Integrated approaches in diagnostics and therapy of allergic diseases

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Allergic diseases are a serious problem in both developed and developing countries. Based on the World Health Organization data over 30-40% of the population have one or more allergic diseases. According to forecasts, by 2050, up to 4 billion people in the world will suffer from asthma, allergic rhinitis or atopic dermatitis. Solving problems related to the complexity of differential diagnosis, false positive and false negative results of clinical and laboratory studies, genetic characteristics of patients and many others, can be realized by integrating approaches of bioinformatics and systems biomedicine based on massive databases of experimental studies on one side and, on the other - on advanced technologies of genotyping and detection of biomarkers. The review analyzes the main resources of international databases on allergens, which help to determine the main characteristics of allergens: molecular weight, epitopes, cross reactivity, geographical prevalence, availability allergens in food. Different approaches are considered in the systematization of data obtained in the study of the genome, transcriptome, microbiome, comparison of data obtained from healthy donors and patients with allergic diseases, genetic mutations, transcriptome and microbiome profiles that cause severe course of allergic diseases. Several ways of depicting relationships in the construction of signaling networks (KEGG, sbvIMPROVER, Cyto scape) are shown, both on the basis of direct influence (KEGG, Cytoscape) and on the basis of OpenBEL - the open-access biological expression language - sbvIMPROVER, capable of displaying complex semantic links between components of the system under consideration.

About the authors

S V Guryanova

Institute of Bioorganic Chemistry. Academicians MM Shemyakin and Yu.A. Ovchinnikov of the Russian Academy of Sciences



