In-vitro Characterization and Phenotypic Assessment of ZMPSTE24 as a Novel Oncological Target
Bibliographic record
Abstract
Colorectal cancer is a predominant cancer type world-wide.Approximately 50% of colorectal cancer patients will experience metastasis, most often to the liver, lungs, and peritoneum.With the rise of drug-resistance amongst cancers, molecules with novel targets may be especially effective as chemotherapeutics.The human zinc metallopeptidase STE24 (ZMPSTE24) is responsible for catalyzing the maturation of lamin A, a critical lamina component.Inhibition of ZMPSTE24 catalytic function has been proposed as a potential novel mechanism for cancer chemotherapy.To establish the physiological role of ZMPSTE24 in colorectal cancer and determine any cancer-specific vulnerabilities two shZMPSTE24 cell lines were established.The first was an HCT-116 colorectal cell line and the second, an IMR90 normal cell line.Phenotypic assays were then conducted to assess the response of each cell line to ZMPSTE24 loss.A ZMPSTE24 knockdown led to an arrest in proliferation in the IMR90 normal cells, but did not impact the HCT-116 cells.The migration ability of cells was also evaluated, no effect was observed in either cell lines.However, limitations in this study may have affected the significance of the results.In addition to exploring the physiological impacts of ZMPSTE24, an in-vitro FRET-based assay was adopted to assess the ability of phosphinyl peptides to block ZMPSTE24 catalytic activity and determine kinetic parameters of the enzyme-substrate pair.Results of the assay contributed to SAR development which can guide our medicinal chemistry studies.The raw time-course fluorescence data from the kinetic studies indicated that the enzyme-substrate pair used did not obey typical Michaelis-Menten kinetics, and that there may be strong product inhibition.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".