Lack of Kcnn4 improves mucociliary clearance in muco-obstructive lung disease.

Airway mucociliary clearance (MCC) is the main mechanism of lung defense keeping airways free of infection and mucus obstruction. Airway surface liquid volume, ciliary beating, and mucus are central for proper MCC and critically regulated by sodium absorption and anion secretion. Impaired MCC is a key feature of muco-obstructive diseases. The calcium-activated potassium channel KCa.3.1, encoded by Kcnn4, participates in ion secretion, and studies showed that its activation increases Na+ absorption in airway epithelia, suggesting that KCa3.1-induced hyperpolarization was sufficient to drive Na+ absorption. However, its role in airway epithelium is not fully understood. We aimed to elucidate the role of KCa3.1 in MCC using a genetically engineered mouse. KCa3.1 inhibition reduced Na+ absorption in mouse and human airway epithelium. Furthermore, the genetic deletion of Kcnn4 enhanced cilia beating frequency and MCC ex vivo and in vivo. Kcnn4 silencing in the Scnn1b-transgenic mouse (Scnn1btg/+), a model of muco-obstructive lung disease triggered by increased epithelial Na+ absorption, improved MCC, reduced Na+ absorption, and did not change the amount of mucus but did reduce mucus adhesion, neutrophil infiltration, and emphysema. Our data support that KCa3.1 inhibition attenuated muco-obstructive disease in the Scnn1btg/+ mice. K+ channel modulation may be a therapeutic strategy to treat muco-obstructive lung diseases.

The activity of IKCa and BKCa channels contributes to insulin-mediated NO synthesis and vascular tone regulation in human umbilical vein.

Insulin regulates the l-arginine/nitric oxide (NO) pathway in human umbilical vein endothelial cells (HUVECs), increasing the plasma membrane expression of the l-arginine transporter hCAT-1 and inducing vasodilation in umbilical and placental veins. Placental vascular relaxation induced by insulin is dependent of large conductance calcium-activated potassium channels (BKCa), but the role of KCa channels on l-arginine transport and NO synthesis is still unknown. The aim of this study was to determine the contribution of KCa channels in both insulin-induced l-arginine transport and NO synthesis, and its relationship with placental vascular relaxation. HUVECs, human placental vein endothelial cells (HPVECs) and placental veins were freshly isolated from umbilical cords and placenta from normal pregnancies. Cells or tissue were incubated in absence or presence of insulin and/or tetraethylammonium, 1-[(2-chlorophenyl)diphenylmethyl]-1H-pyrazole, iberiotoxin or NG-nitro-l-arginine methyl ester. l-Arginine uptake, plasma membrane polarity, NO levels, hCAT-1 expression and placenta vascular reactivity were analyzed. The inhibition of intermediate-conductance KCa (IKCa) and BKCa increases l-arginine uptake, which was related with protein abundance of hCAT-1 in HUVECs. IKCa and BKCa activities contribute to NO-synthesis induced by insulin but are not directly involved in insulin-stimulated l-arginine uptake. Long term incubation (8 h) with insulin increases the plasma membrane hyperpolarization and hCAT-1 expression in HUVECs and HPVECs. Insulin-induced relaxation in placental vasculature was reversed by KCa inhibition. The results show that the activity of IKCa and BKCa channels are relevant for both physiological regulations of NO synthesis and vascular tone regulation in the human placenta, acting as a part of negative feedback mechanism for autoregulation of l-arginine transport in HUVECs.

Ca2+-activated K+ channels of small and intermediate conductance control eNOS activation through NAD(P)H oxidase.

Ca(2+)-activated K(+) channels (K(Ca)) and NO play a central role in the endothelium-dependent control of vasomotor tone. We evaluated the interaction of K(Ca) with NO production in isolated arterial mesenteric beds of the rat. In phenylephrine-contracted mesenteries, acetylcholine (ACh)-induced vasodilation was reduced by NO synthase (NOS) inhibition with N(ω)-nitro-L-arginine (L-NA), but in the presence of tetraethylammonium, L-NA did not further affect the response. In KCl-contracted mesenteries, the relaxation elicited by 100 nM ACh or 1 µM ionomycin was abolished by L-NA, tetraethylammonium, or simultaneous blockade of small-conductance K(Ca) (SK(Ca)) channels with apamin and intermediate-conductance K(Ca) (IK(Ca)) channels with triarylmethane-34 (TRAM-34). Apamin-TRAM-34 treatment also abolished 100 nM ACh-activated NO production, which was associated with an increase in superoxide formation. Endothelial cell Ca(2+) buffering with BAPTA elicited a similar increment in superoxide. Apamin-TRAM-34 treatment increased endothelial NOS phosphorylation at threonine 495 (P-eNOS(Thr495)). Blockade of NAD(P)H oxidase with apocynin or superoxide dismutation with PEG-SOD prevented the increment in superoxide and changes in P-eNOS(Thr495) observed during apamin and TRAM-34 application. Our results indicate that blockade of SK(Ca) and IK(Ca) activates NAD(P)H oxidase-dependent superoxide formation, which leads to inhibition of NO release through P-eNOS(Thr495). These findings disclose a novel mechanism involved in the control of NO production.

Genetic neural network modeling of the selective inhibition of the intermediate-conductance Ca2+ -activated K+ channel by some triarylmethanes using topological charge indexes descriptors.

Selective inhibition of the intermediate-conductance Ca(2+)-activated K(+ )channel (IK (Ca)) by some clotrimazole analogs has been successfully modeled using topological charge indexes (TCI) and genetic neural networks (GNNs). A neural network monitoring scheme evidenced a highly non-linear dependence between the IK (Ca) blocking activity and TCI descriptors. Suitable subsets of descriptors were selected by means of genetic algorithm. Bayesian regularization was implemented in the network training function with the aim of assuring good generalization qualities to the predictors. GNNs were able to yield a reliable predictor that explained about 97% data variance with good predictive ability. On the contrary, the best multivariate linear equation with descriptors selected by linear genetic search, only explained about 60%. In spite of when using the descriptors from the linear equations to train neural networks yielded higher fitted models, such networks were very unstable and had relative low predictive ability. However, the best GNN BRANN 2 had a Q ( 2 ) of LOO of cross-validation equal to 0.901 and at the same time exhibited outstanding stability when calculating 80 randomly constructed training/test sets partitions. Our model suggested that structural fragments of size three and seven have relevant influence on the inhibitory potency of the studied IK (Ca) channel blockers. Furthermore, inhibitors were well distributed regarding its activity levels in a Kohonen self-organizing map (KSOM) built using the inputs of the best neural network predictor.