Salmonella ubiquitination: ARIH1 enters the fray.

© 2017 EMBO. Ubiquitination is a post-translational modification in which ubiquitin, a 76-amino acid polypeptide, is covalently bound to one or more lysines of a target protein. Ubiquitination is mediated by the coordinated activity of ubiquitin activating (E1), conjugating (E2), and ligating (E3) enzymes. Ubiquitin is widely investigated for its ability to regulate key biological processes in the cell, including protein degradation and host-bacteria interactions. The determinants underlying bacterial ubiquitination, and their precise roles in host defense, have not been fully resolved. In this issue of EMBO Reports, Polajnar et al discover that Ring-between-Ring (RBR) E3 ligase ARIH1 (also known as HHARI) is involved in formation of the ubiquitin coat surrounding cytosolic Salmonella. Evidence suggests that ARIH1, in cooperation with E3 ligases LRSAM1 and HOIP, modulates the recognition of intracellular bacteria for cell-autonomous immunity

Inclusion Complex of Docetaxel with Sulfobutyl Ether β-Cyclodextrin: Preparation, In Vitro Cytotoxicity and In Vivo Safety

Docetaxel (DTX), as a first-line anti-tumor drug, has been studied for decades for its diverse bioactivities. However, DTX presents poor solubility in water, low bioavailability and serious toxic side effects which has hindered its application in the clinic. To address these problems, docetaxel-sulfobutyl ether-β-cyclodextrin inclusion complex (DTX-SBE-β-CD) was prepared successfully by saturated aqueous solution method. Sulfobutyl ether β-cyclodetrin (SBE-β-CD) is used as delivery material. For this study, the inclusion complex of docetaxel with sulfobutyl ether β-cyclodetrin (DTX-SBE-β-CD) was prepared and optimized its properties to enhance the cytotoxicity of cancer cells. A large number of physical characterization results showed that DTX-SBE-β-CD inclusion complex was successfully prepared by saturated aqueous solution method. DTX-SBE-β-CD inclusion complex was optimized by Central Composite Design. DTX-SBE-β-CD had an inhibitory effect on the in vitro determination of MCF-7 and HepG2 cells by MTT assay. Pharmacokinetic studies were carried out on male Sprague–Dawley rats by tail injection, including the distribution, metabolism and elimination of DTX-SBE-β-CD in vivo. In the experimental study of inhibition of cancer cells, DTX and DTX-SBE-β-CD showed apparent concentration-dependent inhibitory actions on tumor cells and the inhibition of DTX-SBE-β-CD group was more obvious

Mechanistic Understanding of CO2 Adsorption and Diffusion in the Imidazole Ionic Liquid-Hexafluoroisopropylidene Polyimide Composite Membrane

The integration of molecular design and imidazole ionic liquids produces a synergistic effect to enhance and fine-tune the molecular sieve capability of polyimide (PI) membranes. The most difficult problem was to choose the right ionic liquids and moderate polymer combination. In this work, a series of systems of CO2 in [EMIM][Tf2N], [EMIM][BF4], and [EMIM][PF6] composited with 6FDA-based PI with different IL concentrations were discussed by all-atom molecular dynamics simulations. The results indicated that the CO2 diffusion coefficient decreases with the compatibility of the PI structure with CO2 molecules and increases with an increased IL concentration of up to 75 wt %. Therefore, this study may provide guidance for the design of PI membranes

Modulation-Doped Multiple Quantum Wells of Aligned Single-Wall Carbon Nanotubes

Heterojunctions, quantum wells, and superlattices with precise doping profiles are behind today's electronic and photonic devices based on III–V compound semiconductors such as GaAs. Currently, there is considerable interest in constructing similar artificial 3D architectures with tailored electrical and optical properties by using van der Waals junctions of low-dimensional materials. In this study, the authors have fabricated a novel structure consisting of multiple thin (≈20 nm) layers of aligned single-wall carbon nanotubes with dopants inserted between the layers. This “modulation-doped” multiple-quantum-well structure acts as a terahertz polarizer with an ultra-broadband working frequency range (from ≈0.2 to ≈200 THz), a high extinction ratio (20 dB from ≈0.2 to 1 THz), and a low insertion loss (<2.5 dB from ≈0.2 to 200 THz). The individual carbon nanotube films—highly aligned, densely packed, and large (2 in. in diameter)—were produced using vacuum filtration and then stacked together in the presence of dopants. This simple, robust, and cost-effective method is applicable to the fabrication of a variety of devices relying on macroscopically 1D properties of aligned carbon nanotube assemblies

Structural Analysis and Classification of Low-Molecular-Weight Hyaluronic Acid by Near-Infrared Spectroscopy: A Comparison between Traditional Machine Learning and Deep Learning

Confusing low-molecular-weight hyaluronic acid (LMWHA) from acid degradation and enzymatic hydrolysis (named LMWHA–A and LMWHA–E, respectively) will lead to health hazards and commercial risks. The purpose of this work is to analyze the structural differences between LMWHA–A and LMWHA–E, and then achieve a fast and accurate classification based on near-infrared (NIR) spectroscopy and machine learning. First, we combined nuclear magnetic resonance (NMR), Fourier transform infrared (FTIR) spectroscopy, two-dimensional correlated NIR spectroscopy (2DCOS), and aquaphotomics to analyze the structural differences between LMWHA–A and LMWHA–E. Second, we compared the dimensionality reduction methods including principal component analysis (PCA), kernel PCA (KPCA), and t-distributed stochastic neighbor embedding (t-SNE). Finally, the differences in classification effect of traditional machine learning methods including partial least squares–discriminant analysis (PLS-DA), support vector classification (SVC), and random forest (RF) as well as deep learning methods including one-dimensional convolutional neural network (1D-CNN) and long short-term memory (LSTM) were compared. The results showed that genetic algorithm (GA)–SVC and RF were the best performers in traditional machine learning, but their highest accuracy in the test dataset was 90%, while the accuracy of 1D-CNN and LSTM models in the training dataset and test dataset classification was 100%. The results of this study show that compared with traditional machine learning, the deep learning models were better for the classification of LMWHA–A and LMWHA–E. Our research provides a new methodological reference for the rapid and accurate classification of biological macromolecules

MOESM5 of Combined 13C-assisted metabolomics and metabolic flux analysis reveals the impacts of glutamate on the central metabolism of high β-galactosidase-producing Pichia pastoris

Additional file 5. Measured and estimated mass isotopomer distributions of the intracellular free amino acids and the metabolite pool sizes in G1HL on Glc and Glc/Glu