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MiRNAs in hematopoiesis and leukemogenesis Kuchenbauer, Florian 2009

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MIRNAS IN HEMATOPOIESIS AND LEUKEMOGENESIS by FLORIAN KUCHENBAUER MD, Ludwig-Maximilian University, Munich, 2002  A THESIS SUBMITTED IN PARTIAL FULFILMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY in THE FACULTY OF GRADUATE STUDIES (Experimental Medicine)  THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver)  December 2009 © Florian Kuchenbauer, 2009  Abstract MicroRNAs (miRNAs) have been shown to play important roles in physiological as well as multiple malignant processes including acute myeloid leukemia (AML). In an effort to gain further insight into the role of miRNAs in AML, we have applied the Illumina massively parallel sequencing platform to carry out an in depth analysis of the miRNA transcriptome in a murine leukemia progression model, based on the engineered over-expression of the nucleoporin 98(NUP98)-homeobox HOXD13 fusion gene (ND13), followed by conversion into AML inducing cells upon transduction with the oncogenic collaborator Meis1. Of the over 307 identified miRNA/miRNA* species in both libraries, sequence counts varied between 2 and 136,558, indicating a remarkable expression range. Our finding of extensive sequence variations (isomiRs) for almost all miRNA and miRNA* species adds additional complexity to the miRNA transcriptome. A stringent target prediction analysis coupled with in-vitro target validation revealed the potential for miRNAmediated release of oncogenes that facilitates leukemic progression from the preleukemic to leukemia inducing state. Besides over 50 putative novel miRNAs, we found a high abundance of miRNA* species, implying a functional role for these. To further elucidate the function of miRNA*s, we took advantage of 9 deep sequencing libraries from a variety of cell lines to determine the most abundant complementary strand of know miRNAs. Comparing miRNA/miRNA* ratios across the miRNA sequence libraries revealed that most ratios remain constant across tissues and species, allowing a novel classification of miRNAs into α-duplexes, miRNAs duplexes with a dominant strand and β-duplexes with both strands being abundant. However, certain ratios were highly variable across the libraries examined as ii  exemplified for the ratio of miR-223/miR-223*. Bioinformatics as well as functional analysis revealed a possible supporting function of miR-223* to the differentiating role of miR-223 in normal normal bone marrow as well as AML. Taken together, by using deep sequencing we provided deep insight into the changes of the miRNA transcriptome in the development of AML. Furthermore, we propose a new classification for miRNA duplexes and provide evidence for a possible role a miRNA* in the development of acute myeloid leukemia.  iii  Table of Contents Abstract…………………………………………………………………………………………………………...ii Table of Contents……………………………………………………………………………………………….iv List of Tables…………………………………………………………………………………………………...vii List of Figures………………………………………………………………………………………………….viii List of Abbreviations……………………………………………………………………………………………ix Acknowledgments………………………………………………………………………………………………xi Co-Authorship Statement……………………………………………………………………………………..xii Chapter 1 Introduction........................................................................................................................... 1	
   1.1 Noncoding RNAs ......................................................................................................................... 3	
   1.1.3 MiRNAs ................................................................................................................................. 7	
   1.1.4 Biogenesis of miRNAs .......................................................................................................... 8	
   1.1.5 Mechanisms of posttranscriptional repression by miRNAs ................................................. 10	
   1.2 MiRNAs in hematopoiesis ......................................................................................................... 12	
   1.2.2 Lymphopoiesis .................................................................................................................... 15	
   1.2.3 Granulopoiesis .................................................................................................................... 17	
   1.2.4 Erythropoiesis and megakaryopoiesis ................................................................................ 19	
   1.3 MiRNAs in cancer ...................................................................................................................... 20	
   1.3.1 MiRNAs in acute myeloid leukemia .................................................................................... 23	
   1.4 Detection of miRNAs ................................................................................................................. 25	
   1.4.1 Massively parallel miRNA sequencing ................................................................................ 27	
   1.5 Thesis objectives ....................................................................................................................... 32	
   1.6 Bibliography ............................................................................................................................... 33	
   Chapter 2 In-depth characterization of the microRNA transcriptome in a leukemia progression model ............................................................................................................................................................ 49	
   2.1 Introduction ................................................................................................................................... 50	
   2.2 Materials and methods .............................................................................................................. 52	
   2.2.1 Generation of the ND13 and ND13+Meis1 bone marrow cell lines .................................... 52	
   2.2.2 Small RNA library preparation ............................................................................................ 53	
   2.2.3 Differential expression detection ......................................................................................... 53	
   2.2.4 Cloning, annotation and prediction of novel miRNAs.......................................................... 54	
   2.2.5 Real-time quantitative taqman PCR assays ....................................................................... 54	
   2.2.6 Cooperative miRNA target prediction ................................................................................. 54	
   2.2.7 Luciferase assays ............................................................................................................... 55	
   2.3 Results....................................................................................................................................... 56	
   2.3.1 Sequencing and annotation of small RNAs ........................................................................ 56	
   2.3.2 Sequence variations in miRNAs ......................................................................................... 61	
    iv  2.3.3 MicroRNAs are differentially expressed between the myeloid progenitor ND13 and the leukemic ND13+Meis1 cell line .................................................................................................... 63	
   2.3.4 Novel miRNA Genes ........................................................................................................... 69	
   2.3.5 Targets of differentially expressed miRNAs ........................................................................ 70	
   2.4 Discussion ................................................................................................................................. 74	
   2.5 Bibliography ............................................................................................................................... 80	
   Chapter 3 Differential expression of miRNA*s in cancer and the contribution of miR-223* to the development of acute myeloid leukemia ............................................................................................. 97	
   3.1 Introduction ................................................................................................................................ 98	
   3.2 Materials and methods ............................................................................................................ 100	
   3.2.1 Small RNA library preparation .......................................................................................... 100	
   3.2.2 Computational prediction of target genes ......................................................................... 100	
   3.2.3 AML samples .................................................................................................................... 101	
   3.2.4 Statistical analysis............................................................................................................. 101	
   3.2.5 Isolation of hematopoietic stem and progenitor cells ........................................................ 102	
   3.2.6 Real-time PCR .................................................................................................................. 102	
   3.2.7 Retroviral vectors and cDNA............................................................................................. 103	
   3.2.8 Retroviral infection and clonogenic progenitor assay ....................................................... 104	
   3.2.9 Sorting of AML subpopulations ......................................................................................... 105	
   3.2.10 Pre-miR-223 SNP analysis ............................................................................................. 106	
   3.2.11 Luciferase assays ........................................................................................................... 106	
   3.3 Results..................................................................................................................................... 107	
   3.3.1 The abundance of miRNA*s is tissue dependent ............................................................. 107	
   3.3.2 MiRNA*s can be classified according to their abundance in relation to the corresponding miRNA........................................................................................................................................ 111	
   3.3.2 MiR-223 and miR-223* arm accumulation is a dynamic process during leukemogenesis 114	
   3.3.3 MiR-223* binds to the 3’UTR of the oncogene CUX1 in vitro ........................................... 118	
   3.3.4 A higher relative abundance of miR-223* is associated with a trend towards a better overall survival in AML patients ............................................................................................................. 119	
   3.3.5 MiR-223* and miR-223 correlate with different prognostic markers in AML patient samples ................................................................................................................................................... 122	
   3.3.6 Inactivation of miR-223 decreases the colony forming capacity of normal bone marrow . 124	
   3.4 Discussion ............................................................................................................................... 126	
   3.5 Bibliography ............................................................................................................................. 132	
   Chapter 4 General discussion and conclusion .................................................................................. 137	
   4.1 Summary of thesis findings ..................................................................................................... 137	
   4.2 Evolution and pitfalls in the detection of miRNAs .................................................................... 138	
    v  4.3 MiRNAs in cancer .................................................................................................................... 142	
   4.4 Novel aspects of miRNAs ........................................................................................................ 145	
   4.5 Concluding remarks................................................................................................................. 147	
   4.6 Bibliography ............................................................................................................................. 148	
   Appendices ....................................................................................................................................... 174	
   I Supplementary data .................................................................................................................... 174	
   II UBC Research Ethics Board Certificates of Approval ................................................................ 174	
    vi  List of Tables Table 1.1 Classification of small RNA families based on ago protein association, size and origin ....... 7	
   Table 1.2 Differentially expressed miRNAs in cancer ......................................................................... 23	
   Table 2.1 Overview about detected miRNA/miRNA* species, expression range and distribution ...... 58	
   Table 2.2 Most differentially expressed miRNA/miRNA* species (counts >150 and >1.5 fold change) including miRBase annotation ..................................................................................................... 66	
   Table 2.3 Top 12 most abundant differentially expressed novel miRNAs including their genomic location......................................................................................................................................... 70	
   Table 3.1 Percentage of miRNA* and top 5 of the highest expressed miRNA*s .............................. 111	
   Table 3.2 Examples of α- and β-duplexes ........................................................................................ 113	
   Table 3.3 Clinical characteristics of 93 profiled AML patients ........................................................... 123	
   Table 3.4 Correlation of miR-223/miR-223* expression with clinical parameters ............................. 123	
    vii  List of Figures Figure 1.1 Layers of regulation within the DNARNAProtein dogma............................................... 3	
   Figure 1.2 Classification of small RNAs according to size .................................................................... 4	
   Figure 1.3 Canonical maturation of miRNAs ....................................................................................... 10	
   Figure 1.4 Proposed mechanisms of miRNA mediated translational repression ................................ 12	
   Figure 1.5 Differential expression of miRNAs in hematopoietic lineages............................................ 13	
   Figure 1.6 Illumina sequencing ........................................................................................................... 31	
   Figure 2.1 Overview of small RNA and miRNA gene expression in a preleukemic and leukemic cell model obtained by deep sequencing ........................................................................................... 59	
   Figure 2.2 Example of high frequency of miRNA sequence variation (isomiRs)................................. 63	
   Figure 2.3 Analysis of differentially expressed miRNA genes............................................................. 67	
   Figure 2.4 Venn diagram of predicted miRNA targets ........................................................................ 72	
   Figure 2.5 Dek-3´UTR Luciferase assays for miR-23a and miR-155.................................................. 73	
   Figure 3.1 Distribution and Expression of several miRNA/miRNA* .................................................. 109	
   Figure 3.2 miR-223 and miR-223* expression and characteristics ................................................... 116	
   Figure 3.3 Relative miR-223* expression in AML patient samples ................................................... 120	
   Figure 3.4 Retroviral overexpression of miR-223 and miR-223* in myeloid precursor cells ............. 125	
   Figure 3.5 Schematic model of the concerted action of miR-223 and miR-223* .............................. 131	
   Figure 4.1 Current techniques of miRNA detection........................................................................... 141	
   Figure 4.2 The principle of stemloop PCR ........................................................................................ 142	
   Figure 4.3 The concept of miRNA sponges ...................................................................................... 145	
