Novel Bcl‐2 Associated Transcription Factor (Btf) Interactions and Regulation of pre‐mRNA Splicing
Bibliographic record
Abstract
Previous studies have implicated Bcl‐2 associated transcription factor (Btf) in the regulation of apoptosis (programmed cell death) in vitro through interactions with the Bcl‐2 family proteins. Btf does not share structural similarities with Bcl‐2 members but rather contains tracts of alternating serine/arginine residues, a hallmark of splicing regulators. The exact mechanism whereby Btf exerts its effects is unknown. The goals of my research plan are as follows: To identify and characterize other Btf‐associated partners to clarify the mechanism of action of Btf To determine whether Btf plays a role in the regulation of pre‐mRNA splicing. We screened for Btf‐associated proteins using the yeast two‐hybrid system. Using this strategy I have identified novel interactions between Btf and two proteins that have been previously implicated in pre‐mRNA splicing known as p32 and 9G8. To determine the impact of Btf on alternative splicing, I used a combination of transfection and RT‐PCR techniques to look at the steady‐state mRNAs levels of adenoviral E1A, a well characterized model of alternative splicing in Btf wild‐type and deficient embryonic fibroblasts. Using this approach, Btf appears to act as a negative regulator of splicing. The proposed work has the potential to reveal novel molecular mechanisms whereby developmental cues and external stimuli guide alternative splicing.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".