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.

Advancements in automation for plasma proteomics sample preparation

Automation is necessary to increase sample processing throughput for large-scale clinical analyses. Replacement of manual pipettes with robotic liquid handler systems is especially helpful in processing blood-based samples, such as plasma and serum. These samples are very heterogenous, and protein expression can vary greatly from sample-to-sample, even for healthy controls. Detection of true biological changes requires that variation from sample preparation steps and downstream analytical detection methods, such as mass spectrometry, remains low. In this mini-review, we discuss plasma proteomics protocols and the benefits of automation towards enabling detection of low abundant proteins and providing low sample error and increased sample throughput. This discussion includes considerations for automation of major sample depletion and/or enrichment strategies for plasma toward mass spectrometry detection.

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.

Semiautomated glycoproteomics data analysis workflow for maximized glycopeptide identification and reliable quantification

Glycoproteomic data are often very complex, reflecting the high structural diversity of peptide and glycan portions. The use of glycopeptide-centered glycoproteomics by mass spectrometry is rapidly evolving in many research areas, leading to a demand in reliable data analysis tools. In recent years, several bioinformatic tools were developed to facilitate and improve both the identification and quantification of glycopeptides. Here, a selection of these tools was combined and evaluated with the aim of establishing a robust glycopeptide detection and quantification workflow targeting enriched glycoproteins. For this purpose, a tryptic digest from affinity-purified immunoglobulins G and A was analyzed on a nano-reversed-phase liquid chromatography-tandem mass spectrometry platform with a high-resolution mass analyzer and higher-energy collisional dissociation fragmentation. Initial glycopeptide identification based on MS/MS data was aided by the Byonic software. Additional MS1-based glycopeptide identification relying on accurate mass and retention time differences using GlycopeptideGraphMS considerably expanded the set of confidently annotated glycopeptides. For glycopeptide quantification, the performance of LaCyTools was compared to Skyline, and GlycopeptideGraphMS. All quantification packages resulted in comparable glycosylation profiles but featured differences in terms of robustness and data quality control. Partial cysteine oxidation was identified as an unexpectedly abundant peptide modification and impaired the automated processing of several IgA glycopeptides. Finally, this study presents a semiautomated workflow for reliable glycoproteomic data analysis by the combination of software packages for MS/MS- and MS1-based glycopeptide identification as well as the integration of analyte quality control and quantification.

Tongue Cancer Patients Can be Distinguished from Healthy Controls by Specific N-Glycopeptides Found in Serum

PURPOSE

There are no blood biomarkers to detect early-stage oral cavity squamous cell carcinoma (OSCC) prior to clinical signs. Most OSCC incidence is associated with significant morbidity and poor survival. The authors aimed to use mass-spectrometry (MS) technology to find specific N-glycopeptides potentially serving as serum biomarkers for preclinical OSCC screening.

EXPERIMENTAL DESIGN

Serum samples from 14 patients treated for OSCC (stage I or stage IV) with 12 age- and sex-matched controls are collected. Quantitative label-free N-glycoproteomics is performed, with MS/MS analysis of the statistically significantly different N-glycopeptides.

RESULTS

Combined with a database search using web-based software (GlycopeptideID), MS/MS provided detailed N-glycopeptide information, including glycosylation site, glycan composition, and proposed structures. Thirty-eight tryptic N-glycopeptides are identified, having 19 unique N-glycosylation sites representing 14 glycoproteins. OSCC patients, including stage I tumors, can be differentiated from healthy controls based on the expression levels of these glycoforms. N-glycopeptides of IgG1, IgG4, haptoglobin, and transferrin have statistically significant different abundances between cases and controls.

CONCLUSIONS AND CLINICAL RELEVANCE

The authors are the first to suggest specific N-glycopeptides to serve as potential serum biomarkers to detect preclinical OSCC in patients. These N-glycopeptides are the lead candidates for validation as future diagnostic modalities of OSCC as early as stage I.

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

Bloodstream infections are associated with high morbidity and mortality with rates varying from 10-25% and higher. Appropriate and timely onset of antibiotic therapy influences the prognosis of these patients. It requires the diagnostic accuracy which is not afforded by current gold standards such as blood culture. Moreover, the time from blood sampling to blood culture results is a key determinant of reducing mortality. No established biomarkers exist which can differentiate bloodstream infections from other systemic inflammatory conditions. This calls for studies on biomarkers potential of molecular profiling of plasma as it is affected most by the molecular changes accompanying bloodstream infections. N-glycosylation is a post-translational modification which is very sensitive to changes in physiology. Here we have performed targeted quantitative N-glycoproteomics from plasma samples of patients with confirmed positive blood culture together with age and sex matched febrile controls with negative blood culture reports. Three hundred and sixty eight potential N-glycopeptides were quantified by mass spectrometry and 149 were further selected for identification. Twenty four N-glycopeptides were identified with high confidence together with elucidation of the peptide sequence, N-glycosylation site, glycan composition and proposed glycan structures. Principal component analysis, orthogonal projections to latent structures-discriminant analysis (S-Plot) and self-organizing maps clustering among other statistical methods were employed to analyze the data. These methods gave us clear separation of the two patient classes. We propose high-confidence N-glycopeptides which have the power to separate the bloodstream infections from blood culture negative febrile patients and shed light on host response during bacteremia. Data are available via ProteomeXchange with identifier PXD009048.

Glycosylation of extracellular vesicles: current knowledge, tools and clinical perspectives

It is now acknowledged that extracellular vesicles (EVs) are important effectors in a vast number of biological processes through intercellular transfer of biomolecules. Increasing research efforts in the EV field have yielded an appreciation for the potential role of glycans in EV function. Indeed, recent reports show that the presence of glycoconjugates is involved in EV biogenesis, in cellular recognition and in the efficient uptake of EVs by recipient cells. It is clear that a full understanding of EV biology will require researchers to focus also on EV glycosylation through glycomics approaches. This review outlines the major glycomics techniques that have been applied to EVs in the context of the recent findings. Beyond understanding the mechanisms by which EVs mediate their physiological functions, glycosylation also provides opportunities by which to engineer EVs for therapeutic and diagnostic purposes. Studies characterising the glycan composition of EVs have highlighted glycome changes in various disease states, thus indicating potential for EV glycans as diagnostic markers. Meanwhile, glycans have been targeted as molecular handles for affinity-based isolation in both research and clinical contexts. An overview of current strategies to exploit EV glycosylation and a discussion of the implications of recent findings for the burgeoning EV industry follows the below review of glycomics and its application to EV biology.

