Glioma stem cells invasive phenotype at optimal stiffness is driven by MGAT5 dependent mechanosensing
Bibliographic record
Abstract
BACKGROUND: Glioblastomas stem-like cells (GSCs) by invading the brain parenchyma, remains after resection and radiotherapy and the tumoral microenvironment become stiffer. GSC invasion is reported as stiffness sensitive and associated with altered N-glycosylation pattern. Glycocalyx thickness modulates integrins mechanosensing, but details remain elusive and glycosylation enzymes involved are unknown. Here, we studied the association between matrix stiffness modulation, GSC migration and MGAT5 induced N-glycosylation in fibrillar 3D context. METHOD: To mimic the extracellular matrix fibrillar microenvironments, we designed 3D-ex-polyacrylonitrile nanofibers scaffolds (NFS) with adjustable stiffnesses by loading multiwall carbon nanotubes (MWCNT). GSCs neurosphere were plated on NFSs, allowing GSCs migration and MGAT5 was deleted using CRISPR-Cas9. RESULTS: We found that migration of GSCs was maximum at 166 kPa. Migration rate was correlated with cell shape, expression and maturation of focal adhesion (FA), Epithelial to Mesenchymal Transition (EMT) proteins and (β1,6) branched N-glycan binding, galectin-3. Mutation of MGAT5 in GSC inhibited N-glycans (β1-6) branching, suppressed the stiffness dependence of migration on 166 kPa NFS as well as the associated FA and EMT protein expression. CONCLUSION: MGAT5 catalysing multibranched N-glycans is a critical regulators of stiffness induced invasion and GSCs mechanotransduction, underpinning MGAT5 as a serious target to treat cancer.
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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) | 0.001 | 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".