Inhaltsverzeichnis
Auswahl der wissenschaftlichen Literatur zum Thema „Complexe SWItch/Sucrose Non-Fermentable“
Geben Sie eine Quelle nach APA, MLA, Chicago, Harvard und anderen Zitierweisen an
Machen Sie sich mit den Listen der aktuellen Artikel, Bücher, Dissertationen, Berichten und anderer wissenschaftlichen Quellen zum Thema "Complexe SWItch/Sucrose Non-Fermentable" bekannt.
Neben jedem Werk im Literaturverzeichnis ist die Option "Zur Bibliographie hinzufügen" verfügbar. Nutzen Sie sie, wird Ihre bibliographische Angabe des gewählten Werkes nach der nötigen Zitierweise (APA, MLA, Harvard, Chicago, Vancouver usw.) automatisch gestaltet.
Sie können auch den vollen Text der wissenschaftlichen Publikation im PDF-Format herunterladen und eine Online-Annotation der Arbeit lesen, wenn die relevanten Parameter in den Metadaten verfügbar sind.
Zeitschriftenartikel zum Thema "Complexe SWItch/Sucrose Non-Fermentable"
Choi, Sung Kyung, Myoung Jun Kim und Jueng Soo You. „SMARCB1 Acts as a Quiescent Gatekeeper for Cell Cycle and Immune Response in Human Cells“. International Journal of Molecular Sciences 21, Nr. 11 (01.06.2020): 3969. http://dx.doi.org/10.3390/ijms21113969.
Der volle Inhalt der QuelleNguyen, Thinh T., Joanne G. A. Savory, Travis Brooke-Bisschop, Randy Ringuette, Tanya Foley, Bradley L. Hess, Kirk J. Mulatz, Laura Trinkle-Mulcahy und David Lohnes. „Cdx2 Regulates Gene Expression through Recruitment of Brg1-associated Switch-Sucrose Non-fermentable (SWI-SNF) Chromatin Remodeling Activity“. Journal of Biological Chemistry 292, Nr. 8 (12.01.2017): 3389–99. http://dx.doi.org/10.1074/jbc.m116.752774.
Der volle Inhalt der QuelleLiu, Hongyu, Yang Zhao, Guizhen Zhao, Yongjie Deng, Y. Eugene Chen und Jifeng Zhang. „SWI/SNF Complex in Vascular Smooth Muscle Cells and Its Implications in Cardiovascular Pathologies“. Cells 13, Nr. 2 (16.01.2024): 168. http://dx.doi.org/10.3390/cells13020168.
Der volle Inhalt der QuelleDel Savio, Elisa, und Roberta Maestro. „Beyond SMARCB1 Loss: Recent Insights into the Pathobiology of Epithelioid Sarcoma“. Cells 11, Nr. 17 (24.08.2022): 2626. http://dx.doi.org/10.3390/cells11172626.
Der volle Inhalt der QuelleWanior, Marek, Andreas Krämer, Stefan Knapp und Andreas C. Joerger. „Exploiting vulnerabilities of SWI/SNF chromatin remodelling complexes for cancer therapy“. Oncogene 40, Nr. 21 (03.05.2021): 3637–54. http://dx.doi.org/10.1038/s41388-021-01781-x.
Der volle Inhalt der QuelleSoto-Castillo, Juan José, Lucía Llavata-Marti, Roser Fort-Culillas, Pablo Andreu-Cobo, Rafael Moreno, Carles Codony, Xavier García del Muro, Ramon Alemany, Josep M. Piulats und Juan Martin-Liberal. „SWI/SNF Complex Alterations in Tumors with Rhabdoid Features: Novel Therapeutic Approaches and Opportunities for Adoptive Cell Therapy“. International Journal of Molecular Sciences 24, Nr. 13 (06.07.2023): 11143. http://dx.doi.org/10.3390/ijms241311143.
Der volle Inhalt der QuelleHasan, Nesrin, und Nita Ahuja. „The Emerging Roles of ATP-Dependent Chromatin Remodeling Complexes in Pancreatic Cancer“. Cancers 11, Nr. 12 (25.11.2019): 1859. http://dx.doi.org/10.3390/cancers11121859.
Der volle Inhalt der QuelleCollingwood, TN, FD Urnov und AP Wolffe. „Nuclear receptors: coactivators, corepressors and chromatin remodeling in the control of transcription“. Journal of Molecular Endocrinology 23, Nr. 3 (01.12.1999): 255–75. http://dx.doi.org/10.1677/jme.0.0230255.
