Modulation of the vitamin D/vitamin D receptor system in osteoporosis pathogenesis: insights and therapeutic approaches.

Osteoporosis is a prevalent bone disorder characterized by low bone mineral density (BMD) and deteriorated bone microarchitecture, leading to an increased risk of fractures. Vitamin D (VD), an essential nutrient for skeletal health, plays a vital role in maintaining bone homeostasis. The biological effects of VD are primarily mediated through the vitamin D receptor (VDR), a nuclear receptor that regulates the transcription of target genes involved in calcium and phosphate metabolism, bone mineralization, and bone remodeling. In this review article, we conduct a thorough literature search of the PubMed and EMBASE databases, spanning from January 2000 to September 2023. Utilizing the keywords "vitamin D," "vitamin D receptor," "osteoporosis," and "therapy," we aim to provide an exhaustive overview of the role of the VD/VDR system in osteoporosis pathogenesis, highlighting the most recent findings in this field. We explore the molecular mechanisms underlying VDR's effects on bone cells, including osteoblasts and osteoclasts, and discuss the impact of VDR polymorphisms on BMD and fracture risk. Additionally, we examine the interplay between VDR and other factors, such as hormonal regulation, genetic variants, and epigenetic modifications, that contribute to osteoporosis susceptibility. The therapeutic implications of targeting the VDR pathway for osteoporosis management are also discussed. By bringing together these diverse aspects, this review enhances our understanding of the VD/VDR system's critical role in the pathogenesis of osteoporosis and highlights its significance as a potential therapeutic target.

pGlycoQuant with a deep residual network for quantitative glycoproteomics at intact glycopeptide level.

Large-scale intact glycopeptide identification has been advanced by software tools. However, tools for quantitative analysis remain lagging behind, which hinders exploring the differential site-specific glycosylation. Here, we report pGlycoQuant, a generic tool for both primary and tandem mass spectrometry-based intact glycopeptide quantitation. pGlycoQuant advances in glycopeptide matching through applying a deep learning model that reduces missing values by 19-89% compared with Byologic, MSFragger-Glyco, Skyline, and Proteome Discoverer, as well as a Match In Run algorithm for more glycopeptide coverage, greatly expanding the quantitative function of several widely used search engines, including pGlyco 2.0, pGlyco3, Byonic and MSFragger-Glyco. Further application of pGlycoQuant to the N-glycoproteomic study in three different metastatic HCC cell lines quantifies 6435 intact N-glycopeptides and, together with in vitro molecular biology experiments, illustrates site 979-core fucosylation of L1CAM as a potential regulator of HCC metastasis. We expected further applications of the freely available pGlycoQuant in glycoproteomic studies.

gQuant, an Automated Tool for Quantitative Glycomic Data Analysis.

MALDI-MS-based glycan isotope labeling methods have been effectively and widely used for quantitative glycomics. However, interpretation of the data produced by MALDI-MS is inaccurate and tedious because the bioinformatic tools are inadequate. In this work, we present gQuant, an automated tool for MALDI-MS-based glycan isotope labeling data processing. gQuant was designed with a set of dedicated algorithms to improve the efficiency, accuracy and convenience of quantitation data processing. When tested on the reference data set, gQuant showed a fast processing speed, as it was able to search the glycan data of model glycoproteins in a few minutes and reported more results than the manual analysis did. The reported quantitation ratios matched well with the experimental glycan mixture ratios ranging from 1:10 to 10:1. In addition, gQuant is fully open-source and is coded in Python, which is supported by most operating systems, and it has a user-friendly interface. gQuant can be easily adapted by users for specific experimental designs, such as specific glycan databases, different derivatization types and relative quantitation designs and can thus facilitate fast glycomic quantitation for clinical sample analysis using MALDI-MS-based stable isotope labeling.

OGP: A Repository of Experimentally Characterized O-glycoproteins to Facilitate Studies on O-glycosylation.

