Academic literature on the topic 'GTSE1'
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Journal articles on the topic "GTSE1"
Zhang, Fan, Jingfei Meng, Hong Jiang, Xing Feng, Dongshan Wei, and Wen Meng. "GTSE1 Facilitates the Malignant Phenotype of Lung Cancer Cells via Activating AKT/mTOR Signaling." Analytical Cellular Pathology 2021 (May 1, 2021): 1–11. http://dx.doi.org/10.1155/2021/5589532.
Full textZheng, Yongchang, Yue Shi, Si Yu, Yuanyuan Han, Kai Kang, Haifeng Xu, Huajian Gu, Xinting Sang, Yang Chen, and Jingyu Wang. "GTSE1, CDC20, PCNA, and MCM6 Synergistically Affect Regulations in Cell Cycle and Indicate Poor Prognosis in Liver Cancer." Analytical Cellular Pathology 2019 (December 30, 2019): 1–13. http://dx.doi.org/10.1155/2019/1038069.
Full textBendre, Shweta, Arnaud Rondelet, Conrad Hall, Nadine Schmidt, Yu-Chih Lin, Gary J. Brouhard, and Alexander W. Bird. "GTSE1 tunes microtubule stability for chromosome alignment and segregation by inhibiting the microtubule depolymerase MCAK." Journal of Cell Biology 215, no. 5 (November 23, 2016): 631–47. http://dx.doi.org/10.1083/jcb.201606081.
Full textTipton, Aaron R., Jonathan D. Wren, John R. Daum, Joseph C. Siefert, and Gary J. Gorbsky. "GTSE1 regulates spindle microtubule dynamics to control Aurora B kinase and Kif4A chromokinesin on chromosome arms." Journal of Cell Biology 216, no. 10 (August 18, 2017): 3117–32. http://dx.doi.org/10.1083/jcb.201610012.
Full textShort, Ben. "GTSE1 leads cancer cells into CIN." Journal of Cell Biology 215, no. 5 (November 25, 2016): 593. http://dx.doi.org/10.1083/jcb.2155if.
Full textLi, Ke. "MiR-509-3-5p inhibits colon cancer malignancy by suppressing GTSE1." Biochemical and Biophysical Research Communications 570 (September 2021): 175–83. http://dx.doi.org/10.1016/j.bbrc.2021.07.008.
Full textYao, Chengjiao, Yilin Li, Lihong Luo, Qin Xiong, Xiaowu Zhong, Fengjiao Xie, and Peimin Feng. "Identification of miRNAs and genes for predicting Barrett’s esophagus progressing to esophageal adenocarcinoma using miRNA-mRNA integrated analysis." PLOS ONE 16, no. 11 (November 24, 2021): e0260353. http://dx.doi.org/10.1371/journal.pone.0260353.
Full textScolz, Massimilano, Per O. Widlund, Silvano Piazza, Debora Rosa Bublik, Simone Reber, Leticia Y. Peche, Yari Ciani, et al. "GTSE1 Is a Microtubule Plus-End Tracking Protein That Regulates EB1-Dependent Cell Migration." PLoS ONE 7, no. 12 (December 7, 2012): e51259. http://dx.doi.org/10.1371/journal.pone.0051259.
Full textLei, Xiao, Lehui Du, Pei Zhang, Na Ma, Yanjie Liang, Yanan Han, and Baolin Qu. "Knockdown GTSE1 enhances radiosensitivity in non–small‐cell lung cancer through DNA damage repair pathway." Journal of Cellular and Molecular Medicine 24, no. 9 (March 22, 2020): 5162–67. http://dx.doi.org/10.1111/jcmm.15165.
Full textStelitano, Debora, Yamila Peche Leticia, Emiliano Dalla, Martin Monte, Silvano Piazza, and Claudio Schneider. "GTSE1: a novel TEAD4-E2F1 target gene involved in cell protrusions formation in triple-negative breast cancer cell models." Oncotarget 8, no. 40 (June 27, 2017): 67422–38. http://dx.doi.org/10.18632/oncotarget.18691.
Full textDissertations / Theses on the topic "GTSE1"
Bendre, Shweta [Verfasser], and Andrea [Akademischer Betreuer] Musacchio. "GTSE1 regulates microtubule stability during mitosis through inhibition of the microtubule depolymerase MCAK / Shweta Bendre ; Betreuer: Andrea Musacchio." Duisburg, 2017. http://d-nb.info/1141053675/34.