A Comprehensive, Open-source Platform for Mass Spectrometry-based Glycoproteomics Data Analysis

Glycosylation is among the most abundant and diverse protein post-translational modifications (PTMs) identified to date. The structural analysis of this PTM is challenging because of the diverse monosaccharides which are not conserved among organisms, the branched nature of glycans, their isomeric structures, and heterogeneity in the glycan distribution at a given site. Glycoproteomics experiments have adopted the traditional high-throughput LC-MSⁿ proteomics workflow to analyze site-specific glycosylation. However, comprehensive computational platforms for data analyses are scarce. To address this limitation, we present a comprehensive, open-source, modular software for glycoproteomics data analysis called GlycoPAT (GlycoProteomics Analysis Toolbox; freely available from www.VirtualGlycome.org/glycopat). The program includes three major advances: (1) "SmallGlyPep," a minimal linear representation of glycopeptides for MSⁿ data analysis. This format allows facile serial fragmentation of both the peptide backbone and PTM at one or more locations. (2) A novel scoring scheme based on calculation of the "Ensemble Score (ES)," a measure that scores and rank-orders MS/MS spectrum for N- and O-linked glycopeptides using cross-correlation and probability based analyses. (3) A false discovery rate (FDR) calculation scheme where decoy glycopeptides are created by simultaneously scrambling the amino acid sequence and by introducing artificial monosaccharides by perturbing the original sugar mass. Parallel computing facilities and user-friendly GUIs (Graphical User Interfaces) are also provided. GlycoPAT is used to catalogue site-specific glycosylation on simple glycoproteins, standard protein mixtures and human plasma cryoprecipitate samples in three common MS/MS fragmentation modes: CID, HCD and ETD. It is also used to identify 960 unique glycopeptides in cell lysates from prostate cancer cells. The results show that the simultaneous consideration of peptide and glycan fragmentation is necessary for high quality MSⁿ spectrum annotation in CID and HCD fragmentation modes. Additionally, they confirm the suitability of GlycoPAT to analyze shotgun glycoproteomics data.

Algorithms and design strategies towards automated glycoproteomics analysis

Glycoproteomics involves the study of glycosylation events on protein sequences ranging from purified proteins to whole proteome scales. Understanding these complex post-translational modification (PTM) events requires elucidation of the glycan moieties (monosaccharide sequences and glycosidic linkages between residues), protein sequences, as well as site-specific attachment of glycan moieties onto protein sequences, in a spatial and temporal manner in a variety of biological contexts. Compared with proteomics, bioinformatics for glycoproteomics is immature and many researchers still rely on tedious manual interpretation of glycoproteomics data. As sample preparation protocols and analysis techniques have matured, the number of publications on glycoproteomics and bioinformatics has increased substantially; however, the lack of consensus on tool development and code reuse limits the dissemination of bioinformatics tools because it requires significant effort to migrate a computational tool tailored for one method design to alternative methods. This review discusses algorithms and methods in glycoproteomics, and refers to the general proteomics field for potential solutions. It also introduces general strategies for tool integration and pipeline construction in order to better serve the glycoproteomics community. © 2016 Wiley Periodicals, Inc. Mass Spec Rev 36:475-498, 2017.

A Brief Review of Bioinformatics Tools for Glycosylation Analysis by Mass Spectrometry

The purpose of this review is to provide updated information regarding bioinformatic software for the use in the characterization of glycosylated structures since 2013. A comprehensive review by Woodin et al.Analyst 138: 2793-2803, 2013 (ref. 1) described two main approaches that are introduced for starting researchers in this area; analysis of released glycans and the identification of glycopeptide in enzymatic digests, respectively. Complementary to that report, this review focuses on mass spectrometry related bioinformatics tools for the characterization of N-linked and O-linked glycopeptides. Specifically, it also provides information regarding automated tools that can be used for glycan profiling using mass spectrometry.

Use of an informed search space maximizes confidence of site-specific assignment of glycoprotein glycosylation

In order to interpret glycopeptide tandem mass spectra, it is necessary to estimate the theoretical glycan compositions and peptide sequences, known as the search space. The simplest way to do this is to build a naïve search space from sets of glycan compositions from public databases and to assume that the target glycoprotein is pure. Often, however, purified glycoproteins contain co-purified glycoprotein contaminants that have the potential to confound assignment of tandem mass spectra based on naïve assumptions. In addition, there is increasing need to characterize glycopeptides from complex biological mixtures. Fortunately, liquid chromatography-mass spectrometry (LC-MS) methods for glycomics and proteomics are now mature and accessible. We demonstrate the value of using an informed search space built from measured glycomes and proteomes to define the search space for interpretation of glycoproteomics data. We show this using α-1-acid glycoprotein (AGP) mixed into a set of increasingly complex matrices. As the mixture complexity increases, the naïve search space balloons and the ability to assign glycopeptides with acceptable confidence diminishes. In addition, it is not possible to identify glycopeptides not foreseen as part of the naïve search space. A search space built from released glycan glycomics and proteomics data is smaller than its naïve counterpart while including the full range of proteins detected in the mixture. This maximizes the ability to assign glycopeptide tandem mass spectra with confidence. As the mixture complexity increases, the number of tandem mass spectra per glycopeptide precursor ion decreases, resulting in lower overall scores and reduced depth of coverage for the target glycoprotein. We suggest use of α-1-acid glycoprotein as a standard to gauge effectiveness of analytical methods and bioinformatics search parameters for glycoproteomics studies. Graphical Abstract Assignment of site specific glycosylation from LC-tandemMS data.

