p38 Mitogen-activated protein kinase signaling regulates streptococcal M1 protein-induced neutrophil activation and lung injury

M1 serotype of Streptococcus pyogenes can cause STSS and acute lung damage. Herein, the purpose was to define the role of p38 MAPK signaling in M1 protein-induced pulmonary injury. Male C57BL/6 mice were treated with specific p38 MAPK inhibitors (SB 239063 and SKF 86002) prior to M1 protein challenge. Edema, neutrophil infiltration, and CXC chemokines were determined in the lung, 4 h after M1 protein administration. Flow cytometry was used to determine Mac-1 expression. Phosphorylation and activity of p38 MAPK were determined by immunoprecipitation and Western blot. IVM was used to analyze leukocyte-endothelium interactions in the pulmonary microcirculation. M1 protein challenge increased phosphorylation and activity of p38 MAPK in the lung, which was inhibited by SB 239063 and SKF 86002. Inhibition of p38 MAPK activity decreased M1 protein-induced infiltration of neutrophils, edema, and CXC chemokine formation in the lung, as well as Mac-1 up-regulation on neutrophils. IVM showed that p38 MAPK inhibition reduced leukocyte rolling and adhesion in the pulmonary microvasculature of M1 protein-treated mice. Our results indicate that p38 MAPK signaling regulates neutrophil infiltration in acute lung injury induced by streptococcal M1 protein. Moreover, p38 MAPK activity controls CXC chemokine formation in the lung, as well as neutrophil expression of Mac-1 and recruitment in the pulmonary microvasculature. In conclusion, these findings suggest that targeting the p38 MAPK signaling pathway may open new opportunities to protect against lung injury in streptococcal infections.     

神经节苷脂对小鼠腹腔巨噬细胞吞噬活力的影响

各类神经节苷脂对小鼠腹腔巨噬细胞吞噬酵母菌作用的实验结果表明:GM_3、GM_1可激活巨噬细胞,其相对吞噬激活百分率(RP)分别为108和30,吞噬指数(R)为对照组的二倍;GD_2GD_(1a)、GD_(1b)和唾液酸可抑制巨噬细胞,其RP值分别为-48、-37、-31和-43,R值下降至对照组的50％。在2～30μg/ml范围内,GM_2、GD_3处理组未发现明显剂量效应。脑苷脂对巨噬细胞活力的影响无统计学意义。     

Simvastatin antagonizes CD40L secretion, CXC chemokine formation, and pulmonary infiltration of neutrophils in abdominal sepsis

Statins have been reported to exert anti-inflammatory actions and protect against septic organ dysfunction. Herein, we hypothesized that simvastatin may attenuate neutrophil activation and lung damage in abdominal sepsis. Male C57BL/6 mice were pretreated with simvastatin (0.5 or 10 mg/kg) before CLP. In separate groups, mice received an anti-CD40L antibody or a CXCR2 antagonist (SB225002) prior to CLP. BALF and lung tissue were harvested for analysis of neutrophil infiltration, as well as edema and CXC chemokine formation. Blood was collected for analysis of Mac-1 and CD40L expression on neutrophils and platelets, as well as soluble CD40L in plasma. Simvastatin decreased CLP-induced neutrophil infiltration and edema formation in the lung. Moreover, Mac-1 expression increased on septic neutrophils, which was significantly attenuated by simvastatin. Inhibition of CD40L reduced CLP-induced up-regulation of Mac-1 on neutrophils. Simvastatin prevented CD40L shedding from the surface of platelets and reduced circulating levels of CD40L in septic mice. CXC chemokine-induced migration of neutrophils in vitro was decreased greatly by simvastatin. Moreover, simvastatin abolished CLP-evoked formation of CXC chemokines in the lung, and a CXCR2 antagonist attenuated pulmonary accumulation of neutrophils. Our data suggest that the inhibitory effect of simvastatin on pulmonary accumulation of neutrophils may be related to a reduction of CD40L secretion into the circulation, as well as a decrease in CXC chemokine formation in the lung. Thus, these protective mechanisms help to explain the beneficial actions exerted by statins, such as simvastatin, in sepsis.     

A Multi-Granularity Information-Based Method for Learning High-Dimensional Bayesian Network Structures

The purpose of structure learning is to construct a qualitative relationship of Bayesian networks. Bayesian network with interpretability and logicality is widely applied in a lot of fields. With the extensive development of high-dimensional and low sample size data in some applications, structure learning of Bayesian networks for high dimension and low sample size data becomes a challenging problem. To handle this problem, we propose a method for learning high-dimensional Bayesian network structures based on multi-granularity information. First, an undirected independence graph construction method containing global structure information is designed to optimize the search space of network structure. Then, an improved agglomerative hierarchical clustering method is presented to cluster variables into sub-granules, which reduces the complexity of structure learning by considering the variable community characteristic in high-dimensional data. Finally, the corresponding sub-graphs are formed by learning the internal structure of sub-granules, and the final network structure is constructed based on the proposed construct link graph algorithm. To verify the proposed method, we conduct two types of comparison experiments: comparison experiment and embedded comparison experiment. The results of the experiments show that our approach is superior to the competitors. The results indicate that our method can not only learn structures of Bayesian network from high-dimensional data efficiently but also improve the efficiency and accuracy of network structure generated by other algorithms for high-dimensional data.