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IgE reactivity profiling in an asthma affected cohort

ABSTRACT: Background: Most asthmatic patients have high serum levels of IgE directed against common environmental allergens such as house dust mite, animal danders and moulds. However the presence of specific IgE against individual allergens alone does not account for asthma. Many individuals are atopic but not asthmatic. Precise knowledge of the serum IgE specificity repertoire in asthmatic and non-asthmatic patients would substantially help in understanding the pathogenesis of the disease and at the same time facilitate the treatment and the implementation of preventive measures. Methods: We developed a microarray immunoassay containing 103 common allergens to study the IgE reactivity profiles of 485 asthmatic and 342 non-asthmatic individuals from families whose members had a documented history of asthma and atopy. The results were analyzed using k-means clustering to investigate whether IgE reactivity profiles correlated with asthma, disease severity and age of onset as well as with other atopic conditions such as rhinitis, conjunctivitis and eczema. We trained an artificial neural network (ANN) using the serum reactivity data to identify individuals as asthmatic and non-asthmatic. Results: Individual sera showed clear differences in the number and the combinations of allergens recognized as well as in the level of specific IgE. While the presence of specific IgE against single allergens correlated poorly with the pathological conditions examined k-means clustering analysis unraveled that a particular profile was significantly associated with asthma (p <1E-8). An ANN-based algorithm, calibrated with the profile reactivity data correctly classified as asthmatic or non-asthmatic 78% of the individual examined. Conclusions: Our analysis demonstrates that asthma may be a higher-order phenomenon related to patterns of response and not attributable to single antibody reactions. This information sheds new light on the risk of developing the disease and can be readily utilized in combination with an ANN-based tool to distinguish asthmatic and non-asthmatic individuals on the basis of their serum reactivity profile. The study consisted of a total of 872 sera samples: 485 individuals were diagnosed with asthma, 342 were classified as non asthmatic, a remaining 45 were classified as unconfirmed asthma diagnosis. The serum IgE reactivity profiles were analyzed against 103 allergens representative of 11 distinct allergen classes. Quantification of bound IgE: The fluorescence signal was acquired using ProScanArray Express™ version 3.0 software. PMC reading values of individual spots were corrected against the internal negative control to identify signals above background. Duplicate measurements of individual allergens were utilized. The signal collected from the allergens was interpolated with an external calibration curve to obtain the IU/ml value (see supplementary file GSE20020_IU_ml values.txt), and translated into a Class Score by plotting the data in a standard reactivity scale (see Sample data tables). Class Score values: (CLASS 0 (less than 0.35 IU/ml); CLASS 1 (0.35-0.7 IU/ml); CLASS 2 (0.71-3.5 IU/ml); CLASS 3 (3.51-17.5 IU/ml); CLASS 4 (17.51-50 IU/ml); CLASS 5 (50.01-100 IU/ml).

ORGANISM(S): Homo sapiens

SUBMITTER: tania dottorini

PROVIDER: E-GEOD-20020 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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Project description:Background: Most asthmatic patients have high serum levels of IgE directed against common environmental allergens such as house dust mite, animal danders and moulds. However the presence of specific IgE against individual allergens alone does not account for asthma. Many individuals are atopic but not asthmatic. Precise knowledge of the serum IgE specificity repertoire in asthmatic and non-asthmatic patients would substantially help in understanding the pathogenesis of the disease and at the same time facilitate the treatment and the implementation of preventive measures. Methods: We developed a microarray immunoassay containing 103 common allergens to study the IgE reactivity profiles of 485 asthmatic and 342 non-asthmatic individuals from families whose members had a documented history of asthma and atopy. The results were analyzed using k-means clustering to investigate whether IgE reactivity profiles correlated with asthma, disease severity and age of onset as well as with other atopic conditions such as rhinitis, conjunctivitis and eczema. We trained an artificial neural network (ANN) using the serum reactivity data to identify individuals as asthmatic and non-asthmatic. Results: Individual sera showed clear differences in the number and the combinations of allergens recognized as well as in the level of specific IgE. While the presence of specific IgE against single allergens correlated poorly with the pathological conditions examined k-means clustering analysis unraveled that a particular profile was significantly associated with asthma (p <1E-8). An ANN-based algorithm, calibrated with the profile reactivity data correctly classified as asthmatic or non-asthmatic 78% of the individual examined. Conclusions: Our analysis demonstrates that asthma may be a higher-order phenomenon related to patterns of response and not attributable to single antibody reactions. This information sheds new light on the risk of developing the disease and can be readily utilized in combination with an ANN-based tool to distinguish asthmatic and non-asthmatic individuals on the basis of their serum reactivity profile.

Project description:Background: Respiratory allergy triggered by pollen allergens is increasing at an alarming rate worldwide. Sunflower pollen is thought to be an important source of inhalant allergens. Present study aims to identify the prevalence of sunflower pollinosis among the Indian allergic population and characterizes the pollen allergens using immuno-proteomic tools. Methodology: Clinico-immunological tests were performed to understand the prevalence of sensitivity towards sunflower pollen among the atopic population. Sera from selected sunflower positive patients were used as probe to detect the IgE-reactive proteins from the one and two dimensionally separated proteome of sunflower pollen. The antigenic nature of the sugar moiety of the glycoprotein allergens was studied by meta-periodate modification of IgE-immunoblot. Finally, these allergens were identified by mass-spectrometry (MALDI TOF/TOF and LC ESI qTOF). MASCOT searching was performed against NCBInr database. However, Helianthus annuus genome is not fully sequenced and partially annotated. So in case of low confidence (p> 0.05) protein identification, searching was performed against EST library of Helianthus annuus. Results: Prevalence of sunflower pollen allergy was observed among 21% of the atopic population and associated with elevated level of specific IgE and histamine in the sera of these patients. Immunoscreening of sunflower pollen proteome with patient serum detected seven IgE-reactive proteins with varying molecular weight and pI. Hierarchical clustering of 2D-immunoblot data highlighted three allergens characterized by a more frequent immuno-reactivity and increased levels of IgE antibodies in the sera of susceptible patients. These allergens were considered as the major allergens of sunflower pollen and were found to have their glycan moiety critical for inducing IgE response. Homology driven search of MS/MS data of these IgE-reactive proteins identified seven previously unreported allergens from sunflower pollen. Three major allergenic proteins were identified as two non-isoformic pectate lyases and a cystein protease. Conclusion: Novelty of the present report is the identification of a panel of seven sunflower pollen allergens for the first time at immuno-biochemical and proteomic level, which substantiated the clinical evidence of sunflower allergy. Further purification and recombinant expression of these allergens will improve component-resolved diagnosis and therapy of pollen allergy.

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IgE reactivity profiling in an asthma affected cohort

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