in cells were examined using high-performance liquid chromatography, UDP-Glo glycosyltransferase assays, and glycomic analysis. Subcellular localization of WT and GnT-III mutants was investigated by immunostaining, and degradation rate and secretion were also examined.

RESULTS

The Loop-deletion mutant had higher in vitro and in cellulo activity than GnT-III-WT, indicating that Loop suppresses catalytic activity. In contrast, the Tail-deletion mutant showed weaker activity, increased ER localization, and faster degradation than GnT-III-WT, indicating that Tail is required for proper folding. In addition, deletion of Loop led to aberrant shedding of GnT-III, indicating that Loop contains the cleavage site or regulates GnT-III shedding.

CONCLUSIONS

Loop and Tail of GnT-III play important roles in catalytic activity, folding and shedding.

GENERAL SIGNIFICANCE

Our results provide further understanding of the catalysis and shedding mechanisms of GnT-III and can help in the development of methods for modifying the levels of bisecting GlcNAc on glycoproteins and in cells.

Advances in Prediction of Posttranslational Modification Sites Known to Localize in Protein Supersecondary Structures

Posttranslational modifications (PTMs) play a crucial role in modulating the structure, function, localization, and interactions of proteins, with many PTMs being localized within supersecondary structures, such as helical pairs. These modifications can significantly influence the conformation and stability of these structures. For instance, phosphorylation introduces negative charges that alter electrostatic interactions, while acetylation or methylation of lysine residues affects the stability and interactions of alpha helices or beta strands. Given the pivotal role of supersecondary structures in the overall protein architecture, their modulation by PTMs is essential for protein functionality. This chapter explores the latest advancements in predicting sites for the five PTMs (phosphorylation, acetylation, glycosylation, methylation, and ubiquitination) known to be localized within supersecondary structures. The chapter highlights the recent advances in the prediction of these PTM sites, including the use of global contextualized embeddings from protein language models, integration of structural information, utilization of reliable positive and negative sites, and application of contrastive learning. These methodologies and emerging trends offer a roadmap for novel innovations in addressing PTM prediction challenges, particularly those linked to supersecondary structures.

Processing of N-glycans in the ER and Golgi influences the production of surface sialylated glycoRNA

Glycoconjugates, including glycans on proteins and lipids, have obtained significant attention due to their critical roles in both intracellular and intercellular biological functions and processes. Notably, recent discoveries have revealed the presence of glycosylated RNAs (glycoRNAs) on cell surfaces. Despite the well-characterized roles of RNA modifications, RNA glycosylation remains relatively unexplored. In this study, we investigate the relationship between N-glycosylation and RNA glycosylation. Using a recombinant Siglec11-Fc as a probe, we detected surface sialylated glycoRNAs in human cell lines and identified their dependency on the catalytic isoforms of the oligosaccharyltransferase (OST) complex, implicating STT3A-dependent protein glycosylation as a predominant contributor for affecting indirect generation of glycoRNAs. Additionally, perturbations in N-glycan biosynthesis pathways or changes in N-glycan structure impact surface sialylated glycoRNA levels, indicating a regulatory role of glycan metabolic pathways in RNA glycosylation. Together, our results underscore the intricate relationship between protein N-glycosylation and processing and RNA biology.

Sitetack: a deep learning model that improves PTM prediction by using known PTMs

MOTIVATION

Post-translational modifications (PTMs) increase the diversity of the proteome and are vital to organismal life and therapeutic strategies. Deep learning has been used to predict PTM locations. Still, limitations in datasets and their analyses compromise success.

RESULTS

We evaluated the use of known PTM sites in prediction via sequence-based deep learning algorithms. For each PTM, known locations of that PTM were encoded as a separate amino acid before sequences were encoded via word embedding and passed into a convolutional neural network that predicts the probability of that PTM at a given site. Without labeling known PTMs, our models are on par with others. With labeling, however, we improved significantly upon extant models. Moreover, knowing PTM locations can increase the predictability of a different PTM. Our findings highlight the importance of PTMs for the installation of additional PTMs. We anticipate that including known PTM locations will enhance the performance of other proteomic machine learning algorithms.

AVAILABILITY AND IMPLEMENTATION

Sitetack is available as a web tool at https://sitetack.net; the source code, representative datasets, instructions for local use, and select models are available at https://github.com/clair-gutierrez/sitetack.

Hydrophilic Interaction Chromatography Coupled to Ultraviolet Photodissociation Affords Identification, Localization, and Relative Quantitation of Glycans on Intact Glycoproteins

Protein glycosylation is implicated in a wide array of diseases, yet glycoprotein analysis remains elusive owing to the extreme heterogeneity of glycans, including microheterogeneity of some of the glycosites (amino acid residues). Various mass spectrometry (MS) strategies have proven tremendously successful for localizing and identifying glycans, typically utilizing a bottom-up workflow in which glycoproteins are digested to create glycopeptides to facilitate analysis. An emerging alternative is top-down MS that aims to characterize intact glycoproteins to allow precise identification and localization of glycans. The most comprehensive characterization of intact glycoproteins requires integration of a suitable separation method and high performance tandem mass spectrometry to provide both protein sequence information and glycosite localization. Here, we couple ultraviolet photodissociation and hydrophilic interaction chromatography with high resolution mass spectrometry to advance the characterization of intact glycoproteins ranging from 15 to 34 kDa, offering site localization of glycans, providing sequence coverages up to 93%, and affording relative quantitation of individual glycoforms.

PTMoreR-enabled cross-species PTM mapping and comparative phosphoproteomics across mammals

To support PTM proteomic analysis and annotation in different species, we developed PTMoreR, a user-friendly tool that considers the surrounding amino acid sequences of PTM sites during BLAST, enabling a motif-centric analysis across species. By controlling sequence window similarity, PTMoreR can map phosphoproteomic results between any two species, perform site-level functional enrichment analysis, and generate kinase-substrate networks. We demonstrate that the majority of real P-sites in mice can be inferred from experimentally derived human P-sites with PTMoreR mapping. Furthermore, the compositions of 129 mammalian phosphoproteomes can also be predicted using PTMoreR. The method also identifies cross-species phosphorylation events that occur on proteins with an increased tendency to respond to the environmental factors. Moreover, the classic kinase motifs can be extracted across mammalian species, offering an evolutionary angle for refining current motifs. PTMoreR supports PTM proteomics in non-human species and facilitates quantitative phosphoproteomic analysis.

