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1.  Bioinformatic approaches for functional annotation and pathway inference in metagenomics data 
Briefings in Bioinformatics  2012;13(6):696-710.
Metagenomic approaches are increasingly recognized as a baseline for understanding the ecology and evolution of microbial ecosystems. The development of methods for pathway inference from metagenomics data is of paramount importance to link a phenotype to a cascade of events stemming from a series of connected sets of genes or proteins. Biochemical and regulatory pathways have until recently been thought and modelled within one cell type, one organism, one species. This vision is being dramatically changed by the advent of whole microbiome sequencing studies, revealing the role of symbiotic microbial populations in fundamental biochemical functions. The new landscape we face requires a clear picture of the potentialities of existing tools and development of new tools to characterize, reconstruct and model biochemical and regulatory pathways as the result of integration of function in complex symbiotic interactions of ontologically and evolutionary distinct cell types.
PMCID: PMC3505041  PMID: 23175748
metagenomics; next-generation sequencing; microbiome; pathway analysis; gene annotation
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.
PMCID: PMC3411757  PMID: 22879980
3.  Systems biology of host–fungus interactions: turning complexity into simplicity 
Current Opinion in Microbiology  2012;15(4):440-446.
► 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.
PMCID: PMC3501689  PMID: 22717554
4.  A Systems Biology Approach to Characterize the Regulatory Networks Leading to Trabectedin Resistance in an In Vitro Model of Myxoid Liposarcoma 
PLoS ONE  2012;7(4):e35423.
Trabectedin, a new antitumor compound originally derived from a marine tunicate, is clinically effective in soft tissue sarcoma. The drug has shown a high selectivity for myxoid liposarcoma, characterized by the translocation t(12;16)(q13; p11) leading to the expression of FUS-CHOP fusion gene. Trabectedin appears to act interfering with mechanisms of transcription regulation. In particular, the transactivating activity of FUS-CHOP was found to be impaired by trabectedin treatment. Even after prolonged response resistance occurs and thus it is important to elucidate the mechanisms of resistance to trabectedin. To this end we developed and characterized a myxoid liposarcoma cell line resistant to trabectedin (402-91/ET), obtained by exposing the parental 402-91 cell line to stepwise increases in drug concentration. The aim of this study was to compare mRNAs, miRNAs and proteins profiles of 402-91 and 402-91/ET cells through a systems biology approach. We identified 3,083 genes, 47 miRNAs and 336 proteins differentially expressed between 402-91 and 402-91/ET cell lines. Interestingly three miRNAs among those differentially expressed, miR-130a, miR-21 and miR-7, harbored CHOP binding sites in their promoter region. We used computational approaches to integrate the three regulatory layers and to generate a molecular map describing the altered circuits in sensitive and resistant cell lines. By combining transcriptomic and proteomic data, we reconstructed two different networks, i.e. apoptosis and cell cycle regulation, that could play a key role in modulating trabectedin resistance. This approach highlights the central role of genes such as CCDN1, RB1, E2F4, TNF, CDKN1C and ABL1 in both pre- and post-transcriptional regulatory network. The validation of these results in in vivo models might be clinically relevant to stratify myxoid liposarcoma patients with different sensitivity to trabectedin treatment.
PMCID: PMC3327679  PMID: 22523595
5.  graphite - a Bioconductor package to convert pathway topology to gene network 
BMC Bioinformatics  2012;13:20.
Gene set analysis is moving towards considering pathway topology as a crucial feature. Pathway elements are complex entities such as protein complexes, gene family members and chemical compounds. The conversion of pathway topology to a gene/protein networks (where nodes are a simple element like a gene/protein) is a critical and challenging task that enables topology-based gene set analyses.
Unfortunately, currently available R/Bioconductor packages provide pathway networks only from single databases. They do not propagate signals through chemical compounds and do not differentiate between complexes and gene families.
Here we present graphite, a Bioconductor package addressing these issues. Pathway information from four different databases is interpreted following specific biologically-driven rules that allow the reconstruction of gene-gene networks taking into account protein complexes, gene families and sensibly removing chemical compounds from the final graphs. The resulting networks represent a uniform resource for pathway analyses. Indeed, graphite provides easy access to three recently proposed topological methods. The graphite package is available as part of the Bioconductor software suite.
graphite is an innovative package able to gather and make easily available the contents of the four major pathway databases. In the field of topological analysis graphite acts as a provider of biological information by reducing the pathway complexity considering the biological meaning of the pathway elements.
PMCID: PMC3296647  PMID: 22292714
6.  A computational pipeline to discover highly phylogenetically informative genes in sequenced genomes: application to Saccharomyces cerevisiae natural strains 
Nucleic Acids Research  2012;40(9):3834-3848.
The quest for genes representing genetic relationships of strains or individuals within populations and their evolutionary history is acquiring a novel dimension of complexity with the advancement of next-generation sequencing (NGS) technologies. In fact, sequencing an entire genome uncovers genetic variation in coding and non-coding regions and offers the possibility of studying Saccharomyces cerevisiae populations at the strain level. Nevertheless, the disadvantageous cost-benefit ratio (the amount of details disclosed by NGS against the time-expensive and expertise-demanding data assembly process) still precludes the application of these techniques to the routinely assignment of yeast strains, making the selection of the most reliable molecular markers greatly desirable. In this work we propose an original computational approach to discover genes that can be used as a descriptor of the population structure. We found 13 genes whose variability can be used to recapitulate the phylogeny obtained from genome-wide sequences. The same approach that we prove to be successful in yeasts can be generalized to any other population of individuals given the availability of high-quality genomic sequences and of a clear population structure to be targeted.
PMCID: PMC3351171  PMID: 22266652

Results 1-6 (6)