Numerous studies on cancers, biopharmaceuticals, and clinical trials have necessitated comprehensive and precise analysis of protein O-glycosylation. However, the lack of updated and convenient databases deters the storage of and reference to emerging O-glycoprotein data. To resolve this issue, an O-glycoprotein repository named OGP was established in this work. It was constructed with a collection of O-glycoprotein data from different sources. OGP contains 9354 O-glycosylation sites and 11,633 site-specific O-glycans mapping to 2133 O-glycoproteins, and it is the largest O-glycoprotein repository thus far. Based on the recorded O-glycosylation sites, an O-glycosylation site prediction tool was developed. Moreover, an OGP-based website is already available (https://www.oglyp.org/). The website comprises four specially designed and user-friendly modules: statistical analysis, database search, site prediction, and data submission. The first version of OGP repository and the website allow users to obtain various O-glycoprotein-related information, such as protein accession Nos., O-glycosylation sites, O-glycopeptide sequences, site-specific O-glycan structures, experimental methods, and potential O-glycosylation sites. O-glycosylation data mining can be performed efficiently on this website, which will greatly facilitate related studies. In addition, the database is accessible from OGP website (https://www.oglyp.org/download.php).

Development of a Computational Tool for Automated Interpretation of Intact O-Glycopeptide Tandem Mass Spectra from Single Proteins.

Precise and automated analysis of site-specific O-glycosylation on single proteins is crucial for comprehensive characterization of some important glycoproteins, such as tumor biomarkers and recombinant drug proteins. Mass spectrometry has been proven to be a powerful technique for protein sequencing and N-glycosylation analysis. However, challenges remain in developing computational tools for intact O-glycopeptide analysis, which has greatly hindered the development of mass-spectrometry-based O-glycosylation analysis. Herein, an integrated strategy together with a dedicated automated computational tool termed AOGP was developed for intact O-glycopeptide analysis on single proteins. AOGP utilized de novo sequencing for O-glycans and a database search strategy for peptide backbones. The false discovery rate (FDR) of the identification results was controlled and validated by a mixed Gaussian distribution estimation method. AOGP exhibited superior performance in identifying intact O-glycopeptides of the human erythropoietin with a total of 188 O-glycopeptide spectra reported under 1% FDR. AOGP is developed in Python, is fully open-sourced, and is equipped with a user-friendly interface. Such an easy-operating and robust tool would greatly facilitate O-glycosylation analysis on single proteins in tumor biomarker and recombinant drug protein development.

Aperture-controllable nano-electrospray emitter and its application in cardiac proteome analysis.

The emitter clogging is the most common hardware failure of nano-electrospray ionization, to improve the durability and electrospray stability of fused silica emitters, we demonstrate a means of fabricating nano-electrospray emitters with controllable aperture size and gradually-narrowed channel on the tip. We simulated the fluid morphologies in the emitter channels by computational fluid dynamics and found more stable flow on aperture-controllable nano-electrospray emitter. Besides, we found the unstable flow sections of commercial emitters match the actual clogging sections very well, indicating the main cause of emitter clogging is unstable flow. We further tested the emitters by nano-LC-MS based proteome analysis. Compared with the commercial emitter, aperture-controllable nano-electrospray emitters promoted the total ion chromatogram intensity by 25%, the number of identified proteins by 6.58%, and the number of identified peptides by 7.87%. In total, 989 proteins were identified from 1 µg of extracted mouse cardiac proteins. After the optimization by using mouse samples, we analyzed clinical auricular dextral tissues from patients undergoing cardiac surgery and found 16 proteins related to atrial fibrillation. Overall, aperture-controllable nano-electrospray emitter exhibits better sensitivity and reproducibility in the application of nano-LC-MS cardiac proteome analysis.

Straightforward and Highly Efficient Strategy for Hepatocellular Carcinoma Glycoprotein Biomarker Discovery Using a Nonglycopeptide-Based Mass Spectrometry Pipeline.

