Daniel Aguilar1,2,3, Nathanael Lemonnier4, Gerard H Koppelman5,6, Erik Melén7, Baldo Oliva8, Mariona Pinart2, Stefano Guerra2,9, Jean Bousquet10,11, Josep M Anto2. 1. Biomedical Research Networking Center in Hepatic and Digestive Diseases (CIBEREHD), Instituto de Salud Carlos III, Barcelona, Spain. 2. ISGlobal, Barcelona Institute for Global Health, Barcelona, Spain. 3. 6AM Data Mining, Barcelona, Spain. 4. Institute for Advanced Biosciences, Inserm U 1209 CNRS UMR 5309 Université Grenoble Alpes, Site Santé, Allée des Alpes, La Tronche, France. 5. University of Groningen, University Medical Center Groningen, Beatrix Children's Hospital, Department of Pediatric Pulmonology and Pediatric Allergology, Groningen, Netherlands. 6. University of Groningen, University Medical Center Groningen, GRIAC Research Institute. 7. Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden. 8. Structural Bioinformatics Group, Research Programme on Biomedical Informatics, Department of Experimental and Health Sciences, Universitat Pompeu Fabra, Barcelona, Spain. 9. Asthma and Airway Disease Research Center, University of Arizona, Tucson, Arizona, United States of America. 10. Hopital Arnaud de Villeneuve University Hospital, Montpellier, France. 11. Charité, Universitätsmedizin Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Comprehensive Allergy Center, Department of Dermatology and Allergy, Berlin, Germany.
Abstract
BACKGROUND: The mechanisms explaining multimorbidity between asthma, dermatitis and rhinitis (allergic multimorbidity) are not well known. We investigated these mechanisms and their specificity in distinct cell types by means of an interactome-based analysis of expression data. METHODS: Genes associated to the diseases were identified using data mining approaches, and their multimorbidity mechanisms in distinct cell types were characterized by means of an in silico analysis of the topology of the human interactome. RESULTS: We characterized specific pathomechanisms for multimorbidities between asthma, dermatitis and rhinitis for distinct emergent non-eosinophilic cell types. We observed differential roles for cytokine signaling, TLR-mediated signaling and metabolic pathways for multimorbidities across distinct cell types. Furthermore, we also identified individual genes potentially associated to multimorbidity mechanisms. CONCLUSIONS: Our results support the existence of differentiated multimorbidity mechanisms between asthma, dermatitis and rhinitis at cell type level, as well as mechanisms common to distinct cell types. These results will help understanding the biology underlying allergic multimorbidity, assisting in the design of new clinical studies.
BACKGROUND: The mechanisms explaining multimorbidity between asthma, dermatitis and rhinitis (allergic multimorbidity) are not well known. We investigated these mechanisms and their specificity in distinct cell types by means of an interactome-based analysis of expression data. METHODS: Genes associated to the diseases were identified using data mining approaches, and their multimorbidity mechanisms in distinct cell types were characterized by means of an in silico analysis of the topology of the human interactome. RESULTS: We characterized specific pathomechanisms for multimorbidities between asthma, dermatitis and rhinitis for distinct emergent non-eosinophilic cell types. We observed differential roles for cytokine signaling, TLR-mediated signaling and metabolic pathways for multimorbidities across distinct cell types. Furthermore, we also identified individual genes potentially associated to multimorbidity mechanisms. CONCLUSIONS: Our results support the existence of differentiated multimorbidity mechanisms between asthma, dermatitis and rhinitis at cell type level, as well as mechanisms common to distinct cell types. These results will help understanding the biology underlying allergic multimorbidity, assisting in the design of new clinical studies.
Authors: J Bousquet; J Anto; C Auffray; M Akdis; A Cambon-Thomsen; T Keil; T Haahtela; B N Lambrecht; D S Postma; J Sunyer; R Valenta; C A Akdis; I Annesi-Maesano; A Arno; C Bachert; F Ballester; X Basagana; U Baumgartner; C Bindslev-Jensen; B Brunekreef; K H Carlsen; L Chatzi; R Crameri; E Eveno; F Forastiere; J Garcia-Aymerich; S Guerra; H Hammad; J Heinrich; D Hirsch; B Jacquemin; F Kauffmann; M Kerkhof; M Kogevinas; G H Koppelman; M L Kowalski; S Lau; K C Lodrup-Carlsen; M Lopez-Botet; J Lotvall; C Lupinek; D Maier; M J Makela; F D Martinez; J Mestres; I Momas; M C Nawijn; A Neubauer; S Oddie; S Palkonen; I Pin; C Pison; F Rancé; S Reitamo; E Rial-Sebbag; M Salapatas; V Siroux; D Smagghe; M Torrent; E Toskala; P van Cauwenberge; A J M van Oosterhout; R Varraso; L von Hertzen; M Wickman; C Wijmenga; M Worm; J Wright; T Zuberbier Journal: Allergy Date: 2011-01-24 Impact factor: 13.146
Authors: Juan I Fuxman Bass; Alos Diallo; Justin Nelson; Juan M Soto; Chad L Myers; Albertha J M Walhout Journal: Nat Methods Date: 2013-12 Impact factor: 28.547
Authors: Casey S Greene; Arjun Krishnan; Aaron K Wong; Emanuela Ricciotti; Rene A Zelaya; Daniel S Himmelstein; Ran Zhang; Boris M Hartmann; Elena Zaslavsky; Stuart C Sealfon; Daniel I Chasman; Garret A FitzGerald; Kara Dolinski; Tilo Grosser; Olga G Troyanskaya Journal: Nat Genet Date: 2015-04-27 Impact factor: 38.330