RESUMEN
Exposure to cow's milk constitutes one of the most common causes of food allergy. In addition, exposure to soy proteins has become relevant in a restricted proportion of milk allergic pediatric patients treated with soy formulae as a dairy substitute, because of the cross-allergenicity described between soy and milk proteins. We have previously identified several cross-reactive allergens between milk and soy that may explain this intolerance. The purpose of the present work was to identify epitopes in the purified αS1-casein and the recombinant soy allergen Gly m 5.0101 (Gly m 5) using an α-casein-specific monoclonal antibody (1D5 mAb) through two different approaches for epitope mapping, to understand cross-reactivity between milk and soy. The 1D5 mAb was immobilized onto magnetic beads, incubated with the peptide mixture previously obtained by enzymatic digestion of the allergens, and the captured peptides were identified by MALDI-TOF MS analysis. On a second approach, the peptide mixture was resolved by RP-HPLC and immunodominant peptides were identified by dot blot with the mAb. Finally, recognized peptides were sequenced by MALDI-TOF MS. This novel MS based approach led us to identify and characterize four peptides on α-casein and three peptides on Gly m 5 with a common core motif. Information obtained from these cross-reactive epitopes allows us to gain valuable insight into the molecular mechanisms of cross-reactivity, to further develop new and more effective vaccines for food allergy.
Asunto(s)
Alérgenos/inmunología , Reacciones Cruzadas , Mapeo Epitopo/métodos , Epítopos de Linfocito B/inmunología , Glycine max/química , Leche/química , Espectrometría de Masa por Láser de Matriz Asistida de Ionización Desorción/métodos , Secuencia de Aminoácidos , Animales , Caseínas/análisis , Bovinos , Epítopos de Linfocito B/análisis , Femenino , Humanos , Lactante , Proteínas de la Leche/análisis , Proteínas de la Leche/inmunología , Fragmentos de Péptidos/análisis , Fragmentos de Péptidos/inmunología , Proteínas de Soja/análisisRESUMEN
BACKGROUND: Epitope prediction using computational methods represents one of the most promising approaches to vaccine development. Reduction of time, cost, and the availability of completely sequenced genomes are key points and highly motivating regarding the use of reverse vaccinology. Parasites of genus Leishmania are widely spread and they are the etiologic agents of leishmaniasis. Currently, there is no efficient vaccine against this pathogen and the drug treatment is highly toxic. The lack of sufficiently large datasets of experimentally validated parasites epitopes represents a serious limitation, especially for trypanomatids genomes. In this work we highlight the predictive performances of several algorithms that were evaluated through the development of a MySQL database built with the purpose of: a) evaluating individual algorithms prediction performances and their combination for CD8+ T cell epitopes, B-cell epitopes and subcellular localization by means of AUC (Area Under Curve) performance and a threshold dependent method that employs a confusion matrix; b) integrating data from experimentally validated and in silico predicted epitopes; and c) integrating the subcellular localization predictions and experimental data. NetCTL, NetMHC, BepiPred, BCPred12, and AAP12 algorithms were used for in silico epitope prediction and WoLF PSORT, Sigcleave and TargetP for in silico subcellular localization prediction against trypanosomatid genomes. RESULTS: A database-driven epitope prediction method was developed with built-in functions that were capable of: a) removing experimental data redundancy; b) parsing algorithms predictions and storage experimental validated and predict data; and c) evaluating algorithm performances. Results show that a better performance is achieved when the combined prediction is considered. This is particularly true for B cell epitope predictors, where the combined prediction of AAP12 and BCPred12 reached an AUC value of 0.77. For T CD8+ epitope predictors, the combined prediction of NetCTL and NetMHC reached an AUC value of 0.64. Finally, regarding the subcellular localization prediction, the best performance is achieved when the combined prediction of Sigcleave, TargetP and WoLF PSORT is used. CONCLUSIONS: Our study indicates that the combination of B cells epitope predictors is the best tool for predicting epitopes on protozoan parasites proteins. Regarding subcellular localization, the best result was obtained when the three algorithms predictions were combined. The developed pipeline is available upon request to authors.