Full textYu-Chih, Lin [Verfasser], and Andrea [Akademischer Betreuer] Musacchio. "Adaptor binding sites in the clathrin terminal domain directly recruit the microtubule-stabilizing protein GTSE1 to the mitotic spindle / Lin Yu-Chih ; Betreuer: Andrea Musacchio." Duisburg, 2018. http://d-nb.info/1158496044/34.
Full textCiani, Yari. "Regulatory modules discovery and mesenchymal stem cells characterization from high-throughput cancer genomics data." Doctoral thesis, Università degli studi di Trieste, 2015. http://hdl.handle.net/10077/11111.
Full textIl tumore è una malattia caratterizzata da un’estrema complessità molecolare. Gli approcci di tipo “omic”, collezionando dati sull’intero genoma, sui trascritti e proteine in dataset pubblici, permettono di superare questa complessità e di trovare moduli funzionali che eseguono le funzioni coinvolte nei processi tumorali. Ad esempio, i profili di espressione genica da tessuti vengono usati per definire firme di geni e testarne la rilevanza clinica. Ho usato questo tipo di informazione per caratterizzare specifici geni di interesse in modelli di tumore al seno. Uno dei più recenti progetti di tipo “omic” è il FANTOM5. Questo progetto ha generato una risorsa unica: il primo atlante di espressione in mammifero basato su sequenziamento a singola molecola. Il sistema CAGE (Cap Analysis of Gene Expression) è stato usato per misurare i siti di inizio trascrizione (TSS) e l’utilizzo dei promotori in una collezione di campioni umani: in questo modo sono stati misurati i livelli di espressione di gran parte dei trascritti codificanti e non-codificanti nel genoma umano. Ho usato questo tipo di informazione per caratterizzare una linea staminale mesenchimale/stromale (MSC) derivante da tumori sierosi ovarici di alto grado (HG-SOC-MSCs) o da tessuti normali (N-MSCs) inclusi nel dataset FANTOM5. Ho messo in luce programmi funzionali condivisi tra le due linee cellulari e osservato che le differenze principali tra le funzioni attivate nelle due linee sono di tipo quantitativo più che qualitativo. I risultati suggeriscono inoltre che le HG-SOC-MSCs sono simili alle cellule mesoteliali e alle cellule del tessuto muscolare liscio. Inoltre, ho analizzato l’intero dataset usando ScanAll, un nuovo software utile a predire ab initio la presenza di elementi arricchiti nelle regioni geniche che circondano i promotori trovati del progetto FANTOM5. Ho individuato moduli di regolazione, ossia gruppi di motif che si trovano a distanze predefinite sul genoma uno rispetto all’altro. Questi moduli sono arricchiti in regioni del genoma co-espresse rispetto a sequenze generate casualmente. Infine ho creato un compendio di fattori di trascrizione espressi e che partecipano ad interazione proteina-proteina.
Cancer is a disease characterized by an extreme molecular complexity. Omics approaches, collecting data in public databases for all the genome, transcripts and proteins, attempt to overcome this complexity and find the functional modules that perform the functions involved in tumour related processes. For instance, cancer tissues gene expression profiles are widely used to define genes signatures and test their clinical relevance. I used this kind information in order to characterise interesting genes in breast cancer models. On the other hand, cellular models datasets could provide data that permits to focus on specific molecular mechanisms and probe the effects of molecules in a specific cancer model. One of the most recent omics project is the FANTOM5 project, that has generated a unique resource, the first single molecule sequencing-based expression atlas in mammalian systems. Cap analysis of gene expression (CAGE) was used to measure transcription start sites (TSS) and promoter usage across a wide collection of human samples thereby identifying and measuring levels of the majority of coding and non-coding transcripts in the human genome. I used this information to characterize a mesenchymal/stromal stem cell line (MSC) derived from high-grade serous ovarian cancer (HG-SOC-MSCs) or derived from normal tissue (N-MSCs) included in the entire FANTOM5 human dataset. I highlighted shared functional programs between HG-SOC-MSCs and N-MSCs suggesting that the global differences between the two cell lines are based on quantitative levels of transcriptional output rather than on qualitative differences. The results suggested that HG-SOC-MSCs are close relatives of mesothelial cells and smooth muscle cells. Furthermore, we analysed the entire dataset using ScanAll, a newly developed software, to ab initio predict the presence of enriched elements in the genomic regions surrounding FANTOM5 promoters. I pinpointed regulatory modules, i.e. groups of enriched motifs co-occurring in co-expressed regions within a fixed distance. These modules are enriched in the co-expressed sequences in each sample respect to random generated sequences. Finally, I created a Compendium of putative expressed and directly interacting transcription factors.
