PICK1 regulates the trafficking of ASIC1a and acidotoxicity in a BAR domain lipid binding-dependent manner

<p>Abstract</p> <p>Background</p> <p>Acid-sensing ion channel 1a (ASIC1a) is the major ASIC subunit determining acid-activated currents in brain neurons. Recent studies show that ASIC1a play critical roles in acid-induced cell toxicity. While these studies raise the importance of ASIC1a in diseases, mechanisms for ASIC1a trafficking are not well understood. Interestingly, ASIC1a interacts with PICK1 (protein interacting with C-kinase 1), an intracellular protein that regulates trafficking of several membrane proteins. However, whether PICK1 regulates ASIC1a surface expression remains unknown.</p> <p>Results</p> <p>Here, we show that PICK1 overexpression increases ASIC1a surface level. A BAR domain mutant of PICK1, which impairs its lipid binding capability, blocks this increase. Lipid binding of PICK1 is also required for PICK1-induced clustering of ASIC1a. Consistent with the effect on ASIC1a surface levels, PICK1 increases ASIC1a-mediated acidotoxicity and this effect requires both the PDZ and BAR domains of PICK1.</p> <p>Conclusions</p> <p>Taken together, our results indicate that PICK1 regulates trafficking and function of ASIC1a in a lipid binding-dependent manner.</p

Mechanistic study of visible light-driven CdS or g-C₃N₄-catalyzed C–H direct trifluoromethylation of (hetero)arenes using CF₃SO₂Na as the trifluoromethyl source

The mild and sustainable methods for C–H direct trifluoromethylation of (hetero)arenes without any base or strong oxidants are in extremely high demand. Here, we report that the photo-generated electron-hole pairs of classical semiconductors (CdS or g-C3N4) under visible light excitation are effective to drive C–H trifluoromethylation of (hetero)arenes with stable and inexpensive CF3SO2Na as the trifluoromethyl (TFM) source via radical pathway. Either CdS or g-C3N4 propagated reaction can efficiently transform CF3SO2Na to [rad]CF3 radical and further afford the desired benzotrifluoride derivatives in moderate to good yields. After visible light initiated photocatalytic process, the key elements (such as F, S and C) derived from the starting TFM source of CF3SO2Na exhibited differential chemical forms as compared to those in other oxidative reactions. The photogenerated electron was trapped by chemisorbed O2 on photocatalysts to form superoxide radical anion (O2[rad]−) which will further attack [rad]CF3 radical with the generation of inorganic product F− and CO2. This resulted in a low utilization efficiency of [rad]CF3 (&lt;50%). When nitro aromatic compounds and CF3SO2Na served as the starting materials in inert atmosphere, the photoexcited electrons can be directed to reduce the nitro group to amino group rather than being trapped by O2. Meanwhile, the photogenerated holes oxidize SO2CF3− into [rad]CF3. Both the photogenerated electrons and holes were engaged in reductive and oxidative paths, respectively. The desired product, trifluoromethylated aniline, was obtained successfully via one-pot free-radical synthesis.</p

Data Driven Natural Gas Spot Price Prediction Models Using Machine Learning Methods

Natural gas has been proposed as a solution to increase the security of energy supply and reduce environmental pollution around the world. Being able to forecast natural gas price benefits various stakeholders and has become a very valuable tool for all market participants in competitive natural gas markets. Machine learning algorithms have gradually become popular tools for natural gas price forecasting. In this paper, we investigate data-driven predictive models for natural gas price forecasting based on common machine learning tools, i.e., artificial neural networks (ANN), support vector machines (SVM), gradient boosting machines (GBM), and Gaussian process regression (GPR). We harness the method of cross-validation for model training and monthly Henry Hub natural gas spot price data from January 2001 to October 2018 for evaluation. Results show that these four machine learning methods have different performance in predicting natural gas prices. However, overall ANN reveals better prediction performance compared with SVM, GBM, and GPR

SERS Sensing Using Graphene-Covered Silver Nanoparticles and Metamaterials for the Detection of Thiram in Soil

Multilayer hyperbolic metamaterial (HMM)-based SERS substrates have received special consideration because they accommodate various propagation modes such as surface plasmonic polaritons (SPP). However, the SPP modes are difficult to generate in HMM due to their weak electric field enhancement. In this article, we designed novel SERS substrates consisting of graphene-covered AgNPs and HMM. The graphene-covered AgNPs work as an external coupling structure for hyperbolic metamaterials due to this structure exhibiting significant plasmonic effects as well as unique optical features. The localized surface plasmonic resonance (LSPR) of the graphene-covered AgNPs excited the SPP and thus formed a strong hot spot zone in the nanogap area of the graphene. The Raman experiment was performed using rhodamine 6G (R6G) and crystal violet (CV), which showed high stability and a maximum enhancement factor of 2.12 × 108. The COMSOL simulation further clarified that enhanced SERS performance was due to the presence of monolayer graphene and provided an atomically flat surface for organic molecules in a more controllable manner. Interestingly, the proposed SERS structure carries out quantitative detection of thiram in soil and can satisfy the basic environmental need for pesticide residue in the soil