    viii  List of Abbreviations 5-FU  5-fluorouracil  AML  acute myeloid leukemia  BM  bone marrow  BMT  bone marrow transplantation  CFC  colony-forming cell  CLP  common lymphoid progenitor  CMP  common myeloid progenitor  cDNA  complementary Deoxyribonucleic acid  DMEM  Dulbecco’s modified Eagles medium  Epo  erythropoietin  FACS  fluorescence-activated cell sorting  FL  flt3-ligand  G-CSF  granulocyte colony-stimulating factor  GFP  green fluorescent protein  GM-CSF  granulocyte/macrophage colony-stimulating factor  Hb  Hemoglobin  hd  homeodomain  Hox  clustered homeobox gene  HSCs  hematopoietic stem cells  IL  interleukin  IRES  internal ribosomal entry site  mRNA  messenger ribonucleic acid  miRNA  microRNA  MSCV  murine stem cell virus  NUP98  nucleoporin-98  PB  peripheral blood  PCR  polymerase chain reaction  PE  phycoerythrin  Pep3B  C57Bl6/Ly-Pep3b  PI  propidium iodide ix  PT  post transplantation  RT-PCR  reverse transcriptase-polymerase chain reaction  RBC  red blood cell  RISC  RNA induced silencing complex  SCF  stem cell factor (also called Steel Factor)  SD  standard deviation  TFs  transcription factors  WBC  white blood cells  WT  wild type  x  Acknowledgments First and foremost I wish to thank my supervisor, Keith Humphries, for guiding me with great wisdom and demanding the best of me at all times while remaining compassionate and human. I wish to express my most sincere gratitude to him for all that he has taught me in science and in life throughout these years; I could not have asked for a better teacher. I wish to thank all my lab mates, past and present. Great thanks to the heart of our lab, Patty Rosten, who showed me that there is always a way. Great thanks go to my committee members, Aly Karsan and Andy Weng, for giving my lots of freedom, trust and support throughout my degree. Many thanks to my friends Bob Argiropoulos, Michael Heuser and Dan Starczynowski, who listened to and helped selflessly with all my problems. I also wish to thank the TFL FACS facility staff, especially Lindsey Laycock, Gayle Thornbury and David Ko for their teaching efforts and extensive help with all my sorts as well as the BCCRC animal facility staff for taking care of my mice. My non-scientific friends Rupi and Mike as well as my second family Rudy and Teresa, without whom Vancouver would not have been Vancouver. Last and but not least, I wish to thank my parents, Mami, Papi, Manfred, Tante Lina , Onkel Fritz, Schorschi as well as the love of my life Arefeh. I would like to express my deepest gratitude especially to my mum, who constantly believed and supported me. Without her, I wouldn’t stand near where I am now. I dedicate this work to her and hope to make her proud.  xi  Co-Authorship Statement  Chapter 2 Florian Kuchenbauer and Ryan Morin contributed equally to the manuscript. My supervisor, Keith Humphries, and I designed the experiments and I performed the all experiments, except for the generation of the libraries. The libraries were generated with the help of all co-authors from the Genome Science Centre. Ryan Morin wrote all bioinformatic scripts. Ryan Morin and I analyzed the data. I wrote the manuscript with comments and corrections from my supervisor.  Chapter 3 My supervisor and I designed the experiments, I performed the experiments and analyzed the data. The sequencing libraries were provided by Aly Karsan, Connie Eaves, Sam Aparicio and Yuzhuo Wang and analyzed by Andrew McPherson and Ryan Morin. The patient samples were provided by Michael Heuser, Jürgen Krauter and Arnold Ganser from the university of Hannover. Furthermore, I received help from our coop student Sarah Mah as well as Dr. Tobias Berg, Jens Rüschmann and David Lai. I wrote the manuscript with comments and corrections from my supervisor. Fernando Carmargo provided the miR-223KO model.  xii  Chapter 1 Introduction One of the central ideas in molecular biology has been postulated by Francis Crick in 1958 and published in 1970 (Crick, 1970). This so-called “dogma” described a directed transfer of genetic information from DNA to RNA to protein (DNA  RNA  Protein) (Figure 1.1). Even 50 years later, this observation is still valid, but exceptions such as retroviruses (Coffin, 1979), reversing the flow of information (RNA  DNA), have been discovered (Figure 1.1). More recent exciting discoveries centred on the regulation of the genetic flow of information. Besides the revolutionary concept of inheriting epigenetic regulation, such as histone methylation and DNA imprinting (Feinberg et al., 2002), tiny noncoding RNAs took the centre stage within the last 10 years and resulted in a Nobel prize for A. Fire and C. Mello in 2006. Noncoding RNAs (ncRNAs), such as H19, have been puzzling scientists for almost decades and their functions still remain to be completely understood (Brannan et al., 1990). Only a few years later in 1993 work by Victor Ambros group led to the discovery of the first miRNA, lin-4 and its target lin-14 (Lee et al., 1993). The significance of this discovery was initially underestimated as such so-called st (small temporary) RNAs were thought to represent an “exotic” mechanism limited to the development of C.elegans. Similar findings by Gary Ruvkun’s group (Wightman et al., 1993) and later David Bartel (Lau et al., 2001), Thomas Tuschl (Lagos-Quintana et al., 2002) and again Victor Ambros (Lee and Ambros, 2001) led to the ground breaking observation that many of these small RNA are highly conserved from C.elegans to Homo sapiens and could be even found in plants (Jones, 2002). These 1  concurrent findings in 2000-2002 led to the creation of the name microRNAs (miRNAs) and paved the way for other researchers and the discovery of other small ncRNAs (Ambros et al., 2003a). Within the last 5 years, the description of novel small RNA families, profiling of known small RNAs, especially miRNAs, expanded rapidly. The hopes of finding master-regulators for development and diseases, particularly cancer, were high. Indeed, pioneering work done by Carlo Croce’s group, led to the first functional connection between miRNAs and leukemias (Calin et al., 2002). Despite the recent and rapid progress in elucidating the nature and function of miRNAs, techniques for detecting and quantifying miRNAs have proven challenging. A major breakthrough in both, detecting and quantifying miRNAs was made possible by the development of pyrosequencing (Margulies et al., 2005), resultant in deep sequencing approaches such as the Illumina platform. In this thesis, I report on the first application of such a platform to carry out an in depth characterization of the miRNA transcriptome in a leukemia progression model (Kuchenbauer et al., 2008), including the finding of novel miRNAs, miRNA isoforms and mRNA targets. Based on these sequencing results, I further investigated the expression of miRNA*s and their possible role in the development of acute myeloid leukemia (AML), proposing an additional regulatory layer of miRNAs. In the following sections I provide a brief overview of noncoding RNAs, the genesis of miRNAs and available methods of detection. I then review the state of knowledge at the start of my investigations on the nature and role of miRNAs with special emphasis on hematopoiesis and leukemias.  2  Figure 1.1 Layers of regulation within the DNARNAProtein dogma  1.1 Noncoding RNAs A ncRNA is defined as a functional RNA molecule that is not translated into a protein (Lander et al., 2001). NcRNAs are a heterogeneous group and have been divided into three different classes according to their length and function (reviewed in Costa et al.) (Costa, 2007). With respect to length, ncRNAs can range from ~18 to 25 nucleotides for the families of microRNAs (miRNAs) and small interfering RNAs (siRNAs), ∼20 to 300 nucleotides for small RNAs or up to and beyond 10,000 nucleotides in length for RNAs commonly found as transcriptional and translational regulators (Figure 1.2). The functions of many described ncRNAs are unclear, however well investigated examples are XIST (Brown et al., 1992) and more recently HOTAIR (Rinn et al., 2007), known epigenetic regulators.  3  Small ncRNAs can be broadly distinguished based upon their size (Figure 1.2) and functional properties into structural ncRNA such as tRNAs, rRNAs, snRNAs and snoRNAs as well as trans-acting ncRNAs such as miRNAs, siRNAs and piRNAs. A variety of complex mechanisms including gene silencing (Lee et al., 1993), DNA imprinting (Leighton et al., 1995), chromatin remodeling (Buhler et al., 2006) and others have already been connected to their function. Furthermore, changes in expression levels of ncRNAs have been associated with different types of cancer (Hao et al., 1993) (Tam et al., 2002). The ongoing discovery of new small RNAs and the emerging awareness of their omnipresence in primitive as well as highly developed organisms underlines the importance to research their role in physiological as well as pathophysiological processes such as cancer.  Figure 1.2 Classification of small RNAs according to size  4  1.1.2 The family of small RNAs The first discovered small RNAs (lin4, let-7) were later considered as founding members of corresponding miRNA families, the best-defined class of small transacting ncRNAs. At least three classes of small RNAs are encoded in our genome, which can be classified based on their biogenesis and the type of argonaute (ago) protein they associate with. Argonaute proteins associate with small RNAs and form ribonucleoprotein (RNP) complexes, mandatory for small RNA function (Easow et al., 2007). Based on this, three major families can be distinguished: microRNAs (miRNAs), small interfering RNAs (siRNAs) and Piwi‑interacting RNAs (piRNAs) (Table 1.1). MiRNAs and siRNAs are 19-25 nucleotides (nt) in length and derive from dsRNAs through Dicer1, a double-stranded RNA-specific endoribonuclease (Ketting et al., 2001). A major aid to the study of miRNAs has been the establishment of various databases like miRBase (http://microrna.sanger.ac.uk/sequences/index.shtml) (GriffithsJones et al., 2006).  These databases provide an ongoing registry to track the  nature, number and expression of miRNAs. As of April 2008, miRBase contained some 706 human and 547 mouse miRNA sequences and these numbers appear likely to grow as more sophisticated methods such as high throughput sequencing are used for their detection. Although miRNAs and small inhibiting RNAs (siRNAs) share  structural  similarities,  they  represent  distinct  mechanisms  of  posttranscriptional silencing (PTGS). The active forms of miRNAs and siRNAs are biochemically or/and functionally similar, but derived differently. MiRNAs are generated from the dsRNA region of hairpin shaped precursors and represent a  5  class of their own (Figure 1.2) (Lau et al., 2001). In contrast, siRNAs are endogenously produced in more primitive organisms such as C.elegans or D. melanogaster or upon exogenous delivery or transgenic expression from long dsRNAs in mammals (Ambros et al., 2003b; Aravin et al., 2003), because endogenous sources of siRNAs in mammals are rare (Tam et al., 2008). A very recently discovered class of trans-acting small RNAs are piRNAs (Aravin et al., 2006; Girard et al., 2006). They were originally discovered during small RNA profiling studies of D. melanogaster development and are the largest class of small RNAs expressed in animal cells. PiRNAs form RNA-protein complexes through interactions with Piwi proteins (Grivna et al., 2006). These piRNA complexes have been linked to transcriptional gene silencing of retrotransposons (Houwing et al., 2007) and other genetic elements in germ line cells, particularly those in spermatogenesis (Girard et al., 2006). They are distinct from miRNAs in size (26–31 nt rather than 21–24 nt) and a lack of primary sequence conservation. So far, more than 50,000 unique piRNA sequences have been discovered in mice (Aravin et al., 2006). Based on their origin, they were initially termed repeat‑associated small interfering RNAs (rasiRNAs). Of these small RNA families, miRNAs have been widely implicated in development and disease and are object of intensive research.  6  Subfamily  Ago-family protein  Class of small RNA  Length of small RNA  Origin of small RNA  Mechanism of action  Ago  Ago 1-4  miRNA  21-23nt  miRNA genes  endo-siRNA  21-22nt  repetitive elements, pseudogenes, endo-siRNA clusters  Translational repression, mRNA degradation, mRNA cleavage  prepachytene piRNA,pachy tene piRNA  24-28nt  transposons and piRNA clusters  heterochromatin formation  pachytene piRNA  29-31nt  piRNA clusters  ?  prepachytene piRNA  27-29nt  transposons and piRNA clusters  heterochromatin formation  Piwi  MILI (PIWIL2 in humans) MIWI (PIWIL1 in humans) MIWI2 (PIWIL4 in humans)  mRNA cleavage  Table 1.1 Classification of small RNA families based on ago protein association, size and origin  1.1.3 MiRNAs MiRNAs can be found in primitive organisms, e.g. sponges as well as in highly developed animals such as humans (Grimson et al., 2008). Plant miRNAs are thought to have evolved separately because their sequences, precursor structure and biogenesis are distinct from mammals (Grimson et al., 2008). Many of the animal miRNAs are phylogenetically conserved; ~55% of C. elegans miRNAs have homologues in humans, indicating that miRNAs have had important roles throughout animal evolution (Ibanez-Ventoso et al., 2008). MiRNA species with identical sequences at the seed region, comprising nucleotide positions 2–7 relative to the 5′ end of the miRNA, are often part of a miRNA family. The seed region is considered to be the most important binding site for miRNAs to pair with their mRNA targets  7  (Lewis et al., 2005). Approximately 50% of mammalian miRNAs are found in clusters with other miRNAs, either within a host gene or in intergenic regions.  1.1.4 Biogenesis of miRNAs Not only the structure, but also the biogenesis of miRNAs is conserved and have been studied extensively. In all animals, the canonical maturation of miRNAs is a stepwise process involving nuclear and cytoplasmatic cropping of miRNA precursor forms. Genes encoding miRNAs are mainly transcribed by RNA polymerase II (Lee et al., 2004), although a minor group of miRNAs, mainly associated with Alu repeats (Borchert et al., 2006), are RNA Polymerase III dependent. In all cases, the generated primary transcripts (pri-miRNAs) form an extended stem-loop structure (Figure 1.3) that is processed into hairpin intermediates (pre-miRNAs) by the microprocessor complex, containing the RNase III type enzyme RNASEN (generally known as Drosha in D. melanogaster) (Lee et al., 2006a) and its cofactor DGCR8 in mammals (Figure 1.3) (Han et al., 2004). Export of pre-miRNAs into the cytoplasm requires the formation of a specific complex with the nuclear export receptor XPO5 (Exportin 5) and the Ran-GTP cofactor (Lund et al., 2004). Once in the cytoplasm, pre-miRNAs are cleaved by DICER1 in mammals, an RNase III-family enzyme, resulting in double-stranded RNA with 1–4 nt 3' overhangs at either end (Figure 1.3) (Ketting et al., 2001). Human Dicer has been shown to interact with two related proteins, TARBP2 (Chendrimada et al., 2005) and PACT (Lee et al., 2006b), contributing to the formation of the RNA-induced silencing complex (RISC). Before entering the RISC, Dicer, TRBP, PACT and Ago proteins form a RISC loading  8  complex (Maniataki and Mourelatos, 2005). Although it is currently unknown how the RISC loading complex associates with RNA and initiates Ago protein loading, it is believed that Dicer releases the miRNA duplex cleavage and that the more stable end of the RNA duplex binds to TRBP, whereas the other end interacts with the Ago protein (Gregory et al., 2005; Tomari et al., 2004). Usually, one strand of the ~22‑nt RNA duplex remains in Ago as a mature miRNA (the guide strand or miRNA), whereas the other strand (miRNA* or the passenger strand) is degraded. Interestingly, studies on siRNA duplexes indicate that the relative thermodynamic stability of the two ends of the duplex determines which strand is to be selected (Khvorova et al., 2003; Schwarz et al., 2003). In general, the strand with relatively unstable base pairs at the 5′ end is incorporated into the RISC and does not get degraded. However, strand selection is often not a stringent process and some hairpins produce miRNAs from both strands at comparable frequencies because an imperfect secondary structure, involving gaps, bulges and mismatches is also thought to have significant impact on processing of the miRNA precursor. More recent results, including our own presented in chapter 3, now challenge the theory that miRNA* strands are simply carrier strands destined for degradation (Okamura et al., 2008; Ro et al., 2007). For example, certain miRNA*s are in fact more abundant than previously thought (Kuchenbauer et al., 2008; Okamura et al., 2008). In addition, incorporation of miRNA*s into the RISC and even functional activity have been reported (Okamura et al., 2008). These findings imply that target gene regulation through miRNA*s adds a novel layer of complexity to miRNA induced PTGS, possibly with far-reaching effects, as discussed in chapter 3.  