Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Discovery, Verification, and Validation of Walnut Protein Marker Peptides Using LC-MS/MS Approaches

ABSTRACT: A key requirement of liquid chromatography-mass spectrometry (LC-MS)-based allergenic food protein analysis methods is to use protein marker peptides with good analytical performances in LC-MS analysis of commercial processed foods. In this study, we developed a multistage walnut protein marker peptide selection strategy involving marker peptide discovery and verification and LC-MS validation of chemically equivalent stable isotope-labeled peptides. This strategy proposed three walnut protein marker peptides, including two new marker peptides. Our LC-MS-based walnut protein analysis method using the three stable isotope-labeled peptides showed acceptable linearity (R2 >0.99), matrix effects (coefficient of variation <±15%), sensitivity (limit of detection >0.3 pg/μL, limit of quantification >0.8 pg/μL), recovery (85.1–103.4%), accuracy, and precision (coefficient of variation <10%). In conclusion, our multistage marker peptide selection strategy effectively selects specific protein marker peptides for sensitive detection and absolute quantification of walnut proteins in LC-MS analysis of commercial processed foods.

INSTRUMENT(S): TSQ Altis, Q Exactive Plus

ORGANISM(S): Juglans Regia (english Walnut)

SUBMITTER: Kyungdo Kim

LAB HEAD: Zee-yong Park

PROVIDER: PXD043010 | Pride | 2023-07-31

REPOSITORIES: Pride

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
	180305_Food_Walnut_0_1ug_DDA2_1.pdResult	Other
	180409_Walnut_processed_food.sky	Other
	181010_Accuracy_test_blank_20fmol2.raw	Raw
	181010_Accuracy_test_blank_2fmol.raw	Raw
	181010_Accuracy_test_blank_2fmol2.raw	Raw

Items per page:

1 - 5 of 330

Similar Datasets

Identification of targets of the protein disulfide reductase thioredoxin

Project description:Identification of targets of the protein disulfide reductase thioredoxin using liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS) and thiol specific differential labeling with isotope-coded affinity tags (ICAT). Reduction of specific target disulfides is quantified by measuring ratios of cysteine residues labeled with the heavy (13C) and light (12C) ICAT reagents in peptides derived from tryptic digests of Trx-treated and non-treated samples. Keywords: protein, LC-MS/MS, ICAT

2008-11-20 | GSE11650 | GEO

Mass spectrometry imaging and single-cell transcriptome reveal metabolic profiles of the postauricular glands of the Bufo gargarizans, and hypothesize Bufotoxin biosynthesis pathways

Project description:bufotoxin, which contain alkaloids, small peptides and bufadienolides (BDS), are neurotoxic and cardiotoxic, they are promising medical drugs, but the unclear specifics of bufotoxin in the postauricular glands (PG) and the biosynthetic routes of BDS are unknown. We used MALDI/MS to visualize 1872 components of PG, and established the expression profiles of 9316 cells by single-cell sequencing, which were classified into nine clusters by marker genes. We analyzed the expression differences of key genes for alkaloid, terpene backbone and steroid synthesis, identified optimal and silenced genes involved in key enzymes encoding bufotoxin biosynthesis, and hypothesized the full route of BDS synthesis via by analyzing the cholesterol metabolic pathway. BDS was enriched in EP, FB, and GG2 cells, and genes were expressed mainly in EP and FB cells. Combined with the bulk-RNA transcriptome we suggest that BDS synergistically synthesized by the liver and PG. metabolic mapping of toxin components in the PG of the toad, as demonstrated by our data.

2024-03-20 | GSE261560 | GEO

Proteomics_LC-MS_MS

Project description:Proteomics LC-MS MS dataset

| EGAD00010001006 | EGA

Mus musculus

Project description:Metabolomics LC-MS dataset

| PRJNA811808 | ENA

Improved prediction of endogenous HLA-associated epitopes based on mono-allelic mass spectrometry profiling

Project description:LC-MS/MS-based identification of HLA-peptides is poised to provide a deep understanding of the rules underlying antigen presentation. However, a key obstacle limiting the utility of MS data is the ambiguity arising from the co-expression of multiple HLA alleles. Here, we introduce a strategy for profiling the HLA ligandome one allele at a time. By using cell lines expressing a single HLA allele, optimizing immunopurifications, and developing a novel spectral search algorithm, we identified thousands of peptides bound to 16 different HLA class I alleles. These data enabled the discovery of novel binding motifs, and an integrative analysis quantifying the contribution of factors critical to epitope presentation, such as protein cleavage and gene expression. We trained neural network prediction algorithms with our large dataset (>24,000 peptides) and outperformed algorithms trained on datasets of peptides with measured affinities. We thus demonstrate a scalable strategy for systematically learning the rules of endogenous antigen presentation.

