Glycome Alterations in Psoriasis and an IgA Glycan Prediction Model of Disease Severity
Ontology highlight
ABSTRACT:
INSTRUMENT(S): 6490 Triple Quadrupole LC/MS
ORGANISM(S): Homo Sapiens (ncbitaxon:9606)
SUBMITTER: 
Carlito B. Lebrilla Emanual Maverakis
PROVIDER: MSV000088072 | MassIVE |
REPOSITORIES: MassIVE
Similar Datasets
Project description:The enriched low mass proteome is unexplored as a source of differentiators for diagnosing and monitoring Inflammatory Bowel Disease (IBD) activity, less invasively than colonoscopy and histopathology. Differences in the enriched low mass plasma proteome (<25kDa) were assessed by label-free quantitative mass-spectrometry. A panel of marker candidates were progressed to validation phase and ‘Tier-2’ FDA-level validated quantitative assay. Proteins important in maintaining gut barrier function and homeostasis at the epithelial interface have been quantitated by Multiple Reaction Monitoring (MRM) in plasma and serum including both inflammatory rheumatoid arthritis controls (RA) and non-inflammatory healthy controls (C), ulcerative colitis (UC) and crohn’s disease (CD) patients. Detection by immunoblot confirmed presence at the protein level in serum. Correlation analysis and receiver operator characteristics were used to report the sensitivity and specificity. Five peptides discriminating IBD activity and severity had very little-to-no correlation to Erythrocyte sedimentation rate (ESR), C-reactive protein (CRP), white cell or platelet counts. Three of these peptides were found to be binding partners to SPP24 protein alongside other known matrix proteins. These Proteins have the potentially improve effective diagnosis and evaluate IBD activity, reducing the need for more invasive techniques.
2015-11-06 | PXD002821 | Pride
Project description:The study investigated presentation of HLA A2 restricted H3.3K27M neopeptide using immunopeptidomics followed by DDA and/or targeted multiple monitoring reaction (MRM).
2023-10-24 | PXD038371 | Pride
Project description:Using 30 synthetic bacterial peptides as external standards, we quantified the abundance of identified peptides in the serum and plasma by multiple reaction monitoring (MRM). Of these 30 peptides, 15 were quantified reliably.
2024-06-14 | PXD027793 | Pride
Project description:We applied two-dimensional fluorescence difference gel electrophoresis (2D DIGE) approach to separate depleted serum proteins in CKD and control serum. High abundance proteins were deleted to improve visualization of protein spots. After differentially expressed proteins were identified, these proteins were quantitated with multiple reaction monitoring (MRM) in the serum samples of patients (n=26) and control groups (n=10).
2019-04-16 | PXD013521 | JPOST Repository
Project description:we executed a study of serum proteome differences between trastuzumab-resistance and trastuzumab-response HER2-positive breast cancer patients using an isobaric TMT label-based multiplexed quantitative proteomic method in combination with a comprehensive functional bioinformatics analysis. An LC-MS/MS-based multiple/selective reaction monitoring (MRM/SRM) quantification method was applied to validate several candidate biomarkers.
2022-04-01 | PXD016655 | Pride
Project description:Despite the importance of protein glycosylation to brain health, current knowledge of glycosylated proteoforms or glycoforms in human brain and their alterations in Alzheimer's disease (AD) is limited. Here, we report a proteome-wide glycoform profiling study of human AD and control brains using intact glycopeptide-based quantitative glycoproteomics coupled with systems biology. Our study identified more than 10,000 human brain N-glycoforms from nearly 1200 glycoproteins and uncovered disease signatures of altered glycoforms and glycan modifications, including reduced sialylation and N-glycan branching and elongation as well as elevated mannosylation and N-glycan truncation in AD. Network analyses revealed a higher-order organization of brain glycoproteome into networks of coregulated glycoforms and glycans and discovered glycoform and glycan modules associated with AD clinical phenotype, amyloid-β accumulation, and tau pathology. Our findings provide valuable insights into disease pathogenesis and a rich resource of glycoform and glycan changes in AD and pave the way forward for developing glycosylation-based therapies and biomarkers for AD.