Autoinhibited Protein Database: a curated database of autoinhibitory domains and their autoinhibition mechanisms

Autoinhibition, a crucial allosteric self-regulation mechanism in cell signaling, ensures signal propagation exclusively in the presence of specific molecular inputs. The heightened focus on autoinhibited proteins stems from their implication in human diseases, positioning them as potential causal factors or therapeutic targets. However, the absence of a comprehensive knowledgebase impedes a thorough understanding of their roles and applications in drug discovery. Addressing this gap, we introduce Autoinhibited Protein Database (AiPD), a curated database standardizing information on autoinhibited proteins. AiPD encompasses details on autoinhibitory domains (AIDs), their targets, regulatory mechanisms, experimental validation methods, and implications in diseases, including associated mutations and post-translational modifications. AiPD comprises 698 AIDs from 532 experimentally characterized autoinhibited proteins and 2695 AIDs from their 2096 homologs, which were retrieved from 864 published articles. AiPD also includes 42 520 AIDs of computationally predicted autoinhibited proteins. In addition, AiPD facilitates users in investigating potential AIDs within a query sequence through comparisons with documented autoinhibited proteins. As the inaugural autoinhibited protein repository, AiPD significantly aids researchers studying autoinhibition mechanisms and their alterations in human diseases. It is equally valuable for developing computational models, analyzing allosteric protein regulation, predicting new drug targets, and understanding intervention mechanisms AiPD serves as a valuable resource for diverse researchers, contributing to the understanding and manipulation of autoinhibition in cellular processes. Database URL: http://ssbio.cau.ac.kr/databases/AiPD.

Bisecting GlcNAc modification reverses the chemoresistance via attenuating the function of P-gp

Rationale: Chemoresistance is a key factor contributing to the failure of anti-breast cancer chemotherapy. Although abnormal glycosylation is closely correlated with breast cancer progression, the function of glycoconjugates in chemoresistance remains poorly understood. Methods: Levels and regulatory roles of bisecting N-acetylglucosamine (GlcNAc) in chemoresistant breast cancer cells were determined in vitro and in vivo. Glycoproteomics guided identification of site-specific bisecting GlcNAc on P-glycoprotein (P-gp). Co-immunoprecipitation coupled mass spectrometry (Co-IP-MS) and proximity labelling MS identified the interactome of P-gp, and the biological function of site-specific bisecting GlcNAc was investigated by site/truncation mutation and structural simulations. Results: Bisecting GlcNAc levels were reduced in chemoresistant breast cancer cells, accompanied by an enhanced expression of P-gp. Enhanced bisecting GlcNAc effectively reversed chemoresistance. Mechanical study revealed that bisecting GlcNAc impaired the association between Ezrin and P-gp, leading to a decreased expression of membrane P-gp. Bisecting GlcNAc suppressed VPS4A-mediated P-gp recruitment into microvesicles, and chemoresistance transmission. Structural dynamics analysis suggested that bisecting GlcNAc at Asn494 introduced structural constraints that rigidified the conformation and suppressed the activity of P-gp. Conclusion: Our findings highlight the crucial role of bisecting GlcNAc in chemoresistance and suggest the possibility of reversing chemoresistance by modulating the specific glycosylation in breast cancer therapy.

Recent Advancements in Subcellular Proteomics: Growing Impact of Organellar Protein Niches on the Understanding of Cell Biology

The mammalian cell is a complex entity, with membrane-bound and membrane-less organelles playing vital roles in regulating cellular homeostasis. Organellar protein niches drive discrete biological processes and cell functions, thus maintaining cell equilibrium. Cellular processes such as signaling, growth, proliferation, motility, and programmed cell death require dynamic protein movements between cell compartments. Aberrant protein localization is associated with a wide range of diseases. Therefore, analyzing the subcellular proteome of the cell can provide a comprehensive overview of cellular biology. With recent advancements in mass spectrometry, imaging technology, computational tools, and deep machine learning algorithms, studies pertaining to subcellular protein localization and their dynamic distributions are gaining momentum. These studies reveal changing interaction networks because of "moonlighting proteins" and serve as a discovery tool for disease network mechanisms. Consequently, this review aims to provide a comprehensive repository for recent advancements in subcellular proteomics subcontexting methods, challenges, and future perspectives for method developers. In summary, subcellular proteomics is crucial to the understanding of the fundamental cellular mechanisms and the associated diseases.

Regulation of intracellular activity of N-glycan branching enzymes in mammals

Most proteins in the secretory pathway are glycosylated, and N-glycans are estimated to be attached to over 7000 proteins in humans. As structural variation of N-glycans critically regulates the functions of a particular glycoprotein, it is pivotal to understand how structural diversity of N-glycans is generated in cells. One of the major factors conferring structural variation of N-glycans is the variable number of N-acetylglucosamine branches. These branch structures are biosynthesized by dedicated glycosyltransferases, including GnT-III (MGAT3), GnT-IVa (MGAT4A), GnT-IVb (MGAT4B), GnT-V (MGAT5), and GnT-IX (GnT-Vb, MGAT5B). In addition, the presence or absence of core modification of N-glycans, namely, core fucose (included as an N-glycan branch in this manuscript), synthesized by FUT8, also confers large structural variation on N-glycans, thereby crucially regulating many protein-protein interactions. Numerous biochemical and medical studies have revealed that these branch structures are involved in a wide range of physiological and pathological processes. However, the mechanisms regulating the activity of the biosynthetic glycosyltransferases are yet to be fully elucidated. In this review, we summarize the previous findings and recent updates regarding regulation of the activity of these N-glycan branching enzymes. We hope that such information will help readers to develop a comprehensive overview of the complex system regulating mammalian N-glycan maturation.

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.

Systematic Investigation of the Trafficking of Glycoproteins on the Cell Surface

Glycoproteins located on the cell surface play a pivotal role in nearly every extracellular activity. N-glycosylation is one of the most common and important protein modifications in eukaryotic cells, and it often regulates protein folding and trafficking. Glycosylation of cell-surface proteins undergoes meticulous regulation by various enzymes in the endoplasmic reticulum (ER) and the Golgi, ensuring their proper folding and trafficking to the cell surface. However, the impacts of protein N-glycosylation, N-glycan maturity, and protein folding status on the trafficking of cell-surface glycoproteins remain to be explored. In this work, we comprehensively and site-specifically studied the trafficking of cell-surface glycoproteins in human cells. Integrating metabolic labeling, bioorthogonal chemistry, and multiplexed proteomics, we investigated 706 N-glycosylation sites on 396 cell-surface glycoproteins in monocytes, either by inhibiting protein N-glycosylation, disturbing N-glycan maturation, or perturbing protein folding in the ER. The current results reveal their distinct impacts on the trafficking of surface glycoproteins. The inhibition of protein N-glycosylation dramatically suppresses the trafficking of many cell-surface glycoproteins. The N-glycan immaturity has more substantial effects on proteins with high N-glycosylation site densities, while the perturbation of protein folding in the ER exerts a more pronounced impact on surface glycoproteins with larger sizes. Furthermore, for N-glycosylated proteins, their trafficking to the cell surface is related to the secondary structures and adjacent amino acid residues of glycosylation sites. Systematic analysis of surface glycoprotein trafficking advances our understanding of the mechanisms underlying protein secretion and surface presentation.

