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Multivariate statistical analysis of large-scale IgE antibody measurements reveals allergen extract relationships in sensitized individuals
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences. (Cancer Pharmacology and Informatics)
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2007 (English)In: Journal of Allergy and Clinical Immunology, ISSN 0091-6749, E-ISSN 1097-6825, Vol. 120, no 6, 1433-1440 p.Article in journal (Refereed) Published
Abstract [en]

Background: Many allergenic sources are reportedly cross-reactive because of protein structural similarities. Although several aggregations are well characterized, no holistic mapping of IgE reactivity has hitherto been reported. Objective: The aim of this study was to disclose relevant associations within a large set of allergen preparations, as revealed by specific IgE antibody levels in blood sera of multireactive human donors. Methods: A dataset of recorded IgE antibody serum concentrations of 1011 nonidentifiable multireactive individuals (devoid of clinical records) to 89 allergen extracts was compiled for in silico analysis. Various algorithms were used to identify specific multivariate dependencies between the IgE antibody levels. Results: Exhaustive cluster analysis demonstrates that IgE antibody responses to the 89 extracts can be aggregated into 12 stable formations. These clusters hold both well-known relationships, unexpected patterns, and unknown patterns, the latter categories being exemplified by the coclustering of wasp and certain seafood and a clear differentiation among pollen allergens. Conclusion: Identified relationships within several well-known groups of cross-reactive allergen extracts confirm the applicability of dedicated multivariate data analysis within the allergology field. Moreover, some of the unexpected IgE reactivity associations in sensitized human subjects might help in identifying new relationships with potential importance to allergy. Clinical implications: Although clinical implications from this study should be validated in subsequent investigations with documentation on symptoms included, we believe this seminal approach is a key step toward the development of new analysis tools for interpretation of allergy data generated by using high-throughput recording systems.

Place, publisher, year, edition, pages
2007. Vol. 120, no 6, 1433-1440 p.
Keyword [en]
IgE reactivity, allergens, multivariate data analysis, clustering algorithms
National Category
Medical and Health Sciences
URN: urn:nbn:se:uu:diva-97618DOI: 10.1016/j.jaci.2007.07.021ISI: 000251653800028PubMedID: 17825892OAI: oai:DiVA.org:uu-97618DiVA: diva2:172633
Available from: 2008-10-17 Created: 2008-10-17 Last updated: 2010-05-07Bibliographically approved
In thesis
1. Novel Computational Analyses of Allergens for Improved Allergenicity Risk Assessment and Characterization of IgE Reactivity Relationships
Open this publication in new window or tab >>Novel Computational Analyses of Allergens for Improved Allergenicity Risk Assessment and Characterization of IgE Reactivity Relationships
2008 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Immunoglobulin E (IgE) mediated allergy is a major and seemingly increasing health problem in the Western countries. The combined usage of databases of molecular and clinical information on allergens (allergenic proteins) as well as new experimental platforms capable of generating huge amounts of allergy-related data from a single blood test holds great potential to enhance our knowledge of this complex disease. To maximally benefit from this development, however, both novel and improved methods for computational analysis are urgently required. This thesis concerns two types of important and practical computational analyses of allergens: allergenicity/IgE-cross-reactivity risk assessment and characterization of IgE-reactivity patterns. Both directions rely on development and implementation of bioinformatics and statistical learning algorithms, which are applied to either amino acid sequence information of allergenic proteins or on quantified human blood serum levels of specific IgE-antibodies to allergen preparations (purified extracts of allergenic sources, such as e.g. peanut or birch).

The main application for computational risk assessment of allergenicity is to prevent unintentional introduction of allergen-encoding transgenes in genetically modified (GM) food crops. Two separate classification procedures for potential protein allergenicity are introduced. Both protocols rely on multivariate classification algorithms that are educated to discriminate allergens from presumable non-allergens based on their amino acid sequence. Both classification procedures are thoroughly evaluated and the second protocol shows state-of-the-art performance in comparison to current top-ranked methods. Moreover, several pitfalls in performance estimation of classifiers are demonstrated and procedures to circumvent these are suggested.

Visualization and characterization of IgE-reactivity patterns among allergen preparations are enabled by application of bioinformatics and statistical learning methods to a multivariate dataset holding recorded blood serum IgE-levels of over 1000 sensitized individuals, each measured to 89 allergen preparations. Moreover, a novel framework for divisive hierarchical clustering including graphical representation of the resulting output is introduced, which greatly simplifies analysis of the abovementioned dataset. Important IgE-reactivity relationships within several groups of allergen preparations are identified including well-known groups of clinically relevant cross-reactivities.

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2008. 65 p.
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Medicine, ISSN 1651-6206 ; 385
allergens, bioinformatics, statistical learning, performance estimation, risk assessment
National Category
Biomedical Laboratory Science/Technology
urn:nbn:se:uu:diva-9313 (URN)978-91-554-7308-2 (ISBN)
Public defence
2008-11-07, Lärosal IV, Universitetshuset, Uppsala, 13:00 (English)
Available from: 2008-10-17 Created: 2008-10-17 Last updated: 2009-05-12Bibliographically approved

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