Synuclein-γ Targeting Peptide Inhibitor that Enhances Sensitivity of Breast Cancer Cells to Antimicrotubule Drugs
Bibliographic record
Abstract
Synuclein-gamma (SNCG) plays oncogenic roles in breast carcinogenesis. Although the expression of SNCG is abnormally high in advanced and metastatic breast carcinomas, SNCG is not expressed in normal or benign breast tissues. SNCG is an intrinsically disordered protein known to interact with BubR1, a mitotic checkpoint kinase. The SNCG-BubR1 interaction inhibits mitotic checkpoint control upon spindle damage caused by anticancer drugs, such as nocodazole and taxol. Antimicrotubule drugs that cause mitotic arrest and subsequent apoptosis of cancer cells are frequently used to treat breast cancer patients with advanced or metastatic diseases. However, patient response rates to this class of chemotherapeutic agents vary significantly. In this study, we have designed a novel peptide (ANK) and shown its interaction with SNCG using fluorometry, surface plasmon resonance, and isothermal titration calorimetry. Binding of the ANK peptide did not induce folding of SNCG, suggesting that SNCG can function biologically in its intrinsically disordered state. Microinjection of the ANK peptide in breast cancer cell line overexpressing SNCG (MCF7-SNCG) exhibited a similar cell killing response by nocodazole as in the SNCG-negative MCF7 cells. Overexpression of enhanced green fluorescent protein-tagged ANK reduces SNCG-mediated resistance to paclitaxel treatment by approximately 3.5-fold. Our coimmunoprecipitation and colocalization results confirmed the intracellular association of the ANK peptide with SNCG. This is likely due to the disruption of the interaction of SNCG with BubR1 interaction. Our findings shed light on the molecular mechanism of the ANK peptide in releasing SNCG-mediated drug resistance.
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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) | 0.000 | 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 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".