gFinder: A Web-Based Bioinformatics Tool for the Analysis of N-Glycopeptides

Glycoproteins influence numerous indispensable biological functions, and changes in protein glycosylation have been observed in various diseases. The identification and characterization of glycoprotein and glycosylation sites by mass spectrometry (MS) remain challenging tasks, and great efforts have been devoted to the development of proteome informatics tools that facilitate the MS analysis of glycans and glycopeptides. Here we report on the development of gFinder, a web-based bioinformatics tool that analyzes mixtures of native N-glycopeptides that have been profiled by tandem MS. gFinder not only enables the simultaneous integration of collision-induced dissociation (CID) and high-energy collisional dissociation (HCD) fragmentation but also merges the spectra for high-throughput analysis. These merged spectra expedite the identification of both glycans and N-glycopeptide backbones in tandem MS data using the glycan database and a proteomic search tool (e.g., Mascot). These data can be used to simultaneously characterize peptide backbone sequences and possible N-glycan structures using assigned scores. gFinder also provides many convenient functions that make it easy to perform manual calculations while viewing the spectrum on-screen. We used gFinder to detect an additional protein (Q8N9B8) that was missed from the previously published data set containing N-linked glycosylation. For N-glycan analysis, we used the GlycomeDB glycan structure database, which integrates the structural and taxonomic data from all of the major carbohydrate databases available in the public domain. Thus, gFinder is a convenient, high-throughput analytical tool for interpreting the tandem mass spectra of N-glycopeptides, which can then be used for identification of potential missing proteins having glycans. gFinder is available publicly at http://gFinder.proteomix.org/ .

SweetNET: A Bioinformatics Workflow for Glycopeptide MS/MS Spectral Analysis

Glycoproteomics has rapidly become an independent analytical platform bridging the fields of glycomics and proteomics to address site-specific protein glycosylation and its impact in biology. Current glycopeptide characterization relies on time-consuming manual interpretations and demands high levels of personal expertise. Efficient data interpretation constitutes one of the major challenges to be overcome before true high-throughput glycopeptide analysis can be achieved. The development of new glyco-related bioinformatics tools is thus of crucial importance to fulfill this goal. Here we present SweetNET: a data-oriented bioinformatics workflow for efficient analysis of hundreds of thousands of glycopeptide MS/MS-spectra. We have analyzed MS data sets from two separate glycopeptide enrichment protocols targeting sialylated glycopeptides and chondroitin sulfate linkage region glycopeptides, respectively. Molecular networking was performed to organize the glycopeptide MS/MS data based on spectral similarities. The combination of spectral clustering, oxonium ion intensity profiles, and precursor ion m/z shift distributions provided typical signatures for the initial assignment of different N-, O- and CS-glycopeptide classes and their respective glycoforms. These signatures were further used to guide database searches leading to the identification and validation of a large number of glycopeptide variants including novel deoxyhexose (fucose) modifications in the linkage region of chondroitin sulfate proteoglycans.

Integrated Omics and Computational Glycobiology Reveal Structural Basis for Influenza A Virus Glycan Microheterogeneity and Host Interactions

Despite sustained biomedical research effort, influenza A virus remains an imminent threat to the world population and a major healthcare burden. The challenge in developing vaccines against influenza is the ability of the virus to mutate rapidly in response to selective immune pressure. Hemagglutinin is the predominant surface glycoprotein and the primary determinant of antigenicity, virulence and zoonotic potential. Mutations leading to changes in the number of HA glycosylation sites are often reported. Such genetic sequencing studies predict at best the disruption or creation of sequons for N-linked glycosylation; they do not reflect actual phenotypic changes in HA structure. Therefore, combined analysis of glycan micro and macro-heterogeneity and bioassays will better define the relationships among glycosylation, viral bioactivity and evolution. We present a study that integrates proteomics, glycomics and glycoproteomics of HA before and after adaptation to innate immune system pressure. We combined this information with glycan array and immune lectin binding data to correlate the phenotypic changes with biological activity. Underprocessed glycoforms predominated at the glycosylation sites found to be involved in viral evolution in response to selection pressures and interactions with innate immune-lectins. To understand the structural basis for site-specific glycan microheterogeneity at these sites, we performed structural modeling and molecular dynamics simulations. We observed that the presence of immature, high-mannose type glycans at a particular site correlated with reduced accessibility to glycan remodeling enzymes. Further, the high mannose glycans at sites implicated in immune lectin recognition were predicted to be capable of forming trimeric interactions with the immune-lectin surfactant protein-D.

N-Glycoproteomics of Human Seminal Plasma Glycoproteins

Seminal plasma aids sperm by inhibiting premature capacitation, helping in the intracervical transport and formation of an oviductal sperm reservoir, all of which appear to be important in the fertilization process. Epitopes such as Lewis x and y are known to be present on seminal plasma glycoproteins, which can modulate the maternal immune response. It is suggested by multiple studies that seminal plasma glycoproteins play, largely undiscovered, important roles in the process of fertilization. We have devised a strategy to analyze glycopeptides from a complex, unknown mixture of protease-digested proteins. This analysis provides identification of the glycoproteins, glycosylation sites, glycan compositions, and proposed structures from the original sample. This strategy has been applied to human seminal plasma total glycoproteins. We have elucidated glycan compositions and proposed structures for 243 glycopeptides belonging to 73 N-glycosylation sites on 50 glycoproteins. The majority of the proposed glycan structures were complex type (83%) followed by high-mannose (10%) and then hybrid (7%). Most of the glycoproteins were either sialylated, fucosylated, or both. Many Lewis x/a and y/b epitopes bearing glycans were found, suggesting immune-modulating epitopes on multiple seminal plasma glycoproteins. The study also shows that large scale N-glycosylation mapping is achievable with current techniques and the depth of the analysis is roughly proportional to the prefractionation and complexity of the sample.

