Trafficking and surface expression of hyperpolarization-activated cyclic nucleotide-gated channels in hippocampal neurons.

Hyperpolarization-activated cyclic nucleotide-gated (HCN) channels mediate the hyperpolarization-activated current I(h) and thus play important roles in the regulation of brain excitability. The subcellular distribution pattern of the HCN channels influences the effects that they exert on the properties and activity of neurons. However, little is known about the mechanisms that control HCN channel trafficking to subcellular compartments or that regulate their surface expression. Here we studied the dynamics of HCN channel trafficking in hippocampal neurons using dissociated cultures coupled with time lapse imaging of fluorophore-fused HCN channels. HCN1-green fluorescence protein (HCN1-GFP) channels resided in vesicle-like organelles that moved in distinct patterns along neuronal dendrites, and these properties were isoform-specific. HCN1 trafficking required intact actin and tubulin and was rapidly inhibited by activation of either NMDA or AMPA-type ionotropic glutamate receptors in a calcium-dependent manner. Glutamate-induced inhibition of the movement of HCN1-GFP-expressing puncta was associated with increased surface expression of both native and transfected HCN1 channels, and this surface expression was accompanied by augmented I(h). Taken together, the results reveal the highly dynamic nature of HCN1 channel trafficking in hippocampal neurons and provide a novel potential mechanism for rapid regulation of I(h), and hence of neuronal properties, via alterations of HCN1 trafficking and surface expression.

Residual concentrations and ecological risks of neonicotinoid insecticides in the soils of tomato and cucumber greenhouses in Shouguang, Shandong Province, East China.

Neonicotinoid insecticides (NNIs) are the most widely used insecticides in China and worldwide. Continuous use of NNIs can lead to their accumulation in soil, causing potential ecological risks due to their relatively long half-life. We used liquid chromatography-tandem mass spectrometry (LC-MS/MS) to investigate the residual levels of nine neonicotinoids in greenhouse soils in Shouguang, East China, at different soil depths and with different crops (tomato and cucumber) after varying periods of cultivation. Seven neonicotinoids were detected in the soils of the tomato greenhouses and six were detected in the soils of the cucumber greenhouses, with total concentrations ranging from 0.731 to 11.383 µg kg-1 and 0.363 to 19.224 µg kg-1, respectively. In all samples, the neonicotinoid residues in the soils cultivated for 8-9 years were lower than in those cultivated for 2 years and 14-17 years. In the tomato greenhouse soils, the residual levels of NNIs were highest in the topsoil, with progressively lower concentrations found with depth. Under cucumber cultivation, the NNI residue levels were also highest in the topsoil but there was little difference between the middle and lower soil layers. Total organic carbon (TOC) decreased with soil depth while pH showed the opposite trend, showing a significant negative correlation in both types of soils (tomato soils ρ = -0.900, p = .001; cucumber soils ρ = -0.883, p = .002). Furthermore, TOC was significantly positively correlated, and pH was negatively correlated, with total NNI concentrations in both types of soils (TOC: tomato soils ρ = 0.800, p = .010; cucumber soils ρ = 0.881, p = .004; pH: tomato soils ρ = -0.850, p = .004; cucumber soils ρ = -0.643, p = .086). The results of an ecological risk analysis showed that acetamiprid represents a particularly high toxicity risk in these soils. Based on our analysis, NNI residues in the soils of tomato greenhouses and their associated ecological risks deserve more attention than those of cucumber greenhouse soils.

Effects of pesticide residues on bacterial community diversity and structure in typical greenhouse soils with increasing cultivation years in Northern China.

The understanding of soil microbiome is important for sustainable cultivation, especially under greenhouse conditions. Here, we investigated the changes in soil pesticide residues and microbial diversity and community structure at different cultivation years under a greenhouse system. The 9-to-14 years sites were found to have the least diversity/rich microbial population as compared to sites under 8 years and over 16 years, as analyzed with alpha diversity index. In total, 42 bacterial phyla were identified across soils with different pesticide residues and cultivation ages. Proteobacteria, Acidobacteria, and Bacteroidetes represented the dominant phyla, that accounted for 34.2-43.4%, 9.7-19.3% and 9.2-16.5% of the total population, respectively. Our data prove that certain pesticides contribute to variation in soil microbial community and that soil bacteria respond differently to cultivation years under greenhouse conditions. Thus, this study provides an insight into microbial community structure changes by pesticides under greenhouse systems and natural biodegradation may have an important part in pesticides soil decontamination.

A general-applicable model for estimating the binding coefficient of organic pollutants with dissolved organic matter.

The binding constant (Kdoc) of organic pollutants (OPs) with dissolved organic matter (DOM) is an important parameter in determining the partitioning of OPs in the aquatic environment. Most estimation models have focused on calculating the Kdoc of a specific group of OPs but failed to obtain Kdoc values of different OPs effectively over the last three decades. In this study, we attempted to build a general-applicable Kdoc model based on various organic compounds' Kdoc values from the literature since 1973. Two multiple linear regression models, a DOM nonspecific model and an Aldrich HA model, were developed based on two solid and easy to access parameters-molecular connectivity indices (MCI) and polarity correction factors (PCF). In addition, the models' corresponding Kow-Kdoc models, which were mostly used in previous model studies, were developed for comparison. The adjusted determining coefficient (adj-R2) and standard error of the estimate (SEE) of the DOM nonspecific MCI-PCF-Kdoc model were 0.815 and 0.579, respectively, whereas the adj-R2 and SEE for the MCI-PCF-Kdoc model of Aldrich HA reached 0.907 and 0.438, respectively. The Aldrich HA model showed higher pertinence to the nonspecific model. Furthermore, both models exhibited better fit than the Kow-Kdoc models. The dipole moment modification attempts did not significantly improve either MCI-PCF-Kdoc models; hence, the two models were not altered with the dipole moment. The robustness tests by a Jackknifed method showed that the two MCI-PCF-Kdoc models exhibited higher robustness than the Kow-Kdoc. Of all of the OPs, the phenols contributed the most to their robustness. Furthermore, a sensitivity analysis showed that the two MCI-PCF-Kdoc models were sensitive to the robust parameters.