Inhibition of the ISG15 prevents inflammation-dependent ovarian aging
Bibliographic record
Abstract
Inflammation is a hallmark of aging and negatively affects ovarian function and female fertility. ISGylation is a post-translational modification regulating many life activities, including inflammation, immunomodulation, and embryo implantation. However, the exact role of ISGylation in ovarian aging remains unclear. In this study, age-dependent increase in Isg15 expression was observed in murine ovaries during reproductive aging. Wild-type female mice displayed progressively reduced ovarian reserve, disrupted endocrine function, and ultimately impaired fertility with age, but Isg15 knockdown partly mitigated this phenomenon. Transcriptome sequencing of ovaries from Isg15-/- and WT mice at 12 months of age revealed that Isg15 deletion ameliorated the genes and pathways associated with inflammation process and ovarian function. Meanwhile, Isg15 knockout in mice inhibited ovarian oxidative stress, and then, protected ovarian mitochondrial structure and function. Mechanistically, ISG15 resulted in degradation of proteasome 26S subunit non-ATPase 14 (PSMD14), a negative regulator of inflammasome activation. Furthermore, the degradation of ISGylated PSMD14 suppressed the K63-linked ubiquitination of Pro-IL-1β, and eventually facilitated inflammatory cytokine IL-1β maturation and inflammation activation. These results suggest that Isg15 accelerates the senescence of ovarian granulosa cells by promoting inflammation and thereby reduces reproductive lifespan during aging. The present study demonstrates that a novel regulatory axis of ISG15-PSMD14-IL-1β activates inflammation, and therefore Isg15 deficiency mitigates the age-related decline in female fertility via reducing ovarian inflammation. Overall, our findings provide a new mechanistic insight into the decline of female fertility during ovarian aging, and offer a potential therapeutic strategy for ameliorating age-related female infertility.
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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) | 0.002 | 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".