Modulation of IL-17A and IFNγ by β2-adrenergic agonist terbutaline and inverse-agonist nebivolol, influence of ADRB2 polymorphisms
Bibliographic record
Abstract
Background Upon activation, helper T (Th) cells produce cytokines such as IL-17A and IFNγ, which may exacerbate inflammatory disease and disorders. Adrenergic drugs are emerging as immunomodulatory agents to treat pro-inflammatory diseases, but their function is not completely understood. Th Cells express the β2-adrenergic receptor (β2AR) that is encoded by ADRB2. Agonists of the β2AR decrease IFNγ but can increase IL-17A from Th cells. We compared a β2AR agonist to an inverse-agonist, and assessed the influence of ADRB2 polymorphisms on IL-17A and IFNγ responses. Methods Peripheral blood mononuclear cells (PBMCs) from venous blood of healthy human participants were cultured with T cell activators anti-CD3 and anti-CD28 antibodies. Terbutaline, a β2AR agonist or nebivolol, a β1AR antagonist and β2AR inverse-agonist, were added in vitro. Cytokines IL-17A and IFNγ were measured using enzyme-linked immunosorbent assay. Genomic ADRB2 and its immediate upstream region were sequenced using Sanger's method. Cytokine response to drug was analyzed based on ADRB2 polymorphisms. Results Terbutaline consistently inhibited IFNγ from activated PBMC samples. In contrast, it increased IL-17A in PBMC homozygous for Gly16 codon of ADRB2. Nebivolol inhibited IL-17A and IFNγ from activated Th cells. When applied to activated-PBMCs, nebivolol inhibited IL-17A but did not significantly inhibit IFNγ although a trend was observed. The ability of nebivolol to inhibit IL-17A was attenuated by a β2AR-specific antagonist. Cellular proliferation and viability was not significantly changed by nebivolol. Nebivolol suppressed IL-17A in all of the samples regardless of ADRB2 polymorphisms. Conclusions This data demonstrates that terbutaline inhibited IFNγ, however, it increased IL-17A in samples with the common Gly16 polymorphism of ADRB2. Nebivolol inhibited IL-17A regardless of ADRB2 polymorphisms. Thus, nebivolol is a strong candidate for treating inflammatory diseases or disorders where IL-17A exacerbates symptoms.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".