Nonglycosylated Peptides Research Articles

IgG glycopeptide enrichment using hydrophilic interaction chromatography-based solid-phase extraction on anaminopropyl column.

The sample preparation step is pivotal in glycoproteomic analysis. An effective approach in glycoprotein sample preparation involves enriching glycopeptides by solid-phase extraction (SPE) using polar stationary phases in hydrophilic interaction liquid chromatography (HILIC) mode. The aim of this work is to show how different experimental conditions influence the enrichment efficiency of glycopeptides from human immunoglobulin G (IgG) on an aminopropyl-modified SPE column. Different compositions of the elution solvent (acetonitrile, methanol, and isopropanol), along with varying concentrations of elution solvent acidifiers (formic and acetic acid), and different concentrations of acetonitrile for the conditioning and washing solvents (65%, 75%, and 85% acetonitrile) were tested to observe their effects on the glycopeptide enrichment process. Isopropanol proved less effective in enriching glycopeptides, while acetonitrile was the most efficient, with methanol in between. Higher formic acid concentrations in the elution solvent weakened the ionic interactions, particularly with sialylated glycopeptides. Substituting formic acid with acetic acid led to earlier elution of more glycopeptides. The acetonitrile concentration in conditioning and washing solutions played a key role; at 65% acetonitrile, glycopeptides were not retained on the SPE column and were detected in the flow-through fraction. Ultimately, it was proven that the enrichment method was applicable to human plasma samples, resulting in a significant decrease in the abundances of non-glycosylated peptides. To the best of our knowledge, this study represents the first systematic investigation into the impact of the mobile phase on glycopeptide enrichment using an aminopropyl-modified SPE column in HILIC mode. This study demonstrates the substantial impact of even minor variations in experimental conditions, which havenot yet beenconsidered in the literature, on SPE-HILIC glycopeptide enrichment. Consequently, meticulous optimization of these conditions is imperative to enhance the specificity and selectivity of glycoproteomic analysis, ensuring accurate and reliable quantification.

The power of glycobiology: Precision of polyp detection using glycoproteomic biomarkers.

22 Background: Glycosylation plays a pivotal role in host immune response during cancer development. Recently, we have reported specific changes in blood-based glycoforms for the early detection of advanced adenomas (AAs) and colorectal cancer (CRC). This study aims to identify glycoproteomic biomarkers associated with various polyp types, as glycosylation abnormalities are linked to neoplastic transformation, and CRC risk varies by polyp histology. Methods: Samples from an observational study (NCT05445570) were used to identify three distinct histologic polyp groups: adenomatous (AP), hyperplastic (HP), or serrated (SP). Combining liquid-chromatography/mass-spectrometry and artificial-intelligence powered data processing, our glycoproteomic profiling platform assessed glycopeptide (GP) and non-glycosylated peptide quantification transitions from the serum samples. We used multi-nominal response logit models to examine the relationship between polyp types and biomarkers. An a priori biomarker panel was interjected as exposures in the model. Each marker is assessed independently for inclusion into our final model. The final model utilized biomarkers found to have a significant association with the presence of AP, SP, or HPs along with age, sex, and BMI. Results: We identified 1,035 subjects with distinct polyp histologies and 1,300 polyp-free controls. We found a significant increase in relative risk (RR) for polyp presence in patients who were: >60 years (AP RR 1.40), male (AP RR 1.74), or had a BMI> 25. From the GP biomarker panel, 15 GPs were associated with risk of polyp presence; however, only 7 GP markers were significantly associated after adjusting for age, sex, and BMI. Two biomarkers were found to have a protective effect for the risk of an adenomatous polyp (RR 0.37; RR 0.39). Three biomarkers increased the risk of an adenomatous polyp (RR 1.31; RR 1.42; RR 1.98). One biomarker was found to increase the risk of a hyperplastic polyp (RR 1.42). Two biomarkers were found to increase the risk of serrated polyps (RR 1.67; RR 2.08) and one biomarker was found to decrease the risk of serrated polyps (RR 0.77). Conclusions: This is a novel exploratory study using GP biomarkers as it relates to risk of polyp presence by distinct histology. The use of these biomarkers allows us to harness the power of glycobiology in the liquid biopsy domain for AAs/CRC detection. The exact function of these GP markers during the development of different polyp types needs to be further explored through future studies.

Anti-diabetic properties of brewer's spent yeast peptides. In vitro, in silico and ex vivo study after simulated gastrointestinal digestion.

