Vai ai contenuti. | Spostati sulla navigazione | Spostati sulla ricerca | Vai al menu | Contatti | Accessibilità

| Crea un account

Saccoman, Claudia (2015) microRNAs impact in cancer: from non canonical biogenesis and functions to methodological aspects. [Tesi di dottorato]

Full text disponibile come:

[img]Documento PDF
Tesi non accessible fino a 31 Dicembre 2018 per motivi correlati alla proprietà intellettuale.
Visibile a: nessuno

13Mb

Abstract (inglese)

The DNA information appears nowadays more stratified than it was supposed to be a decade before. In this scenario, non coding RNAs have been introduced in the fraction of functional RNA, carrying information and underpinning regulatory circuits of complex genetic phenomena in eukaryotes. microRNAs are endogenous single stranded ~22 nt long transcripts among that unveiled non coding RNA with regulatory functions, detected both in animals and plants. Increasing evidence shows that deregulation of microRNAs (miRNAs) plays an important role in both solid and hematologic malignancies. In this work we considered microRNA non canonical functions and involvement in tumours, integrating computational analyses of genome-wide datasets and targeted experimental results, with a critical approach to the specific adopted computational tools.
First, we studied miRNAs role in myeloproliferative disorders, more specifically in primary myelofibrosis, considering also other small RNAs detected in RNA-seq data. Myeloproliferative neoplasms are chronic myeloid cancers involving CD34+ hematopoietic stem cells alterations, evolving to acute leukemia in the most severe forms. This study deals indeed with an Illumina sequencing of small RNAs samples of CD34+ hematopoietic stem cells of patients affected by primary myelofibrosis and of controls, in order to characterize miRNAs profile and find relevant differentially expressed elements, as putative effectors of a disrupted post-transcriptional regulation involved in PMF initiation and progression.
Then, in order to have a better understanding of each step of a computational analysis of RNA-seq data, we studied the impact on small RNAs differential expression analysis of normalization methods developed for long RNA. We evaluated five commonly used normalization methods to pinpoint a procedure to perform a robust RNA-seq analysis. We estimated statistical distribution parameters from a real microRNA numerous dataset and we simulated a huge number of small RNAs dataset. We controlled datasets characteristics in order to generate 9 different testing scenarios and measure the normalization impact on differentially expressed elements recognition, through ROC and AUC curves. We ascertain that normalization methods still need strong efforts in developing new algorithms in order to fill the wide room for improvement.
Thereafter we evaluated the implication of microRNAs in the gene expression changes observed after H-ferritin silencing. We explored whether different FHC amounts might modulate miRNA expression levels in K562 cells and we studied the impact of miRNAs in gene expression profile modifications. To this aim, we performed a miRNA-mRNA integrative analysis in K562 silenced for FHC (K562shFHC) comparing it with K562 transduced with scrambled RNA (K562shRNA). The remarkable up-regulation of four miRNAs, hsa-let-7g-5p, hsa-let-7f-5p, hsa-let-7i-5p and hsa-miR-125b-5p, in silenced cells and their down-regulation when FHC expression was rescued supported a specific relation between FHC silencing and miRNA-modulation. The integration of target predictions with miRNA and gene expression profiles led to the identification of a regulatory network. Our data, confirmed by an experimental validation, indicate that, FHC silencing may affect RAF1/pERK1/2 levels through the modulation of a specific set of miRNAs and they add new insights to the relationship among iron homeostasis and miRNAs.
We further explored a putative non canonical role of microRNAs, more specifically, in the context of the always more evident complex cross talk between protein-coding and non-protein coding RNAs. We worked on a preliminary study that deals with the involvement of microRNAs in the regulation of alternative translation (AT) and thus of protein isoform equilibrium. There is an increasing appreciation of the high prevalence of alternative translation in mammals. Complex and regulated translation pattern are achieved thanks to multiple Open Reading Frame (ORFs) and Translation Initiation Sites (TISs) in the same mRNA that can influence each other in different ways. miRNAs were recently demonstrated to be involved in modulation of protein isoform equilibrium binding to TISs. We provided novel data on the overlap of active TISs of mRNAs, experimentally defined using GTI-seq, to miRNA-binding sites, experimentally determined using CLASH technique. The genes whose sites were recognized are supposed to be involved in miRNA-modulated AT and we modelled the interaction mechanism. The miRNA-based regulation of mRNA alternative translation surely deserves further investigation to clarify if and how it impacts on cell processes and on disease.

Abstract (italiano)

L’informazione contenuta nel DNA appare oggi sempre più stratificata di quanto non si pensasse. In questo scenario, gli RNA non codificanti sono stati riconosciuti come RNA funzionali, portatori di informazione e parti fondamentali dei più complessi circuiti regolativi negli eucarioti. Tra i più studiati RNA non codificanti con funzioni regolative ci sono i microRNA (miRNA), RNA a singolo filamento lunghi circa 22 nucleotidi, presenti sia in piante che animali. Ci sono prove sempre più evidenti che la deregolazione dei miRNA abbia un ruolo fondamentale nei tumori solidi e del sangue. In questo lavoro abbiamo preso in considerazione le funzioni non canoniche dei miRNA, il loro coinvolgimento nei tumori, integrando analisi computazionali di dati genome-wide e dati sperimentali più specifici, con un approccio critico rispetto gli strumenti computazionali.
Abbiamo innanzitutto studiato il ruolo dei miRNA nelle neoplasie mieloproliferative, più specificamente nella mielofibrosi, considerando anche altri small RNA presenti nei dati RNA-seq. Le malattie mieloproliferative sono tumori cronici della linea mieloide che vedono l’alterazione delle cellule emopoietiche CD34+, ed evolvono in leucemia acuta nei casi più gravi. In questo studio abbiamo pertanto analizzato dati di RNA-seq, prodotti con tecnologia Illumina, di cellule raccolte da pazienti affetti da mielofibrosi primaria e da controlli sani, al fine di caratterizzare i profili di microRNA e trovare gli elementi differenzialmente espressi, in quanto possibili elementi di regolazione post trascrizionale alterata e coinvolti nella genesi e nello sviluppo della mielofibrosi.
Successivamente, al fine di aver piena consapevolezza dei vari passi di un’analisi computazionale, abbiamo studiato l’impatto dell’applicazione su dati di RNA corti di algoritmi di normalizzazione, sviluppati per RNA lunghi, valutato a livello dei risultati dell’analisi differenziale. Abbiamo preso in considerazione cinque tra i più comunemente usati algoritmi, per individuare la procedura che permetta di svolgere in modo più robusto l’analisi di dati RNA-seq. Abbiamo stimato i parametri della distribuzione statistica di un dataset reale di microRNA particolarmente numeroso, e abbiamo simulato un numero sostanzioso di dataset. Abbiamo generato nove tipi di data set con diverse caratteristiche controllate e abbiamo misurato l’impatto della normalizzazione nei vari casi, quantificando l’impatto sull’analisi differenziale attraverso curve ROC e AUC. Abbiamo evidenziato la necessità di nuovi algoritmi di normalizzazione, più specifici per i miRNA, in grado di colmare le grosse lacune dei metodi attuali.
Ci siamo in seguito concentrati sul coinvolgimento dei microRNA nei cambiamenti dei valori di espressione genica, rilevati in cellule K562 in cui fosse silenziata la ferritina FHC. Abbiamo indagato se diversi livelli di FHC potessero modulare i livelli di espressione dei microRNA e abbiamo monitorato l’impatto dei miRNAs rispetto le modificazioni dei livelli d’espressione dei geni. A tal fine abbiamo, condotto un’analisi integrata di miRNA-mRNA in cellule K562 silenziate per la FHC (K562shFHC) confrontandole con cellule K562 trasdotte con RNA scrambled (K562shRNA). La notevole up-regolazione di quattro miRNA, hsa-let-7g-5p, hsa-let-7f-5p, hsa-let-7i-5p e hsa-miR-125b-5p, nelle cellule silenziate e il fatto che i loro livelli di espressione scendessero quando fosse riattivata l’espressione di FHC, supporta l’esistenza di una relazione tra FHC e la modulazione dei miRNA. Integrando le informazioni sui target dei miRNA e i profili di espressione dei geni, abbiamo identificato dei network regolativi. I nostri dati, confermati con validazioni sperimentali, indicano che il silenziamento di FHC potrebbe impattare sui livelli di RAF1/pERK1/2 attraverso la modulazione di specifici gruppi di microRNA, fornendo nuove informazioni sul rapporto tra omeostasi del ferro e miRNA.
Infine, ci siamo occupati di un ruolo non canonico dei microRNA, più specificamente nel contesto delle sempre più evidenti interazioni tra RNA codificanti e RNA non codificanti. Abbiamo condotto uno studio preliminare sul coinvolgimento dei microRNA nella regolazione della traduzione alternativa e di conseguenza dell’equilibrio delle varie isoforme proteiche. C’è una maggior consapevolezza della diffusione del meccanismo della traduzione alternativa nei mammiferi. Si realizzano pattern complessi di regolazione delle isoforme grazie alla presenza, nello stesso mRNA, di più Open Reading Frame (ORF) e Translation Initiation Sites (TISs) utilizzati. Questi sono in grado di influenzarsi a vicenda in maniera diversa. E’ stato recentemente dimostrato che i miRNA sono coinvolti nella modulazione dell’equilibrio delle isoforme proteiche, legandosi ai TIS. Noi abbiamo individuato la corrispondenza di siti TIS attivi nei trascritti di mRNA, trovati sperimentalmente con GTI-seq, e siti di legame di miRNA nelle sequenze di mRNA, determinati sperimentalmente con tecnica CLASH. Questi geni in cui sono stati riconosciuti siti di legame, si suppongono coinvolti in un meccanismo di traduzione alternativa modulata da miRNAs. Alcune interazioni miRNA-tis sono state confermate sperimentalmente, ma ulteriori studi sono necessari per valutare se il meccanismo di modulazione della traduzione alternativa da parte dei miRNA possa impattare su processi cellulari e nella malattia.

Aggiungi a RefWorks
Tipo di EPrint:Tesi di dottorato
Relatore:Casadoro, Giorgio
Correlatore:Bortoluzzi , Stefania
Dottorato (corsi e scuole):Ciclo 27 > scuole 27 > BIOSCIENZE E BIOTECNOLOGIE > BIOLOGIA EVOLUZIONISTICA
Data di deposito della tesi:31 Gennaio 2015
Anno di Pubblicazione:31 Gennaio 2015
Parole chiave (italiano / inglese):microRNA/microRNA
Settori scientifico-disciplinari MIUR:Area 05 - Scienze biologiche > BIO/13 Biologia applicata
Struttura di riferimento:Dipartimenti > Dipartimento di Biologia
Codice ID:7852
Depositato il:23 Nov 2015 15:21
Simple Metadata
Full Metadata
EndNote Format

Bibliografia

I riferimenti della bibliografia possono essere cercati con Cerca la citazione di AIRE, copiando il titolo dell'articolo (o del libro) e la rivista (se presente) nei campi appositi di "Cerca la Citazione di AIRE".
Le url contenute in alcuni riferimenti sono raggiungibili cliccando sul link alla fine della citazione (Vai!) e tramite Google (Ricerca con Google). Il risultato dipende dalla formattazione della citazione.