Liquid chromatography-tandem mass spectrometry-based fragmentation analysis of glycopeptides

The use of liquid chromatography-electrospray ionization-tandem mass spectrometry (LC-ESI-MS(n)) for the glycoproteomic characterization of glycopeptides is a growing field of research. The N- and O-glycosylated peptides (N- and O-glycopeptides) analyzed typically originate from protease-digested glycoproteins where many of them are expected to be biomedically important. Examples of LC-MS(2) and MS(3) fragmentation strategies used to pursue glycan structure, peptide identity and attachment-site identification analyses of glycopeptides are described in this review. MS(2) spectra, using the CID and HCD fragmentation techniques of a complex biantennary N-glycopeptide and a core 1 O-glycopeptide, representing two examples of commonly studied glycopeptide types, are presented. A few practical tips for accomplishing glycopeptide analysis using reversed-phase LC-MS(n) shotgun proteomics settings, together with references to the latest glycoproteomic studies, are presented.

A review of methods for interpretation of glycopeptide tandem mass spectral data

Despite the publication of several software tools for analysis of glycopeptide tandem mass spectra, there remains a lack of consensus regarding the most effective and appropriate methods. In part, this reflects problems with applying standard methods for proteomics database searching and false discovery rate calculation. While the analysis of small post-translational modifications (PTMs) may be regarded as an extension of proteomics database searching, glycosylation requires specialized approaches. This is because glycans are large and heterogeneous by nature, causing glycopeptides to exist as multiple glycosylated variants. Thus, the mass of the peptide cannot be calculated directly from that of the intact glycopeptide. In addition, the chemical nature of the glycan strongly influences product ion patterns observed for glycopeptides. As a result, glycopeptidomics requires specialized bioinformatics methods. We summarize the recent progress towards a consensus for effective glycopeptide tandem mass spectrometric analysis.

Engaging challenges in glycoproteomics: recent advances in MS-based glycopeptide analysis

The proteomic analysis of glycosylation is uniquely challenging. The numerous and varied biological roles of protein-linked glycans have fueled a tremendous demand for technologies that enable rapid, in-depth structural examination of glycosylated proteins in complex biological systems. In turn, this demand has driven many innovations in wide ranging fields of bioanalytical science. This review will summarize key developments in glycoprotein separation and enrichment, glycoprotein proteolysis strategies, glycopeptide separation and enrichment, the role of mass measurement accuracy in glycopeptide detection, glycopeptide ion dissociation methods for MS/MS, and informatic tools for glycoproteomic analysis. In aggregate, this selection of topics serves to encapsulate the present status of MS-based analytical technologies for engaging the challenges of glycoproteomic analysis.

Integration of systems glycobiology with bioinformatics toolboxes, glycoinformatics resources, and glycoproteomics data

The glycome constitutes the entire complement of free carbohydrates and glycoconjugates expressed on whole cells or tissues. 'Systems Glycobiology' is an emerging discipline that aims to quantitatively describe and analyse the glycome. Here, instead of developing a detailed understanding of single biochemical processes, a combination of computational and experimental tools are used to seek an integrated or 'systems-level' view. This can explain how multiple biochemical reactions and transport processes interact with each other to control glycome biosynthesis and function. Computational methods in this field commonly build in silico reaction network models to describe experimental data derived from structural studies that measure cell-surface glycan distribution. While considerable progress has been made, several challenges remain due to the complex and heterogeneous nature of this post-translational modification. First, for the in silico models to be standardized and shared among laboratories, it is necessary to integrate glycan structure information and glycosylation-related enzyme definitions into the mathematical models. Second, as glycoinformatics resources grow, it would be attractive to utilize 'Big Data' stored in these repositories for model construction and validation. Third, while the technology for profiling the glycome at the whole-cell level has been standardized, there is a need to integrate mass spectrometry derived site-specific glycosylation data into the models. The current review discusses progress that is being made to resolve the above bottlenecks. The focus is on how computational models can bridge the gap between 'data' generated in wet-laboratory studies with 'knowledge' that can enhance our understanding of the glycome.

Comparison of sialylated N-glycopeptide levels in serum of pancreatic cancer patients, acute pancreatitis patients, and healthy controls

Serum protein glycosylation is known to be affected by pathological conditions, including cancer and inflammatory diseases. Pancreatic cancer patients would benefit from early diagnosis, as the disease is often detected in an advanced stage and has poor prognosis. Searching for changes in serum protein site-specific glycosylation could reveal novel glycoprotein biomarkers. We used Sambucus nigra lectin affinity chromatography to enrich α-2,6 sialylated tryptic N-glycopeptides from albumin-depleted sera of pancreatic cancer patients, acute pancreatitis patients, and healthy individuals, and compared their relative abundance using ultra performance LC-MS. Relative quantitation was done using the spectrum processing software MZmine. Identification was performed on the web-based tool GlycopeptideID, developed for in silico analysis of intact N-glycopeptides. Seventeen high-abundance serum proteins, mainly acute-phase proteins, and immunoglobulins, with total 27 N-glycosylation sites, and 62 glycoforms, were identified. Pancreatitis patient sera contained 38, and pancreatic cancer patients sera contained 13 glycoform changes with statistical significance (p < 0.05). In pancreatitis, up to tenfold changes were found in some glycoforms, and in pancreatic cancer, threefold. Analysis showed that the changes often concerned one or two, but not all, N-glycosylation sites in a specific glycoprotein. In conclusion, the analysis shows that pancreatic cancer, and acute pancreatitis are associated with changes in concentrations of intact sialylated N-glycopeptides derived from acute-phase proteins, and immunoglobulins, and that changes are site specific.

Quantitative mass spectrometric analysis of glycoproteins combined with enrichment methods

Mass spectrometry (MS) has been a core technology for high sensitive and high-throughput analysis of the enriched glycoproteome in aspects of quantitative assays as well as qualitative profiling of glycoproteins. Because it has been widely recognized that aberrant glycosylation in a glycoprotein may involve in progression of a certain disease, the development of efficient analysis tool for the aberrant glycoproteins is very important for deep understanding about pathological function of the glycoprotein and new biomarker development. This review first describes the protein glycosylation-targeting enrichment technologies mainly employing solid-phase extraction methods such as hydrizide-capturing, lectin-specific capturing, and affinity separation techniques based on porous graphitized carbon, hydrophilic interaction chromatography, or immobilized boronic acid. Second, MS-based quantitative analysis strategies coupled with the protein glycosylation-targeting enrichment technologies, by using a label-free MS, stable isotope-labeling, or targeted multiple reaction monitoring (MRM) MS, are summarized with recent published studies.

