Regulation of Kruppel-like factor 4, 9, and 13 genes and the steroidogenic genes LDLR, StAR, and CYP11A in ovarian granulosa cells
Bibliographic record
Abstract
Kruppel-like factors (KLFs) are important Sp1-like eukaryotic transcriptional proteins. The LDLR, StAR, and CYP11A genes exhibit GC-rich Sp1-like sites, which have the potential to bind KLFs in multiprotein complexes. We now report that KLF4, KLF9, and KLF13 transcripts are expressed in and regulate ovarian cells. KLF4 and 13, but not KLF9, mRNA expression was induced and then repressed over time (P < 0.001). Combined LH and IGF-I stimulation increased KLF4 mRNA at 2 h (P < 0.01), whereas LH decreased KLF13 mRNA at 6 h (P < 0.05), and IGF-I reduced KLF13 at 24 h (P < 0.01) compared with untreated control. KLF9 was not regulated by either hormone. Transient transfection of KLF4, KLF9, and KLF13 suppressed LDLR/luc, StAR/luc, and CYP11A/luc by 80-90% (P < 0.001). Histone-deacetylase (HDAC) inhibitors stimulated LDLR/luc five- to sixfold and StAR/luc and CYP11A/luc activity twofold (P < 0.001) and partially reversed suppression by all three KLFs (P < 0.001). Deletion of the zinc finger domain of KLF13 abrogated repression of LDLR/luc. Lentiviral overexpression of the KLF13 gene suppressed LDLR mRNA (P < 0.001) and CYP11A mRNA (P = 0.003) but increased StAR mRNA (P = 0.007). Collectively, these data suggest that KLFs may recruit inhibitory complexes containing HDAC corepressors, thereby repressing LDLR and CYP11A transcription. Conversely, KLF13 may recruit unknown coactivators or stabilize StAR mRNA, thereby explaining enhancement of in situ StAR gene expression. These data introduce new potent gonadal transregulators of genes encoding proteins that mediate sterol uptake and steroid biosynthesis.
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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".