Inhibiting Protein Kinase D Promotes Airway Epithelial Barrier Integrity in Mouse Models of Influenza A Virus Infection.

Rationale: Protein kinase D (PKD) is a serine/threonine kinase family that is involved in a wide array of signaling pathways. Although PKD has been implicated in immune responses, relatively little is known about the function of PKD in the lung or during viral infections. Objectives: We investigated the hypothesis that PKD is involved in multiple aspects of host response to viral infection. Methods: The selective PKD inhibitor CRT0010166 was administered to C57BL/6 mice prior to and during challenge with either inhaled double-stranded RNA or Influenza A Virus. PKD signaling pathways were investigated in human bronchial epithelial cells treated with CRT0010166, double-stranded RNA, and/or infected with Influenza A Virus. Measurements: Total protein and albumin accumulation in the bronchoalveolar fluid was used to asses inside/out leak. Clearance of inhaled FITC-dextran out of the airspace was used to assess outside/in leak. Cytokines and neutrophils in bronchoalveolar lavage were assayed with ELISAs and cytospins respectively. Viral RNA level was assessed with RT-PCR and protein level assessed by ELISA. Main Results: PKD inhibition prevented airway barrier dysfunction and pro-inflammatory cytokine release. Epithelial cells express PKD3, and PKD3 siRNA knock-down inhibited polyI:C induced cytokine production. Lung epithelial-specific deletion of PKD3 (CC10-Cre x PKD3-floxed mice) partially attenuated polyI:C-induced barrier disruption in vivo. Mechanistically, we found that PKD promoted cytokine mRNA transcription, not secretion, likely through activating the transcription factor Sp1. Finally, prophylactic CRT treatment of mice promoted barrier integrity during influenza virus infection and reduced viral burden. Conclusions: Inhibiting PKD promotes barrier integrity, limit pathogenic cytokine levels, and restrict Influenza A Virus infection. Therefore, PKD is an attractive target for novel antiviral therapeutics.

Statistical Coupling Analysis-Guided Library Design for the Discovery of Mutant Luciferases.

Directed evolution has proven to be an invaluable tool for protein engineering; however, there is still a need for developing new approaches to continue to improve the efficiency and efficacy of these methods. Here, we demonstrate a new method for library design that applies a previously developed bioinformatic method, Statistical Coupling Analysis (SCA). SCA uses homologous enzymes to identify amino acid positions that are mutable and functionally important and engage in synergistic interactions between amino acids. We use SCA to guide a library of the protein luciferase and demonstrate that, in a single round of selection, we can identify luciferase mutants with several valuable properties. Specifically, we identify luciferase mutants that possess both red-shifted emission spectra and improved stability relative to those of the wild-type enzyme. We also identify luciferase mutants that possess a >50-fold change in specificity for modified luciferins. To understand the mutational origin of these improved mutants, we demonstrate the role of mutations at N229, S239, and G246 in altered function. These studies show that SCA can be used to guide library design and rapidly identify synergistic amino acid mutations from a small library.