Brewer's spent yeast (BSY) hydrolysates are a source of antidiabetic peptides. Nevertheless, the impact of in vitro gastrointestinal digestion of BSY derived peptides on diabetes has not been assessed. In this study, two BSY hydrolysates were obtained (H1 and H2) using β-glucanase and alkaline protease, with either 1 h or 2 h hydrolysis time for H1 and H2, respectively. These hydrolysates were then subjected to simulated gastrointestinal digestion (SGID), obtaining dialysates D1 and D2, respectively. BSY hydrolysates inhibited the activity of α-glucosidase and dipeptidyl peptidase IV (DPP-IV) enzymes. Moreover, although D2 was inactive against these enzymes, D1 IC50 value was lower than those found for the hydrolysates. Interestingly, after electrophoretic separation, D1 mannose-linked peptides showed the highest α-glucosidase inhibitory activity, while non-glycosylated peptides had the highest DPP-IV inhibitory activity. Kinetic analyses showed a non-competitive mechanism in both cases. After peptide identification, GILFVGSGVSGGEEGAR and IINEPTAAAIAYGLDK showed the highest in silico anti-diabetic activities among mannose-linked and non-glycosylated peptides, respectively (AntiDMPpred score: 0.70 and 0.77). Molecular docking also indicated that these peptides act as non-competitive inhibitors. Finally, an ex vivo model of mouse jejunum organoids was used to study the effect of D1 on the expression of intestinal epithelial genes related to diabetes. The reduction of the expression of genes that codify lactase, sucrase-isomaltase and glucose transporter 2 was observed, as well as an increase in the expression of Gip (glucose-dependent insulinotropic peptide) and Glp1 (glucagon-like peptide 1). This is the first report to evaluate the anti-diabetic effect of BSY peptides in mouse jejunum organoids.

IGF2 Peptide-Based LYTACs for Targeted Degradation of Extracellular and Transmembrane Proteins.

Lysosome-targeting chimeras (LYTACs) have recently been developed to facilitate the lysosomal degradation of specific extracellular and transmembrane molecular targets. However, the LYTAC particles described to date are based on glycopeptide conjugates, which are difficult to prepare and produce on a large scale. Here, we report on the development of pure protein LYTACs based on the non-glycosylated IGF2 peptides, which can be readily produced in virtually any facility capable of monoclonal antibody production. These chimeras utilize the IGF2R/CI-M6PR pathway for lysosomal shuttling and, in our illustrative example, target programmed death ligand 1 (PD-L1), eliciting physiological effects analogous to immune checkpoint blockade. Results from in vitro assays significantly exceed the effects of anti-PD-L1 antibodies alone.

Glycopeptide-Based Supramolecular Hydrogels Induce Differentiation of Adipose Stem Cells into Neural Lineages.

We applied a bottom-up approach to develop biofunctional supramolecular hydrogels from an aromatic glycodipeptide. The self-assembly of the glycopeptide was induced by either temperature manipulation (heating-cooling cycle) or solvent (DMSO to water) switch. The sol-gel transition was salt-triggered in cell culture media and resulted in gels with the same chemical compositions but different mechanical properties. Human adipose derived stem cells (hASCs) cultured on these gels under basal conditions (i.e., without differentiation factors) overexpressed neural markers, such as GFAP, Nestin, MAP2, and βIII-tubulin, confirming the differentiation into neural lineages. The mechanical properties of the gels influenced the number and distribution of the adhered cells. A comparison with gels obtained from the nonglycosylated peptide showed that glycosylation is crucial for the biofunctionality of the hydrogels by capturing and preserving essential growth factors, e.g., FGF-2.

Abstract 5314: A peripheral blood-based glycoproteomic predictor of checkpoint inhibitor treatment benefit in advanced non-small cell lung cancer

