Regulatory modules function in a non-autonomous manner to control transcription of the mbp gene
Bibliographic record
Abstract
Multiple regulatory modules contribute to the complex expression programs realized by many loci. Although long thought of as isolated components, recent studies demonstrate that such regulatory sequences can physically associate with promoters and with each other and may localize to specific sub-nuclear transcription factories. These associations provide a substrate for putative interactions and have led to the suggested existence of a transcriptional interactome. Here, using a controlled strategy of transgenesis, we analyzed the functional consequences of regulatory sequence interaction within the myelin basic protein (mbp) locus. Interactions were revealed through comparisons of the qualitative and quantitative expression programs conferred by an allelic series of 11 different enhancer/inter-enhancer combinations ligated to a common promoter/reporter gene. In a developmentally contextual manner, the regulatory output of all modules changed markedly in the presence of other sequences. Predicted by transgene expression programs, deletion of one such module from the endogenous locus reduced oligodendrocyte expression levels but unexpectedly, also attenuated expression of the overlapping golli transcriptional unit. These observations support a regulatory architecture that extends beyond a combinatorial model to include frequent interactions capable of significantly modulating the functions conferred through regulatory modules in isolation.
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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 | 0.001 |
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".