bioRxivDevelopmental BiologyPreprint
Genetic background and transient prenatal disruption of vitamin A signaling determine susceptibility to airway hyperresponsiveness in mice
Takehiro Otoshi, Ayyappa Kumar Sista KameshwariD, Benjamin D. Kotton, Yoshinori Seki, Zachary Cardell, Xiangyi Ke, Yuta MatsunoiD, Pooja Rajaram, Youn‐Kyung KimiD, Sarah M. Sharpton, Loredana QuadroiD, Wellington Veras CardosoiD, Masako SuzukiiD
Abstract
Airway structural changes and hyperresponsiveness (AHR), hallmarks of asthma, are crucially influenced by genetic variations and adverse exposures. While intrauterine environmental perturbations leading to dysfunctional lung development have been linked to adult pulmonary disease, still little is known about the developmental events leading to these postnatal abnormalities. Here, we provide evidence of genetic background playing a key role in this process. Using A/J and C57BL/6J mice known for their distinct susceptibility to AHR, we show that A/J but not C57BL/6J develop an aberrant airway smooth muscle (SM) program and AHR in adulthood when exposed transiently to a vitamin A/retinoic acid (RA)-disrupted intrauterine environment in vivo by a maternal BMS493 administration. Single nuclei multiomics analysis identified a subpopulation of mesenchymal cells that overactivated TGF{beta} targets in response to BMS selectively in A/J, but not C57BL/6J, embryonic lungs. These cells, localized to sites of airway SM initiation, exhibited robust BMS-mediated upregulation of SMAD2/3 targets, including regulators of SM program Pdgfra and Tnc, and showed stable cell proportions despite the marked transcriptional rewiring following RA disruption. These findings identify TGF{beta}-activating mesenchymal cells as a critical niche responsive to RA signaling and reveal how genetic background determines developmental susceptibility to micronutrient perturbations with long-term impact on airway function.
- organism: mouse
- assay: bulk RNA-seq
- research focus: cell type annotation
- research focus: differential expression
- research focus: gene regulatory network inference
- approach: statistical model
- Citations
- 0
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