Transcriptional Characterization of Glioma Neural Stem Cells Diva ...

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6.7 Isoform Differential Expression Results may have therapeutic relevance, as inhibitors of their homolog PARP1 are in clinical trials for brain tumours [270]. Our comparison between GNS and NS cell lines, thus, highlights genes and pathways that are known to be affected in glioma as well as novel candidates, suggesting that the GNS vs NS comparison is a promising approach for further understanding molecular aspects of glioma. 6.7 Isoform Differential Expression To establish Tag-seq as a sensitive technique for the identification of tran- script isoforms differentially expressed between our GNS and NS libraries, we performed a parametric and a non-parametric test on the genes identified by multiple tag mappings using the "Ref_best" collection (see Fig 6.5). With the non-parametric χ 2 test, 2,682 isoforms were found to be differentially expressed as detected by the sum over all tags for each isoform. With the parametric approach, in which the logarithmic ratio of expression method adapted from Morrissy et al [346] was used, we computed a ratio change for each pair of tags that identified an isoform, so that a total of 2,040 differentially expressed isoforms were detected. The two methods share a total of 1,454 genes that have differentially expressed isoforms in the GNS cell lines with respect to the NS cell lines at significant levels (p-value2 for parametric), with 1,228 genes uniquely identified by the logarithmic method and 586 genes uniquely identified by the non-parametric method. In the attempt to focus on isoforms relevant to the pathways affected in glioma, we overlaid both lists of differentially expressed isoforms on the in- tegrated glioblastoma pathway described in section 7.5 and shown in figure 7.8. This highlighted the presence of 48 matching genes from the logarithmic (non-parametric) method and 57 matching genes from the parametric χ 2 test method, with 35 genes shared between the two methods. Each of the 35 genes, as well as the 13 genes identified exclusively through the logarithmic method and the 22 genes identified exclusively through the parametric χ 2 test method, were manually scrutinised on the University of California Santa Cruz (UCSC) genome browser by adding our tag mappings as a custom track together with the UCSC gene track and the gene predicted Ensembl track, both in "full" mode for the NCBI37.2/Ensembl 64 human genome assembly. For the follow- 144
6.7 Isoform Differential Expression Results Table 6.6: Genes with limited evidence or no evidence of implication in glioblastoma that appear in our main glioblastoma pathway. In "Others" the genes with an important physiological role in the brain that do not appear in our glioblastoma pathway are listed. Gene* Log2(FC)** Association Implications in Citations with glioma other neoplasms CACNA1A 7.1 None Prostate cancer (mouse model) [235] CACNA1C -8.2 None Liver cancer [25] CACNG7 -2.6 None None - CACNG8 -2.6 None None - CAMK1D -2.4 None Breast cancer [51] CPLX2 6.4 None None - DDIT3 (CHOP, GADD153) 4.4 Limited General (cellular stress response) [218,227,330,415] DUSP16 4.2 None Burkitt’s lymphoma [265] FGF19 - None Liver, lung and colon cancer [119] ITGA4 (CD49D) 3.0 Limited Chronic lymphocytic leukaemia, breast cancer and others [244,263,309] ITGBL1 + None None - MAP3K5 (ASK1) 5.1 Limited Gastric cancer and histiocytoma [94,189,504] NFATC2 (NFAT1) + Limited Breast cancer [64,95,311,348] NFKBIZ 5.1 None Liposarcoma [170] NR0B1 + None Lung adenocarcinoma and Ew- [?,236] (DAX1) ing’s sarcoma NR1D1 2.9 None Breast cancer [246] PARP3, 4.1, 2.9 Homology*** The PARP gene family is in- [312,463] PARP12 volved in DNA repair and several other processes related to tumourigenesis PERP 3.8 None Lung and skin cancer [92,317] PPEF1 4.4 Limited None [296] SNAP25 3.3 None Lung cancer [175] SYT1 -2.5 None None - TNFRSF14 4.0 None Follicular lymphoma [93] TNFSF4 4.0 None Generally implicated in immune [424] (OX40L) response to tumours * Aliases are listed in parentheses.** Gene expression log2FC between the GNS and NS lines compared by Tag-seq. Some genes were detected exclusively in GNS or NS lines (indicated in column two by + or -, respectively).*** The homolog PARP1 has been implicated in glioma. ing list of genes an alternative tag mapping-based isoform was observed, three of which are reported as adapted images from the UCSC genome browser in figures 6.15 and 6.16: 145
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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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7.5 Glioblastoma Pathway Analysis R
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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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