Abstract Background: Protein glycosylation is the most common and complex form of post-translational protein modification. Glycosylation profoundly affects protein structure, conformation, and function. The elucidation of the potential role of differential protein glycosylation as biomarkers has been limited by the technical complexity of generating and interpreting this information. We have recently established a novel, powerful platform that combines liquid chromatography-mass spectrometry with a proprietary artificial-intelligence-based data processing engine that allows, for the first time, highly scalable interrogation of the glycoproteome. Here we report the performance of this platform to predict likely benefit from immune-checkpoint inhibitor (ICI) therapy in advanced non-small cell lung cancer (NSCLC). Methods: Our platform was utilized to assess 532 glycopeptide (GP) and peptide signatures representing 75 serum proteins in pretreatment blood samples from a cohort of 123 individuals (54 females, 69 males, age range 30 to 88 years). Inclusion criteria were a diagnosis of unresectable stage 3 or 4 NSCLC, treatment with pembrolizumab monotherapy (26 patients), or treatment with combination pembrolizumab-chemotherapy (97 patients). Overall survival (OS) data were available for all patients. Results: An ensemble multivariable-model-based glycoproteomic classifier consisting of 7 GP and non-glycosylated peptide biomarker features selected from a generalized additive model for OS was developed using ≈2/3rds of the full cohort (n=88) and validated in the remainder of patients (n=35). The classifier yielded similar statistical significance in Cox regression analysis for separating patients who are likely to benefit from ICI therapy from those who are not, to accurately predict likely ICI benefit with a sensitivity of >95% while performing at a specificity of 33% to predict those who are unlikely to benefit. Results were further analyzed in patients with either non-squamous or squamous NSCLC with first-line therapy (n=98). The classifier yielded a hazard ratio (HR) for prediction of likely ICI benefit of 3.6 with median OS of 13.9 vs. 4.2 months, and of 3.5 with median OS of 13.5 vs. 4.5 months in the entire cohort and the first-line treated patients, respectively. Conclusions: The glycoproteomic classifier described here predicts with high sensitivity which patients are likely to benefit from ICI therapy. In addition to potentially reducing the use of ICIs in a safe manner in patients who would be unnecessarily subjected to possible adverse drug reactions, our classifier simultaneously has the potential of reducing the burden of health care expenditures. Our results indicate that glycoproteomics holds a strong promise as a predictor for ICI treatment benefit which appears to significantly outperform other currently pursued biomarker approaches. Citation Format: Klaus Lindpaintner, Chad Pickering, Alan Mitchell, Gege Xu, Xin Cong, Daniel Serie. A peripheral blood-based glycoproteomic predictor of checkpoint inhibitor treatment benefit in advanced non-small cell lung cancer. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5314.

Structural Basis for the IgE-Binding Cross-Reacting Epitopic Peptides of Cup s 3, a PR-5 Thaumatin-like Protein Allergen from Common Cypress (Cupressus sempervirens) Pollen

The present work was aimed at identifying the IgE-binding epitopic regions on the surface of the Cup s 3 allergen from the common cypress Cupressus sempervirens, that are possibly involved in the IgE-binding cross-reactivity reported between Cupressaceae species. Three main IgE-binding epitopic regions were mapped on the molecular surface of Cup s 3, the PR-5 thaumatin-like allergen of common cypress Cupressus sempervirens. They correspond to exposed areas containing either electropositive (R, K) or electronegative (D, E) residues. A coalescence occurs between epitopes #1 and #2, that creates an extended IgE-binding regions on the surface of the allergen. Epitope #3 contains a putative N-glycosylation site which is actually glycosylated and could therefore comprise a glycotope. However, most of the allergenic potency of Cup s 3 depends on non-glycosylated epitopic peptides. The corresponding regions of thaumatin-like allergens from other closely related Cupressaceae (Cryptomeria, Juniperus, Thuja) exhibit a very similar conformation that should account for the IgE-binding cross-reactivity observed among the Cupressaceae allergens.

Insights into the Role of Sialylation in Cancer Metastasis, Immunity, and Therapeutic Opportunity.

Sialylation is an enzymatic process that covalently attaches sialic acids to glycoproteins and glycolipids and terminates them by creating sialic acid-containing glycans (sialoglycans). Sialoglycans, usually located in the outmost layers of cells, play crucial biological roles, notably in tumor transformation, growth, metastasis, and immune evasion. Thus, a deeper comprehension of sialylation in cancer will help to facilitate the development of innovative cancer therapies. Cancer sialylation-related articles have consistently increased over the last four years. The primary subjects of these studies are sialylation, cancer, immunotherapy, and metastasis. Tumor cells activate endothelial cells and metastasize to distant organs in part by the interactions of abnormally sialylated integrins with selectins. Furthermore, cancer sialylation masks tumor antigenic epitopes and induces an immunosuppressive environment, allowing cancer cells to escape immune monitoring. Cytotoxic T lymphocytes develop different recognition epitopes for glycosylated and nonglycosylated peptides. Therefore, targeting tumor-derived sialoglycans is a promising approach to cancer treatments for limiting the dissemination of tumor cells, revealing immunogenic tumor antigens, and boosting anti-cancer immunity. Exploring the exact tumor sialoglycans may facilitate the identification of new glycan targets, paving the way for the development of customized cancer treatments.

Multidimensional separation and analysis of alpha-1-acid glycoprotein N-glycopeptides using high-field asymmetric waveform ion mobility spectrometry (FAIMS) and nano-liquid chromatography tandem mass spectrometry.