The Immunometabolic Gene N-Acetylglucosamine Kinase Is Uniquely Involved in the Heritability of Multiple Sclerosis Severity

The clinical severity of multiple sclerosis (MS), an autoimmune disorder of the central nervous system, is thought to be determined by environmental and genetic factors that have not yet been identified. In a recent genome-wide association study (GWAS), a single nucleotide polymorphism (SNP), rs10191329, has been associated with MS severity in two large independent cohorts of patients. Different approaches were followed by the authors to prioritize the genes that are transcriptionally regulated by such an SNP. It was concluded that the identified SNP regulates a group of proximal genes involved in brain resilience and cognitive abilities rather than immunity. Here, by conducting an alternative strategy for gene prioritization, we reached the opposite conclusion. According to our re-analysis, the main target of rs10191329 is N-Acetylglucosamine Kinase (NAGK), a metabolic gene recently shown to exert major immune functions via the regulation of the nucleotide-binding oligomerization domain-containing protein 2 (NOD2) pathway. To gain more insights into the immunometabolic functions of NAGK, we analyzed the currently known list of NAGK protein partners. We observed that NAGK integrates a dense network of human proteins that are involved in glucose metabolism and are highly expressed by classical monocytes. Our findings hold potentially major implications for the understanding of MS pathophysiology.

N-Glycosylation as a Modulator of Protein Conformation and Assembly in Disease

Glycosylation, a prevalent post-translational modification, plays a pivotal role in regulating intricate cellular processes by covalently attaching glycans to macromolecules. Dysregulated glycosylation is linked to a spectrum of diseases, encompassing cancer, neurodegenerative disorders, congenital disorders, infections, and inflammation. This review delves into the intricate interplay between glycosylation and protein conformation, with a specific focus on the profound impact of N-glycans on the selection of distinct protein conformations characterized by distinct interactomes-namely, protein assemblies-under normal and pathological conditions across various diseases. We begin by examining the spike protein of the SARS virus, illustrating how N-glycans regulate the infectivity of pathogenic agents. Subsequently, we utilize the prion protein and the chaperone glucose-regulated protein 94 as examples, exploring instances where N-glycosylation transforms physiological protein structures into disease-associated forms. Unraveling these connections provides valuable insights into potential therapeutic avenues and a deeper comprehension of the molecular intricacies that underlie disease conditions. This exploration of glycosylation's influence on protein conformation effectively bridges the gap between the glycome and disease, offering a comprehensive perspective on the therapeutic implications of targeting conformational mutants and their pathologic assemblies in various diseases. The goal is to unravel the nuances of these post-translational modifications, shedding light on how they contribute to the intricate interplay between protein conformation, assembly, and disease.

Identification and characterization of intact glycopeptides in human urine

Glycoproteins in urine have the potential to provide a rich class of informative molecules for studying human health and disease. Despite this promise, the urine glycoproteome has been largely uncharacterized. Here, we present the analysis of glycoproteins in human urine using LC-MS/MS-based intact glycopeptide analysis, providing both the identification of protein glycosites and characterization of the glycan composition at specific glycosites. Gene enrichment analysis reveals differences in biological processes, cellular components, and molecular functions in the urine glycoproteome versus the urine proteome, as well as differences based on the major glycan class observed on proteins. Meta-heterogeneity of glycosylation is examined on proteins to determine the variation in glycosylation across multiple sites of a given protein with specific examples of individual sites differing from the glycosylation trends in the overall protein. Taken together, this dataset represents a potentially valuable resource as a baseline characterization of glycoproteins in human urine for future urine glycoproteomics studies.

Hydrophilic Peptide and Glycopeptide as Immobilized Sorbents for Glycosylation Analysis

Hydrophilic interaction liquid chromatography (HILIC) is widely used for glycopeptide enrichment in shot-gun glycoproteomics to enhance the glycopeptide signal and minimize the ionization competition of peptides. In this work, we have developed a novel hydrophilic material (glycoHILIC) based on glycopeptides and peptides to provide hydrophilic properties. GlycoHILIC was synthesized by oxidizing cotton and then reacting the resulting aldehyde with the N-terminus of the glycopeptide or peptide by reductive amination. Due to the large amount of hydrophilic carbohydrates and hydrophilic amino acids contained in glycopeptides, glycoHILIC showed significantly better enrichment of glycopeptides than cotton itself. Our results demonstrate that glycoHILIC has high selectivity, a low detection limit, and good stability. Over 257 unique N-linked glycosylation sites in 1477 intact N-glycopeptides from 146 glycoproteins were identified from 1 μL of human serum using glycoHILIC. Serum analysis of pancreatic cancer patients found that 38 N-glycopeptides among 21 glycoproteins changed significantly, of which 7 N-glycopeptides increased and 31 N-glycopeptides decreased. These results demonstrate that glycoHILIC can be used for glycopeptide enrichment and analysis.

EMNGly: predicting N-linked glycosylation sites using the language models for feature extraction

MOTIVATION

N-linked glycosylation is a frequently occurring post-translational protein modification that serves critical functions in protein folding, stability, trafficking, and recognition. Its involvement spans across multiple biological processes and alterations to this process can result in various diseases. Therefore, identifying N-linked glycosylation sites is imperative for comprehending the mechanisms and systems underlying glycosylation. Due to the inherent experimental complexities, machine learning and deep learning have become indispensable tools for predicting these sites.

RESULTS

In this context, a new approach called EMNGly has been proposed. The EMNGly approach utilizes pretrained protein language model (Evolutionary Scale Modeling) and pretrained protein structure model (Inverse Folding Model) for features extraction and support vector machine for classification. Ten-fold cross-validation and independent tests show that this approach has outperformed existing techniques. And it achieves Matthews Correlation Coefficient, sensitivity, specificity, and accuracy of 0.8282, 0.9343, 0.8934, and 0.9143, respectively on a benchmark independent test set.

