Scleraxis Regulation of Snail1 and Twist1 Gene Expression in Epithelial – Mesenchymal Transition
Notice bibliographique
Résumé
Epithelial‐Mesenchymal Transition (EMT) is a process by which epithelial cells lose their connectivity with each other and convert to a migratory, mesenchymal phenotype, and plays an important role in development as well as pathogenesis of diseases such as cancer and fibrosis. EMT is induced in part by the up‐regulation of the transcription factors Twist1 as well as Snai1, which directly represses transcription of E‐cadherin, an integral component of adherens junctions: tight cell‐cell connections prominent in epithelial cell sheets. Our previous data in mice demonstrated that gene knockout of the transcription factor scleraxis, which is involved in cell phenotype conversion, resulted in fewer cardiac fibroblasts, which arise developmentally from proepicardial cells undergoing EMT. We observed an increase in epithelial markers concomitant with a loss of mesenchymal markers in scleraxis null hearts, suggesting that EMT was impaired by scleraxis deletion. Our data indicated that scleraxis directly transactivates the Twist1 and Snail1 gene promoters, and that scleraxis was sufficient to induce EMT in A549 epithelial cells, but the mechanistic details of this regulation were unclear. Here we examined the Twist1 and Snail1 gene promoters for E‐boxes – CANNTG sequences to which scleraxis binds. By in silico analysis, we identified one and four putative E‐boxes in the Twist1 and Snail1 proximal promoters, respectively. Using luciferase reporter assays, we found that scleraxis directly transactivated both promoters. The single E‐box in the Twist1 promoter was required for this effect, as mutation of the E‐box abolished scleraxis‐mediated reporter induction. Conversely, three out of four E‐boxes within the Snail1 promoter were similarly required. Results for both studies were confirmed by electrophoretic mobility shift and chromatin immunoprecipitation assays, which demonstrated that scleraxis directly interacted with the E‐boxes in question. Scleraxis over‐expression resulted in up‐regulation of both Snail1 and Twist1, and increased migration of A549 epithelial cells as measured via scratch assay, indicating progression of EMT. TGFβ 1 , a known inducer of both EMT and scleraxis expression, increased Snail1 and Twist1 expression, but scleraxis knockdown via shRNA attenuated this induction, demonstrating that TGFβ 1 ‐mediated EMT requires scleraxis. These findings suggest that scleraxis is an important factor in driving EMT, and may be a novel therapeutic target to arrest cell conversion in EMT‐dependent pathological processes. Support or Funding Information DSA was supported by funding from the GETS program of the University of Manitoba. MPC was supported by an Open Operating Grant from the Canadian Institutes of Health Research (MOP136862). This abstract is from the Experimental Biology 2018 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .
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 machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| 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) | 0,002 | 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 source (Gemma direct ou Codex distillé), 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 ».