The single-strand DNA/RNA-binding protein, Purβ, regulates serum response factor (SRF)-mediated cardiac muscle gene expressionThis paper is one of a selection of papers published in this Special Issue, entitled The Cellular and Molecular Basis of Cardiovascular Dysfunction, Dhalla 70th Birthday Tribute.
Bibliographic record
Abstract
Single-strand DNA-binding proteins, Puralpha and Purbeta, play a role in cell growth and differentiation by modulating both transcriptional and translational controls of gene expression. We have previously characterized binding of Puralpha and Purbeta proteins to a purine-rich negative regulatory (PNR) element of the rat cardiac alpha-myosin heavy chain (MHC) gene that controls cardiac muscle specificity. In this study we investigated the role of upstream sequences of the alpha-MHC promoter in Purbeta-mediated gene repression. In the transient transfection analysis overexpression of Purbeta revealed a negative regulatory effect on serum response factor (SRF)-dependent alpha-MHC and alpha-skeletal actin expression in muscle cell background. Contrary, in nonmuscle cells, Purbeta showed no repressive effect. The results obtained from gel-shift assays demonstrated a sequence specific competitive binding of Purbeta to the minus strand of the SRF-binding, CArG box sequences of different muscle genes, but not to the SRF-binding, SRE sequences of the c-fos gene. These element-specific associations of Purbeta with muscle CArG boxes may, in part, explain why muscle gene expression is downregulated in disease states in which Purbeta levels are elevated. This data also provide a mechanistic distinction between muscle CArG boxes and nonmuscle serum response element (SRE) sequences in terms of their affinity to bind to SRF and their ability to regulate cell-specific gene expression.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.002 | 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".