References introduction Cerca con Google

1. Mortazavi, A., Williams, B. A., McCue, K., Schaeffer, L. & Wold, B. Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nature Methods 5, 621-628 (2008). Cerca con Google

2. Nagalakshmi, U. et al. The Transcriptional Landscape of the Yeast Genome Defined by RNA Sequencing. Science 320, 1344-1349 (2008). Cerca con Google

3. Lister, R. et al. Highly Integrated Single-Base Resolution Maps of the Epigenome in Arabidopsis. Cell 133, 523-536 (2008). Cerca con Google

4. Consortium, T. E. N. C. O. D. E. P. The ENCODE (ENCyclopedia Of DNA Elements) Project. Science 306, 636-640 (2004). Cerca con Google

5. Consortium, T. E. N. C. O. D. E. P. An integrated encyclopedia of DNA elements in the human genome. Nature 489, 57-74 (2012). Cerca con Google

6. Djebali, S. et al. Landscape of transcription in human cells. Nature 489, 101-108 (2012). Cerca con Google

7. Gerstein, M. B. et al. Architecture of the human regulatory network derived from ENCODE data. Nature 489, 91-100 (2012). Cerca con Google

8. Celniker, S. E. et al. Unlocking the secrets of the genome. Nature 459, 927-930 (2009). Cerca con Google

9. Tuck, A. C. & Tollervey, D. RNA in pieces. Trends in Genetics 27, 422-432 (2011). Cerca con Google

10. Mattick, J. S. The genetic signatures of noncoding RNAs. PLoS Genetics 5 (2009). Cerca con Google

11. Costa, F. F. Non-coding RNAs, epigenetics and complexity. Gene 410, 9-17 (2008). Cerca con Google

12. Bartel, D. P. MicroRNAs: genomics, biogenesis, mechanism, and function. Cell 116, 281-297 (2004). Cerca con Google

13. Lee, R. C., Feinbaum, R. L. & Ambros, V. The C. elegans heterochronic gene lin-4 encodes small RNAs with antisense complementarity to lin-14. Cell 75, 843-854 (1993). Cerca con Google

14. Reinhart, B. J. et al. The 21-nucleotide let-7 RNA regulates developmental timing in Caenorhabditis elegans. Nature 403, 901-906 (2000). Cerca con Google

15. Pasquinelli, A. E. et al. Conservation of the sequence and temporal expression of let-7 heterochronic regulatory RNA. Nature 408, 86-89 (2000). Cerca con Google

16. Griffiths-Jones, S., Saini, H. K., Dongen, S. v. & Enright, A. J. miRBase: tools for microRNA genomics. Nucleic Acids Res. 36, D154-D158 (2008). Cerca con Google

17. Lagos-Quintana, M., Rauhut, R., Lendeckel, W. & Tuschl, T. Identification of Novel Genes Coding for Small Expressed RNAs. Science 294, 853-858 (2001). Cerca con Google

18. Lau, N. C., Lim, L. P., Weinstein, E. G. & Bartel, D. P. An Abundant Class of Tiny RNAs with Probable Regulatory Roles in Caenorhabditis elegans. Science 294, 858-862 (2001). Cerca con Google

19. Niwa, R. & Slack, F. J. The evolution of animal microRNA function. Curr. Opin. Genet. Dev. 17, 145-150 (2007). Cerca con Google

20. Luo, Y., Guo, Z. & Li, L. Evolutionary conservation of microRNA regulatory programs in plant flower development. Dev. Biol. 380, 133-144 (2013). Cerca con Google

21. Salmena, L., Poliseno, L., Tay, Y., Kats, L. & Pandolfi, P. A ceRNA Hypothesis: The Rosetta Stone of a Hidden RNA Language? Cell 146, 353-358 (2011). Cerca con Google

22. Lee, Y. et al. The nuclear RNase III Drosha initiates microRNA processing. Nature 425, 415-419 (2003). Cerca con Google

23. Lee, Y., Jeon, K., Lee, J., Kim, S. & Kim, V. N. MicroRNA maturation: stepwise processing and subcellular localization. EMBO J. 21, 4663-4670 (2002). Cerca con Google

24. Zeng, Y., Yi, R. & Cullen, B. R. MicroRNAs and small interfering RNAs can inhibit mRNA expression by similar mechanisms. Proceedings of the National Academy of Sciences 100, 9779-9784 (2003). Cerca con Google

25. Yi, R., Qin, Y., Macara, I. G. & Cullen, B. R. Exportin-5 mediates the nuclear export of pre-microRNAs and short hairpin RNAs. Genes Dev. 17, 3011-3016 (2003). Cerca con Google

26. Lund, E., Güttinger, S., Calado, A., Dahlberg, J. E. & Kutay, U. Nuclear Export of MicroRNA Precursors. Science 303, 95-98 (2004). Cerca con Google

27. Dueck, A., Ziegler, C., Eichner, A., Berezikov, E. & Meister, G. microRNAs associated with the different human Argonaute proteins. Nucleic Acids Res. 40, 9850-9862 (2012). Cerca con Google

28. Ro, S., Park, C., Young, D., Sanders, K. M. & Yan, W. Tissue-dependent paired expression of miRNAs. Nucleic Acids Res. 35, 5944-5953 (2007). Cerca con Google

29. Axtell, M. J., Westholm, J. O. & Lai, E. C. Vive la différence: biogenesis and evolution of microRNAs in plants and animals. Genome Biol. 12 (2011). Cerca con Google

30. Olena, A. F. & Patton, J. G. Genomic organization of microRNAs. J. Cell. Physiol. 222, 540-545 (2010). Cerca con Google

31. Westholm, J. O. & Lai, E. C. Mirtrons: microRNA biogenesis via splicing. Biochimie 93, 1897-1904 (2011). Cerca con Google

32. Lee, C., Risom, T. & Strauss, W. M. Evolutionary Conservation of MicroRNA Regulatory Circuits: An Examination of MicroRNA Gene Complexity and Conserved MicroRNA-Target Interactions through Metazoan Phylogeny. DNA Cell Biol. 26, 209-218 (2007). Cerca con Google

33. Wienholds, E. et al. MicroRNA Expression in Zebrafish Embryonic Development. Science 309, 310-311 (2005). Cerca con Google

34. Sempere, L. F., Cole, C. N., Mcpeek, M. A. & Peterson, K. J. The phylogenetic distribution of metazoan microRNAs: insights into evolutionary complexity and constraint. Journal of Experimental Zoology Part B: Molecular and Developmental Evolution 306B, 575-588 (2006). Cerca con Google

35. Hertel, J. et al. The expansion of the metazoan microRNA repertoire. BMC Genomics 7 (2006). Cerca con Google

36. Guo, Y. E. & Steitz, J. A. Virus Meets Host MicroRNA: the Destroyer, the Booster, the Hijacker. Mol. Cell. Biol. 34, 3780-3787 (2014). Cerca con Google

37. Ha, M. & Kim, V. N. Regulation of microRNA biogenesis. Nature Reviews Molecular Cell Biology 15, 509-524 (2014). Cerca con Google

38. Mingyi Xie, Joan A. Steitz. Versatile microRNA biogenesis in animals and their viruses. RNA biology 11 (2014). Cerca con Google

39. Babiarz, J. E., Ruby, J. G., Wang, Y., Bartel, D. P. & Blelloch, R. Mouse ES cells express endogenous shRNAs, siRNAs, and other Microprocessor-independent, Dicer-dependent small RNAs. Genes Dev. 22, 2773-2785 (2008). Cerca con Google

40. Maute, R. L. et al. tRNA-derived microRNA modulates proliferation and the DNA damage response and is down-regulated in B cell lymphoma. Proceedings of the National Academy of Sciences 110, 1404-1409 (2013). Cerca con Google

41. Ender, C. et al. A Human snoRNA with MicroRNA-Like Functions. Mol. Cell 32, 519-528 (2008). Cerca con Google

42. Cazalla, D., Xie, M. & Steitz, J. A Primate Herpesvirus Uses the Integrator Complex to Generate Viral MicroRNAs. Mol. Cell 43, 982-992 (2011). Cerca con Google

43. Neilsen, C. T., Goodall, G. J. & Bracken, C. P. IsomiRs, the overlooked repertoire in the dynamic microRNAome. Trends in Genetics 28, 544-549 (2012). Cerca con Google

44. Azuma-Mukai, A. et al. Characterization of endogenous human Argonautes and their miRNA partners in RNA silencing. Proceedings of the National Academy of Sciences 105, 7964-7969 (2008). Cerca con Google

45. Fernandez-Valverde, S., Taft, R. J. & Mattick, J. S. Dynamic isomiR regulation in Drosophila development. RNA 16, 1881-1888 (2010). Cerca con Google

46. Tan, G. C. et al. 5' isomiR variation is of functional and evolutionary importance. Nucleic Acids Res. 42, 9424-9435 (2014). Cerca con Google

47. Jaskiewicz, L. & Zavolan, M. Dicer partners expand the repertoire of miRNA targets. Genome Biol. 13 (2012). Cerca con Google

48. Neilsen, C. T., Goodall, G. J. & Bracken, C. P. IsomiRs, the overlooked repertoire in the dynamic microRNAome. Trends in Genetics 28, 544-549 (2012). Cerca con Google

49. Fukunaga, R. et al. Dicer Partner Proteins Tune the Length of Mature miRNAs in Flies and Mammals. Cell 151, 533-546 (2012). Cerca con Google

50. Wu, H., Ye, C., Ramirez, D. & Manjunath, N. Alternative Processing of Primary microRNA Transcripts by Drosha Generates 5′ End Variation of Mature microRNA. PLoS ONE 4 (2009). Cerca con Google

51. Morin, R. D. et al. Application of massively parallel sequencing to microRNA profiling and discovery in human embryonic stem cells. Genome Res. 18, 610-621 (2008). Cerca con Google

52. Chen, K. & Rajewsky, N. Natural selection on human microRNA binding sites inferred from SNP data. Nat. Genet. 38, 1452-1456 (2006). Cerca con Google

53. Saunders, M. A., Liang, H. & Li, W. Human polymorphism at microRNAs and microRNA target sites. Proceedings of the National Academy of Sciences 104, 3300-3305 (2007). Cerca con Google

54. Llorens, F. et al. A highly expressed miR-101 isomiR is a functional silencing small RNA. BMC Genomics 14 (2013). Cerca con Google

55. Cloonan, N. et al. MicroRNAs and their isomiRs function cooperatively to target common biological pathways. Genome Biol. 12 (2011). Cerca con Google

56. Bortoluzzi, S., Biasiolo, M. & Bisognin, A. MicroRNA-offset RNAs (moRNAs): by-product spectators or functional players? Trends Mol. Med. 17, 473-474 (2011). Cerca con Google

57. Bortoluzzi, S. et al. Characterization and discovery of novel miRNAs and moRNAs in JAK2V617F-mutated SET2 cells. Blood 119, e120-e130 (2012). Cerca con Google

58. Langenberger, D. et al. Evidence for human microRNA-offset RNAs in small RNA sequencing data. Bioinformatics 25, 2298-2301 (2009). Cerca con Google

