Re-evaluation of protein kinase CK2 pleiotropy: new insights provided by a phosphoproteomics analysis of CK2 knockout cells

CK2 denotes a ubiquitous and pleiotropic protein kinase whose holoenzyme is composed of two catalytic (α and/or α′) and two regulatory β subunits. The CK2 consensus sequence, S/T-x-x-D/E/pS/pT is present in numerous phosphosites, but it is not clear how many of these are really generated by CK2. To gain information about this issue, advantage has been taken of C2C12 cells entirely deprived of both CK2 catalytic subunits by the CRISPR/Cas9 methodology. A comparative SILAC phosphoproteomics analysis reveals that, although about 30% of the quantified phosphosites do conform to the CK2 consensus, only one-third of these are substantially reduced in the CK2α/α′^(?/?) cells, consistent with their generation by CK2. A parallel study with C2C12 cells deprived of the regulatory β subunit discloses a role of this subunit in determining CK2 targeting. We also find that phosphosites notoriously generated by CK2 are not fully abrogated in CK2α/α′^(?/?) cells, while some phosphosites unrelated to CK2 are significantly altered. Collectively taken our data allow to conclude that the phosphoproteome generated by CK2 is not as ample and rigidly pre-determined as it was believed before. They also show that the lack of CK2 promotes phosphoproteomics perturbations attributable to kinases other than CK2.     

Forecasting Government Bond Yields with Neural Networks Considering Cointegration

This paper discusses techniques that might be helpful in predicting interest rates and tries to evaluate a new hybrid forecasting approach. Results of examining government bond yields in Germany and France reported in this study indicate that a hybrid forecasting approach which combines techniques of cointegration analysis with neural network (NN) forecasting models can produce superior results to the use of NN forecasting models alone. The findings documented in this paper could be a consequence of the fact that examining differenced data under certain conditions will lead to a loss of information and that the inclusion of the error correction term from the cointegration model can help to cope with this problem. The paper also discusses some possibly interesting directions for further research. Copyright © 2015 John Wiley & Sons, Ltd.     

The Role of Survey Data in Nowcasting Euro Area GDP Growth

This paper evaluates the impact of new releases of financial, real activity and survey data on nowcasting euro area gross domestic product (GDP). We show that all three data categories positively impact on the accuracy of GDP nowcasts, whereby the effect is largest in the case of real activity data. When treating variables as if they were all published at the same time and without any time lag, financial series lose all their significance, while survey data remain an important ingredient for the nowcasting exercise. The subsequent analysis shows that the sectoral coverage of survey data, which is broader than that of timely available real activity data, as well as their information content stemming from questions focusing on agents' expectations, are the main sources of the ‘genuine’ predictive power of survey data. When the forecast period is restricted to the 2008–09 financial crisis, the main change is an enhanced forecasting role for financial data. Copyright © 2015 John Wiley & Sons, Ltd.     

Common variants near MC4R are associated with fat mass, weight and risk of obesity

To identify common variants influencing body mass index (BMI), we analyzed genome-wide association data from 16,876 individuals of European descent. After previously reported variants in FTO, the strongest association signal (rs17782313, P = 2.9 x 10(-6)) mapped 188 kb downstream of MC4R (melanocortin-4 receptor), mutations of which are the leading cause of monogenic severe childhood-onset obesity. We confirmed the BMI association in 60,352 adults (per-allele effect = 0.05 Z-score units; P = 2.8 x 10(-15)) and 5,988 children aged 7-11 (0.13 Z-score units; P = 1.5 x 10(-8)). In case-control analyses (n = 10,583), the odds for severe childhood obesity reached 1.30 (P = 8.0 x 10(-11)). Furthermore, we observed overtransmission of the risk allele to obese offspring in 660 families (P (pedigree disequilibrium test average; PDT-avg) = 2.4 x 10(-4)). The SNP location and patterns of phenotypic associations are consistent with effects mediated through altered MC4R function. Our findings establish that common variants near MC4R influence fat mass, weight and obesity risk at the population level and reinforce the need for large-scale data integration to identify variants influencing continuous biomedical traits.     

SLC2A9 influences uric acid concentrations with pronounced sex-specific effects

Serum uric acid concentrations are correlated with gout and clinical entities such as cardiovascular disease and diabetes. In the genome-wide association study KORA (Kooperative Gesundheitsforschung in der Region Augsburg) F3 500K (n = 1,644), the most significant SNPs associated with uric acid concentrations mapped within introns 4 and 6 of SLC2A9, a gene encoding a putative hexose transporter (effects: -0.23 to -0.36 mg/dl per copy of the minor allele). We replicated these findings in three independent samples from Germany (KORA S4 and SHIP (Study of Health in Pomerania)) and Austria (SAPHIR; Salzburg Atherosclerosis Prevention Program in Subjects at High Individual Risk), with P values ranging from 1.2 x 10(-8) to 1.0 x 10(-32). Analysis of whole blood RNA expression profiles from a KORA F3 500K subgroup (n = 117) showed a significant association between the SLC2A9 isoform 2 and urate concentrations. The SLC2A9 genotypes also showed significant association with self-reported gout. The proportion of the variance of serum uric acid concentrations explained by genotypes was about 1.2% in men and 6% in women, and the percentage accounted for by expression levels was 3.5% in men and 15% in women.     

Meta-analysis of genome-wide association data and large-scale replication identifies additional susceptibility loci for type 2 diabetes

Genome-wide association (GWA) studies have identified multiple loci at which common variants modestly but reproducibly influence risk of type 2 diabetes (T2D). Established associations to common and rare variants explain only a small proportion of the heritability of T2D. As previously published analyses had limited power to identify variants with modest effects, we carried out meta-analysis of three T2D GWA scans comprising 10,128 individuals of European descent and approximately 2.2 million SNPs (directly genotyped and imputed), followed by replication testing in an independent sample with an effective sample size of up to 53,975. We detected at least six previously unknown loci with robust evidence for association, including the JAZF1 (P = 5.0 x 10(-14)), CDC123-CAMK1D (P = 1.2 x 10(-10)), TSPAN8-LGR5 (P = 1.1 x 10(-9)), THADA (P = 1.1 x 10(-9)), ADAMTS9 (P = 1.2 x 10(-8)) and NOTCH2 (P = 4.1 x 10(-8)) gene regions. Our results illustrate the value of large discovery and follow-up samples for gaining further insights into the inherited basis of T2D.