N-linked (N-) glycoproteomics of urinary exosomes. [Corrected]

Epithelial cells lining the urinary tract secrete urinary exosomes (40-100 nm) that can be targeted to specific cells modulating their functionality. One potential targeting mechanism is adhesion between vesicle surface glycoproteins and target cells. This makes the glycopeptide analysis of exosomes important. Exosomes reflect the physiological state of the parent cells; therefore, they are a good source of biomarkers for urological and other diseases. Moreover, the urine collection is easy and noninvasive and urinary exosomes give information about renal and systemic organ systems. Accordingly, multiple studies on proteomic characterization of urinary exosomes in health and disease have been published. However, no systematic analysis of their glycoproteomic profile has been carried out to date, whereas a conserved glycan signature has been found for exosomes from urine and other sources including T cell lines and human milk. Here, we have enriched and identified the N-glycopeptides from these vesicles. These enriched N-glycopeptides were solved for their peptide sequence, glycan composition, structure, and glycosylation site using collision-induced dissociation MS/MS (CID-tandem MS) data interpreted by a publicly available software GlycopeptideId. Released glycans from the same sample was also analyzed with MALDI-MS. We have identified the N-glycoproteome of urinary exosomes. In total 126 N-glycopeptides from 51 N-glycosylation sites belonging to 37 glycoproteins were found in our results. The peptide sequences of these N-glycopeptides were identified unambiguously and their glycan composition (for 125 N-glycopeptides) and structures (for 87 N-glycopeptides) were proposed. A corresponding glycomic analysis with released N-glycans was also performed. We identified 66 unique nonmodified N-glycan compositions and in addition 13 sulfated/phosphorylated glycans were also found. This is the first systematic analysis of N-glycoproteome of urinary exosomes.

Advances in mass spectrometry driven O-glycoproteomics

BACKGROUND

Global analyses of proteins and their modifications by mass spectrometry are essential tools in cell biology and biomedical research. Analyses of glycoproteins represent particular challenges and we are only at the beginnings of the glycoproteomic era. Some of the challenges have been overcome with N-glycoproteins and proteome-wide analysis of N-glycosylation sites is accomplishable today but only by sacrificing information of structures at individual glycosites. More recently advances in analysis of O-glycoproteins have been made and proteome-wide analysis of O-glycosylation sites is becoming available as well.

SCOPE OF REVIEW

Here we discuss the challenges of analysis of O-glycans and new O-glycoproteomics strategies focusing on O-GalNAc and O-Man glycoproteomes.

MAJOR CONCLUSIONS

A variety of strategies are now available for proteome-wide analysis of O-glycosylation sites enabling functional studies. However, further developments are still needed for complete analysis of glycan structures at individual sites for both N- and O-glycoproteomics strategies.

GENERAL SIGNIFICANCE

The advances in O-glycoproteomics have led to identification of new biological functions of O-glycosylation and a new understanding of the importance of where O-glycans are positioned on proteins.

Advances in LC-MS/MS-based glycoproteomics: getting closer to system-wide site-specific mapping of the N- and O-glycoproteome

Site-specific structural characterization of glycoproteins is important for understanding the exact functional relevance of protein glycosylation. Resulting partly from the multiple layers of structural complexity of the attached glycans, the system-wide site-specific characterization of protein glycosylation, defined as glycoproteomics, is still far from trivial leaving the N- and O-linked glycoproteomes significantly under-defined. However, recent years have seen significant advances in glycoproteomics driven, in part, by the developments of dedicated workflows and efficient sample preparation, including glycopeptide enrichment and prefractionation. In addition, glycoproteomics has benefitted from the continuous performance enhancement and more intelligent use of liquid chromatography and tandem mass spectrometry (LC-MS/MS) instrumentation and a wider selection of specialized software tackling the unique challenges of glycoproteomics data. Together these advances promise more streamlined N- and O-linked glycoproteome analysis. Tangible examples include system-wide glycoproteomics studies detecting thousands of intact glycopeptides from hundreds of glycoproteins from diverse biological samples. With a strict focus on the system-wide site-specific analysis of protein N- and O-linked glycosylation, we review the recent advances in LC-MS/MS based glycoproteomics. The review opens with a more general discussion of experimental designs in glycoproteomics and sample preparation prior to LC-MS/MS based data acquisition. Although many challenges still remain, it becomes clear that glycoproteomics, one of the last frontiers in proteomics, is gradually maturing enabling a wider spectrum of researchers to access this new emerging research discipline. The next milestone in analytical glycobiology is being reached allowing the glycoscientist to address the functional importance of protein glycosylation in a system-wide yet protein-specific manner.

Software for automated interpretation of mass spectrometry data from glycans and glycopeptides

The purpose of this review is to provide those interested in glycosylation analysis with the most updated information on the availability of automated tools for MS characterization of N-linked and O-linked glycosylation types. Specifically, this review describes software tools that facilitate elucidation of glycosylation from MS data on the basis of mass alone, as well as software designed to speed the interpretation of glycan and glycopeptide fragmentation from MS/MS data. This review focuses equally on software designed to interpret the composition of released glycans and on tools to characterize N-linked and O-linked glycopeptides. Several websites have been compiled and described that will be helpful to the reader who is interested in further exploring the described tools.

