Transcriptional Characterization of Glioma Neural Stem Cells Diva ...

More documents

Recommendations

Info

Abstract Tumours affecting the glial portion of brain parenchyma are termed gliomas and consti- tute the most frequent and lethal cancers affecting the central nervous system. Glioblastoma multiforme is the most aggressive glioma in adults and a World Health Organisation classified grade IV astrocytoma, characterised by widespread intra-tumoural heterogeneity. A recent advance in the study of gliomas has been the establishment of glioma-derived neural stem (GNS) cell lines that may represent the glioma cell of origin. While these cell lines show many similarities to normal neural stem (NS) cells, an important difference is their capacity to give rise to authentic glioma-like tumours when xenografted into subventricular strata of immunocompromised mice. Here I describe an in-depth characterisation of the transcriptome of GNS cells, to identify differences in the gene expression between normal and glioma-derived cell lines that may underlie tumorigenesis. Analyses were carried out at the levels of gene expression, molecular signature profiling, transcript isoform detection and the quantitation of small non-coding RNAs, taking genetic alterations into account at both the karyotype and mutational level. Importantly, the cell lines studied were established from tumours with differing histology, allowing us to sample the breadth of the disease rather than focus on the differences between unhealthy versus healthy counterparts. We identified a large cohort of significantly differentially-expressed genes and a smaller subset of strictly up- and down-regulated ones, including several known glioma oncogenes as well as novel candidates. An extensive glioblastoma pathway was manually curated to show the expression of our dataset on the known and unknown glioblastoma-affected pathways. Interestingly, gene set enrichment analysis revealed a consistent up-regulation of inflammatory genes in the GNS lines belonging to the MHC class II family, suggesting an immune-evasion phenotype that has been noted in a number of early glioma studies. Gliomas have been classified into a small number of subtypes on the basis of patient survival and response to therapy. We found that the expression signatures of GNS cell lines closely resembled the mesenchymal and proneural subtypes, as well as reflecting their known histopathological features. To characterise genes correlating with patient survival time, we tested for the association between survival time and gene expression in publicly available glioma and glioblastoma data sets and found four genes to be strongly positively correlated with patient survival time and patient age. Together these studies provide an in-depth analysis of a model of glioma pathology driven by an aberrant population of NS cells. Finally, a package for the performance evaluation of eight leading microRNA target prediction algorithms was built using exon array and microRNA array data from the same GNS cell lines. This data was used to validate experimentally the target prediction algorithms that were assessed in their performance as single and combinations of them. The combi- natorial weight analysis allowed us to conclude that (i) tissue specificity bears a non-trivial weight in predicting what set of genes a certain microRNA regulates and, therefore, should be included in future versions of these algorithms, and that (ii) the ElMMO prediction algorithm fares better than any other combination of prediction algorithms.
