bioRxivMolecular BiologyPreprint
The lncRNA FENDRR fine-tunes FOXF1 protein levels through a negative feedback loop governing human embryonic lung fibroblast-to-myofibroblast transition
Jean-Francois LaurendeauiD, Sophie MocklyiD, Gilberto Duran-Bishop, Sophie EhresmanniD, Elena GorettiiD, Virginie CalderoniD, Mohan K. MalleshaiahiD, Martin SauvageauiD
Abstract
Precise control of transcription factor dosage is critical for lung mesenchymal development. The forkhead box transcription factor FOXF1 is a dosage-sensitive regulator of pulmonary vascularization and fibroblast differentiation, with haploinsufficiency causing the lethal neonatal disorder alveolar capillary dysplasia with misalignment of pulmonary veins (ACDMPV). A syntenically conserved long noncoding RNA (lncRNA), FENDRR, is divergently transcribed [~]1.7 kb upstream of FOXF1. Notably, ACDMPV-associated chromosomal deletions disrupting FENDRR or a distal enhancer regulating both FENDRR and FOXF1 have been identified. Consistent with a role in lung development, selective deletion of Fendrr in mice causes neonatal lethality with lung growth defects, as well as alveolar and vascular abnormalities. Here, we define the conserved expression patterns, isoform diversity, and subcellular localization of FENDRR and FOXF1 across human and murine embryonic lung cell types. Functional perturbation reveals a bidirectional regulatory circuit in which FOXF1 promotes FENDRR transcription, while FENDRR limits FOXF1 protein abundance without affecting mRNA levels. Transcriptomic analyses demonstrate overlapping target gene networks and opposing effects on fibroblast-to-myofibroblast differentiation. These findings uncover a rheostat-like regulatory layer by which the lncRNA FENDRR fine-tunes FOXF1 protein dosage to influence lung fibroblast cell fate response and offers additional context for the significance of FOXF1-FENDRR dysregulation in ACDMPV.
- organism: human
- organism: mouse
- assay: bulk RNA-seq
- research focus: gene regulatory network inference
- research focus: differential expression
- research focus: cell type annotation
- approach: statistical model
- Citations
- 0
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