Bottom-up nLC-MS/MS-based glycoprotein mass spectrometry workflows rely on the generation of a mixture of non-glycosylated and glycosylated peptides via proteolysisof glycoproteins. Such methods are challenged by suppression of hydrophilic glycopeptide ions by more abundant, hydrophobic, and readily ionizable non-glycosylated peptides. Commercially available high-field asymmetric waveform ion mobility spectrometry (FAIMS) devices have recently been introduced and present a potential benefit for glycoproteomic workflows by enabling orthogonal separation of non-glycosylated peptides and glycopeptides following chromatographic separation, and prior to MS/MS analysis. However, knowledge is lacking regarding optimal FAIMS conditions for glycopeptide analyses. Here, we document optimal FAIMS compensation voltages for the transmission and analysis of human alpha-1-acid glycoprotein (AGP) tryptic N-glycopeptide ions. Further, we evaluate the effect of FAIMS on AGP glycopeptide assignment confidence by comparing the number of assigned glycopeptides at different confidence levels using a standard nLC-MS/MS method or an otherwise identical method employing FAIMS. Optimized methods will potentiate glycoproteomic analyses by increasing the number of unique glycopeptide identifications and the confidence of glycopeptide assignments. Data are available via ProteomeXchange with identifier PXD036667. Analysis of alpha-1-acid glycoprotein (AGP) tryptic digests via nLC-FAIMS-MS/MS (top) led to the establishment of ideal FAIMS voltages for the analysis of AGP N-glycopeptides (bottom), suggesting that FAIMS can improve the depth of glycoproteome characterization. Pairs of CV magnitudes are shown along the x-axis.

Glycoproteomics as a powerful liquid biopsy-based screening tool for non-small cell lung cancer.

e21148 Background: Protein glycosylation is the most abundant and complex form of post-translational protein modification. Glycosylation profoundly affects protein structure, conformation, and function. The elucidation of the potential role of differential protein glycosylation as biomarkers has so far been limited by the technical complexity of generating and interpreting this information. We have recently established a novel, powerful platform that combines liquid chromatography/mass spectrometry with a proprietary artificial-intelligence-based data processing engine that allows, for the first time, highly scalable interrogation of the glycoproteome. Methods: Using this platform, we interrogated 694 glycopeptide (GP) and non-glycosylated peptide transitions derived from 74 serum proteins in pre-treatment peripheral blood samples from a cohort of 316 individuals with non-small-cell lung cancer (NSCLC) (128 females, 187 males, 1 with unknown sex, median age 66 years, age range 31-89 years, stage 0-4 N’s: 1 / 99 / 80 / 84 / 49, 3 missing) and a comparison cohort of 194 healthy control samples (102 females, 92 males, median age 52 years, age range 30-63 years). Age- and sex-adjusted differential expression analysis for 596 normalized biomarkers were performed to evaluate statistically significant differential abundances using an FDR-adjusted q-value of 0.05 as a cutoff. Repeated five-fold cross-validated LASSO-regularized logistic regression was performed to create a multivariable classifier that predicts whether a serum sample belongs to the healthy or NSCLC cohort. Results: We identified 432 biomarkers with significant abundance differences at FDR ≤ 0.05 between samples with NSCLC and healthy controls. Using 70% of the complete cohort (balanced by case/control membership, NSCLC stage, sex, and age quartile) as a training set, we selected a total of 375 glycopeptide and non-glycosylated peptide biomarker features that remained differentially expressed at FDR-adjusted q-value ≤ 0.05 as input into a LASSO-regularized multivariable classifier. This resulting in a 19-biomarker model exhibiting an accuracy of 94.8% (96.9% sensitivity, 91.2% specificity) and AUC of 0.989. This classifier was validated in an independent test set comprising the remaining 30% of subjects, yielding an accuracy of 94.5% (95.5% sensitivity, 93.0% specificity) and AUC of 0.975. Sensitivity in the test set was 100% / 96% / 99% / 96% / 94% / 10%, in stages 0-4 and missing, respectively. Conclusions: Our results indicate that glycoproteomic biomarkers can be leveraged as a strong liquid biopsy-based screening tool for patients at high risk of NSCLC, as an alternative to imaging modalities.

