EP300 contributes to high-altitude adaptation in Tibetans by regulating nitric oxide production

The genetic adaptation of Tibetans to high altitude hypoxia likely involves a group of genes in the hypoxic pathway,as suggested by earlier studies.To test the adaptive role of the previously reported candidate gene EP300 (histone acetyltransferase p300),we conducted resequencing of a 108.9 kb gene region of EP300 in 80 unrelated Tibetans.The allele-frequency and haplotype-based neutrality tests detected signals of positive Darwinian selection on EP300 in Tibetans,with a group of variants showing allelic divergence between Tibetans and lowland reference populations,including Han Chinese,Europeans,and Africans.Functional prediction suggested the involvement of multiple EP300 variants in gene expression regulation.More importantly,genetic association tests in 226 Tibetans indicated significant correlation of the adaptive EP300 variants with blood nitric oxide (NO) concentration.Collectively,we propose that EP300 harbors adaptive variants in Tibetans,which might contribute to high-altitude adaptation through regulating NO production.     

Role of IKK/NF-κB signaling in extinction of conditioned place aversion memory in rats

The inhibitor κB protein kinase/nuclear factor κB (IKK/NF-κB) signaling pathway is critical for synaptic plasticity. However, the role of IKK/NF-κB in drug withdrawal-associated conditioned place aversion (CPA) memory is unknown. Here, we showed that inhibition of IKK/NF-κB by sulphasalazine (SSZ; 10 mM, i.c.v.) selectively blocked the extinction but not acquisition or expression of morphine-induced CPA in rats. The blockade of CPA extinction induced by SSZ was abolished by sodium butyrate, an inhibitor of histone deacetylase. Thus, the IKK/NF-κB signaling pathway might play a critical role in the extinction of morphine-induced CPA in rats and might be a potential pharmacotherapy target for opiate addiction.     

Statin Use and the Risk of Parkinson's Disease: An Updated Meta-Analysis

Introduction

In response to the ongoing debate over the relationship between the use of statins and the risk of Parkinson''s disease (PD), we performed a systematic review and meta-analysis of observational studies to examine their association.

Methods

We conducted a review of the literature using electronic databases supplemented by a manual search to identify potentially relevant case-control or cohort studies. Summary relative risk (RRs) and 95% confidence intervals (CIs) were calculated using a random-effects model. Sensitivity and subgroup analyses were also conducted.

Results

Eleven studies (five case-control and six cohort) with a total of 3,513,209 participants and 21,011 PD cases were included. Statin use was associated with a lower risk of PD, with a summary RR of 0.81 (95% CI 0.71–0.92). Sensitivity analysis demonstrated the robustness of results. Subgroup analyses showed that neither study design nor study region significantly influenced the effect estimates. However, subgroup studies adjusted for age or sex had a greater inverse association than did unadjusted analyses (age-adjusted RR 0.75, 95% CI 0.60–0.95; age-unadjusted RR 0.86, 95% CI 0.75–0.99 and sex-adjusted RR 0.76, 95% CI 0.59–0.98; sex-unadjusted RR 0.85, 95% CI 0.79–0.92).

Conclusions

Results of this systematic review suggest that statin use is associated with a reduced PD risk. However, randomized controlled trials and more observational studies should be performed before strong conclusions are drawn.     

Equivalent potential of water for the electronic structure of glycine

First-principles, all-electron, ab initio calculations have been performed to construct an equivalent potential of water for the electronic structure of glycine (Gly) in solution. The calculation involved three steps. The first step was to search for the minimum-energy geometric structure of the Gly + nH₂O system. The second step was to calculate the electronic structure of Gly with the potential of water molecules via the self-consistent cluster-embedding method (SCCE), based on the result obtained in the first step. The last step was to calculate the electronic structure of Gly with the potential of dipoles after replacing the water molecules with dipoles. The results show that the occupied molecular orbitals of Gly are raised by about 0.0524 Ry on average due to the effect of water. The effect of water can be simulated well using the dipole potential. The equivalent potential obtained can be applied directly to electronic structure calculations of proteins in solution using the SCCE method.     

A New Statistic to Evaluate Imputation Reliability

Background

As the amount of data from genome wide association studies grows dramatically, many interesting scientific questions require imputation to combine or expand datasets. However, there are two situations for which imputation has been problematic: (1) polymorphisms with low minor allele frequency (MAF), and (2) datasets where subjects are genotyped on different platforms. Traditional measures of imputation cannot effectively address these problems.

Methodology/Principal Findings

We introduce a new statistic, the imputation quality score (IQS). In order to differentiate between well-imputed and poorly-imputed single nucleotide polymorphisms (SNPs), IQS adjusts the concordance between imputed and genotyped SNPs for chance. We first evaluated IQS in relation to minor allele frequency. Using a sample of subjects genotyped on the Illumina 1 M array, we extracted those SNPs that were also on the Illumina 550 K array and imputed them to the full set of the 1 M SNPs. As expected, the average IQS value drops dramatically with a decrease in minor allele frequency, indicating that IQS appropriately adjusts for minor allele frequency. We then evaluated whether IQS can filter poorly-imputed SNPs in situations where cases and controls are genotyped on different platforms. Randomly dividing the data into “cases” and “controls”, we extracted the Illumina 550 K SNPs from the cases and imputed the remaining Illumina 1 M SNPs. The initial Q-Q plot for the test of association between cases and controls was grossly distorted (λ = 1.15) and had 4016 false positives, reflecting imputation error. After filtering out SNPs with IQS<0.9, the Q-Q plot was acceptable and there were no longer false positives. We then evaluated the robustness of IQS computed independently on the two halves of the data. In both European Americans and African Americans the correlation was >0.99 demonstrating that a database of IQS values from common imputations could be used as an effective filter to combine data genotyped on different platforms.

Conclusions/Significance

IQS effectively differentiates well-imputed and poorly-imputed SNPs. It is particularly useful for SNPs with low minor allele frequency and when datasets are genotyped on different platforms.     

Two‐Dimensional Materials for Beyond‐Lithium‐Ion Batteries

Lithium‐ion batteries (LIBs) have dominated the portable electronics industry and solid‐state electrochemical research and development for the past two decades. In light of possible concerns over the cost and future availability of lithium, sodium‐ion batteries (SIBs) and other new technologies have emerged as candidates for large‐scale stationary energy storage. Research in these technologies has increased dramatically with a focus on the development of new materials for both the positive and negative electrodes that can enhance the cycling stability, rate capability, and energy density. Two‐dimensional (2D) materials are showing promise for many energy‐related applications and particularly for energy storage, because of the efficient ion transport between the layers and the large surface areas available for improved ion adsorption and faster surface redox reactions. Recent research highlights on the use of 2D materials in these future ‘beyond‐lithium‐ion’ battery systems are reviewed, and strategies to address challenges are discussed as well as their prospects.     

An improved classification of G-protein-coupled receptors using sequence-derived features

Background  

G-protein-coupled receptors (GPCRs) play a key role in diverse physiological processes and are the targets of almost two-thirds of the marketed drugs. The 3 D structures of GPCRs are largely unavailable; however, a large number of GPCR primary sequences are known. To facilitate the identification and characterization of novel receptors, it is therefore very valuable to develop a computational method to accurately predict GPCRs from the protein primary sequences.