Performance evaluation of deleteriousness prediction methods for intronic SNVs in next generation sequences

Introduction: Alterations in splicing sites (ss) are estimated to explain approximately 10% of human disease causal variants. Mutations outside the ss but affecting ?regulatory elements? can be up to 25%. Accurate deleteriousness prediction for intronic variants is crucial for diagnostic purposes. Many deleteriousness prediction methods have been developed, but their relative values are still unclear in practical applications. We comprehensively evaluated the predictive performance of two complementary deleteriousness-scoring methods using information from real patients. Material and Methods: We selected the dbscSNV (both ADA and RF scores) and SPIDEX algorithms, that study variants in splicing consensus regions or in regulatory regions respectively. The tools, either alone or in combination, were tested on 29294 gene intronic SNVs that have previously been characterised by ClinVar as either ?pathogenic? (430) or ?benign? (28864). The sensitivity, specificity and positive and negative predictive values were calculated. Moreover, we applied the algorithms to WES data from undiagnosed patients, and we analysed the mRNA sequence from genes that fitted the patient?s phenotype. Results: The highest sensitivity corresponds to dbscSNV with 96.55% while the best specificity is for SPIDEX with 95.78%. When considering the 3 scores (SPIDEX, dbscSNV ADA and RF Score), the sensitivity and specificity values were 60.7% and 94.6%. The Positive and Negative Predictive Value were 14.45% and 99.39%. The results for 20 undiagnosed cases are presented. Conclusions: Besides the low positive predictive value, the combination of both algorithms leads less than 1% of false negatives, so their routine use can be recommended for diagnostic purposes

Characterization of New Proteomic Biomarker Candidates in Mucopolysaccharidosis Type IVA

Mucopolysaccharidosis type IVA (MPS IVA) is a lysosomal storage disease caused by mutations in the N-acetylgalactosamine-6-sulfatase (GALNS) gene. Skeletal dysplasia and the related clinical features of MPS IVA are caused by disruption of the cartilage and its extracellular matrix, leading to a growth imbalance. Enzyme replacement therapy (ERT) with recombinant human GALNS has yielded positive results in activity of daily living and endurance tests. However, no data have demonstrated improvements in bone lesions and bone grow thin MPS IVA after ERT, and there is no correlation between therapeutic efficacy and urine levels of keratan sulfate, which accumulates in MPS IVA patients. Using qualitative and quantitative proteomics approaches, we analyzed leukocyte samples from healthy controls (n = 6) and from untreated (n = 5) and ERT-treated (n = 8, sampled before and after treatment) MPS IVA patients to identify potential biomarkers of disease. Out of 690 proteins identified in leukocytes, we selected a group of proteins that were dysregulated in MPS IVA patients with ERT. From these, we identified four potential protein biomarkers, all of which may influence bone and cartilage metabolism: lactotransferrin, coronin 1A, neutral alpha-glucosidase AB, and vitronectin. Further studies of cartilage and bone alterations in MPS IVA will be required to verify the validity of these proteins as potential biomarkers of MPS IVA

Wage inequality, segregation by skill and the price of capital in an assignment model

Some pieces of empirical evidence suggest that in the U.S., over the last few decades, (i) wage inequality between-plants has risen much more than wage inequality within-plants and (ii) there has been an increase in the segregation of workers by skill into separate plants. This paper presents a frictionless assignment model in which these two features can be explained simultaneously as the result of the decline in the relative price of capital. Additional implications of the model regarding the skill premium and the dispersion in labor productivity across plants are also consistent with the empirical evidence. [resumen de autor

The labor market effects of technology shocks

We analyze the effects of neutral and investment-specific technology shocks on hours worked and unemployment. We characterize the response of unemployment in terms of job separation and job finding rates. We find that job separation rates mainly account for the impact response of unemployment while job finding rates for movements along its adjustment path. Neutral shocks increase unemployment and explain a substantial portion of unemployment and output volatilityinvestment-specific shocks expand employment and hours worked and mostly contribute to hours worked volatility. We show that this evidence is consistent with the view that neutral technological progress prompts Schumpeterian creative destruction, while investment specific technological progress has standard neoclassical feature

1D coordination polymer based on copper(II)-containing tetrameric 1,2,3- triazole ligand from click chemistry : magnetic and catalytic properties.

A novel tetrameric tetra[O-((1-benzyl-1H-1,2,3-triazol-4-yl)methyl)]-pentaerythritol (TBTP) has been synthesized using click chemistry strategy. TBTP was characterized and used as ligand to form new Cu(II) complexes, forming 1-D coordination polymers. Two square planar complexes were characterized by single-crystal X-ray diffraction, presenting formula [Cu(TBTP)][Cu(NO3)4] (1) and [Cu(TBTP)](NO3)2 (2). In both structures, a cationic 1-D coordination polymer (CP) has been formed. The CP contain a 1:1 Cu(II)/TBTP ratio with four neutral triazole groups coordinating the Cu(II) center, forming a CuN bonds ranging 1.988(2)?2.001(2)??. The study of the magnetic properties of compounds 1 and 2 pointed to an antiferromagnetic behavior for both compounds, defined by inter- and intra-chain dipolar interactions among their metallic centers. In addition, the complex 1 was found to be an efficient catalyst for selective oxidation of aniline to azobenzene under mild reaction conditions