Molecular basis for bacterial N-glycosylation by a soluble HMW1C-like N-glycosyltransferase

Soluble HMW1C-like N-glycosyltransferases (NGTs) catalyze the glycosylation of Asn residues in proteins, a process fundamental for bacterial autoaggregation, adhesion and pathogenicity. However, our understanding of their molecular mechanisms is hindered by the lack of structures of enzymatic complexes. Here, we report structures of binary and ternary NGT complexes of Aggregatibacter aphrophilus NGT (AaNGT), revealing an essential dyad of basic/acidic residues located in the N-terminal all α-domain (AAD) that intimately recognizes the Thr residue within the conserved motif Asn0-X+1-Ser/Thr+2. Poor substrates and inhibitors such as UDP-galactose and UDP-glucose mimetics adopt non-productive conformations, decreasing or impeding catalysis. QM/MM simulations rationalize these results, showing that AaNGT follows a SN2 reaction mechanism in which the acceptor asparagine uses its imidic form for catalysis and the UDP-glucose phosphate group acts as a general base. These findings provide key insights into the mechanism of NGTs and will facilitate the design of structure-based inhibitors to treat diseases caused by non-typeable H. influenzae or other Gram-negative bacteria.

Whole proteome copy number dataset in primary mouse cortical neurons

The functional diversity of neurons is specified through their proteome resulting in elaborate and tightly regulated protein interaction networks and signalling that regulates neuronal processes. Dysregulation of these dynamic networks in development or in adulthood lead to neurodevelopmental or neurological disorders respectively. Over the past few decades, mass spectrometry has become a powerful tool for quantifying and resolving any proteome, including complex tissues such as the brain proteome, with technological advances leading to higher levels of resolution and throughput than traditional biochemical techniques. In this article, we provide a proteomic reference dataset that has been generated to identify proteins and quantify their level of expression in primary mouse cortical neurons. It represents a summary analysis of previously published data in (Antico et al., 2021). Mouse cortical neurons were isolated from E16.5 C57Bl/6J mice and cultured for 21 days in vitro (DIV). We employed the mitochondrial uncouplers AntimycinA/Oligomycin (AO) to induce mitochondrial depolarisation that is a well-established paradigm to assess mitophagic signalling. Total lysates from mouse primary cortical neurons were subjected to label-free quantitative proteomic analysis using both data dependent acquisition (DDA) and data independent acquisition (DIA) modes. DDA proteomic analysis identified a total dataset of 9367 proteins in mouse cortical neurons and absolute abundance of proteins was calculated as copy numbers per cell. DDA dataset was also processed to generate a reference spectral library to fit in and quantify MS spectra generated in DIA mode. Quantitative DIA analysis identified more than 6000 protein groups and statistical comparison of the two analysed groups (untreated and AO-treated) revealed that the neuronal proteome was largely unchanged post mitochondrial depolarisation for 5 hours. To our knowledge, these files represent the most comprehensive DDA and DIA reference datasets of fully functional maturated mouse primary cortical neurons and serve as a valuable resource for further investigating the role of specific proteins involved in neurobiology and neurological disorders such as Alzheimer's disease (AD), Parkinson's disease (PD) and Autism Spectrum Disorders (ASD).

Integrated glycoproteomic characterization of clear cell renal cell carcinoma

Clear cell renal cell carcinoma (ccRCC), a common form of RCC, is responsible for the high mortality rate of kidney cancer. Dysregulations of glycoproteins have been shown to associate with ccRCC. However, the molecular mechanism has not been well characterized. Here, a comprehensive glycoproteomic analysis is conducted using 103 tumors and 80 paired normal adjacent tissues. Altered glycosylation enzymes and corresponding protein glycosylation are observed, while two of the major ccRCC mutations, BAP1 and PBRM1, show distinct glycosylation profiles. Additionally, inter-tumor heterogeneity and cross-correlation between glycosylation and phosphorylation are observed. The relation of glycoproteomic features to genomic, transcriptomic, proteomic, and phosphoproteomic changes shows the role of glycosylation in ccRCC development with potential for therapeutic interventions. This study reports a large-scale tandem mass tag (TMT)-based quantitative glycoproteomic analysis of ccRCC that can serve as a valuable resource for the community.

Critical considerations in N-glycoproteomics

N-Glycoproteomics, the system-wide study of glycans asparagine-linked to protein carriers, holds a unique and still largely untapped potential to provide deep insights into the complexity and dynamics of the heterogeneous N-glycoproteome. Despite the advent of innovative analytical and informatics tools aiding the analysis, N-glycoproteomics remains challenging and consequently largely restricted to specialised laboratories. Aiming to stimulate discussions of method harmonisation, data standardisation and reporting guidelines to make N-glycoproteomics more reproducible and accessible to the community, we here discuss critical considerations related to the design and execution of N-glycoproteomics experiments and highlight good practices in N-glycopeptide data collection, analysis, interpretation and sharing. Giving the rapid maturation and, expectedly, a wide-spread implementation of N-glycoproteomics capabilities across the community in future years, this piece aims to point out common pitfalls, to encourage good data sharing and documentation practices, and to highlight practical solutions and strategies to enhance the insight into the N-glycoproteome.

N-glycosylation of serum proteins in adult type 1 diabetes mellitus exposes further changes compared to children at the disease onset

OBJECTIVE

Previously we have shown that plasma protein N-glycosylation is changed in children at the onset of type 1 diabetes. In this study, we aim to identify N-glycan changes in adults with T1DM, compare them to those in children, and investigate their associations with disease duration, complications, glycaemic status, and smoking.

METHODS

Serum protein N-glycans from 200 adults with type 1 diabetes and 298 healthy controls were analysed using ultra-high performance liquid chromatography and divided into 39 directly measured glycan groups from which 16 derived traits were calculated.

RESULTS

Compared to healthy controls, subjects with type 1 diabetes showed differences in 19 glycan groups and a decrease in monogalactosylated, an increase in digalactosylated, monosialylated, and antennary fucosylated derived traits, from which changes in monogalactosylation and seven directly measured traits overlapped with previously reported in children. Changes in four directly measured and two derived traits previously seen in children were not detected in adults. HbA1c was positively associated with sialylated and highly branched structures, whereas N-glycome was not influenced by disease duration or diabetic complications.

CONCLUSIONS

Our results suggest potential N-glycome involvement in different stages of type 1 diabetes, including processes underlying its development, the disease itself, as well as those occurring after disease establishment.

Post-translational proteomics platform identifies neurite outgrowth impairments in Parkinson's disease GBA-N370S dopamine neurons

Variants at the GBA locus, encoding glucocerebrosidase, are the strongest common genetic risk factor for Parkinson's disease (PD). To understand GBA-related disease mechanisms, we use a multi-part-enrichment proteomics and post-translational modification (PTM) workflow, identifying large numbers of dysregulated proteins and PTMs in heterozygous GBA-N370S PD patient induced pluripotent stem cell (iPSC) dopamine neurons. Alterations in glycosylation status show disturbances in the autophagy-lysosomal pathway, which concur with upstream perturbations in mammalian target of rapamycin (mTOR) activation in GBA-PD neurons. Several native and modified proteins encoded by PD-associated genes are dysregulated in GBA-PD neurons. Integrated pathway analysis reveals impaired neuritogenesis in GBA-PD neurons and identify tau as a key pathway mediator. Functional assays confirm neurite outgrowth deficits and identify impaired mitochondrial movement in GBA-PD neurons. Furthermore, pharmacological rescue of glucocerebrosidase activity in GBA-PD neurons improves the neurite outgrowth deficit. Overall, this study demonstrates the potential of PTMomics to elucidate neurodegeneration-associated pathways and potential drug targets in complex disease models.