Liquid-biopsy-derived glycoproteomic profiling as a novel means for noninvasive diagnosis of ovarian cancer.

e17604 Background: Ovarian cancer (OC) is the fifth- leading cause of cancer-related deaths among women, and the most lethal gynecological cancer. Currently available biomarkers, including CA-125 and HE4, show suboptimal diagnostic performance for differentiating among benign and malignant pelvic tumors, and the early recognition of OC. Differentiation of benign and malignant pelvic tumors is required for proper patient triaging and management, yet non-invasive methods remain a largely unmet medical need. Methods: We applied a novel platform for characterizing peripheral blood glycoproteomic biomarkers, combining liquid-chromatography/mass spectrometry (LC-MS) with artificial intelligence/neural networks (AI-NN) for the targeted quantification of serum protein glycosylation at intact glycopeptide level to analyze serum samples from 296 treatment-naïve women with histopathology-confirmed diagnosis of either benign (n = 151) or malignant (n = 145) tumors, and from 55 healthy control subjects, procured from a commercial biobank. Using data-dependent acquisition, a panel of 683 serum glycosylated and non-glycosylated peptides, representing 71 serum proteins, was interrogated. Samples were randomly divided into a training and a testing set for multivariable analysis. Data processing was performed using PB-Net, an in-house-developed high-throughput peak integration software. Raw data were normalized, processed by statistical analysis, and applied to machine learning models. Results: Comparison of glycopeptide abundances among patients with OC and benign pelvic tumors yielded 428 statistically significantly differentially expressed glycopeptides/peptides (at FDR < 0.05). A subpanel of these markers used to generate a score for predicting OC yielded areas under the receiver-operating-characteristic of 0.955 and 0.894 in the training and testing sets, respectively. The predicted probability of malignancy increased with cancer stage, and probability distributions were similar across training and test sets. Applying the model to healthy subjects, not utilized in training, resulted in few misclassifications and a spread nearly equivalent to that of the benign tumor cases. This indicates the signature robustly predicts malignancy and severity of disease. Conclusions: Our novel approach exhibited impressive levels of accuracy for the noninvasive differentiation of benign and malignant pelvic masses, compared with existing biomarkers and algorithms, thereby demonstrating the utility of glycoprotein profiles as a powerful, noninvasive new diagnostic modality.

Glycoproteomics as a powerful liquid biopsy-based predictor of checkpoint inhibitor treatment benefit in metastatic malignant melanoma.

9545 Background: Protein glycosylation is the most abundant and complex form of post-translational protein modification. Glycosylation profoundly affects protein structure, conformation, and function. The elucidation of the potential role of differential protein glycosylation as biomarkers has so far been limited by the technical complexity of generating and interpreting this information. We have recently established a novel, powerful platform that combines liquid chromatography/mass spectrometry with a proprietary artificial-intelligence-based data processing engine that allows, for the first time highly scalable interrogation of the glycoproteome. Methods: Using this platform, we interrogated 526 glycopeptide (GP) signatures derived from 75 serum proteins in pretreatment blood samples from a cohort of 205 individuals (66 females, 139 males, age range 24 to 97 years) with metastatic malignant melanoma treated either with nivolumab plus ipilimumab (95 patients) or pembrolizumab (110 patients) immune-checkpoint inhibitor (ICI) therapy. Results: In an optimized assay containing 27 glycopeptides and 20 non-glycosylated peptides, we identified 14 GPs with abundance differences at FDR q≤0.05 with regard to PFS. Using 40% of the cohort as a training set and selecting 12 glycopeptide and non-glycosylated peptide biomarker features of the 47 total by LASSO shrinkage, we created a multivariable-model-based classifier for PFS that yielded a hazard ratio (HR) for prediction of likely ICI benefit of 7.5 at p < 0.0001. This classifier was validated in the test set comprised of the held-out 60% of patients, yielding a HR of 4.7 at a similar p-value for separating patients likely benefiting from ICI therapy and those likely not benefiting from ICI therapy (50% PFS of 18 months vs. 3 months based on classifier score above/below cutoff). This classifier has a sensitivity of > 99% to predict likely ICI benefit, while still performing at a specificity of 26%, thus helping to safely reduce ultimately unnecessary and non-beneficial exposure to these agents of one in four who otherwise would unnecessarily be exposed to them. Conclusions: Our results indicate that glycoproteomics holds a strong promise as a predictor for checkpoint inhibitor treatment benefit that appears to significantly outperform other currently pursued biomarker approaches in this context.

