Lumican Expression in Diaphragm Induced by Mechanical Ventilation

Diaphragmatic dysfunction found in the patients with acute lung injury required prolonged mechanical ventilation. Mechanical ventilation can induce production of inflammatory cytokines and excess deposition of extracellular matrix proteins via up-regulation of transforming growth factor (TGF)-β1. Lumican is known to participate in TGF-β1 signaling during wound healing. The mechanisms regulating interactions between mechanical ventilation and diaphragmatic injury are unclear. We hypothesized that diaphragmatic damage by short duration of mechanical stretch caused up-regulation of lumican that modulated TGF-β1 signaling.Male C57BL/6 mice, either wild-type or lumican-null, aged 3 months, weighing between 25 and 30 g, were exposed to normal tidal volume (10 ml/kg) or high tidal volume (30 ml/kg) mechanical ventilation with room air for 2 to 8 hours. Nonventilated mice served as control groups.High tidal volume mechanical ventilation induced interfibrillar disassembly of diaphragmatic collagen fiber, lumican activation, type I and III procollagen, fibronectin, and α-smooth muscle actin (α-SMA) mRNA, production of free radical and TGF-β1 protein, and positive staining of lumican in diaphragmatic fiber. Mechanical ventilation of lumican deficient mice attenuated diaphragmatic injury, type I and III procollagen, fibronectin, and α-SMA mRNA, and production of free radical and TGF-β1 protein. No significant diaphragmatic injury was found in mice subjected to normal tidal volume mechanical ventilation.Our data showed that high tidal volume mechanical ventilation induced TGF-β1 production, TGF-β1-inducible genes, e.g., collagen, and diaphragmatic dysfunction through activation of the lumican

Assessing Approaches for Inferring Species Trees from Multi-Copy Genes

Abstract.—With the availability of genomic sequence data, there is increasing interest in using genes with a possible history of duplication and loss for species tree inference. Here we assess the performance of both nonprobabilistic and probabilistic species tree inference approaches using gene duplication and loss and coalescence simulations. We evaluated the performance of gene tree parsimony (GTP) based on duplication (Only-dup), duplication and loss (Dup-loss), and deep coalescence (Deep-c) costs, the NJst distance method, the MulRF supertree method, and PHYLDOG, which jointly estimates gene trees and species tree using a hierarchical probabilistic model. We examined the effects of gene tree and species sampling, gene tree error, and duplication and loss rates on the accuracy of phylogenetic estimates. In the 10-taxon duplication and loss simulation experiments, MulRF is more accurate than the other methods when the duplication and loss rates are low, and Dup-loss is generally the most accurate when the duplication and loss rates are high. PHYLDOG performs well in 10-taxon duplication and loss simulations, but its run time is prohibitively long on larger data sets. In the larger duplication and loss simulation experiments, MulRF outperforms all other methods in experiments with at most 100 taxa; however, in the larger simulation, Dup-loss generally performs best. In all duplication and loss simulation experiment