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      IgE Recognition Patterns of Profilin, PR-10, and Tropomyosin Panallergens Tested in 3,113 Allergic Patients by Allergen Microarray-Based Technology

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          Abstract

          Background

          IgE recognition of panallergens having highly conserved sequence regions, structure, and function and shared by inhalant and food allergen sources is often observed.

          Methods

          We evaluated the IgE recognition profile of profilins (Bet v 2, Cyn d 12, Hel a 2, Hev b 8, Mer a 1, Ole e 2, Par j 3, Phl p 12, Pho d 2), PR-10 proteins (Aln g 1, Api g 1, Bet v 1.0101, Bet v 1.0401, Cor a 1, Dau c 1 and Mal d 1.0108) and tropomyosins (Ani s 3, Der p 10, Hel as 1, Pen i 1, Pen m 1, Per a 7) using the Immuno-Solid phase Allergen Chip (ISAC) microarray system. The three panallergen groups were well represented among the allergenic molecules immobilized on the ISAC. Moreover, they are distributed in several taxonomical allergenic sources, either close or distant, and have a route of exposure being either inhalation or ingestion.

          Results

          3,113 individuals (49.9% female) were selected on the basis of their reactivity to profilins, PR-10 or tropomyosins. 1,521 (48.8%) patients were reactive to profilins (77.6% Mer a 1 IgE +), 1,420 (45.6%) to PR-10 (92.5% Bet v 1 IgE +) and 632 (20.3%) to tropomyosins (68% Der p 10 IgE +). A significant direct relationship between different representative molecules within each group of panallergens was found. 2,688 patients (86.4%) recognized only one out of the three distinct groups of molecules as confirmed also by hierarchical clustering analysis.

          Conclusions

          Unless exposed to most of the allergens in the same or related allergenic sources, a preferential IgE response to distinct panallergens has been recorded. Allergen microarray IgE testing increases our knowledge of the IgE immune response and related epidemiological features within and between homologous molecules better describing the patients' immunological phenotypes.

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          Most cited references47

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          Cluster analysis and display of genome-wide expression patterns.

          A system of cluster analysis for genome-wide expression data from DNA microarray hybridization is described that uses standard statistical algorithms to arrange genes according to similarity in pattern of gene expression. The output is displayed graphically, conveying the clustering and the underlying expression data simultaneously in a form intuitive for biologists. We have found in the budding yeast Saccharomyces cerevisiae that clustering gene expression data groups together efficiently genes of known similar function, and we find a similar tendency in human data. Thus patterns seen in genome-wide expression experiments can be interpreted as indications of the status of cellular processes. Also, coexpression of genes of known function with poorly characterized or novel genes may provide a simple means of gaining leads to the functions of many genes for which information is not available currently.
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            Genesis: cluster analysis of microarray data.

            A versatile, platform independent and easy to use Java suite for large-scale gene expression analysis was developed. Genesis integrates various tools for microarray data analysis such as filters, normalization and visualization tools, distance measures as well as common clustering algorithms including hierarchical clustering, self-organizing maps, k-means, principal component analysis, and support vector machines. The results of the clustering are transparent across all implemented methods and enable the analysis of the outcome of different algorithms and parameters. Additionally, mapping of gene expression data onto chromosomal sequences was implemented to enhance promoter analysis and investigation of transcriptional control mechanisms.
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              Allergens are distributed into few protein families and possess a restricted number of biochemical functions.

              Existing allergen databases classify their entries by source and route of exposure, thus lacking an evolutionary, structural, and functional classification of allergens. We sought to build AllFam, a database of allergen families, and use it to extract common structural and functional properties of allergens. Allergen data from the Allergome database and protein family definitions from the Pfam database were merged into AllFam, a database that is freely accessible on the Internet at http://www.meduniwien.ac.at/allergens/allfam/. A structural classification of allergens was established by matching Pfam families with families from the Structural Classification of Proteins database. Biochemical functions of allergens were extracted from the Gene Ontology Annotation database. Seven hundred seven allergens were classified by sequence into 134 AllFam families containing 184 Pfam domains (2% of 9318 Pfam families). A random set of 707 sequences with the same taxonomic distribution contained a significantly higher number of different Pfam domains (479 +/- 17). Classifying allergens by structure revealed that 5% of 3012 Structural Classification of Proteins families contained allergens. The biochemical functions of allergens most frequently found were limited to hydrolysis of proteins, polysaccharides, and lipids; binding of metal ions and lipids; storage; and cytoskeleton association. The small number of protein families that contain allergens and the narrow functional distribution of most allergens confirm the existence of yet unknown factors that render proteins allergenic.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS One
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, USA )
                1932-6203
                2011
                15 September 2011
                : 6
                : 9
                : e24912
                Affiliations
                [1]Center for Molecular Allergology, IDI-IRCCS, Rome, Italy
                Albany Medical College, United States of America
                Author notes

                Conceived and designed the experiments: AM ES. Performed the experiments: ES CA MLB RF DZ AM PP DP ML. Analyzed the data: ES MS CR. Contributed reagents/materials/analysis tools: AM. Wrote the paper: ES CA AM.

                Article
                PONE-D-11-07902
                10.1371/journal.pone.0024912
                3174236
                21949785
                ce96dfa3-d83e-4bf7-855b-cbac9504dc1d
                Scala et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
                History
                : 4 May 2011
                : 22 August 2011
                Page count
                Pages: 8
                Categories
                Research Article
                Biology
                Immunology
                Immunologic Techniques
                Immunoassays
                Allergy and Hypersensitivity
                Immune Response
                Population Biology
                Epidemiology
                Disease Informatics
                Medicine
                Clinical Immunology
                Immunologic Techniques
                Immunoassays
                Allergy and Hypersensitivity
                Immune Response
                Diagnostic Medicine
                Clinical Laboratory Sciences
                Clinical Immunology
                Test Evaluation
                Epidemiology
                Biomarker Epidemiology
                Clinical Epidemiology
                Disease Informatics
                Molecular Epidemiology
                Survey Methods

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                Uncategorized

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