Protein glycosylation in urine as a biomarker of diseases

Human body fluids have become an indispensable resource for clinical research, diagnosis and prognosis. Urine is widely used to discover disease-specific glycoprotein biomarkers because of its recurrently non-invasive collection and disease-indicating properties. While urine is an unstable fluid in that its composition changes with ingested nutrients and further as it is excreted through micturition, urinary proteins are more stable and their abnormal glycosylation is associated with diseases. It is known that aberrant glycosylation can define tumor malignancy and indicate disease initiation and progression. However, a thorough and translational survey of urinary glycosylation in diseases has not been performed. In this article, we evaluate the clinical applications of urine, introduce methods for urine glycosylation analysis, and discuss urine glycoprotein biomarkers. We emphasize the importance of mining urinary glycoproteins and searching for disease-specific glycosylation in various diseases (including cancer, neurodegenerative diseases, diabetes, and viral infections). With advances in mass spectrometry-based glycomics/glycoproteomics/glycopeptidomics, characterization of disease-specific glycosylation will optimistically lead to the discovery of disease-related urinary biomarkers with better sensitivity and specificity in the near future.

Systematic characterization of extracellular glycoproteins using mass spectrometry

Surface and secreted glycoproteins are essential to cells and regulate many extracellular events. Because of the diversity of glycans, the low abundance of many glycoproteins, and the complexity of biological samples, a system-wide investigation of extracellular glycoproteins is a daunting task. With the development of modern mass spectrometry (MS)-based proteomics, comprehensive analysis of different protein modifications including glycosylation has advanced dramatically. This review focuses on the investigation of extracellular glycoproteins using MS-based proteomics. We first discuss the methods for selectively enriching surface glycoproteins and investigating protein interactions on the cell surface, followed by the application of MS-based proteomics for surface glycoprotein dynamics analysis and biomarker discovery. We then summarize the methods to comprehensively study secreted glycoproteins by integrating various enrichment approaches with MS-based proteomics and their applications for global analysis of secreted glycoproteins in different biological samples. Collectively, MS significantly expands our knowledge of extracellular glycoproteins and enables us to identify extracellular glycoproteins as potential biomarkers for disease detection and drug targets for disease treatment.

GlycoSLASH: Concurrent Glycopeptide Identification from Multiple Related LC-MS/MS Data Sets by Using Spectral Clustering and Library Searching

Liquid chromatography coupled with tandem mass spectrometry is commonly adopted in large-scale glycoproteomic studies involving hundreds of disease and control samples. The software for glycopeptide identification in such data (e.g., the commercial software Byonic) analyzes the individual data set and does not exploit the redundant spectra of glycopeptides presented in the related data sets. Herein, we present a novel concurrent approach for glycopeptide identification in multiple related glycoproteomic data sets by using spectral clustering and spectral library searching. The evaluation on two large-scale glycoproteomic data sets showed that the concurrent approach can identify 105%-224% more spectra as glycopeptides compared to the glycopeptide identification on individual data sets using Byonic alone. The improvement of glycopeptide identification also enabled the discovery of several potential biomarkers of protein glycosylations in hepatocellular carcinoma patients.

Quantitative analysis of fucosylated glycoproteins by immobilized lectin-affinity fluorescent labeling

Human biofluids are often used to discover disease-specific glycosylation, since abnormal changes in protein glycosylation can discern physiopathological states. Highly glycosylated proteins in biofluids make it possible to identify disease signatures. Glycoproteomic studies on saliva glycoproteins showed that fucosylation was significantly increased during tumorigenesis and that glycoproteins became hyperfucosylated in lung metastases, and tumor stage is associated with fucosylation. Quantification of salivary fucosylation can be achieved by mass spectrometric analysis of fucosylated glycoproteins or fucosylated glycans; however, the use of mass spectrometry is non-trivial for clinical practice. Here, we developed a high-throughput quantitative method, lectin-affinity fluorescent labeling quantification (LAFLQ), to quantify fucosylated glycoproteins without relying on mass spectrometry. Lectins with a specific affinity for fucoses are immobilized on the resin and effectively capture fluorescently labeled fucosylated glycoproteins, which are further quantitatively characterized by fluorescence detection in a 96-well plate. Our results demonstrated that serum IgG can be accurately quantified by lectin and fluorescence detection. Quantification in saliva showed significantly higher fucosylation in lung cancer patients compared to healthy controls or other non-cancer diseases, suggesting that this method has the potential to quantify stage-related fucosylation in lung cancer saliva.

Lectin-Based Affinity Enrichment and Characterization of N-Glycoproteins from Human Tear Film by Mass Spectrometry

The glycosylation of proteins is one of the most common post-translational modifications (PTMs) and plays important regulatory functions in diverse biological processes such as protein stability or cell signaling. Accordingly, glycoproteins are also a consistent part of the human tear film proteome, maintaining the proper function of the ocular surface and forming the first defense barrier of the ocular immune system. Irregularities in the glycoproteomic composition of tear film might promote the development of chronic eye diseases, indicating glycoproteins as a valuable source for biomarker discovery or drug target identification. Therefore, the present study aimed to develop a lectin-based affinity method for the enrichment and concentration of tear glycoproteins/glycopeptides and to characterize their specific N-glycosylation sites by high-resolution mass spectrometry (MS). For method development and evaluation, we first accumulated native glycoproteins from human tear sample pools and assessed the enrichment efficiency of different lectin column systems by 1D gel electrophoresis and specific protein stainings (Coomassie and glycoproteins). The best-performing multi-lectin column system (comprising the four lectins ConA, JAC, WGA, and UEA I, termed 4L) was applied to glycopeptide enrichment from human tear sample digests, followed by MS-based detection and localization of their specific N-glycosylation sites. As the main result, our study identified a total of 26 N glycosylation sites of 11 N-glycoproteins in the tear sample pools of healthy individuals (n = 3 biological sample pools). Amongst others, we identified tear film proteins lactotransferrin (N497 and N642, LTF), Ig heavy chain constant α-1 (N144 and 340, IGHA1), prolactin-inducible protein (N105, PIP), and extracellular lacritin (N105, LACRT) as highly reliable and significant N glycoproteins, already associated with the pathogenesis of various chronic eye diseases such as dry eye syndrome (DES). In conclusion, the results of the present study will serve as an important tear film N-glycoprotein catalog for future studies focusing on human tear film and ocular surface-related inflammatory diseases.

