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1.  Strain Dependent Variation of Immune Responses to A. fumigatus: Definition of Pathogenic Species 
PLoS ONE  2013;8(2):e56651.
For over a century microbiologists and immunologist have categorized microorganisms as pathogenic or non-pathogenic species or genera. This definition, clearly relevant at the strain and species level for most bacteria, where differences in virulence between strains of a particular species are well known, has never been probed at the strain level in fungal species. Here, we tested the immune reactivity and the pathogenic potential of a collection of strains from Aspergillus spp, a fungus that is generally considered pathogenic in immuno-compromised hosts. Our results show a wide strain-dependent variation of the immune response elicited indicating that different isolates possess diverse virulence and infectivity. Thus, the definition of markers of inflammation or pathogenicity cannot be generalized. The profound understanding of the molecular mechanisms subtending the different immune responses will result solely from the comparative study of strains with extremely diverse properties.
doi:10.1371/journal.pone.0056651
PMCID: PMC3575482  PMID: 23441211
2.  The Modular Nature of Dendritic Cell Responses to Commensal and Pathogenic Fungi 
PLoS ONE  2012;7(8):e42430.
The type of adaptive immune response following host-fungi interaction is largely determined at the level of the antigen-presenting cells, and in particular by dendritic cells (DCs). The extent to which transcriptional regulatory events determine the decision making process in DCs is still an open question. By applying the highly structured DC-ATLAS pathways to analyze DC responses, we classified the various stimuli by revealing the modular nature of the different transcriptional programs governing the recognition of either pathogenic or commensal fungi. Through comparison of the network parts affected by DC stimulation with fungal cells and purified single agonists, we could determine the contribution of each receptor during the recognition process. We observed that initial recognition of a fungus creates a temporal window during which the simultaneous recruitment of cell surface receptors can intensify, complement and sustain the DC activation process. The breakdown of the response to whole live cells, through the purified components, showed how the response to invading fungi uses a set of specific modules. We find that at the start of fungal recognition, DCs rapidly initiate the activation process. Ligand recognition is further enhanced by over-expression of the receptor genes, with a significant correspondence between gene expression and protein levels and function. Then a marked decrease in the receptor levels follows, suggesting that at this moment the DC commits to a specific fate. Overall our pathway based studies show that the temporal window of the fungal recognition process depends on the availability of ligands and is different for pathogens and commensals. Modular analysis of receptor and signalling-adaptor expression changes, in the early phase of pathogen recognition, is a valuable tool for rapid and efficient dissection of the pathogen derived components that determine the phenotype of the DC and thereby the type of immune response initiated.
doi:10.1371/journal.pone.0042430
PMCID: PMC3411757  PMID: 22879980
3.  Systems biology of host–fungus interactions: turning complexity into simplicity 
Current Opinion in Microbiology  2012;15(4):440-446.
Highlights
► Understanding the complexity of host–fungus interactions during commensalism. ► Genes mediating host colonization or fitness can evolve into infection-associated traits. ► Using bioinformatics to unravel functional genomics in dual-genome datasets. ► Modeling both fungal and host immune responses using network analysis tools. ► Databases and web-based resources for investigating host–pathogen interactions.
Modeling interactions between fungi and their hosts at the systems level requires a molecular understanding both of how the host orchestrates immune surveillance and tolerance, and how this activation, in turn, affects fungal adaptation and survival. The transition from the commensal to pathogenic state, and the co-evolution of fungal strains within their hosts, necessitates the molecular dissection of fungal traits responsible for these interactions. There has been a dramatic increase in publically available genome-wide resources addressing fungal pathophysiology and host–fungal immunology. The integration of these existing data and emerging large-scale technologies addressing host–pathogen interactions requires novel tools to connect genome-wide data sets and theoretical approaches with experimental validation so as to identify inherent and emerging properties of host–pathogen relationships and to obtain a holistic view of infectious processes. If successful, a better understanding of the immune response in health and microbial diseases will eventually emerge and pave the way for improved therapies.
doi:10.1016/j.mib.2012.05.001
PMCID: PMC3501689  PMID: 22717554
4.  The dectin-1/inflammasome pathway is responsible for the induction of protective T-helper 17 responses that discriminate between yeasts and hyphae of Candida albicans 
Journal of Leukocyte Biology  2011;90(2):357-366.
