Quantitative N-glycoproteomics reveals altered glycosylation levels of various plasma proteins in bloodstream infected patients

Plasma glycoproteomics delivers high-specificity disease biomarkers by detecting site-specific glycosylation abnormalities

INTRODUCTION

The human plasma glycoproteome holds enormous potential to identify personalized biomarkers for diagnostics. Glycoproteomics has matured into a technology for plasma N-glycoproteome analysis but further evolution towards clinical applications depends on the clinical validity and understanding of protein- and site-specific glycosylation changes in disease.

OBJECTIVES

Here, we exploited the uniqueness of a patient cohort of genetic defects in well-defined glycosylation pathways to assess the clinical applicability of plasma N-glycoproteomics.

METHODS

Comparative glycoproteomics was performed of blood plasma from 40 controls and 74 patients with 13 different genetic diseases that impact the protein N-glycosylation pathway. Baseline glycosylation in healthy individuals was compared to reference glycome and intact transferrin protein mass spectrometry data. Use of glycoproteomics data for biomarker discovery and sample stratification was evaluated by multivariate chemometrics and supervised machine learning. Clinical relevance of site-specific glycosylation changes were evaluated in the context of genetic defects that lead to distinct accumulation or loss of specific glycans. Integrated analysis of site-specific glycoproteome changes in disease was performed using chord diagrams and correlated with intact transferrin protein mass spectrometry data.

RESULTS

Glycoproteomics identified 191 unique glycoforms from 58 unique peptide sequences of 34 plasma glycoproteins that span over 3 magnitudes of abundance in plasma. Chemometrics identified high-specificity biomarker signatures for each of the individual genetic defects with better stratification performance than the current diagnostic standard method. Bioinformatic analyses revealed site-specific glycosylation differences that could be explained by underlying glycobiology and protein-intrinsic factors.

CONCLUSION

Our work illustrates the strong potential of plasma glycoproteomics to significantly increase specificity of glycoprotein biomarkers with direct insights in site-specific glycosylation changes to better understand the glycobiological mechanisms underlying human disease.

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.

Altered microheterogeneity at several N-glycosylation sites in OPSCC in constant protein expression conditions

Protein glycosylation responds sensitively to disease states. It is implicated in every hallmark of cancer and has recently started to be considered as a hallmark itself. Changes in N-glycosylation microheterogeneity are more dramatic than those of protein expression due to the non-template nature of protein glycosylation. This enables their potential use in serum-based diagnostics. Here, we perform glycopeptidomics on serum from patients with oropharyngeal squamous cell carcinoma (OPSCC), compared to controls and comparing between cancers based on etiology (human papilloma virus- positive or negative). Using MS2, we then targeted glycoforms, significantly different between the groups, to identify their glycopeptide compositions. Simultaneously we investigate the same serum proteins, comparing whether N-glycosylation changes reflect protein-level changes. Significant glycoforms were identified from proteins such as alpha-1-antitrypsin (SERPINA1), haptoglobin, and different immunoglobulins. SERPINA1 had glycovariance at 2 N-glycosylation sites, that were up to 35 times more abundant in even early-stage OPSCCs, despite minimal differences between SERPINA1 protein levels between groups. Some identified glycoforms' fold changes (FCs) were in line with serum protein level FCs, others were less abundant in early-stage cancers but with great variance in higher-stage cancers, such as on immunoglobulin heavy constant gamma 2, despite no change in protein levels. Such findings indicate that glycovariant analysis might be more beneficial than proteomic analysis, which is yet to be fruitful in the search for biomarkers. Highly sensitive glycopeptide changes could potentially be used in the future for cancer screening. Additionally, characterizing the glycopeptide changes in OPSCC is valuable in the search for potential therapeutic targets.

Transcriptome analysis of six tissues obtained post-mortem from sepsis patients

Septic shock is a life-threatening clinical condition characterized by a robust immune inflammatory response to disseminated infection. Little is known about its impact on the transcriptome of distinct human tissues. To address this, we performed RNA sequencing of samples from the prefrontal cortex, hippocampus, heart, lung, kidney and colon of seven individuals who succumbed to sepsis and seven uninfected controls. We identified that the lungs and colon were the most affected organs. While gene activation dominated, strong inhibitory signals were also detected, particularly in the lungs. We found that septic shock is an extremely heterogeneous disease, not only when different individuals are investigated, but also when comparing different tissues of the same patient. However, several pathways, such as respiratory electron transport and other metabolic functions, revealed distinctive alterations, providing evidence that tissue specificity is a hallmark of sepsis. Strikingly, we found evident signals of accelerated ageing in our sepsis population.