9  Figure 1.3 Canonical maturation of miRNAs  1.1.5 Mechanisms of posttranscriptional repression by miRNAs Once the miRNA duplex has been unwound and strand selection occurs, the RISC loading complex leads to association of the miRNA strand with an AGO protein and thus formation of the actual RISC. In reality, these steps are most probably 10  inseparable and occur at the same time. The silencing complex consists of the miRNA strand, an argonaute protein as well as other proteins such as GW182 (Liu et al., 2005; Meister et al., 2005). In general, mammalian miRNA binding sites reside within the 3’UTR. Most animal miRNAs display imperfect binding which is in contrast to siRNAs, that are perfectly complementary to their binding mRNA (recently reviewed by David Bartel) (Bartel, 2009). The binding region and thus the most conserved part of miRNAs is the seed region, consisting of nucleotides 2-8 (Lewis et al., 2005). Similar to siRNAs, most plant miRNAs bind with near-perfect complementarity to sites within the coding sequence of their targets (Bartel, 2009). The degree of miRNA-mRNA complementarity has been considered a key determinant of the regulatory mechanism. Nearly perfect complementarity, as seen for miR-196 and HoxB8 (Yekta et al., 2004) allows Ago-catalyzed cleavage of the mRNA strand, comparable to siRNAs, whereas mismatches promote repression of mRNA translation. Several models regarding the mechanisms by which miRNAs regulate translation have been proposed and are summarized in Figure 1.4. It is not clear if repression occurs before initiation of translation or after and likely both can occur. Several mechanisms of translation repression are currently considered operative. For example, translation might be stopped by inhibiting initiation through repression of the cap recognition stage or the 60S recruitment stage (Humphreys et al., 2005; Pillai et al., 2005). Alternatively, induction of deadenylation can inhibit circularization of the mRNA and lead to a block of translational initiation (Wakiyama et al., 2007). Progression past the post-initiation stage can be inhibited by inducing a ribosomal drop-off (Petersen et al., 2006) or promoting mRNA degradation by  11  inducing deadenylation followed by mRNA decapping (Nottrott et al., 2006) (Figure 1.4).  Figure 1.4 Proposed mechanisms of miRNA mediated translational repression The figure was adapted from Carthew and Sontheimer, Cell 136:642-655, 2009.  1.2 MiRNAs in hematopoiesis Recent profiling approaches have revealed tissue-specific and/or developmentally regulated expression patterns that point to a role of miRNAs in complex biological processes such as cellular differentiation. Mechanisms of differentiation have been extensively studied in the hematopoietic system, facilitated through relatively easy access and well-established techniques, such as murine transplantation models.  12  The same applies to leukemias, representing deregulated differentiation and selfrenewal in stem and progenitor cells. Indeed the hematopoietic system and its related neoplasias have served as an excellent model to study the expression and function of miRNAs. Half a decade ago, the pioneering work of Chen et al. demonstrated the presence and relevance of miRNAs in hematopoiesis (Chen et al., 2004). In this work, more than 100 miRNAs were cloned from mouse bone marrow and their expression in different hematopoietic tissues validated. Based on this study and the pivotal studies of Carlo Croce’s group in chronic lymphocytic leukemia (CLL) (Calin et al., 2002), many publications followed elucidating the complex role of miRNAs in the bone marrow. Figure 1.5 summarizes the expression of some miRNAs in the different hematopoietic lineages.  Figure 1.5 Differential expression of miRNAs in hematopoietic lineages  13  1.2.1 Hematopoietic stem cells Considering that more than a hundred miRNAs have been cloned from total bone marrow and certain miRNAs, such as miR-223, exhibited lineage specific expression, it is not difficult to imagine that miRNAs have the potential to act at multiple stages in the control of hematopoiesis, potentially from stem cells to end cells. In an effort to detect miRNAs in hematopoietic stem cells and formulate a model for their role in hematopoietic differentiation Georgantas et al. combined miRNA and mRNA microarrays to search for multiple layers of regulation (Georgantas et al., 2007). Based on measured miRNA levels and correlated mRNA levels coupled with  predicted miRNA targets they proposed a model of  hematopoietic differentiation controlled by miRNAs. In total, the authors found 33 miRNAs expressed in CD34+ hematopoietic stem-progenitor cells from normal human bone marrow and mobilized human peripheral blood stem cell harvests. Based on their miRNA/mRNA-target analysis, no miRNAs specific for self-renewal could be predicted. MiR-181a and miR-128 were predicted to inhibit differentiation of all hematopoietic lineages by regulating molecules critical to very early steps in hematopoiesis. Other miRNAs such as miR-146 were predicted to regulate general lymphoid differentiation on the progenitor level, in contrast to miR-155, miR-24a and miR-17 that were predicted to be potential regulators of myeloid progenitors. However, none of these predictions have been tested in in vitro or vivo models, except for miR-155. Therefore, the regulatory potential of other miRNAs cannot be excluded.  14  A recent cloning-based survey of miRNAs from more than 250 tissue libraries surprisingly identified only 5 miRNAs that were highly specific for hematopoietic cells: miR-142, miR-144, miR-150, miR-155, and miR-223 (Landgraf et al., 2007). Here, similar to the study of Georgantas et al., miR-155 was found to be highly expressed in primitive CD34+ cells as well as lymphoid cells, suggesting a dual role in hematopoietic stem cells as well as in the lymphoid system. In an effort to test the influence of miR-155 on hematopoietic cell development, miR-155 was retrovirally expressed in murine progenitor and stem cells (O'Connell et al., 2008). Sustained expression of miR-155 had profound effects on hematopoietic populations, resulting in a myeloproliferative disorder characterized by an increase of Gr-1/Mac-1++ cells after at least 8 weeks of engraftment. However, it is not clear if these mice developed AML over time. In vitro transduction of miR-155 in human CD34+ cells led to decreased myeloid and erythroid colony formation, only partially mimicking the profound effects seen in the murine system (Georgantas et al., 2007). These observations demonstrate that deregulated miR-155 expression can affect myeloid or lymphoid development depending on the context of its expression and indicate the potential for miR-155 to direct hematopoietic cell fate decisions.  1.2.2 Lymphopoiesis The first studies of miRNAs in the hematopoietic system were based on analyses in conditions of abnormal lymphopoiesis such as chronic lymphocytic leukemia (CLL) and high-grade lymphomas. Subsequently a number of studies have focused on Tcells where for example a conditional loss of Dicer demonstrated an essential role  15  for miRNAs in T-cell development (Cobb et al., 2005). The most comprehensive miRNA profiling study was performed by Neilson et al., measuring the expression of miRNAs by cloning and sequencing from 6 stages of T-cell development (Neilson et al., 2007). This study revealed distinct miRNA expression patterns in thymocyte maturation from various double-negative (DN) stages to mature CD4+ and CD8+ cells and suggested a significant regulatory role for miR-181 also in T-cell development. Similar results were seen in earlier works by Monticelli et al. (Monticelli et al., 2005). Both studies also found miR-155 to be upregulated in the lymphoid system. The importance of miR-155 became obvious through two miR-155 knockout models (Thai et al., 2007; Vigorito et al., 2007) which revealed defective humoral responses after immunization, related to impaired germinal center formation and less antibody class switching to immunoglobulin G1, mediated by the miR-155 target PU.1 (Vigorito et al., 2007). Furthermore, miR-155 may regulate T cell lineage fate by promoting T helper type 1 versus T helper type 2 differentiation, possibly by targeting the transcription factor c-MAF (Rodriguez et al., 2007). Similar to T-cells, B cell development also appears to be strongly dependent on miRNAs as shown by a targeted deletion of miR-17-92 (Ventura et al., 2008). Loss of this cluster in murine hematopoiesis led to inhibition of B-cell development especially the pro-B to pre-B transition, and an associated increase in levels of the miR-17-92 target Bim, a proapoptotic protein (Ventura et al., 2008). These experiments suggest that the miR-17-92 cluster is crucial for the transition from preB to pro-B lymphocyte development, enhancing the survival of the B-cells at this stage by targeting Bim. In a comprehensive study of miRNAs and mRNAs in various  16  stages of B-cell development as well as B-cell tumors, a direct role for the miRNAregulation of key transcription factors in B-cell differentiation, specifically LMO2 and PRDM1 (Blimp1), was identified (Zhang et al., 2009). Again, this study underlined the relevance of miRNAs expressed from the miR-17-92 and the miR-30 cluster in B-cell development. In the transition of naïve B-cells to germinal centre B-cells an upregulation of miR-17-5p, miR-20b, miR-93, miR-28 and miR-181b was detected (Zhang et al., 2009). Interestingly, at a later stage, the transition from germinal centre B-cells to plasma cells was characterized by a down-regulation of miR-30 cluster members as well as miR-17-5p, miR-20b, miR-93, miR-28 and miR-181b (Zhang et al., 2009).  1.2.3 Granulopoiesis Due to its myeloid specific expression, miR-223 has been one of the mostinvestigated miRNAs in the hematopoietic system. However, the role of miR-223 in myeloid differentiation is not completely understood, as contradicting data exist. MiR-223 is expressed at low levels in the stem cell compartment and increases throughout myeloid differentiation (Chen et al., 2004). The first study describing a regulatory circuit involving miR-223 and the transcription factors NFIA as well as C/EBPa during human granulocytic differentiation came from Irene Bozzoni’s group (Fazi et al., 2005). In short, it was proposed that NFI-A binds the miR-223 promoter and negatively regulates its expression. Upon differentiation induction with retinoic acid, C/EBPa displaces NFI-A from the miR-223 promoter and activates miR-223 expression. In a negative regulatory loop, miR-223 represses NFI-A translation and  17  promotes granulocytic differentiation. However, in a recent report by Fukao et al. the unique C/EBPa binding element that overlaps with NFIA in the mouse miR-223 upstream sequence could not be identified (Fukao et al., 2007). Fazi et al. also showed that lentiviral overexpression of miR-223 in NB4 cells, a human acute promyelocytic leukemia cell line, induces myeloid differentiation (Fazi et al., 2005). Similar results were shown later in primary AML cells from the same group with minimal overexpression levels of miR-223, ranging below 2 fold (Fazi et al., 2007). These results indicate that discrete changes in miR-223 expression levels might be enough to promote myeloid differentiation. Considering that miR-223 is highly conserved, one would assume that its regulatory mechanisms have also been conserved during myeloid differentiation. Indeed, Fukao et al. described a conserved miR-223 promoter site, where both PU.1 and C/EBPb, two important transcription factors in myeloid differentiation, bind and lead to transcriptional activation of miR-223, probably in a dose-dependent manner (Fukao et al., 2007). Interestingly, overexpression of miR-223 and loss of miR-223 might have distinct effects, as genetic depletion of miR-223 led to a significant increase of myeloid progenitor cells as well as the number of circulating and bone marrow neutrophils (Johnnidis et al., 2008). The neutrophils exhibited an unusual morphology, aberrant pattern of lineage specific markers expression, increased reactivity to activating stimuli and display increased fungicidal activity. These findings by Johnnidis et al suggest that miR-223 acts as negative regulator of myeloid progenitor cells as well as fine-tuner of granulocytic production and immune response. This apparent contradiction, that miR-223 can act as negative regulator of  18  differentiation and at the same time as enhancer of differentiation could be due to a dose dependent mechanisms of action. This would be consistent with evidence that CEBPa exerts its functions in a dose dependent manner (Rosenbauer et al., 2005). Other miRNAs that have been implicated in myeloid differentiation are as already mentioned miR-155 (O'Connell et al., 2008), miR-196b and miR-21 (Velu et al., 2009) as well as the miR-17-106a cluster (Fontana et al., 2007) and miR-424 in monocytopoiesis (Rosa et al., 2007).  1.2.4 Erythropoiesis and megakaryopoiesis In vitro megakaryocyte differentiation assays led to the identification of 20 downregulated miRNAs, including miR-10a, miR-126, miR-106, miR-10b, miR-17 and miR-20 as detected by miRNA microarrays (Garzon et al., 2006). In addition in this study by Garzon et al. miR-130a was found to target MAFB, a factor involved in platelet function (Garzon, 2009; Garzon et al., 2006). A similar approach was chosen to profile miRNAs during erythropoiesis (Bruchova et al., 2007). Felli et al. found that KIT, a key protein in the development of red blood cells, is regulated through miR221 and miR-222 and that a decrease of miR-221 and miR-222 could be linked to changes of kit receptor levels during erythrocyte differentiation (Felli et al., 2005). MiRNA mediated modulation of the kit receptor led to expansion of early erythroblasts. Other miRNAs that have been identified in erythrocyte precursors, undergoing a progressive downregulation during erythropoeisis were miR-150, driving MEP differentiation through targeting the transcription factor MYB (Lu et al., 2008), miR-155, miR-221 and miR-222 combined with upregulation of miR-16 and  19  miR-451 at late stages of differentiation and a biphasic regulation of miR-339 and miR-378 (Lu et al., 2008). In addition by using a conditional GATA-1 cell line, Dore et al. identified that the miR-144/451 cluster is under the transcriptional control of the master erythrocyte regulator GATA-1 in zebrafish (Dore et al., 2008).  1.3 MiRNAs in cancer Much evidence implicates miRNAs as contributing factors in the pathogenesis of cancers. A provocative observation was made by Calin et al. was that a large number of known recurrent genomic alterations involved in cancer are in close proximity to miRNA genes (Calin et al., 2004b) and thus suggesting that these rearrangements affect the expression of miRNAs with tumour suppressive or oncogenic properties. Indeed, numerous reports have now appeared of candidate miRNA expression signatures in various neoplasias (Table 1.2). Translocations can result in activation of genes involved in apoptosis or cell cycle progression. Some of these oncogenes directly mediate the expression of miRNAs. For example, overexpression of c-Myc leads directly to transcription of the miR-17-92 miRNA cluster (which, in turn, is a posttranscriptional regulator of c-Myc) (O'Donnell et al., 2005). Hence, miRNA profiling can reveal expression changes that are either directly causative or the result of upstream changes in the development of cancer. Other documented mechanisms altering miRNA expression in cancer include epigenetic changes (reviewed in Rouhi et al.) (Rouhi et al., 2008), A->I editing of the premiRNA (Blow et al., 2006; Luciano et al., 2004), polymorphisms (Jazdzewski et al., 2008) or somatic mutations (Calin et al., 2005) which can affect either miRNA  20  transcription or maturation. Identifying the miRNAs that are deregulated in cancers is a first important step, reflected in a growing number of profiling approaches in all kinds of cancers. However, the next major hurdle is to reveal their pathogenic targets. Though many approaches for target prediction exist, the targets of most miRNAs are not known. Depending on their targets, miRNAs can act as tumour suppressor genes or oncogenes. Examples are miR-15 and miR-16, two miRNAs that have tumour suppressor functions by down-regulating the anti-apoptotic BCL2 gene in normal Bcells (Cimmino et al., 2005). BCL2 over-expression is observed in multiple B-Cell malignancies including B-CLL and non-Hodgkins lymphomas. Reduction of miR-15b and/or miR-16 is one of a number of mechanisms leading to BCL2 over-expression and it is thought to be the primary mechanism in development of B-CLL (Cimmino et al., 2005).  Another illustrative example of tumour suppressive miRNAs is the  repression of RAS oncogenes as well as HMGA2 by let-7, a relationship that is conserved between nematodes and humans (Johnson et al., 2005; Mayr et al., 2007). In normal lung tissues, the let-7 family tightly regulates the expression of KRAS, and the reduced expression of let-7 is a signature of non-small cell lung cancers (Johnson et al., 2005) (Table 1.2). Affirming the importance of this relationship, a common polymorphism in the let-7 binding site of KRAS leads to an increased risk of developing non-small cell lung cancer (Chin et al., 2008). This finding adds a complication to miRNA target predictions, since a somatic mutation in the binding site of a miRNA would mimic a miRNA KO on that particular target. In contrast to tumour-suppressive miRNAs, the set of miRNAs with oncogenic  21  properties are termed ‘oncomiRs’ (Esquela-Kerscher and Slack, 2006), such as miR21, miR-155 or the miR-17-92 cluster. MiR-21 is commonly over-expressed in many cancers including hepatocellular carcinoma and has been shown to a regulator of the PTEN tumour suppressor (Gramantieri et al., 2008) (Table 1.2). Another wellstudied set of 6 oncogenic miRNAs was derived from the miR-17-92 cluster. These co-transcribed miRNAs, which share many common targets, are abundant in hepatocellular carcinoma (Connolly et al., 2008) as well as various classes of B-cell lymphoma (Ota et al., 2004; Tagawa and Seto, 2005). Retroviral overexpression of miR-17-92 in a c-myc background resulted in accelerated tumor development in murine B-cell lymphoma model (He et al., 2005). Similar observations were made in an Eµ-mmu-miR155 transgenic mouse model, leading to an aggressive B-cell malignancy (Costinean et al., 2006).  