Dataset Information

Development of a novel peptide microarray for large-scale epitope mapping of food allergens.

ABSTRACT:

Background

The peptide microarray is a novel assay that facilitates high-throughput screening of peptides with a small quantity of sample.

Objective

We sought to use overlapping peptides of milk allergenic proteins as a model system to establish a reliable and sensitive peptide microarray-based immunoassay for large-scale epitope mapping of food allergens.

Methods

A milk peptide microarray was developed by using commercially synthesized peptides (20-mers, 3 offset) covering the primary sequences of alpha(s1)-casein, alpha(s2)-casein, beta-casein, kappa-casein, and beta-lactoglobulin. Conditions for printing and immunolabeling were optimized using a serum pool of 5 patients with milk allergy. Reproducibility of the milk peptide microarray was evaluated using replicate arrays immunolabeled with the serum pool, whereas specificity and sensitivity were assessed by using serial dilution of the serum pool and a peptide inhibition assay.

Results

Our results show that epitopes identified by the peptide microarray were mostly consistent with those identified previously by SPOT membrane technology, but with specific binding to a few newly identified epitopes of milk allergens. Data from replicate arrays were reproducible (r > or = 0.92) regardless of printing lots, immunolabeling, and serum pool batches. Using the serially diluted serum pool, we confirmed that IgE antibody binding detected in the array was specific. Peptide inhibition of IgE binding to the same peptide and overlapping peptides further confirmed the specificity of the array.

Conclusion

A reliable peptide microarray was established for large-scale IgE epitope mapping of milk allergens, and this robust technology could be applied for epitope mapping of other food allergens.

SUBMITTER: Lin J

PROVIDER: S-EPMC2757036 | biostudies-literature | 2009 Aug

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Development of a novel peptide microarray for large-scale epitope mapping of food allergens.

Lin Jing J Bardina Ludmilla L Shreffler Wayne G WG Andreae Doerthe A DA Ge Yongchao Y Wang Julie J Bruni Francesca M FM Fu Zhiyan Z Han Youngshin Y Sampson Hugh A HA

The Journal of allergy and clinical immunology 20090703 2

<h4>Background</h4>The peptide microarray is a novel assay that facilitates high-throughput screening of peptides with a small quantity of sample.<h4>Objective</h4>We sought to use overlapping peptides of milk allergenic proteins as a model system to establish a reliable and sensitive peptide microarray-based immunoassay for large-scale epitope mapping of food allergens.<h4>Methods</h4>A milk peptide microarray was developed by using commercially synthesized peptides (20-mers, 3 offset) covering ...[more]

PMID: 19577281

Similar Datasets

Project description:Aim of Study: The use of weighed food diaries in nutritional studies provides a powerful method to quantify food and nutrient intakes. Yet, mapping these records onto food composition tables (FCTs) is a challenging, time-consuming and error-prone process. Experts make this effort manually and no automation has been previously proposed. Our study aimed to assess automated approaches to map food items onto FCTs. Methods: We used food diaries (~170,000 records pertaining to 4,200 unique food items) from the DiOGenes randomized clinical trial. We attempted to map these items onto six FCTs available from the EuroFIR resource. Two approaches were tested: the first was based solely on food name similarity (fuzzy matching). The second used a machine learning approach (C5.0 classifier) combining both fuzzy matching and food energy. We tested mapping food items using their original names and also an English-translation. Top matching pairs were reviewed manually to derive performance metrics: precision (the percentage of correctly mapped items) and recall (percentage of mapped items). Results: The simpler approach: fuzzy matching, provided very good performance. Under a relaxed threshold (score > 50%), this approach enabled to remap 99.49% of the items with a precision of 88.75%. With a slightly more stringent threshold (score > 63%), the precision could be significantly improved to 96.81% while keeping a recall rate > 95% (i.e., only 5% of the queried items would not be mapped). The machine learning approach did not lead to any improvements compared to the fuzzy matching. However, it could increase substantially the recall rate for food items without any clear equivalent in the FCTs (+7 and +20% when mapping items using their original or English-translated names). Our approaches have been implemented as R packages and are freely available from GitHub. Conclusion: This study is the first to provide automated approaches for large-scale food item mapping onto FCTs. We demonstrate that both high precision and recall can be achieved. Our solutions can be used with any FCT and do not require any programming background. These methodologies and findings are useful to any small or large nutritional study (observational as well as interventional).

Dataset Information

Development of a novel peptide microarray for large-scale epitope mapping of food allergens.

Background

Objective

Methods

Results

Conclusion

Publications

Development of a novel peptide microarray for large-scale epitope mapping of food allergens.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure