Abstract 4603: To analyse PIMT function in glioblastoma cell invasion
Bibliographic record
Abstract
The protein L-isoaspartyl (D-aspartyl) methyltransferase (PIMT) is an enzyme able to repair abnormal L-aspartyl residues in damaged proteins. Among examined tissues, PIMT shows the highest level in brain. U87-MG cells are a commonly used grade IV cellular model to study the most frequent brain tumor, the glioblastoma. Previously, we reported that PIMT amount increased when U87-MG cells were detached from the extracellular matrix. Recently, our laboratory also showed that PIMT possessed pro-angiogenic properties. Together, these PIMT features led us to postulate that PIMT could play a critical role in glioblastoma growth. To investigate PIMT involvement during glioblastoma growth, we analyzed its role in U87-MG cell viability, adhesion, migration, invasion, clonogenic properties and the reorganization of the actin cytoskeleton. For instance, PIMT inhibition by siRNA reduced cell migration and invasion by different assays: in wound healing assay, in Boyden chambers coated with gelatin and in Matrigel invasion assay. Conversely, in stably transfected U87-MG cells overexpressing wild-type PIMT, cell migration, invasive capacity and colony formation significantly increased. However, in stably transfected cells with the gene encoding for the inactive PIMT D83V, in spite of its overexpression, the migration and invasion remained similar to that observed in control cells transfected with the empty plasmid. In all these conditions, cell viability was unaffected. Finally, the reorganization of the actin cytoskeleton is regulated by PIMT protein level. Overall, these data enlighten the importance of PIMT level and mainly of its catalytic activity in migration and invasion of malignant glioma U87-MG cells and its possible contribution in cancer cell invasion during glioblastoma growth.Citation Format: Fatima Belkourchia, Richard Desrosiers. To analyse PIMT function in glioblastoma cell invasion [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 4603.
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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) | 0.001 | 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) | 0.005 | 0.002 |
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".