Oscillatory Co-expression of HES1 and HES5 Enables a Hybrid State in a Bistable Transcription Factor Regulatory Motif
Bibliographic record
Abstract
Abstract Many cell fate decisions in the developing neural tube are directed by cross-repressive transcription factor (TF) motifs that generate bistability, enforcing expression of one dominant TF. However, evidence of hybrid states, where cells co-express opposing fate determinants, challenges this model. We hypothesised that oscillatory expression enables co-existence of cross-repressive TFs within single cells, allowing hybrid states in bistable motifs. To test this, we focused on HES1 and HES5, oscillatory, cross-repressive TFs that regulate neural progenitor maintenance and are expressed in adjacent dorsoventral domains in the developing spinal cord. Using live-cell imaging of fluorescent reporters and computational modelling, we show that HES1 and HES5 co-express and oscillate in-phase within single cells. Differences in protein stability result in distinct free-running periodicity, but co-expression results in entrainment and phase-locking. Modulating cross-repression strength and/or abundance shifts the system towards bistability and dominance of a single TF oscillator. Consistent with this, we observe progressive separation of the HES expression domains in vivo, through a decrease in oscillatory co-expression. Our findings provide a mechanism for hybrid states to emerge in a developmental bistable motif.
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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".