Lower NPAS3 expression during the later stages of abnormal lung development in rat congenital diaphragmatic hernia

Purpose Congenital diaphragmatic hernia (CDH) is characterized by a developmental defect in the diaphragm, pulmonary hypoplasia and pulmonary hypertension. NPAS3 is a PAS domain transcription factor regulating Drosophila tracheogenesis. NPAS3 null mice develop pulmonary hypoplasia in utero and die after birth due to respiratory failure. We aimed to evaluate NPAS3 expres- sion during normal and abnormal lung development due to CDH. Methods CDH was induced by administering 100 mg/ml nitrofen to time-pregnant dams on embryonic day (E) 9 of gestation. Lungs were isolated on E15, E18 and E21 and NPAS3 localization was determined by immunohisto- chemistry and quantified using Western blotting. Results We found that only E21 hypoplastic CDH lungs have reduced expression of NPAS3 in the terminal sac- cules. Western blotting confirmed the down-regulation of NPAS3 protein in the nitrofen-induced hypoplastic lungs. Conclusions We demonstrate for the first time that ni- trofen-induced hypoplastic CDH lungs have reduced NPAS3 expression in the terminal saccules during the later stages of abnormal lung development. Our findings suggest that NPAS3 is associated with pulmonary hypoplasia in CDH.

An R package for the integrated analysis of metabolomics and spectral data

Recently, there has been a growing interest in the field of metabolomics, materialized by a remarkable growth in experimental techniques, available data and related biological applications. Indeed, techniques as Nuclear Magnetic Resonance, Gas or Liquid Chromatography, Mass Spectrometry, Infrared and UV-visible spectroscopies have provided extensive datasets that can help in tasks as biological and biomedical discovery, biotechnology and drug development. However, as it happens with other omics data, the analysis of metabolomics datasets provides multiple challenges, both in terms of methodologies and in the development of appropriate computational tools. Indeed, from the available software tools, none addresses the multiplicity of existing techniques and data analysis tasks. In this work, we make available a novel R package, named specmine, which provides a set of methods for metabolomics data analysis, including data loading in different formats, pre-processing, metabolite identification, univariate and multivariate data analysis, machine learning, and feature selection. Importantly, the implemented methods provide adequate support for the analysis of data from diverse experimental techniques, integrating a large set of functions from several R packages in a powerful, yet simple to use environment. The package, already available in CRAN, is accompanied by a web site where users can deposit datasets, scripts and analysis reports to be shared with the community, promoting the efficient sharing of metabolomics data analysis pipelines.

From perfect strangers to research partners: a study of relationships, resource sharing and perceived benefits within R&D cooperation networks

Doctoral thesis in Marketing and Strategy.