22  microRNA(s)  Cancers (up/down) d  d  d  Type  d  d  let-7/miR-98  Lung , colon , ovarian , NSCLC , adenocarcinoma  miR-9  Hypermethylated in breast carcinoma  Unknown  miR-10b  Breast cancer  Unknown  miR-15  CLL  d  miR-16  CLL  d  Tumour suppressive Tumour suppressive  u  u  miR-21  glioblastoma , breast carcinoma , lymphoma  miR-29  CLL , NSCLC  miR-34a  Lung , colon , neuroblastoma , CLL ,  d  d  u  Tumour suppressive  d  Tumour suppressive  d  d  u  Either  d  miR-34b/c  NSCLC  miR-100  Pancreatic  miR-122a  HCC  miR-126  metastatic breast carcinoma  Tumour suppressive u  Oncogenic  d  Tumour suppressive d  Tumour suppressive  B-cell leukemias  u  miR-143  B-cell leukemias  d  miR-145  B-cell malignancies , colorectal carcinoma  miR-146  Papillary thyroid carcinoma, Breast, Ovarian, Prostate  miR-142  Oncogenic Tumour suppressive  d  u  d  Tumour suppressive  u  miR-155  FLT3+ leukemias , lymphomas  miR-195  CLL  miR-199a miR-221/222  Hepatocellular carcinoma u u glioblastoma , papillary thyroid carcinoma , prostate , u pancreatic  miR-223  bladder cancer , B-cell lymphoma , ovarian cancer  Tumour suppressive Oncogenic  u d  u  u  u  miR-335  metastatic breast carcinoma  miR-372/373  testicular  miR-378  B-cell lymphoma  Tumour suppressive  u  d  d  u  u d  miR-106-363 cluster  T-cell leukemias , CLL  miR-17-92 cluster  breast , lung , B-cell lymphoma  d  Tumour suppressive  u  Both u  Oncogenic  Table 1.2 Differentially expressed miRNAs in cancer  1.3.1 MiRNAs in acute myeloid leukemia The miRNA expression profile of AML patient samples has been recently addressed by several studies, using different methodological approaches and patient subgroups (Garzon et al., 2008a; Garzon et al., 2008b; Isken et al., 2008; JongenLavrencic et al., 2008; Marcucci et al., 2008). Garzon et al. profiled 240 AML patient samples with predominantly intermediate and poor cytogenetics and identified  23  molecular signatures associated with balanced 11q23 translocations, isolated trisomy 8 and FLT3 mutations (FLT3-ITD), implying a miRNA profile driven by cytogenetics (Garzon et al., 2008b). Interestingly, only this study identified two miRNAs, miR-191 and miR-199a, that correlate to overall and disease-free survival. In a second study by the same group, Garzon et al. investigated the role of miRNAs in AML patients carrying NPM1 and FLT3-ITD, the most frequent molecular aberrations in AML patients (Garzon et al., 2008a). A signature, distinguishing mutated NPM1 from unmutated NPM1 cases included the upregulation of miR-10a, miR-10b as well as let-7 and miR-29 family members (Garzon et al., 2008a). Interestingly, 2 independent studies found a correlation of FLT3-ITD+ samples and upregulation of miR-155 (Garzon et al., 2008a; Jongen-Lavrencic et al., 2008). However, FLT-3 inhibitor studies showed that the upregulation of miR-155 was independent from FLT3 signaling (Garzon et al., 2008a). Despite its role in myelopoiesis, none of these studies connected miR-223 expression to a particular AML subtype. However, Fazi et al. demonstrated that the AML1/ETO oncoprotein, the fusion product of the t(8;21), induces heterochromatic silencing of miR-223 expression by recruiting chromatin remodeling enzymes at an AML1-binding site on the miR-223 gene (Fazi et al., 2007). This is the first study showing epigenetic silencing of a miRNA locus within the pathogenesis of AML. Similar to the study of Garzon et al., the group of Bob Löwenberg explored the expression of miRNAs in a mixed AML patient group (Jongen-Lavrencic et al., 2008). They could associate miRNA expression patterns with cytogenetic and molecular subtypes. Interestingly, the amount of miRNAs necessary for a particular signature varied between AML  24  subtypes drastically. For example, a class predictor of 10 miRNAs could predict AML with t(8;21) and a set of 7 miRNAs predicted AML with t(15;17). In contrast, a predictor of 72 miRNAs was necessary for AML with inv(16). No single miRNAs could be identified that correlated with overall or event-free survival. These obvious differences might be partially due to the different patient groups, but also to the methodical approach, as the studies by Garzon et al were performed on miRNA microarrays whereas the study by Jongen-Lavrencic was based on Taqman assays. Despite the broad range of detectable miRNAs in these assays, they do not fully capture all aspects of a deregulated miRNA transcriptome in leukemia. Novel approaches, such as deep sequencing, might significantly add to the understanding how miRNAs contribute to the development of cancer, by revealing novel miRNAs, miRNA isoforms, mutations and absolute sequence counts.  1.4 Detection of miRNAs For many years, the study of mature miRNAs involved laborious techniques such as cloning (for discovery) (Lau et al., 2001) and northern blots (for measuring expression) (Chen et al., 2004). Northern blotting based assay is a powerful method to quantify miRNAs as it provides information about the size and quantity of the detected molecule. Unfortunately, large amounts of RNA, often >5µg, are necessary and it is questionable that very closely related miRNA can be reliably distinguished (Ambros and Lee, 2004). As an alternative approach, various PCR based methods involving real-time PCR have been developed (Chen et al., 2005; Ro et al., 2006). All real-time PCR techniques are based on the addition of adapters by reverse  25  transcription to the miRNA target and miRNA specific primers to detect the miRNA. The differences are found in the composition of the miRNA specific reverse transcription primers, e.g. with a stem-loop structure (Chen et al., 2005) as well as in the miRNA specific PCR primers (Jiang et al., 2005), e.g. containing Locked Nucleic Acid (LNA) to facilitate binding (Raymond et al., 2005). The main advantage of PCR based techniques lies in the high sensitivity, allowing starting amounts of 1-10 nanograms RNA. Furthermore, it is possible to discriminate miRNAs by a single base, facilitating the detection of closely related miRNA family members without cross hybridization as seen in northern blots. Other more “exotic” methods to detect small RNA expression include RNase protection assays, primer extension and in situ hybridization (Aravin and Tuschl, 2005). Cloning of miRNAs from cDNA libraries identified the majority of known small RNAs. Most of these strategies involve multiple cloning steps for which different protocols exist (Lagos-Quintana et al., 2002; Lau et al., 2001). All these cloning strategies use linkers to elongate miRNAs and PCR to amply them, followed by Sanger sequencing. Variations exist in the sequences of the linkers. Until recently, miRNA microarrays were the only cost effective way to simultaneously measure the expression of many miRNAs. Array experiments yield reproducible results that facilitate classification of cancers by virtue of similar miRNA expression profiles (Davison et al., 2006) while also enabling differentiation of subclasses of diagnostically similar (yet clinically distinct) cancers (Alizadeh and Staudt, 2000). Interestingly, it has been shown that an expression profile of 217 miRNAs can better distinguish cancers of different subtypes and stages of differentiation than ~16,000  26  protein-coding genes from the same samples (Lu et al., 2005). Two recent studies have shown a similar result in the ability to differentiate the two common subtypes of diffuse large B-cell lymphoma (DLBCL), albeit using cell line models of the disease (Lawrie et al., 2007). Though arrays have enabled pivotal discoveries in a many normal biological processes and diseases, their use and interpretation can be challenged by technical limitations. For example, unlike standard mRNA microarrays, there are limited options for probe design. Confounding this issue are the numerous miRNA families encoding highly similar mature sequences. The incorporation of LNA nucleotide analogs into probes provided a considerable enhancement in the robustness of these technologies, potentially facilitating discrimination between miRNAs with single nucleotide differences.  1.4.1 Massively parallel miRNA sequencing Despite ongoing improvements to miRNA microarrays, they are restricted to detection and profiling of known or predicted miRNAs previously described (Berezikov et al., 2006c). Another limitation arose from the observation that mature miRNAs have been shown to exhibit sequence variability owing to polymorphisms, mutations and enzymatic modifications (Morin et al., 2008; Reid et al., 2008). A rather predominant modification that appears to affect most (if not all) miRNAs is the addition of 3’ nucleotides (Morin et al., 2008). This may not be a problem as long as these additions affect all miRNAs equally. However, if there are sequence alterations that affect certain molecules (for example, a polymporphism or mutation), an assay may erroneously report this as a reduction in the expression level of that miRNA.  27  Another benefit of sequencing is that it provides the potential to discover novel miRNA genes. Though many uncharacterized small RNAs are observed in sequencing libraries, a large number of these do not appear to derive from transcripts able to form stable substrates for Drosha and Dicer, key enzymes involved in miRNA maturation. These include a recently uncovered class of miRNA termed miRtrons, whose maturation does not follow that of typical miRNAs (Ruby et al., 2007). Application of rigorous approaches involving RNA folding and machine learning-based classifiers has resulted in the discovery of many new human miRNA genes in the past few years. These approaches typically rely on small-RNA sequence data to identify candidate miRNA genes (Berezikov et al., 2006b; Friedlander et al., 2008; Morin et al., 2008). Given that only a few tissue types have thus far been profiled using deep sequencing strategies, many more novel miRNAs likely remain to be annotated. Though it is reasonable to assume that most of the highly conserved miRNAs have already been identified by computational means, we speculate that undiscovered non-conserved miRNA genes might play important roles in human disease. The quantitative detection of miRNA molecules via sequencing only recently became cost effective. Previously, miRNA sequences could only be obtained by capturing small RNAs (using single stranded ligation) followed by reverse transcription, cloning and sequencing of individual cDNAs using capillary sequencing (Lau et al., 2001). In contrast, massively parallel sequencing strategies allow the simultaneous ‘reading’ of sequences of up to millions of cDNAs derived from small RNA fragments. The first such approach (termed 454) is based on pyrosequencing technology (Margulies et  28  al., 2005). In essence, this method involves the concurrent synthesis of cDNA molecules representing small RNAs on hundreds of thousands of beads. Each of the four nucleoside triphosphates is introduced (in series) and each bead on which that nucleotide is incorporated produces visible light (due to a chemical reaction involving the pyrophospate byproduct). This process is repeated for over 100 cycles. By capturing the signal from each bead during these cycles, a read-out of signal intensity results in a sequence ‘read’ from each bead representing the sequence of the original template molecule. This technology was the first step towards routine profiling of the entire miRNA transcriptome of a single sample. A more recently available technology known as Illumina (or Solexa) sequencing is capable of producing read numbers that are orders of magnitude greater (Bentley et al., 2008). Rather than using beads, the Illumina technology involves universal primers that are physically anchored to a fixed surface inside a “flow cell” (Figure 1.6). A sequenceready library (with flanking sequences complementary to the universal primers) is introduced into a fresh flowcell and bridge amplification produces clusters comprising double-stranded DNA each derived from a single template molecule (Figure 1.6). As in 454 sequencing, each cluster is individually sequenced in parallel by successive addition of the four nucleoside triphospates. This process is distinct in that the nucleotides have a 3’ reversible terminator moiety (which is actually the fluorophore). This effectively prevents successive incorporation of multiple nucleotides in polynucleotide tracts, a major issue with early 454 sequencing. Images of the laser-excited fluorophores are captured after each nucleotide addition step and an overlay of all images is used to produce full-length sequence reads.  29  Individual reads produced by this technology are inherently shorter (currently 3650nt) but this is not a concern in small RNA applications as the read length need only exceed the length of a miRNA. Because the diversity of the miRNA transcriptome is much less than the number of reads, many identical sequences are produced. By considering each read as a single observation of a molecule of that miRNA, massively parallel sequence data provides the expression profile of a sample while offering the potential to reveal sequence discrepancies between miRNAs and the human reference genome. However, with only a few exceptions (Morin et al., 2008; Reid et al., 2008), miRNA reads that do not match the genome perfectly are generally discarded due to difficulties in alignment and separating the real observations from noise arising from difficulties in alignment to the human reference genome or sequencing errors. This mindset was encouraged by the knowledge that each individual sequence read had a high error rate (relative to classical capillary sequence data). Next-generation alignment software such as SOAP or Novoalign are capable of finding optimal alignments in the human genome while allowing multiple mismatches and variable read length. Considering that the variability of miRNAs at the sequence level is poorly understood, routine global analysis of reads (including those with imperfect alignments) is important for proper quantitation of miRNAs and for discovery of known or novel sequence variants.  30  Figure 1.6 Illumina sequencing Illumina massively parallel sequencing. After total RNA is size-fractionated to retrieve the desired size range, universal sequencing adaptors are ligated on to either end of the small RNAs. The complementary strand is synthesized (by reversetranscription) resulting in a sequence-ready cDNA library. This library is applied to a flow-cell and millions of clusters are generated by bridge amplification (red arrow). The primers for bridge-amplification (blue/violet) are complementary to the universal sequencing adaptor. Clusters are sequenced by successive additions of fluorescent ddNTPs. Images from each step are overlaid to extract a sequence read from every cluster on the flowcell.  31  1.5 Thesis objectives From the above review (chapter1), it became obvious that miRNAs and their roles in hematopoiesis and as well as leukemogenesis are complex. Given the constantly increasing number of newly discovered miRNAs, their complexity of expression and the lack of functional in vivo studies, only the tip of the iceberg has been described, but yet remains to be fully understood. Therefore, I decided to take advantage of recently established deep-sequencing approaches in combination with novel leukemia progression models to carry out a detailed quantitative examination of miRNA expression during the development of acute myeloid leukemia. The findings of this work are presented in chapter 2 as recently published in Genome Research (In-depth characterization of the microRNA transcriptome in a leukemia progression model. Kuchenbauer F, Morin RD, Argiropoulos B, Petriv OI, Griffith M, Heuser M, Yung E, Piper J, Delaney A, Prabhu AL, Zhao Y, McDonald H, Zeng T, Hirst M, Hansen CL, Marra MA, Humphries RK. Genome Res. 2008 Nov;18(11):1787-97). Based on these initial findings, we extended our examination of miRNA*s to more cancer tissues across species and found that miRNAs* behave less erratically than previously assumed. These results as presented in chapter 3 challenge the current view of an unambiguous functional miRNA for each miRNA duplex and I propose a novel classification for miRNA duplexes and how they might exert their functions. This chapter also presents results of studies examining a possible contribution of miR-223* to the function of miR-223 in the biology of myeloid cells. Finally, in chapter 4, I summarise and discuss my work in the context of the field of miRNAs in cancer.  32  1.6 Bibliography Alizadeh, A. A., and Staudt, L. M. (2000). Genomic-scale gene expression profiling of normal and malignant immune cells. Curr Opin Immunol 12, 219-225. Ambros, V., Bartel, B., Bartel, D. P., Burge, C. B., Carrington, J. C., Chen, X., Dreyfuss, G., Eddy, S. R., Griffiths-Jones, S., Marshall, M., et al. (2003a). A uniform system for microRNA annotation. Rna 9, 277-279. Ambros, V., and Lee, R. C. (2004). Identification of microRNAs and other tiny noncoding RNAs by cDNA cloning. Methods Mol Biol 265, 131-158. Ambros, V., Lee, R. C., Lavanway, A., Williams, P. T., and Jewell, D. (2003b). MicroRNAs and other tiny endogenous RNAs in C. elegans. Curr Biol 13, 807-818. Aravin, A., Gaidatzis, D., Pfeffer, S., Lagos-Quintana, M., Landgraf, P., Iovino, N., Morris, P., Brownstein, M. J., Kuramochi-Miyagawa, S., Nakano, T., et al. (2006). A novel class of small RNAs bind to MILI protein in mouse testes. Nature 442, 203207. Aravin, A., and Tuschl, T. (2005). Identification and characterization of small RNAs involved in RNA silencing. FEBS Lett 579, 5830-5840. Aravin, A. A., Lagos-Quintana, M., Yalcin, A., Zavolan, M., Marks, D., Snyder, B., Gaasterland, T., Meyer, J., and Tuschl, T. (2003). The small RNA profile during Drosophila melanogaster development. Dev Cell 5, 337-350. Bartel, D. P. (2009). MicroRNAs: target recognition and regulatory functions. Cell 136, 215-233. Bentley, D. R., Balasubramanian, S., Swerdlow, H. P., Smith, G. P., Milton, J., Brown, C. G., Hall, K. P., Evers, D. J., Barnes, C. L., Bignell, H. R., et al. (2008). 