Signaling pathway of globo-series glycosphingolipids and ß1,3-galactosyltransferase V (ß3GalT5) in breast cancer.

The globo-series glycosphingolipids (GSLs) SSEA3, SSEA4, and Globo-H specifically expressed on cancer cells are found to correlate with tumor progression and metastasis, but the functional roles of these GSLs and the key enzyme ß1,3-galactosyltransferase V (ß3GalT5) that converts Gb4 to SSEA3 remain largely unclear. Here we show that the expression of ß3GalT5 significantly correlates with tumor progression and poor survival in patients, and the globo-series GSLs in breast cancer cells form a complex in membrane lipid raft with caveolin-1 (CAV1) and focal adhesion kinase (FAK) which then interact with AKT and receptor-interacting protein kinase (RIP), respectively. Knockdown of ß3GalT5 disrupts the complex and induces apoptosis through dissociation of RIP from the complex to interact with the Fas death domain (FADD) and trigger the Fas-dependent pathway. This finding provides a link between SSEA3/SSEA4/Globo-H and the FAK/CAV1/AKT/RIP complex in tumor progression and apoptosis and suggests a direction for the treatment of breast cancer, as demonstrated by the combined use of antibodies against Globo-H and SSEA4.

MAGIC-web: a platform for untargeted and targeted N-linked glycoprotein identification.

MAGIC-web is the first web server, to the best of our knowledge, that performs both untargeted and targeted analyses of mass spectrometry-based glycoproteomics data for site-specific N-linked glycoprotein identification. The first two modules, MAGIC and MAGIC+, are designed for untargeted and targeted analysis, respectively. MAGIC is implemented with our previously proposed novel Y1-ion pattern matching method, which adequately detects Y1- and Y0-ion without prior information of proteins and glycans, and then generates in silico MS(2) spectra that serve as input to a database search engine (e.g. Mascot) to search against a large-scale protein sequence database. On top of that, the newly implemented MAGIC+ allows users to determine glycopeptide sequences using their own protein sequence file. The third module, Reports Integrator, provides the service of combining protein identification results from Mascot and glycan-related information from MAGIC-web to generate a complete site-specific protein-glycan summary report. The last module, Glycan Search, is designed for the users who are interested in finding possible glycan structures with specific numbers and types of monosaccharides. The results from MAGIC, MAGIC+ and Reports Integrator can be downloaded via provided links whereas the annotated spectra and glycan structures can be visualized in the browser. MAGIC-web is accessible from http://ms.iis.sinica.edu.tw/MAGIC-web/index.html.

Adapting Data-Independent Acquisition for Mass Spectrometry-Based Protein Site-Specific N-Glycosylation Analysis.

A hallmark of protein N-glycosylation is extensive heterogeneity associated with each glycosylation site. In human cells, the constituent glycoforms differ mostly in numerous ways of extensions from an invariable trimannosyl core and terminal modifications. The efficient identification of these glycoforms at the glycopeptide level by mass spectrometry (MS) requires a precursor sampling technique that is not dictated by signal intensity or by preset targets during MS2 data acquisition. We show here that the recently developed data-independent acquisition (DIA) approach is best suited to this demanding task. It allows postacquisition extraction of glycopeptide-specific fragment-ion chromatograms to be aligned with that of precursor MS1 ion by nanoLC elution time. For any target glycoprotein, judicious selection of the most favorable MS1/MS2 transitions can first be determined from prior analysis of a purified surrogate standard that carries similar site-specific glycosylation but may differ in its exact range of glycoforms. Since the MS2 transitions to be used for extracting DIA data is common to that glycosylation site and not dictated by a specific MS1 value, our workflow applies equally well to the identification of both targeted and unexpected glycoforms. Using a case example, we show that, in targeted mode, it identified more site-specific glycoforms than the more commonly used data-dependent acquisition method when the amount of the target glycoprotein was limited in a sample of high complexity. In discovery mode, it allows detection, with supporting MS2 evidence, of under-sampled glycoforms and of those that failed to be identified by searching against a predefined glycan library owing to unanticipated modifications.

MAGIC: an automated N-linked glycoprotein identification tool using a Y1-ion pattern matching algorithm and in silico MS² approach.

