We developed a novel software tool, EXCAVATOR, for the detection of copy number variants (CNVs) from whole-exome sequencing data. EXCAVATOR combines a three-step normalization procedure with a novel heterogeneous hidden Markov model algorithm and a calling method that classifies genomic regions into five copy number states. We validate EXCAVATOR on three datasets and compare the results with three other methods. These analyses show that EXCAVATOR outperforms the other methods and is therefore a valuable tool for the investigation of CNVs in largescale projects, as well as in clinical research and diagnostics. EXCAVATOR is freely available at http://sourceforge.net/projects/excavatortool/.

The glucose and lipid metabolism reprogramming is gradedependent in clear cell renal cell carcinoma primary cultures and is targetable to modulate cell viability and proliferation The glucose and lipid metabolism reprogramming is grade-dependent in clear cell renal cell carcinoma primary cultures and is targetable to modulate cell viability and proliferation

Sequencing large number of individuals, which is often needed for population genetics studies, is still economically challenging despite falling costs of Next Generation Sequencing (NGS). Pool-seq is an alternative cost- and time-effective option in which DNA from several individuals is pooled for sequencing. However, pooling of DNA creates new problems and challenges for accurate variant call and allele frequency (AF) estimation. In particular, sequencing errors confound with the alleles present at low frequency in the pools possibly giving rise to false positive variants. We sequenced 996 individuals in 83 pools (12 individuals/pool) in a targeted re-sequencing experiment. We show that Pool-seq AFs are robust and reliable by comparing them with public variant databases and in-house SNP-genotyping data of individual subjects of pools. Furthermore, we propose a simple filtering guideline for the removal of spurious variants based on the Kolmogorov-Smirnov statistical test. We experimentally validated our filters by comparing Pool-seq to individual sequencing data showing that the filters remove most of the false variants while retaining majority of true variants. The proposed guideline is fairly generic in nature and could be easily applied in other Pool-seq experiments.

Oxidative stress, pulmonary and systemic inflammation, endothelial cell dysfunction, atherosclerosis and cardiac autonomic dysfunction have been linked to urban particulate matter exposure. The chemical composition of airborne pollutants in Milano is similar to those of other European cities though with a higher PM2.5 fraction. Milano winter fine particles (PM2.5win) are characterized by the presence of nitrate, organic carbon fraction, with high amount of polycyclic aromatic hydrocarbons and elements such as Pb, Al, Zn, V, Fe, Cr and others, with a negligible endotoxin presence. In BALB/c mice, we examined, at biochemical and transcriptomic levels, the adverse effects of repeated Milano PM2.5win exposure in lung and heart. We found that ET-1, Hsp70, Cyp1A1, Cyp1B1 and Hsp-70, HO-1, MPO respectively increased within lung and heart of PM2.5win-treated mice. The PM2.5win exposure had a strong impact on global gene expression of heart tissue (181 up-regulated and 178 down-regulated genes) but a lesser impact on lung tissue (14 up-regulated genes and 43 down-regulated genes). Focusing on modulated genes, in lung we found two- to three-fold changes of those genes related to polycyclic aromatic hydrocarbons exposure and calcium signalling. Within heart the most striking aspect is the twofold to threefold increase in collagen and laminin related genes as well as in genes involved in calcium signaling. The current study extends our previous findings, showing that repeated instillations of PM2.5win trigger systemic adverse effects. PM2.5win thus likely poses an acute threat primarily to susceptible people, such as the elderly and those with unrecognized coronary artery or structural heart disease. The study of genomic responses will improve understanding of disease mechanisms and enable future clinical testing of interventions against the toxic effects of air pollutant.

Metabolism is directly and indirectly fine-tuned by a complex web of interacting regulatory mechanisms that fall into two major classes. On the one hand, the expression level of the catalyzing enzyme sets the maximal theoretical flux level (i.e., the net rate of the reaction) for each enzyme-controlled reaction. On the other hand, metabolic regulation controls the metabolic flux through the interactions of metabolites (substrates, cofactors, allosteric modulators) with the responsible enzyme. High-throughput data, such as metabolomics and transcriptomics data, if analyzed separately, do not accurately characterize the hierarchical regulation of metabolism outlined above. They must be integrated to disassemble the interdependence between different regulatory layers controlling metabolism. To this aim, we propose INTEGRATE, a computational pipeline that integrates metabolomics and transcriptomics data, using constraint-based stoichiometric metabolic models as a scaffold. We compute differential reaction expression from transcriptomics data and use constraint-based modeling to predict if the differential expression of metabolic enzymes directly originates differences in metabolic fluxes. In parallel, we use metabolomics to predict how differences in substrate availability translate into differences in metabolic fluxes. We discriminate fluxes regulated at the metabolic and/or gene expression level by intersecting these two output datasets. We demonstrate the pipeline using a set of immortalized normal and cancer breast cell lines. In a clinical setting, knowing the regulatory level at which a given metabolic reaction is controlled will be valuable to inform targeted, truly personalized therapies in cancer patients.