Deciphering O-glycoprotease substrate preferences with O-Pair Search

O-Glycoproteases are an emerging class of enzymes that selectively digest glycoproteins at positions decorated with specific O-linked glycans. O-Glycoprotease substrates range from any O-glycoprotein (albeit with specific O-glycan modifications) to only glycoproteins harboring specific O-glycosylated sequence motifs, such as those found in mucin domains. Their utility for multiple glycoproteomic applications is driving the search to both discover new O-glycoproteases and to understand how structural features of characterized O-glycoproteases influence their substrate specificities. One challenge of defining O-glycoprotease specificity restraints is the need to characterize O-glycopeptides with site-specific analysis of O-glycosites. Here, we demonstrate how O-Pair Search, a recently developed O-glycopeptide-centric identification platform that enables rapid searches and confident O-glycosite localization, can be used to determine substrate specificities of various O-glycoproteases de novo from LC-MS/MS data of O-glycopeptides. Using secreted protease of C1 esterase inhibitor (StcE) from enterohemorrhagic Escherichia coli and O-endoprotease OgpA from Akkermansia mucinophila, we explore numerous settings that effect O-glycopeptide identification and show how non-specific and semi-tryptic searches of O-glycopeptide data can produce candidate cleavage motifs. These putative motifs can be further used to define new protease cleavage settings that lower search times and improve O-glycopeptide identifications. We use this platform to generate a consensus motif for the recently characterized immunomodulating metalloprotease (IMPa) from Pseudomonas aeruginosa and show that IMPa is a favorable O-glycoprotease for characterizing densely O-glycosylated mucin-domain glycoproteins.

Glycoinformatics in the Artificial Intelligence Era

Artificial intelligence (AI) methods have been and are now being increasingly integrated in prediction software implemented in bioinformatics and its glycoscience branch known as glycoinformatics. AI techniques have evolved in the past decades, and their applications in glycoscience are not yet widespread. This limited use is partly explained by the peculiarities of glyco-data that are notoriously hard to produce and analyze. Nonetheless, as time goes, the accumulation of glycomics, glycoproteomics, and glycan-binding data has reached a point where even the most recent deep learning methods can provide predictors with good performance. We discuss the historical development of the application of various AI methods in the broader field of glycoinformatics. A particular focus is placed on shining a light on challenges in glyco-data handling, contextualized by lessons learnt from related disciplines. Ending on the discussion of state-of-the-art deep learning approaches in glycoinformatics, we also envision the future of glycoinformatics, including development that need to occur in order to truly unleash the capabilities of glycoscience in the systems biology era.

qPTM: an updated database for PTM dynamics in human, mouse, rat and yeast

Post-translational modifications (PTMs) are critical molecular mechanisms that regulate protein functions temporally and spatially in various organisms. Since most PTMs are dynamically regulated, quantifying PTM events under different states is crucial for understanding biological processes and diseases. With the rapid development of high-throughput proteomics technologies, massive quantitative PTM proteome datasets have been generated. Thus, a comprehensive one-stop data resource for surfing big data will benefit the community. Here, we updated our previous phosphorylation dynamics database qPhos to the qPTM (http://qptm.omicsbio.info). In qPTM, 11 482 553 quantification events among six types of PTMs, including phosphorylation, acetylation, glycosylation, methylation, SUMOylation and ubiquitylation in four different organisms were collected and integrated, and the matched proteome datasets were included if available. The raw mass spectrometry based false discovery rate control and the recurrences of identifications among datasets were integrated into a scoring system to assess the reliability of the PTM sites. Browse and search functions were improved to facilitate users in swiftly and accurately acquiring specific information. The results page was revised with more abundant annotations, and time-course dynamics data were visualized in trend lines. We expected the qPTM database to be a much more powerful and comprehensive data repository for the PTM research community.

Mass spectrometry based biomarkers for early detection of HCC using a glycoproteomic approach

Hepatocellular carcinoma (HCC) is the fourth most common cause of cancer-related mortality worldwide and 80%-90% of HCC develops in patients that have underlying cirrhosis. Better methods of surveillance are needed to increase early detection of HCC and the proportion of patients that can be offered curative therapies. Recent work in novel mass spec-based methods for glycomic and glycopeptide analysis for discovery and confirmation of markers for early detection of HCC versus cirrhosis is reviewed in this chapter. Results from recent work in these fields by several groups and the progress made in developing markers of early HCC which can outperform the current serum-based markers are described and discussed. Also, recent developments in isoform analysis of glycans and glycopeptides and in various mass spec fragmentation methods will be described and discussed.

Mass spectrometry-based N-glycosylation analysis in kidney disease

Kidney disease is a global health concern with an enormous expense. It is estimated that more than 10% of the population worldwide is affected by kidney disease and millions of patients would progress to death prematurely and unnecessarily. Although creatinine detection and renal biopsy are well-established tools for kidney disease diagnosis, they are limited by several inevitable defects. Therefore, diagnostic tools need to be upgraded, especially for the early stage of the disease and possible progression. As one of the most common post-translational modifications of proteins, N-glycosylation plays a vital role in renal structure and function. Deepening research on N-glycosylation in kidney disease provides new insights into the pathophysiology and paves the way for clinical application. In this study, we reviewed recent N-glycosylation studies on several kidney diseases. We also summarized the development of mass spectrometric methods in the field of N-glycoproteomics and N-glycomics.

TNFα-induced metabolic reprogramming drives an intrinsic anti-viral state

Cytokines induce an anti-viral state, yet many of the functional determinants responsible for limiting viral infection are poorly understood. Here, we find that TNFα induces significant metabolic remodeling that is critical for its anti-viral activity. Our data demonstrate that TNFα activates glycolysis through the induction of hexokinase 2 (HK2), the isoform predominantly expressed in muscle. Further, we show that glycolysis is broadly important for TNFα-mediated anti-viral defense, as its inhibition attenuates TNFα’s ability to limit the replication of evolutionarily divergent viruses. TNFα was also found to modulate the metabolism of UDP-sugars, which are essential precursor substrates for glycosylation. Our data indicate that TNFα increases the concentration of UDP-glucose, as well as the glucose-derived labeling of UDP-glucose and UDP-N-acetyl-glucosamine in a glycolytically-dependent manner. Glycolysis was also necessary for the TNFα-mediated accumulation of several glycosylated anti-viral proteins. Consistent with the importance of glucose-driven glycosylation, glycosyl-transferase inhibition attenuated TNFα’s ability to promote the anti-viral cell state. Collectively, our data indicate that cytokine-mediated metabolic remodeling is an essential component of the anti-viral response.

