Quantitative Proteomics Identifies DNA Repair as a Novel Biological Function for Hepatocyte Nuclear Factor 4α in Colorectal Cancer Cells
Bibliographic record
Abstract
Hepatocyte nuclear factor 4α (HNF4α) is a transcription factor that acts as a master regulator of genes for several endoderm-derived tissues, including the intestine, in which it plays a central role during development and tumorigenesis. To better define the mechanisms by which HNF4α can influence these processes, we identified proteins interacting with HNF4α using stable isotope labelling with amino acids in cell culture (SILAC)-based quantitative proteomics with either immunoprecipitation of green fluorescent protein (GFP) or with proximity-dependent purification by the biotin ligase BirA (BioID), both fused to HNF4α. Surprisingly, these analyses identified a significant enrichment of proteins characterized with a role in DNA repair, a so far unidentified biological feature of this transcription factor. Several of these proteins including PARP1, RAD50, and DNA-PKcs were confirmed to interact with HNF4α in colorectal cancer cell lines. Following DNA damage, HNF4α was able to increase cell viability in colorectal cancer cells. Overall, these observations identify a potential role for this transcription factor during the DNA damage response.
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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 | 0.000 |
| 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".