Glycosylation is a highly complex modification influencing the functions and activities of proteins. Interpretation of intact glycopeptide spectra is crucial but challenging. In this paper, we present a mass spectrometry-based automated glycopeptide identification platform (MAGIC) to identify peptide sequences and glycan compositions directly from intact N-linked glycopeptide collision-induced-dissociation spectra. The identification of the Y1 (peptideY0 + GlcNAc) ion is critical for the correct analysis of unknown glycoproteins, especially without prior knowledge of the proteins and glycans present in the sample. To ensure accurate Y1-ion assignment, we propose a novel algorithm called Trident that detects a triplet pattern corresponding to [Y0, Y1, Y2] or [Y0-NH3, Y0, Y1] from the fragmentation of the common trimannosyl core of N-linked glycopeptides. To facilitate the subsequent peptide sequence identification by common database search engines, MAGIC generates in silico spectra by overwriting the original precursor with the naked peptide m/z and removing all of the glycan-related ions. Finally, MAGIC computes the glycan compositions and ranks them. For the model glycoprotein horseradish peroxidase (HRP) and a 5-glycoprotein mixture, a 2- to 31-fold increase in the relative intensities of the peptide fragments was achieved, which led to the identification of 7 tryptic glycopeptides from HRP and 16 glycopeptides from the mixture via Mascot. In the HeLa cell proteome data set, MAGIC processed over a thousand MS(2) spectra in 3 min on a PC and reported 36 glycopeptides from 26 glycoproteins. Finally, a remarkable false discovery rate of 0 was achieved on the N-glycosylation-free Escherichia coli data set. MAGIC is available at http://ms.iis.sinica.edu.tw/COmics/Software_MAGIC.html .

Interaction modes and approaches to glycopeptide and glycoprotein enrichment.

Protein glycosylation has received increased attention for its critical role in cell biology and diseases. Developing new methodologies to discern phenotype-dependent glycosylation will not only elucidate the mechanistic aspects of cell signaling cascades but also accelerate biomarker discovery for disease diagnosis or prognosis. In the analytical pipeline, enrichment at either the protein or peptide level is the most critical prerequisite for analyzing heterogeneous glycan composition, linkage, site occupancy and carrier proteins. Because the critical factor for choosing a suitable enrichment method is primarily a particular technique's selectivity and affinity towards target glycoproteins/glycopeptides, it is important to fully understand the working principles for the different approaches. For mechanistic insight into the enrichment protocol, we focused on the fundamental chemical and physical processes for the commonly used approaches based on: (a) glycan/peptide physicochemical properties (hydrophilic interactions, chelation/coordination chemistry) and (b) glycan-specific recognition (lectin-based affinity, covalent bond formation by hydrazide/boronic acid). Various interaction modes, such as hydrogen bonding, van der Waals interaction, multivalency, and metal- or water-mediated stabilization, are discussed in detail. In addition, we will review the design of and modifications to such methods, hyphenated approaches, and glycoproteomic applications. Finally, we will outline challenges to existing strategies and offer novel proposals for glycoproteome enrichment.

Single Site N-Glycosylation of B Cell Maturation Antigen (BCMA) Inhibits γ-Secretase-Mediated Shedding and Improves Surface Retention and Cell Survival.

B cell maturation antigen (BCMA), a member of the tumor necrosis factor receptor (TNFR) family, on the cell surface plays a key role in maintaining the survival of plasma cells and malignant as well as inflammatory accessory cells. Therefore, targeting BCMA or disrupting its interaction with ligands has been a potential approach to cancer therapy. BCMA contains a single N-glycosylation site, but the function of N-glycan on BCMA is not understood. Here, we found that the N-glycosylation of BCMA promoted its cell-surface retention while removing the N-glycan increased BCMA secretion through γ-secretase-mediated shedding. Addition of γ-secretase inhibitor prevented nonglycosylated BCMA from shedding and protected cells from dexamethasone and TRAIL-induced apoptosis.

Quantitative mass spectrometric analysis of hepatocellular carcinoma biomarker alpha-fetoprotein.

Serum alpha-fetoprotein (AFP) has been used as a marker for the diagnosis of hepatocellular carcinoma (HCC) and its core fucosylation is associated with the early stage of HCC. However, current methods for the detection of AFP with core fucose are not highly accurate for early diagnosis. In this study, we established an enzyme-assisted mass spectrometric method for the quantitative analysis of AFP/core fucose with high specificity and sensitivity. We employed endoglycosidase treatment of AFP to improve the biomarker analysis. The accuracy and precision are within the US FDA-suggested value, and a good linearity (r2 = 0.9930) and a detection limit of 15.6 ng mL-1 can be achieved.

Development of in silico models to predict viscosity and mouse clearance using a comprehensive analytical data set collected on 83 scaffold-consistent monoclonal antibodies.