Invading C. albicans hyphae are recognized by macrophages, activate the caspase-1/IL-1β pathway, and lead to the activation of IL-17 pathway to control the C. albicans infection.
In the mucosa, the immune pathways discriminating between colonizing and invasive Candida, thus inducing tolerance or inflammation, are poorly understood. Th17 responses induced by Candida albicans hyphae are central for the activation of mucosal antifungal immunity. An essential step for the discrimination between yeasts and hyphae and induction of Th17 responses is the activation of the inflammasome by C. albicans hyphae and the subsequent release of active IL-1β in macrophages. Inflammasome activation in macrophages results from differences in cell-wall architecture between yeasts and hyphae and is partly mediated by the dectin-1/Syk pathway. These results define the dectin-1/inflammasome pathway as the mechanism that enables the host immune system to mount a protective Th17 response and distinguish between colonization and tissue invasion by C. albicans.
doi:10.1189/jlb.1210702
PMCID: PMC3513931  PMID: 21531876
Candida; colonization; invasion; IL-1β; IL-17
5.  The Biological Connection Markup Language: a SBGN-compliant format for visualization, filtering and analysis of biological pathways 
Bioinformatics  2011;27(15):2127-2133.
Motivation: Many models and analysis of signaling pathways have been proposed. However, neither of them takes into account that a biological pathway is not a fixed system, but instead it depends on the organism, tissue and cell type as well as on physiological, pathological and experimental conditions.
Results: The Biological Connection Markup Language (BCML) is a format to describe, annotate and visualize pathways. BCML is able to store multiple information, permitting a selective view of the pathway as it exists and/or behave in specific organisms, tissues and cells. Furthermore, BCML can be automatically converted into data formats suitable for analysis and into a fully SBGN-compliant graphical representation, making it an important tool that can be used by both computational biologists and ‘wet lab’ scientists.
Availability and implementation: The XML schema and the BCML software suite are freely available under the LGPL for download at http://bcml.dc-atlas.net. They are implemented in Java and supported on MS Windows, Linux and OS X.
Contact: duccio.cavalieri@unifi.it; sorin@wayne.edu
Supplementary information: Supplementary data are available at Bioinformatics online.
doi:10.1093/bioinformatics/btr339
PMCID: PMC3137220  PMID: 21653523
6.  DC-ATLAS: a systems biology resource to dissect receptor specific signal transduction in dendritic cells 
Immunome Research  2010;6:10.
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.
doi:10.1186/1745-7580-6-10
PMCID: PMC3000836  PMID: 21092113
7.  Using Pathway Signatures as Means of Identifying Similarities among Microarray Experiments 
PLoS ONE  2009;4(1):e4128.
Widespread use of microarrays has generated large amounts of data, the interrogation of the public microarray repositories, identifying similarities between microarray experiments is now one of the major challenges. Approaches using defined group of genes, such as pathways and cellular networks (pathway analysis), have been proposed to improve the interpretation of microarray experiments. We propose a novel method to compare microarray experiments at the pathway level, this method consists of two steps: first, generate pathway signatures, a set of descriptors recapitulating the biologically meaningful pathways related to some clinical/biological variable of interest, second, use these signatures to interrogate microarray databases. We demonstrate that our approach provides more reliable results than with gene-based approaches. While gene-based approaches tend to suffer from bias generated by the analytical procedures employed, our pathway based method successfully groups together similar samples, independently of the experimental design. The results presented are potentially of great interest to improve the ability to query and compare experiments in public repositories of microarray data. As a matter of fact, this method can be used to retrieve data from public microarray databases and perform comparisons at the pathway level.
doi:10.1371/journal.pone.0004128
PMCID: PMC2610483  PMID: 19125200

Results 1-7 (7)