Viral infection often activates a host cell’s intrinsic immune response resulting in the cellular secretion of cytokines, important host-defense molecules. These cytokines act on neighboring cells to make them less permissive to viral infection. Many of the mechanisms through which cytokines promote a less permissive cell state remain unclear. Our data indicate that treatment with the anti-viral cytokine TNFα induces substantial changes to cellular metabolic activity, including activating glucose metabolism. We find that these TNFα-induced metabolic changes are critical for TNFα to limit the replication of diverse viruses including Human Cytomegalovirus and two Coronaviruses, OC43 and SARS-CoV-2. Inhibition of glucose metabolism during TNFα treatment prevented the expression of a variety of known cellular anti-viral proteins. Collectively, our data indicate that cytokine-induced metabolic remodeling is an important component of TNFα’s ability to promote a less permissive cell state and raises further questions about the mechanisms through which specific cytokine-induced metabolic activities contribute to various aspects of anti-viral defense.

Mass Spectrometry-Based Analysis of Serum N-Glycosylation Changes in Patients with Parkinson's Disease

It is urgently needed to find reliable biofluid biomarkers for early diagnosis of Parkinson's disease in order to achieve better treatment. Promising biomarkers can be found in Parkinson's disease-related glycoproteins as aberrant protein glycosylation plays an important role in disease progression. However, current information on serum N-glycoproteomic changes in Parkinson's disease is still limited. Here, we used glycoproteomics methods, which combine the solid-phase chemoenzymatic method, lectin affinity chromatography, and hydrophilic interaction chromatography with high-resolution mass spectrometry, to analyze the glycans, glycosites, and intact glycopeptides of serum. Increased abundance of glycans containing core fucose, sialic acid, and bisecting N-acetyl glucosamine was detected at the overall glycan level and also at specific glycosites of glycopeptides. Five Parkinson's disease-associated proteins with this type of N-glycosylation changes were also identified. We propose that the revealed site-specific N-glycosylation changes in serum can be potential biomarkers for Parkinson's disease.

Cell-type-specific labeling and profiling of glycans in living mice

Metabolic labeling of glycans with clickable unnatural sugars has enabled glycan analysis in multicellular systems. However, cell-type-specific labeling of glycans in vivo remains challenging. Here we develop genetically encoded metabolic glycan labeling (GeMGL), a cell-type-specific strategy based on a bump-and-hole pair of an unnatural sugar and its matching engineered enzyme. N-pentynylacetylglucosamine (GlcNAl) serves as a bumped analog of N-acetylglucosamine (GlcNAc) that is specifically incorporated into glycans of cells expressing a UDP-GlcNAc pyrophosphorylase mutant, AGX2^F383G. GeMGL with the 1,3-di-O-propionylated GlcNAl (1,3-Pr₂GlcNAl) and AGX2^F383G pair was demonstrated in cell cocultures, and used for specific labeling of glycans in mouse xenograft tumors. By generating a transgenic mouse line with AGX2^F383G expressed under a cardiomyocyte-specific promoter, we performed specific imaging of cardiomyocyte glycans in the heart and identified 582 cardiomyocyte O-GlcNAcylated proteins with no interference from other cardiac cell types. GeMGL will facilitate cell-type-specific glycan imaging and glycoproteomics in various tissues and disease models.

Recent advancements in glycoproteomic studies: Glycopeptide enrichment and derivatization, characterization of glycosylation in SARS CoV2, and interacting glycoproteins

Proteomics studies allow for the determination of the identity, amount, and interactions of proteins under specific conditions that allow the biological state of an organism to ultimately change. These conditions can be either beneficial or detrimental. Diseases are due to detrimental changes caused by either protein overexpression or underexpression caused by as a result of a mutation or posttranslational modifications (PTM), among other factors. Identification of disease biomarkers through proteomics can be potentially used as clinical information for diagnostics. Common biomarkers to look for include PTM. For example, aberrant glycosylation of proteins is a common marker and will be a focus of interest in this review. A common way to analyze glycoproteins is by glycoproteomics involving mass spectrometry. Due to factors such as micro- and macroheterogeneity which result in a lower abundance of each version of a glycoprotein, it is difficult to obtain meaningful results unless rigorous sample preparation procedures are in place. Microheterogeneity represents the diversity of glycans at a single site, whereas macroheterogeneity depicts glycosylation levels at each site of a protein. Enrichment and derivatization of glycopeptides help to overcome these limitations. Over the time range of 2016 to 2020, several methods have been proposed in the literature and have contributed to drastically improve the outcome of glycosylation analysis, as presented in the sampling surveyed in this review. As a current topic in 2020, glycoproteins carried by pathogens can also cause disease and this is seen with SARS CoV2, causing the COVID-19 pandemic. This review will discuss glycoproteomic studies of the spike glycoprotein and interacting proteins such as the ACE2 receptor.

Precision glycoproteomics reveals distinctive N-glycosylation in human spermatozoa

Spermatozoon represents a very special cell type in human body, and glycosylation plays essential roles in its whole life including spermatogenesis, maturation, capacitation, sperm–egg recognition, and fertilization. In this study, by mapping the most comprehensive N-glycoproteome of human spermatozoa using our recently developed site-specific glycoproteomic approaches, we show that spermatozoa contain a number of distinctive glycoproteins, which are mainly involved in spermatogenesis, acrosome reaction and sperm:oocyte membrane binding, and fertilization. Heavy fucosylation is observed on 14 glycoproteins mostly located at extracellular and cell surface regions in spermatozoa but not in other tissues. Sialylation and Lewis epitopes are enriched in the biological process of immune response in spermatozoa, while bisected core structures and LacdiNAc structures are highly expressed in acrosome. These data deepen our knowledge about glycosylation in spermatozoa and lay the foundation for functional study of glycosylation and glycan structures in male infertility.

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A precision site-specific glycoproteome is documented in human spermatozoa.

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Distinctive glycoproteins and heavy fucosylation are detected in spermatozoa.

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Sialylation and Lewis epitopes are related to immune response of spermatozoa.

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Bisected core structures and LacdiNAc are enriched on acrosome of spermatozoa.

