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Record W2094260855 · doi:10.1371/journal.pone.0116773

Cytokine-Induced Loss of Glucocorticoid Function: Effect of Kinase Inhibitors, Long-Acting β2-Adrenoceptor Agonist and Glucocorticoid Receptor Ligands

2015· article· en· W2094260855 on OpenAlexafffund
Christopher F. Rider, Suharsh Shah, Anna Miller‐Larsson, Mark A. Giembycz, Robert Newton

Bibliographic record

VenuePLoS ONE · 2015
Typearticle
Languageen
FieldMedicine
TopicAsthma and respiratory diseases
Canadian institutionsUniversity of Calgary
FundersCanadian Institutes of Health ResearchAlberta InnovatesGilead SciencesGlaxoSmithKlineAstraZeneca
KeywordsGlucocorticoidGlucocorticoid receptorEndocrinologyInternal medicineTumor necrosis factor alphaReporter geneAgonistCytokineKinaseBiologyChemistryReceptorGene expressionMedicineCell biologyBiochemistry

Abstract

fetched live from OpenAlex

Acting on the glucocorticoid receptor (NR3C1), glucocorticoids are widely used to treat inflammatory diseases. However, glucocorticoid resistance often leads to suboptimal asthma control. Since glucocorticoid-induced gene expression contributes to glucocorticoid activity, the aim of this study was to use a 2 × glucocorticoid response element (GRE) reporter and glucocorticoid-induced gene expression to investigate approaches to combat cytokine-induced glucocorticoid resistance. Pre-treatment with tumor necrosis factor-α (TNF) or interleukin-1β inhibited dexamethasone-induced mRNA expression of the putative anti-inflammatory genes RGS2 and TSC22D3, or just TSC22D3, in primary human airway epithelial and smooth muscle cells, respectively. Dexamethasone-induced DUSP1 mRNA was unaffected. In human bronchial epithelial BEAS-2B cells, dexamethasone-induced TSC22D3 and CDKN1C expression (at 6 h) was reduced by TNF pre-treatment, whereas DUSP1 and RGS2 mRNAs were unaffected. TNF pre-treatment also reduced dexamethasone-dependent 2×GRE reporter activation. This was partially reversed by PS-1145 and c-jun N-terminal kinase (JNK) inhibitor VIII, inhibitors of IKK2 and JNK, respectively. However, neither inhibitor affected TNF-dependent loss of dexamethasone-induced CDKN1C or TSC22D3 mRNA. Similarly, inhibitors of the extracellular signal-regulated kinase, p38, phosphoinositide 3-kinase or protein kinase C pathways failed to attenuate TNF-dependent repression of the 2×GRE reporter. Fluticasone furoate, fluticasone propionate and budesonide were full agonists relative to dexamethasone, while GSK9027, RU24858, des-ciclesonide and GW870086X were partial agonists on the 2×GRE reporter. TNF reduced reporter activity in proportion with agonist efficacy. Full and partial agonists showed various degrees of agonism on RGS2 and TSC22D3 expression, but were equally effective at inducing CDKN1C and DUSP1, and did not affect the repression of CDKN1C or TSC22D3 expression by TNF. Finally, formoterol-enhanced 2×GRE reporter activity was also proportional to agonist efficacy and functionally reversed repression by TNF. As similar effects were apparent on glucocorticoid-induced gene expression, the most effective strategy to overcome glucocorticoid resistance in this model was addition of formoterol to high efficacy NR3C1 agonists.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.031
GPT teacher head0.255
Teacher spread0.225 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations30
Published2015
Admission routes2
Has abstractyes

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