DC-ATLAS: a systems biology resource to dissect receptor specific signal transduction in dendritic cells

Van, Cavalieri Duccio, Rivero Damariz, Beltrame Luca, Buschow, Calura Enrica, Rizzetto Lisa, Gessani Sandra, Gauzzi, Reith Walter, Baur Andreas, Bonaiuti Roberto, Brandizi Marco, De, D'Oro Ugo, Draghici Sorin, Dunand-Sauthier Isabelle, Gatti Evelina, Granucci Francesca, Gündel Michaela, Kramer Matthijs, Kuka Mirela, Lanyi Arpad, Melief, Ostuni Renato, Philippe Pierre, Popovici Razvan, Rajnavolgyi Eva, Schierer Stephan, Schuler Gerold, Soumelis Vassili, Splendiani Andrea, Stefanini Irene, Torcia, Zanoni Ivan, Zollinger Raphael, Figdor, Austyn

Description

The advent of Systems Biology has been accompanied by the blooming of pathway databases. Currently pathways are defined generically with respect to the organ or cell type where a reaction takes place. The cell type specificity of the reactions is the foundation of immunological research, and capturing this specificity is of paramount importance when using pathway-based analyses to decipher complex immunological datasets. Here, we present DC-ATLAS, a novel and versatile resource for the interpretation of high-throughput data generated perturbing the signaling network of dendritic cells (DCs). Results Pathways are annotated using a novel data model, the Biological Connection Markup Language (BCML), a SBGN-compliant data format developed to store the large amount of information collected. The application of DC-ATLAS to pathway-based analysis of the transcriptional program of DCs stimulated with agonists of the toll-like receptor family allows an integrated description of the flow of information from the cellular sensors to the functional outcome, capturing the temporal series of activation events by grouping sets of reactions that occur at different time points in well-defined functional modules. Conclusions The initiative significantly improves our understanding of DC biology and regulatory networks. Developing a systems biology approach for immune system holds the promise of translating knowledge on the immune system into more successful immunotherapy strategies.

Informations

• Publié par: biomed
• Publié le: 01 janvier 2010
• Nombre de lectures: 13
• Langue: English
• Poids de l'ouvrage: 1 Mo

Extrait

Cavalieriet al.Immunome Research2010,6:10 http://www.immunomeresearch.com/content/6/1/10

IMMUNOME RESEARCH

R E S E A R C HOpen Access DCATLAS: a systems biology resource to dissect receptor specific signal transduction in dendritic cells 1* 11 21,3 1 Duccio Cavalieri, Damariz Rivero , Luca Beltrame , Sonja I Buschow , Enrica Calura, Lisa Rizzetto , 4 45 61 7 Sandra Gessani , Maria C Gauzzi , Walter Reith , Andreas Baur , Roberto Bonaiuti , Marco Brandizi , 1 89 510 11 Carlotta De Filippo , Ugo D’, Francesca Granucci,Oro , Sorin Draghici , Isabelle DunandSauthier , Evelina Gatti 7 128 1314 14 Michaela Gündel , Matthijs Kramer, Mirela Kuka , Arpad Lanyi, Cornelis JM Melief, Nadine van Montfoort, 11 1015 136 6 Renato Ostuni, Philippe Pierre, Razvan Popovici, Eva Rajnavolgyi, Stephan Schierer , Gerold Schuler , 16 71 1711 16 Vassili Soumelis, Andrea Splendiani , Irene Stefanini , Maria G Torcia, Ivan Zanoni, Raphael Zollinger, 2 18 Carl G Figdor , Jonathan M Austyn

Abstract Background:The advent of Systems Biology has been accompanied by the blooming of pathway databases. Currently pathways are defined generically with respect to the organ or cell type where a reaction takes place. The cell type specificity of the reactions is the foundation of immunological research, and capturing this specificity is of paramount importance when using pathwaybased analyses to decipher complex immunological datasets. Here, we present DCATLAS, a novel and versatile resource for the interpretation of highthroughput data generated perturbing the signaling network of dendritic cells (DCs). Results:Pathways are annotated using a novel data model, the Biological Connection Markup Language (BCML), a SBGNcompliant data format developed to store the large amount of information collected. The application of DC ATLAS to pathwaybased analysis of the transcriptional program of DCs stimulated with agonists of the tolllike receptor family allows an integrated description of the flow of information from the cellular sensors to the functional outcome, capturing the temporal series of activation events by grouping sets of reactions that occur at different time points in welldefined functional modules. Conclusions:The initiative significantly improves our understanding of DC biology and regulatory networks. Developing a systems biology approach for immune system holds the promise of translating knowledge on the immune system into more successful immunotherapy strategies.

Background Dendritic cells (DCs) orchestrate a repertoire of immune responses that endow resistance to infections and toler ance to self. DC plasticity has a prominent role in elicit ing the proper immune response. Different DC subsets display different receptors and surface molecules and express different sets of cytokines/chemokines, all of which lead to distinct immunological outcomes. Among the receptors are the innate pattern recognition

* Correspondence: [e-mail] 1 Department of Pharmacology, University of Firenze, Firenze, Italy Full list of author information is available at the end of the article

receptors (PRRs) that mediate the initial sensing of an infection. These include Tolllike receptors (TLRs), RIG Ilike receptors (RLRs), NODlike receptors (NLRs), and Ctype lectin receptors (CLRs) [1]. TLRs recognize con served structures of microbes and are localized on the cell surface (TLR1, TLR2, TLR4, TLR5 and TLR6) to recognize bacterial and fungal cell wall components or in intracellular membranes such as endosomes or pha gosomes (TLR3, TLR7, TLR8 and TLR9) where they recognize viral or microbial nucleic acids [1]. Thus, dif ferent TLRs are amenable to targeting by different types of agents [2].

© 2010 Cavalieri et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.