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A high-throughput 3D cantilever array to model airway smooth muscle hypercontractility in asthma

Pranjali Beri, Christopher Plunkett, Joshua Barbara, Chien-Cheng Shih, S. Whitney Barnes, Olivia Ross, Paula Choconta, Ton Trinh, Datzael Gomez, Bella Litvin, John Walker, Minhua Qiu,Scott Hammack,Erin Quan Toyama

APL Bioengineering(2023)

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Abstract
Asthma is often characterized by tissue-level mechanical phenotypes that include remodeling of the airway and an increase in airway tightening, driven by the underlying smooth muscle. Existing therapies only provide symptom relief and do not improve the baseline narrowing of the airway or halt progression of the disease. To investigate such targeted therapeutics, there is a need for models that can recapitulate the 3D environment present in this tissue, provide phenotypic readouts of contractility, and be easily integrated into existing assay plate designs and laboratory automation used in drug discovery campaigns. To address this, we have developed DEFLCT, a high-throughput plate insert that can be paired with standard labware to easily generate high quantities of microscale tissues in vitro for screening applications. Using this platform, we exposed primary human airway smooth muscle cell-derived microtissues to a panel of six inflammatory cytokines present in the asthmatic niche, identifying TGF-beta 1 and IL-13 as inducers of a hypercontractile phenotype. RNAseq analysis further demonstrated enrichment of contractile and remodeling-relevant pathways in TGF-beta 1 and IL-13 treated tissues as well as pathways generally associated with asthma. Screening of 78 kinase inhibitors on TGF-beta 1 treated tissues suggests that inhibition of protein kinase C and mTOR/Akt signaling can prevent this hypercontractile phenotype from emerging, while direct inhibition of myosin light chain kinase does not. Taken together, these data establish a disease-relevant 3D tissue model for the asthmatic airway, which combines niche specific inflammatory cues and complex mechanical readouts that can be utilized in drug discovery efforts. VC 2023 Author(s). All article content, except where otherwise noted, is licensed under a Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
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