The Role of Activating Transcription Factor 3 (ATF3) in Chemotherapeutic Induced Cytotoxicity
Notice bibliographique
Résumé
Understanding the specific mechanisms regulating chemotherapeutic drug anti-cancer activities will uncover novel strategies to enhance the efficacy of these drugs in clinical settings. Activating Transcription Factor 3 (ATF3) is a stress inducible gene whose expression has been associated with survival outcomes in cancer models. This study characterizes the chemotherapeutic drugs, cisplatin and Histone Deacetylase Inhibitor (HDACi), M344 as novel inducers of ATF3 expression. Cisplatin is a DNA damaging agent widely used in various tumour types including lung, head and neck, and ovarian carcinomas. The HDAC inhibitor, SAHA, has recently been approved as a single agent in the treatment of subcutaneous T-cell lymphoma and HDACis themselves show potential for synergistic anti-cancer effects when used in combination with established chemotherapeutic drugs, including cisplatin. This study evaluates the mechanisms by which cisplatin and HDACi induce ATF3, as well as the role ATF3 plays as a mediator of cisplatin-induced cytotoxicity and the enhanced cytotoxicity between HDACi and cisplatin in combination. In this study, we demonstrate that cytotoxic doses of cisplatin and carboplatin consistently induced ATF3 expression in a panel of human tumour derived cell lines. Characterization of this induction revealed a p53, BRCA1, and integrated stress response (ISR) independent mechanism, all previously implicated in stress mediated ATF3 induction. Analysis of MAPKinase pathway involvement in ATF3 induction by cisplatin revealed a MAPKinase dependent mechanism. Cisplatin treatment, in combination with specific inhibitors to each MAPKinase pathway (JNK, ERK and p38) resulted in decreased ATF3 induction at the protein level. MAPKinase pathway inhibition led to decreased ATF3 mRNA expression and a reduction in the cytotoxic effects of cisplatin as measured by MTT cell viability assay. In A549 lung carcinoma cells, targeting ATF3 with specific shRNAs also attenuated the cytotoxic effects of cisplatin. Similarly, ATF3 -/- MEFs were shown to be less sensitive to cisplatin induced cytotoxicity as compared with ATF3+/+ MEFs. Taken together, we identified cisplatin as a MAPKinase pathway dependent inducer of ATF3 whose expression regulates in part cisplatin’s cytotoxic effects. Furthermore, we demonstrated that the HDAC inhibitor M344 was also an inducer of ATF3 expression at the protein and mRNA level in the same human derived cancer cell lines. Combination treatment with M344 and cisplatin lead to increased induction of ATF3 compared with cisplatin alone. Utilizing the MTT cell viability assay, M344 treatment was also shown to enhance the cytotoxic effects of cisplatin in these cancer cell lines. Unlike cisplatin, the mechanism of ATF3 induction by M344 was found to be independent of MAPKinase pathways. Utilizing ATF4 heterozygote (+/-) and knock out (-/-) mouse embryonic fibroblast (MEF) M334 induction of ATF3 was shown to depend on the presence of ATF4, a known regulator of ATF3 expression as part of the ISR pathway. HDACi treatment did not affect the level of histone acetylation associated with the ATF3 promoter as determined through Chromatin immunoprecipitation (ChIP) analysis, suggesting that ATF3 induction was not a direct effect of HDACi mediated histone acetylation. We also demonstrated that ATF3 regulates the enhanced cytotoxicity of M344 in combination with cisplatin as evidenced by attenuation of cytotoxicity in shRNAs targeting ATF3 expressing cells. This study identifies the pro-apoptotic factor, ATF3 as a novel target of M344, as well as a mediator of the co-operative effects of cisplatin and M344 induced tumour cell cytotoxicity.
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 ».