Stroke genetics informs drug discovery and risk prediction across ancestries

Previous genome-wide association studies (GWASs) of stroke — the second leading cause of death worldwide — were conducted predominantly in populations of European ancestry1,2. Here, in cross-ancestry GWAS meta-analyses of 110,182 patients who have had a stroke (five ancestries, 33% non-European) and 1,503,898 control individuals, we identify association signals for stroke and its subtypes at 89 (61 new) independent loci: 60 in primary inverse-variance-weighted analyses and 29 in secondary meta-regression and multitrait analyses. On the basis of internal cross-ancestry validation and an independent follow-up in 89,084 additional cases of stroke (30% non-European) and 1,013,843 control individuals, 87% of the primary stroke risk loci and 60% of the secondary stroke risk loci were replicated (P < 0.05). Effect sizes were highly correlated across ancestries. Cross-ancestry fine-mapping, in silico mutagenesis analysis3, and transcriptome-wide and proteome-wide association analyses revealed putative causal genes (such as SH3PXD2A and FURIN) and variants (such as at GRK5 and NOS3). Using a three-pronged approach4, we provide genetic evidence for putative drug effects, highlighting F11, KLKB1, PROC, GP1BA, LAMC2 and VCAM1 as possible targets, with drugs already under investigation for stroke for F11 and PROC. A polygenic score integrating cross-ancestry and ancestry-specific stroke GWASs with vascular-risk factor GWASs (integrative polygenic scores) strongly predicted ischaemic stroke in populations of European, East Asian and African ancestry5. Stroke genetic risk scores were predictive of ischaemic stroke independent of clinical risk factors in 52,600 clinical-trial participants with cardiometabolic disease. Our results provide insights to inform biology, reveal potential drug targets and derive genetic risk prediction tools across ancestries

Source voltage sensorless estimation scheme for PWM rectifiers under unbalanced conditions

A source voltage sensorless estimation scheme is proposed for a pulsewidth-modulation (PWM) rectifier in unbalanced circumstances. The negative sequence is accompanied by the unbalance among phases, and acts as an ac disturbance to a normal-mode estimator and controller. Hence, without considering voltage unbalance, a PWM rectifier yields a voltage ripple in the dc-link voltage and large reactive currents. With the proposed sensorless scheme, both positive and negative components are estimated separately by using a full- (or reduced-) order estimator. The feasibility of the proposed sensorless scheme is confirmed through computer simulation and experiment.X1135sciescopu

Pharmacogenetic analysis of cinacalcet response in secondary hyperparathyroidism patients

Sohyun Jeong,1 In-Wha Kim,1 Kook-Hwan Oh,2 Nayoung Han,1 Kwon Wook Joo,2 Hyo Jin Kim,2 Jung Mi Oh1 1College of Pharmacy, Research Institute of Pharmaceutical&nbsp;Sciences, Seoul National University, 2Department of Internal Medicine, Division of Nephrology, Seoul National University Hospital, Seoul,&nbsp;Korea Background: Secondary hyperparathyroidism (SHPT) is one of the major risk factors of morbidity and mortality in end-stage renal disease. Cinacalcet effectively controls SHPT without causing hypercalcemia and hyperphosphatemia. However, there is significant inter-individual response variance to cinacalcet treatment. Therefore, we aimed to evaluate the genetic effects related with parathyroid hormone regulation as factors for cinacalcet response variance. Methods: Patients with a diagnosis of SHPT based on intact parathyroid hormone (iPTH) &gt;300 pg/mL on dialysis were included in this study. They were over 18 years and have been treated by cinacalcet for more than 3 months. Responders and nonresponders were grouped by the serum iPTH changes. Twenty-four single nucleotide polymorphisms of CASR, VDR, FGFR1, KL, ALPL, RGS14, NR4A2, and PTHLH genes were selected for the pharmacogenetic analysis. Results: After adjusting for age, sex, and calcium level, CASR rs1042636 (odds ratio [OR]: 0.066, P=0.027) and rs1802757 (OR: 10.532, P=0.042) were associated with cinacalcet response. The association of haplotypes of CASR rs1042636, rs10190, and rs1802757; GCC (OR: 0.355, P=0.015); and ATT (OR: 2.769, P=0.014) with cinacalcet response was also significant. Conclusion: We obtained supporting information of the associations between cinacalcet response and CASR polymorphisms. CASR single nucleotide polymorphisms (SNPs) rs1802757, rs1042636, and haplotypes of rs1042636, rs10190, and rs1802757 were significantly associated with cinacalcet response variance. Keywords: CASR, calcium sensing receptor, SHPT, genetic polymorphisms, haplotype, single nucleotide polymorphism

Effect of conformational heterogeneity on excitation energy transfer efficiency in directly meso-meso linked Zn(II) porphyrin arrays

We have investigated the overall excitation energy relaxation dynamics in linear porphyrin arrays as well as the energy transport phenomena by attaching an energy acceptor to one end of a linear porphyrin array by using steady state and time-resolved spectroscopic measurements. We have revealed that the solvation dynamics as well as the conformational dynamics contributes significantly to the energy relaxation processes of linear porphyrin arrays. Consequently, long porphyrin arrays no longer serve as good energy transmission elements in donor-acceptor linked systems due to conformational heterogeneities which provide the nonradiative deactivation channels as energy quenchers.X113430sciescopu

Improved network community structure improves function prediction

We are overwhelmed by experimental data, and need better ways to understand large interaction datasets. While clustering related nodes in such networks—known as community detection—appears a promising approach, detecting such communities is computationally difficult. Further, how to best use such community information has not been determined. Here, within the context of protein function prediction, we address both issues. First, we apply a novel method that generates improved modularity solutions than the current state of the art. Second, we develop a better method to use this community information to predict proteins' functions. We discuss when and why this community information is important. Our results should be useful for two distinct scientific communities: first, those using various cost functions to detect community structure, where our new optimization approach will improve solutions, and second, those working to extract novel functional information about individual nodes from large interaction datasets