The human Ã-globin LCR HS2 and HS3 : a tale of two position effects and mechanisms of transcriptional enhancement
Bibliographic record
Abstract
The human beta-globin LCR is a tissue-specific enhancer composed of 4 major erythroid-specific DNaseI hypersensitive sites (HS 1 to 4). The beta-globin LCR has previously been reported to confer high-level, position independent expression onto globin transgenes. Each HS has been shown to have some enhancer activity individually; however, there is evidence that they may work by different mechanisms. In this study, we have explored this possibility by assessing what type of position effects arise in individual clones having integrated either muLCR, HS2 or HS3 linked to the human beta-globin gene at different integration sites. We used Fluorescence Activated Cell Sorting (FACS) in conjunction with immunochemistry to measure human beta-globin expression in single murine erythroid leukemia cells (MEL). MEL cells (c88 strain) were induced to produce human beta-globin by treatment using 2% DMSO or by 10 nM TSA treatment so as to gain further insight on the molecular mechanism of these enhancers. Our results suggest that (1) HS3 is generally a stronger enhancer than HS2. (2) HS2 is exclusively affected by a graded position effect whereas HS3 (and the [LCR to a lesser extent) is prone to position effect variegation (PEV). (3) PEV was alleviated in HS3 and muLCR clones when we used 10nM TSA as the induction method but did not change HS2 position effects. (4) The expression per expressing cell seemed copy number dependent for HS3 whereas it was not for HS2 This data taken together with evidence from the literature suggests that HS2 and HS3 function by different mechanisms whereby HS2 would play a role in acetylating histones/protection from DNA methylation and open chromatin whereas HS3 would increase the total amount of transcripts in each expressing cell.
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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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".