Characterization of the innate immune response to chronic aspiration in a novel rodent model

<p>Abstract</p> <p>Background</p> <p>Although chronic aspiration has been associated with several pulmonary diseases, the inflammatory response has not been characterized. A novel rodent model of chronic aspiration was therefore developed in order to investigate the resulting innate immune response in the lung.</p> <p>Methods</p> <p>Gastric fluid or normal saline was instilled into the left lung of rats (n = 48) weekly for 4, 8, 12, or 16 weeks (n = 6 each group). Thereafter, bronchoalveolar lavage specimens were collected and cellular phenotypes and cytokine concentrations of IL-1alpha, IL-1beta, IL-2, IL-4, IL-6, IL-10, GM-CSF, IFN-gamma, TNF-alpha, and TGF-beta were determined.</p> <p>Results</p> <p>Following the administration of gastric fluid but not normal saline, histologic specimens exhibited prominent evidence of giant cells, fibrosis, lymphocytic bronchiolitis, and obliterative bronchiolitis. Bronchoalveolar lavage specimens from the left (treated) lungs exhibited consistently higher macrophages and T cells with an increased CD4:CD8 T cell ratio after treatment with gastric fluid compared to normal saline. The concentrations of IL-1alpha, IL-1beta, IL-2, TNF-alpha and TGF-beta were increased in bronchoalveolar lavage specimens following gastric fluid aspiration compared to normal saline.</p> <p>Conclusion</p> <p>This represents the first description of the pulmonary inflammatory response that results from chronic aspiration. Repetitive aspiration events can initiate an inflammatory response consisting of macrophages and T cells that is associated with increased TGF-beta, TNF-alpha, IL-1alpha, IL-1beta, IL-2 and fibrosis in the lung. Combined with the observation of gastric fluid-induced lymphocyitic bronchiolitis and obliterative bronchiolitis, these findings further support an association between chronic aspiration and pulmonary diseases, such as obliterative bronchiolitis, pulmonary fibrosis, and asthma.</p

Network Models of TEM β-Lactamase Mutations Coevolving under Antibiotic Selection Show Modular Structure and Anticipate Evolutionary Trajectories

Understanding how novel functions evolve (genetic adaptation) is a critical goal of evolutionary biology. Among asexual organisms, genetic adaptation involves multiple mutations that frequently interact in a non-linear fashion (epistasis). Non-linear interactions pose a formidable challenge for the computational prediction of mutation effects. Here we use the recent evolution of β-lactamase under antibiotic selection as a model for genetic adaptation. We build a network of coevolving residues (possible functional interactions), in which nodes are mutant residue positions and links represent two positions found mutated together in the same sequence. Most often these pairs occur in the setting of more complex mutants. Focusing on extended-spectrum resistant sequences, we use network-theoretical tools to identify triple mutant trajectories of likely special significance for adaptation. We extrapolate evolutionary paths (n = 3) that increase resistance and that are longer than the units used to build the network (n = 2). These paths consist of a limited number of residue positions and are enriched for known triple mutant combinations that increase cefotaxime resistance. We find that the pairs of residues used to build the network frequently decrease resistance compared to their corresponding singlets. This is a surprising result, given that their coevolution suggests a selective advantage. Thus, β-lactamase adaptation is highly epistatic. Our method can identify triplets that increase resistance despite the underlying rugged fitness landscape and has the unique ability to make predictions by placing each mutant residue position in its functional context. Our approach requires only sequence information, sufficient genetic diversity, and discrete selective pressures. Thus, it can be used to analyze recent evolutionary events, where coevolution analysis methods that use phylogeny or statistical coupling are not possible. Improving our ability to assess evolutionary trajectories will help predict the evolution of clinically relevant genes and aid in protein design