59. Shi, W., Hendrix, D., Levine, M. & Haley, B. A distinct class of small RNAs arises from pre-miRNA–proximal regions in a simple chordate. Nature Structural & Molecular Biology 16, 183-189 (2009). Cerca con Google

60. Berezikov, E. et al. Deep annotation of Drosophila melanogaster microRNAs yields insights into their processing, modification, and emergence. Genome Res. 21, 203-215 (2011). Cerca con Google

61. Ruby, J. G. et al. Evolution, biogenesis, expression, and target predictions of a substantially expanded set of Drosophila microRNAs. Genome Res. 17, 1850-1864 (2007). Cerca con Google

62. Zhou, H. et al. Deep annotation of mouse iso-miR and iso-moR variation. Nucleic Acids Res. 40, 5864-5875 (2012). Cerca con Google

63. Taft, R. J. et al. Nuclear-localized tiny RNAs are associated with transcription initiation and splice sites in metazoans. Nature Structural & Molecular Biology 17, 1030-1034 (2010). Cerca con Google

64. Meiri, E. et al. Discovery of microRNAs and other small RNAs in solid tumors. Nucleic Acids Res. 38, 6234-6246 (2010). Cerca con Google

65. Eichhorn, S. et al. mRNA Destabilization Is the Dominant Effect of Mammalian MicroRNAs by the Time Substantial Repression Ensues. Mol. Cell 56, 104-115 (2014). Cerca con Google

66. Djuranovic, S., Nahvi, A. & Green, R. miRNA-Mediated Gene Silencing by Translational Repression Followed by mRNA Deadenylation and Decay. Science 336, 237-240 (2012). Cerca con Google

67. Guo, H., Ingolia, N. T., Weissman, J. S. & Bartel, D. P. Mammalian microRNAs predominantly act to decrease target mRNA levels. Nature 466, 835-840 (2010). Cerca con Google

68. Voinnet, O. Origin, Biogenesis, and Activity of Plant MicroRNAs. Cell 136, 669-687 (2009). Cerca con Google

69. Lytle, J. R., Yario, T. A. & Steitz, J. A. Target mRNAs are repressed as efficiently by microRNA-binding sites in the 5'-UTR as in the 3'- UTR. Proceedings of the National Academy of Sciences 104, 9667-9672 (2007). Cerca con Google

70. Lim, L. P. et al. The microRNAs of Caenorhabditis elegans. Genes Dev. 17, 991-1008 (2003). Cerca con Google

71. Grimson, A. et al. MicroRNA Targeting Specificity in Mammals: Determinants beyond Seed Pairing. Mol. Cell 27, 91-105 (2007). Cerca con Google

72. Lai, E. C. Predicting and validating microRNA targets. Genome Biol. 5 (2004). Cerca con Google

73. Brennecke, J., Stark, A., Russell, R. B. & Cohen, S. M. Principles of MicroRNA–Target Recognition. PLoS Biol 3 (2005). Cerca con Google

74. Doench, J. G., Petersen, C. P. & Sharp, P. A. siRNAs can function as miRNAs. Genes Dev. 17, 438-442 (2003). Cerca con Google

75. Witkos, T. M., Koscianska, E. & Krzyzosiak, W. J. Practical aspects of microRNA target prediction. Curr. Mol. Med. 11 (2011). Cerca con Google

76. Hofacker, I. L. How microRNAs choose their targets. Nat. Genet. 39, 1191-1192 (2007). Cerca con Google

77. Bartel, D. P. MicroRNAs: Target Recognition and Regulatory Functions. Cell 136, 215-233 (2009). Cerca con Google

78. Baek, D. et al. The impact of microRNAs on protein output. Nature 455, 64-71 (2008). Cerca con Google

79. Pasquinelli, A. E. MicroRNAs and their targets: recognition, regulation and an emerging reciprocal relationship. Nature Reviews Genetics 13, 271-282 (2012). Cerca con Google

80. Enright, A. J. et al. MicroRNA targets in Drosophila. Genome Biol. 5, R1-R1 (2004). Cerca con Google

81. Kiriakidou, M. A combined computational-experimental approach predicts human microRNA targets. Genes Dev. 18, 1165-1178 (2004). Cerca con Google

82. Rehmsmeier, M. Fast and effective prediction of microRNA/target duplexes. RNA 10, 1507-1517 (2004). Cerca con Google

83. Rusinov, V., Baev, V., Minkov, I. N. & Tabler, M. MicroInspector: a web tool for detection of miRNA binding sites in an RNA sequence. Nucleic Acids Res. 33, W696-W700 (2005). Cerca con Google

84. Lewis, B. P. et al. Prediction of mammalian microRNA targets. Cell 115, 787-798 (2003). Cerca con Google

85. Lewis, B. P., Burge, C. B. & Bartel, D. P. Conserved Seed Pairing, Often Flanked by Adenosines, Indicates that Thousands of Human Genes are MicroRNA Targets. Cell 120, 15-20 (2005). Cerca con Google

86. Krek, A. et al. Combinatorial microRNA target predictions. Nat. Genet. 37, 495-500 (2005). Cerca con Google

87. Thadani, R. & Tammi, M. T. MicroTar: predicting microRNA targets from RNA duplexes. BMC Bioinformatics 7 (2006). Cerca con Google

88. Kertesz, M., Iovino, N., Unnerstall, U., Gaul, U. & Segal, E. The role of site accessibility in microRNA target recognition. Nat. Genet. 39, 1278-1284 (2007). Cerca con Google

89. Miranda, K. C. et al. A Pattern-Based Method for the Identification of MicroRNA Binding Sites and Their Corresponding Heteroduplexes. Cell 126, 1203-1217 (2006). Cerca con Google

90. Yue, D., Liu, H. & Huang, Y. Survey of computational algorithms for MicroRNA target prediction. Curr. Genomics 10 (2009). Cerca con Google

91. Reyesâ Herrera, P. H. & Ficarra, E. One Decade of Development and Evolution of MicroRNA Target Prediction Algorithms. Genomics, Proteomics & Bioinformatics 10, 254-263 (2012). Cerca con Google

92. Peterson, S. M. et al. Common features of microRNA target prediction tools. Frontiers in Genetics 5 (2014). Cerca con Google

93. Friedman, R. C., Farh, K. K., Burge, C. B. & Bartel, D. P. Most mammalian mRNAs are conserved targets of microRNAs. Genome Res. 19, 92-105 (2009). Cerca con Google

94. van, d. G. & Nolte-’t Hoen, Esther N. M. “Small Talk” in the Innate Immune System via RNA-Containing Extracellular Vesicles. Frontiers in Immunology 5 (2014). Cerca con Google

95. Kosaka, N. et al. Trash or Treasure: extracellular microRNAs and cell-to-cell communication. Non-Coding RNA 4 (2013). Cerca con Google

96. Hawkins, P. & Morris, K. V. RNA and transcriptional modulation of gene expression. Cell cycle (Georgetown, Tex.) 7, 602-607 (2008). Cerca con Google

97. Tan, Y. et al. Transcriptional inhibiton of Hoxd4 expression by miRNA-10a in human breast cancer cells. BMC Molecular Biology 10 (2009). Cerca con Google

98. Zardo, G. et al. Polycombs and microRNA-223 regulate human granulopoiesis by transcriptional control of target gene expression. Blood 119, 4034-4046 (2012). Cerca con Google

99. Sinkkonen, L. et al. MicroRNAs control de novo DNA methylation through regulation of transcriptional repressors in mouse embryonic stem cells. Nature Structural & Molecular Biology 15, 259-267 (2008). Cerca con Google

100. Zardo, G. et al. Transcriptional targeting by microRNA-Polycomb complexes: A novel route in cell fate determination. Cell Cycle 11, 3543-3549 (2012). Cerca con Google

101. Zardo, G. et al. Polycombs and microRNA-223 regulate human granulopoiesis by transcriptional control of target gene expression. Blood 119, 4034-4046 (2012). Cerca con Google

102. Kochetov, A. V. Alternative translation start sites and hidden coding potential of eukaryotic mRNAs. Bioessays 30, 683-691 (2008). Cerca con Google

103. Kochetov, A. V. et al. uORFs, reinitiation and alternative translation start sites in human mRNAs. FEBS Lett. 582, 1293-1297 (2008). Cerca con Google

104. Menschaert, G. et al. Deep proteome coverage based on ribosome profiling aids mass spectrometry-based protein and peptide discovery and provides evidence of alternative translation products and near-cognate translation initiation events. Molecular & cellular proteomics: MCP 12, 1780-1790 (2013). Cerca con Google

105. Vanderperre, B. et al. Direct Detection of Alternative Open Reading Frames Translation Products in Human Significantly Expands the Proteome. PLoS ONE 8 (2013). Cerca con Google

106. Smith, E. et al. Leaky ribosomal scanning in mammalian genomes: significance of histone H4 alternative translation in vivo. Nucleic Acids Res. 33, 1298-1308 (2005). Cerca con Google

107. Wang, Y. et al. Gene Expression Profiles and Molecular Markers To Predict Recurrence of Dukes' B Colon Cancer. Journal of Clinical Oncology 22, 1564-1571 (2004). Cerca con Google

108. Morris, D. R. & Geballe, A. P. Upstream Open Reading Frames as Regulators of mRNA Translation. Mol. Cell. Biol. 20, 8635-8642 (2000). Cerca con Google

109. Skabkin, M., Skabkina, O., Hellen, C. T. & Pestova, T. Reinitiation and Other Unconventional Posttermination Events during Eukaryotic Translation. Mol. Cell 51, 249-264 (2013). Cerca con Google

110. Lytle, J. R., Yario, T. A. & Steitz, J. A. Target mRNAs are repressed as efficiently by microRNA-binding sites in the 5′ UTR as in the 3′ UTR. Proceedings of the National Academy of Sciences 104, 9667-9672 (2007). Cerca con Google

111. Helwak, A., Kudla, G., Dudnakova, T. & Tollervey, D. Mapping the Human miRNA Interactome by CLASH Reveals Frequent Noncanonical Binding. Cell 153, 654-665 (2013). Cerca con Google

112. Tay, Y., Zhang, J., Thomson, A. M., Lim, B. & Rigoutsos, I. MicroRNAs to Nanog, Oct4 and Sox2 coding regions modulate embryonic stem cell differentiation. Nature 455, 1124-1128 (2008). Cerca con Google

113. Liu, C. et al. CLIP-based prediction of mammalian microRNA binding sites. Nucleic Acids Res. 41, e138-e138 (2013). Cerca con Google

114. Sonda, N. et al. miR-142-3p Prevents Macrophage Differentiation during Cancer-Induced Myelopoiesis. Immunity 38, 1236-1249 (2013). Cerca con Google

115. Garzon, R., Calin, G. A. & Croce, C. M. MicroRNAs in Cancer. Annu. Rev. Med. 60, 167-179 (2009). Cerca con Google

116. Mendell, J. & Olson, E. MicroRNAs in Stress Signaling and Human Disease. Cell 148, 1172-1187 (2012). Cerca con Google

117. Palanichamy, J. K. & Rao, D. S. miRNA dysregulation in cancer: towards a mechanistic understanding. Frontiers in Genetics 5 (2014). Cerca con Google

