Antiproteases as therapeutics to target inflammation in cystic fibrosis.

Cystic Fibrosis (CF) is the most common fatal inherited disease of Caucasians, affecting about 1 in 3000 births. Patients with CF have a recessive mutation in the gene encoding the CF transmembrane conductance regulator (CFTR). CFTR is expressed in the epithelium of many organs throughout the exocrine system, however, inflammation and damage of the airways as a result of persistent progressive endobronchial infection is a central feature of CF. The inflammatory response to infection brings about a sustained recruitment of neutrophils to the site of infection. These neutrophils release various pro-inflammatory compounds including proteases, which when expressed at aberrant levels can overcome the endogenous antiprotease defence mechanisms of the lung. Unregulated, these proteases can exacerbate inflammation and result in the degradation of structural proteins and tissue damage leading to bronchiectasis and loss of respiratory function. Other host-derived and bacterial proteases may also contribute to the inflammation and lung destruction observed in the CF lung. Antiprotease strategies to dampen the excessive inflammatory response and concomitant damage to the airways remains an attractive therapeutic option for CF patients.

Ranking of microRNA target prediction scores by Pareto front analysis

Over the past ten years, a variety of microRNA target prediction methods has been developed, and many of the methods are constantly improved and adapted to recent insights into miRNA-mRNA interactions. In a typical scenario, different methods return different rankings of putative targets, even if the ranking is reduced to selected mRNAs that are related to a specific disease or cell type. For the experimental validation it is then difficult to decide in which order to process the predicted miRNA-mRNA bindings, since each validation is a laborious task and therefore only a limited number of mRNAs can be analysed. We propose a new ranking scheme that combines ranked predictions from several methods and - unlike standard thresholding methods - utilises the concept of Pareto fronts as defined in multi-objective optimisation. In the present study, we attempt a proof of concept by applying the new ranking scheme to hsa-miR-21, hsa-miR-125b, and hsa-miR-373 and prediction scores supplied by PITA and RNAhybrid. The scores are interpreted as a two-objective optimisation problem, and the elements of the Pareto front are ranked by the STarMir score with a subsequent re-calculation of the Pareto front after removal of the top-ranked mRNA from the basic set of prediction scores. The method is evaluated on validated targets of the three miRNA, and the ranking is compared to scores from DIANA-microT and TargetScan. We observed that the new ranking method performs well and consistent, and the first validated targets are elements of Pareto fronts at a relatively early stage of the recurrent procedure. which encourages further research towards a higher-dimensional analysis of Pareto fronts. (C) 2010 Elsevier Ltd. All rights reserved.