XXVII Ciclo
1986
Liperis, Georgios. "The function of gametocyte specific factor 1 (GTSF1) in mammalian oocyte and ovarian follicle development." Thesis, University of Leeds, 2013. http://etheses.whiterose.ac.uk/6895/.
Full textArif, Amena. "Biochemical Mechanism of Gene Expression Silencing by piRNA-directed PIWI-Clade Argonautes." eScholarship@UMMS, 2021. https://escholarship.umassmed.edu/gsbs_diss/1154.
Full textO'Brien, Paul. "Biomolecular NMR spectroscopy: Application to the study of the piRNA-pathway protein GTSF1, and backbone and side-chain spin relaxation methods development." Thesis, 2019. https://doi.org/10.7916/d8-rg5e-gf61.
Full textBooks on the topic "GTSE1"
Biomolecular NMR spectroscopy: Application to the study of the piRNA-pathway protein GTSF1, and backbone and side-chain spin relaxation methods development. [New York, N.Y.?]: [publisher not identified], 2019.
Find full textConference papers on the topic "GTSE1"
Perry, Dewayne E., and Don Batory. "A Theory about the Structure of GTSEs." In 2015 IEEE/ACM 4th SEMAT Workshop on a General Theory of Software Engineering (GTSE). IEEE, 2015. http://dx.doi.org/10.1109/gtse.2015.13.
Full textMegawati, Rizna Triana Dewi, Hanny Mulyani, Faiza Maryani, Puspa Dewi N. Lotullung, and Minarti. "Rubrofusarin from Aspergillus niger GTS01-4 and its biological activity." In INTERNATIONAL SYMPOSIUM ON APPLIED CHEMISTRY (ISAC) 2016. Author(s), 2017. http://dx.doi.org/10.1063/1.4973153.
Full textBoehm, Barry. "General Theories of Software Engineering (GTSE): Key Criteria and an Example." In 2015 IEEE/ACM 4th SEMAT Workshop on a General Theory of Software Engineering (GTSE). IEEE, 2015. http://dx.doi.org/10.1109/gtse.2015.18.
Full textRalph, Paul, Gregor Engels, Ivar Jacobson, and Michael Goedicke. "4th SEMAT Workshop on General Theory of Software Engineering (GTSE 2015)." In 2015 IEEE/ACM 37th IEEE International Conference on Software Engineering (ICSE). IEEE, 2015. http://dx.doi.org/10.1109/icse.2015.316.
Full textVillarreal, Amelia Martínez, Jennifer Gantchev, and Ivan Litvinov. "Abstract 2415: The role of GTSF1 as a regulator of retrotransposons and its impact on carcinogenesis." In Proceedings: AACR Annual Meeting 2021; April 10-15, 2021 and May 17-21, 2021; Philadelphia, PA. American Association for Cancer Research, 2021. http://dx.doi.org/10.1158/1538-7445.am2021-2415.
Full textJohnson, Pontus, Michael Goedicke, Ivar Jacobson, and Mira Kajko-Mattsson. "2nd SEMAT workshop on a general theory of software engineering (GTSE 2013)." In 2013 35th International Conference on Software Engineering (ICSE). IEEE, 2013. http://dx.doi.org/10.1109/icse.2013.6606769.
Full text"Committees." In 2015 IEEE/ACM 4th SEMAT Workshop on a General Theory of Software Engineering (GTSE). IEEE, 2015. http://dx.doi.org/10.1109/gtse.2015.6.
Full textBarn, Balbir, and Ravinder Barn. "An Approximate Theory for Value Sensitivity." In 2015 IEEE/ACM 4th SEMAT Workshop on a General Theory of Software Engineering (GTSE). IEEE, 2015. http://dx.doi.org/10.1109/gtse.2015.10.
Full textGhazarian, Arbi. "A Theory of Software Complexity." In 2015 IEEE/ACM 4th SEMAT Workshop on a General Theory of Software Engineering (GTSE). IEEE, 2015. http://dx.doi.org/10.1109/gtse.2015.11.
Full textPark, June Sung. "Essence-Based, Goal-Driven Adaptive Software Engineering." In 2015 IEEE/ACM 4th SEMAT Workshop on a General Theory of Software Engineering (GTSE). IEEE, 2015. http://dx.doi.org/10.1109/gtse.2015.12.
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