Extracellular matrix degradation pathways and fatty acid metabolism regulate distinct pulmonary vascular cell types in Pulmonary Arterial Hypertension

Pulmonary arterial hypertension (PAH) describes a group of diseases characterized by raised pulmonary vascular resistance, resulting from vascular remodelling in the pre-capillary resistance arterioles. Left untreated, patients die from right heart failure. Pulmonary vascular remodelling involves all cell types but to date the precise roles of the different cells is unknown. This study investigated differences in basal gene expression between PAH and controls using both human pulmonary microvascular endothelial (HPMEC) and pulmonary artery smooth muscle cells (HPASMC). HPMEC and HPASMC from PAH patients and controls were cultured to confluence, harvested and RNA extracted. Whole genome sequencing was performed and after transcript quantification and normalization, we examined differentially expressed genes (DEGs) and applied gene set enrichment analysis (GSEA) to the DEGs to identify putative activated pathways. HPMEC displayed 1008 significant (p≤0.0001) DEGs in PAH samples compared to controls. In HPASMC there were 229 significant (p≤0.0001) DEGs between PAH and controls. Pathway analysis revealed distinctive differences: HPMEC display down-regulation of extracellular matrix organisation, collagen formation and biosynthesis, focal- and cell- adhesion molecules suggesting severe endothelial barrier dysfunction and vascular permeability in PAH pathogenesis. In contrast pathways in HPASMC were mainly up-regulated, including those for fatty acid metabolism, biosynthesis of unsaturated fatty acids, cell-cell and adherens junction interactions suggesting a more energy-driven proliferative phenotype. This suggests that the two cell types play different mechanistic roles in PAH pathogenesis and further studies are required to fully elucidate the role each plays and the interactions between these cell types in vascular remodelling in disease progression

PlatformTM, a standards-based data custodianship platform for translational medicine research

Biomedical informatics has traditionally adopted a linear view of the informatics process (collect, store and analyse) in translational medicine (TM) studies; focusing primarily on the challenges in data integration and analysis. However, a data management challenge presents itself with the new lifecycle view of data emphasized by the recent calls for data re-use, long term data preservation, and data sharing. There is currently a lack of dedicated infrastructure focused on the 'manageability' of the data lifecycle in TM research between data collection and analysis. Current community efforts towards establishing a culture for open science prompt the creation of a data custodianship environment for management of TM data assets to support data reuse and reproducibility of research results. Here we present the development of a lifecycle-based methodology to create a metadata management framework based on community driven standards for standardisation, consolidation and integration of TM research data. Based on this framework, we also present the development of a new platform (PlatformTM) focused on managing the lifecycle for translational research data assets

PlatformTM, a standards-based data custodianship platform for translational medicine research.

Biomedical informatics has traditionally adopted a linear view of the informatics process (collect, store and analyse) in translational medicine (TM) studies; focusing primarily on the challenges in data integration and analysis. However, a data management challenge presents itself with the new lifecycle view of data emphasized by the recent calls for data re-use, long term data preservation, and data sharing. There is currently a lack of dedicated infrastructure focused on the 'manageability' of the data lifecycle in TM research between data collection and analysis. Current community efforts towards establishing a culture for open science prompt the creation of a data custodianship environment for management of TM data assets to support data reuse and reproducibility of research results. Here we present the development of a lifecycle-based methodology to create a metadata management framework based on community driven standards for standardisation, consolidation and integration of TM research data. Based on this framework, we also present the development of a new platform (PlatformTM) focused on managing the lifecycle for translational research data assets

"T2-high" in severe asthma related to blood eosinophil, exhaled nitric oxide and serum periostin

BACKGROUND: Type-2 (T2) immune responses in airway epithelial cells (AECs) classifies mild-moderate asthma into a T2-high phenotype. We examined whether currently-available clinical biomarkers can predict AEC-defined T2-high phenotype within U-BIOPRED cohort. METHODS: The transcriptomic profile of AECs obtained from brushings of 103 patients with asthma and 44 healthy controls was obtained and gene set variation analysis used to determine the relative expression score of T2 asthma using a signature from IL-13-exposed AECs. RESULTS: 37% of asthmatics (45% non-smoking severe asthma, n=49, 33% of smoking or ex-smoking severe asthma, n=18 and 28% mild-moderate asthma, n=36) were T2-high using AEC gene expression. They were more symptomatic with higher levels of nitric oxide in exhaled breath (FeNO) and of blood and sputum eosinophils but not of serum IgE or periostin. Sputum eosinophilia correlated best with the T2-high signature. FeNO (≥30 ppb) and blood eosinophils (≥300/µL) gave a moderate prediction of T2-high asthma. Sputum IL-4, IL-5 and IL-13 protein levels did not correlate with gene expression. CONCLUSION: T2-high severe asthma can be predicted to some extent from raised levels of FeNO, blood and sputum eosinophil counts, but serum IgE or serum periostin were poor predictors. Better bedside biomarkers are needed to detect T2-high