33  Accurate whole human genome sequencing using reversible terminator chemistry. Nature 456, 53-59. Berezikov, E., Thuemmler, F., van Laake, L. W., Kondova, I., Bontrop, R., Cuppen, E., and Plasterk, R. H. (2006a). Diversity of microRNAs in human and chimpanzee brain. Nat Genet 38, 1375-1377. Berezikov, E., van Tetering, G., Verheul, M., van de Belt, J., van Laake, L., Vos, J., Verloop, R., van de Wetering, M., Guryev, V., Takada, S., et al. (2006b). Many novel mammalian microRNA candidates identified by extensive cloning and RAKE analysis. Genome Res 16, 1289-1298. Blow, M. J., Grocock, R. J., van Dongen, S., Enright, A. J., Dicks, E., Futreal, P. A., Wooster, R., and Stratton, M. R. (2006). RNA editing of human microRNAs. Genome Biol 7, R27. Borchert, G. M., Lanier, W., and Davidson, B. L. (2006). RNA polymerase III transcribes human microRNAs. Nat Struct Mol Biol 13, 1097-1101. Brannan, C. I., Dees, E. C., Ingram, R. S., and Tilghman, S. M. (1990). The product of the H19 gene may function as an RNA. Mol Cell Biol 10, 28-36. Brown, C. J., Hendrich, B. D., Rupert, J. L., Lafreniere, R. G., Xing, Y., Lawrence, J., and Willard, H. F. (1992). The human XIST gene: analysis of a 17 kb inactive Xspecific RNA that contains conserved repeats and is highly localized within the nucleus. Cell 71, 527-542. Bruchova, H., Yoon, D., Agarwal, A. M., Mendell, J., and Prchal, J. T. (2007). Regulated  expression  of  microRNAs  in  normal  and  polycythemia  vera  erythropoiesis. Exp Hematol 35, 1657-1667.  34  Buhler, M., Verdel, A., and Moazed, D. (2006). Tethering RITS to a nascent transcript initiates RNAi- and heterochromatin-dependent gene silencing. Cell 125, 873-886. Calin, G. A., Dumitru, C. D., Shimizu, M., Bichi, R., Zupo, S., Noch, E., Aldler, H., Rattan, S., Keating, M., Rai, K., et al. (2002). Frequent deletions and downregulation of micro- RNA genes miR15 and miR16 at 13q14 in chronic lymphocytic leukemia. Proc Natl Acad Sci U S A 99, 15524-15529. Calin, G. A., Ferracin, M., Cimmino, A., Di Leva, G., Shimizu, M., Wojcik, S. E., Iorio, M. V., Visone, R., Sever, N. I., Fabbri, M., et al. (2005). A MicroRNA signature associated with prognosis and progression in chronic lymphocytic leukemia. N Engl J Med 353, 1793-1801. Calin, G. A., Sevignani, C., Dumitru, C. D., Hyslop, T., Noch, E., Yendamuri, S., Shimizu, M., Rattan, S., Bullrich, F., Negrini, M., and Croce, C. M. (2004). Human microRNA genes are frequently located at fragile sites and genomic regions involved in cancers. Proc Natl Acad Sci U S A 101, 2999-3004. Chen, C., Ridzon, D. A., Broomer, A. J., Zhou, Z., Lee, D. H., Nguyen, J. T., Barbisin, M., Xu, N. L., Mahuvakar, V. R., Andersen, M. R., et al. (2005). Real-time quantification of microRNAs by stem-loop RT-PCR. Nucleic Acids Res 33, e179. Chen, C. Z., Li, L., Lodish, H. F., and Bartel, D. P. (2004). MicroRNAs modulate hematopoietic lineage differentiation. Science 303, 83-86. Chendrimada, T. P., Gregory, R. I., Kumaraswamy, E., Norman, J., Cooch, N., Nishikura, K., and Shiekhattar, R. (2005). TRBP recruits the Dicer complex to Ago2 for microRNA processing and gene silencing. Nature 436, 740-744.  35  Chin, L. J., Ratner, E., Leng, S., Zhai, R., Nallur, S., Babar, I., Muller, R. U., Straka, E., Su, L., Burki, E. A., et al. (2008). A SNP in a let-7 microRNA complementary site in the KRAS 3' untranslated region increases non-small cell lung cancer risk. Cancer Res 68, 8535-8540. Cimmino, A., Calin, G. A., Fabbri, M., Iorio, M. V., Ferracin, M., Shimizu, M., Wojcik, S. E., Aqeilan, R. I., Zupo, S., Dono, M., et al. (2005). miR-15 and miR-16 induce apoptosis by targeting BCL2. Proc Natl Acad Sci U S A 102, 13944-13949. Cobb, B. S., Nesterova, T. B., Thompson, E., Hertweck, A., O'Connor, E., Godwin, J., Wilson, C. B., Brockdorff, N., Fisher, A. G., Smale, S. T., and Merkenschlager, M. (2005). T cell lineage choice and differentiation in the absence of the RNase III enzyme Dicer. J Exp Med 201, 1367-1373. Coffin, J. M. (1979). Structure, replication, and recombination of retrovirus genomes: some unifying hypotheses. J Gen Virol 42, 1-26. Connolly, E., Melegari, M., Landgraf, P., Tchaikovskaya, T., Tennant, B. C., Slagle, B. L., Rogler, L. E., Zavolan, M., Tuschl, T., and Rogler, C. E. (2008). Elevated expression of the miR-17-92 polycistron and miR-21 in hepadnavirus-associated hepatocellular carcinoma contributes to the malignant phenotype. Am J Pathol 173, 856-864. Costa, F. F. (2007). Non-coding RNAs: lost in translation? Gene 386, 1-10. Costinean, S., Zanesi, N., Pekarsky, Y., Tili, E., Volinia, S., Heerema, N., and Croce, C. M. (2006). Pre-B cell proliferation and lymphoblastic leukemia/high-grade lymphoma in E(mu)-miR155 transgenic mice. Proc Natl Acad Sci U S A 103, 70247029. Crick, F. (1970). Central dogma of molecular biology. Nature 227, 561-563.  36  Davison, T. S., Johnson, C. D., and Andruss, B. F. (2006). Analyzing micro-RNA expression using microarrays. Methods Enzymol 411, 14-34. Dore, L. C., Amigo, J. D., Dos Santos, C. O., Zhang, Z., Gai, X., Tobias, J. W., Yu, D., Klein, A. M., Dorman, C., Wu, W., et al. (2008). A GATA-1-regulated microRNA locus essential for erythropoiesis. Proc Natl Acad Sci U S A 105, 3333-3338. Easow, G., Teleman, A. A., and Cohen, S. M. (2007). Isolation of microRNA targets by miRNP immunopurification. Rna 13, 1198-1204. Esquela-Kerscher, A., and Slack, F. J. (2006). Oncomirs - microRNAs with a role in cancer. Nat Rev Cancer 6, 259-269. Fazi, F., Racanicchi, S., Zardo, G., Starnes, L. M., Mancini, M., Travaglini, L., Diverio, D., Ammatuna, E., Cimino, G., Lo-Coco, F., et al. (2007). Epigenetic Silencing of the Myelopoiesis Regulator microRNA-223 by the AML1/ETO Oncoprotein. Cancer Cell 12, 457-466. Fazi, F., Rosa, A., Fatica, A., Gelmetti, V., De Marchis, M. L., Nervi, C., and Bozzoni, I. (2005). A minicircuitry comprised of microRNA-223 and transcription factors NFI-A and C/EBPalpha regulates human granulopoiesis. Cell 123, 819-831. Feinberg, A. P., Oshimura, M., and Barrett, J. C. (2002). Epigenetic mechanisms in human disease. Cancer Res 62, 6784-6787. Felli, N., Fontana, L., Pelosi, E., Botta, R., Bonci, D., Facchiano, F., Liuzzi, F., Lulli, V., Morsilli, O., Santoro, S., et al. (2005). MicroRNAs 221 and 222 inhibit normal erythropoiesis and erythroleukemic cell growth via kit receptor down-modulation. Proc Natl Acad Sci U S A 102, 18081-18086.  37  Fontana, L., Pelosi, E., Greco, P., Racanicchi, S., Testa, U., Liuzzi, F., Croce, C. M., Brunetti, E., Grignani, F., and Peschle, C. (2007). MicroRNAs 17-5p-20a-106a control monocytopoiesis through AML1 targeting and M-CSF receptor upregulation. Nat Cell Biol 9, 775-787. Friedlander, M. R., Chen, W., Adamidi, C., Maaskola, J., Einspanier, R., Knespel, S., and Rajewsky, N. (2008). Discovering microRNAs from deep sequencing data using miRDeep. Nat Biotechnol 26, 407-415. Fukao, T., Fukuda, Y., Kiga, K., Sharif, J., Hino, K., Enomoto, Y., Kawamura, A., Nakamura, K., Takeuchi, T., and Tanabe, M. (2007). An evolutionarily conserved mechanism for microRNA-223 expression revealed by microRNA gene profiling. Cell 129, 617-631. Garzon, R. (2009). MicroRNA profiling of megakaryocytes. Methods Mol Biol 496, 293-298. Garzon, R., Garofalo, M., Martelli, M. P., Briesewitz, R., Wang, L., FernandezCymering, C., Volinia, S., Liu, C. G., Schnittger, S., Haferlach, T., et al. (2008a). Distinctive microRNA signature of acute myeloid leukemia bearing cytoplasmic mutated nucleophosmin. Proc Natl Acad Sci U S A 105, 3945-3950. Garzon, R., Pichiorri, F., Palumbo, T., Iuliano, R., Cimmino, A., Aqeilan, R., Volinia, S., Bhatt, D., Alder, H., Marcucci, G., et al. (2006). MicroRNA fingerprints during human megakaryocytopoiesis. Proc Natl Acad Sci U S A 103, 5078-5083. Garzon, R., Volinia, S., Liu, C. G., Fernandez-Cymering, C., Palumbo, T., Pichiorri, F., Fabbri, M., Coombes, K., Alder, H., Nakamura, T., et al. (2008b). MicroRNA signatures associated with cytogenetics and prognosis in acute myeloid leukemia. Blood.  38  Georgantas, R. W., 3rd, Hildreth, R., Morisot, S., Alder, J., Liu, C. G., Heimfeld, S., Calin, G. A., Croce, C. M., and Civin, C. I. (2007). CD34+ hematopoietic stemprogenitor cell microRNA expression and function: a circuit diagram of differentiation control. Proc Natl Acad Sci U S A 104, 2750-2755. Girard, A., Sachidanandam, R., Hannon, G. J., and Carmell, M. A. (2006). A germline-specific class of small RNAs binds mammalian Piwi proteins. Nature 442, 199-202. Gramantieri, L., Fornari, F., Callegari, E., Sabbioni, S., Lanza, G., Croce, C. M., Bolondi, L., and Negrini, M. (2008). MicroRNA involvement in hepatocellular carcinoma. J Cell Mol Med 12, 2189-2204. Gregory, R. I., Chendrimada, T. P., Cooch, N., and Shiekhattar, R. (2005). Human RISC couples microRNA biogenesis and posttranscriptional gene silencing. Cell 123, 631-640. Griffiths-Jones, S., Grocock, R. J., van Dongen, S., Bateman, A., and Enright, A. J. (2006). miRBase: microRNA sequences, targets and gene nomenclature. Nucleic Acids Res 34, D140-144. Grimson, A., Srivastava, M., Fahey, B., Woodcroft, B. J., Chiang, H. R., King, N., Degnan, B. M., Rokhsar, D. S., and Bartel, D. P. (2008). Early origins and evolution of microRNAs and Piwi-interacting RNAs in animals. Nature 455, 1193-1197. Grivna, S. T., Beyret, E., Wang, Z., and Lin, H. (2006). A novel class of small RNAs in mouse spermatogenic cells. Genes Dev 20, 1709-1714. Han, J., Lee, Y., Yeom, K. H., Kim, Y. K., Jin, H., and Kim, V. N. (2004). The Drosha-DGCR8 complex in primary microRNA processing. Genes Dev 18, 30163027.  39  Hao, Y., Crenshaw, T., Moulton, T., Newcomb, E., and Tycko, B. (1993). Tumoursuppressor activity of H19 RNA. Nature 365, 764-767. He, L., Thomson, J. M., Hemann, M. T., Hernando-Monge, E., Mu, D., Goodson, S., Powers, S., Cordon-Cardo, C., Lowe, S. W., Hannon, G. J., and Hammond, S. M. (2005). A microRNA polycistron as a potential human oncogene. Nature 435, 828833. Houwing, S., Kamminga, L. M., Berezikov, E., Cronembold, D., Girard, A., van den Elst, H., Filippov, D. V., Blaser, H., Raz, E., Moens, C. B., et al. (2007). A role for Piwi and piRNAs in germ cell maintenance and transposon silencing in Zebrafish. Cell 129, 69-82. Humphreys, D. T., Westman, B. J., Martin, D. I., and Preiss, T. (2005). MicroRNAs control translation initiation by inhibiting eukaryotic initiation factor 4E/cap and poly(A) tail function. Proc Natl Acad Sci U S A 102, 16961-16966. Ibanez-Ventoso, C., Vora, M., and Driscoll, M. (2008). Sequence relationships among C. elegans, D. melanogaster and human microRNAs highlight the extensive conservation of microRNAs in biology. PLoS ONE 3, e2818. Isken, F., Steffen, B., Merk, S., Dugas, M., Markus, B., Tidow, N., Zuhlsdorf, M., Illmer, T., Thiede, C., Berdel, W. E., et al. (2008). Identification of acute myeloid leukaemia associated microRNA expression patterns. Br J Haematol 140, 153-161. Jazdzewski, K., Murray, E. L., Franssila, K., Jarzab, B., Schoenberg, D. R., and de la Chapelle, A. (2008). Common SNP in pre-miR-146a decreases mature miR expression and predisposes to papillary thyroid carcinoma. Proc Natl Acad Sci U S A 105, 7269-7274.  40  Jiang, J., Lee, E. J., Gusev, Y., and Schmittgen, T. D. (2005). Real-time expression profiling of microRNA precursors in human cancer cell lines. Nucleic Acids Res 33, 5394-5403. Johnnidis, J. B., Harris, M. H., Wheeler, R. T., Stehling-Sun, S., Lam, M. H., Kirak, O., Brummelkamp, T. R., Fleming, M. D., and Camargo, F. D. (2008). Regulation of progenitor cell proliferation and granulocyte function by microRNA-223. Nature 451, 1125-1129. Johnson, S. M., Grosshans, H., Shingara, J., Byrom, M., Jarvis, R., Cheng, A., Labourier, E., Reinert, K. L., Brown, D., and Slack, F. J. (2005). RAS is regulated by the let-7 microRNA family. Cell 120, 635-647. Jones, L. (2002). Revealing micro-RNAs in plants. Trends Plant Sci 7, 473-475. Jongen-Lavrencic, M., Sun, S. M., Dijkstra, M. K., Valk, P. J., and Lowenberg, B. (2008). MicroRNA expression profiling in relation to the genetic heterogeneity of acute myeloid leukemia. Blood. Ketting, R. F., Fischer, S. E., Bernstein, E., Sijen, T., Hannon, G. J., and Plasterk, R. H. (2001). Dicer functions in RNA interference and in synthesis of small RNA involved in developmental timing in C. elegans. Genes Dev 15, 2654-2659. Khvorova, A., Reynolds, A., and Jayasena, S. D. (2003). Functional siRNAs and miRNAs exhibit strand bias. Cell 115, 209-216. Kuchenbauer, F., Morin, R. D., Argiropoulos, B., Petriv, O., Griffith, M., Heuser, M., Yung, E., Piper, J., Delaney, A., Prabhu, A. L., et al. (2008). In depth characterization of the microRNA transcriptome in a leukemia progression model. Genome Research 11, 1787-97.  41  Lagos-Quintana, M., Rauhut, R., Yalcin, A., Meyer, J., Lendeckel, W., and Tuschl, T. (2002). Identification of tissue-specific microRNAs from mouse. Curr Biol 12, 735739. Lander, E. S., Linton, L. M., Birren, B., Nusbaum, C., Zody, M. C., Baldwin, J., Devon, K., Dewar, K., Doyle, M., FitzHugh, W., et al. (2001). Initial sequencing and analysis of the human genome. Nature 409, 860-921. Landgraf, P., Rusu, M., Sheridan, R., Sewer, A., Iovino, N., Aravin, A., Pfeffer, S., Rice, A., Kamphorst, A. O., Landthaler, M., et al. (2007). A mammalian microRNA expression atlas based on small RNA library sequencing. Cell 129, 1401-1414. Lau, N. C., Lim, L. P., Weinstein, E. G., and Bartel, D. P. (2001). An abundant class of tiny RNAs with probable regulatory roles in Caenorhabditis elegans. Science 294, 858-862. Lawrie, C. H., Soneji, S., Marafioti, T., Cooper, C. D., Palazzo, S., Paterson, J. C., Cattan, H., Enver, T., Mager, R., Boultwood, J., et al. (2007). Microrna expression distinguishes between germinal center B cell-like and activated B cell-like subtypes of diffuse large B cell lymphoma. Int J Cancer 121, 1156-1161. Lee, R. C., and Ambros, V. (2001). An extensive class of small RNAs in Caenorhabditis elegans. Science 294, 862-864. Lee, R. C., Feinbaum, R. L., and Ambros, V. (1993). The C. elegans heterochronic gene lin-4 encodes small RNAs with antisense complementarity to lin-14. Cell 75, 843-854. Lee, Y., Han, J., Yeom, K. H., Jin, H., and Kim, V. N. (2006a). Drosha in primary microRNA processing. Cold Spring Harb Symp Quant Biol 71, 51-57.  42  Lee, Y., Hur, I., Park, S. Y., Kim, Y. K., Suh, M. R., and Kim, V. N. (2006b). The role of PACT in the RNA silencing pathway. Embo J 25, 522-532. Lee, Y., Kim, M., Han, J., Yeom, K. H., Lee, S., Baek, S. H., and Kim, V. N. (2004). MicroRNA genes are transcribed by RNA polymerase II. Embo J 23, 4051-4060. Leighton, P. A., Ingram, R. S., Eggenschwiler, J., Efstratiadis, A., and Tilghman, S. M. (1995). Disruption of imprinting caused by deletion of the H19 gene region in mice. Nature 375, 34-39. Lewis, B. P., Burge, C. B., and Bartel, D. P. (2005). Conserved seed pairing, often flanked by adenosines, indicates that thousands of human genes are microRNA targets. Cell 120, 15-20. Liu, J., Rivas, F. V., Wohlschlegel, J., Yates, J. R., 3rd, Parker, R., and Hannon, G. J. (2005). A role for the P-body component GW182 in microRNA function. Nat Cell Biol 7, 1261-1266. Lu, J., Getz, G., Miska, E. A., Alvarez-Saavedra, E., Lamb, J., Peck, D., SweetCordero, A., Ebert, B. L., Mak, R. H., Ferrando, A. A., et al. (2005). MicroRNA expression profiles classify human cancers. Nature 435, 834-838. Lu, J., Guo, S., Ebert, B. L., Zhang, H., Peng, X., Bosco, J., Pretz, J., Schlanger, R., Wang, J. Y., Mak, R. H., et al. (2008). MicroRNA-mediated control of cell fate in megakaryocyte-erythrocyte progenitors. Dev Cell 14, 843-853. Luciano, D. J., Mirsky, H., Vendetti, N. J., and Maas, S. (2004). RNA editing of a miRNA precursor. Rna 10, 1174-1177. Lund, E., Guttinger, S., Calado, A., Dahlberg, J. E., and Kutay, U. (2004). Nuclear export of microRNA precursors. Science 303, 95-98.  43  Maniataki, E., and Mourelatos, Z. (2005). A human, ATP-independent, RISC assembly machine fueled by pre-miRNA. Genes Dev 19, 2979-2990. Marcucci, G., Radmacher, M. D., Maharry, K., Mrozek, K., Ruppert, A. S., Paschka, P., Vukosavljevic, T., Whitman, S. P., Baldus, C. D., Langer, C., et al. (2008). MicroRNA expression in cytogenetically normal acute myeloid leukemia. N Engl J Med 358, 1919-1928. Margulies, M., Egholm, M., Altman, W. E., Attiya, S., Bader, J. S., Bemben, L. A., Berka, J., Braverman, M. S., Chen, Y. J., Chen, Z., et al. (2005). Genome sequencing in microfabricated high-density picolitre reactors. Nature 437, 376-380. Mayr, C., Hemann, M. T., and Bartel, D. P. (2007). Disrupting the pairing between let-7 and Hmga2 enhances oncogenic transformation. Science 315, 1576-1579. Meister, G., Landthaler, M., Peters, L., Chen, P. Y., Urlaub, H., Luhrmann, R., and Tuschl, T. (2005). Identification of novel argonaute-associated proteins. Curr Biol 15, 2149-2155. Monticelli, S., Ansel, K. M., Xiao, C., Socci, N. D., Krichevsky, A. M., Thai, T. H., Rajewsky, N., Marks, D. S., Sander, C., Rajewsky, K., et al. (2005). MicroRNA profiling of the murine hematopoietic system. Genome Biol 6, R71. Morin, R. D., O'Connor, M. D., Griffith, M., Kuchenbauer, F., Delaney, A., Prabhu, A. L., Zhao, Y., McDonald, H., Zeng, T., Hirst, M., et al. (2008). Application of massively parallel sequencing to microRNA profiling and discovery in human embryonic stem cells. Genome Res 18, 610-621.  