Glioma stem cells invasive phenotype at optimal stiffness is driven by MGAT5 dependent mechanosensing
Bibliographic record
Abstract
Abstract Glioblastomas stem-like cells (GSCs) by invading the brain parenchyma escape resection and radiotherapy. GSC invasion is associated with altered N-glycosylation pattern of integrins and other transmembrane proteins resulting in changed mechanosensing but details are elusive. Because the tumour microenvironment has an increased stiffness we studied the interaction between matrix stiffness, N-glycosylation and GSC migration. To mimic the fibrillar microenvironments, we designed 3D-ex-polyacrylonitrile nanofibers scaffolds (NFS) with adjustable stiffnesses by loading multiwall carbon nanotubes (MWCNT). We found that migration of GSCs was maximum at 166 kPa. Migration rate was correlated with cell shape, expression 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 FA and EMT protein expression as well as migration on 166kPa NFS; underpinning the role of multibranched N-glycans as a critical regulator of mechanotransduction by GSC. Significance Statement During pathological processes in which cell migration is involved, cells undergo important functional changes in protein glycosylation and are responsive to environmental mechanical modifications. We addressed the question of the glycosylation role in mechanotransduction regulation of glioma stem cells. We created a bio-inspired 3D nanofiber scaffold (NFS) loaded with multiwall carbon nanotubes to obtain NFS of adjustable stiffness in physiological and pathological ranges. We highlighted and described a mechanism of fine mechanotransduction leading to a nonlinear migration response regarding to 3D microenvironment stiffness values. We show the importance to develop mechano-pharmacology as new therapeutic target by demonstrating the relationship existing between environmental stiffness and multibranched N-glycans catalysed by the MGAT5 enzyme to optimize directed migration.
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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".