Predicting breast cancer in women using liquid biopsy-derived glycoproteomic markers.

e12545 Background: Breast cancer is the most common cancer among women worldwide.Traditional methods of cancer detection such as tissue biopsy are invasive, costly, time consuming and not amenable for repetition. As a result, minimally invasive liquid biopsies, especially blood-based biomarkers show potential value for breast cancer risk prediction and early detection. In this study, we investigated the use of serum glycoproteins circulating in blood to identify a panel of potential prognostic markers that may aid in predicting breast cancer in women. Methods: We applied a novel platform for characterizing blood glycoproteomic biomarkers, combining liquid-chromatography/mass spectrometry (LC-MS) with artificial intelligence/neural networks (AI-NN) to analyze serum samples from 279 breast cancer patients (median age 56 years, with stage 0-4 N’s: 1 / 83 / 114 / 56 / 25) and 102 healthy control samples (median age 52 years). A panel of 596 serum glycosylated and non-glycosylated peptides, representing 71 serum proteins, were analyzed. Age-adjusted differential expression analysis for 596 normalized biomarkers were performed to evaluate statistically significant differential abundances using an FDR q-value of 0.05 as a cutoff. Using the top differentially expressed markers as input, a LASSO penalized logistic regression model with 5-fold repeated cross validation was applied to identify the top biomarkers contributing to the separation between healthy controls and breast cancer patients. Results: We identified 243 out of 596 markers that were differentially expressed (FDR <<0.05) between breast cancer samples and healthy controls. Out of those, 11 markers were obtained as the top predictors in classifying breast cancer patients and healthy controls. The classification algorithm yielded an accuracy of 94% (95.9% sensitivity, 88.7% specificity) and an AUC of 0.983 on the training set. This classifier was validated on an independent test set with 30% of the subjects, yielding an accuracy of 93% (96.4% sensitivity, 83.9% specificity) and an AUC of 0.974. Test sensitivity was high across stages, at 96% / 90% / 95% / 90% in stages 1-4, respectively. Conclusions: Based on the results, we conclude that circulating glycoproteins in serum may be useful in screening applications in breast cancer, and strongly demonstrates the utility of glycoprotein profiles as a powerful non-invasive diagnostic tool.

Novel plasma glycoprotein biomarkers predict progression-free survival in surgically resected clear cell renal cell carcinoma.

Limited data exists on utilization of protein post-translational modifications as biomarkers for clear cell renal cell carcinoma (ccRCC).We employed high-throughput glycoproteomics to evaluate differential expression of glycoprotein-isoforms as novel markers for ccRCC progression-free survival (PFS). Plasma samples were obtained from 77 patients treated surgically for ccRCC. Glycoproteomic analyses were carried out after liquid chromatography tandem mass spectrometry.Age-adjusted Cox proportional hazard models were constructed to evaluate PFS. Optimized Harrell's C-index was employed to dichotomize the collective for the construction of Kaplan-Meier curves. The average length of follow-up was 3.4 (range: 0.04-9.83) years. Glycoproteomic analysis identified 39 glycopeptides and 14 non-glycosylated peptides that showed statistically significant (false discovery rate P ≤ 0.05) differential expression associated with PFS. Five of the glycosylated peptides conferred continuous hazard ratio (HR) of > 6 (range 6.3-11.6).These included prothrombin A2G2S glycan motif (HR = 6.47, P = 9.53E-05), immunoglobulin J chain FA2G2S2 motif (HR = 10.69, P = 0.001), clusterin A2G2 motif (HR = 7.38, P = 0.002), complement component C8A A2G2S2 motif (HR = 11.59, P = 0.002), and apolipoprotein M glycopeptide with non-fucosylated and non-sialylated hybrid-type glycan (HR = 6.30, P = 0.003). Kaplan-Meier curves based on dichotomous expression of these five glycopeptides resulted in hazard ratios of 3.9 to 10.7, all with P-value < 0.03. Kaplan-Meyer plot using the multivariable model comprising 3 of the markers yielded HR of 11.96 (P < 0.0001). Differential glyco-isoform abundance of plasma proteins may be a useful source of biomarkers for the clinical course and prognosis of ccRCC.

Microflow Liquid Chromatography Coupled to Mass Spectrometry (μLC-MS) Workflow for O-Glycopeptides Isomers Analysis Combining Differential Mobility Spectrometry and Collision Induced and Electron Capture Dissociation.

Peptide and protein O-glycosylation can occur mostly on any serine or threonine and could generate several positional isomers, which may coelute during liquid chromatography (LC) separation, challenging their characterization. Ion mobility has emerged as a technique of interest to separate isomeric compounds. In the different ion mobility techniques, differential ion mobility (DMS) includes the particular interest to tune ion separation by the possible addition of an organic modifier. Different microflow liquid chromatography coupled to mass spectrometry (μLC-MS) workflows were investigated for the analysis of a set of four model peptides made of three isomeric glycopeptides and a corresponding nonglycosylated peptide using differential mobility spectrometry (DMS), collision induced dissociation (CID), and electron capture dissociation (ECD). Neither DMS nor LC provided sufficient separation of the three isomeric O-glycopeptides while the nonmodified one was clearly separated by LC. The hyphenation of LC with DMS led to differentiating the three glycopeptides, and further detection and characterization (ECD/CID) with a chimeric collision cell were achieved in a single LC run. The position of the modification was determined from ECD data, while CID data characterized the sugar through its distinctive oxoniums ions in the low mass range.

