Characterizing the interaction between insulin-like growth factor 2 mRNA-binding protein 1 (IMP1) and KRAS expression
Bibliographic record
Abstract
Insulin-like growth factor 2 mRNA-binding protein-1 (IMP1) has high affinity for KRAS mRNA, and it can regulate KRAS expression in cells. We first characterized the molecular interaction between IMP1 and KRAS mRNA. Using IMP1 variants with a point mutation in the GXXG motif at each KH domain, we showed that all KH domains play a critical role in the binding of KRAS RNA. We mapped the IMP1-binding sites on KRAS mRNA and show that IMP1 has the highest affinity for nts 1–185. Although it has lower affinity, IMP1 does bind to other coding regions and the 3′-UTR of KRAS mRNA. Eight antisense oligonucleotides (AONs) were designed against KRAS RNA in the nts 1–185 region, but only two, SM6 and SM7, show potent inhibition of the IMP1–KRAS RNA interaction in vitro. To test the activity of these two AONs in SW480 human colon cancer cells, we used 2′-O-methyl-modified versions of SM6 and SM7 in an attempt to down-regulate KRAS expression. To our surprise, both SM6 and SM7 had no effect on KRAS mRNA and protein expression, but significantly inhibited IMP1 protein expression without altering IMP1 mRNA level. On the other hand, knockdown of IMP1 using siRNA lowered the expression of KRAS. Using Renilla luciferase as a reporter, we found that IMP1 translation is significantly reduced in SM7-treated cells with no change in let-7a levels. The present study shows that the regulation of KRAS expression by IMP1 is complex and may involve both the IMP1 protein and its mRNA transcript.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".