Project description:To identify genes relevant for cystic fibrosis pathophysiology, we profiled blood samples in CF patients and healthy controls using RNA-seq. Weighted Gene Co-expression Network Analysis of a transcriptomic dataset allowed us to identify 28 co-expressed modules that correlated with clinical traits of interest in cystic fibrosis.
Project description:Coding and long non-coding RNA metabolism is now revealing its crucial role in Amyotrophic Lateral Sclerosis (ALS) pathogenesis. In this work, we performed Illumina RNA-seq analysis on Peripheral Blood Mononuclear Cells (PBMCs) from Sporadic and mutated ALS patients (mutations in FUS, TARDBP, SOD1, C9Orf72 and VCP genes) and healthy controls. Our aim is the whole-transcriptome characterization of PBMCs content, both in terms of coding and non coding RNAs, in order to compare the disease state to the healthy controls, both for sporadic patients and for mutated patients. Out dataset is a starting point for the omni-comprehensive analysis of coding and long non coding RNAs, from an easy to withdraw, manage and store tissue that shows to be a suitable model for RNA profiling in ALS.