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.

Glycopeptide-Centric Approaches for the Characterization of Microbial Glycoproteomes.

Protein glycosylation is increasingly recognized as a common class of modifications within microbial species that can shape protein functions and the proteome at large. Due to this, there is an increasing need for robust analytical methods, which allow for the identification and characterization of microbial glycopeptides from proteome samples in a high-throughput manner. Using affinity-based enrichment (either hydrophilicity or antibody-based approaches) glycopeptides can easily be separated from non-glycosylated peptides and analyzed using mass spectrometry. By utilizing multiple mass spectrometry fragmentation approaches and open searching-based bioinformatic techniques, novel glycopeptides can be identified and characterized without prior knowledge of the glycans used for glycosylation. Using these approaches, glycopeptides within samples can rapidly be identified as well as quantified to understand how glycosylation changes in response to stimuli or how changes in glycosylation systems impact the glycoproteome. This chapter outlines a set of robust protocols for the initial preparation, enrichment, and analysis of microbial glycopeptides for both qualitative and quantitative glycoproteomic studies. Using these approaches, glycosylation events can be easily identified by researchers without the need for extensive manual analysis of proteomic datasets.

Deep Membrane Proteome Profiling Reveals Overexpression of Prostate-Specific Membrane Antigen (PSMA) in High-Risk Human Paraganglioma and Pheochromocytoma, Suggesting New Theranostic Opportunity.

Pheochromocytomas and paragangliomas (PPGLs) are rare neuroendocrine tumors arising from chromaffin cells of adrenal medulla or sympathetic or parasympathetic paraganglia, respectively. To identify new therapeutic targets, we performed a detailed membrane-focused proteomic analysis of five human paraganglioma (PGL) samples. Using the Pitchfork strategy, which combines specific enrichments of glycopeptides, hydrophobic transmembrane segments, and non-glycosylated extra-membrane peptides, we identified over 1800 integral membrane proteins (IMPs). We found 45 “tumor enriched” proteins, i.e., proteins identified in all five PGLs but not found in control chromaffin tissue. Among them, 18 IMPs were predicted to be localized on the cell surface, a preferred drug targeting site, including prostate-specific membrane antigen (PSMA), a well-established target for nuclear imaging and therapy of advanced prostate cancer. Using specific antibodies, we verified PSMA expression in 22 well-characterized human PPGL samples. Compared to control chromaffin tissue, PSMA was markedly overexpressed in high-risk PPGLs belonging to the established Cluster 1, which is characterized by worse clinical outcomes, pseudohypoxia, multiplicity, recurrence, and metastasis, specifically including SDHB, VHL, and EPAS1 mutations. Using immunohistochemistry, we localized PSMA expression to tumor vasculature. Our study provides the first direct evidence of PSMA overexpression in PPGLs which could translate to therapeutic and diagnostic applications of anti-PSMA radio-conjugates in high-risk PPGLs.

O-glycosylated HBsAg peptide can induce specific antibody neutralizing HBV infection

BackgroundHepatitis B virus (HBV), which causes hepatitis, liver cirrhosis, and hepatocellular carcinoma, is a global human health problem. HBV contains three envelope proteins, S-, M-, and L-hepatitis B surface antigen (HBsAg). We recently found that O-glycosylated M-HBsAg, reactive with jacalin lectin, is one of the primary components of HBV DNA-containing virus particles. Thus, we aimed to analyze and target the glycosylation of HBsAg. MethodsHBsAg prepared from the serum of Japanese patients with HBV were analyzed using mass spectrometry. The glycopeptide modified with O-glycan was generated and used for immunization. The specificity of the generated antibody and the HBV infection inhibition activity was examined. ResultsMass spectrometry analysis revealed that T37 and/or T38 on M-HBsAg of genotype C were modulated by ±NeuAc(α2,3)Gal(β1,3)GalNAc. Chemically and enzymatically synthesized O-glycosylated peptide (Glyco-PS2) induced antibodies that recognize mainly PreS2 in M-HBsAg not in L-HBsAg, whereas the non-glycosylated peptide (PS2) induced antisera recognizing L-HBsAg but not O-glycosylated M-HBsAg. The removal of O-glycan from M-HBsAg partly decreased the reactivity of the Glyco-PS2 antibody, suggesting that peptide part was also recognized by the antibody. The antibody further demonstrated the inhibition of HBV infection in human hepatic cells in vitro. ConclusionsGlycosylation of HBsAg occurs differently in different HBsAgs in a site-specific manner. The new Glyco-PS2 antibody, recognizing O-glycosylated M-HBsAg of genotype C, could inhibit HBV infection. General significanceThe detailed analysis of HBsAg identified different glycosylations of HBV surface. The glycosylated peptide based on mass spectrometry analysis showed higher potential to induce functional antibody against HBV.

