Mass spectrometric detection and characterization of atypical membrane-bound zinc-sensitive phosphatases modulating GABAA receptors

Non-redox cycling mechanisms of oxidative stress induced by PM metals

Metallic compounds contribute to the oxidative stress of ambient particulate matter (PM) exposure. The toxicity of redox inert ions of cadmium, mercury, lead and zinc, as well as redox-active ions of vanadium and chromium is underlain by dysregulation of mitochondrial function and loss of signaling quiescence. Central to the initiation of these effects is the interaction of metal ions with cysteinyl thiols on glutathione and key regulatory proteins, which leads to impaired mitochondrial electron transport and persistent pan-activation of signal transduction pathways. The mitochondrial and signaling effects are linked by the production of H₂O₂, generated from mitochondrial superoxide anion or through the activation of NADPH oxidase, which extends the range and amplifies the magnitude of the oxidative effects of the metals. This oxidative burden can be further potentiated by inhibitory effects of the metals on the enzymes of the glutathione and thioredoxin systems. Along with the better-known Fenton-based mechanisms, the non-redox cycling mechanisms of oxidative stress induced by metals constitute significant pathways for cellular injury induced by PM inhalation.

Zinc signaling and epilepsy

Evidence from both preclinical and clinical studies suggest the importance of zinc homeostasis in seizures/epilepsy. Undoubtedly, zinc, via modulation of a variety of targets, is necessary for maintaining the balance between neuronal excitation and inhibition, while an imbalance between excitation and inhibition underlies seizures. However, the relationship between zinc signaling and seizures/epilepsy is complex as both extracellular and intracellular zinc may produce either protective or detrimental effects. This review provides an overview of preclinical/behavioral, functional and molecular studies, as well as clinical data on the involvement of zinc in the pathophysiology and treatment of seizures/epilepsy. Furthermore, the potential of targeting elements associated with zinc signaling or homeostasis and zinc levels as a therapeutic strategy for epilepsy is discussed.

In Silico Prediction of Gamma-Aminobutyric Acid Type-A Receptors Using Novel Machine-Learning-Based SVM and GBDT Approaches

Gamma-aminobutyric acid type-A receptors (GABA_ARs) belong to multisubunit membrane spanning ligand-gated ion channels (LGICs) which act as the principal mediators of rapid inhibitory synaptic transmission in the human brain. Therefore, the category prediction of GABA_ARs just from the protein amino acid sequence would be very helpful for the recognition and research of novel receptors. Based on the proteins' physicochemical properties, amino acids composition and position, a GABA_AR classifier was first constructed using a 188-dimensional (188D) algorithm at 90% cd-hit identity and compared with pseudo-amino acid composition (PseAAC) and ProtrWeb web-based algorithms for human GABA_AR proteins. Then, four classifiers including gradient boosting decision tree (GBDT), random forest (RF), a library for support vector machine (libSVM), and k-nearest neighbor (k-NN) were compared on the dataset at cd-hit 40% low identity. This work obtained the highest correctly classified rate at 96.8% and the highest specificity at 99.29%. But the values of sensitivity, accuracy, and Matthew's correlation coefficient were a little lower than those of PseAAC and ProtrWeb; GBDT and libSVM can make a little better performance than RF and k-NN at the second dataset. In conclusion, a GABA_AR classifier was successfully constructed using only the protein sequence information.