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1.  Transcriptome Analysis of Ullrich Congenital Muscular Dystrophy Fibroblasts Reveals a Disease Extracellular Matrix Signature and Key Molecular Regulators 
PLoS ONE  2015;10(12):e0145107.
Collagen VI related myopathies encompass a range of phenotypes with involvement of skeletal muscle, skin and other connective tissues. They represent a severe and relatively common form of congenital disease for which there is no treatment. Collagen VI in skeletal muscle and skin is produced by fibroblasts.
Aims & Methods
In order to gain insight into the consequences of collagen VI mutations and identify key disease pathways we performed global gene expression analysis of dermal fibroblasts from patients with Ullrich Congenital Muscular Dystrophy with and without vitamin C treatment. The expression data were integrated using a range of systems biology tools. Results were validated by real-time PCR, western blotting and functional assays.
We found significant changes in the expression levels of almost 600 genes between collagen VI deficient and control fibroblasts. Highly regulated genes included extracellular matrix components and surface receptors, including integrins, indicating a shift in the interaction between the cell and its environment. This was accompanied by a significant increase in fibroblasts adhesion to laminin. The observed changes in gene expression profiling may be under the control of two miRNAs, miR-30c and miR-181a, which we found elevated in tissue and serum from patients and which could represent novel biomarkers for muscular dystrophy. Finally, the response to vitamin C of collagen VI mutated fibroblasts significantly differed from healthy fibroblasts. Vitamin C treatment was able to revert the expression of some key genes to levels found in control cells raising the possibility of a beneficial effect of vitamin C as a modulator of some of the pathological aspects of collagen VI related diseases.
PMCID: PMC4686057  PMID: 26670220
2.  Improving Assessment of Lipoprotein Profile in Type 1 Diabetes by 1H NMR Spectroscopy 
PLoS ONE  2015;10(8):e0136348.
Patients with type 1 diabetes (T1D) present increased risk of cardiovascular disease (CVD). The aim of this study is to improve the assessment of lipoprotein profile in patients with T1D by using a robust developed method 1H nuclear magnetic resonance spectroscopy (1H NMR), for further correlation with clinical factors associated to CVD. Thirty patients with T1D and 30 non-diabetes control (CT) subjects, matched for gender, age, body composition (DXA, BMI, waist/hip ratio), regular physical activity levels and cardiorespiratory capacity (VO2peak), were analyzed. Dietary records and routine lipids were assessed. Serum lipoprotein particle subfractions, particle sizes, and cholesterol and triglycerides subfractions were analyzed by 1H NMR. It was evidenced that subjects with T1D presented lower concentrations of small LDL cholesterol, medium VLDL particles, large VLDL triglycerides, and total triglycerides as compared to CT subjects. Women with T1D presented a positive association with HDL size (p<0.005; R = 0.601) and large HDL triglycerides (p<0.005; R = 0.534) and negative (p<0.005; R = -0.586) to small HDL triglycerides. Body fat composition represented an important factor independently of normal BMI, with large LDL particles presenting a positive correlation to total body fat (p<0.005; R = 0.505), and total LDL cholesterol and small LDL cholesterol a positive correlation (p<0.005; R = 0.502 and R = 0.552, respectively) to abdominal fat in T1D subjects; meanwhile, in CT subjects, body fat composition was mainly associated to HDL subclasses. VO2peak was negatively associated (p<0.005; R = -0.520) to large LDL-particles only in the group of patients with T1D. In conclusion, patients with T1D with adequate glycemic control and BMI and without chronic complications presented a more favourable lipoprotein profile as compared to control counterparts. In addition, slight alterations in BMI and/or body fat composition showed to be relevant to provoking alterations in lipoproteins profiles. Finally, body fat composition appears to be a determinant for cardioprotector lipoprotein profile.
PMCID: PMC4552656  PMID: 26317989
4.  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
5.  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
6.  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 1-6 (6)