Phosphorylation-Dependent Sumoylation Regulates Estrogen-Related Receptor-α and -γ Transcriptional Activity through a Synergy Control Motif
Bibliographic record
Abstract
Interplay between different posttranslational modifications of transcription factors is an important mechanism to achieve an integrated regulation of gene expression. For the estrogen-related receptors (ERRs) alpha and gamma, regulation by posttranslational modifications is still poorly documented. Here we show that transcriptional repression associated with the ERR amino-terminal domains is mediated through sumoylation at a conserved phospho-sumoyl switch, psiKxEPxSP, that exists within a larger synergy control motif. Arginine substitution of the sumoylatable lysine residue or alanine substitution of a nearby phosphorylatable serine residue (serine 19 in ERRalpha) increased the transcriptional activity of both ERRalpha and -gamma. In addition, phospho-mimetic substitution of the serine residue with aspartate restored the sumoylation and transcriptional repression activity. The increased transcriptional activity of the sumoylation-deficient mutants was more pronounced in the presence of multiple adjacent ERR response elements. We also identified protein inhibitor of activated signal transducer and activator of transcription y as an interacting partner and a small ubiquitin-related modifier E3 ligase for ERRalpha. Importantly, analysis with a phospho-specific antibody revealed that sumoylation of ERRalpha in mouse liver requires phosphorylation of serine 19. Taken together, these results show that the interplay of phosphorylation and sumoylation in the amino-terminal domain provides an additional mechanism to regulate the transcriptional activity of ERRalpha and -gamma.
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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".