Abstract #1291: Efficient knockdown of MMR proteins in human CRCcells using chained microRNA constructs
Bibliographic record
Abstract
In colorectal cancer, microsatellite instability (MSI) is a frequent occurrence which causes an increase in mutation rate leading to tumor progression. Previous studies in our laboratory established that MSH2 expression is mediated by ischemia, and that this leads to de novo point mutations in the transforming oncogene KRAS. Since the cells previously used had been genetically manipulated to loose an activating allele of KRAS, we wished to create an in vitro model that is able to sufficiently mimic the mutator phenotype seen in MSI colorectal cancer earlier in progression (i.e. prior to KRAS activation). We achieved this by knocking-down the expression of two key mismatch repair proteins, MSH2 and MLH1. This was done by stably transfecting Caco-2 colorectal cells (which are mismatch repair proficient and microsatellite stable) with a plasmid (BLOCK-iT; Invitrogen) containing chained microRNAs (miRNAs) designed to target both MSH2 and MLH1 mRNAs. Cells were then seeded at low density in selection media containing blasticidin and different clones were isolated. Protein knock-down in each clone was then examined through western blotting. Greater than 99% protein knockdown was achieved through miRNA targeting of MSH2 alone, and we found chained constructs were more effective than single miRNA sequences. Co-repression of MSH6 but not MLH1 was also observed in MSH2 knockdown cells. Further studies will be done to determine the MSI status of these cells as well as other molecular changes they have undergone. This study will further elucidate the effects of decreased MMR protein expression on cell behavior and aid in our understanding of its involvement in tumor progression. Citation Information: In: Proc Am Assoc Cancer Res; 2009 Apr 18-22; Denver, CO. Philadelphia (PA): AACR; 2009. Abstract nr 1291.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.005 |
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".