  1. Masoli M, Fabian D, Holt S, Beasley R. The global burden of asthma: executive summary of the GINA Dissemination Committee report. Allergy 2004;59:469-478.
  2. Ault A. Report blames global warming for rising asthma. Lancet. 2004;363(9420):1532.
  3. Global Atlas of Allergy. EAACI, 2014. 383 р.
  4. Kattan JD, Scott H. Sicherer. Optimizing the Diagnosis of Food allergy. Immunol Allergy Clin North Am. 2015 Feb; 35(1): 61-76.
  5. Santos F, Shreffler WG. Road map for the clinical application of the basophil activation test in food allergy. Clin Exp Allergy. 2017 Sep; 47(9): 1115-1124.
  6. Kowalski ML, Ansotegui I, Aberer W. Risk and safety requirements for diagnostic and therapeutic procedures in allergology: World Allergy Organization Statement. World Allergy Organ J. 2017; 10(1): 6.
  7. Fu Z, Lin J. An Overview of Bioinformatics Tools and Resources in Allergy. Methods Mol Biol. 2017;1592:223-245.
  8. Mari A, Scala E, Palazzo P, et al. Bioinformatics applied to allergy: Allergen databases, from collecting sequence information to data integration. The Allergome platform as a model. Cellular Immunology 244 ; 2006: 97-100.
  9. Werchan B, Werchan M, Mücke H, Gauger U, Simoleit A, Zuberbier T, Bergmann KC. Spatial distribution of allergenic pollen through a large metropolitan area. Environ Monit Assess. 2017 Apr;189(4):169.
  10. Hernández-Cadena L, Zeldin DC, Barraza-Villarreal A. et al. Indoor determinants of dustborne allergens in Mexican homes. Allergy Asthma Proc. 2015 Mar-Apr;36(2): 130-7.
  11. Ailin Tao, EyalRaz. Allergy Bioinformatics. in: Ttanslational Bioinformatics. Springer 2015. Edit. Eyal Raz. p. 16.
  12. Martínez-Cañavate Burgos A, Torres-Borrego J, Molina Terán AB. Molecular sensitization patterns and influence of molecular diagnosis in immunotherapy prescription in children sensitized to both grass and olive pollen. Pediatr Allergy Immunol. 2018 Jan 25.
  13. Nakamura R, Nakamura R, Teshima R. Major revision of the allergen database for food safety (ADFS) and validation of the motif-based allergenicity prediction tool. (Japan). Kokuritsu Iyakuhin Shokuhin Eisei Kenkyusho Hokoku. 2009;(127):44-9.
  14. Radauer C. Navigating through the Jungle of Allergens: Features and Applications of Allergen Databases. Int Arch Allergy Immunol. 2017;173(1):1-11.
  15. Negi SS, Braun W. Cross-React: a new structural bioinformatics method for predicting allergen cross-reactivity. Bioinformatics. 2017 Apr 1;33(7):1014-1020.
  16. Bunyavanich S, Schadt E. Systems Biology of Asthma and Allergic Diseases: A Multiscale Approach. J Allergy Clin Immunol. 2015 Jan; 135(1): 31-42.
  17. Mathias RA. Introduction to genetics and genomics in asthma: genetics of asthma. Adv Exp Med Biol. 2014;795: 125-55/
  18. Moffatt MF, Kabesch M, Liang L, Dixon AL, et al. Genetic variants regulating ORMDL3 expression contribute to the risk of childhood asthma. Nature. 2007 Jul 26; 448(7152):470-3.
  19. Torgerson DG, Ampleford EJ, Chiu GY, Gauderman WJ, Gignoux CR, Graves PE, et al. Meta-analysis of genome-wide association studies of asthma in ethnically diverse North American populations. Nat Genet. 2011;43:887-92.
  20. Wan YI, Shrine NR, Soler Artigas M, Wain LV, Blakey JD, Moffatt MF, et al. Genome-wide association study to identify genetic determinants of severe asthma. Thorax. 2012;67:762-8. [PubMed]
  21. Ferreira MA, Matheson MC, Duffy DL, Marks GB, Hui J, Le Souef P, et al. Identification of IL6R and chromosome 11q13. 5 as risk loci for asthma. Lancet. 2011; 378:1006-14.
  22. Cantero-Recasens G, Fandos C, Rubio-Moscardo F, Valverde MA, Vicente R. The asthma-associated ORMDL3 gene product regulates endoplasmic reticulum-mediated calcium signaling and cellular stress. Hum Mol Genet. 2010;19:111-21.
  23. Cole C, Kroboth K, Schurch NJ, Sandilands A. Filaggrin-stratified transcriptomic analysis of pediatric skin identifies mechanistic pathways in patients with atopic dermatitis. J Allergy ClinImmunol. 2014 Jul;134(1):82-91.
  24. Margolis DJ, Gupta J, Apter AJ, Hoffstad O, Papadopoulos M, Rebbeck TR, et al. Exome sequencing of filaggrin and related genes in african-american children with atopic dermatitis. J Invest Dermatol. 2014;134:2272-4.