Der volle Inhalt der QuellePadilla-Benavides, Teresita, Pablo Reyes-Gutierrez und Anthony N. Imbalzano. „Regulation of the Mammalian SWI/SNF Family of Chromatin Remodeling Enzymes by Phosphorylation during Myogenesis“. Biology 9, Nr. 7 (03.07.2020): 152. http://dx.doi.org/10.3390/biology9070152.
Der volle Inhalt der QuelleWu, Shuai, Nail Fatkhutdinov, Leah Rosin, Jennifer M. Luppino, Osamu Iwasaki, Hideki Tanizawa, Hsin-Yao Tang et al. „ARID1A spatially partitions interphase chromosomes“. Science Advances 5, Nr. 5 (Mai 2019): eaaw5294. http://dx.doi.org/10.1126/sciadv.aaw5294.
Der volle Inhalt der QuelleDissertationen zum Thema "Complexe SWItch/Sucrose Non-Fermentable"
Bretones, Santamarina Jorge. „Integrated multiomic analysis, synthetic lethality inference and network pharmacology to identify SWI/SNF subunit-specific pathway alterations and targetable vulnerabilities“. Electronic Thesis or Diss., université Paris-Saclay, 2024. http://www.theses.fr/2024UPASL049.
Der volle Inhalt der QuelleNowadays the cancer community agrees on the need for patient-tailored diagnostics and therapies, which calls for the design of translational studies combining experimental and statistical approaches. Current challenges include the validation of preclinical experimental models and their multi-omics profiling, along with the design of dedicated bioinformatics and mathematical pipelines (i.e. dimension reduction, multi-omics integration, mechanism-based digital twins) for identifying patient-specific optimal drug combinations.To address these challenges, we designed bioinformatics and statistical approaches to analyze various large-scale data types and integrate them to identify targetable vulnerabilities in cancer cell lines. We developed our pipeline in the context of alterations of the SWItch Sucrose Non-Fermentable (SWI/SNF) chromatin remodeling complex. SWI/SNF mutations occur in ~20% of all cancers, but such malignancies still lack efficient therapies. We leveraged a panel of HAP1 isogenic cell lines mutated for SWI/SNF subunits or other epigenetic enzymes for which transcriptomics, proteomics and drug screening data were available.We worked on four methodological axes, the first one being the design of an optimized pathway enrichment pipeline to detect pathways differentially activated in the mutants against the wild-type. We developed a pruning algorithm to reduce gene and pathway redundancy in the Reactome database and improve the interpretability of the results. We evidenced the bad performance of first-generation enrichment methods and proposed to combine the topology-based method ROntoTools with pre-ranked GSEA to increase enrichment performance .Secondly, we analyzed drug screens, processed drug-gene interaction databases to obtain genes and pathways targeted by effective drugs and integrated them with proteomics enrichment results to infer targetable vulnerabilities selectively harming mutant cell lines. The validation of potential targets was achieved using a novel method detecting synthetic lethality from transcriptomics and CRISPR data of independent cancer cell lines in DepMap, run for each studied epigenetic enzyme. Finally, to further inform multi-agent therapy optimization, we designed a first digital representation of targetable pathways for SMARCA4-mutated tumors by building a directed protein-protein interaction network connecting targets inferred from multi-omics HAP1 and DepMap CRISPR analyses. We used the OmniPath database to retrieve direct protein interactions and added the connecting neighboring genes with the Neko algorithm.These methodological developments were applied to the HAP1 panel datasets. Using our optimized enrichment pipeline, we identified Metabolism of proteins as the most frequently dysregulated pathway category in SWI/SNF-KO lines. Next, the drug screening analysis revealed cytotoxic and epigenetic drugs selectively targeting SWI/SNF mutants, including CBP/EP300 or mitochondrial respiration inhibitors, also identified as synthetic lethal by our Depmap CRISPR analysis. Importantly, we validated these findings in two independent isogenic cancer-relevant experimental models. The Depmap CRISPR analysis was also used in a separate project to identify synthetic lethal interactions in glioblastoma, which proved relevant for patient-derived cell lines and are being validated in dedicated drug screens.To sum up, we developed computational methods to integrate multi-omics expression data with drug screening and CRISPR assays and identified new vulnerabilities in SWI/SNF mutants which were experimentally revalidated. This study was limited to the identification of effective single agents. As a future direction, we propose to design mathematical models representing targetable protein networks using differential equations and their use in numerical optimization and machine learning procedures as a key tool to investigate concomitant druggable targets and personalize drug combinations