Mechanistic Insights into the Notch signalling pathway through Molecular Condensation
Bibliographic record
Abstract
The ability of a cell to undergo cellular decisions is ingrained in the integration of cues from its local environments. One such cue is relaying information based on local cell density, neighbouring cell identity, and cell positional information. A key signalling pathway in interpreting this information is the Notch signalling pathway. It is currently poorly understood how Notch is able to play a titratable role in signal transduction as our current model does not effectively translate how fluctuations in Notch signalling lead to dynamic changes in Notch target gene expression. One potential mechanism that has recently been shown to play a role in titratable gene expression of several signalling pathways is the ability for proteins to undergo molecular condensation within living cells. To date, Notch signalling has been difficult to study because of the lack of live cell systems that allow us to study both Notch protein localization and Notch target gene expression in real-time. Using PlayBack, a system I developed that allows for cost-effective plasmid cloning, I was able to develop both a light controlled Notch construct, OptoNotch, and a Live Notch target gene promoter reporter, called Hes1-Live-RNA, to allow for both the visualization and quantification of Notch activity within living cells. Using these tools, I discovered that the Notch 1 intracellular domain forms molecular condensates which in turn positively facilitates both Notch1 target gene expression as well as facilitating Notch’s ability to regulate enhancer looping. This is the first instance of N1ICD undergoing condensation within live cells and offers a mechanism by which Notch can have a titratable effect on target gene expression.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".