  25. Hinds DA, McMahon G, Kiefer AK, Do CB, Eriksson N, Evans DM, et al. A genome-wide association meta-analysis of self-reported allergy identifies shared and allergy-specific susceptibility loci. Nat Genet. 2013;45:907-11.
  26. Esparza-Gordillo J, Weidinger S, Folster-Holst R, Bauerfeind A, Ruschendorf F, Patone G, et al. A common variant on chromosome 11q13 is associated with atopic dermatitis. Nat Genet. 2009;41:596-601.
  27. Hinds DA, McMahon G, Kiefer AK, Do CB, Eriksson N, Evans DM, et al. A genome-wide association meta-analysis of self-reported allergy identifies shared and allergy-specific susceptibility loci. Nat Genet. 2013;45:907-11.
  28. Bonnelykke K, Matheson MC, Pers TH, Granell R, Strachan DP, Alves AC, et al. Meta-analysis of genome-wide association studies identifies ten loci influencing allergic sensitization. Nat Genet. 2013;45:902-6.
  29. Yick CY, Zwinderman AH, Kunst PW, et al. Transcriptome sequencing (RNA-Seq) of human endobronchial biopsies: asthma versus controls. Eur Respir J. 2013;42:662-70.
  30. Poole A, Urbanek C, Eng C, Schageman J, et al. Dissecting childhood asthma with nasal transcriptomics distinguishes subphenotypes of disease. Journal of Allergy and Clinical Immunology. 2014;133:670-8. e12.
  31. Pascual M, Roa S, Garcia-Sanchez A, Sanz C, et al. Genome 698 wide expression profiling of B lymphocytes reveals IL4R increase in allergic asthma. J Allergy Clin Immunol. 2014 Oct; 134(4):972-5.
  32. PeñalverBernabé B, Cralle L, Gilbert JA. Systems biology of the human microbiome. Curr Opin Biotechnol. 2018 Feb 13;51:146-153.
  33. Hormannsperger G, Clavel T, Haller D. Gut matters: microbe-host interactions in allergic diseases. J Allergy Clin Immunol. 2012;129:1452-9.
  34. Gilstrap DL, Kraft M. Asthma and the host-microbe interaction. Journal of Allergy and Clinical Immunology. 2013;131:1449-50. e3.
  35. Bisgaard H, Li N, Bonnelykke K, et al. Reduced diversity of the intestinal microbiota during infancy is associated with increased risk of allergic disease at school age. J Allergy Clin Immunol. 2011;128:646-52. e1-5.
  36. Tipton L, Müller CL, Kurtz ZD, et al. Fungi stabilize connectivity in the lung and skin microbial ecosystems. Microbiome. 2018 Jan 15;6(1):12. doi: 10.1186/s40168-017-0393-0.
  37. Huang YJ, Marsland BJ, Bunyavanich S, et al. The Microbiome in Allergic Disease: Current Understanding and Future Opportunities - 2017 PRACTALL Document of the American Academy of Allergy, Asthma & Immunology and the European Academy of Allergy and Clinical Immunology. J Allergy ClinImmunol. 2017 Feb 28.
  38. Barabási AL, Gulbahce N, Loscalzo J. Network medicine: a network-based approach to human disease. Nat Rev Genet. 2011 Jan;12(1):56-68.
  39. Sieberts SK, Schadt EE. Moving toward a system genetics view of disease. Mamm Genome. 2007 Jul; 18(6-7): 389-401.
  40. Kanehisa M., Goto S. KEGG: kyoto encyclopedia of genes and genomes. Nucleic acids research. 2000. Vol. 28, no. 1. P. 27-30.
  41. Guryanova S, Guryanova A. Sbv IMPROVER - Modern Approach to Systems Biology. Methods Mol Biol. 2017;1613:21-29.
  42. Smoot ME, Ono K, Ruscheinski J, et al. Cytoscape 2.8: new features for data integration and network visualization. Bioinformatics, 2011;27, 431-432.
  43. Chen Y, Zhu J, Lum PY, et al. Variations in DNA elucidate molecular networks that cause disease. Nature. 2008 Mar 27;452(7186):429-35.
  44. Fodor LE, Gézsi A, Ungvári L, et al. Investigation of the Possible Role of the Hippo/YAP1 Pathway in Asthma and Allergy. Allergy Asthma Immunol Res. 2017 May; 9(3):247-256.
  45. Slater T. Recent advances in modeling languages for pathway maps and computable biological networks. Drug Discov Today. 2014 Feb;19(2):193-8.
  46. Pillich RT, Chen J, Rynkov V, Welker D, Pratt D. A Community Resource for Sharing and Publishing of Biological Networks. Methods Mol Biol. 2017;1558:271-301.
  47. Boue S, Fields B, Hoeng J, et al. Enhancement of COPD biological networks using a web-based collaboration interface. F1000 RESEARCH 4, JANUARY 2015.
  48. sbv IMPROVER project team and challenge best performers Namasivayam AA, Morales AF, Lacave ÁM, et al. Community-Reviewed Biological Network Models for Toxicology and Drug Discovery Applications. Gene Regul Syst Bio. 2016 Jul 12;10:51-66.

Copyright (c) 2018 Guryanova S.V.

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