118. Iorio, M. V. & Croce, C. M. MicroRNA dysregulation in cancer: diagnostics, monitoring and therapeutics. A comprehensive review. EMBO Molecular Medicine 4, 143-159 (2012). Cerca con Google

119. Bommer, G. T. et al. p53-Mediated Activation of miRNA34 Candidate Tumor-Suppressor Genes. Current Biology 17, 1298-1307 (2007). Cerca con Google

120. He, L. et al. A microRNA component of the p53 tumour suppressor network. Nature 447, 1130-1134 (2007). Cerca con Google

121. Chang, T. et al. Transactivation of miR-34a by p53 broadly influences gene expression and promotes apoptosis. Mol. Cell 26, 745-752 (2007). Cerca con Google

122. Johnson, S. M. et al. RAS Is Regulated by the let-7 MicroRNA Family. Cell 120, 635-647 (2005). Cerca con Google

123. Sampson, V. B. et al. MicroRNA Let-7a Down-regulates MYC and Reverts MYC-Induced Growth in Burkitt Lymphoma Cells. Cancer Res. 67, 9762-9770 (2007). Cerca con Google

124. Le Quesne, J. & Caldas, C. Micro-RNAs and breast cancer. Molecular Oncology 4, 230-241 (2010). Cerca con Google

125. Iorio, M. V. et al. MicroRNA Gene Expression Deregulation in Human Breast Cancer. Cancer Res. 65, 7065-7070 (2005). Cerca con Google

126. Iorio, M. V. & Croce, C. M. microRNA involvement in human cancer. Carcinogenesis 33, 1126-1133 (2012). Cerca con Google

127. Hurst, D. R., Edmonds, M. D. & Welch, D. R. Metastamir - the field of metastasis-regulatory microRNA is spreading. Cancer Res. 69, 7495-7498 (2009). Cerca con Google

128. Huang, Q. et al. The microRNAs miR-373 and miR-520c promote tumour invasion and metastasis. Nat. Cell Biol. 10, 202-210 (2008). Cerca con Google

129. Mittal, N. & Zavolan, M. Seq and CLIP through the miRNA world. Genome Biol. 15 (2014). Cerca con Google

130. Zheng, G. et al. Serum microRNA panel as biomarkers for early diagnosis of colorectal adenocarcinoma. Br. J. Cancer 111, 1985-1992 (2014). Cerca con Google

131. Qin, X., Xu, H., Gong, W. & Deng, W. The tumor cytosol miRNAs, fluid miRNAs, and exosome miRNAs in lung cancer. Cancer Endocrinology 4 (2015). Cerca con Google

132. Bahn, J. H. et al. The Landscape of MicroRNA, Piwi-Interacting RNA, and Circular RNA in Human Saliva. Clin. Chem. 61, 221-230 (2015). Cerca con Google

133. Weber, J. A. et al. The MicroRNA Spectrum in 12 Body Fluids. Clin. Chem. 56, 1733-1741 (2010). Cerca con Google

References chapter 1 Cerca con Google

1. Tefferi A and Vardiman JW. Classification and diagnosis of myeloproliferative neoplasms: The 2008 World Health Organization criteria and point-of-care diagnostic algorithms. Leukemia. 2007;22(1):14-22. Cerca con Google

2. Vannucchi AM, Guglielmelli P, Tefferi A. Advances in Understanding and Management of Myeloproliferative Neoplasms. CA: A Cancer Journal for Clinicians. 2009;59(3):171-191. Cerca con Google

3. Vannucchi AM. Management of myelofibrosis. ASH Education Program Book. 2011;2011(1):222-230. Cerca con Google

4. Vannucchi AM. From Palliation to Targeted Therapy in Myelofibrosis. N Engl J Med. 2010;363(12):1180-1182. Cerca con Google

5. Baxter EJ, Scott LM, Campbell PJ, et al. Acquired mutation of the tyrosine kinase JAK2 in human myeloproliferative disorders. The Lancet. 2005;365(9464):1054-1061. Cerca con Google

6. Jones AV, Kreil S, Zoi K, et al. Widespread occurrence of the JAK2 V617F mutation in chronic myeloproliferative disorders. Blood. 2005;106(6):2162-2168. Cerca con Google

7. James C, Ugo V, Le Couédic J, et al. A unique clonal JAK2 mutation leading to constitutive signalling causes polycythaemia vera. Nature. 2005;434(7037):1144-1148. Cerca con Google

8. Kralovics R, Passamonti F, Buser AS, et al. A Gain-of-Function Mutation of JAK2 in Myeloproliferative Disorders. N Engl J Med. 2005;352(17):1779-1790. Cerca con Google

9. Zhao R, Xing S, Li Z, et al. Identification of an Acquired JAK2 Mutation in Polycythemia Vera. J Biol Chem. 2005;280(24):22788-22792. Cerca con Google

10. Klampfl T, Harutyunyan A, Berg T, et al. Genome integrity of myeloproliferative neoplasms in chronic phase and during disease progression. Blood. 2011;118(1):167-176. Cerca con Google

11. Vannucchi AM, Lasho TL, Guglielmelli P, et al. Mutations and prognosis in primary myelofibrosis. Leukemia. 2013;27(9):1861-1869. Cerca con Google

12. Vannucchi AM and Biamonte F. Epigenetics and mutations in chronic myeloproliferative neoplasms. Haematologica. 2011. Cerca con Google

13. Mullally A, Lane SW, Ball B, et al. Physiological Jak2V617F expression causes a lethal myeloproliferative neoplasm with differential effects on hematopoietic stem and progenitor cells. Cancer Cell. 2010;17(6):584-596. Cerca con Google

14. Chen E, Beer PA, Godfrey AL, et al. Distinct Clinical Phenotypes Associated with JAK2V617F Reflect Differential STAT1 Signaling. Cancer Cell. 2010;18(5):524-535. Cerca con Google

15. Bartel DP. MicroRNAs: genomics, biogenesis, mechanism, and function. Cell. 2004;116(2):281-297. Cerca con Google

16. Eichhorn S, Guo H, McGeary S, et al. mRNA Destabilization Is the Dominant Effect of Mammalian MicroRNAs by the Time Substantial Repression Ensues. Mol Cell. 2014;56(1):104-115. Cerca con Google

17. Iorio MV and Croce CM. MicroRNA dysregulation in cancer: diagnostics, monitoring and therapeutics. A comprehensive review. EMBO Molecular Medicine. 2012;4(3):143-159. Cerca con Google

18. Guglielmelli P, Tozzi L, Pancrazzi A, et al. MicroRNA expression profile in granulocytes from primary myelofibrosis patients. Exp Hematol. 2007;35(11):1708.e1-1708.e12. Cerca con Google

19. Hussein K, Theophile K, Dralle W, Wiese B, Kreipe H, Bock O. MicroRNA expression profiling of megakaryocytes in primary myelofibrosis and essential thrombocythemia. Platelets. 2009;20(6):391-400. Cerca con Google

20. Guglielmelli P, Tozzi L, Bogani C, et al. Overexpression of microRNA-16-2 contributes to the abnormal erythropoiesis in polycythemia vera. Blood. 2011;117(25):6923-6927. Cerca con Google

21. Guo S, Lu J, Schlanger R, et al. MicroRNA miR-125a controls hematopoietic stem cell number. Proceedings of the National Academy of Sciences. 2010;107(32):14229-14234. Cerca con Google

22. O'Connell RM, Chaudhuri AA, Rao DS, Gibson WSJ, Balazs AB, Baltimore D. MicroRNAs enriched in hematopoietic stem cells differentially regulate long-term hematopoietic output. Proceedings of the National Academy of Sciences. 2010;107(32):14235-14240. Cerca con Google

23. Ooi AGL, Sahoo D, Adorno M, Wang Y, Weissman IL, Park CY. MicroRNA-125b expands hematopoietic stem cells and enriches for the lymphoid-balanced and lymphoid-biased subsets. Proceedings of the National Academy of Sciences. 2010;107(50):21505-21510. Cerca con Google

24. Lu J, Guo S, Ebert BL, et al. MicroRNA-Mediated Control of Cell Fate in Megakaryocyte-Erythrocyte Progenitors. Developmental Cell. 2008;14(6):843-853. Cerca con Google

25. Kumar MS, Narla A, Nonami A, et al. Coordinate loss of a microRNA and protein-coding gene cooperate in the pathogenesis of 5q− syndrome. Blood. 2011;118(17):4666-4673. Cerca con Google

26. Chen C, Li L, Lodish HF, Bartel DP. MicroRNAs Modulate Hematopoietic Lineage Differentiation. Science. 2004;303(5654):83-86. Cerca con Google

27. Zhan H, Cardozo C, Raza A. MicroRNAs in myeloproliferative neoplasms. Br J Haematol. 2013;161(4):471-483. Cerca con Google

28. Zhang L, Sankaran VG, Lodish HF. MicroRNAs in erythroid and megakaryocytic differentiation and megakaryocyte–erythroid progenitor lineage commitment. Leukemia. 2012;26(11):2310-2316. Cerca con Google

29. Báez A, Martìn-Antonio B, Piruat JI, et al. Gene and miRNA Expression Profiles of Hematopoietic Progenitor Cells Vary Depending on Their Origin. Biology of Blood and Marrow Transplantation. 2014;20(5):630-639. Cerca con Google

30. Raghavachari N, Liu P, Barb JJ, et al. Integrated analysis of miRNA and mRNA during differentiation of human CD34+ cells delineates the regulatory roles of microRNA in hematopoiesis. Exp Hematol. 2014;42(1):14-27.e2. Cerca con Google

31. Bruchova H, Yoon D, Agarwal AM, Mendell J, Prchal JT. Regulated expression of microRNAs in normal and polycythemia vera erythropoiesis. Exp Hematol. 2007;35(11):1657-1667. Cerca con Google

32. Bruchova H, Merkerova M, Prchal JT. Aberrant expression of microRNA in polycythemia vera. Haematologica. 2008;93(7):1009-1016. Cerca con Google

33. Vian L, Di Carlo M, Pelosi E, et al. Transcriptional fine-tuning of microRNA-223 levels directs lineage choice of human hematopoietic progenitors. Cell Death & Differentiation. 2013;21(2):290-301. Cerca con Google

34. Su R, Lin H, Zhang X, et al. MiR-181 family: regulators of myeloid differentiation and acute myeloid leukemia as well as potential therapeutic targets. Oncogene. 2014. Cerca con Google

35. Lin X, Rice KL, Buzzai M, et al. miR-433 is aberrantly expressed in myeloproliferative neoplasms and suppresses hematopoietic cell growth and differentiation. Leukemia. 2013;27(2):344-352. Cerca con Google

36. Slezak S, Jin P, Caruccio L, et al. Gene and microRNA analysis of neutrophils from patients with polycythemia vera and essential thrombocytosis: down-regulation of micro RNA-1 and -133a. Journal of Translational Medicine. 2009;7(1). Cerca con Google

37. Norfo R, Zini R, Pennucci V, et al. miRNA-mRNA integrative analysis in primary myelofibrosis CD34+ cells unveils the role of miR-155/JARID2 axis in abnormal megakaryopoiesis. Blood. 2014. Cerca con Google