Quantitative glycoproteomics of human milk and association with atopic disease

The prevalence of allergic diseases and asthma is increasing rapidly worldwide, with environmental and lifestyle behaviors implicated as a reason. Epidemiological studies have shown that children who grow up on farms are at lower risk of developing childhood atopic disease, indicating the presence of a protective “farm effect”. The Old Order Mennonite (OOM) community in Upstate New York have traditional, agrarian lifestyles, a low rate of atopic disease, and long periods of exclusive breastfeeding. Human milk proteins are heavily glycosylated, although there is a paucity of studies investigating the milk glycoproteome. In this study, we have used quantitative glycoproteomics to compare the N-glycoprotein profiles of 54 milk samples from Rochester urban/suburban and OOM mothers, two populations with different lifestyles, exposures, and risk of atopic disease. We also compared N-glycoprotein profiles according to the presence or absence of atopic disease in the mothers and, separately, the children. We identified 79 N-glycopeptides from 15 different proteins and found that proteins including immunoglobulin A1, polymeric immunoglobulin receptor, and lactotransferrin displayed significant glycan heterogeneity. We found that the abundances of 38 glycopeptides differed significantly between Rochester and OOM mothers and also identified four glycopeptides with significantly different abundances between all comparisons. These four glycopeptides may be associated with the development of atopic disease. The findings of this study suggest that the differential glycosylation of milk proteins could be linked to atopic disease.

Differential Peripheral Blood Glycoprotein Profiles in Symptomatic and Asymptomatic COVID-19

Glycosylation is the most common form of post-translational modification of proteins, critically affecting their structure and function. Using liquid chromatography and mass spectrometry for high-resolution site-specific quantification of glycopeptides coupled with high-throughput artificial intelligence-powered data processing, we analyzed differential protein glycoisoform distributions of 597 abundant serum glycopeptides and nonglycosylated peptides in 50 individuals who had been seriously ill with COVID-19 and in 22 individuals who had recovered after an asymptomatic course of COVID-19. As additional comparison reference phenotypes, we included 12 individuals with a history of infection with a common cold coronavirus, 16 patients with bacterial sepsis, and 15 healthy subjects without history of coronavirus exposure. We found statistically significant differences, at FDR < 0.05, for normalized abundances of 374 of the 597 peptides and glycopeptides interrogated between symptomatic and asymptomatic COVID-19 patients. Similar statistically significant differences were seen when comparing symptomatic COVID-19 patients to healthy controls (350 differentially abundant peptides and glycopeptides) and common cold coronavirus seropositive subjects (353 differentially abundant peptides and glycopeptides). Among healthy controls and sepsis patients, 326 peptides and glycopeptides were found to be differentially abundant, of which 277 overlapped with biomarkers that showed differential expression between symptomatic COVID-19 cases and healthy controls. Among symptomatic COVID-19 cases and sepsis patients, 101 glycopeptide and peptide biomarkers were found to be statistically significantly abundant. Using both supervised and unsupervised machine learning techniques, we found specific glycoprotein profiles to be strongly predictive of symptomatic COVID-19 infection. LASSO-regularized multivariable logistic regression and K-means clustering yielded accuracies of 100% in an independent test set and of 96% overall, respectively. Our findings are consistent with the interpretation that a majority of glycoprotein modifications observed which are shared among symptomatic COVID-19 and sepsis patients likely represent a generic consequence of a severe systemic immune and inflammatory state. However, there are glycoisoform changes that are specific and particular to severe COVID-19 infection. These may be representative of either COVID-19-specific consequences or susceptibility to or predisposition for a severe course of the disease. Our findings support the potential value of glycoproteomic biomarkers in the biomedical understanding and, potentially, the clinical management of serious acute infectious conditions.

Extensive heterogeneity of glycopeptides in plasma revealed by deep glycoproteomic analysis using size-exclusion chromatography

Several plasma glycoproteins are clinically useful as biomarkers in a variety of diseases. Although thousands of proteins are present in plasma, >95% of the plasma proteome by mass is represented by only 22 proteins. This necessitates strategies to deplete the abundant proteins and enrich other subsets of proteins. Although glycoproteins are abundant in plasma, in routine proteomic analyses, glycopeptides are not often investigated. Traditional methods such as lectin-based enrichment of glycopeptides followed by deglycosylation have helped understand the glycoproteome, but they lack any information about the attached glycans. Here, we apply size-exclusion chromatography (SEC) as a simple strategy to enrich intact N-glycopeptides based on their larger size which achieves broad selectivity regardless of the nature of attached glycans. Using this approach, we identified 1317 N-glycopeptides derived from 266 glycosylation sites on 154 plasma glycoproteins. The deep coverage achieved by this approach was evidenced by extensive heterogeneity that was observed. For instance, 20-100 glycopeptides were observed per protein for the 15 most-glycosylated glycoproteins. Notably, we discovered 615 novel glycopeptides of which 39 glycosylation sites (from 38 glycoproteins) were not included in protein databases such as Uniprot and GlyConnectDB. Finally, we also identified 12 novel glycopeptides containing di-sialic acid, which is a rare glycan epitope. Our results demonstrate the utility of SEC for efficient LC-MS/MS-based deep glycoproteomics analysis of human plasma. Overall, the SEC-based method described here is a simple, rapid and high-throughput strategy for characterization of any glycoproteome.

