DUSP5 functions as a feedback regulator of TNFα-induced ERK1/2 dephosphorylation and inflammatory gene expression in adipocytes
Bibliographic record
Abstract
Adipose tissue inflammation is a central pathological element that regulates obesity-mediated insulin resistance and type II diabetes. Evidence demonstrates that extracellular signal-regulated kinase (ERK 1/2) activation (i.e. phosphorylation) links tumor necrosis factor α (TNFα) to pro-inflammatory gene expression in the nucleus. Dual specificity phosphatases (DUSPs) inactivate ERK 1/2 through dephosphorylation and can thus inhibit inflammatory gene expression. We report that DUSP5, an ERK1/2 phosphatase, was induced in epididymal white adipose tissue (WAT) in response to diet-induced obesity. Moreover, DUSP5 mRNA expression increased during obesity development concomitant to increases in TNFα expression. Consistent with in vivo findings, DUSP5 mRNA expression increased in adipocytes in response to TNFα, parallel with ERK1/2 dephosphorylation. Genetic loss of DUSP5 exacerbated TNFα-mediated ERK 1/2 signaling in 3T3-L1 adipocytes and in adipose tissue of mice. Furthermore, inhibition of ERK 1/2 and c-Jun N terminal kinase (JNK) signaling attenuated TNFα-induced DUSP5 expression. These data suggest that DUSP5 functions in the feedback inhibition of ERK1/2 signaling in response to TNFα, which resulted in increased inflammatory gene expression. Thus, DUSP5 potentially acts as an endogenous regulator of adipose tissue inflammation; although its role in obesity-mediated inflammation and insulin signaling remains unclear.
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 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".