Abstract 2886: The mechanistic effect of DHA on FABP7 associated membrane lipid order & nanodomain distribution in glioblastoma migration
Bibliographic record
Abstract
Abstract Glioblastoma (GBM) is the most common primary brain malignancy. Brain fatty acid binding protein (B-FABP, FABP7) promotes glioblastoma (GBM) migration and correlates to dismal prognosis in GBM patients. Long chain polyunsaturated fatty acids (PUFAs) such as docosahexaenoic acid (DHA) and arachidonic acid (AA) are abundant in brain cell membranes. DHA and AA are preferred ligands of FABP7, with DHA having the stronger affinity for FABP7. Previous studies have shown that DHA inhibits GBM migration in an FABP7-dependent manner. In the current study, we have demonstrated that DHA dramatically altered membrane FABP7 nanoscale distribution pattern, which resulting from plasma membrane lipid ordered domain disruption in GBM cells and neurosphere cultures. We used quantitative membrane order assay to show that FABP7-expressing cells has increased membrane lipid order which is tightly correlated with higher GBM migration property. Using stimulated emission depletion (STED) microscopy, we observed lipid ordered domain was associated with FABP7 nanoscale domain formation. Interestingly, super-resolution microscopy quantitative image analysis revealed that FABP7 membrane nanodomains distribution was dramatically altered upon DHA treatment, which is associated with DHA inhibitory effect on membrane lipid order. Furthermore, STED microscopy has also revealed that reduced FABP7 nanodomain formation in slow migrating GBM cells are similar as FABP7-expressing GBM cells upon DHA treatment. Therefore, we hypothesize that these alterations are likely to be the mechanism of FABP7-mediated DHA inhibitory role in GBM migration. Citation Format: Xia Xu, Yixiong Wang, Wonshik Choi, Roseline Godbout. The mechanistic effect of DHA on FABP7 associated membrane lipid order & nanodomain distribution in glioblastoma migration [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 2886.
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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) | 0.002 | 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".