α and β catalytic subunits of cAMP‐dependent protein kinase regulate formoterol‐induced inflammatory gene expression changes in human bronchial epithelial cells
Bibliographic record
Abstract
Background and Purpose It has been proposed that genomic mechanisms contribute to adverse effects often experienced by asthmatic subjects who take regular, inhaled β2‐adrenoceptor agonists as a monotherapy. Moreover, data from preclinical models of asthma suggest that these gene expression changes are mediated by β‐arrestin‐2 rather than PKA. Herein, we tested this hypothesis by comparing the genomic effects of formoterol, a β2‐adrenoceptor agonist, with forskolin in human primary bronchial epithelial cells (HBEC). Experimental Approach Gene expression changes were determined by RNA‐sequencing. Gene silencing and genome editing were employed to explore the roles of β‐arrestin‐2 and PKA. Key Results The formoterol‐regulated transcriptome in HBEC treated concurrently with TNFα was defined by 1480 unique gene expression changes. TNFα‐induced transcripts modulated by formoterol were annotated with enriched gene ontology terms related to inflammation and proliferation, notably “GO:0070374~positive regulation of ERK1 and ERK2 cascade,” which is an apparent β‐arrestin‐2 target. However, expression of the formoterol‐ and forskolin‐regulated transcriptomes were highly rank‐order correlated and the effects of formoterol on TNFα‐induced inflammatory genes were abolished by an inhibitor of PKA. Furthermore, formoterol‐induced gene expression changes in BEAS‐2B bronchial epithelial cell clones deficient in β‐arrestin‐2 were comparable with those expressed by their parental counterparts. Contrariwise, gene expression was partially inhibited in clones lacking the α‐catalytic subunit (Cα) of PKA and abolished following the additional knockdown of the β‐catalytic subunit (Cβ) paralogue. Conclusions The effects of formoterol on inflammatory gene expression in airway epithelia are mediated by PKA and involve the cooperation of PKA‐Cα and PKA‐Cβ.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".