| S-EPMC10997212 | biostudies-literature
Project description:BackgroundChildren account for a significant proportion of COVID-19 hospitalizations, but data on the predictors of disease severity in children are limited. We aimed to identify risk factors associated with moderate/severe COVID-19 and develop a nomogram for predicting children with moderate/severe COVID-19.MethodsWe identified children ≤ 12 years old hospitalized for COVID-19 across five hospitals in Negeri Sembilan, Malaysia, from 1 January 2021 to 31 December 2021 from the state's pediatric COVID-19 case registration system. The primary outcome was the development of moderate/severe COVID-19 during hospitalization. Multivariate logistic regression was performed to identify independent risk factors for moderate/severe COVID-19. A nomogram was constructed to predict moderate/severe disease. The model performance was evaluated using the area under the curve (AUC), sensitivity, specificity, and accuracy.ResultsA total of 1,717 patients were included. After excluding the asymptomatic cases, 1,234 patients (1,023 mild cases and 211 moderate/severe cases) were used to develop the prediction model. Nine independent risk factors were identified, including the presence of at least one comorbidity, shortness of breath, vomiting, diarrhea, rash, seizures, temperature on arrival, chest recessions, and abnormal breath sounds. The nomogram's sensitivity, specificity, accuracy, and AUC for predicting moderate/severe COVID-19 were 58·1%, 80·5%, 76·8%, and 0·86 (95% CI, 0·79 - 0·92) respectively.ConclusionOur nomogram, which incorporated readily available clinical parameters, would be useful to facilitate individualized clinical decisions.
| S-EPMC10259359 | biostudies-literature
Project description:Ranging from bacterial cell adhesion over viral cell entry to human innate immunity, glycan-binding proteins or lectins are abound in nature. Widely used as staining and characterization reagents in cell biology and crucial for understanding the interactions in biological systems, lectins are a focal point of study in glycobiology. Yet the sheer breadth and depth of specificity for diverse oligosaccharide motifs has made studying lectins a largely piecemeal approach, with few options to generalize. Here, LectinOracle, a model combining transformer-based representations for proteins and graph convolutional neural networks for glycans to predict their interaction, is presented. Using a curated data set of 564,647 unique protein-glycan interactions, it is shown that LectinOracle predictions agree with literature-annotated specificities for a wide range of lectins. Using a range of specialized glycan arrays, it is shown that LectinOracle predictions generalize to new glycans and lectins, with qualitative and quantitative agreement with experimental data. It is further demonstrated that LectinOracle can be used to improve lectin classification, accelerate lectin directed evolution, predict epidemiological outcomes in the context of influenza virus, and analyze whole lectomes in host-microbe interactions. It is envisioned that the herein presented platform will advance both the study of lectins and their role in (glyco)biology.
| S-EPMC8728848 | biostudies-literature
Project description:Absolute quantification of clinical biomarkers by mass spectrometry (MS) has been challenged due to low sample-throughput of current multiple reaction monitoring (MRM) methods. For this problem to be overcome, in this work, a novel high-sample-throughput multiple reaction monitoring mass spectrometric (HST-MRM-MS) quantification approach is developed to achieve simultaneous quantification of 24 samples. Briefly, triplex dimethyl reagents (L, M, and H) and eight-plex iTRAQ reagents were used to label the N- and C-termini of the Lys C-digested peptides, respectively. The triplex dimethyl labeling produces three coelute peaks in MRM traces, and the iTRAQ labeling produces eight peaks in MS2, resulting in 24 (3×8) channels in a single experiment. HST-MRM-MS has shown good accuracy (R2 > 0.98 for absolute quantification), reproducibility (RSD < 15%), and linearity (2–3 orders of magnitude). Moreover, the novel method has been successfully applied in quantifying serum biomarkers in hepatocellular carcinoma (HCC)-related serum samples. In conclusion, HST-MRM-MS is an accurate, high-sample-throughput, and broadly applicable MS-based absolute quantification method.
2020-02-23 | PXD040708 |
Project description:Despite a clinical, economic, and regulatory imperative to develop companion diagnostics, precious few new biomarkers have been successfully translated into clinical use, due in part to inadequate protein assay technologies to support large-scale testing of hundreds of candidate biomarkers in formalin-fixed paraffin embedded (FFPE) tissues. While the feasibility of using targeted, multiple reaction monitoring mass spectrometry (MRM-MS) for quantitative analyses of FFPE tissues has been demonstrated, protocols have not been systematically optimized for robust quantification across a large number of analytes, nor has the performance of immuno-MRM been evaluated. To address this gap, we used a test battery approach coupled with MRM-MS (targeting 515 analytes) to evaluate quantitatively the performance of three extraction protocols in combination with three trypsin digestion protocols (9 processes). An optimum process based on RapiGest buffer extraction and urea-based digestion was identified. The optimized process is generally amenable and enables highly reproducible, multiplex, standardizable quantitative MRM in archival specimens.
2016-07-25 | PXD003770 | Pride