Inflammatory mediators mediate airway smooth muscle contraction through a G protein-coupled receptor-transmembrane protein 16A-voltage-dependent Ca2+ channel axis and contribute to bronchial hyperresponsiveness in asthma.

BACKGROUND: Allergic inflammation has long been implicated in asthmatic hyperresponsiveness of airway smooth muscle (ASM), but its underlying mechanism remains incompletely understood. Serving as G protein-coupled receptor agonists, several inflammatory mediators can induce membrane depolarization, contract ASM, and augment cholinergic contractile response. We hypothesized that the signal cascade integrating on membrane depolarization by the mediators might involve asthmatic hyperresponsiveness. OBJECTIVE: We sought to investigate the signaling transduction of inflammatory mediators in ASM contraction and assess its contribution in the genesis of hyperresponsiveness. METHODS: We assessed the capacity of inflammatory mediators to induce depolarization currents by electrophysiological analysis. We analyzed the phenotypes of transmembrane protein 16A (TMEM16A) knockout mice, applied pharmacological reagents, and measured the Ca2+ signal during ASM contraction. To study the role of the depolarization signaling in asthmatic hyperresponsiveness, we measured the synergistic contraction by methacholine and inflammatory mediators both ex vivo and in an ovalbumin-induced mouse model. RESULTS: Inflammatory mediators, such as 5-hydroxytryptamin, histamine, U46619, and leukotriene D4, are capable of inducing Ca2+-activated Cl- currents in ASM cells, and these currents are mediated by TMEM16A. A combination of multiple analysis revealed that a G protein-coupled receptor-TMEM16A-voltage-dependent Ca2+ channel signaling axis was required for ASM contraction induced by inflammatory mediators. Block of TMEM16A activity may significantly inhibit the synergistic contraction of acetylcholine and the mediators and hence reduces hypersensitivity. CONCLUSIONS: A G protein-coupled receptor-TMEM16A-voltage-dependent Ca2+ channel axis contributes to inflammatory mediator-induced ASM contraction and synergistically activated TMEM16A by allergic inflammatory mediators with cholinergic stimuli.

[LLE-PLS nonlinear modeling method for near infrared spectroscopy and its application].

The traditional near infrared (NIR) spectra modeling algorithm-partial least squares (PLS) can't effectively reflect the nonlinear correlations existing between the near infrared spectra and the chemical or physical properties of samples. Locally linear embedding (LLE) is a newly proposed nonlinear dimension reduction algorithm, which is a kind of manifold learning algorithm. It can find out the intrinsic dimension from high dimensional data effectively, and map the high dimensional input data points to a global low dimensional coordinates while keeping the spatial relations of the adjacent points, i. e. the geometry structure of the high dimensional space. No application of LLE in the information processing of NIR spectra has been reported. By combining LLE and PLS, a novel nonlinear modeling method LLE-PLS for NIR spectra was proposed. In the proposed method, LLE and PLS were adopted to deduct the dimensions of NIR spectra and build regressor, respectively. The LLE-PLS method was applied to correlate the NIR spectra with the concentrations of salvia acid B in the elution of column chromatography of Salvianolate. The results showed that LLE-PLS outperformed other preprocessing methods such as multiplicative scattering correction, the 1st derivative, vector normalization, minimum-maximum normalization, detrend, debias, and the 2nd derivative. After parameter optimization, LLE-PLS can accurately predict the concentration of salvia acid B, with a minimum RMSECV of 0.128 mg x mL(-1) and r2 of 0.9988, suggesting that LLE-PLS is better than PLS in modeling and prediction. The parameter of the number of nearest neighbor k of LLE-PLS and output dimension d can affect the performance of the method. The research showed that k is robust to RMSECV, and an excessively low or high output dimension d will result in a greater error because of insufficient or excessive information extraction. It can be concluded that LLE-PLS can effectively model the nonlinear correlations between spectra and physicochemical properties of the samples. And it is feasible to actualize online monitoring of the process of column chromatography of Salvianolate by coupling NIR spectra with LLE-PLS modeling method.