Transcriptional Characterization of Glioma Neural Stem Cells Diva ...

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6.7 Isoform Differential Expression Results Table 6.8: MicroRNA array results for GNS cell lines with respect to NS cell lines. Based on a literature survey, these microRNAs would be interesting candidates to validate experimentally in a TLDA assay. microRNA log 2(F C) FDR miR-128 0.9 0.000206443 miR-137 1 0.000121777 miR-34a -1.5 0.000004.43 miR-26a 1.4 0.0000143 miR-10b 2.9 1.03E-08 miR-451 1 3.15E-05 miR-129-3p 1 9.63E-05 · The levels of miR-128 have been found to be consistently lower in glioblastoma compared with normal brain tissue [259,364]. Opposite findings were observed in our microRNA array, in which miR-128 is up- regulated in GNS cells with respect to NS cells (Table 6.8). miR-128 directly targets the transcription factor E2F3a, which activates genes necessary for the progression of cell-cycle and can thus inhibit proliferation of brain cells by negatively regulating E2F3a. This microRNA also directly targets BMI1, a gene that is thought to act as an oncogene in glioblastoma by regulating tumour suppressors like P53 and CDKN2A. BMI1 also promotes stem cell renewal by acting as part of a Polycomb Si- lencing Complex to silence the expression of genes - including CDKN2A and CDKN1A tumour suppressors - involved in differentiation and senes- cence. Low levels of miR-128 in glioblastoma may contribute to glioma growth by allowing the increased expression of BMI1 to promote an un- differentiated self-renewing state. High levels of miR-128 in GNS cells with respect to NS cells may identify a stem cell pool specific regulation that has yet to be studied in detail. miR-128 is known to be highly expressed in neurons but its role in the brain is still unknown and is surmised to be the promotion of neuronal differentiation through pre- vention of stem cell self-renewal. Our isoform data analysis with the non-parametric method showed that miR-128 is predicted to target the nerve growth factor receptor associated protein 1 NGFRAP1, a p75NTR- associated cell-death executor mediated by the common neutrophin receptor p75NTR [350] that also plays a role in NGF-induced apopto- sis in oligodendrocytes [351]. Analsysis with the logarithmic method showed that miR-128 is predicted to target the Rab GTPase guanine nu- 152
6.7 Isoform Differential Expression Results cleotide exchange factor GAPVD1, a gene fundamental for the activation of RAB5A during the engulfment of apoptotic cells [238] (Fig 6.19). · miR-137 is one of the most down-regulated microRNAs in glioblastoma compared with normal brain tissue. In our microRNA microarray dataset, miR-137 is up-regulated in GNS cell lines with respect to NS cell lines. Since miR-137 directly targets CDK6, if over-expressed in glioblastoma it would induce cell-cycle arrest. The level of miR-137 increases upon differentiation of glioma neurosphere cultures and if over-expressed in these cells it leads to the expression of markers consistent with neuronal differentiation. These data suggest that the lower expression of this microRNA in glioblastoma, and the higher expression in GNS cells with respect to NS cells, reflects the lack of tumour cell differentiation in the former and the presence of regulatory mechanisms downstream of miR-137 in the latter, since cancer stem cells are the least differentiated within the lineage hierarchy according to the cancer stem cell hypothesis [364]. In our microRNA prediction analysis, miR-137 was predicted to target the serine/threonine-protein kinase 40 STK40 gene, a nega- tive regulator of NFKB and p53-mediated gene transcription [200] and the transcription factor TCF4, which associated with β catenin mediates Wnt signaling by trans-activating downstream target genes [11]. · miR-34a expression is down-regulated in glioblastoma and in p53-null mutant gliomas since non p53-null mutants, expressed in many tumours and that can possess gain-of-function activities, do not regulate transcription of miR-34a. This suggests miR-34a as a transcriptional target of P53 [278]. In our microRNA microarray dataset miR-34a is found to be down-regulated as well. Furthermore, miR-34a potently inhibits the protein expression of MET, a hepatocyte growth factor receptor encod- ing a tyrosine-kinase, as well as MET 3'UTR reporter activity in glioma, medulloblastoma cells and astrocytes. miR-34a also inhibits Notch-1 and Notch-2 protein expression and their 3'UTR reporter activities, as well as CDK6 protein expression in glioma cells. Transient transfection of miR-34a into brain tumour cell lines inhibited cell proliferation, cell cycle progression, cell survival, and cell invasion but did not affect hu- man astrocyte cell survival and cell cycle. miR-34a transfection, also, did not affect the protein levels of PDGFRA in any tested cell line, al- though miR-34a has predicted seed matches in the 3'UTR of PDGFRA. 153
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Transcriptional Characterization of
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This dissertation is my own work an
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Acknowledgements I would like to th
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5.9 External Dataset Expression Cor
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Introduction 1
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1.1 Glial Cells in the Central Nerv
