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CF airway smooth muscle transcriptome reveals a role for PYK2
Daniel P. Cook, … , Kin Fai Au, David A. Stoltz
Daniel P. Cook, … , Kin Fai Au, David A. Stoltz
Published September 7, 2017
Citation Information: JCI Insight. 2017;2(17):e95332. https://doi.org/10.1172/jci.insight.95332.
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Research Article Pulmonology

CF airway smooth muscle transcriptome reveals a role for PYK2

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Abstract

Abnormal airway smooth muscle function can contribute to cystic fibrosis (CF) airway disease. We previously found that airway smooth muscle from newborn CF pigs had increased basal tone, an increased bronchodilator response, and abnormal calcium handling. Since CF pigs lack airway infection and inflammation at birth, these findings suggest intrinsic airway smooth muscle dysfunction in CF. In this study, we tested the hypothesis that CFTR loss in airway smooth muscle would produce a distinct set of changes in the airway smooth muscle transcriptome that we could use to develop novel therapeutic targets. Total RNA sequencing of newborn wild-type and CF airway smooth muscle revealed changes in muscle contraction–related genes, ontologies, and pathways. Using connectivity mapping, we identified several small molecules that elicit transcriptional signatures opposite of CF airway smooth muscle, including NVP-TAE684, an inhibitor of proline-rich tyrosine kinase 2 (PYK2). In CF airway smooth muscle tissue, PYK2 phosphorylation was increased and PYK2 inhibition decreased smooth muscle contraction. In vivo NVP-TAE684 treatment of wild-type mice reduced methacholine-induced airway smooth muscle contraction. These findings suggest that studies in the newborn CF pig may provide an important approach to enhance our understanding of airway smooth muscle biology and for discovery of novel airway smooth muscle therapeutics for CF and other diseases of airway hyperreactivity.

Authors

Daniel P. Cook, Ryan J. Adam, Keyan Zarei, Benjamin Deonovic, Mallory R. Stroik, Nicholas D. Gansemer, David K. Meyerholz, Kin Fai Au, David A. Stoltz

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Figure 3

Connectivity mapping can be used to generate therapeutic targets based upon the CF airway smooth muscle transcriptional signature.

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Connectivity mapping can be used to generate therapeutic targets based u...
(A) Overview of approach. Transcriptional profiles from two distinct groups (denoted by blue and orange in this schematic; for our study WT and CFTR−/− porcine airway smooth muscle) are queried against the LINCS database, which contains compound signatures. Compounds are ranked based upon the direction and strength of enrichment with the transcriptional profile of CFTR−/− airway smooth muscle to generate a list of positively and negatively correlated compounds. (B) From this nonbiased approach, a list of potential candidate small molecules that elicit a transcriptional signature negatively correlated to the CF airway smooth muscle transcriptome was generated. (C) The odds ratio of an association for LINCS-generated compounds from the CFTR−/− airway smooth muscle gene signature with the PubMed database terms “asthma,” “chronic kidney disease,” and “diabetes” was determined (see Methods for further details). Data are plotted with mean and 95% confidence intervals. “Diabetes” and “chronic kidney disease” were used as non–airway smooth muscle–mediated disease controls.

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