The Structural Basis of Gas-Responsive Transcription by the Human Nuclear Hormone Receptor REV-ERBβ
Bibliographic record
Abstract
Heme is a ligand for the human nuclear receptors (NR) REV-ERBalpha and REV-ERBbeta, which are transcriptional repressors that play important roles in circadian rhythm, lipid and glucose metabolism, and diseases such as diabetes, atherosclerosis, inflammation, and cancer. Here we show that transcription repression mediated by heme-bound REV-ERBs is reversed by the addition of nitric oxide (NO), and that the heme and NO effects are mediated by the C-terminal ligand-binding domain (LBD). A 1.9 A crystal structure of the REV-ERBbeta LBD, in complex with the oxidized Fe(III) form of heme, shows that heme binds in a prototypical NR ligand-binding pocket, where the heme iron is coordinately bound by histidine 568 and cysteine 384. Under reducing conditions, spectroscopic studies of the heme-REV-ERBbeta complex reveal that the Fe(II) form of the LBD transitions between penta-coordinated and hexa-coordinated structural states, neither of which possess the Cys384 bond observed in the oxidized state. In addition, the Fe(II) LBD is also able to bind either NO or CO, revealing a total of at least six structural states of the protein. The binding of known co-repressors is shown to be highly dependent upon these various liganded states. REV-ERBs are thus highly dynamic receptors that are responsive not only to heme, but also to redox and gas. Taken together, these findings suggest new mechanisms for the systemic coordination of molecular clocks and metabolism. They also raise the possibility for gas-based therapies for the many disorders associated with REV-ERB biological functions.
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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".