Efficient detection of aberrant glycoproteins in serum is particularly important for biomarker discovery. However, direct quantitation of glycoproteins in serum remains technically challenging because of the extraordinary complexity of the serum proteome. In the current work, we proposed a straightforward and highly efficient strategy by using the nonglycopeptides releasing from the specifically enriched glycoproteins for targeted glycoprotein quantification. With this so-called nonglycopeptide-based mass spectrometry (NGP-MS) strategy, a powerful and nondiscriminatory pipeline for hepatocellular carcinoma (HCC) glycoprotein biomarker discovery, verification, and validation has been developed. First, a data set of 234 NGPs was strictly established for multiple-reaction monitoring (MRM) quantification in serum. Second, the NGPs enriched from 20 HCC serum mixtures and 20 normal serum mixtures were labeled with mTRAQ reagents (Δ0 and Δ8, respectively) to find the differentially expressed glycoproteins in HCC. A total of 97 glycoprotein candidates were preliminarily screened and submitted for absolute quantitation with NGP-based stable-isotope-labeled (SID)-MRM in the individual samples of 38 HCC serum and 24 normal controls. Finally, 21 glycoproteins were absolutely quantified with high quality. The diagnostic sensitivity results showed that three glycoproteins, ß-2-glycoprotein 1 (APOH), α-1-acid glycoprotein 2 (ORM2), and complement C3 (C3), could be used for the discrimination between HCC patients and healthy people. A novel glycoprotein biomarker panel [APOH, ORM2, C3, and α-fetoprotein (AFP)] has proven to outperform AFP, the known HCC serum biomarker, alone, in this study. We believe that this strategy and the panel of glycoproteins might hold great clinical value for HCC detection in the future.

A multi-parallel N-glycopeptide enrichment strategy for high-throughput and in-depth mapping of the N-glycoproteome in metastatic human hepatocellular carcinoma cell lines.

N-glycosylation is deeply involved in many biological processes, and approximately 50% of mammalian proteins are predicted to be glycosylated. Many large-scale studies have been carried out to reveal the glycosylation status involved in different physiological pathologies across species. However, the lack of a highly specific and high-throughput N-glycosylated enrichment method not only results in extended time requirements but also limits the depth of mapping when handling a large number of samples. In this study, we firstly optimized traditional zwitterionic hydrophilic interaction liquid chromatography (ZIC-HILIC) enrichment and found that using of 70% acetonitrile (ACN), 0.1% trifluoroacetic acid (TFA) as the enrichment buffer, 2800 g as the washing speed and 600 µL as the washing volume achieved the best specificity, which is higher than 75%. On this basis, we developed a multi-parallel enrichment strategy assisted by a filter-coated 96-well plate, which achieved high specificity and high throughput simultaneously. This strategy allowed us to enrich large numbers of fractionated samples from hepatocellular carcinoma (HCC) cell lines in less than 2 h. Its good specificity helped us achieve in-depth mapping of the N-glycoproteome in metastatic HCC cell lines. A total of 5466 N-glycosites from 2383 glycoproteins were identified, among which 1900 N-glycosites were unannotated in UniProt. The in-depth glycoproteome mapping provides insight into the N-glycosylation status in HCC cell lines with differences in metastatic potential and contributes to biomarker discovery.

In Situ Synthesis of Magnetic Mesoporous Phenolic Resin for the Selective Enrichment of Glycopeptides.

