Abstract B04: In vivo BioID identifies novel Myc interacting partners
Bibliographic record
Abstract
Abstract Myc oncoprotein is a major driver of cancer initiation and progression, and thus targeting its activity would mark a key therapeutic advance. In a genetic preclinical mouse model, systemic Myc inhibition using the dominant-negative Myc mutant, termed Omomyc, showed that Ras-driven lung cancer could be eradicated without any harmful long-term effects to the animal. However, developing an anti-cancer agent that directly binds and inhibits Myc has not been possible, to date. Therefore, new strategies are required to inhibit Myc in cancer. Understanding the Myc interactome may unravel novel approaches to target Myc in cancer. The BioID proximity-based biotin labeling technique was recently developed for the characterization of protein-protein interaction networks. In BioID, the protein of interest is expressed as a fusion partner biotin ligase (BirA*), which activates biotin. The active biotin reacts with lysine residues on nearby polypeptides. Following a stringent cell lysis and streptavidin-sepharose pulldown, biotinylated proteins can be identified using MS. To date, this method has been applied to a number of different polypeptides expressed in cultured cells. Here we report the adaptation of BioID to the identification of protein-protein interactions surrounding the Myc oncoprotein in human cells grown both under standard culture conditions and in mice as tumor xenografts. Notably, in vivo BioID yielded >100 high confidence Myc interacting proteins, including >30 known binding partners such as MAX (Myc-associated factor X), TRRAP (transformation/transcription domain-associated protein), the enhancer of polycomb homologs 1 and 2 (EPC1, EPC2), lysine acetyltransferase 5 (KAT5). Putative novel Myc interactors include components of the STAGA/KAT5 and SWI/SNF chromatin remodelling complexes (see Penn lab abstract Tu et al), DNA repair and replication factors, general transcription and elongation factors, and transcriptional co-regulators such as the DNA helicase chromodomain 8 (CHD8). Providing additional confidence in these findings, ENCODE ChIP-seq datasets highlight significant coincident binding throughout the genome for the Myc interactors identified here, and we validate the previously unreported CHD8 (an ATP-dependent helicase)-Myc interaction using both a yeast two hybrid analysis and the proximity-based ligation assay (PLA). Additionally, we also validate Myc-BRD4 and Myc-TRIM24 interaction by PLA. In sum, here we identify bona fide interacting partners of Myc in vivo by use of BioID. Our study shows for the first time Myc interactome in vivo, understanding these interactors will shed more light on Myc oncogenesis, which can be used to therapeutically target Myc in cancer. Citation Format: Dharmendra Dingar, Manpreet Kalkat, Pak-Kei Chan, Swneke D. Bailey, Tharan Srikumar, William B. Tu, Etienne Coyaud, Romina Ponzielli, Max Kolyar, Igor Jurisica, Annie Huang, Mathieu Lupien, Brian Raught, Linda Z. Penn. In vivo BioID identifies novel Myc interacting partners. [abstract]. In: Proceedings of the AACR Special Conference on Myc: From Biology to Therapy; Jan 7-10, 2015; La Jolla, CA. Philadelphia (PA): AACR; Mol Cancer Res 2015;13(10 Suppl):Abstract nr B04.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 teacher head, 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".