Sodium channels play a critical role in the generation and propagation of action potentials in excitable tissues, such as nerves, cardiac muscle, and skeletal muscle, and are the primary targets of toxins found in animal venoms. Here, two novel peptide toxins (Cl6a and Cl6b) were isolated from the venom of the spider Cyriopagopus longipes and characterized. Cl6a and Cl6b were shown to be inhibitors of tetrodotoxin-sensitive (TTX-S), but not TTX-resistant, sodium channels. Among the TTX-S channels investigated, Cl6a and Cl6b showed the highest degree of inhibition against NaV1.7 (half-maximal inhibitory concentration (IC50) of 11.0 ± 2.5 nM and 18.8 ± 2.4 nM, respectively) in an irreversible manner that does not alter channel activation, inactivation, or repriming kinetics. Moreover, analysis of NaV1.7/NaV1.8 chimeric channels revealed that Cl6b is a site 4 neurotoxin. Site-directed mutagenesis analysis indicated that D816, V817, and E818 observably affected the efficacy of the Cl6b-NaV1.7 interaction, suggesting that these residues might directly affect the interaction of NaV1.7 with Cl6b. Taken together, these two novel peptide toxins act as potent and sustained NaV1.7 blockers and may have potential in the pharmacological study of sodium channels.
Voltage-gated sodium (Nav) channels are indispensable membrane elements for the generation and propagation of electric signals in excitable cells. The successes in the crystallographic studies on prokaryotic Nav channels in recent years greatly promote the mechanistic investigation of these proteins and their eukaryotic counterparts. In this paper, we mainly review the progress in computational studies, especially the simulation studies, on these proteins in the past years.
Axonal transport of mitochondria is critical for neuronal survival and function. Automatically quantifying and analyzing mitochondrial movement in a large quantity remain challenging. Here, we report an efficient method for imaging and quantifying axonal mitochondrial transport using microfluidic-chamber-cultured neurons together with a newly developed analysis package named "MitoQuant". This tool-kit consists of an automated program for tracking mitochondrial movement inside live neuronal axons and a transient-velocity analysis program for analyzing dynamic movement patterns of mitochondria. Using this method, we examined axonal mitochondrial movement both in cultured mammalian neurons and in motor neuron axons of Drosophila in vivo. In 3 different paradigms (temperature changes, drug treatment and genetic manipulation) that affect mitochondria, we have shown that this new method is highly efficient and sensitive for detecting changes in mitochondrial movement. The method significantly enhanced our ability to quantitatively analyze axonal mitochondrial movement and allowed us to detect dynamic changes in axonal mitochondrial transport that were not detected by traditional kymographic analyses.
Alzheimer disease (AD), a central nervous system degenerative disease, is characterized by abnormal deposition of amyloid-β peptide (Aβ), neurofibrillary tangles formed by hyperphosphorylated tau and synaptic loss. It is widely accepted that Aβ is the chief culprit of AD. Aβ peptide is the cleavage product of amyloid-β precursor protein (APP). Recently, more attention has been paid to O-linked β-N-acetylglucosaminylation (O-GlcNAcylation) modification of protein. O-GlcNAcylation plays a significant role in hippocampal synaptic function. Abated O-GlcNAcylation might be a modulator in progression of AD through regulating activity of pertinent enzymes and factors. Evidence suggests that enhanced O-GlcNAcylation interacts with tau phosphorylation and prevents brain from tau and Aβ-induced impairment. Here, we review the roles of O-GlcNAcylation in APP cleavage, tau phosphorylation and hippocampal synapses function.
Objective The co-occurrence of kidney disease in patients with type 2 diabetes (T2D) is a major public health challenge. Although early detection and intervention can prevent or slow down the progression, the commonly used estimated glomerular filtration rate (eGFR) based on serum creatinine may be influenced by factors unrelated to kidney function. Therefore, there is a need to identify novel biomarkers that can more accurately assess renal function in T2D patients. In this study, we employed an interpretable machine-learning framework to identify plasma metabolomic features associated with GFR in T2D patients. Methods We retrieved 1626 patients with type 2 diabetes (T2D) in Liaoning Medical University First Affiliated Hospital (LMUFAH) as a development cohort and 716 T2D patients in Second Affiliated Hospital of Dalian Medical University (SAHDMU) as an external validation cohort. The metabolite features were screened by the orthogonal partial least squares discriminant analysis (OPLS-DA). We compared machine learning prediction methods, including logistic regression (LR), support vector machine (SVM), random forest (RF), and eXtreme Gradient Boosting (XGBoost). The Shapley Additive exPlanations (SHAP) were used to explain the optimal model. Results For T2D patients, compared with the normal or elevated eGFR group, glutarylcarnitine (C5DC) and decanoylcarnitine (C10) were significantly elevated in GFR mild reduction group, and citrulline and 9 acylcarnitines were also elevated significantly (FDR<0.05, FC > 1.2 and VIP > 1) in moderate or severe reduction group. The XGBoost model with metabolites had the best performance: in the internal validate dataset (AUROC=0.90, AUPRC=0.65, BS=0.064) and external validate cohort (AUROC=0.970, AUPRC=0.857, BS=0.046). Through the SHAP method, we found that C5DC higher than 0.1μmol/L, Cit higher than 26 μmol/L, triglyceride higher than 2 mmol/L, age greater than 65 years old, and duration of T2D more than 10 years were associated with reduced GFR. Conclusion Elevated plasma levels of citrulline and a panel of acylcarnitines were associated with reduced GFR in T2D patients, independent of other conventional risk factors.
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