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1.2 Glioblastoma Multiforme Introdu
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1.3 Primary and Secondary Glioblast
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1.4 Pathways Involved in Glioblasto
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1.5 Pathway Crosstalk Introduction
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Chapter 2 Neurogenesis Contents 2.1
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2.1 Radial Glia Introduction VZ adj
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2.2 Neural Stem Cells Introduction
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Chapter 3 Brain Cancer Stem Cells C
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3.1 The Cancer Stem Cell Hypothesis
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3.1 The Cancer Stem Cell Hypothesis
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3.2 Brain Cancer Stem Cells Introdu
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3.3 Glioma Culture Systems Introduc
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Chapter 4 The Non-Coding RNA World
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4.1 MicroRNA regulation Introductio
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4.1 MicroRNA regulation Introductio
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4.2 Target Prediction and Validatio
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Methods 93
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5.1 Tag-sequencing Data Processing
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5.1 Tag-sequencing Data Processing
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5.2 Array Comparative Genomic Hybri
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8.1 Principles Results say, this is
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8.3 Databases Results Figure 8.1: O
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8.3 Databases Results Figure 8.3: W
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8.4 Filters Results Bayesian method
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8.5 Target Prediction Ensemble Anal
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9.1. Digital Profiling of GNS Cell
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9.2. MicroRNA Target Prediction Ana
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9.3. Concluding Remarks Appendix pr
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A.1 Differential Expression Appendi
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A.2 Classified Differential Express
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A.3 Quantitative RT-PCR Appendix Ta
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A.3 Quantitative RT-PCR Appendix We
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A.3 Quantitative RT-PCR Appendix Ta
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A.3 Quantitative RT-PCR Appendix We
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A.4 Tag-seq vs qRT-PCR Correlation
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Appendix "BEX5", "DIAPH2", "GBP3",
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End Select End If End Sub Appendix
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If NameStr.ToLower().Equals("query"
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Appendix C Long ncRNAs We detected
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C. Long ncRNAs Appendix Tag Accessi
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D.1 Pathway Interactions Appendix F
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D.2 Pathway Images Appendix Figure
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D.2 Pathway Images Appendix Figure
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E. Exon Array Data Appendix Average
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F. MicroRNA Array Data Appendix Tab
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F. MicroRNA Array Data Appendix GNS
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F. MicroRNA Array Data Appendix GNS
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Ln Natural logarithm LOH Loss of He
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3.3 Schematisation of cell cycle ph
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8.3 Workflows at the core of the pr
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7.1 Selected Gene Ontology terms an
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Bibliography [1] Cell signaling tec
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[38] G. Bain, D. Kitchens, M. Yao,
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[78] S. Bustin, V. Benes, J. Garson
Page 329 and 330:
[112] M. Czystowska, J. Han, M. Szc
Page 331 and 332:
[153] T. Fujiwara, M. Bandi, M. Nit
Page 333 and 334:
[192] M. Hernandez, M. Nieto, and M
Page 335 and 336:
[231] T.-M. Kim, W. Huang, R. Park,
Page 337 and 338:
[268] H. Lemjabbar-Alaoui, A. van Z
Page 339 and 340:
[305] K. MAEDA, S. MATSUHASHI, K. T
Page 341 and 342:
[342] H. Moon, M. Ahn, J. Park, K.
Page 343 and 344:
[379] D. Park and J. Rich. Biology
Page 345 and 346:
[417] P. Rakic. Guidance of neurons
Page 347 and 348:
[456] T. Shima, N. Okumura, T. Taka
Page 349 and 350:
[490] P. A. C. t’Hoen, Y. Ariyure
Page 351 and 352:
[523] T. Watanabe, A. Takeda, T. Ts
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