Biologic drug discovery pipelines are designed to deliver protein therapeutics that have exquisite functional potency and selectivity while also manifesting biophysical characteristics suitable for manufacturing, storage, and convenient administration to patients. The ability to use computational methods to predict biophysical properties from protein sequence, potentially in combination with high throughput assays, could decrease timelines and increase the success rates for therapeutic developability engineering by eliminating lengthy and expensive cycles of recombinant protein production and testing. To support development of high-quality predictive models for antibody developability, we designed a sequence-diverse panel of 83 effector functionless IgG1 antibodies displaying a range of biophysical properties, produced and formulated each protein under standard platform conditions, and collected a comprehensive package of analytical data, including in vitro assays and in vivo mouse pharmacokinetics. We used this robust training data set to build machine learning classifier models that can predict complex protein behavior from these data and features derived from predicted and/or experimental structures. Our models predict with 87% accuracy whether viscosity at 150 mg/mL is above or below a threshold of 15 centipoise (cP) and with 75% accuracy whether the area under the plasma drug concentration-time curve (AUC0-672 h) in normal mouse is above or below a threshold of 3.9 × 106 h x ng/mL.

Computer-aided scoring of erb-b2 receptor tyrosine kinase 2 (HER2) gene amplification status in breast cancer.

Background: Identification of HER2 protein overexpression and/or amplification of the HER2 gene are required to qualify breast cancer patients for HER2 targeted therapies. In situ hybridization (ISH) assays that identify HER2 gene amplification function as a stand-alone test for determination of HER2 status and rely on the manual quantification of the number of HER2 genes and copies of chromosome 17 to determine HER2 amplification. Methods: To assist pathologists, we have developed the uPath HER2 Dual ISH Image Analysis for Breast (uPath HER2 DISH IA) algorithm, as an adjunctive aid in the determination of HER2 gene status in breast cancer specimens. The objective of this study was to compare uPath HER2 DISH image analysis vs manual read scoring of VENTANA HER2 DISH-stained breast carcinoma specimens with ground truth (GT) gene status as the reference. Three reader pathologists reviewed 220, formalin-fixed, paraffin-embedded (FFPE) breast cancer cases by both manual and uPath HER2 DISH IA methods. Scoring results from manual read (MR) and computer-assisted scores (image analysis, IA) were compared against the GT gene status generated by consensus of a panel of pathologists. The differences in agreement rates of HER2 gene status between manual, computer-assisted, and GT gene status were determined. Results: The positive percent agreement (PPA) and negative percent agreement (NPA) rates for image analysis (IA) vs GT were 97.2% (95% confidence interval [CI]: 95.0, 99.3) and 94.3% (95% CI: 90.8, 97.3) respectively. Comparison of agreement rates showed that the lower bounds of the 95% CIs for the difference of PPA and NPA for IA vs MR were -0.9% and -6.2%, respectively. Further, inter- and intra-reader agreement rates in the IA method were observed with point estimates of at least 96.7%. Conclusions: Overall, our data show that the uPath HER2 DISH IA is non-inferior to manual scoring and supports its use as an aid for pathologists in routine diagnosis of breast cancer.

Performance comparison of anti-p504s (SP116) Rabbit Monoclonal Primary Antibody vs. Monoclonal Rabbit Anti-Human AMACR clone 13H4 when duplexed with VENTANA Basal Cell Cocktail (34ßE12+p63) as a diagnostic aid for prostatic adenocarcinoma using immunohistochemistry.

Alpha-methylacyl-coenzyme A-racemase (AMACR), also known as p504s, is overexpressed in prostatic adenocarcinoma and is frequently used in combination with basal cell markers to aid in diagnosing difficult prostate adenocarcinoma cases. In this retrospective method comparison study, we examined the sensitivity and specificity of the ready-to-use anti-p504s (SP116) Rabbit Monoclonal Primary Antibody compared to the monoclonal rabbit anti-human AMACR clone 13H4 in prostatic adenocarcinoma samples. De-identified prostatic adenocarcinoma tissue samples were stained with either the SP116 or 13H4 antibody clone in combination with the VENTANA Basal Cell Cocktail (34ßE12+p63) and scored as positive or negative for prostatic adenocarcinoma. The scoring pathologist was blinded to the known historical diagnosis of each sample. The scoring pathologist correctly diagnosed each sample regardless of which p504s clone was used. Both assays using either clone were 100% concordant in their sensitivity and specificity. This study demonstrates that the ready-to-use anti-p504s (SP116) Rabbit Monoclonal Primary Antibody is equivalent to clone 13H4 concentrate when used according to package insert instructions in combination with the VENTANA Basal Cell Cocktail (34ßE12+p63) to aid pathologists in the diagnosis of prostatic adenocarcinoma.

End-stage renal disease in patients with rheumatoid arthritis.