Longitudinal proteomic profiling provides insights into host response and proteome dynamics in COVID-19 progression

In managing patients with coronavirus disease 2019 (COVID‐19), early identification of those at high risk and real‐time monitoring of disease progression to severe COVID‐19 is a major challenge. We aimed to identify potential early prognostic protein markers and to expand understanding of proteome dynamics during clinical progression of the disease. We performed in‐depth proteome profiling on 137 sera, longitudinally collected from 25 patients with COVID‐19 (non‐severe patients, n = 13; patients who progressed to severe COVID‐19, n = 12). We identified 11 potential biomarkers, including the novel markers IGLV3‐19 and BNC2, as early potential prognostic indicators of severe COVID‐19. These potential biomarkers are mainly involved in biological processes associated with humoral immune response, interferon signalling, acute phase response, lipid metabolism, and platelet degranulation. We further revealed that the longitudinal changes of 40 proteins persistently increased or decreased as the disease progressed to severe COVID‐19. These 40 potential biomarkers could effectively reflect the clinical progression of the disease. Our findings provide some new insights into host response to SARS‐CoV‐2 infection, which are valuable for understanding of COVID‐19 disease progression. This study also identified potential biomarkers that could be further validated, which may support better predicting and monitoring progression to severe COVID‐19.

Interlaboratory evaluation of plasma N-glycan antennary fucosylation as a clinical biomarker for HNF1A-MODY using liquid chromatography methods

Antennary fucosylation alterations in plasma glycoproteins have been previously proposed and tested as a biomarker for differentiation of maturity onset diabetes of the young (MODY) patients carrying a functional mutation in the HNF1A gene. Here, we developed a novel LC-based workflow to analyze blood plasma N-glycan fucosylation in 320 diabetes cases with clinical features matching those at risk of HNF1A-MODY. Fucosylation levels measured in two independent research centers by using similar LC-based methods were correlated to evaluate the interlaboratory performance of the biomarker. The interlaboratory study showed good correlation between fucosylation levels measured for the 320 cases in the two centers with the correlation coefficient (r) of up to 0.88 for a single trait A3FG3S2. The improved chromatographic separation allowed the identification of six single glycan traits and a derived antennary fucosylation trait that were able to differentiate individuals carrying pathogenic mutations from benign or no HNF1A mutation cases, as determined by the area under the curve (AUC) of up to 0.94. The excellent (r = 0.88) interlaboratory performance of the glycan biomarker for HNF1A-MODY further supports the development of a clinically relevant diagnostic test measuring antennary fucosylation levels.

Serum N-Glycomics Stratifies Bacteremic Patients Infected with Different Pathogens

Bacteremia—i.e., the presence of pathogens in the blood stream—is associated with long-term morbidity and is a potential precursor condition to life-threatening sepsis. Timely detection of bacteremia is therefore critical to reduce patient mortality, but existing methods lack precision, speed, and sensitivity to effectively stratify bacteremic patients. Herein, we tested the potential of quantitative serum N-glycomics performed using porous graphitized carbon liquid chromatography tandem mass spectrometry to stratify bacteremic patients infected with Escherichia coli (n = 11), Staphylococcus aureus (n = 11), Pseudomonas aeruginosa (n = 5), and Streptococcus viridans (n = 5) from healthy donors (n = 39). In total, 62 N-glycan isomers spanning 41 glycan compositions primarily comprising complex-type core fucosylated, bisecting N-acetylglucosamine (GlcNAc), and α2,3-/α2,6-sialylated structures were profiled across all samples using label-free quantitation. Excitingly, unsupervised hierarchical clustering and principal component analysis of the serum N-glycome data accurately separated the patient groups. P. aeruginosa-infected patients displayed prominent N-glycome aberrations involving elevated levels of fucosylation and bisecting GlcNAcylation and reduced sialylation relative to other bacteremic patients. Notably, receiver operating characteristic analyses demonstrated that a single N-glycan isomer could effectively stratify each of the four bacteremic patient groups from the healthy donors (area under the curve 0.93–1.00). Thus, the serum N-glycome represents a new hitherto unexplored class of potential diagnostic markers for bloodstream infections.

