A Tubulin Binding Peptide Targets Glioma Cells Disrupting Their Microtubules, Blocking Migration, and Inducing Apoptosis
Bibliographic record
Abstract
Despite aggressive treatment regimes, glioma remains a largely fatal disease. Current treatment limitations are attributed to the precarious locations within the brain where such tumors grow, their highly infiltrative nature precluding complete resection and lack of specificity among agents capable of attenuating their growth. Here, we show that in vitro, glioma cells of diverse origins internalize a peptide encompassing a tubulin-binding site (TBS) on the neurofilament light protein. The internalized peptide disrupts the microtubule network, inhibits migration and proliferation, and leads to apoptosis. Using an intracerebral transplant model, we show that most, if not all, of these responses to peptide exposure also occur in vivo. Notably, a single intratumor injection significantly attenuates tumor growth, while neither peptide uptake nor downstream consequences are observed elsewhere in the host nervous system. Such preferential uptake suggests that the peptide may have potential as a primary or supplementary glioblastoma treatment modality by exploiting its autonomous microtubule-disrupting activity or engaging its capacity to selectively target glioma cells with other cell-disrupting cargos.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".