44  Neilson, J. R., Zheng, G. X., Burge, C. B., and Sharp, P. A. (2007). Dynamic regulation of miRNA expression in ordered stages of cellular development. Genes Dev 21, 578-589. Nottrott, S., Simard, M. J., and Richter, J. D. (2006). Human let-7a miRNA blocks protein production on actively translating polyribosomes. Nat Struct Mol Biol 13, 1108-1114. O'Connell, R. M., Rao, D. S., Chaudhuri, A. A., Boldin, M. P., Taganov, K. D., Nicoll, J., Paquette, R. L., and Baltimore, D. (2008). Sustained expression of microRNA155 in hematopoietic stem cells causes a myeloproliferative disorder. J Exp Med 205, 585-594. O'Donnell, K. A., Wentzel, E. A., Zeller, K. I., Dang, C. V., and Mendell, J. T. (2005). c-Myc-regulated microRNAs modulate E2F1 expression. Nature 435, 839-843. Okamura, K., Phillips, M. D., Tyler, D. M., Duan, H., Chou, Y. T., and Lai, E. C. (2008). The regulatory activity of microRNA* species has substantial influence on microRNA and 3' UTR evolution. Nat Struct Mol Biol 15, 354-363. Ota, A., Tagawa, H., Karnan, S., Tsuzuki, S., Karpas, A., Kira, S., Yoshida, Y., and Seto, M. (2004). Identification and characterization of a novel gene, C13orf25, as a target for 13q31-q32 amplification in malignant lymphoma. Cancer Res 64, 30873095. Petersen, C. P., Bordeleau, M. E., Pelletier, J., and Sharp, P. A. (2006). Short RNAs repress translation after initiation in mammalian cells. Mol Cell 21, 533-542. Pillai, R. S., Bhattacharyya, S. N., Artus, C. G., Zoller, T., Cougot, N., Basyuk, E., Bertrand, E., and Filipowicz, W. (2005). Inhibition of translational initiation by Let-7 MicroRNA in human cells. Science 309, 1573-1576.  45  Raymond, C. K., Roberts, B. S., Garrett-Engele, P., Lim, L. P., and Johnson, J. M. (2005). Simple, quantitative primer-extension PCR assay for direct monitoring of microRNAs and short-interfering RNAs. Rna 11, 1737-1744. Reid, J. G., Nagaraja, A. K., Lynn, F. C., Drabek, R. B., Muzny, D. M., Shaw, C. A., Weiss, M. K., Naghavi, A. O., Khan, M., Zhu, H., et al. (2008). Mouse let-7 miRNA populations exhibit RNA editing that is constrained in the 5'-seed/ cleavage/anchor regions and stabilize predicted mmu-let-7a:mRNA duplexes. Genome Res 18, 15711581. Rinn, J. L., Kertesz, M., Wang, J. K., Squazzo, S. L., Xu, X., Brugmann, S. A., Goodnough, L. H., Helms, J. A., Farnham, P. J., Segal, E., and Chang, H. Y. (2007). Functional demarcation of active and silent chromatin domains in human HOX loci by noncoding RNAs. Cell 129, 1311-1323. Ro, S., Park, C., Jin, J., Sanders, K. M., and Yan, W. (2006). A PCR-based method for detection and quantification of small RNAs. Biochem Biophys Res Commun 351, 756-763. Ro, S., Park, C., Young, D., Sanders, K. M., and Yan, W. (2007). Tissue-dependent paired expression of miRNAs. Nucleic Acids Res 35, 5944-5953. Rodriguez, A., Vigorito, E., Clare, S., Warren, M. V., Couttet, P., Soond, D. R., van Dongen, S., Grocock, R. J., Das, P. P., Miska, E. A., et al. (2007). Requirement of bic/microRNA-155 for normal immune function. Science 316, 608-611. Rosa, A., Ballarino, M., Sorrentino, A., Sthandier, O., De Angelis, F. G., Marchioni, M., Masella, B., Guarini, A., Fatica, A., Peschle, C., and Bozzoni, I. (2007). The interplay between the master transcription factor PU.1 and miR-424 regulates human monocyte/macrophage differentiation. Proc Natl Acad Sci U S A 104, 1984919854.  46  Rosenbauer, F., Koschmieder, S., Steidl, U., and Tenen, D. G. (2005). Effect of transcription-factor concentrations on leukemic stem cells. Blood 106, 1519-1524. Rouhi, A., Mager, D. L., Humphries, R. K., and Kuchenbauer, F. (2008). MiRNAs, epigenetics, and cancer. Mamm Genome. Ruby, J. G., Jan, C. H., and Bartel, D. P. (2007). Intronic microRNA precursors that bypass Drosha processing. Nature 448, 83-86. Schwarz, D. S., Hutvagner, G., Du, T., Xu, Z., Aronin, N., and Zamore, P. D. (2003). Asymmetry in the assembly of the RNAi enzyme complex. Cell 115, 199-208. Tagawa, H., and Seto, M. (2005). A microRNA cluster as a target of genomic amplification in malignant lymphoma. Leukemia 19, 2013-2016. Tam, O. H., Aravin, A. A., Stein, P., Girard, A., Murchison, E. P., Cheloufi, S., Hodges, E., Anger, M., Sachidanandam, R., Schultz, R. M., and Hannon, G. J. (2008). Pseudogene-derived small interfering RNAs regulate gene expression in mouse oocytes. Nature 453, 534-538. Tam, W., Hughes, S. H., Hayward, W. S., and Besmer, P. (2002). Avian bic, a gene isolated from a common retroviral site in avian leukosis virus-induced lymphomas that encodes a noncoding RNA, cooperates with c-myc in lymphomagenesis and erythroleukemogenesis. J Virol 76, 4275-4286. Thai, T. H., Calado, D. P., Casola, S., Ansel, K. M., Xiao, C., Xue, Y., Murphy, A., Frendewey, D., Valenzuela, D., Kutok, J. L., et al. (2007). Regulation of the germinal center response by microRNA-155. Science 316, 604-608.  47  Tomari, Y., Matranga, C., Haley, B., Martinez, N., and Zamore, P. D. (2004). A protein sensor for siRNA asymmetry. Science 306, 1377-1380. Velu, C. S., Baktula, A. M., and Grimes, H. L. (2009). Gfi1 regulates miR-21 and miR-196b to control myelopoiesis. Blood. Ventura, A., Young, A. G., Winslow, M. M., Lintault, L., Meissner, A., Erkeland, S. J., Newman, J., Bronson, R. T., Crowley, D., Stone, J. R., et al. (2008). Targeted deletion reveals essential and overlapping functions of the miR-17 through 92 family of miRNA clusters. Cell 132, 875-886. Vigorito, E., Perks, K. L., Abreu-Goodger, C., Bunting, S., Xiang, Z., Kohlhaas, S., Das, P. P., Miska, E. A., Rodriguez, A., Bradley, A., et al. (2007). microRNA-155 regulates the generation of immunoglobulin class-switched plasma cells. Immunity 27, 847-859. Wakiyama, M., Takimoto, K., Ohara, O., and Yokoyama, S. (2007). Let-7 microRNAmediated mRNA deadenylation and translational repression in a mammalian cellfree system. Genes Dev 21, 1857-1862. Wightman, B., Ha, I., and Ruvkun, G. (1993). Posttranscriptional regulation of the heterochronic gene lin-14 by lin-4 mediates temporal pattern formation in C. elegans. Cell 75, 855-862. Yekta, S., Shih, I. H., and Bartel, D. P. (2004). MicroRNA-directed cleavage of HOXB8 mRNA. Science 304, 594-596. Zhang, J., Jima, D. D., Jacobs, C., Fischer, R., Gottwein, E., Huang, G., Lugar, P. L., Lagoo, A. S., Rizzieri, D. A., Friedman, D. R., et al. (2009). Patterns of microRNA expression characterize stages of human B cell differentiation. Blood.  48  Chapter 2 In-depth characterization of the microRNA transcriptome in a leukemia progression model1  1  A version of this chapter has been published. Kuchenbauer, F., Morin, R.D., Argiropoulos, B.A., Petriv I.O.,  Griffith, M., Heuser, M., Yung, E., Piper, J., Delaney, A., Prabhu,A.L., Zhao, Y., McDonald, H., Zeng, T., Hirst, M., Hansen,C.L., Marra, M.A. and Humphries, R.K. (2008) In depth characterization of the microRNA transcriptome in a leukemia progression model. Genome Res. 18:1787-1797.  49  2.1 Introduction MicroRNAs (miRNAs) are short RNA molecules, 19-25 nucleotides (nt) in length, recently identified to play key roles in regulating gene expression by inhibiting translation and/or triggering degradation of target mRNAs (Bartel, 2004). The emerging awareness of the large number of miRNAs, their complex expression patterns and broad range of potential targets has triggered major interest in understanding their possible regulatory functions. Indeed it is now clear that miRNAs play critical roles in physiological (Looijenga et al., 2007; Park et al., 2007; Tang et al., 2007; Thatcher et al., 2007; Wang et al., 2007) as well as multiple malignant processes (Bandres et al., 2007; Hernando, 2007; Jay et al., 2007; Looijenga et al., 2007; Lui et al., 2007; Negrini et al., 2007; Porkka et al., 2007; Sevignani et al., 2007; Shell et al., 2007; Tran et al., 2007; Yu et al., 2007b). Specifically in the context of hematologic malignancies, seminal studies from the group of Carlo Croce have strongly linked miRNAs to lymphoma development (Calin et al., 2002; Calin et al., 2005; Calin et al., 2004a). Recent findings indicate miRNA expression profiling as a useful tool for classification and prognostic purposes in acute myelogenous leukemia (AML). These initial findings encourage further efforts directed at obtaining a comprehensive and quantitative picture of the miRNA transcriptome to gain further insights into the multistep process of AML development. Such efforts to date have principally relied on methods to detect single miRNAs or on a larger scale to profile the miRNA transcriptome using real-time PCR or microarray platforms. These methods are limited as they are restricted to the detection and profiling of known miRNA sequences previously identified by sequencing or homology searches  50  (Griffiths-Jones, 2006). In an effort to gain further insights into the role of miRNAs in AML, we have applied the Illumina massively parallel sequencing platform to carry out an in depth, quantitative comparative analysis of miRNA expression in a murine model of leukemia progression (Pineault et al., 2005). This leukemia model simulates the stepwise conversion of a non-leukemic myeloid progenitor cell, induced from normal mouse bone marrow by engineered over-expression of the nucleoporin 98 (NUP98) - homeobox gene HOXD13 (ND13) fusion gene, to a highly aggressive AML inducing cells upon transduction with the potent oncogenic collaborator Meis1 (Pineault et al., 2005; Pineault et al., 2003). Our results provide a comprehensive view of the miRNA transcriptome in a well-defined leukemia progression model and reveal both a striking repertoire of expressed miRNAs, including identification of 55 novel miRNAs, and a remarkable range of expression levels spanning some 5 orders of magnitude. Interestingly, few miRNAs were detected that were uniquely expressed in the preleukemic versus leukemic state but multiple differentially expressed miRNAs were identified and thus suggesting that the functional role for miRNAs in leukemic transformation may be highly complex. Adding to this complexity, we show that almost all miRNAs exhibited isoforms of variable length and thus potentially distinct function.  51  2.2 Materials and methods 2.2.1 Generation of the ND13 and ND13+Meis1 bone marrow cell lines Mice were bred and maintained at the British Columbia Cancer Research Center Animal Facility (Vancouver, BC). All experimental protocols were approved by the University of British Columbia Animal Care Committee. Establishment and characterization of the ND13 pre-leukemic BM cell lines was as previously described (Pineault et al., 2005). In brief, a polyclonal representative line was established from BM cells from C57Bl/6J mice freshly transduced with the ND13-PAC virus, selected with puromycin at a concentration of 3mg/ml for 5 days and maintained at a concentration 2mg/ml in liquid culture (Dulbecco's modified Eagle's medium (DMEM)) supplemented with 15% fetal bovine serum (FBS), 10 ng/ml of human interleukin-6 (hIL-6), 6 ng/ml of murine interleukin-3 (mIL3), and 100 ng/ml of murine stem cell factor (mSCF). All culture media and growth factors were obtained from StemCell Technologies Inc. (Vancouver, BC, Canada). Cells were counted with the Vi-Cell XR Cell Viability Analyzer (Beckman Coulter Inc., Fullerton, California). To generate the ND13+Meis1 BM cell line, puromycin-selected ND13 BM cells as described above were transduced by co-cultivation on irradiated (4,000 cGy) E86 producers for Meis1-YFP, respectively, for a period of 2 days in the presence of 5 mg/ml of protamine sulfate (Sigma, Oakville, ON, Canada) and sorted for YFP positive cells with the FACSVantage SE (Becton Dickinson, Mississauga, ON, Canada). Both, ND13 and ND13+Meis1 bone marrow cell lines were kept in culture for 40-50 days and tested for YFP by flow-cytometry analysis (FACS), immunophenotyped by FACS and injected into C57Bl/6J mice to test their in-vivo  52  properties. The lines were frozen in multiple vials with 1-3x106 cells from both lines in 1ml of 90% newborn calf serum (Invitrogen, Burlington, ON, Canada) and 10% DMSO (Sigma, Oakville, ON, Canada).  2.2.2 Small RNA library preparation Cultured ND13 and ND13+Meis1 cells were harvested and RNA extracted with Trizol as previously described (Argiropoulos et al., 2008). The extracted RNA was subjected to miRNA library construction (ND13, ND13+Meis1) according to the protocol published in Morin et al. (Morin et al., 2008).  2.2.3 Differential expression detection All unique small RNA sequences were compared between the two libraries (ND13 and ND13+Meis1) for differential expression using the Fisher exact test and Bonferroni correction. Sequences were deemed significantly differentially expressed if the p-value given by this method was < 0.001 and there was at least a 1.5-fold change in sequence counts between the two libraries. In practice, all miRNAs with this combination of fold change and expression level were deemed statistically significant. Unless stated otherwise, comparison of miRNA expression between libraries regards the most frequently observed isomiR as the diagnostic sequence for evaluation of differential expression.  53  2.2.4 Cloning, annotation and prediction of novel miRNAs A limited small RNA sequence analysis was performed according to the protocol of Fu et al. (Fu et al., 2005). The annotation procedure was performed as described but employed annotations from miRBase version 11 and the mus musculus genome (NCBI build 37). Novel miRNAs were predicted as previously described (Morin et al, 2008).  2.2.5 Real-time quantitative taqman PCR assays MiRNA real time quantification was performed using the BioMark™ 48.48 Dynamic Array System (Fluidigm Corporation, San Francisco) and TaqMan MicroRNA Assays (Applied Biosystems, Foster City) according to the manufacture’s instructions. The reverse transcriptase reaction using Taqman stemloop primers was performed according to the protocol of Tang et al (Tang et al., 2006).  2.2.6 Cooperative miRNA target prediction Predicted targets for the 19 highest expressed miRNAs from each library were downloaded  from  TargetScan  (http://www.targetscan.org),  (http://bibiserv.techfak.uni-bielefeld.de/rnahybrid/)  and  RNAHybrid miRanda  (http://www.microrna.org/miranda_new.html). Only miRNAs with counts of at least 100 in ND13 or ND13+Meis1 were included in the target analyses. Genes with target sites for at least two co-expressed miRNAs from one or both libraries were identified as potential cooperative targets. To compensate for potential bias, genes with numerous predicted miRNA target sites were given a lower rank than those with few  54  predicted target sites. This ranking was was calculated by dividing the number of target sites for co-expressed miRNAs by the total number of target sites for that gene.  We used a cutoff of 0.15 (rank) to produce the two sets of high-ranked  candidate cooperative targets of ND13-enriched and ND13+Meis1-enriched miRNAs. Predicted targets were only considered relevant if their 3’UTR was accessible based by secondary structure folding predicted by mfold 3.2 (Zuker, 2003).  2.2.7 Luciferase assays Two fragments of Dek-3´UTR with binding sites for either miR-23a (Dek23; chr13:47180220-47180392)  or  miR-155  (Dek155;  chr13:47180967-47181135)  (Figure 1.5B) were cloned into pMirReport (Ambion, Austin, Tx) and transfected with hsa-miR-155 (Ambion, Austin, Tx), hsa-miR-23a (Ambion, Austin, Tx) or a negative control miRNA (Ambion, Austin, Tx) into 293T cells. For the 3’UTR-luciferase assays, 200 ng of pMirReport-3’UTR, 10pmol of miRNAs and 0.17 ng of thymidine kinase-renilla were cotransfected into 6x105 293T cells (24-well format) using the Lipofectamine 2000 transfection reagent (Invitrogen, Burlington, ON, Canada). The assays were read in the Lumat LB 9507 tube luminometer (EG&G BERTHOLD, Germany) and the Luciferase/Renilla ratio calculated. Student’s t-test was used for statistical analysis and p<0.05 considered as significant.  55  2.3 Results All experiments were carried out using a Hox-based leukemia progression model as previously described (Pineault et al., 2005). To model the preleukemic state we used a murine bone marrow derived cell line generated by transduction with the ND13 fusion gene. These cells are growth factor dependent and are transplantable giving rise to short-term myeloid restricted repopulation without evolution to leukemia over extended in vivo follow-up (Pineault et al., 2005). As a model of progression to the leukemic state, we transduced the ND13 cell line with the Hox co-factor Meis1 thus generating a cell line that remains growth factor dependent but induces aggressive and rapidly fatal myeloid leukemia upon transplantation (Pineault et al., 2005; Pineault et al., 2003). Both cell lines exhibit stable, homogenous and almost identical immune phenotypes of primitive hematopoietic progenitors and stable differential in vivo functional properties of pre-leukemic versus leukemic cells (for details refer to Methods and Supplementary Figure 2.1).  