Transcriptional Characterization of Glioma Neural Stem Cells Diva ...

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9.1. Digital Profiling of GNS Cell Lines Discussion morphogenetic proteins that plays a key role in the transformation of mes- enchymal cells into bone and cartilage [245], and ERBB2, HLA-A and PTEN. We found that 226 of the approx. 2,000 differentially expressed isoforms hosted at least one microRNA seed sequence between at least two tags, which identified microRNA regulation as a widely adopted mechanism of regulation of gene and isoform expression in GNS cell lines. An important follow-up experiment for which data came in only recently in our laboratory, is the measurement of microRNA expression levels in our GNS cell lines on a microRNA microarray platform. Analysis of this data revealed that, of the 226 microRNAs identified on a prediction basis as regulators of isoforms within our GNS study, miR- 10b (see discussion above) and miR-26a were consistently up-regulated in our GNS cell lines, and miR-137, miR 128, miR-34a, miR-129-3p, and miR-451 consistently down-regulated, in line with the glioblastoma microRNA literature [96,278,364]. In the assessment of the regulation potentially performed by long non-coding RNAs, we found 18 up-regulated and 7 down-regulated putative ncRNAs, three of which are known to be long antisense RNAs: CDKN2BAS, HOTAIRM1 and NEAT1. Although long-non coding RNA regulation still needs to be thoroughly elucidated, an interesting pattern was observed with the levels of expression of HOTAIRM1 in our GNS cell lines and the expression trend found in literature in human NB4 promyelocytic cell lines. In fact, upon induction of granulocytic differentiation, HOTAIRM1 becomes strongly up-regulated in human NB4 promyelocytic cell lines and normal hematopoietic cells, and its knock-down in NB4 cells causes down-regulation of the HOXA1 and HOXA4 genes [547]. In line with these observations, HOTAIRM1 is found to be up- regulated in our GNS cell lines compared to our NS lines, and the HOXA1 and HOXA4 genes are also up-regulated (Fig 6.22), which potentially identifies them as conserved targets of HOTAIRM1, although an immuno-precipitation assay should be performed in order to conclude that. Overall, in this thesis I demonstrate that our results support GNS lines as suitable model for understanding the molecular basis of glioblastoma, and use of NS cell lines as controls in this setting. By this approach, we have identified several likely oncogenes and tumour suppressors that have not previously been associated with glioblastoma. With the advent of GNS cell lines, the glioblastoma research field is bound to be enriched by experiments that will represent 230
9.2. MicroRNA Target Prediction Analysis Discussion in their results also the stem cell component of the cancer and therefore enable more accurate targeting therapeutic strategies. Outside of the glioblastoma research field, similar concepts will be applied for the adherent culturing of other solid cancers with a preponderant stem cell component, such as other brain cancers and breast as well as colon, pancreatic and lymphoma cancers. The ability to work in an adherent culture that maintains intact the stem cell component of the cancer for long periods of time is an enabling feature for the entire cancer research field that will enable researchers to move forward faster towards the achievement of effective cancer therapeutic strategies. 9.2 MicroRNA Target Prediction Analysis In this thesis I have also constructed a microRNA target prediction analysis tool for the manipulation of microRNA target prediction data and combined the exon array expression data (Appendix E) with the microRNA array expression data (Appendix E) to produce, using the GenemiR software tool, an ensemble analysis that would help me answer the question whether any com- bination of prediction algorithms together were more effective and accurate at predicting mRNA targets that any prediction algorithm alone. microRNA target prediction is the result of the factoring of several different variables that are each treated differently, depending on the algorithm at work, in terms of the importance that is assigned to each within the algorithm itself. Although several robust prediction algorithms have been developed, they all have the problem of predicting with a very high percentage of false positives. Furthermore, the poor agreement that is so often observed between sets of results originating from exactly the same input list but processed by different algorithms, tells us that there needs to be more to be then account of. Thus, the characterisation of microRNA function and regulation of mRNAs is de- pendent on a robust strategy for managing disparate results. Some prediction algorithms generate a large number of putative regulatory targets, many of which are exclusive to a particular method. Moreover, those results that do agree will not necessarily exhibit higher prediction accuracy. Even in the best case where the degree of overlap is approximately 70% (between TargetScanS and PicTar), it is not obvious which algorithm produces the optimal set of target predictions. When predictions are made, they must then be viewed 231
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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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5.4 Quantitative Real Time-PCR Vali
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5.5 Literature Mining Methods Figur
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5.5 Literature Mining Methods How m
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5.6 Differential Isoform Expression
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5.9 External Dataset Expression Cor
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5.10 Glioblastoma Pathway Construct
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5.11 MicroRNA Target Prediction Ana
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Results 119
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6.2 Tag mapping Results Table 6.1:
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6.2 Tag mapping Results 123 Figure
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6.3 Copy Number Aberrations Results
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6.3 Copy Number Aberrations Results
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6.4 Core Differentially Expressed G
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6.5 Large-scale qRT-PCR Validation
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6.6 Literature Mining for Different
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6.7 Isoform Differential Expression
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6.8 Long ncRNA Differential Express
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6.8 Long ncRNA Differential Express
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Chapter 7 Dataset Correlation Analy
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7.1 Enrichment Analysis Results Tab
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7.1 Enrichment Analysis Results Fig
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7.1 Enrichment Analysis Results Tab
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7.2 Glioblastoma Expression Signatu
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7.3 Tumour Expression Correlation R
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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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