| 1. |
- Dass, Randall A., et al.
(författare)
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Wnt5a Signals through DVL1 to Repress Ribosomal DNA Transcription by RNA Polymerase I
- 2016
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Ingår i: PLoS Genetics. - : Public Library of Science (PLoS). - 1553-7390 .- 1553-7404. ; 12:8
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Tidskriftsartikel (refereegranskat)abstract
- Ribosome biogenesis is essential for cell growth and proliferation and is commonly elevated in cancer. Accordingly, numerous oncogene and tumor suppressor signaling pathways target rRNA synthesis. In breast cancer, non-canonical Wnt signaling by Wnt5a has been reported to antagonize tumor growth. Here, we show that Wnt5a rapidly represses rDNA gene transcription in breast cancer cells and generates a chromatin state with reduced transcription of rDNA by RNA polymerase I (Pol I). These effects were specifically dependent on Dishevelled1 (DVL1), which accumulates in nucleolar organizer regions (NORs) and binds to rDNA regions of the chromosome. Upon DVL1 binding, the Pol I transcription activator and deacetylase Sirtuin 7 (SIRT7) releases from rDNA loci, concomitant with disassembly of Pol I transcription machinery at the rDNA promoter. These findings reveal that Wnt5a signals through DVL1 to suppress rRNA transcription. This provides a novel mechanism for how Wnt5a exerts tumor suppressive effects and why disruption of Wnt5a signaling enhances mammary tumor growth in vivo.
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| 2. |
- Niklasson, Mia, et al.
(författare)
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Membrane-Depolarizing Channel Blockers Induce Selective Glioma Cell Death by Impairing Nutrient Transport and Unfolded Protein/Amino Acid Responses
- 2017
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Ingår i: Cancer Research. - : AMER ASSOC CANCER RESEARCH. - 0008-5472 .- 1538-7445. ; 77:7, s. 1741-1752
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Tidskriftsartikel (refereegranskat)abstract
- Glioma-initiating cells (GIC) are considered the underlying cause of recurrences of aggressive glioblastomas, replenishing the tumor population and undermining the efficacy of conventional chemotherapy. Here we report the discovery that inhibiting T-type voltage-gated Ca2+ and KCa channels can effectively induce selective cell death of GIC and increase host survival in an orthotopic mouse model of human glioma. At present, the precise cellular pathways affected by the drugs affecting these channels are unknown. However, using cell-based assays and integrated proteomics, phosphoproteomics, and transcriptomics analyses, we identified the downstreamsignaling events these drugs affect. Changes in plasma membrane depolarization and elevated intracellular Na+, which compromised Na+-dependent nutrient transport, were documented. Deficits in nutrient deficit acted in turn to trigger the unfolded protein response and the amino acid response, leading ultimately to nutrient starvation and GIC cell death. Our results suggest new therapeutic targets to attack aggressive gliomas.
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| 3. |
- Prakash, Varsha, et al.
(författare)
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Ribosome biogenesis during cell cycle arrest fuels EMT in development and disease
- 2019
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Ingår i: Nature Communications. - : Springer Science and Business Media LLC. - 2041-1723. ; 10
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Tidskriftsartikel (refereegranskat)abstract
- Ribosome biogenesis is a canonical hallmark of cell growth and proliferation. Here we show that execution of Epithelial-to-Mesenchymal Transition (EMT), a migratory cellular program associated with development and tumor metastasis, is fueled by upregulation of ribosome biogenesis during G1/S arrest. This unexpected EMT feature is independent of species and initiating signal, and is accompanied by release of the repressive nucleolar chromatin remodeling complex (NoRC) from rDNA, together with recruitment of the EMT-driving transcription factor Snai1 (Snail1), RNA Polymerase I (Pol I) and the Upstream Binding Factor (UBF). EMT-associated ribosome biogenesis is also coincident with increased nucleolar recruitment of Rictor, an essential component of the EMT-promoting mammalian target of rapamycin complex 2 (mTORC2). Inhibition of rRNA synthesis in vivo differentiates primary tumors to a benign, Estrogen Receptor-alpha (ER alpha) positive, Rictor-negative phenotype and reduces metastasis. These findings implicate the EMT-associated ribosome biogenesis program with cellular plasticity, de-differentiation, cancer progression and metastatic disease.
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| 4. |
- Strbkova, Lenka, et al.
(författare)
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Automated interpretation of time-lapse quantitative phase image by machine learning to study cellular dynamics during epithelial-mesenchymal transition
- 2020
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Ingår i: Journal of Biomedical Optics. - : SPIE - The International Society for Optics and Photonics. - 1083-3668 .- 1560-2281. ; 25:8
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Tidskriftsartikel (refereegranskat)abstract
- Significance: Machine learning is increasingly being applied to the classification of microscopic data. In order to detect some complex and dynamic cellular processes, time-resolved live-cell imaging might be necessary. Incorporating the temporal information into the classification process may allow for a better and more specific classification.Aim: We propose a methodology for cell classification based on the time-lapse quantitative phase images (QPIs) gained by digital holographic microscopy (DHM) with the goal of increasing performance of classification of dynamic cellular processes.Approach: The methodology was demonstrated by studying epithelial-mesenchymal transition (EMT) which entails major and distinct time-dependent morphological changes. The time-lapse QPIs of EMT were obtained over a 48-h period and specific novel features representing the dynamic cell behavior were extracted. The two distinct end-state phenotypes were classified by several supervised machine learning algorithms and the results were compared with the classification performed on single-time-point images.Results: In comparison to the single-time-point approach, our data suggest the incorporation of temporal information into the classification of cell phenotypes during EMT improves performance by nearly 9% in terms of accuracy, and further indicate the potential of DHM to monitor cellular morphological changes.Conclusions: Proposed approach based on the time-lapse images gained by DHM could improve the monitoring of live cell behavior in an automated fashion and could be further developed into a tool for high-throughput automated analysis of unique cell behavior.
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