Inhibition of IFN-γ-dependent antiviral airway epithelial defense by cigarette smoke

<p>Abstract</p> <p>Background</p> <p>Although individuals exposed to cigarette smoke are more susceptible to respiratory infection, the effects of cigarette smoke on lung defense are incompletely understood. Because airway epithelial cell responses to type II interferon (IFN) are critical in regulation of defense against many respiratory viral infections, we hypothesized that cigarette smoke has inhibitory effects on IFN-γ-dependent antiviral mechanisms in epithelial cells in the airway.</p> <p>Methods</p> <p>Primary human tracheobronchial epithelial cells were first treated with cigarette smoke extract (CSE) followed by exposure to both CSE and IFN-γ. Epithelial cell cytotoxicity and IFN-γ-induced signaling, gene expression, and antiviral effects against respiratory syncytial virus (RSV) were tested without and with CSE exposure.</p> <p>Results</p> <p>CSE inhibited IFN-γ-dependent gene expression in airway epithelial cells, and these effects were not due to cell loss or cytotoxicity. CSE markedly inhibited IFN-γ-induced Stat1 phosphorylation, indicating that CSE altered type II interferon signal transduction and providing a mechanism for CSE effects. A period of CSE exposure combined with an interval of epithelial cell exposure to both CSE and IFN-γ was required to inhibit IFN-γ-induced cell signaling. CSE also decreased the inhibitory effect of IFN-γ on RSV mRNA and protein expression, confirming effects on viral infection. CSE effects on IFN-γ-induced Stat1 activation, antiviral protein expression, and inhibition of RSV infection were decreased by glutathione augmentation of epithelial cells using N-acetylcysteine or glutathione monoethyl ester, providing one strategy to alter cigarette smoke effects.</p> <p>Conclusions</p> <p>The results indicate that CSE inhibits the antiviral effects of IFN-γ, thereby presenting one explanation for increased susceptibility to respiratory viral infection in individuals exposed to cigarette smoke.</p

Advanced Feature-Selection-Based Hybrid Ensemble Learning Algorithms for Network Intrusion Detection Systems

As cyber-attacks become remarkably sophisticated, effective Intrusion Detection Systems (IDSs) are needed to monitor computer resources and to provide alerts regarding unusual or suspicious behavior. Despite using several machine learning (ML) and data mining methods to achieve high effectiveness, these systems have not proven ideal. Current intrusion detection algorithms suffer from high dimensionality, redundancy, meaningless data, high error rate, false alarm rate, and false-negative rate. This paper proposes a novel Ensemble Learning (EL) algorithm-based network IDS model. The efficient feature selection is attained via a hybrid of Correlation Feature Selection coupled with Forest Panelized Attributes (CFS&ndash;FPA). The improved intrusion detection involves exploiting AdaBoosting and bagging ensemble learning algorithms to modify four classifiers: Support Vector Machine, Random Forest, Na&iuml;ve Bayes, and K-Nearest Neighbor. These four enhanced classifiers have been applied first as AdaBoosting and then as bagging, using the aggregation technique through the voting average technique. To provide better benchmarking, both binary and multi-class classification forms are used to evaluate the model. The experimental results of applying the model to CICIDS2017 dataset achieved promising results of 99.7%accuracy, a 0.053 false-negative rate, and a 0.004 false alarm rate. This system will be effective for information technology-based organizations, as it is expected to provide a high level of symmetry between information security and detection of attacks and malicious intrusion

Advanced Feature-Selection-Based Hybrid Ensemble Learning Algorithms for Network Intrusion Detection Systems

As cyber-attacks become remarkably sophisticated, effective Intrusion Detection Systems (IDSs) are needed to monitor computer resources and to provide alerts regarding unusual or suspicious behavior. Despite using several machine learning (ML) and data mining methods to achieve high effectiveness, these systems have not proven ideal. Current intrusion detection algorithms suffer from high dimensionality, redundancy, meaningless data, high error rate, false alarm rate, and false-negative rate. This paper proposes a novel Ensemble Learning (EL) algorithm-based network IDS model. The efficient feature selection is attained via a hybrid of Correlation Feature Selection coupled with Forest Panelized Attributes (CFS–FPA). The improved intrusion detection involves exploiting AdaBoosting and bagging ensemble learning algorithms to modify four classifiers: Support Vector Machine, Random Forest, Naïve Bayes, and K-Nearest Neighbor. These four enhanced classifiers have been applied first as AdaBoosting and then as bagging, using the aggregation technique through the voting average technique. To provide better benchmarking, both binary and multi-class classification forms are used to evaluate the model. The experimental results of applying the model to CICIDS2017 dataset achieved promising results of 99.7%accuracy, a 0.053 false-negative rate, and a 0.004 false alarm rate. This system will be effective for information technology-based organizations, as it is expected to provide a high level of symmetry between information security and detection of attacks and malicious intrusion

Normal CFTR Inhibits Epidermal Growth Factor Receptor-Dependent Pro-Inflammatory Chemokine Production in Human Airway Epithelial Cells

Mutations in cystic fibrosis transmembrane conductance regulator (CFTR) protein cause cystic fibrosis, a disease characterized by exaggerated airway epithelial production of the neutrophil chemokine interleukin (IL)-8, which results in exuberant neutrophilic inflammation. Because activation of an epidermal growth factor receptor (EGFR) signaling cascade induces airway epithelial IL-8 production, we hypothesized that normal CFTR suppresses EGFR-dependent IL-8 production and that loss of CFTR at the surface exaggerates IL-8 production via activation of a pro-inflammatory EGFR cascade. We examined this hypothesis in human airway epithelial (NCI-H292) cells and in normal human bronchial epithelial (NHBE) cells containing normal CFTR treated with a CFTR-selective inhibitor (CFTR-172), and in human airway epithelial (IB3) cells containing mutant CFTR versus isogenic (C38) cells containing wild-type CFTR. In NCI-H292 cells, CFTR-172 induced IL-8 production EGFR-dependently. Pretreatment with an EGFR neutralizing antibody or the metalloprotease TACE inhibitor TAPI-1, or TACE siRNA knockdown prevented CFTR-172-induced EGFR phosphorylation (EGFR-P) and IL-8 production, implicating TACE-dependent EGFR pro-ligand cleavage in these responses. Pretreatment with neutralizing antibodies to IL-1R or to IL-1alpha, but not to IL-1beta, markedly suppressed CFTR-172-induced EGFR-P and IL-8 production, suggesting that binding of IL-1alpha to IL-1R stimulates a TACE-EGFR-IL-8 cascade. Similarly, in NHBE cells, CFTR-172 increased IL-8 production EGFR-, TACE-, and IL-1alpha/IL-1R-dependently. In IB3 cells, constitutive IL-8 production was markedly increased compared to C38 cells. EGFR-P was increased in IB3 cells compared to C38 cells, and exaggerated IL-8 production in the IB3 cells was EGFR-dependent. Activation of TACE and binding of IL-1alpha to IL-1R contributed to EGFR-P and IL-8 production in IB3 cells but not in C38 cells. Thus, we conclude that normal CFTR suppresses airway epithelial IL-8 production that occurs via a stimulatory EGFR cascade, and that loss of normal CFTR activity exaggerates IL-8 production via activation of a pro-inflammatory EGFR cascade