Exploring site-specific N-glycosylation microheterogeneity of haptoglobin using glycopeptide CID tandem mass spectra and glycan database search

Glycosylation is a common protein modification with a significant role in many vital cellular processes and human diseases, making the characterization of protein-attached glycan structures important for understanding cell biology and disease processes. Direct analysis of protein N-glycosylation by tandem mass spectrometry of glycopeptides promises site-specific elucidation of N-glycan microheterogeneity, something that detached N-glycan and deglycosylated peptide analyses cannot provide. However, successful implementation of direct N-glycopeptide analysis by tandem mass spectrometry remains a challenge. In this work, we consider algorithmic techniques for the analysis of LC-MS/MS data acquired from glycopeptide-enriched fractions of enzymatic digests of purified proteins. We implement a computational strategy that takes advantage of the properties of CID fragmentation spectra of N-glycopeptides, matching the MS/MS spectra to peptide-glycan pairs from protein sequences and glycan structure databases. Significantly, we also propose a novel false discovery rate estimation technique to estimate and manage the number of false identifications. We use a human glycoprotein standard, haptoglobin, digested with trypsin and GluC, enriched for glycopeptides using HILIC chromatography, and analyzed by LC-MS/MS to demonstrate our algorithmic strategy and evaluate its performance. Our software, GlycoPeptideSearch (GPS), assigned glycopeptide identifications to 246 of the spectra at a false discovery rate of 5.58%, identifying 42 distinct haptoglobin peptide-glycan pairs at each of the four haptoglobin N-linked glycosylation sites. We further demonstrate the effectiveness of this approach by analyzing plasma-derived haptoglobin, identifying 136 N-linked glycopeptide spectra at a false discovery rate of 0.4%, representing 15 distinct glycopeptides on at least three of the four N-linked glycosylation sites. The software, GlycoPeptideSearch, is available for download from http://edwardslab.bmcb.georgetown.edu/GPS .

Automated assignments of N- and O-site specific glycosylation with extensive glycan heterogeneity of glycoprotein mixtures

Site-specific glycosylation (SSG) of glycoproteins remains a considerable challenge and limits further progress in the areas of proteomics and glycomics. Effective methods require new approaches in sample preparation, detection, and data analysis. While the field has advanced in sample preparation and detection, automated data analysis remains an important goal. A new bioinformatics approach implemented in software called GP Finder automatically distinguishes correct assignments from random matches and complements experimental techniques that are optimal for glycopeptides, including nonspecific proteolysis and high mass resolution liquid chromatography/tandem mass spectrometry (LC/MS/MS). SSG for multiple N- and O-glycosylation sites, including extensive glycan heterogeneity, was annotated for single proteins and protein mixtures with a 5% false-discovery rate, generating hundreds of nonrandom glycopeptide matches and demonstrating the proof-of-concept for a self-consistency scoring algorithm shown to be compliant with the target-decoy approach (TDA). The approach was further applied to a mixture of N-glycoproteins from unprocessed human milk and O-glycoproteins from very-low-density-lipoprotein (vLDL) particles.

GlycoPep Detector: a tool for assigning mass spectrometry data of N-linked glycopeptides on the basis of their electron transfer dissociation spectra

Electron transfer dissociation (ETD) is commonly used in fragmenting N-linked glycopeptides in their mass spectral analyses to complement collision-induced dissociation (CID) experiments. The glycan remains intact through ETD, while the peptide backbone is cleaved, providing the sequence of amino acids for a glycopeptide. Nonetheless, data analysis is a major bottleneck to high-throughput glycopeptide identification based on ETD data, due to the complexity and diversity of ETD mass spectra compared to CID counterparts. GlycoPep Detector (GPD) is a web-based tool to address this challenge. It filters out noise peaks that interfere with glycopeptide sequencing, correlates input glycopeptide compositions with the ETD spectra, and assigns a score for each candidate. By considering multiple ion series (c-, z-, and y-ions) and scoring them separately, the software gives more weighting to the ion series that matches peaks of high intensity in the spectra. This feature enables the correct glycopeptide to receive a high score while keeping scores of incorrect compositions low. GPD has been utilized to interpret data collected on six model glycoproteins (RNase B, avidin, fetuin, asialofetuin, transferrin, and AGP) as well as a clade C HIV envelope glycoprotein, C.97ZA012 gp140ΔCFI. In every assignment made by GPD, the correct glycopeptide composition earns a score that is about 2-fold higher than other incorrect glycopeptide candidates (decoys). The software can be accessed at http://glycopro.chem.ku.edu/ZZKHome.php .

Sweet-Heart - an integrated suite of enabling computational tools for automated MS2/MS3 sequencing and identification of glycopeptides

High efficiency identification of intact glycopeptides from a shotgun glycoproteomic LC-MS(2) dataset remains problematic. The prevalent mode of identifying the de-N-glycosylated peptides is littered with false positives and addresses only the issue of site occupancy. Here, we present Sweet-Heart, a computational tool set developed to tackle the heart of the problems in MS(2) sequencing of glycopeptide. It accepts low resolution and low accuracy ion trap MS(2) data, filters for glycopeptides, couples knowledge-based de novo interpretation of glycosylation-dependent fragmentation pattern with protein database search, and uses machine-learning algorithm to score the computed glyco and peptide combinations. Higher ranking candidates are then compiled into a list of MS(2)/MS(3) entries to drive subsequent rounds of targeted MS(3) sequencing of putative peptide backbone, allowing its validation by database search in a fully automated fashion. With additional fishing out of all related glycoforms and final data integration, the platform proves to be sufficiently sensitive and selective, conducive to novel glycosylation discovery, and robust enough to discriminate, among others, N-glycolyl neuraminic acid/fucose from N-acetyl neuraminic acid/hexose. A critical appraisal of its computing performance shows that Sweet-Heart allows high sensitivity comprehensive mapping of site-specific glycosylation for isolated glycoproteins and facilitates analysis of glycoproteomic data.

BIOLOGICAL SIGNIFICANCE

The biological relevance of protein site-specific glycosylation cannot be meaningfully addressed without first defining its pattern by direct analysis of glycopeptides. Sweet-Heart is a novel suite of computational tools allowing for automated analysis of mass spectrometry-based glycopeptide sequencing data. It is developed to accept ion trap MS2/MS3 data and uses a machine learning algorithm to score and rank the candidate peptide core and glycosyl substituent combinations. By eliminating the need for manual, labor-intensive, and subjective data interpretation, it facilitates high throughput shotgun glycoproteomic data analysis and is conducive to identification of unanticipated glycosylation, as demonstrated here with a recombinant EGFR.

