Genomic structure, transcriptional control, and tissue distribution of<i>HERG1</i>and<i>KCNQ1</i>genes
Bibliographic record
Abstract
The long QT syndrome genes human ether-a-go-go-related gene (HERG1) and voltage-gated K+ channel, KQT-like subfamily, member 1, gene (KCNQ1), encoding K+ channels critical to the repolarization rate and repolarization reserve in cardiac cells, and thereby the likelihood of arrhythmias, are both composed of two isoforms: HERG1a and HERG1b and KCNQ1a and KCNQ1b, respectively. Expression of these genes is dynamic, depending on the differentiation status and disease states. We identified their core promoter regions and transcription start sites. Our data suggest that HERG1a and HERG1b, and KCNQ1a and KCNQ1b, represent independent transcripts instead of being alternatively spliced variants of the same gene, for they each have their own transcription start sites and their own promoter regions. We obtained data pointing to the potential role of stimulating protein 1 (Sp1) in the transactivation of these genes. We compared expression profiling of these genes across a variety of human tissues. Consistent with the general lack of cis elements for cardiac-specific transcription factors and the presence of multiple sites for ubiquitous Sp1 sites in the core promoter regions of HERG1a/HERG1b and KCNQ1a/KCNQ1b genes, the transcripts demonstrated widespread distribution across a variety of human tissues. We further revealed that the mRNA levels of all HERG1 and KCNQ1 isoforms were asymmetrically distributed within the heart, being more abundant in the right atria and ventricles relative to the left atria and ventricles. These findings open up an opportunity for studying interventricular gradients of slow and rapid delayed rectifier K+ current and of cardiac repolarization as well. Our study might help us understand the molecular mechanisms for arrhythmias since heterogeneity of ion channel activities is an important substrate for arrhythmogenesis.
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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 | 0.001 | 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 | 0.000 |
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".