38. Zhan H, Cardozo C, Yu W, et al. MicroRNA deregulation in polycythemia vera and essential thrombocythemia patients. Blood Cells, Molecules, and Diseases. 2013;50(3):190-195. Cerca con Google

39. Bortoluzzi S, Bisognin A, Biasiolo M, et al. Characterization and discovery of novel miRNAs and moRNAs in JAK2V617F-mutated SET2 cells. Blood. 2012;119(13):e120-e130. Cerca con Google

40. Gaffo E, Zambonelli P, Bisognin A, Bortoluzzi S, Davoli R. miRNome of Italian Large White pig subcutaneous fat tissue: new miRNAs, isomiRs and moRNAs. Anim Genet. 2014;45(5):685-698. Cerca con Google

41. Kent WJ. BLAT--the BLAST-like alignment tool. Genome Res. 2002;12(4):656-664. Cerca con Google

42. Gee HE, Camps C, Buffa FM, et al. MicroRNA-10b and breast cancer metastasis. Nature. 2008;455(7216):E8-E9. Cerca con Google

43. Ma L, Teruya-Feldstein J, Weinberg RA. Tumour invasion and metastasis initiated by microRNA-10b in breast cancer. Nature. 2007;449(7163):682-688. Cerca con Google

44. Chan M, Liaw CS, Ji SM, et al. Identification of Circulating MicroRNA Signatures for Breast Cancer Detection. Clinical Cancer Research. 2013;19(16):4477-4487. Cerca con Google

45. Ouyang M, Li Y, Ye S, et al. MicroRNA Profiling Implies New Markers of Chemoresistance of Triple-Negative Breast Cancer. PLoS ONE. 2014;9(5). Cerca con Google

46. Tsukamoto O, Miura K, Mishima H, et al. Identification of endometrioid endometrial carcinoma-associated microRNAs in tissue and plasma. Gynecol Oncol. 2014;132(3):715-721. Cerca con Google

47. Zaravinos A, Radojicic J, Lambrou GI, et al. Expression of miRNAs Involved in Angiogenesis, Tumor Cell Proliferation, Tumor Suppressor Inhibition, Epithelial-Mesenchymal Transition and Activation of Metastasis in Bladder Cancer. J Urol. 2012;188(2):615-623. Cerca con Google

48. Huang L, Lin J, Yu Y, Zhang M, Wang H, Zheng M. Downregulation of Six MicroRNAs Is Associated with Advanced Stage, Lymph Node Metastasis and Poor Prognosis in Small Cell Carcinoma of the Cervix. PLoS ONE. 2012;7(3). Cerca con Google

49. Wu X, Weng L, Li X, et al. Identification of a 4-microRNA Signature for Clear Cell Renal Cell Carcinoma Metastasis and Prognosis. PLoS ONE. 2012;7(5). Cerca con Google

50. Han Y-, Park CY, Bhagat G, et al. microRNA-29a induces aberrant self-renewal capacity in hematopoietic progenitors, biased myeloid development, and acute myeloid leukemia. J Exp Med. 2010;207(3):475-489. Cerca con Google

51. Umbach JL, Strelow LI, Wong SW, Cullen BR. Analysis of rhesus rhadinovirus microRNAs expressed in virus-induced tumors from infected rhesus macaques. Virology. 2010;405(2):592-599. Cerca con Google

52. Bortoluzzi S, Biasiolo M, Bisognin A. MicroRNA–offset RNAs (moRNAs): by-product spectators or functional players? Trends Mol Med. 2011;17(9):473-474. Cerca con Google

53. Enright AJ, John B, Gaul U, et al. MicroRNA targets in Drosophila. Genome Biol. 2004;5(1):R1-R1. Cerca con Google

54. Kertesz M, Iovino N, Unnerstall U, Gaul U, Segal E. The role of site accessibility in microRNA target recognition. Nat Genet. 2007;39(10):1278-1284. Cerca con Google

55. Brooks AN, Kilgour E, Smith PD. Molecular Pathways: Fibroblast Growth Factor Signaling: A New Therapeutic Opportunity in Cancer. Clinical Cancer Research. 2012;18(7):1855-1862. Cerca con Google

56. Tiong KH, Mah LY, Leong C. Functional roles of fibroblast growth factor receptors (FGFRs) signaling in human cancers. Apoptosis. 2013;18(12):1447-1468. Cerca con Google

57. Katoh M and Nakagama H. FGF Receptors: Cancer Biology and Therapeutics. Med Res Rev. 2014;34(2):280-300. Cerca con Google

58. Tan L, Wang J, Tanizaki J, et al. Development of covalent inhibitors that can overcome resistance to first-generation FGFR kinase inhibitors. Proceedings of the National Academy of Sciences. 2014. Cerca con Google

59. Aoki N, Kimura S, Takiyama Y, et al. The Role of the DAP12 Signal in Mouse Myeloid Differentiation. The Journal of Immunology. 2000;165(7):3790-3796. Cerca con Google

60. Aoki N, Kimura S, Oikawa K, et al. DAP12 ITAM Motif Regulates Differentiation and Apoptosis in M1 Leukemia Cells. Biochem Biophys Res Commun. 2002;291(2):296-304. Cerca con Google

61. Gingras M, Lapillonne H, Margolin JF. TREM-1, MDL-1, and DAP12 expression is associated with a mature stage of myeloid development. Mol Immunol. 2002;38(11):817-824. Cerca con Google

62. Bakker ABH, Baker E, Sutherland GR, Phillips JH, Lanier LL. Myeloid DAP12-associating lectin (MDL)-1 is a cell surface receptor involved in the activation of myeloid cells. Proceedings of the National Academy of Sciences. 1999;96(17):9792-9796. Cerca con Google

63. Chen X, Bai F, Sokol L, et al. A critical role for DAP10 and DAP12 in CD8+ T cell–mediated tissue damage in large granular lymphocyte leukemia. Blood. 2009;113(14):3226-3234. Cerca con Google

64. Aird KM and Zhang R. Nucleotide metabolism, oncogene-induced senescence and cancer. Cancer Lett. Cerca con Google

65. Grasso D and Vaccaro MI. Macroautophagy and the oncogene-induced senescence. Endocrinology of Aging. 2014;5. Cerca con Google

66. Hills S and Diffley JX. DNA Replication and Oncogene-Induced Replicative Stress. Current Biology. 2014;24(10):R435-R444. Cerca con Google

67. Ma H, Wu Y, Choi JG, Wu H. Lower and upper stem-single-stranded RNA junctions together determine the Drosha cleavage site. Proc Natl Acad Sci U S A. 2013;110(51):20687-20692. Cerca con Google

68. Romero-Cordoba SL, Salido-Guadarrama I, Rodriguez-Dorantes M, Hidalgo-Miranda A. miRNA biogenesis: Biological impact in the development of cancer. Cancer Biol Ther. 2014;15(11):1444-1455. Cerca con Google

69. Winter J and Diederichs S. Argonaute-3 activates the let-7a passenger strand microRNA. RNA Biol. 2013;10(10):1631-1643. Cerca con Google

70. Yuen HF, Chan KK, Grills C, et al. Ran is a potential therapeutic target for cancer cells with molecular changes associated with activation of the PI3K/Akt/mTORC1 and Ras/MEK/ERK pathways. Clin Cancer Res. 2012;18(2):380-391. Cerca con Google

71. Singh CP, Singh J, Nagaraju J. A baculovirus-encoded MicroRNA (miRNA) suppresses its host miRNA biogenesis by regulating the exportin-5 cofactor Ran. J Virol. 2012;86(15):7867-7879. Cerca con Google

72. Azuma-Mukai A, Oguri H, Mituyama T, et al. Characterization of endogenous human Argonautes and their miRNA partners in RNA silencing. Proceedings of the National Academy of Sciences. 2008;105(23):7964-7969. Cerca con Google

73. Fernandez-Valverde S, Taft RJ, Mattick JS. Dynamic isomiR regulation in Drosophila development. RNA. 2010;16(10):1881-1888. Cerca con Google

74. Tan GC, Chan E, Molnar A, et al. 5' isomiR variation is of functional and evolutionary importance. Nucleic Acids Res. 2014;42(14):9424-9435. Cerca con Google

75. Jaskiewicz L and Zavolan M. Dicer partners expand the repertoire of miRNA targets. Genome Biol. 2012;13(11). Cerca con Google

76. Neilsen CT, Goodall GJ, Bracken CP. IsomiRs – the overlooked repertoire in the dynamic microRNAome. Trends in Genetics. 2012;28(11):544-549. Cerca con Google

77. Fukunaga R, Han B, Hung J, Xu J, Weng Z, Zamore P. Dicer Partner Proteins Tune the Length of Mature miRNAs in Flies and Mammals. Cell. 2012;151(3):533-546. Cerca con Google

78. Morin RD, O'Connor MD, Griffith M, et al. Application of massively parallel sequencing to microRNA profiling and discovery in human embryonic stem cells. Genome Res. 2008;18(4):610-621. Cerca con Google

79. Cloonan N, Wani S, Xu Q, et al. MicroRNAs and their isomiRs function cooperatively to target common biological pathways. Genome Biol. 2011;12(12). Cerca con Google

80. Chan Y, Lin Y, Lin R, et al. Concordant and Discordant Regulation of Target Genes by miR-31 and Its Isoforms. PLoS ONE. 2013;8(3). Cerca con Google

81. Langenberger D, Bermudez-Santana C, Hertel J, Hoffmann S, Khaitovich P, Stadler PF. Evidence for human microRNA-offset RNAs in small RNA sequencing data. Bioinformatics. 2009;25(18):2298-2301. Cerca con Google

References chapter 2 Cerca con Google

1. Mortazavi, A., Williams, B. A., McCue, K., Schaeffer, L. & Wold, B. Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nature Methods 5, 621-628 (2008). Cerca con Google

2. Nagalakshmi, U. et al. The Transcriptional Landscape of the Yeast Genome Defined by RNA Sequencing. Science 320, 1344-1349 (2008). Cerca con Google

3. Lister, R. et al. Highly Integrated Single-Base Resolution Maps of the Epigenome in Arabidopsis. Cell 133, 523-536 (2008). Cerca con Google

4. The Status, Quality, and Expansion of the NIH Full-Length cDNA Project: The Mammalian Gene Collection (MGC). Genome Res. 14, 2121-2127 (2004). Cerca con Google

5. Boguski, M. S., Tolstoshev, C. M. & Bassett, D. E. Gene discovery in dbEST. Science 265, 1993-1994 (1994). Cerca con Google

6. Sanger, F., Nicklen, S. & Coulson, A. R. DNA sequencing with chain-terminating inhibitors. Proceedings of the National Academy of Sciences 74, 5463-5467 (1977). Cerca con Google

7. Sanger, F. & Coulson, A. R. A rapid method for determining sequences in DNA by primed synthesis with DNA polymerase. J. Mol. Biol. 94, 441-448 (1975). Cerca con Google

8. Velculescu, V. E., Zhang, L., Vogelstein, B. & Kinzler, K. W. Serial Analysis of Gene Expression. Science 270, 484-487 (1995). Cerca con Google

