Transcription regulation of human NCR1/NKp46, an activating NK cell receptor (113.4)
Bibliographic record
Abstract
Abstract Natural Killer (NK) lymphocytes are innate immune cells, mainly known to eliminate cancerous and virus infected cells. The NCR1/NKp46 receptor is expressed specifically in the NK lineage. Activation of NK effector functions results when the receptor binds viral haemagglutinin or unidentified tumor ligands. Knockout studies have shown that the molecule is indispensible in the control of influenza infection, lymphoma growth, and the development of type I diabetes. Yet the molecular basis behind the specificity of NCR1 expression is unknown. We seek to uncover the genetic and epigenetic mechanisms that regulate human NCR1 transcription. Comparative genomics and transient luciferase assays suggest that crucial cis-regulatory elements are found within 300bp upstream of the gene. Within this proximal region, luciferase experiments also indicate the presence of a basal, non-specific promoter as well as a tissue-specific regulatory switch. Through bioinformatics and transcription factor RNA expression screens, RUNX3 was identified as a probable regulator of the switch. Chromatin immunoprecipitation (ChIP) studies were conducted to confirm RUNX3 binding in NK cells. A proteomics approach is currently underway to identify additional factors. Finally, preliminary ChIP data hints at transcriptional control by histone 3 lysine 4 methylation in blood lineages.
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".