O-GlcNAcylation regulates β1,4-GlcNAc-branched N-glycan biosynthesis via the OGT/SLC35A3/GnT-IV axis

N-Linked glycosylation and O-linked N-acetylglucosamine (O-GlcNAc) are important protein post-translational modifications that are orchestrated by a diverse set of gene products. Thus far, the relationship between these two types of glycosylation has remained elusive, and it is unclear whether one influences the other via UDP-GlcNAc, which is a common donor substrate. Theoretically, a decrease in O-GlcNAcylation may increase the products of GlcNAc-branched N-glycans. In this study, via examination by lectin blotting, HPLC, and mass spectrometry analysis, however, we found that the amounts of GlcNAc-branched tri-antennary N-glycans catalyzed by N-acetylglucosaminyltransferase IV (GnT-IV) and tetra-antennary N-glycans were significantly decreased in O-GlcNAc transferase knockdown cells (OGT-KD) compared with those in wild type cells. We examined this specific alteration by focusing on SLC35A3, which is the main UDP-GlcNAc transporter in mammals that is believed to modulate GnT-IV activation. It is interesting that a deficiency of SLC35A3 specifically leads to a decrease in the amounts of GlcNAc-branched tri- and tetra-antennary N-glycans. Furthermore, co-immunoprecipitation experiments have shown that SLC35A3 interacts with GnT-IV, but not with N-acetylglucosaminyltransferase V. Western blot and chemoenzymatic labeling assay have confirmed that OGT modifies SLC35A3 and that O-GlcNAcylation contributes to its stability. Furthermore, we found that SLC35A3-KO enhances cell spreading and suppresses both cell migration and cell proliferation, which is similar to the phenomena observed in the OGT-KD cells. Taken together, these data are the first to demonstrate that O-GlcNAcylation specifically governs the biosynthesis of tri- and tetra-antennary N-glycans via the OGT-SLC35A3-GnT-IV axis.

OmicsOne: associate omics data with phenotypes in one-click

BACKGROUND

The rapid advancements of high throughput "omics" technologies have brought a massive amount of data to process during and after experiments. Multi-omic analysis facilitates a deeper interrogation of a dataset and the discovery of interesting genes, proteins, lipids, glycans, metabolites, or pathways related to the corresponding phenotypes in a study. Many individual software tools have been developed for data analysis and visualization. However, it still lacks an efficient way to investigate the phenotypes with multiple omics data. Here, we present OmicsOne as an interactive web-based framework for rapid phenotype association analysis of multi-omic data by integrating quality control, statistical analysis, and interactive data visualization on 'one-click'.

MATERIALS AND METHODS

OmicsOne was applied on the previously published proteomic and glycoproteomic data sets of high-grade serous ovarian carcinoma (HGSOC) and the published proteome data set of lung squamous cell carcinoma (LSCC) to confirm its performance. The data was analyzed through six main functional modules implemented in OmicsOne: (1) phenotype profiling, (2) data preprocessing and quality control, (3) knowledge annotation, (4) phenotype associated features discovery, (5) correlation and regression model analysis for phenotype association analysis on individual features, and (6) enrichment analysis for phenotype association analysis on interested feature sets.

RESULTS

We developed an integrated software solution, OmicsOne, for the phenotype association analysis on multi-omics data sets. The application of OmicsOne on the public data set of ovarian cancer data showed that the software could confirm the previous observations consistently and discover new evidence for HNRNPU and a glycopeptide of HYOU1 as potential biomarkers for HGSOC data sets. The performance of OmicsOne was further demonstrated in the Tumor and NAT comparison study on the proteome data set of LSCC.

CONCLUSIONS

OmicsOne can effectively simplify data analysis and reveal the significant associations between phenotypes and potential biomarkers, including genes, proteins, and glycopeptides, in minutes to assist users to understand aberrant biological processes.

DeepNGlyPred: A Deep Neural Network-Based Approach for Human N-Linked Glycosylation Site Prediction

Protein N-linked glycosylation is a post-translational modification that plays an important role in a myriad of biological processes. Computational prediction approaches serve as complementary methods for the characterization of glycosylation sites. Most of the existing predictors for N-linked glycosylation utilize the information that the glycosylation site occurs at the N-X-[S/T] sequon, where X is any amino acid except proline. Not all N-X-[S/T] sequons are glycosylated, thus the N-X-[S/T] sequon is a necessary but not sufficient determinant for protein glycosylation. In that regard, computational prediction of N-linked glycosylation sites confined to N-X-[S/T] sequons is an important problem. Here, we report DeepNGlyPred a deep learning-based approach that encodes the positive and negative sequences in the human proteome dataset (extracted from N-GlycositeAtlas) using sequence-based features (gapped-dipeptide), predicted structural features, and evolutionary information. DeepNGlyPred produces SN, SP, MCC, and ACC of 88.62%, 73.92%, 0.60, and 79.41%, respectively on N-GlyDE independent test set, which is better than the compared approaches. These results demonstrate that DeepNGlyPred is a robust computational technique to predict N-Linked glycosylation sites confined to N-X-[S/T] sequon. DeepNGlyPred will be a useful resource for the glycobiology community.

Proteogenomic characterization of pancreatic ductal adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive cancer with poor patient survival. Toward understanding the underlying molecular alterations that drive PDAC oncogenesis, we conducted comprehensive proteogenomic analysis of 140 pancreatic cancers, 67 normal adjacent tissues, and 9 normal pancreatic ductal tissues. Proteomic, phosphoproteomic, and glycoproteomic analyses were used to characterize proteins and their modifications. In addition, whole-genome sequencing, whole-exome sequencing, methylation, RNA sequencing (RNA-seq), and microRNA sequencing (miRNA-seq) were performed on the same tissues to facilitate an integrated proteogenomic analysis and determine the impact of genomic alterations on protein expression, signaling pathways, and post-translational modifications. To ensure robust downstream analyses, tumor neoplastic cellularity was assessed via multiple orthogonal strategies using molecular features and verified via pathological estimation of tumor cellularity based on histological review. This integrated proteogenomic characterization of PDAC will serve as a valuable resource for the community, paving the way for early detection and identification of novel therapeutic targets.

The structure of an archaeal oligosaccharyltransferase provides insight into the strict exclusion of proline from the N-glycosylation sequon

Oligosaccharyltransferase (OST) catalyzes oligosaccharide transfer to the Asn residue in the N-glycosylation sequon, Asn-X-Ser/Thr, where Pro is strictly excluded at position X. Considering the unique structural properties of proline, this exclusion may not be surprising, but the structural basis for the rejection of Pro residues should be explained explicitly. Here we determined the crystal structure of an archaeal OST in a complex with a sequon-containing peptide and dolichol-phosphate to a 2.7 Å resolution. The sequon part in the peptide forms two inter-chain hydrogen bonds with a conserved amino acid motif, TIXE. We confirmed the essential role of the TIXE motif and the adjacent regions by extensive alanine-scanning of the external loop 5. A Ramachandran plot revealed that the ring structure of the Pro side chain is incompatible with the ϕ backbone dihedral angle around -150° in the rigid sequon-TIXE structure. The present structure clearly provides the structural basis for the exclusion of Pro residues from the N-glycosylation sequon.