Automated glycopeptide analysis--review of current state and future directions

Glycosylation of proteins is involved in immune defense, cell-cell adhesion, cellular recognition and pathogen binding and is one of the most common and complex post-translational modifications. Science is still struggling to assign detailed mechanisms and functions to this form of conjugation. Even the structural analysis of glycoproteins-glycoproteomics-remains in its infancy due to the scarcity of high-throughput analytical platforms capable of determining glycopeptide composition and structure, especially platforms for complex biological mixtures. Glycopeptide composition and structure can be determined with high mass-accuracy mass spectrometry, particularly when combined with chromatographic separation, but the sheer volume of generated data necessitates computational software for interpretation. This review discusses the current state of glycopeptide assignment software-advances made to date and issues that remain to be addressed. The various software and algorithms developed so far provide important insights into glycoproteomics. However, there is currently no freely available software that can analyze spectral data in batch and unambiguously determine glycopeptide compositions for N- and O-linked glycopeptides from relevant biological sources such as human milk and serum. Few programs are capable of aiding in structural determination of the glycan component. To significantly advance the field of glycoproteomics, analytical software and algorithms are required that: (i) solve for both N- and O-linked glycopeptide compositions, structures and glycosites in biological mixtures; (ii) are high-throughput and process data in batches; (iii) can interpret mass spectral data from a variety of sources and (iv) are open source and freely available.

GlycoPep grader: a web-based utility for assigning the composition of N-linked glycopeptides

GlycoPep grader (GPG) is a freely available software tool designed to accelerate the process of accurately determining glycopeptide composition from tandem mass spectrometric data. GPG relies on the identification of unique dissociation patterns shown for high mannose, hybrid, and complex N-linked glycoprotein types, including patterns specific to those structures containing fucose or sialic acid residues. The novel GPG scoring algorithm scores potential candidate compositions of the same nominal mass against MS/MS data through evaluation of the Y(1) ion and other peptide-containing product ions, across multiple charge states, when applicable. In addition to evaluating the peptide portion of a given glycopeptide, the GPG algorithm predicts and scores product ions that result from unique neutral losses of terminal glycans. GPG has been applied to a variety of glycoproteins, including RNase B, asialofetuin, and transferrin, and the HIV envelope glycoprotein, CON-S gp140ΔCFI. The GPG software is implemented predominantly in PostgreSQL, with PHP as the presentation tier, and is publicly accessible online. Thus far, the algorithm has identified the correct compositional assignment from multiple candidate N-glycopeptides in all tests performed.

Detection and characterization of low abundance glycopeptides via higher-energy C-trap dissociation and orbitrap mass analysis

Broad-scale mass spectrometric analyses of glycopeptides are constrained by the considerable complexity inherent to glycoproteomics, and techniques are still being actively developed to address the associated analytical difficulties. Here we apply Orbitrap mass analysis and higher-energy C-trap dissociation (HCD) to facilitate detailed insights into the compositions and heterogeneity of complex mixtures of low abundance glycopeptides. By generating diagnostic oxonium product ions at mass measurement errors of <5 ppm, highly selective glycopeptide precursor ion detections are made at sub-fmol limits of detection: analyses of proteolytic digests of a hen egg glycoprotein mixture detect 88 previously uncharacterized glycopeptides from 666 precursor ions selected for MS/MS, with only one false positive due to co-fragmentation of a non-glycosylated peptide with a glycopeptide. We also demonstrate that by (1) identifying multiple series of glycoforms using high mass accuracy single stage MS spectra, and (2) performing product ion scans at optimized HCD collision energies, the identification of peptide + N-acetylhexosamine (HexNAc) ions (Y1 ions) can be readily achieved at <5 ppm mass measurement errors. These data allow base peptide sequences and glycan compositional information to be attained with high confidence, even for glycopeptides that produce weak precursor ion signals and/or low quality MS/MS spectra. The glycopeptides characterized from low fmol abundances using these methods allow two previously unreported glycosylation sites on the Gallus gallus protein ovoglycoprotein (amino acids 82 and 90) to be confirmed; considerable glycan heterogeneities at amino acid 90 of ovoglycoprotein, and amino acids 34 and 77 of Gallus gallus ovomucoid are also revealed.

Toward automated glycan analysis

As drastic structural changes in cell-surface glycans of glycoproteins and glycosphingolipids, as well as serum glycoproteins, are often observed during cell differentiation and cancer progression, it is considered that glycans can be potential candidates for novel diagnostic and therapeutic biomarkers. Although there have been substantial advances in our understanding of the effects of glycosylation on some biological systems, we still do not fully understand the significance and mechanism of glycoform alteration that is widely observed in many human diseases. This is due to the highly complicated structures of the glycans and the extremely tedious and time-consuming processes required for their separation from complex mixtures and their subsequent analysis. As a result, with a few notable exceptions, the therapeutic potential of complex glycans has not been well exploited. This article is focused on the state of the art and current advances in glycomics, and efforts for the development of automated glycan analysis, which should greatly accelerate functional glycobiology and its medical/pharmaceutical applications. The "glycoblotting method" is the only method currently available that allows rapid and large-scale clinical glycomics of human whole-serum glycoproteins, because it requires very little material and, when combined with an automated system "SweetBlot," takes only ∼14h to complete whole glycan profiling by mass spectrometry. The upcoming goal is to combine glycoblotting methods and various MS-based platforms for the development of a fully automated glycan analytical system and accelerating research to discover highly sensitive and clinically important biomarker molecules.

Mass spectrometry of peptides and proteins from human blood

It is difficult to convey the accelerating rate and growing importance of mass spectrometry applications to human blood proteins and peptides. Mass spectrometry can rapidly detect and identify the ionizable peptides from the proteins in a simple mixture and reveal many of their post-translational modifications. However, blood is a complex mixture that may contain many proteins first expressed in cells and tissues. The complete analysis of blood proteins is a daunting task that will rely on a wide range of disciplines from physics, chemistry, biochemistry, genetics, electromagnetic instrumentation, mathematics and computation. Therefore the comprehensive discovery and analysis of blood proteins will rank among the great technical challenges and require the cumulative sum of many of mankind's scientific achievements together. A variety of methods have been used to fractionate, analyze and identify proteins from blood, each yielding a small piece of the whole and throwing the great size of the task into sharp relief. The approaches attempted to date clearly indicate that enumerating the proteins and peptides of blood can be accomplished. There is no doubt that the mass spectrometry of blood will be crucial to the discovery and analysis of proteins, enzyme activities, and post-translational processes that underlay the mechanisms of disease. At present both discovery and quantification of proteins from blood are commonly reaching sensitivities of ∼1 ng/mL.