Protein glycosylation is a significant participant in a mass of biological processes, which is a pivotal protein post-translational modification. Due to the low contents of glycopeptides compared with nonglycopeptides and the microheterogeneity of glycosylation sites, highly selective enrichment methods for the purification of glycopeptides are required for the comprehensive characterization of glycoproteomics. In this work, a type of magnetic mesoporous phenolic resin (MMP) was prepared using branched polyethylenimine (PEI) as a cross-linker from a homogeneous magnetic Fe3O4@SiO2 solution in a resorcinol/formaldehyde monomer aqueous system via an in situ emulsion polymerization procedure. The results showed that MMP exhibited good biocompatibility, a mesoporous structure, nitrogen-containing functionality, excellent hydrophilicity, and solvent resistance by using multiple characterization methods. By taking advantage of the interaction between hydrophilic groups on the MMP and glycan components on the glycopeptides, the acquired MMP was utilized to the selective capture of N-glycopeptides (human IgG or HRP tryptic digests/BSA proteins = 1:50), good recovery yield (70.18-97.23%), superior binding capacity (400 mg g-1), and excellent reproducibility. Based on the outstanding performance in standard glycoproteins tryptic digests enrichment, MMP was further used to capture N-glycopeptides from tryptic digests of human serum. A total of 15 unique N-glycopeptides were identified from an ultralow sample volume (0.025 µL) of human serum. Overall, we identified 356 unique N-glycopeptides corresponding to 119 glycoproteins from human serum (0.35 µL) in the overlap of three replicate analyses. All the results have demonstrated that MMP has great potential in large-scale N-glycoproteomics research.

pGlyco 2.0 enables precision N-glycoproteomics with comprehensive quality control and one-step mass spectrometry for intact glycopeptide identification.

The precise and large-scale identification of intact glycopeptides is a critical step in glycoproteomics. Owing to the complexity of glycosylation, the current overall throughput, data quality and accessibility of intact glycopeptide identification lack behind those in routine proteomic analyses. Here, we propose a workflow for the precise high-throughput identification of intact N-glycopeptides at the proteome scale using stepped-energy fragmentation and a dedicated search engine. pGlyco 2.0 conducts comprehensive quality control including false discovery rate evaluation at all three levels of matches to glycans, peptides and glycopeptides, improving the current level of accuracy of intact glycopeptide identification. The N-glycoproteome of samples metabolically labeled with 15N/13C were analyzed quantitatively and utilized to validate the glycopeptide identification, which could be used as a novel benchmark pipeline to compare different search engines. Finally, we report a large-scale glycoproteome dataset consisting of 10,009 distinct site-specific N-glycans on 1988 glycosylation sites from 955 glycoproteins in five mouse tissues.Protein glycosylation is a heterogeneous post-translational modification that generates greater proteomic diversity that is difficult to analyze. Here the authors describe pGlyco 2.0, a workflow for the precise one step identification of intact N-glycopeptides at the proteome scale.

Highly Selective Enrichment of Glycopeptides Based on Zwitterionically Functionalized Soluble Nanopolymers.

Efficient glycopeptides enrichment prior to mass spectrometry analysis is essential for glycoproteome study. ZIC-HILIC (zwitterionic hydrophilic interaction liquid chromatography) based glycopeptides enrichment approaches have been attracting more attention for several benefits like easy operating, high enrichment specificity and intact glycopeptide retained. In this study, Poly (amidoamine) dendrimer (PAMAM) was adopted for the synthesis of zwitterionically functionalized (ZICF) materials for glycopeptide enrichment. The multiple branched structure and good solubility of ZICF-PAMAM enables a sufficient interaction with glycopeptides. The ZICF-PAMAM combined with the FASP-mode enrichment strategy exhibits more superior performance compared with the existing methods. It has the minimum detectable concentration of femtomolar level and high recovery rate of over 90.01%, and can efficiently enrich glycopeptides from complex biological samples even for merely 0.1 µL human serum. The remarkable glycopeptides enrichment capacity of ZICF-PAMAM highlights the potential application in in-depth glycoproteome research, which may open up new opportunities for the development of glycoproteomics.

Glycan reducing end dual isotopic labeling (GREDIL) for mass spectrometry-based quantitative N-glycomics.

A general and effective enzymatic labeling method, termed glycan reducing end dual isotopic labeling (GREDIL), was developed for mass spectrometry-based quantitative N-glycomics.