2.3.1 Sequencing and annotation of small RNAs Small RNAs were isolated from the preleukemic progenitor line, hereafter referred to as ND13, and the leukemic line, hereafter referred to as ND13+Meis1, and processed to allow deep sequencing on the Illumina platform. A total of 9.56 x 107 and 7.23 x 107 nucleotides were sequenced from the myeloid progenitor ND13 and leukemic ND13+Meis1 cell lines respectively, producing (after removal of ambiguous reads), 3.4 x 106 (ND13) and 2.6 x 106 (ND13+Meis1) unique 27 nucleotide (nt) reads. After mapping the sequences to the mouse genome (NCBI Build 37), a total 56  of 3.90 x 105 (ND13) and 2.96 x 105 (ND13+Meis1) unique small RNA sequences remained. Each of these sequences was classified either as a known class of small RNAs, genomic repeats, degradation fragments of larger non-coding RNAs, known mRNA sequences or small RNAs deriving from un-annotated intergenic regions (Figure 2.1A). The most abundant (based on read count) RNA species in both libraries were classifiable as known miRNAs (65% of total) (Figure 2.1A) and corresponding to some 220 miRNA genes (see below and Table 2.1). The ranges of all sequence counts for each miRNA gene are plotted in Figure 2.1B. Both libraries show a similar distribution of expression levels with count ranges for a given unique miRNA species, spanning 2 (to minimize consideration of reads deriving from sequencing errors, singletons were excluded, see Methods section) to over 1.3 x 105 (ND13) and 1 x 106 (ND13+Meis1) sequence counts, respectively. Exemplified for ND13 cells, approximately 8% of miRNAs and miRNA*s were detected at high sequence counts (greater than 104) and approximately 14% were detected in the intermediate 103-104 sequence count range; the remaining 77% were detected in the low range of 2-1000 sequence counts. Thus the sequence data reveal a wide range of expression levels for miRNAs spanning over 5 orders of magnitude (Table 2.1).  57  total miRNAs/miRNA* species derived from miRNA genes most abundant miRNA/miRNA* matches miRBase ref seq # miRNAs miRNAs* annotated miRNAs* not annotated sequence count range of the most abundant miRNA/miRNA* species ## miRNAs upregulated ## miRNA downregulated # miRNA/miRNAs* exclusively expresssed in each library  ND13 305 (387) 223 110 (117) 99 (42) 51 (63) 53 (81) 1-136558 / / 12  ND13+Meis1 306 (381) 219 118 (127) 103 (35) 51 (65) 52 (78) 1-104331 16 49 8  ()=including singletons #  based on the most common sequence  ##  filtered for the most common sequence, >= 150 sequence counts, >=1.5 fold  Table 2.1 Overview about detected miRNA/miRNA* species, expression range and distribution  58  Figure 2.1 Overview of small RNA and miRNA gene expression in a preleukemic and leukemic cell model obtained by deep sequencing A. Breakdown of the proportions (in %) of various classes of small RNAs detected by sequencing of the preleukemic ND13 library. The percentages are comparable to those found in the leukemic ND13+Meis1 library. Small RNAs belonging to the miRNA family constitute the majority (65.7% in the preleukemic and 66.2% in the leukemic cells). Abreviations used:  scRNA = small cytoplasmic RNA; snRNA =  small nuclear RNA; sno RNA = small nucleolar RNA; rRNA = ribosomal RNA; tRNA = transferRNA; unknown = derived from un-annotated /intergenic regions. B. Distribution of miRNA genes expressed according to their sequence counts in the preleukemic (ND13) compared to leukemic (ND13+Meis1) cells. Shown are the numbers of unique miRNA genes plotted as a function of their expression levels as defined by a given range of sequence counts in the respective libraries of small RNAs. The total numbers of miRNA sequence counts were 1,240,570 and 1,030,414 for the preleukemic and leukemic libraries respectively.  59  60  2.3.2 Sequence variations in miRNAs In both libraries, miRNA sequences frequently exhibited variations from their ‘reference’ sequences as currently described in miRBase and thus indicating multiple mature variants that we hereafter refer to as isomiRs. Evidence of isomiRs of a similar nature were also detected in a limited sequence analysis using a linkerbased miRNA cloning approach from the same RNA pools (Fu et al., 2005) suggesting that isomiRs are not due to artifacts created from massively parallel sequencing (Figure 2.2). Our analysis revealed 2 major classes of variants or isomiRs. Most isomiRs show variability at their 5’ and/or 3’ ends likely resulting from variations in the pre-miRNA secondary structures that result in variable cleavage sites for Rnasen and Dicer1. In total, we found 3,390 isomiRs for a total of 336 sequenced miRNAs and miRNA*s, corresponding to 225 miRNA genes from both libraries (Table 2.1 and Supplementary Table 2.1). The number of different isomiRs for a given miRNA ranged from 1 to 74. Only 22 miRNAs or miRNA*s (all with very low sequence counts, <50), did not exhibit any isomiRs. However, the number of isomiRs showed only a moderate correlation with the absolute expression levels for each miRNA (R-squared=0.40, Pearson), suggesting that the number of observed isomiRs is not directly related to the abundance of a miRNA. The more prevalent type of modification noted amongst the miRNAs were single-nucleotide 3’ extensions (3390 isomiRs, Supplementary Table 2.1), compared to 151 miRNAs/miRNA*s with 3’ variations not matching the genome (Supplementary Table 2.2, Figure 2.2), possibly arising from novel mechanisms of miRNA processing. An example is given  61  in Figure 2.2, demonstrating sequenced isomiRs for miR-181a. As seen for miR181a, we frequently found that the miRBase reference sequence was not the dominant species. Therefore, the apparent relative expression levels could substantially depend on which isomiR is interrogated and challenge current real-time PCR approaches, which are based on the miRBase reference sequence. Our own experiments (unpublished data) using stemloop primers lacking only one nucleotide at the 3' end, which should theoretically amplify different IsomiRs, showed deltaCT differences between isomiRs of the same miRNA as large as 2. However, these CT differences were not predictable based on the ratio of read counts for the longer and shorter isomiR, implying that more than one isomiR was amplified by either primer.  62  Figure 2.2 Example of high frequency of miRNA sequence variation (isomiRs) Shown are the unique sequences and number of times this sequence was detected matching the pre-miRNA sequence of miR-181a. The most frequent occurring miR181a sequence is not in accordance with the miRBase reference sequence. The three most common sequences were also detectable by linker-based cloning as indicated in the figure. An example of a miR-181 isomiR not matching the genome is shown in the bottom part of the figure.  2.3.3 MicroRNAs are differentially expressed between the myeloid progenitor ND13 and the leukemic ND13+Meis1 cell line Measuring the abundance of a miRNA or miRNA* using the sum of all isomiR sequence counts correlated well with the expression level of the most abundant miRNA / miRNA* sequence (ND13: R-squared=0.98, Pearson; ND13+Meis1: R-  63  squared=0.97, Pearson). As the most abundant miRNA/miRNA* sequence detected did not correspond to the current miRBase reference sequence (Table 2.2 and for all sequence counts please refer to Supplementary Table 2.3), we focused on the most abundant miRNA/miRNA* sequence for differential abundance analyses as previously described (Morin et al, 2008). Applying this measure of miRNA expression, we identified 336 miRNAs and miRNA*s in both libraries, corresponding to 225 miRNA genes. Only twelve miRNAs were unique to ND13 and 8 were unique to ND13+Meis1 (Table 2.1) although all of these corresponded to the very low (<10) sequence count range and thus their observed differential expression may represent limits of detection rather than biological variability. Of the miRNAs detected in both the preleukemic and leukemic cells, 65 were significantly differentially expressed (≥1.5 fold change, ≥150 sequence counts, p<0.001). Table 2.2 lists all upregulated and the top 20 downregulated miRNAs with a sequence count ≥150 and a ≥1.5 fold change, thresholds set to minimize inflated fold changes values from miRNAs with very low expression levels. Supplementary Table 2.3 summarizes all differentially expressed miRNAs for different metrics. In general, more miRNAs were downregulated (49 miRNAs) than upregulated (16 miRNAs) (Table 2.2, Supplementary Table 2.3), a phenomenon that is consistent with recent profiling reports in leukemias (Garzon et al., 2008b; Lu et al., 2005). Considering fold change, the most significant miRNA was miR-196b, exhibiting a 4.4x increase in the ND13/MEIS library.  Few miRNAs showed predominant  expression in only one of the two libraries, with miR-223* (2684 counts) sequestered to ND13 cells (Table 2.2). The fact that the 5’ arm of miR-223, miR-223* is the  64  highest downregulated sequence, implies that miR-223*, previously thought to be nonfunctional, might also be an important factor for leukemic transformation. Most differentially expressed miRNA/miRNA*s were detected with intermediate sequence count levels as depicted in Figure 2.4B, which shows the abundance of a miRNA and its relative expression. An exception is miR-10a, which is expressed at high levels (ND13: 14,700, ND13+Meis1: 32,064 counts) and displays a 2.18 fold upregulation. Notably, all miRNAs located in the Hox cluster (miR-10a, mir-10b and miR-196b) (Mansfield et al., 2004) and previously implicated in regulating Hox gene expression in AML (Debernardi et al., 2007; Isken et al., 2008) were upregulated in the leukemic ND13+Meis1 cells. In contrast, almost all members of the let-7 family, some of them with very high expression levels (Supplementary Table 3), were found to be downregulated in the ND13+Meis1 library (1.7 - to 2.6 - fold), and consistent with the proposed role of let-7 family members as tumor suppressors (Lee and Dutta, 2007). In order to compare our sequencing results with a secondary method, we used Taqman probes to assay 23 of 27 miRNAs as presented in Table 2.2. Consistently, 17 out of 23 miRNAs (73.9%) correlated with the differential expression detected by Illumina sequencing (Table 2.2). In summary, these results from comparing a preleukemic versus leukemic cell state, document a large and overlapping repertoire of miRNA species, many of which are differentially expressed, and spanning a large range of expression levels. These results suggest that processes involved in or reflecting leukemogenic states are dictated by a complex repertoire of overlapping, but differentially expressed miRNAs, rather than complete presence or absence of miRNAs between these cellular states.  65  miRNA  miRNA star  ND13  ND13+ Meis1  fold change  mmu-miR-196b 209 918 mmu-miR-467a* x 56 194 mmu-miR-30b 49 160 mmu-miR-18a 53 171 mmu-miR-23a 594 1594 mmu-miR-652 679 1565 mmu-miR-10a 14700 32064 mmu-miR-140 73 153 mmu-miR-155 187 373 mmu-miR-192 669 1291 mmu-miR-22 175 314 mmu-miR-365 152 268 mmu-miR-15a 164 286 mmu-miR-29c 99 170 mmu-miR-669c 1347 2292 mmu-miR-674 110 171 mmu-mir-223-5-p x 2684 1 mmu-miR-296-3p 151 3 mmu-miR-298 1143 26 mmu-miR-877 262 28 mmu-miR-351 872 94 mmu-mir-365-1-5-p x 408 76 mmu-miR-27b* x 471 90 mmu-miR-7a 3090 605 mmu-miR-542-3p 1157 245 mmu-mir-301a-5-p x 165 37 mmu-miR-805 5558 1353 mmu-miR-450b-5p 173 43 mmu-mir-193b-5-p x 1277 332 mmu-mir-23b-5-p x 689 192 mmu-miR-503 2713 889 mmu-miR-33 610 204 mmu-miR-210 240 81 mmu-miR-27a 2020 700 mmu-mir-25-5-p x 3123 1139 mmu-miR-222 4723 1768 no* = no miRNA* sequence in miRBase published a = differential expression corresponds to Taqman assay b = miRNA is upregulated in Taqman assay  4.39 3.46 3.27 3.23 2.68 2.3 2.18 2.1 1.99 1.93 1.79 1.76 1.74 1.72 1.7 1.55 2684 50.33 43.96 9.36 9.28 5.37 5.23 5.11 4.72 4.46 4.11 4.02 3.85 3.59 3.05 2.99 2.96 2.89 2.74 2.67  up (1) / down (-1) a  1 1 b 1 a 1 1 1 a 1 a 1 a 1 1 1 a 1 a 1 a 1 1 1 -1 a -1 a -1 -1 a -1 -1 -1 b -1 a -1 b -1 -1 a -1 -1 a -1 a -1 b -1 a -1 a -1 b -1 b -1  miRBase reference sequence yes no yes no no yes no yes no yes yes yes yes yes no no no* no no no yes no* no no no no* no no no* no* no yes yes no no* no  p-value 3.61E-143 2.97E-26 6.21E-21 6.31E-22 2.27E-158 4.93E-128 0 3.63E-12 1.29E-25 3.87E-80 3.03E-18 2.76E-15 7.37E-16 1.20E-09 2.66E-110 5.13E-08 0 1.53E-34 1.38E-249 1.93E-38 2.62E-124 1.28E-41 1.09E-46 4.56E-291 3.19E-102 4.63E-15 0 4.06E-14 7.20E-90 3.79E-45 3.88E-135 1.18E-30 1.14E-12 1.10E-91 1.21E-127 2.86E-182  Table 2.2 Most differentially expressed miRNA/miRNA* species (counts >150 and >1.5 fold change) including miRBase annotation  66  Figure 2.3 Analysis of differentially expressed miRNA genes A Distribution of differentially expressed miRNA genes according to their fold changes. Shown are the number of miRNA genes whose expression was upregulated (positive values) or downregulated (negative values) in the leukemic cells as a function of the fold change. Only changes >1.5 and achieving a p value <0.05 were included. B Bubble plot depicting the abundance of selected miRNA/miRNA* species and their relative expression levels. The bubbles represent the sum of the most common sequence counts from both libraries for a miRNA/miRNA* species plotted as a function of fold difference between the leukemic versus preleukemic cells.  67  68  2.3.4 Novel miRNA Genes We sought to identify novel miRNA genes amongst the un-classified sequences in our libraries. After annotation, 81,316 of the small RNA sequences in the ND13 library and 57,015 in the ND13+Meis1 library remained unclassified because they derived from un-annotated regions of the mouse genome. To identify candidate novel miRNAs amongst these, we employed both in-house and publicly available algorithms as published (Morin et al., 2008). The total set of novel miRNA candidates, comprises 94 unique miRNA sequences with mainly low expression levels, of which 55 have been accepted by miRBase (Supplementary Table 2.4) as novel miRNAs. Three novel miRNAs exhibited sequence counts >100 and a ≥1.5 fold change between the two libraries and thus represent potentially interesting candidates as having functional relevance in leukemia (Table 2.3).  69  Name  Genomic location  Mature Sequence  ND1  ND13  3  +Meis1  mmu-mir-1937a  chr16:4736234-4736256  ATCCCGGACGAGCCCCCA  7063  3114  mmu-mir-1937b  chr12:18343927-18343950  AATCCCGGACGAGCCCCCA  295  210  mmu-mir-1964  chr7:29482102-29482126  CCGACTTCTGGGCTCCGGCTTT  92  71  mmu-mir-1306  chr16:18197810-18197833  ACGTTGGCTCTGGTGGTGATG  55  44  mmu-mir-1965  chr7:80026402-80026430  AAGCCGGGCCGTAGTGGCGCA  200  33  mmu-mir-1274a  chrX:63213849-63213872  TCAGGTCCCTGTTCAGGCGCCA  11  27  mmu-mir-1948  chr18:12858029-12858051  TTTAGGCAGAGCACTCGTACAG  48  15  mmu-mir-1937c  chr3:23759265-23759286  ATCCCGGAAGAGCCCCCA  29  11  mmu-mir-1943  chr15:79202525-79202549  AAGGGAGGATCTGGGCACCTGGA  7  8  mmu-mir-669  chr2:10432170-10432194  TAGTTGTGTGTGCATGTTTATGT  3  8  mmu-mir-1933-5p  chr11:21244601-  CCAGGACCATCAGTGTGACTAT  4  7  CCAGTGCTGTTAGAAGAGGGCT  14  7  21244625_5p mmu-mir-1960  chr5:30501537-30501560  Table 2.3 Top 12 most abundant differentially expressed novel miRNAs including their genomic location  2.3.5 Targets of differentially expressed miRNAs In an attempt to highlight the regulation of oncogenes through miRNAs in preleukemic ND13 and leukemic ND13+Meis1 cells, target sites of the 19 most abundant miRNAs from each cell line were predicted with the following three computational algorithms:  TargetScan (http://www.targetscan.org), RNAHybrid  (http://bibiserv.techfak.uni-bielefeld.de/rnahybrid/)  and  miRanda  (http://www.microrna.org/miranda_new.html). Only genes with at least two predicted target sites and an accessible 3’ UTR were considered relevant (Long et al., 2007; Zuker, 2003). Based on this, we identified 1,461 and 937 target genes for miRNAs enriched in ND13 and ND13+Meis1 cells, respectively. In order to identify possible oncogenes from these lists, the predicted targets were compared with the Sanger 70  Cancer Gene Census (http://www.sanger.ac.uk/genetics/CGP/Census/), a list comprising 367 genes documented to be involved in cancer development (Futreal et al., 2004). Remarkably, almost half of the 17 predicted cancer gene census targets of ND13-enriched miRNAs represented AML-specific oncogenes including MLL genes and HOXA11 (Figure 2.4). However, only three AML specific oncogenes (DEK, CBFB and WT1) out of 15 predicted gene census targets were identified as targets for miRNAs of the ND13+Meis1 library (Figure 2.4). Predicted miRNAs targeting DEK include miR-155 and miR-23a, which could also be confirmed as regulators in luciferase assays (Figure 2.5A+B). Other predicted miRNAs, but not experimentally validated for CBFB and WT1 were miR-19b, miR-30e and miR-669c (Supplementary Table 2.5). In addition, the downregulation of the ND13+Meis1 specific tumor suppressor gene NR4A3 has been recently shown to cause AML (Mullican et al., 2007). Thus, the observation of an accentuated miRNA downregulation and the miRNA-mediated release of oncogenes might facilitate leukemic progression from a preleukemic to leukemic state.  71  Figure 2.4 Venn diagram of predicted miRNA targets Venn diagram of predicted miRNA targets for the 19 most abundant miRNAs from each library and their shared targets with the Sanger Cancer Gene Census. The dark boxes indicate AML specific oncogenes, whereas the grey box highlights a tumor suppressor gene targeted by miRNAs enriched in ND13+Meis1 cells.  72  Figure 2.5 Dek-3´UTR Luciferase assays for miR-23a and miR-155 A Bar diagram demonstrating the binding of miR-23a and miR-155 to the 3´UTR of the Dek oncogene. Dek23 comprises only binding sites for miR-23a, whereas Dek155 exhibits only predicted binding sites for miR-155. A non-binding miRNA was used as negative control. *=p<0.05 B Schematic representation of the Dek 3´UTR constructs and the predicted miRNA binding sites.  