9. Harbers, M. & Carninci, P. Tag-based approaches for transcriptome research and genome annotation. Nature Methods 2, 495-502 (2005). Cerca con Google

10. Kodzius, R. et al. CAGE: cap analysis of gene expression. Nature Methods 3, 211-222 (2006). Cerca con Google

11. Nakamura, M. & Carninci, P. Cap analysis gene expression: CAGE]. Tanpakushitsu Kakusan Koso.Protein, Nucleic Acid, Enzyme 49, 2688-2693 (2004). Cerca con Google

12. Shiraki, T. et al. Cap analysis gene expression for high-throughput analysis of transcriptional starting point and identification of promoter usage. Proceedings of the National Academy of Sciences 100, 15776-15781 (2003). Cerca con Google

13. Brenner, S. et al. Gene expression analysis by massively parallel signature sequencing (MPSS) on microbead arrays. Nat. Biotechnol. 18, 630-634 (2000). Cerca con Google

14. Peiffer, J. A. et al. A spatial dissection of the Arabidopsis floral transcriptome by MPSS. BMC Plant Biology 8 (2008). Cerca con Google

15. Reinartz, J. et al. Massively parallel signature sequencing (MPSS) as a tool for in-depth quantitative gene expression profiling in all organisms. Briefings in Functional Genomics & Proteomics 1, 95-104 (2002). Cerca con Google

16. Wang, Z., Gerstein, M. & Snyder, M. RNA-Seq: a revolutionary tool for transcriptomics. Nature Reviews Genetics 10, 57-63 (2009). Cerca con Google

17. Nagalakshmi, U. et al. The Transcriptional Landscape of the Yeast Genome Defined by RNA Sequencing. Science 320, 1344-1349 (2008). Cerca con Google

18. Wilhelm, B. T. et al. Dynamic repertoire of a eukaryotic transcriptome surveyed at single-nucleotide resolution. Nature 453, 1239-1243 (2008). Cerca con Google

19. Lister, R. et al. Highly Integrated Single-Base Resolution Maps of the Epigenome in Arabidopsis. Cell 133, 523-536 (2008). Cerca con Google

20. Marioni, J. C., Mason, C. E., Mane, S. M., Stephens, M. & Gilad, Y. RNA-seq: An assessment of technical reproducibility and comparison with gene expression arrays. Genome Res. 18, 1509-1517 (2008). Cerca con Google

21. Morin, R. et al. Profiling the HeLa S3 transcriptome using randomly primed cDNA and massively parallel short-read sequencing. BioTechniques 45, 81-94 (2008). Cerca con Google

22. Cloonan, N. et al. Stem cell transcriptome profiling via massive-scale mRNA sequencing. Nature Methods 5, 613-619 (2008). Cerca con Google

23. Barbazuk, W. B., Emrich, S. J., Chen, H. D., Li, L. & Schnable, P. S. SNP discovery via 454 transcriptome sequencing. The Plant Journal 51, 910-918 (2007). Cerca con Google

24. Vera, J. C. et al. Rapid transcriptome characterization for a nonmodel organism using 454 pyrosequencing. Mol. Ecol. 17, 1636-1647 (2008). Cerca con Google

25. Emrich, S. J., Barbazuk, W. B., Li, L. & Schnable, P. S. Gene discovery and annotation using LCM-454 transcriptome sequencing. Genome Res. 17, 69-73 (2007). Cerca con Google

26. Pareek, C. S., Smoczynski, R. & Tretyn, A. Sequencing technologies and genome sequencing. J. Appl. Genet. 52, 413-435 (2011). Cerca con Google

27. Marguerat, S. & Bähler, J. RNA-seq: from technology to biology. Cellular and Molecular Life Sciences 67, 569-579 (2010). Cerca con Google

28. Oshlack, A., Robinson, M. D. & Young, M. D. From RNA-seq reads to differential expression results. Genome Biol. 11 (2010). Cerca con Google

29. Srivastava, S. & Chen, L. A two-parameter generalized Poisson model to improve the analysis of RNA-seq data. Nucleic Acids Res. 38, e170-e170 (2010). Cerca con Google

30. Taslim, C. et al. Comparative study on ChIP-seq data: normalization and binding pattern characterization. Bioinformatics 25, 2334-2340 (2009). Cerca con Google

31. Robinson, M. D. & Oshlack, A. A scaling normalization method for differential expression analysis of RNA-seq data. Genome Biol. 11 (2010). Cerca con Google

32. Zhou, X., Oshlack, A. & Robinson, M. D. miRNA-Seq normalization comparisons need improvement. RNA 19, 733-734 (2013). Cerca con Google

33. Risso, D., Ngai, J., Speed, T. P. & Dudoit, S. Normalization of RNA-seq data using factor analysis of control genes or samples. Nat. Biotechnol. 32, 896-902 (2014). Cerca con Google

34. Pickrell, J. K. et al. Understanding mechanisms underlying human gene expression variation with RNA sequencing. Nature 464, 768-772 (2010). Cerca con Google

35. Bullard, J., Purdom, E., Hansen, K. & Dudoit, S. Evaluation of statistical methods for normalization and differential expression in mRNA-Seq experiments. BMC Bioinformatics 11 (2010). Cerca con Google

36. Oshlack, A. & Wakefield, M. J. Transcript length bias in RNA-seq data confounds systems biology. Biology Direct 4 (2009). Cerca con Google

37. Bolstad, B. M., Irizarry, R. A., Ã…strand, M. & Speed, T. P. A comparison of normalization methods for high density oligonucleotide array data based on variance and bias. Bioinformatics 19, 185-193 (2003). Cerca con Google

38. Ogunnaike, B. A., Gelmi, C. A. & Edwards, J. S. A probabilistic framework for microarray data analysis: Fundamental probability models and statistical inference. J. Theor. Biol. 264, 211-222 (2010). Cerca con Google

39. Sebastiani, P., Gussoni, E., Kohane, I. S. & Ramoni, M. F. Statistical Challenges in Functional Genomics. Statistical Science 18, 33-70 (2003). Cerca con Google

40. Farztdinov, V. & McDyer, F. Distributional fold change test, a statistical approach for detecting differential expression in microarray experiments. Algorithms for Molecular Biology 7 (2012). Cerca con Google

41. Dillies, M. et al. A comprehensive evaluation of normalization methods for Illumina high-throughput RNA sequencing data analysis. Briefings in Bioinformatics (2012). Cerca con Google

42. Sun, Z. & Zhu, Y. Systematic comparison of RNA-Seq normalization methods using measurement error models. Bioinformatics 28, 2584-2591 (2012). Cerca con Google

43. Soneson, C. & Delorenzi, M. A comparison of methods for differential expression analysis of RNA-seq data. BMC Bioinformatics 14 (2013). Cerca con Google

44. Guo, Y., Li, C., Ye, F. & Shyr, Y. Evaluation of read count based RNAseq analysis methods. BMC Genomics 14 (2013). Cerca con Google

45. Garmire, L. X. & Subramaniam, S. Evaluation of normalization methods in mammalian microRNA-Seq data. RNA 18, 1279-1288 (2012). Cerca con Google

46. Anders, S. & Huber, W. Differential expression analysis for sequence count data. Genome Biol. 11 (2010). Cerca con Google

47. Auer, P. L. & Doerge, R. W. Statistical Design and Analysis of RNA Sequencing Data. Genetics 185, 405-416 (2010). Cerca con Google

48. Robinson, M. D. & Smyth, G. K. Small-sample estimation of negative binomial dispersion, with applications to SAGE data. Biostatistics 9, 321-332 (2008). Cerca con Google

49. Hardcastle, T. J. & Kelly, K. A. baySeq: Empirical Bayesian methods for identifying differential expression in sequence count data. BMC Bioinformatics 11 (2010). Cerca con Google

50. Zhou, Y., Xia, K. & Wright, F. A. A powerful and flexible approach to the analysis of RNA sequence count data. Bioinformatics 27, 2672-2678 (2011). Cerca con Google

51. Bartel, D. P. MicroRNAs: genomics, biogenesis, mechanism, and function. Cell 116, 281-297 (2004). Cerca con Google

52. Bartel, D. P. MicroRNAs: Target Recognition and Regulatory Functions. Cell 136, 215-233 (2009). Cerca con Google

53. Iorio, M. V. & Croce, C. M. MicroRNA dysregulation in cancer: diagnostics, monitoring and therapeutics. A comprehensive review. EMBO Molecular Medicine 4, 143-159 (2012). Cerca con Google

54. Iorio, M. V. & Croce, C. M. microRNA involvement in human cancer. Carcinogenesis 33, 1126-1133 (2012). Cerca con Google

55. Pasquinelli, A. E. MicroRNAs and their targets: recognition, regulation and an emerging reciprocal relationship. Nature reviews.Genetics 13, 271-282 (2012). Cerca con Google

56. Anders, S. & Huber, W. Differential expression analysis for sequence count data. Genome Biol. 11 (2010). Cerca con Google

57. Robinson, M. D. & Smyth, G. K. Moderated statistical tests for assessing differences in tag abundance. Bioinformatics 23, 2881-2887 (2007). Cerca con Google

58. Robinson, M. D., McCarthy, D. J. & Smyth, G. K. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139-140 (2010). Cerca con Google

59. Smyth, G. K. in (eds Gentleman, R., Carey, V. J., Huber, W., Irizarry, R. A. & Dudoit, S.) 397-420 (Springer New York, 2005). Cerca con Google

60. Di, Y., Schafer, D. W., Cumbie, J. S. & Chang, J. H. The NBP Negative Binomial Model for Assessing Differential Gene Expression from RNA-Seq. Statistical Applications in Genetics and Molecular Biology 10, 1-28 (2011). Cerca con Google

61. Kadota, K., Nishiyama, T. & Shimizu, K. A normalization strategy for comparing tag count data. Algorithms for Molecular Biology 7 (2012). Cerca con Google

62. Di, Y., Schafer, D. W. & Di, M. Y. Package "NBPSeq". Mol. Biol. (N. Y. ) 10 (2012). Cerca con Google

63. Hardcastle, T. J. & Kelly, K. A. baySeq: empirical Bayesian methods for identifying differential expression in sequence count data. BMC Bioinformatics 11 (2010). Cerca con Google

64. Benjamini, Y. & Hochberg, Y. Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing. Journal of the Royal Statistical Society.Series B (Methodological) 57, 289-300 (1995). Cerca con Google

References chapter 3 Cerca con Google

1. Beard JL, Connor JR, Jones BC. (1993) Iron in the brain. Nutr Rev 51: 157–170. Cerca con Google

2. Arosio P, Ingrassia R, Cavadini P. (2009) Ferritins: a family of molecules for iron storage, antioxidation and more. Biochim Biophys Acta 1790(7):589-599. Cerca con Google

3. Costanzo F, Colombo M, Staempfli S, Santoro C, Marone M, et al. (1986) Structure of gene and pseudogenes of human apoferritin HNucleic Acids Res 14:721–736. Cerca con Google

4. Levi S, Luzzago A, Cesareni G, Cozzi A, Franceschinelli F. (1988) Mechanism of ferritin iron uptake: activity of the H-chain and deletion mapping of the ferro-oxidase site.A study of iron uptake and ferro-oxidase activity of human liver, recombinant H-chain ferritins, and of two H-chain deletion mutants. J Biol Chem 263(34):18086-18092. Cerca con Google