Acknowledgements I would like to thank on a professional note all the members of my lab, starting from my supervisor Paul Bertone, who has given me the opportunity of con- ducting research at the University of Cambridge and has taught me over the years invaluable life lessons that I will always remember. An incredibly special thank you goes to Pär Engstrom, who supervised my work throughout my time as a doctoral student and has always been there for me in any matter scientific. On a more personal note, I would like to thank my parents. Mamma, grazie dell’appoggio psicologico, culinario, telefonico, automobilistico e soprattutto affettivo che mi hai dato negli anni passati lontano da casa. Senza le tue cure ed il tuo affetto non sarei mai arrivata viva a questo giorno. Babbo, grazie del DNA che condividiamo, grazie del costante supporto mentale, pecuniario e filosofico che mi dai sempre incondizionatamente, a mo’ di martello pneu- matico. Grazie degli stimoli incessanti sui quali posso sempre contare. I also want to thank Sean Cheng for being there for me since March of 2009. Thanks for these past four years of exciting PhD life that you lived with me. Who knows what’s next for us. Thanks for being a great person, thanks for under- standing what goes on in my mind, thanks for the love and affection that you give me every day and thanks for ruining movies by always anticipating what will happen next. I secretly love that.
Page 1 and 2: Transcriptional Characterization of
Page 3: This dissertation is my own work an
Page 7 and 8: 5.9 External Dataset Expression Cor
Page 9 and 10: Introduction 1
Page 11 and 12: 1.1 Glial Cells in the Central Nerv
Page 13 and 14: 1.2 Glioblastoma Multiforme Introdu
Page 19 and 20: 1.3 Primary and Secondary Glioblast
Page 29 and 30: 1.4 Pathways Involved in Glioblasto
Page 39 and 40: 1.5 Pathway Crosstalk Introduction
Page 41 and 42: Chapter 2 Neurogenesis Contents 2.1
Page 43 and 44: 2.1 Radial Glia Introduction VZ adj
Page 45 and 46: 2.2 Neural Stem Cells Introduction
Page 55 and 56:
2.2 Neural Stem Cells Introduction
Page 57 and 58:
Page 59 and 60:
Page 61 and 62:
Page 63 and 64:
Page 65 and 66:
Chapter 3 Brain Cancer Stem Cells C
Page 67 and 68:
3.1 The Cancer Stem Cell Hypothesis
Page 69 and 70:
3.1 The Cancer Stem Cell Hypothesis
Page 71 and 72:
3.2 Brain Cancer Stem Cells Introdu
Page 73 and 74:
Page 75 and 76:
Page 77 and 78:
Page 79 and 80:
Page 81 and 82:
3.3 Glioma Culture Systems Introduc
Page 83 and 84:
Page 85 and 86:
Page 87 and 88:
Page 89 and 90:
Page 91 and 92:
Page 93 and 94:
Chapter 4 The Non-Coding RNA World
Page 95 and 96:
4.1 MicroRNA regulation Introductio
Page 97 and 98:
4.1 MicroRNA regulation Introductio
Page 99 and 100:
4.2 Target Prediction and Validatio
Page 101 and 102:
Methods 93
Page 103 and 104:
5.1 Tag-sequencing Data Processing
Page 105 and 106:
5.1 Tag-sequencing Data Processing
Page 107 and 108:
5.2 Array Comparative Genomic Hybri
Page 109 and 110:
5.4 Quantitative Real Time-PCR Vali
Page 111 and 112:
Page 113 and 114:
Page 115 and 116:
5.5 Literature Mining Methods Figur
Page 117 and 118:
5.5 Literature Mining Methods How m
Page 119 and 120:
5.6 Differential Isoform Expression
Page 121 and 122:
5.9 External Dataset Expression Cor
Page 123 and 124:
5.10 Glioblastoma Pathway Construct
Page 125 and 126:
5.11 MicroRNA Target Prediction Ana
Page 127 and 128:
Results 119
Page 129 and 130:
6.2 Tag mapping Results Table 6.1:
Page 131 and 132:
6.2 Tag mapping Results 123 Figure
Page 133 and 134:
6.3 Copy Number Aberrations Results
Page 135 and 136:
6.3 Copy Number Aberrations Results
Page 137 and 138:
6.4 Core Differentially Expressed G
