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1.  Synergistic anti-tumor activity of acadesine (AICAR) in combination with the anti-CD20 monoclonal antibody rituximab in in vivo and in vitro models of mantle cell lymphoma 
Oncotarget  2014;5(3):726-739.
Mantle cell lymphoma (MCL) is considered one of the most challenging lymphoma, with limited responses to current therapies. Acadesine, a nucleoside analogue has shown antitumoral effects in different preclinical cancer models as well as in a recent phase I/II clinical trial conducted in patients with chronic lymphocytic leukemia. Here we observed that acadesine exerted a selective antitumoral activity in the majority of MCL cell lines and primary MCL samples, independently of adverse cytogenetic factors. Moreover, acadesine was highly synergistic, both in vitro and in vivo, with the anti-CD20 monoclonal antibody rituximab, commonly used in combination therapy for MCL. Gene expression profiling analysis in harvested tumors suggested that acadesine modulates immune response, actin cytoskeleton organization and metal binding, pointing out a substantial impact on metabolic processes by the nucleoside analog. Rituximab also induced changes on metal binding and immune responses. The combination of both drugs enhanced the gene signature corresponding to each single agent, showing an enrichment of genes involved in inflammation, metabolic stress, apoptosis and proliferation. These effects could be important as aberrant apoptotic and proinflammatory pathways play a significant role in the pathogenesis of MCL. In summary, our results suggest that acadesine exerts a cytotoxic effect in MCL in combination with rituximab, by decreasing the proliferative and survival signatures of the disease, thus supporting the clinical examination of this strategy in MCL patients.
PMCID: PMC3996675  PMID: 24519895
Acadesine; rituximab mantle cell lymphoma; xenograft mouse model
2.  Gene Expression Profiling Identifies Molecular Pathways Associated with Collagen VI Deficiency and Provides Novel Therapeutic Targets 
PLoS ONE  2013;8(10):e77430.
Ullrich congenital muscular dystrophy (UCMD), caused by collagen VI deficiency, is a common congenital muscular dystrophy. At present, the role of collagen VI in muscle and the mechanism of disease are not fully understood. To address this we have applied microarrays to analyse the transcriptome of UCMD muscle and compare it to healthy muscle and other muscular dystrophies. We identified 389 genes which are differentially regulated in UCMD relative to controls. In addition, there were 718 genes differentially expressed between UCMD and dystrophin deficient muscle. In contrast, only 29 genes were altered relative to other congenital muscular dystrophies. Changes in gene expression were confirmed by real-time PCR. The set of regulated genes was analysed by Gene Ontology, KEGG pathways and Ingenuity Pathway analysis to reveal the molecular functions and gene networks associated with collagen VI defects. The most significantly regulated pathways were those involved in muscle regeneration, extracellular matrix remodelling and inflammation. We characterised the immune response in UCMD biopsies as being mainly mediated via M2 macrophages and the complement pathway indicating that anti-inflammatory treatment may be beneficial to UCMD as for other dystrophies. We studied the immunolocalisation of ECM components and found that biglycan, a collagen VI interacting proteoglycan, was reduced in the basal lamina of UCMD patients. We propose that biglycan reduction is secondary to collagen VI loss and that it may be contributing towards UCMD pathophysiology. Consequently, strategies aimed at over-expressing biglycan and restore the link between the muscle cell surface and the extracellular matrix should be considered.
PMCID: PMC3819505  PMID: 24223098
3.  Knowledge management for systems biology a general and visually driven framework applied to translational medicine 
BMC Systems Biology  2011;5:38.
To enhance our understanding of complex biological systems like diseases we need to put all of the available data into context and use this to detect relations, pattern and rules which allow predictive hypotheses to be defined. Life science has become a data rich science with information about the behaviour of millions of entities like genes, chemical compounds, diseases, cell types and organs, which are organised in many different databases and/or spread throughout the literature. Existing knowledge such as genotype - phenotype relations or signal transduction pathways must be semantically integrated and dynamically organised into structured networks that are connected with clinical and experimental data. Different approaches to this challenge exist but so far none has proven entirely satisfactory.
To address this challenge we previously developed a generic knowledge management framework, BioXM™, which allows the dynamic, graphic generation of domain specific knowledge representation models based on specific objects and their relations supporting annotations and ontologies. Here we demonstrate the utility of BioXM for knowledge management in systems biology as part of the EU FP6 BioBridge project on translational approaches to chronic diseases. From clinical and experimental data, text-mining results and public databases we generate a chronic obstructive pulmonary disease (COPD) knowledge base and demonstrate its use by mining specific molecular networks together with integrated clinical and experimental data.
We generate the first semantically integrated COPD specific public knowledge base and find that for the integration of clinical and experimental data with pre-existing knowledge the configuration based set-up enabled by BioXM reduced implementation time and effort for the knowledge base compared to similar systems implemented as classical software development projects. The knowledgebase enables the retrieval of sub-networks including protein-protein interaction, pathway, gene - disease and gene - compound data which are used for subsequent data analysis, modelling and simulation. Pre-structured queries and reports enhance usability; establishing their use in everyday clinical settings requires further simplification with a browser based interface which is currently under development.
PMCID: PMC3060864  PMID: 21375767

Results 1-3 (3)