Abstract 2740: Identification of new binding partners of the DNA repair protein MGMT using a proteomic discovery-based approach in glioblastoma
Bibliographic record
Abstract
Abstract Background: Glioblastoma multiforme (GBM) is characterized by aberrant angiogenesis and widespread invasion through the brain parenchyma. The DNA repair protein O6-methylguanine-DNA methyltransferase (MGMT) has been extensively characterized for its role in resistance to alkylating agents used in treatment of GBM. Our team discovered an inverse relationship between expression of MGMT and GBM angiogenesis and invasion. To gain new insights into how MGMT affects angiogenesis and invasion, we used a proteomic-based approach integrated with bioinformatics analysis to identify potential MGMT-binding partners (BPs) for the first time in GBM. Methods: We used T98G, a human GBM cell line with constitutive expression of MGMT and performed direct immunoprecipitation (IP) of endogenous MGMT using an anti-MGMT antibody or the isotype control. Following elution of the antibody, proteins were resolved by SDS-PAGE, stained, excised from the gel then subjected to trypsin digestion and identified by liquid chromatography-tandem mass spectrometry using the LTQ-Orbitrap Elite mass analyzer. The resulting tryptic peptides were purified and identified by LC-tandem mass spectrometry (MS/MS). The resultant MS/MS spectra were searched against a proteome database for peptide matching and protein identification. Proteins identified with high confidence (Scaffold software) were used to construct the biological network of MGMT-BPs in GBM using the Build Network tool provided by MetaCore. Results: We identified a total of 186 MGMT-BPs, which were not identified in the elution from the isotype control. Using gene ontology (GO) database, we determined the function and biological role of identified proteins (mitochondrial metabolism, DNA repair and replication, ubiquitin pathway, transcription regulators, RNA post-transcriptional processing, transcriptional splicing, protein biosynthesis and trafficking, cellular metabolism, cell cycle and division, response to stress and cell death, cell migration and invasion). The list of new BPs was uploaded to MetaCore and the most relevant biological process was enriched based on public GO databases. Among the top proteins identified with a very high confidence, we found newly identified MGMT-BPs, which may underlie the role of MGMT in angiogenesis and invasion, namely the splicing factor heterogeneous nuclear ribonucleoprotein A1 (hnRNPA1), known for its role in the packaging of pre-mRNA into hnRNP particles and alternative splicing of angiogenic factors (VEGF-A and the fibroblast growth factor 2) and annexin A2. Knockdown of annexin A2 decreased invasion, angiogenesis and proliferation in vivo. Conclusion: Our study provides new mechanistic insights into how MGMT affects angiogenesis and invasion in GBM, which may ultimately lead to the identification of new therapeutic targets to improve the poor outcome of this devastating disease. Note: This abstract was not presented at the meeting. Citation Format: Siham Sabri, Yaoxian Xu, Bassam Abdulkarim. Identification of new binding partners of the DNA repair protein MGMT using a proteomic discovery-based approach in glioblastoma. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 2740. doi:10.1158/1538-7445.AM2014-2740
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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 | 0.001 |
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".