5. Alkhateeb AA, Connor JR. (2013) The significance of ferritin in cancer: anti-oxidation, inflammation and tumorigenesis. Biochim Biophys Acta 1836(2):245-254. Cerca con Google

6. Coffman LG, Parsonage D, D'Agostino R Jr, Torti FM, Torti SV. (2009) Regulatory effects of ferritin on angiogenesis. Proc Natl Acad Sci U S A 106(2):570-575. Cerca con Google

7. Li R, Luo C, Mines M, Zhang J, Fan GH.(2006). Chemokine CXCL12 induces binding of ferritin heavy chain to the chemokine receptor CXCR4, alters CXCR4 signaling, and induces phosphorylation and nuclear translocation of ferritin heavy chain. J Biol Chem. 281(49):37616-27. Cerca con Google

8. Bevilacqua MA, Costanzo F, Buonaguro L, Cimino F. (1988) Ferritin H and L mRNAs in human neoplastic tissues. Ital J Biochem 37(1):1-7. Cerca con Google

9. Bevilacqua MA, Faniello MC, Quaresima B, Tiano MT, Giuliano P, et al. (1997) A common mechanism underlying the E1A repression and the cAMP stimulation of the H ferritin transcription. J Biol Chem 272(33):20736-20741. Cerca con Google

10. Faniello MC, Di Sanzo M, Quaresima B, Baudi F, Di Caro V, et al. (2008) p53-mediated downregulation of H ferritin promoter transcriptional efficiency via NF-Y. Int J Biochem Cell Biol 40(10):2110-2109. Cerca con Google

11. Wu KJ, Polack A, Dalla-Favera R. (1999) Coordinated regulation of iron-controlling genes, H-ferritin and IRP2, by c-MYC. Science 283(5402):676-679. Cerca con Google

12. Faniello MC, Chirico G, Quaresima B, Cuda G, Allevato G, et al. (2002). An alternative model of H ferritin promoter transactivation by c-Jun. Biochem J 363(Pt 1):53-58. Cerca con Google

13. Lee JH, Jang H, Cho EJ, Youn HD. (2009) Ferritin binds and activates p53 under oxidative stress Biochem Biophys Res Commun 389(3):399-404. Cerca con Google

14. Tsuji Y, Miller LL, Miller SC, Torti SV, Torti FM. (1991) Tumor necrosis factor-alpha and interleukin 1-alpha regulate transferrin receptor in human diploid fibroblasts. Relationship to the induction of ferritin heavy chain.J Biol Chem. 266(11):7257-7261. Cerca con Google

15. Di Sanzo M, Gaspari M, Misaggi R, Romeo F, Falbo L, et al.(2011) H ferritin gene silencing in a human metastatic melanoma cell line: a proteomic analysis. J Proteome Res 10(12):5444-5453. Cerca con Google

16. Iwasaki K, Mackenzie EL, Hailemariam K, Sakamoto K, Tsuji Y. (2006) Hemin-mediated regulation of an antioxidant-responsive element of the human ferritin H gene and role of Ref-1 during erythroid differentiation of K562 cells. Mol Cell Biol 26(7):2845-56. Cerca con Google

17. Bevilacqua MA, Faniello MC, Iovine B, Russo T, Cimino F et al. (2002) Transcription factor NF-Y regulates differentiation of CaCo-2 cells. Arch Biochem Biophys 407: 39–44. Cerca con Google

18. Festa M, Ricciardelli G, Mele G, Pietropaolo C, Ruffo A, et al. (2000) Overexpression of H ferritin and up-regulation of iron regulatory protein genes during differentiation of 3T3-L1 pre-adipocytes. J Biol Chem 275(47):36708-36712. Cerca con Google

19. Kim VN, Han J, Siomi MC. (2009) Biogenesis of small RNAs in animals. Nat Rev Mol Cell Biol 10(2):126-139. Cerca con Google

20. Friedman RC, Farh KK, Burge CB, Bartel DP. (2009) Most mammalian mRNAs are conserved targets of microRNAs. Genome Research 19: 92–105. Cerca con Google

21. Kim VN. (2005) MicroRNA biogenesis: coordinated cropping and dicing. Nature Reviews Molecular Cell Biology 6: 376–385. Cerca con Google

22. Montagner S, Dehó L, Monticelli S. (2014) MicroRNAs in hematopoietic development BMC Immunol 15:14. Cerca con Google

23. O'Connell RM, Zhao JL, Rao DS. (2011) MicroRNA function in myeloid biology. Blood 118(11):2960-2969. Cerca con Google

24. Calin GA, Croce CM. (2006) MicroRNA-cancer connection: the beginning of a new tale. Cancer Res 66(15):7390-7394. Cerca con Google

25. Jansson MD, Lund AH. 2012 MicroRNA and cancer. Mol Oncol 6(6):590-610. Cerca con Google

26. Davis M, Clarke S. (2013) Influence of microRNA on the maintenance of human iron metabolism. Nutrients 5(7):2611-2628. Cerca con Google

27. Misaggi R, Di Sanzo M, Cosentino C, Bond HM, Scumaci D, et al. (2014) Identification of H ferritin-dependent and independent genes in K562 differentiating cells by targeted gene silencing and expression profiling. Gene 535(2):327-335. Cerca con Google

28. Smyth GK. (2004) Linear models and empirical Bayes methods for assessing differential expression in microarray experiments. Stat Appl Genet Mol Biol 3:Article3.Wu et al. 2010 Cerca con Google

29. Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, et al. (2003). Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res 13(11):2498-2504. Cerca con Google

30. J. Wang, S. Sen. (2011) MicroRNA functional network in pancreatic cancer: from biology to biomarkers of disease. J. Biosci 36(3):481-91. Cerca con Google

31. Bradford MM. (1976) A rapid and sensitive method for the quantitation of microgram quantities of protein utilizing the principle of protein-dye binding. Anal Biochem 72:248-254. Cerca con Google

32. Zhang W, Liu HT. (2002) MAPK signal pathways in the regulation of cell proliferation in mammalian cells. Cell Research 12(1):9-18. Cerca con Google

33. Yuan X, Cong Y, Hao J, Shan Y, Zhao Z, et al. (2004) Regulation of LIP level and ROS formation through interaction of H-ferritin with G-CSF receptor. J Mol Biol 339(1):131-144. Cerca con Google

34. Rashid KA, Hevi S, Chen Y, Le Cahérec F, Chuck SL. (2002) A proteomic approach identifies proteins in hepatocytes that bind nascent apolipoprotein B. J Biol Chem 277(24):22010-22017. Cerca con Google

35. Johnson SM, Grosshans H, Shingara J, Byrom M, Jarvis R, et al. (2005) RAS is regulated by the let-7 microRNA family. Cell 120(5):635-647. Cerca con Google

36. Akao Y, Nakagawa Y, Naoe T. (2006) let-7 microRNA functions as a potential growth suppressor in human colon cancer cells. Biol Pharm Bull 29(5):903-906. Cerca con Google

37. Sampson VB, Rong NH, Han J, Yang Q, Aris V, et al (2007) MicroRNA let-7a down-regulates MYC and reverts MYC-induced growth in Burkitt lymphoma cells. Cancer Res 67(20):9762-9770. Cerca con Google

38. Lee YS, Dutta A. (2007) The tumor suppressor microRNA let-7 represses the HMGA2 oncogene. Genes Dev 21(9):1025-1030. Cerca con Google

39. Boyerinas B, Park SM, Hau A, Murmann AE, Peter ME. (2010) The role of let 7 in cell differentiation and cancer. Endocr Relat Cancer 17:F19-F36. Cerca con Google

40. Banzhaf-Strathmann J, Edbauer D. (2014) Good guy or bad guy: the opposing roles of microRNA 125b in cancer. Cell Commun Signal 12:30. Cerca con Google

41. Bousquet M et al. (2008) Myeloid cell differentiation arrest by miR-125b-1 in myelodysplastic syndrome and acute myeloid leukemia with the t(2;11)(p21;q23) translocation. J Exp Med. 205(11):2499-2506. Cerca con Google

42. Xiong Q, Yang Y, Wang H, Li J, Wang S et al. (2014) Characterization of miRNomes in acute and chronic myeloid leukemia cell lines. Genomics Proteomics Bioinformatics 12(2):79-91. Cerca con Google

43. Roskoski R Jr. (2012) ERK1/2 MAP kinases: structure, function, and regulation. Pharmacol Res 66(2):105-143. Cerca con Google

44. Coccia EM, Stellacci E, Orsatti R, Testa U, Battistini A. (1995) Regulation of ferritin H-chain expression in differentiating Friend leukemia cells. Blood. 86(4):1570-1579. Cerca con Google

45. Hou W, Tian Q, Steuerwald NM, Schrum LW, Bonkovsky HL. (2012) The let-7 microRNA enhances heme oxygenase-1 by suppressing Bach1 and attenuates oxidant injury in human hepatocytes. Biochim Biophys Acta. 2012 Nov-Dec;1819(11-12):1113-1122. Cerca con Google

46. Manca S, Magrelli A, Cialfi S, Lefort K, Ambra R, et al. (2011) Oxidative stress activation of miR-125b is part of the molecular switch for Hailey-Hailey disease manifestation. Exp Dermatol 20(11):932-937. Cerca con Google

References chapter 4 Cerca con Google

Abreu, M.M. and Sealy, L. 2010. The C/EBPbeta isoform, liver-inhibitory protein (LIP), induces autophagy in breast cancer cell lines. Exp. Cell Res. 316: 3227-3238. Cerca con Google

Albergaria, A., Resende, C., Nobre, A.R., Ribeiro, A.S., Sousa, B., Machado, J.C., Seruca, R., Paredes, J. and Schmitt, F. 2013. CCAAT/enhancer binding protein β (C/EBPβ) isoforms as transcriptional regulators of the pro-invasive CDH3/P-cadherin gene in human breast cancer cells. PloS One 8. Cerca con Google

Bartel, D.P. 2009. MicroRNAs: Target Recognition and Regulatory Functions. Cell 136: 215-233. Cerca con Google

Bazykin, G.A. and Kochetov, A.V. 2011. Alternative translation start sites are conserved in eukaryotic genomes. Nucleic Acids Res. 39: 567-577. Cerca con Google

Candeias, M.M., Powell, D.J., Roubalova, E., Apcher, S., Bourougaa, K., Vojtesek, B., Bruzzoni-Giovanelli, H. and Fåhraeus, R. 2006. Expression of p53 and p53/47 are controlled by alternative mechanisms of messenger RNA translation initiation. Oncogene 25: 6936-6947. Cerca con Google

Calvo SE, Pagliarini DJ, Mootha VK. Upstream open reading frames cause widespread reduction of protein expression and are polymorphic among humans. ProcNatl Acad Sci U S A. 2009;106(18):7507-12. Cerca con Google

Cao, D., Li, J., Guo, C.C., Allan, R.W. and Humphrey, P.A. 2009. SALL4 is a novel diagnostic marker for testicular germ cell tumors. Am. J. Surg. Pathol. 33: 1065-1077. Cerca con Google

