IPF lung fibroblasts have a senescent phenotype.
Diana ÁlvarezNayra CárdenesJacobo SellarésMarta BuenoCatherine CoreyVidya Sagar HanumanthuYating PengHannah D'CunhaJohn SembratMehdi NouraieSwaroop ShankerChandler CaufieldSruti ShivaMary ArmaniosAna L MoraMauricio M RojasPublished in: American journal of physiology. Lung cellular and molecular physiology (2017)
The mechanisms of aging that are involved in the development of idiopathic pulmonary fibrosis (IPF) are still unclear. Although it has been hypothesized that the proliferation and activation of human lung fibroblasts (hLFs) are essential in IPF, no studies have assessed how this process works in an aging lung. Our goal was to elucidate if there were age-related changes on primary hLFs isolated from IPF lungs compared with age-matched controls. We investigated several hallmarks of aging in hLFs from IPF patients and age-matched controls. IPF hLFs have increased cellular senescence with higher expression of β-galactosidase, p21, p16, p53, and cytokines related to the senescence-associated secretory phenotype (SASP) as well as decreased proliferation/apoptosis compared with age-matched controls. Additionally, we observed shorter telomeres, mitochondrial dysfunction, and upon transforming growth factor-β stimulation, increased markers of endoplasmic reticulum stress. Our data suggest that IPF hLFs develop senescence resulting in a decreased apoptosis and that the development of SASP may be an important contributor to the fibrotic process observed in IPF. These results might change the existing paradigm, which describes fibroblasts as aberrantly activated cells, to a cell with a senescence phenotype.
Keyphrases
- idiopathic pulmonary fibrosis
- endoplasmic reticulum stress
- induced apoptosis
- interstitial lung disease
- cell cycle arrest
- transforming growth factor
- dna damage
- signaling pathway
- oxidative stress
- cell death
- stress induced
- end stage renal disease
- newly diagnosed
- chronic kidney disease
- stem cells
- ejection fraction
- prognostic factors
- machine learning
- big data
- electronic health record
- bone marrow
- pi k akt
- atomic force microscopy
- binding protein
- patient reported