The ion channels of endomembranes.

The endomembrane system consists of organellar membranes in the biosynthetic pathway [endoplasmic reticulum (ER), Golgi apparatus, and secretory vesicles] as well as those in the degradative pathway (early endosomes, macropinosomes, phagosomes, autophagosomes, late endosomes, and lysosomes). These endomembrane organelles/vesicles work together to synthesize, modify, package, transport, and degrade proteins, carbohydrates, and lipids, regulating the balance between cellular anabolism and catabolism. Large ion concentration gradients exist across endomembranes: Ca2+ gradients for most endomembrane organelles and H+ gradients for the acidic compartments. Ion (Na+, K+, H+, Ca2+, and Cl-) channels on the organellar membranes control ion flux in response to cellular cues, allowing rapid informational exchange between the cytosol and organelle lumen. Recent advances in organelle proteomics, organellar electrophysiology, and luminal and juxtaorganellar ion imaging have led to molecular identification and functional characterization of about two dozen endomembrane ion channels. For example, whereas IP3R1-3 channels mediate Ca2+ release from the ER in response to neurotransmitter and hormone stimulation, TRPML1-3 and TMEM175 channels mediate lysosomal Ca2+ and H+ release, respectively, in response to nutritional and trafficking cues. This review aims to summarize the current understanding of these endomembrane channels, with a focus on their subcellular localizations, ion permeation properties, gating mechanisms, cell biological functions, and disease relevance.

Comparative analysis and application of soft sensor models in domestic wastewater treatment for advancing sustainability.

This study focuses on the development and evaluation of soft sensor models for predicting NH3-N values in a wastewater treatment process. The study compares the performance of linear regression (LR), neural networks (NN) and random forest regression (RFR) models. The proposed methodology involves optimizing the sequencing batch reactor process using artificial intelligence and an automatic control system. Real-time NH3-N values are obtained by inputting data from electronic conductivity and temperature sensors into the prediction models. Once the predicted NH3-N value falls below the effluent standard, the cycle ends, improving energy efficiency and sustainability by cutting down the agitator and aerator. The research results demonstrate that the RNN-based NH3-N soft sensor built in this study exhibits the best performance, which is promising for wastewater treatment process optimization and evaluation. The results show that sensor model NNR[0.5Y]H exhibits exceptional performance, utilizing recurrent neural network with 5-step input delays. Sensor NNR[0.5Y]H exhibits an R2 of 0.921, an RMSE of 6.110, and an MAE of 4.558. Based on the findings, recurrent neural network (RNN) variants emerge as the most effective modeling technique due to their ability to capture temporal dependencies and handle variable-length sequences. This study provides satisfied performance results for the NNR[0.5Y]H soft sensor model in NH3-N monitoring and process optimization in wastewater treatment, highlighting the effectiveness of recurrent neural networks and their contribution to improving interpretability, accuracy, and adaptability of soft sensor models.

Structural Exploration of Polycationic Nanoparticles for siRNA Delivery.

RNA interference (RNAi) is a promising approach to the treatment of genetic diseases by the specific knockdown of target genes. Functional polymers are potential vehicles for the effective delivery of vulnerable small interfering RNA (siRNA), which is required for the broad application of RNAi-based therapeutics. The development of methods for the facile modulation of chemical structures of polymeric carriers and an elucidation of detailed delivery mechanisms remain important areas of research. In this paper, we synthesized a series of methacrylate-based polymers with controllable structures and narrow distributions by atom transfer radical polymerization using various combinations of cationic monomers (2-dimethylaminoethyl methacrylate, 2-diethylaminoethyl methacrylate, and 2-dibutylaminoethyl methacrylate) and hydrophobic monomers (2-butyl methacrylate (BMA), cyclohexyl methacrylate, and 2-ethylhexyl methacrylate). These polymers exhibited varying hydrophobicities, charge densities, and pKa values, enabling the discovery of effective carriers for siRNA by in vitro delivery assays. For the polymers with BMA segments, 50% of cationic segments were beneficial to the formation of siRNA nanoparticles (NPs) and the in vitro delivery of siRNA. The optimal ratio varied for different combinations of cationic and hydrophobic segments. In particular, 20k PMB 0.5, PME 0.5, and PEB 1.0 showed >75% luciferase knockdown. Efficacious delivery was dependent on high siRNA binding, the small size of NPs, and balanced hydrophobicity and charge density. Cellular uptake and endosomal escape experiments indicated that carboxybetaine modification of 20k PMB 0.5 did not remarkably affect the internalization of corresponding NPs after incubation for 6 h but significantly reduced the endosomal escape of NPs, which leads to the notable decrease in delivery efficacy of polymers. These results provide insights into the mechanism of polymer-based siRNA delivery and may inspire the development of novel polymeric carriers.

Construction of Degradable and Amphiphilic Triblock Polymer Carriers for Effective Delivery of siRNA.

The development of effective and safe delivery carriers is one of the prerequisites for the clinical translation of siRNA-based therapeutics. In this study, a library of 144 functional triblock polymers using ring-opening polymerization (ROP) and thiol-ene click reaction is constructed. These triblock polymers are composed of hydrophilic poly (ethylene oxide) (PEO), hydrophobic poly (ε-caprolactone) (PCL), and cationic amine blocks. Three effective carriers are discovered by high-throughput screening of these polymers for siRNA delivery to HeLa-Luc cells. In vitro evaluation shows that siLuc-loaded nanoparticles (NPs) fabricated with leading polymer carriers exhibit sufficient knockdown of luciferase genes and relatively low cytotoxicity. The chemical structure of polymers significantly affects the physicochemical properties of the resulting siRNA-loaded NPs, which leads to different cellular uptake of NPs and endosomal escape of loaded siRNA and thus the overall in vitro siRNA delivery efficacy. After systemic administration to mice with xenograft tumors, siRNA NPs based on P2-4.5A8 are substantially accumulated at tumor sites, suggesting that PEO and PCL blocks are beneficial for improving blood circulation and biodistribution of siRNA NPs. This functional triblock polymer platform may have great potential in the development of siRNA-based therapies for the treatment of cancers.