2017-02-21 | GSE93315 | GEO

Her2/Neu breast cancer mouse model whole tissue transcriptome

Project description:Purpose: We generated extensive transcriptional and proteomic profiles from a Her2-driven mouse model of breast cancer that closely recapitulates human breast cancer. This report makes these data publicly available in raw and processed forms, as a resource to the community. Importantly, we previously made biospecimens from this same mouse model freely available through a sample repository, so researchers can obtain samples to test biological hypotheses without the need of breeding animals and collecting biospecimens. Experimental design: Twelve datasets are available, encompassing 841 LC-MS/MS experiments (plasma and tissues) and 255 microarray analyses of multiple tissues (thymus, spleen, liver, blood cells, and breast). Cases and controls were rigorously paired to avoid bias. Results: In total, 18,880 unique peptides were identified (PeptideProphet peptide error rate ≤1%), with 3884 and 1659 non-redundant protein groups identified in plasma and tissue datasets, respectively. Sixty-one of these protein groups overlapped between cancer plasma and cancer tissue. Conclusions and clinical relevance: These data are of use for advancing our understanding of cancer biology, for software and quality control tool development, investigations of analytical variation in MS/MS data, and selection of proteotypic peptides for MRM-MS. The availability of these datasets will contribute positively to clinical proteomics.

2010-10-22 | GSE20465 | GEO

Her2/Neu breast cancer mouse model transcriptome

2010-10-18 | GSE20280 | GEO

YangziChen2024 - Metabolic Machine Learning Predictor Model for Diagnosis of Gastric Cancer

Project description:The paper "Metabolomic Machine Learning Predictor for Diagnosis and Prognosis of Gastric Cancer" addresses the need for non-invasive diagnostic tools for gastric cancer (GC). Traditional methods like endoscopy are invasive and expensive. The authors conducted a targeted metabolomics analysis of 702 plasma samples to develop machine learning models for GC diagnosis and prognosis. The diagnostic model, using 10 metabolites, achieved a sensitivity of 0.905, outperforming conventional protein marker-based methods. The prognostic model effectively stratified patients into risk groups, surpassing traditional clinical models. I have successfully reproduced the diagnosis model from the paper. This machine learning-based system differentiates GC patients from non-GC controls using metabolomics data from plasma samples analyzed by liquid chromatography-mass spectrometry (LC-MS). The model focuses on 10 metabolites, including succinate, uridine, lactate, and serotonin. Employing LASSO regression and a random forest classifier, the model achieved an AUROC of 0.967, with a sensitivity of 0.854 and specificity of 0.926. This model significantly outperforms traditional diagnostic methods and underscores the potential of integrating machine learning with metabolomics for early GC detection and treatment.

2024-07-23 | MODEL2407230001 | BioModels

Metagenomic, metatranscriptomic and metaproteomic study of enrichment culture of M. oxyfera (denitrifying methanotroph)

Project description:DNA, RNA and protein were extracted from the culture and subjected to massive parallel sequencing and nano-LC-MS-MS respectively

2010-03-25 | GSE18535 | GEO

Roasted English Walnut Label-Free LC-MS/MS Quantification

Project description:Thermal processing of allergenic foods has the potential to induce a number of changes in the constituent allergens, but many of the effects of heat treatment are poorly defined. Like numerous other commonly allergenic tree nuts, walnuts often undergo heat treatment, such as roasting, prior to consumption. This study evaluated the changes in solubility and detectability of allergens from roasted walnuts using tandem mass spectrometry methods. Walnuts were roasted at 132 °C or 180 °C for 5, 10, or 20 minutes prior to preparation for LC-MS/MS using sequential or simultaneous protocols for extraction and trypsin digestion. LC-MS/MS data analysis incorporated label-free quantification and Maillard adduct screening. Relatively minor changes in detection were demonstrated following roasting in some allergenic proteins (2S albumin, LTP, and the 7S vicilin N-terminal region) with the LC-MS/MS method. The mature 7S vicilin and 11S legumin, however, showed significantly increased detection in the highest heat treatment sample when using the simultaneous extraction/digestion protocol, an effect likely due to increased digestibility of the proteins following heating. The results of this study indicate that individual walnut allergens respond differently to thermal processing, and the detection of these proteins by LC-MS/MS is dependent on the protein in question, its susceptibility to proteolytic digestion, the degree of thermal processing, and the sample preparation methodology.

2016-05-18 | PXD000907 | Pride

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data