Page 139 and 140:
Page 141 and 142:
Page 143 and 144:
Page 145 and 146:
6.5 Large-scale qRT-PCR Validation
Page 147 and 148:
Page 149 and 150:
Page 151 and 152:
6.6 Literature Mining for Different
Page 153 and 154:
6.7 Isoform Differential Expression
Page 155 and 156:
Page 157 and 158:
Page 159 and 160:
Page 161 and 162:
Page 163 and 164:
Page 165 and 166:
6.8 Long ncRNA Differential Express
Page 167 and 168:
6.8 Long ncRNA Differential Express
Page 169 and 170:
Chapter 7 Dataset Correlation Analy
Page 171 and 172:
7.1 Enrichment Analysis Results Tab
Page 173 and 174:
7.1 Enrichment Analysis Results Fig
Page 175 and 176:
7.1 Enrichment Analysis Results Tab
Page 177 and 178:
7.2 Glioblastoma Expression Signatu
Page 179 and 180:
7.3 Tumour Expression Correlation R
Page 181 and 182:
Page 183 and 184:
Page 185 and 186:
Page 187 and 188:
Page 189 and 190:
Page 191 and 192:
7.4 Survival Analysis Results Table
Page 193 and 194:
7.4 Survival Analysis Results 867 g
Page 195 and 196:
7.5 Glioblastoma Pathway Analysis R
Page 197 and 198:
Page 199 and 200:
Page 201 and 202:
Page 203 and 204:
Page 205 and 206:
Page 207 and 208:
Page 209 and 210:
Page 211 and 212:
8.1 Principles Results say, this is
Page 213 and 214:
8.3 Databases Results Figure 8.1: O
Page 215 and 216:
8.3 Databases Results Figure 8.3: W
Page 217 and 218:
8.4 Filters Results Bayesian method
Page 219 and 220:
8.5 Target Prediction Ensemble Anal
Page 221 and 222:
Page 223 and 224:
Page 225 and 226:
Page 227 and 228:
Page 229 and 230:
9.1. Digital Profiling of GNS Cell
Page 231 and 232:
Page 233 and 234:
Page 235 and 236:
Page 237 and 238:
Page 239 and 240:
9.2. MicroRNA Target Prediction Ana
Page 241 and 242:
9.3. Concluding Remarks Appendix pr
Page 243 and 244:
A.1 Differential Expression Appendi
Page 245 and 246:
Page 247 and 248:
Page 249 and 250:
Page 251 and 252:
Page 253 and 254:
Page 255 and 256:
Page 257 and 258:
Page 259 and 260:
A.2 Classified Differential Express
Page 261 and 262:
Page 263 and 264:
Page 265 and 266:
A.3 Quantitative RT-PCR Appendix Ta
Page 267 and 268:
A.3 Quantitative RT-PCR Appendix We
Page 269 and 270:
A.3 Quantitative RT-PCR Appendix Ta
Page 271 and 272:
A.3 Quantitative RT-PCR Appendix We
Page 273 and 274:
A.4 Tag-seq vs qRT-PCR Correlation
Page 275 and 276:
Appendix "BEX5", "DIAPH2", "GBP3",
Page 277 and 278:
End Select End If End Sub Appendix
Page 279 and 280:
If NameStr.ToLower().Equals("query"
Page 281 and 282:
Appendix C Long ncRNAs We detected
Page 283 and 284:
C. Long ncRNAs Appendix Tag Accessi
Page 285 and 286:
D.1 Pathway Interactions Appendix F
Page 287 and 288:
Page 289 and 290:
Page 291 and 292:
D.2 Pathway Images Appendix Figure
Page 293 and 294:
D.2 Pathway Images Appendix Figure
Page 295 and 296:
E. Exon Array Data Appendix Average
Page 297 and 298:
Page 299 and 300:
Page 301 and 302:
Page 303 and 304:
Page 305 and 306:
Page 307 and 308:
Page 309 and 310:
F. MicroRNA Array Data Appendix Tab
Page 311 and 312:
F. MicroRNA Array Data Appendix GNS
Page 313 and 314:
F. MicroRNA Array Data Appendix GNS
Page 315 and 316:
Ln Natural logarithm LOH Loss of He
Page 317 and 318:
3.3 Schematisation of cell cycle ph
Page 319 and 320:
8.3 Workflows at the core of the pr
Page 321 and 322:
7.1 Selected Gene Ontology terms an
Page 323 and 324:
Bibliography [1] Cell signaling tec
Page 325 and 326:
[38] G. Bain, D. Kitchens, M. Yao,
Page 327 and 328:
[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
show all

Transcriptional Characterization of Glioma Neural Stem Cells Diva ...

You also want an ePaper? Increase the reach of your titles

Delete template?

Save as template?