Structural and functional diversity of neutrophil glycosylation in innate immunity and related disorders

The granulated neutrophils are abundant innate immune cells that utilize bioactive glycoproteins packed in cytosolic granules to fight pathogenic infections, but the neutrophil glycobiology remains poorly understood. Facilitated by technological advances in glycoimmunology, systems glycobiology and glycoanalytics, a considerable body of literature reporting on novel aspects of neutrophil glycosylation has accumulated. Herein, we summarize the building knowledge of the structural and functional diversity displayed by N- and O-linked glycoproteins spatiotemporally expressed and sequentially brought-into-action across the diverse neutrophil life stages during bone marrow maturation, movements to, from and within the blood circulation and microbicidal processes at the inflammatory sites in peripheral tissues. It transpires that neutrophils abundantly decorate their granule glycoproteins including neutrophil elastase, myeloperoxidase and cathepsin G with peculiar glyco-signatures not commonly reported in other areas of human glycobiology such as hyper-truncated chitobiose core- and paucimannosidic-type N-glycans and monoantennary complex-type N-glycans. Sialyl Lewis^x, Lewis^x, poly-N-acetyllactosamine extensions and core 1-/2-type O-glycans are also common neutrophil glyco-signatures. Granule-specific glycosylation is another fascinating yet not fully understood feature of neutrophils. Recent literature suggests that unconventional biosynthetic pathways and functions underpin these prominent neutrophil-associated glyco-phenotypes. The impact of glycosylation on key neutrophil effector functions including extravasation, degranulation, phagocytosis and formation of neutrophil extracellular traps during normal physiological conditions and in innate immune-related diseases is discussed. We also highlight new technologies that are expected to further advance neutrophil glycobiology and briefly discuss the untapped diagnostic and therapeutic potential of neutrophil glycosylation that could open avenues to combat the increasingly prevalent innate immune disorders.

Thromboinflammatory changes in plasma proteome of pregnant women with PCOS detected by quantitative label-free proteomics

Polycystic ovary syndrome (PCOS) is the most common endocrinological disorder of fertile-aged women. Several adverse pregnancy outcomes and abnormalities of the placenta have been associated with PCOS. By using quantitative label-free proteomics we investigated whether changes in the plasma proteome of pregnant women with PCOS could elucidate the mechanisms behind the pathologies observed in PCOS pregnancies. A total of 169 proteins with ≥2 unique peptides were detected to be differentially expressed between women with PCOS (n = 7) and matched controls (n = 20) at term of pregnancy, out of which 35 were significant (p-value < 0.05). A pathway analysis revealed that networks related to humoral immune responses, inflammatory responses, cardiovascular disease and cellular growth and proliferation were affected by PCOS. Classification of cases and controls was carried out using principal component analysis, orthogonal projections on latent structure-discriminant analysis (OPLS-DA), hierarchical clustering, self-organising maps and ROC-curve analysis. The most significantly enriched proteins in PCOS were properdin and insulin-like growth factor II. In the dataset, properdin had the best predictive accuracy for PCOS (AUC = 1). Additionally, properdin abundances correlated with AMH levels in pregnant women.

Microchip gel electrophoretic analysis of perchloric acid-soluble serum proteins in systemic inflammatory disorders

Perchloric acid (PCA) precipitation is a well-known method for the separation of heavily glycosylated proteins and for reducing the masking effect of major serum proteins. The aim of this study is to characterize PCA-soluble serum proteins in healthy individuals and in patients with systemic inflammatory diseases, such as Crohn's disease and sepsis. A PCA precipitation protocol was prepared and adapted to the analytical methods. After PCA treatment of the serum, the soluble proteins in the supernatant were analyzed by SDS-PAGE and by microchip gel electrophoresis (MGE). Characteristic changes of the electrophoretic patterns of the PCA-soluble fractions were observed. Four characteristic bands (at ∼11, ∼65, ∼85, and ∼120 kDa) with varying intensity were detected by MGE. The proportion of the ∼65, ∼85, and ∼120 kDa bands were significantly higher in systemic inflammatory conditions than in healthy individuals (p < 0.001), and characteristic patterns were observed in patients with acute inflammation. The marked differences in the acid-soluble protein patterns, which were observed in patients with ongoing systemic inflammation, might be a good indicator of inflammation. The MGE analysis is a fast screening and quantification method for the detection of characteristic changes among acid-soluble serum proteins.