ETS1 governs pathological tissue-remodeling programs in disease-associated fibroblasts.
Minglu YanNoriko KomatsuRyunosuke MuroNam Cong-Nhat HuynhYoshihiko TomofujiYukinori OkadaHiroshi I SuzukiHiroyuki TakabaRiko KitazawaSohei KitazawaWarunee PluemsakunthaiYuichi MitsuiTakashi SatohTadashi OkamuraTakeshi NittaSin-Hyeog ImChan Johng KimGeorge KolliasSakae TanakaKazuo OkamotoMasayuki TsukasakiHiroshi TakayanagiPublished in: Nature immunology (2022)
Fibroblasts, the most abundant structural cells, exert homeostatic functions but also drive disease pathogenesis. Single-cell technologies have illuminated the shared characteristics of pathogenic fibroblasts in multiple diseases including autoimmune arthritis, cancer and inflammatory colitis. However, the molecular mechanisms underlying the disease-associated fibroblast phenotypes remain largely unclear. Here, we identify ETS1 as the key transcription factor governing the pathological tissue-remodeling programs in fibroblasts. In arthritis, ETS1 drives polarization toward tissue-destructive fibroblasts by orchestrating hitherto undescribed regulatory elements of the osteoclast differentiation factor receptor activator of nuclear factor-κB ligand (RANKL) as well as matrix metalloproteinases. Fibroblast-specific ETS1 deletion resulted in ameliorated bone and cartilage damage under arthritic conditions without affecting the inflammation level. Cross-tissue fibroblast single-cell data analyses and genetic loss-of-function experiments lent support to the notion that ETS1 defines the perturbation-specific fibroblasts shared among various disease settings. These findings provide a mechanistic basis for pathogenic fibroblast polarization and have important therapeutic implications.
Keyphrases
- transcription factor
- nuclear factor
- extracellular matrix
- single cell
- oxidative stress
- rheumatoid arthritis
- public health
- toll like receptor
- dna binding
- multiple sclerosis
- induced apoptosis
- rna seq
- immune response
- squamous cell carcinoma
- cell death
- cell proliferation
- copy number
- postmenopausal women
- young adults
- cell cycle arrest
- machine learning
- big data
- deep learning
- endoplasmic reticulum stress