bioRxivImmunologyPreprint
NF-kB-Dependent Transcriptional Regulation of Piezo1 Mediates Bacterial Clearance on Stiffened Lung
Erica M. OrsiniiD, Adam M. BoultoniD, Susamma AbrahamiD, Alyson Brown, Haley Ricci, Akash AhujaiD, Bishnu Datt PantiD, Caitlin M. Snyder, Amanda Reinhardt, Amy Houghton AttawayiD, Ryan J. MusichiD, Lisa M. GroveiD, Mitchell A. OlmaniD, Vidula VachharajaniiD, Rachel Greenberg ScheragaiD
Abstract
Respiratory pathogens, such as Pseudomonas aeruginosa damage the alveolar-capillary barrier leading to lung injury and stiffness. Lung stiffness is a key macrophage signal for bacterial clearance, but it remains unknown how stiffness-sensing mechanosensitive ion channels in macrophages are regulated during pneumonia. Macrophage Piezo1 is critical to bacterial clearance in experimental pneumonia in vivo; however, identification of putative matrix-derived signals and the mechanism of their effects remain to be determined. To our knowledge, our work is the first to show that during pneumonia, transcription of the mechanosensitive ion channel Piezo1 is increased in macrophages by the NF-{kappa}B transcription factor, p65, through its signaling adaptor protein, MyD88, leading to increased Piezo1 Ca2+ channel activity. Piezo1 mRNA abundance is increased in association with open chromatin at the Piezo1 promoter in macrophages. The enhanced level of Piezo1 increases the abundance of transcription factor EB (Tfeb) resulting in lysosome biogenesis and stiffness-dependent phagolysosome maturation, a critical step for macrophage bacterial clearance. Our data support the mechanism whereby transcription of macrophage Piezo1 is enhanced by p65 to augment bacterial clearance on an injured, stiffened lung matrix during pneumonia. Therefore, Piezo1 is a future therapeutic target against pneumonia-induced lung injury.
- organism: mouse
- assay: bulk RNA-seq
- assay: proteomics
- research focus: cell signaling & trafficking
- research focus: host-pathogen & immune response
- research focus: gene regulatory network inference
- approach: statistical model
- approach: experimental assay
- Citations
- 0
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