O-Fucose and Fringe-Modified Recombinant Notch Peptides As Decoy Molecules to Promote Hematopoietic Progenitor Mobilization

Nearly all autologous hematopoietic cell therapy (HCT) procedures are performed using mobilized hematopoietic progenitor cells (HPCs). Successful outcome is dependent on infusing an adequate number of functionally active HPCs. Mobilization strategy of AMD3100, in combination with G-CSF, increases total CD34+ cells mobilized, and often is used for HPC mobilization in myeloma and non-Hodgkin lymphoma patients. Unfortunately, this approach does not always lead to adequate HPC collection in patients who previously have been exposed to extensive cytotoxic therapy. Furthermore, suboptimal number of HPCs mobilized in some patients may result in slower hematopoietic reconstitution and increased risk of complications during the transplant. Hence developing new mobilization strategy is highly desired for patients who are considered poor-mobilizers. Notch is a signaling molecule important for stem cell self-renewal and fate determination in many tissues including the hematopoietic system. An important feature of Notch is its adhesive nature which was first described by cell aggregation assays in Drosophila studies. We previously reported that hematopoietic stem cell and progenitors (HSPCs) with faulty Notch-ligand interaction display increased cell cycling, decreased adhesion to marrow osteoblastic lineage cells, and enhanced egress from the marrow.Since Notch2 is the major Notch isotype expressed on HSPCs, we showed that prior treatment in mice with Notch2 blocking antibody but not Notch1 blocking antibody sensitizes HSPC to the mobilizing stimuli of G-CSF and AMD3100 with a 3~4 - fold increase in mobilization. We showed that transient Notch2 blockade leads to decreased CXCR4 expression by HSPCs with CXCR4 transcription being directly regulated by the Notch transcriptional protein RBPJ. Notch2 blockade also increases cell cycling of HPCs during mobilization temporarily without affecting hematopoietic stem cell (HSC) homeostasis and self-renewal. Moreover, transient Notch2 blockade leads to greater HSPC homing to the marrow that is accompanied with a faster hematopoietic recovery and a competitive repopulating advantage of HSPCs.Herein, to search for alternative agents to Notch blocking antibody, we evaluate the ability of recombinant Notch peptides as decoy molecules to promote disengagement of Notch2-ligand interaction between HSPCs and niche supporting cells. By using OP9 cell-based binding assay and surface plasmon resonance (SPR) analyses, we showed that recombinant Notch111-13, Notch19-13, Notch18-13, Notch211-13, Notch29-13, and Notch28-13 peptide comprising EGF-like repeats 11-13, 8-13, and 9-13, of Notch1 or Notch2, respectively, all bind to Notch ligand Dll4, Dll1 and Jag2. Because EGF-like repeat 12 (EGF 12) has been shown located within the essential ligand binding domain, and the O -fucose attached to the threonine residue of Notch1 EGF12 acts as a surrogate amino acid to make specific and functional contact to residues of Notch ligand, we generated peptides in CHO cells with O -fucose attached to the threonine residue of EGF12 and further extended by Fringe. We found that binding affinities of the glycosylated peptides further increased compared to those of non-glycosylated peptides. Compared to Notch2 peptides, O -fucose and Fringe-modified Notch111-13 peptide shows the highest biding affinity to Notch ligand Dll4 with dissociation constant (Kd) reaching 34 nM. Similar to Notch2 blocking antibody, the O -fucose and Fringe-modified Notch111-13 peptide effectively outcompetes Notch2-ligand binding and thus blocks the adhesion of marrow HSPCs to the primary osteoblasts or OP9 cells expressing Notch ligands. Further, O -fucose and Fringe-modified Notch111-13 peptide can effectively induce HSPCs emigration from a 3D osteoblastic “spheroid niche”, an in vitro model of HSC niche. Compared to Notch2 blocking antibody, O -fucose and Fringe-modified Notch111-13 peptide caused a further decrease of HSPC cells remained in the “spheroid niche” and more HSPC emigration out of the “niche”.In summary, our findings suggest that recombinant Notch decoy peptides may be considered as alternatives to Notch2 blockade to be combined with the mobilizing regimen of AMD3100 and G-CSF for more effective HSPC mobilization. DisclosuresNo relevant conflicts of interest to declare.