Chiba, M. 2012. Exosomes secreted from human colorectal cancer cell lines contain mRNAs, microRNAs and natural antisense RNAs, that can transfer into the human hepatoma HepG2 and lung cancer A549 cell lines. Oncol. Rep. Cerca con Google

Fukushima, M., Tomita, T., Janoshazi, A. and Putney, J.W. 2012. Alternative translation initiation gives rise to two isoforms of Orai1 with distinct plasma membrane mobilities. J. Cell. Sci. 125: 4354-4361. Cerca con Google

Gao, Y., Wei, J., Han, J., Wang, X., Su, G., Zhao, Y., Chen, B., Xiao, Z., Cao, J. and Dai, J. 2012. The Novel Function of OCT4B Isoform-265 in Genotoxic Stress. Stem Cells 30: 665-672. Cerca con Google

Grzybowska, E.A. 2012. Human intronless genes: Functional groups, associated diseases, evolution, and mRNA processing in absence of splicing. Biochem. Biophys. Res. Commun. 424: 1-6. Cerca con Google

Helwak, A., Kudla, G., Dudnakova, T. and Tollervey, D. 2013. Mapping the Human miRNA Interactome by CLASH Reveals Frequent Noncanonical Binding. Cell 153: 654-665. Cerca con Google

Huang, D.W., Sherman, B.T. and Lempicki, R.A. 2008. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nature Protocols 4: 44-57. Cerca con Google

Jackson, R.J., Hellen, C.U.T. and Pestova, T.V. 2012. Termination and post-termination events in eukaryotic translation. Advances in Protein Chemistry and Structural Biology 86: 45-93. Cerca con Google

Kochetov, A.V. 2008. Alternative translation start sites and hidden coding potential of eukaryotic mRNAs. Bioessays 30: 683-691. Cerca con Google

Kochetov, A.V., Ahmad, S., Ivanisenko, V., Volkova, O.A., Kolchanov, N.A. and Sarai, A. 2008. uORFs, reinitiation and alternative translation start sites in human mRNAs. FEBS Lett. 582: 1293-1297. Cerca con Google

Kosaka, N., Yusuke, Y., Hagiwara, K., Tominaga, N., Katsuda, T. and Ochiya, T. 2013. Trash or Treasure: extracellular microRNAs and cell-to-cell communication. Non-Coding RNA 4. Cerca con Google

Lee, I., Ajay, S.S., Yook, J.I., Kim, H.S., Hong, S.H., Kim, N.H., Dhanasekaran, S.M., Chinnaiyan, A.M. and Athey, B.D. 2009. New class of microRNA targets containing simultaneous 5′-UTR and 3′-UTR interaction sites. Genome Res. 19: 1175-1183. Cerca con Google

Lee S, Liu B, Lee S, Huang SX, Shen B, Qian SB. Global mapping of translation initiation sites in mammalian cells at single-nucleotide resolution. Proc Natl Acad Sci U S A. 2012;109(37):E2424-32. Cerca con Google

Liang H, He S, Yang J, Jia X, Wang P, Chen X, Zhang Z, Zou X, McNutt MA, Shen WH, Yin Y. PTENα, a PTEN isoform translated through alternative initiation, regulates mitochondrial function and energy metabolism. Cell Metab. 2014 19(5):836-48. Cerca con Google

Liao, J., Ma, L., Guo, Y., Zhang, Y., Zhou, H., Shao, P., Chen, Y. and Qu, L. 2010. Deep Sequencing of Human Nuclear and Cytoplasmic Small RNAs Reveals an Unexpectedly Complex Subcellular Distribution of miRNAs and tRNA 3′ Trailers. PLoS ONE 5. Cerca con Google

Liu, C., Mallick, B., Long, D., Rennie, W.A., Wolenc, A., Carmack, C.S. and Ding, Y. 2013. CLIP-based prediction of mammalian microRNA binding sites. Nucleic Acids Res. 41: e138-e138. Cerca con Google

Lytle, J.R., Yario, T.A. and Steitz, J.A. 2007. Target mRNAs are repressed as efficiently by microRNA-binding sites in the 5′ UTR as in the 3′ UTR. Proceedings of the National Academy of Sciences 104: 9667-9672. Cerca con Google

Menschaert, G., Van Criekinge, W., Notelaers, T., Koch, A., Crappé, J., Gevaert, K. and Van Damme, P. 2013. Deep proteome coverage based on ribosome profiling aids mass spectrometry-based protein and peptide discovery and provides evidence of alternative translation products and near-cognate translation initiation events. Molecular & Cellular Proteomics: MCP 12: 1780-1790. Cerca con Google

Miloslavski R, Cohen E, Avraham A, Iluz Y, Hayouka Z, Kasir J, Mudhasani R,Jones SN, Cybulski N, Rüegg MA, Larsson O, Gandin V, Rajakumar A, Topisirovic I, Meyuhas O. Oxygen sufficiency controls TOP mRNA translation via the TSC-Rheb-mTOR pathway in a 4E-BP-independent manner. J Mol Cell Biol. 2014; 6(3):255-66. Cerca con Google

Mittal, N. and Zavolan, M. 2014. Seq and CLIP through the miRNA world. Genome Biol. 15. Cerca con Google

Morris, D.R. and Geballe, A.P. 2000. Upstream Open Reading Frames as Regulators of mRNA Translation. Mol. Cell. Biol. 20: 8635-8642. Cerca con Google

Ørom, U.A., Nielsen, F.C. and Lund, A.H. 2008. MicroRNA-10a Binds the 5′UTR of Ribosomal Protein mRNAs and Enhances Their Translation. Mol. Cell 30: 460-471. Cerca con Google

Park, B., Kook, S., Lee, S., Jeong, J., Brufsky, A. and Lee, B. 2013. An isoform of C/EBPβ, LIP, regulates expression of the chemokine receptor CXCR4 and modulates breast cancer cell migration. The Journal of Biological Chemistry 288: 28656-28667. Cerca con Google

Schnall-Levin, M., Rissland, O.S., Johnston, W.K., Perrimon, N., Bartel, D.P. and Berger, B. 2011. Unusually effective microRNA targeting within repeat-rich coding regions of mammalian mRNAs. Genome Res. 21: 1395-1403. Cerca con Google

Sinkkonen, L., Hugenschmidt, T., Berninger, P., Gaidatzis, D., Mohn, F., Artus-Revel, C., Zavolan, M., Svoboda, P. and Filipowicz, W. 2008. MicroRNAs control de novo DNA methylation through regulation of transcriptional repressors in mouse embryonic stem cells. Nature Structural & Molecular Biology 15: 259-267. Cerca con Google

Skabkin, M., Skabkina, O., Hellen, C.T. and Pestova, T. 2013. Reinitiation and Other Unconventional Posttermination Events during Eukaryotic Translation. Mol. Cell 51: 249-264. Cerca con Google

Slavoff, S.A., Mitchell, A.J., Schwaid, A.G., Cabili, M.N., Ma, J., Levin, J.Z., Karger, A.D., Budnik, B.A., Rinn, J.L. and Saghatelian, A. 2013. Peptidomic discovery of short open reading frame-encoded peptides in human cells. Nature Chemical Biology 9: 59-64. Cerca con Google

Smith, E., Meyerrose, T.E., Kohler, T., Namdar-Attar, M., Bab, N., Lahat, O., Noh, T., Li, J., Karaman, M.W., Hacia, J.G. et al. 2005. Leaky ribosomal scanning in mammalian genomes: significance of histone H4 alternative translation in vivo. Nucleic Acids Res. 33: 1298-1308. Cerca con Google

Sonda, N., Simonato, F., Peranzoni, E., Calì, B., Bortoluzzi, S., Bisognin, A., Wang, E., Marincola, F., Naldini, L., Gentner, B. et al. 2013. miR-142-3p Prevents Macrophage Differentiation during Cancer-Induced Myelopoiesis. Immunity 38: 1236-1249. Cerca con Google

Szamecz, B., Rutkai, E., Cuchalová, L., Munzarová, V., Herrmannová, A., Nielsen, K.H., Burela, L., Hinnebusch, A.G. and Valášek, L. 2008. eIF3a cooperates with sequences 5′ of uORF1 to promote resumption of scanning by post-termination ribosomes for reinitiation on GCN4 mRNA. Genes Dev. 22: 2414-2425. Cerca con Google

Tay, S., Blythe, J. and Lipovich, L. 2009. Global discovery of primate-specific genes in the human genome. Proceedings of the National Academy of Sciences 106: 12019-12024. Cerca con Google

Tay, Y., Zhang, J., Thomson, A.M., Lim, B. and Rigoutsos, I. 2008. MicroRNAs to Nanog, Oct4 and Sox2 coding regions modulate embryonic stem cell differentiation. Nature 455: 1124-1128. Cerca con Google

Touriol, C., Bornes, S., Bonnal, S., Audigier, S., Prats, H., Prats, A. and Vagner, S. 2003. Generation of protein isoform diversity by alternative initiation of translation at non-AUG codons. Biol. Cell 95: 169-178. Cerca con Google

Valasek, L.S. 2012. 'Ribozoomin' - Translation Initiation from the Perspective of the Ribosome-bound Eukaryotic Initiation Factors (eIFs). Curr. Protein Peptide Sci. 13: 305-330. Cerca con Google

Vanderperre, B., Lucier, J., Bissonnette, C., Motard, J., Tremblay, G., Vanderperre, S., Wisztorski, M., Salzet, M., Boisvert, F. and Roucou, X. 2013. Direct Detection of Alternative Open Reading Frames Translation Products in Human Significantly Expands the Proteome. PLoS ONE 8. Cerca con Google

Vanderperre, B., Staskevicius, A.B., Tremblay, G., McCoy, M., O'Neill, M.,A., Cashman, N.R. and Roucou, X. 2011. An overlapping reading frame in the PRNP gene encodes a novel polypeptide distinct from the prion protein. FASEB Journal: Official Publication of the Federation of American Societies for Experimental Biology 25: 2373-2386. Cerca con Google

Wan J, Qian SB. TISdb: a database for alternative translation initiation in mammalian cells. Nucleic Acids Res. 2014;42(Database issue):D845-50. Cerca con Google

Wang, X., Zhao, Y., Xiao, Z., Chen, B., Wei, Z., Wang, B., Zhang, J., Han, J., Gao, Y., Li, L. et al. 2009. Alternative Translation of OCT4 by an Internal Ribosome Entry Site and its Novel Function in Stress Response. Stem Cells 27: 1265-1275. Cerca con Google

Wang, Y., Jatkoe, T., Zhang, Y., Mutch, M.,G., Talantov, D., Jiang, J., McLeod, H.,L. and Atkins, D. 2004. Gene expression profiles and molecular markers to predict recurrence of Dukes' B colon cancer. J. Clin. Oncol. 22: 1564-1571. Cerca con Google

Zardo, G., Ciolfi, A., Vian, L., Starnes, L.M., Billi, M., Racanicchi, S., Maresca, C., Fazi, F., Travaglini, L., Noguera, N. et al. 2012. Polycombs and microRNA-223 regulate human granulopoiesis by transcriptional control of target gene expression. Blood 119: 4034 Cerca con Google

Solo per lo Staff dell Archivio: Modifica questo record