DNA Methylome Alterations Are Associated with Airway Macrophage Differentiation and Phenotype during Lung Fibrosis.
Peter McErleanChristopher G BellRichard J HewittZabreen BusharatPatricia P OggerPoonam GhaiGesa J AlbersEmily CalamitaShaun KingstonPhilip L MolyneauxStephan BeckClare M LloydToby M MaherAdam J ByrnePublished in: American journal of respiratory and critical care medicine (2021)
Rationale: Airway macrophages (AMs) are key regulators of the lung environment and are implicated in the pathogenesis of idiopathic pulmonary fibrosis (IPF), a fatal respiratory disease with no cure. However, knowledge about the epigenetics of AMs in IPF is limited. Objectives: To assess the role of epigenetic regulation of AMs during lung fibrosis. Methods: We undertook DNA methylation (DNAm) profiling by using Illumina EPIC (850k) arrays in sorted AMs from healthy donors (n = 14) and donors with IPF (n = 30). Cell-type deconvolution was performed by using reference myeloid-cell DNA methylomes. Measurements and Main Results: Our analysis revealed that epigenetic heterogeneity was a key characteristic of IPF AMs. DNAm "clock" analysis indicated that epigenetic alterations in IPF AMs were not associated with accelerated aging. In differential DNAm analysis, we identified numerous differentially methylated positions (n = 11) and differentially methylated regions (n = 49) between healthy and IPF AMs, respectively. Differentially methylated positions and differentially methylated regions encompassed genes involved in lipid (LPCAT1 [lysophosphatidylcholine acyltransferase 1]) and glucose (PFKFB3 [6-phosphofructo-2-kinase/fructose-2,6-biphosphatase 3]) metabolism, and importantly, the DNAm status was associated with disease severity in IPF. Conclusions: Collectively, our data identify that changes in the epigenome are associated with the development and function of AMs in the IPF lung.
Keyphrases
- idiopathic pulmonary fibrosis
- dna methylation
- interstitial lung disease
- gene expression
- single cell
- healthcare
- circulating tumor
- type diabetes
- blood pressure
- cell free
- stem cells
- metabolic syndrome
- machine learning
- single molecule
- electronic health record
- systemic sclerosis
- insulin resistance
- weight loss
- big data
- artificial intelligence
- blood glucose
- circulating tumor cells