Abstract 2493: Identification of MGMT-binding proteins involved in the negative regulation of angiogenesis and invasion.
Bibliographic record
Abstract
Abstract Background: Glioblastoma multiforme (GBM) is the most frequent and most aggressive form of primary malignant brain tumors in adults. The dismal prognosis of GBM patients stems from the highly angiogenic and invasive behavior of GBM tumor cells. O6-methylguanine-DNA methyltransferase (MGMT), a DNA repair protein ubiquitously expressed in normal tissues has been extensively characterized for its role in resistance to alkylating agents used in GBM treatment. We reported for the first time an inverse relationship between expression of MGMT and the angiogenic and invasive profile of GBM cell lines. The mechanisms by which MGMT affects angiogenesis and invasion are unknown. We hypothesized that interactions of MGMT with binding proteins (BPs) may account for additional functions beyond its known role as a DNA repair protein. Methods: As a first screening to identify MGMT-BPs with a functional relevance for invasion and angiogenesis, we performed affinity purification of MGMT-BPs following overexpression of FLAG-tagged MGMT and mass spectrometry analysis using 293T-Flag/MGMT and control Flag-tagged empty vector (293T-Flag/EV). Lysates were subjected to affinity purification using an anti-Flag monoclonal antibody covalently attached to agarose resin. The affinity bound FLAG fusion proteins were eluted and separated on SDS-PAGE. Coomassie Blue staining enabled the identification of 6 bands including Flag-MGMT in 293T-Flag/MGMT but not the Flag/EV control. The bands were excised from the gel, subjected to trypsin digestion and identified by liquid chromatography-tandem mass spectrometry. The resultant MS/MS spectra were searched against a proteome database for peptide matching and protein identification. Proteins were identified with high confidence using Scaffold software. Results: Our analysis provided evidence for binding of MGMT to 120 BPs. Using gene ontology (GO) database to search for functional categories, we identified proteins involved in DNA repair, ubiquitin pathway, DNA replication and transcription, RNA metabolism and processing, cell cycle and division, response to stress and cell death. Importantly, we identified proteins involved in cell motility and/or angiogenesis, cytoskeletal-related proteins (15 proteins), small GTPases family and their regulators (10 proteins, such as Rho guanine nucleotide exchange factor 2) and two proteins involved in angiogenesis (Endoribonuclease Dicer and Ribonuclease inhibitor). We also used T98G a human GBM cell line with constitutive expression of MGMT to perform immunoprecipitation of endogenous MGMT (anti-MGMT antibody or the IgG1 isotype control). Mass spectrometry and proteomic analysis of MGMT-BPs in T98G is underway. Conclusion: Our data provide new structural aspects of MGMT and shed light into the multifaceted role of MGMT, which may lead to the identification of novel therapeutic targets in GBM. Citation Format: Siham Sabri, Yaoxian Xu, Nicolas Stifani, Bassam S. Abdulkarim. Identification of MGMT-binding proteins involved in the negative regulation of angiogenesis and invasion. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 2493. doi:10.1158/1538-7445.AM2013-2493
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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) | 0.003 | 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".