Mass spectrometry based glycoproteomics--from a proteomics perspective

Glycosylation is one of the most important and common forms of protein post-translational modification that is involved in many physiological functions and biological pathways. Altered glycosylation has been associated with a variety of diseases, including cancer, inflammatory and degenerative diseases. Glycoproteins are becoming important targets for the development of biomarkers for disease diagnosis, prognosis, and therapeutic response to drugs. The emerging technology of glycoproteomics, which focuses on glycoproteome analysis, is increasingly becoming an important tool for biomarker discovery. An in-depth, comprehensive identification of aberrant glycoproteins, and further, quantitative detection of specific glycosylation abnormalities in a complex environment require a concerted approach drawing from a variety of techniques. This report provides an overview of the recent advances in mass spectrometry based glycoproteomic methods and technology, in the context of biomarker discovery and clinical application.

GlycoSpectrumScan: fishing glycopeptides from MS spectra of protease digests of human colostrum sIgA

With the emergence of glycoproteomics, there is a need to develop bioinformatic tools to identify glycopeptides in protease digests of glycoproteins. GlycoSpectrumScan is a web-based tool that identifies the glycoheterogeneity on a peptide from mass spectrometric data. Two experimental data sets are required as inputs: (1) oligosaccharide compositions of the N- and/or O-linked glycans present in the sample and (2) in silico derived peptide masses of proteolytically digested proteins with a potential number of N- and/or O-glycosylation sites. GlycoSpectrumScan uses MS data, rather than MS/MS data, to identify glycopeptides and determine the relative distribution of N- and O-glycoforms at each site. It is functional for assigning monosaccharide compositions on glycopeptides with single and multiple sites of glycosylation. The algorithm allows the input of raw mass data, including multiply charged ions, making it applicable for both ESI and MALDI data from all mass spectrometer platforms. Manual analysis time for identifying glycosylation heterogeneity at each site on glycoprotein(s) is substantially decreased. The application of this tool to characterize the N- and O-linked glycopeptides from human secretory IgA (sIgA), consisting of secretory component (7 N-linked sites), IgA1 (2 N-linked, <or=5 O-linked sites), IgA2 (4 N-linked sites) and J-chain (1 N-linked site) is described. GlycoSpectrumScan is freely available at www.glycospectrumscan.org .

Determination of glycosylation sites and site-specific heterogeneity in glycoproteins

Glycosylation is one of the most common post-translational modifications (PTMs) of proteins. At least 50% of human proteins are glycosylated with some estimates being as high as 70%. Glycoprotein analysis requires determining both the sites of glycosylation as well as the glycan structures associated with each site. Recent advances have led to the development of new analytical methods that employ mass spectrometry extensively making it possible to obtain the glycosylation site and the site microheterogeneity. These tools will be important for the eventual development of glycoproteomics.

Label-free quantitation: a new glycoproteomics approach

We demonstrate herein a method for quantifying glycosylation changes on glycoproteins. This novel method uses MS data of characterized glycopeptides to analyze glycosylation profiles, and several quality control tests were done to demonstrate that the method is reproducible, robust, applicable to different types of glycoproteins, and tolerant of instrumental variability during ionization of the analytes. This method is unique in that it is the first label-free quantitative method specifically designed for glycopeptide analysis. It can be used to monitor changes in glycosylation in a glycosylation site-specific manner on a single glycoprotein, or it can be used to quantify glycosylation in a glycoprotein mixture. During mixture analysis, the method can discriminate between changes in glycosylation of a given protein, and changes in the glycoprotein's concentration in the mixture. This method is useful for quantitative analyses in biochemical studies of glycoproteins, where changes in glycosylation composition can be linked to functional differences; it could also be implemented in the pharmaceutical industry, where glycosylation profiles of glycoprotein-based therapeutics must be quantified. Finally, quantification of glycopeptides is an important aspect of glycopeptide-based biomarker discovery, and our quantitative approach could be a valuable asset to this field as well, provided the compositions of the glycopeptides to be quantified are identifiable using other methods.

Caveolar transport through nasal epithelium of birch pollen allergen Bet v 1 in allergic patients

BACKGROUND

Previous work in type I pollen allergies has focused on aberrant immunoresponses.

OBJECTIVE

Our systems-level analyses explore the role of epithelium in early pathogenesis of type I allergic reactions.

METHODS

We began top-down analyses of differences in human nasal epithelial cells and biopsy specimens obtained from patients with birch allergy and healthy control subjects in the resting state and after intranasal in vivo birch pollen challenges. Immunohistochemistry, immunotransmission electron microscopy, mass spectrometry, transcriptomics, and integration of data to a pathway were conducted.

RESULTS

Bet v 1 allergen bound to epithelium immediately after in vivo birch pollen challenge during winter only in allergic individuals. It also travelled through epithelium with caveolae to mast cells. Sixteen unique proteins were found to bind to the Bet v 1 column only in lysates from allergic epithelial cells; 6 of these were caveolar and 6 were cytoskeletal proteins. The nasal epithelial transcriptome analysis from allergic and healthy subjects differed during the winter season, and these subjects also responded differentially to birch pollen challenge. Within this pollen-induced response, the gene ontology categories of cytoskeleton and actin cytoskeleton were decreased in allergic patients, whereas the actin-binding category was enriched in healthy subjects. Integration of microscopic, mass spectrometric, and transcriptomic data to a common protein-protein binding network showed how these were connected to each other.

CONCLUSION

We propose a hypothesis of caveolae-dependent uptake and transport of birch pollen allergen in the epithelium of allergic patients only. Application of discovery-driven methodologies can provide new hypotheses worth further analysis of complex multifactorial diseases, such as type I allergy.