OBJECTIVES: To determine the frequency of end-stage renal disease (ESRD) in patients with rheumatoid arthritis (RA), the causes of ESRD, and the treatment of RA in the setting of ESRD. METHODS: Cross-sectional study of RA (N = 3754) and non-RA (N = 326,776) patients in the United States Renal Data System (USRDS) during 2011 (N = 330,530). The epidemiology of ESRD in RA was determined and the etiology of ESRD in patients with and without RA was compared. The frequency of patients with RA with at least one filled prescription for prednisone/prednisolone, a DMARD, and/or a biologic in 2011 was determined. RESULTS: The prevalence of RA with ESRD in the USRDS in 2011 was 1.1%. There were significant differences in age, race, sex, and BMI category between the groups (p < 0.01). Diabetes (33.5%) and hypertension (30.6%) were the most common primary causes of ESRD in patients with RA. Amyloidosis, vasculitis, and analgesic nephropathy combined accounted for less than 10% of cases of ESRD. Prednisone was the most commonly filled medication that could be used to treat RA (45.9% of RA patients). Hydroxychloroquine was the most frequently filled DMARD (13.5%); biologics were uncommon (etanercept 2.5%, adalimumab 1.5%; golimumab, infliximab, anakinra, and abatacept <1%). CONCLUSIONS: The co-occurrence of RA with ESRD was 1.1% in the USRDS by 2011. Physicians should be aware of the critical impact of the comorbidities of diabetes and hypertension in causing ESRD in RA patients. Use of DMARDS other than hydroxychloroquine and biologics to treat RA in the setting of ESRD appears to be infrequent. Further prospective studies of treatment strategies for RA in ESRD are needed.

MicroRNA-223 Expression is Upregulated in Insulin Resistant Human Adipose Tissue.

MicroRNAs (miRNAs) are short noncoding RNAs involved in posttranscriptional regulation of gene expression and influence many cellular functions including glucose and lipid metabolism. We previously reported that adipose tissue (AT) from women with polycystic ovary syndrome (PCOS) or controls with insulin resistance (IR) revealed a differentially expressed microRNA (miRNA) profile, including upregulated miR-93 in PCOS patients and in non-PCOS women with IR. Overexpressed miR-93 directly inhibited glucose transporter isoform 4 (GLUT4) expression, thereby influencing glucose metabolism. We have now studied the role of miR-223, which is also abnormally expressed in the AT of IR subjects. Our data indicates that miR-223 is significantly overexpressed in the AT of IR women, regardless of whether they had PCOS or not. miR-223 expression in AT was positively correlated with HOMA-IR. Unlike what is reported in cardiomyocytes, overexpression of miR-223 in human differentiated adipocytes was associated with a reduction in GLUT4 protein content and insulin-stimulated glucose uptake. In addition, our data suggests miR-223 regulates GLUT4 expression by direct binding to its 3' untranslated region (3'UTR). In conclusion, in AT miR-223 is an IR-related miRNA that may serve as a potential therapeutic target for the treatment of IR-related disorders.

Nanoparticles can cross mouse placenta and induce trophoblast apoptosis.

INTRODUCTION: The effects of nanoparticles on pregnancy remain unclear. In this study, we investigate whether nanoparticles of a specific size can cross the placenta and affect trophoblast function. METHODS: Fluorescently labelled carboxylate-modified polystyrene beads with diameters of 20, 40, 100, 200, and 500 nm were chosen as model particles. In vitro, trophoblast cell line (3A-Sub-E) or primary culture of term trophoblasts was used for nanoparticle uptake analysis using flow cytometry, confocal microscopy, BrdU proliferation assay and analysis of cell apoptosis using Western blot. Intravenous injection of nanoparticles into pregnant mice at embryonic day 17 was used to study whether nanoparticles can cross the placenta. The mouse placentas were collected and quantitatively analyzed using high-performance liquid chromatography for nanoparticle uptake. RESULTS: Fluorescent polystyrene particles with diameters of up to 500 nm were taken up by the placenta and were able to cross the placental barrier. The fluorescent polystyrene particles were observed in various organs of fetuses after 4 h of administration to pregnant mice. The nanoparticle uptake by placental tissue was significantly increased in nanoparticles with a diameter of 40 nm. No linear association was evident between nanoparticle size and uptake. Nanoparticles with diameters of 20 nm (200 µg/ml) and 40 nm (500 µg/ml) could induce trophoblast cell apoptosis with increased cleaved caspase 3 and reduced cell proliferation. DISCUSSION: Our findings suggest that nanoparticles can cross the placenta and be taken up by fetal organs. Certain concentrations of carboxylate-modified polystyrene nanoparticles may be cytotoxic to trophoblasts, which could alter placental function.