Abstract 2010: Charactering the interactomes of the Myc family of oncogenes
Notice bibliographique
Résumé
Abstract The Myc family of transcription factors, c-Myc, N-Myc and L-Myc, are known to be deregulated in a large variety of cancers. Mechanisms responsible for the deregulation of the activity of Myc family members in cancer are not well understood. A number of protein-protein interactions and post-translational modifications have been suggested to promote the oncogenic activity of Myc. Traditional biochemical approaches have not been successful at mapping the interactors of Myc family members due to their tight association with chromatin and the labile nature of Myc proteins. Mapping the protein-protein interactions that support the aberrant activity of Myc family of oncoproteins in cancer cells is of high interest, particularly in a more natural context in xenograft models in vivo. A new mass spectrometry-based technique, BioID-MS, which relies on proximity-based biotin labeling, has recently emerged as a key advance for the characterization of hard-to-detect protein-protein interactions in living cells. Herein, we are reporting a new application of the BioID-MS technique for the characterization of c-Myc interactors in human cell line in vivo, in mouse tumor xenografts. Using the in vivo BioID assay, we were able to identify more than 30 known and validated c-Myc interactors, some of which include the components of the STAGA complex and SWI/SNF chromatin remodelling complex. We were further able to identify more than 100 novel high-confidence c-Myc interactors, which include components of the DNA repair and replication machinery, general transcription and elongation factors, and the co-regulator of transcription-like DNA helicase protein chromodomain 8 (CHD8). Using ENCODE ChIP-seq datasets we were able to map some of the high-confidence interactors to coincident binding sites with c-Myc throughout the genome. This provided further credibility that the newly identified putative interactors could co-occupy sites on chromatin with c-Myc and could be functionally important for activity. Furthermore, we validated the Myc-CHD8 interaction using a number of approaches, including yeast two hybrid and proximity-based ligation assays. These findings suggest that the BioID-MS technique can be used to extend the mapping of the Myc interactome and contribute to a greater understanding of Myc regulation by protein-protein interactions. Furthermore, we are currently in the process of employing this technique to map the interactomes of two other Myc family members, N-Myc and L-Myc. We are interested in identifying common interactors of the Myc family members that contribute to their oncogenic activity, validate them, and explore whether these interactors could be potential therapeutic targets in cancers with deregulated Myc activity. Citation Format: Diana Resetca, Dharmesh Dingar, Manpreet Kalkat, Brian Raught, Linda Z. Penn. Charactering the interactomes of the Myc family of oncogenes. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 2010.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».