73  2.4 Discussion To gain insight into the miRNA transcriptome in the development of leukemia, we have exploited a massively parallel sequencing strategy coupled to a novel leukemic progression cell line model. Our results reveal a large number and remarkable overlap in miRNAs detected in both libraries and also a broad range in miRNA expression levels. Furthermore, we support the recent finding in humans that the miRNA transcriptome includes a diversity of miRNA isoforms and have identified over a hundred putative novel miRNAs (Morin et al., 2008). Though these two cellular states share many miRNAs, we documented numerous differences in miRNA expression levels between the preleukemic versus leukemic stages in the Hox-based progression model. Among the dramatic findings from this analysis are the large numbers of 205 different miRNA genes and 55 novel miRNAs detected. This exceeds a previous estimate of only 43 miRNAs in various leukemia cell lines as detected by Northern blot analysis (Yu et al., 2006). Recent analysis of the miRNA transcriptome in ES cells using the Illumina high throughput sequencing platform also revealed a remarkable number of miRNA species (Morin et al., 2008). Strikingly, over 60% of the miRNAs detected in ES cells were also found in our analysis of a leukemia progression model. Such overlaps of miRNA expression patterns suggest that many key functional roles of miRNAs depend more on relative levels rather than unique, tissue specific expression (Morin et al., 2008). Indeed, we observed a high overlap (~80%) in the miRNA sequences expressed in both preleukemic and leukemic bone marrow cells. All miRNAs unique to a single library were found at very low  74  expression levels (<30 copies), and thus likely at the limits of detection and of questionable functional significance. An exception with relatively high expression levels was miR-223*, highly detected in non-leukemic cells (2684 sequence counts) and with only one copy in the leukemic cells. The fact that a miRNA* species from a well characterized miRNA like miR-223 (Fazi et al., 2007; Fazi et al., 2005) was overrepresented in ND13 bone marrow cells, supports current speculations that both pre/miRNA arms can be tissue-dependently expressed and may have functional relevance (Okamura et al., 2008; Ro et al., 2007; Seitz et al., 2008). Recently published works (Okamura et al., 2008; Seitz et al., 2008) imply that the miRNA* strand is not merely a carrier strand, but can bind to the RISC complex and have inhibitory potential. Interestingly, based on the targetscan prediction algorithm (www.targetscan.com) the only target with 2 conserved target sites for the miR-223* seed region (GUGUAUU) is CUTL1, a gene essentially involved in the pathogenesis of leukemia. Another potential highly ranked target of miR-223* is MDS1 (also known as EVI1) a known oncogene involved in AML pathogenesis. Based on these facts it is not unlikely that miR-223* might contribute to the myeloid differentiation potential of mir-223. The complexity of the miRNA transcriptome dramatically increases when the miRNA miRBase reference sequences and their isoforms (isomiRs) (Morin et al., 2008), are taken into consideration. Our analysis revealed a large number of isomiRs, derived from almost all detected miRNAs, in total we detected 3390 isomiRs in both libraries, greatly exceeding and extending previous reports (Landgraf et al., 2007; Ruby et al., 2006), but comparable to Illumina sequencing of human ES cells (Morin et al.,  75  2008). This confirms recent suggestions that the miRNA transcriptome is more complex than previously assumed. Despite the large number of isomiRs detected, their role in post-transcriptional regulation remains to be experimentally determined. However, isomiRs resulting from variation at the 5’ end may be of particular interest as they have different seed sequences than the reference miRNA, with the ability to potentially target different transcripts. Besides changes of the seed region, end variations can putatively change the secondary structure of the miRNA and thus facilitate or prevent target UTR binding. These results suggest that a fuller description of the expression of isomiRs for each miRNA will be of interest to determine if there are tissue specific isomiR distributions relevant to development and disease. Another striking observation was the large range in miRNA expression levels for both libraries with count ranges for a given unique miRNA and miRNA* species, spanning 2 to over 1.3 x 107 sequence counts. This documented range in expression spanning over 5 orders of magnitude is some 100-fold greater than reported in previous studies (Berezikov et al., 2006a) likely reflecting the improved sampling depth possible with the Illumina sequencing method. MiRNAs with high expression levels in both libraries included members of the let-7 family, miR-21 and miR-25 suggesting a fundamental role in cell survival and/or proliferation. Indeed, some of these miRNAs like the let-7 family and miR-21 have been shown to be highly expressed in other tissue libraries (Ibarra et al., 2007; Morin et al., 2008) and in the context of cancer (Chan et al., 2005b; Mayr et al., 2007).  76  Interestingly, all miRNAs located in the Hox cluster (miR-10a, miR-10b and miR196b) were upregulated in the leukemic ND13+Meis1 cells. Although the function of these miRNAs in AML is unknown, recent AML profiling reports point towards distinct roles in leukemogenesis (Garzon et al., 2008b; Isken et al., 2008; JongenLavrencic et al., 2008). Furthermore, miR-10b seems to be a key factor modulating the ability of breast cancer cells to metastasize (Ma et al., 2007). In contrast, almost all members of the known tumor suppressor miRNA family let-7 (Akao et al., 2006; Johnson et al., 2005; Lee and Dutta, 2006, 2007; Mayr et al., 2007; Yu et al., 2007a) were downregulated in the leukemic state examined here, raising the intriguing possibility that this downregulation is linked to the erosion of key self-renewal and differentiation programs in leukemic stem cells similar to breast cancer stem cells as shown by Yu et al.. (Akao et al., 2006; Johnson et al., 2005; Lee and Dutta, 2006, 2007; Mayr et al., 2007; Yu et al., 2007a). Although it is difficult to compare our model system to recent miRNA profiling approaches in human AML, the depth of the presented work might complement these studies (Garzon et al., 2008b; Isken et al., 2008; Jongen-Lavrencic et al., 2008). Similar as in the mentioned works, we also found an upregulation of all miRNAs located in the Hox cluster, as well as a downregulation of let-7 family members. However, all published works do not provide more than a quantitative approach, which does not cover questions about isomiRs, novel miRNAs and disease specific mutations within the miRNA transcriptome. These questions will be more likely covered by future high throughput sequencing approaches, providing the genomic resolution to understand the undergoing changes of the miRNA transcriptome within the development of AML.  77  In order to highlight potential oncogenes as targets for miRNAs highly expressed in both libraries, we took into account cooperative target selection and 3’UTR accessibility. Comparing all predicted targets with the Sanger Cancer Gene Census (Futreal et al., 2004), we identified unique cancer related target genes for miRNAs of both libraries. Notably, our analysis suggested that in non-leukemic ND13 cell leukemia-specific oncogenes were more frequently targeted, whereas within ND13+Meis1 almost no leukemia-specific oncogenes were targeted. These predictions were experimentally validated for the Dek oncogene. Therefore, it can be speculated if targeting of specific oncogenes through miRNAs could tip the balance from the preleukemic to leukemic state. With few exceptions, recent large-scale cloning efforts have provided minimal yields of new miRNA genes, mainly due to the dominance of the highly expressed miRNAs in small RNA libraries (Landgraf et al., 2007). We present 55 novel miRNA genes that have passed multiple levels of annotation criteria (Morin et al., 2008). Although the majority of these novel miRNAs were expressed at modest to low levels and only 3 showed differential expression in this model, further assessment of their expression and roles in other tissues and diseases will be of interest. Notably, the predicted novel miRNAs also exhibit 3’ variations, which match or do not match the genome, similar to the above-mentioned isomiRs (data not shown). Applying the massively parallel Illumina sequencing platform has allowed us to generate an accurate and comprehensive picture of the miRNA transcriptome in a Hox/Meis1 leukemia progression model at great depth. Following a combination of novel miRNA annotation and discovery techniques, we have revealed a large list of  78  expressed miRNA/miRNA* sequences. In addition to a large range of expressed miRNA genes with dramatic expression ranges, we detected massive 5’ and 3’ sequence variations within each miRNA/miRNA* species, called isomiRs, adding an additional layer of complexity to the known miRNA sequences.  79  2.5 Bibliography Alizadeh, A. A., and Staudt, L. M. (2000). Genomic-scale gene expression profiling of normal and malignant immune cells. Curr Opin Immunol 12, 219-225. Ambros, V., Bartel, B., Bartel, D. P., Burge, C. B., Carrington, J. C., Chen, X., Dreyfuss, G., Eddy, S. R., Griffiths-Jones, S., Marshall, M., et al. (2003a). A uniform system for microRNA annotation. Rna 9, 277-279. Ambros, V., and Lee, R. C. (2004). Identification of microRNAs and other tiny noncoding RNAs by cDNA cloning. Methods Mol Biol 265, 131-158. Ambros, V., Lee, R. C., Lavanway, A., Williams, P. T., and Jewell, D. (2003b). MicroRNAs and other tiny endogenous RNAs in C. elegans. Curr Biol 13, 807-818. Aravin, A., Gaidatzis, D., Pfeffer, S., Lagos-Quintana, M., Landgraf, P., Iovino, N., Morris, P., Brownstein, M. J., Kuramochi-Miyagawa, S., Nakano, T., et al. (2006). A novel class of small RNAs bind to MILI protein in mouse testes. Nature 442, 203207. Aravin, A., and Tuschl, T. (2005). Identification and characterization of small RNAs involved in RNA silencing. FEBS Lett 579, 5830-5840. Aravin, A. A., Lagos-Quintana, M., Yalcin, A., Zavolan, M., Marks, D., Snyder, B., Gaasterland, T., Meyer, J., and Tuschl, T. (2003). The small RNA profile during Drosophila melanogaster development. Dev Cell 5, 337-350. Bartel, D. P. (2009). MicroRNAs: target recognition and regulatory functions. Cell 136, 215-233.  80  Bentley, D. R., Balasubramanian, S., Swerdlow, H. P., Smith, G. P., Milton, J., Brown, C. G., Hall, K. P., Evers, D. J., Barnes, C. L., Bignell, H. R., et al. (2008). Accurate whole human genome sequencing using reversible terminator chemistry. Nature 456, 53-59. Berezikov, E., Thuemmler, F., van Laake, L. W., Kondova, I., Bontrop, R., Cuppen, E., and Plasterk, R. H. (2006a). Diversity of microRNAs in human and chimpanzee brain. Nat Genet 38, 1375-1377. Berezikov, E., van Tetering, G., Verheul, M., van de Belt, J., van Laake, L., Vos, J., Verloop, R., van de Wetering, M., Guryev, V., Takada, S., et al. (2006b). Many novel mammalian microRNA candidates identified by extensive cloning and RAKE analysis. Genome Res 16, 1289-1298. Blow, M. J., Grocock, R. J., van Dongen, S., Enright, A. J., Dicks, E., Futreal, P. A., Wooster, R., and Stratton, M. R. (2006). RNA editing of human microRNAs. Genome Biol 7, R27. Borchert, G. M., Lanier, W., and Davidson, B. L. (2006). RNA polymerase III transcribes human microRNAs. Nat Struct Mol Biol 13, 1097-1101. Brannan, C. I., Dees, E. C., Ingram, R. S., and Tilghman, S. M. (1990). The product of the H19 gene may function as an RNA. Mol Cell Biol 10, 28-36. Brown, C. J., Hendrich, B. D., Rupert, J. L., Lafreniere, R. G., Xing, Y., Lawrence, J., and Willard, H. F. (1992). The human XIST gene: analysis of a 17 kb inactive Xspecific RNA that contains conserved repeats and is highly localized within the nucleus. Cell 71, 527-542.  81  Bruchova, H., Yoon, D., Agarwal, A. M., Mendell, J., and Prchal, J. T. (2007). Regulated  expression  of  microRNAs  in  normal  and  polycythemia  vera  erythropoiesis. Exp Hematol 35, 1657-1667. Buhler, M., Verdel, A., and Moazed, D. (2006). Tethering RITS to a nascent transcript initiates RNAi- and heterochromatin-dependent gene silencing. Cell 125, 873-886. Calin, G. A., Dumitru, C. D., Shimizu, M., Bichi, R., Zupo, S., Noch, E., Aldler, H., Rattan, S., Keating, M., Rai, K., et al. (2002). Frequent deletions and downregulation of micro- RNA genes miR15 and miR16 at 13q14 in chronic lymphocytic leukemia. Proc Natl Acad Sci U S A 99, 15524-15529. Calin, G. A., Ferracin, M., Cimmino, A., Di Leva, G., Shimizu, M., Wojcik, S. E., Iorio, M. V., Visone, R., Sever, N. I., Fabbri, M., et al. (2005). A MicroRNA signature associated with prognosis and progression in chronic lymphocytic leukemia. N Engl J Med 353, 1793-1801. Calin, G. A., Sevignani, C., Dumitru, C. D., Hyslop, T., Noch, E., Yendamuri, S., Shimizu, M., Rattan, S., Bullrich, F., Negrini, M., and Croce, C. M. (2004). Human microRNA genes are frequently located at fragile sites and genomic regions involved in cancers. Proc Natl Acad Sci U S A 101, 2999-3004. Chen, C., Ridzon, D. A., Broomer, A. J., Zhou, Z., Lee, D. H., Nguyen, J. T., Barbisin, M., Xu, N. L., Mahuvakar, V. R., Andersen, M. R., et al. (2005). Real-time quantification of microRNAs by stem-loop RT-PCR. Nucleic Acids Res 33, e179. Chen, C. Z., Li, L., Lodish, H. F., and Bartel, D. P. (2004). MicroRNAs modulate hematopoietic lineage differentiation. Science 303, 83-86.  82  Chendrimada, T. P., Gregory, R. I., Kumaraswamy, E., Norman, J., Cooch, N., Nishikura, K., and Shiekhattar, R. (2005). TRBP recruits the Dicer complex to Ago2 for microRNA processing and gene silencing. Nature 436, 740-744. Chin, L. J., Ratner, E., Leng, S., Zhai, R., Nallur, S., Babar, I., Muller, R. U., Straka, E., Su, L., Burki, E. A., et al. (2008). A SNP in a let-7 microRNA complementary site in the KRAS 3' untranslated region increases non-small cell lung cancer risk. Cancer Res 68, 8535-8540. Cimmino, A., Calin, G. A., Fabbri, M., Iorio, M. V., Ferracin, M., Shimizu, M., Wojcik, S. E., Aqeilan, R. I., Zupo, S., Dono, M., et al. (2005). miR-15 and miR-16 induce apoptosis by targeting BCL2. Proc Natl Acad Sci U S A 102, 13944-13949. Cobb, B. S., Nesterova, T. B., Thompson, E., Hertweck, A., O'Connor, E., Godwin, J., Wilson, C. B., Brockdorff, N., Fisher, A. G., Smale, S. T., and Merkenschlager, M. (2005). T cell lineage choice and differentiation in the absence of the RNase III enzyme Dicer. J Exp Med 201, 1367-1373. Coffin, J. M. (1979). Structure, replication, and recombination of retrovirus genomes: some unifying hypotheses. J Gen Virol 42, 1-26. Connolly, E., Melegari, M., Landgraf, P., Tchaikovskaya, T., Tennant, B. C., Slagle, B. L., Rogler, L. E., Zavolan, M., Tuschl, T., and Rogler, C. E. (2008). Elevated expression of the miR-17-92 polycistron and miR-21 in hepadnavirus-associated hepatocellular carcinoma contributes to the malignant phenotype. Am J Pathol 173, 856-864. Costa, F. F. (2007). Non-coding RNAs: lost in translation? Gene 386, 1-10. Costinean, S., Zanesi, N., Pekarsky, Y., Tili, E., Volinia, S., Heerema, N., and Croce, C. M. (2006). Pre-B cell proliferation and lymphoblastic leukemia/high-grade  83  lymphoma in E(mu)-miR155 transgenic mice. Proc Natl Acad Sci U S A 103, 70247029. Crick, F. (1970). Central dogma of molecular biology. Nature 227, 561-563. Davison, T. S., Johnson, C. D., and Andruss, B. F. (2006). Analyzing micro-RNA expression using microarrays. Methods Enzymol 411, 14-34. Dore, L. C., Amigo, J. D., Dos Santos, C. O., Zhang, Z., Gai, X., Tobias, J. W., Yu, D., Klein, A. M., Dorman, C., Wu, W., et al. (2008). A GATA-1-regulated microRNA locus essential for erythropoiesis. Proc Natl Acad Sci U S A 105, 3333-3338. Easow, G., Teleman, A. A., and Cohen, S. M. (2007). Isolation of microRNA targets by miRNP immunopurification. Rna 13, 1198-1204. Esquela-Kerscher, A., and Slack, F. J. (2006). Oncomirs - microRNAs with a role in cancer. Nat Rev Cancer 6, 259-269. Fazi, F., Racanicchi, S., Zardo, G., Starnes, L. M., Mancini, M., Travaglini, L., Diverio, D., Ammatuna, E., Cimino, G., Lo-Coco, F., et al. (2007). Epigenetic Silencing of the Myelopoiesis Regulator microRNA-223 by the AML1/ETO Oncoprotein. Cancer Cell 12, 457-466. Fazi, F., Rosa, A., Fatica, A., Gelmetti, V., De Marchis, M. L., Nervi, C., and Bozzoni, I. (2005). A minicircuitry comprised of microRNA-223 and transcription factors NFI-A and C/EBPalpha regulates human granulopoiesis. Cell 123, 819-831. Feinberg, A. P., Oshimura, M., and Barrett, J. C. (2002). Epigenetic mechanisms in human disease. Cancer Res 62, 6784-6787.  84  Felli, N., Fontana, L., Pelosi, E., Botta, R., Bonci, D., Facchiano, F., Liuzzi, F., Lulli, V., Morsilli, O., Santoro, S., et al. (2005). MicroRNAs 221 and 222 inhibit normal erythropoiesis and erythroleukemic cell growth via kit receptor down-modulation. Proc Natl Acad Sci U S A 102, 18081-18086. Fontana, L., Pelosi, E., Greco, P., Racanicchi, S., Testa, U., Liuzzi, F., Croce, C. M., Brunetti, E., Grignani, F., and Peschle, C. (2007). MicroRNAs 17-5p-20a-106a control monocytopoiesis through AML1 targeting and M-CSF receptor upregulation. Nat Cell Biol 9, 775-787. Friedlander, M. R., Chen, W., Adamidi, C., Maaskola, J., Einspanier, R., Knespel, S., and Rajewsky, N. (2008). Discovering microRNAs from deep sequencing data using miRDeep. Nat Biotechnol 26, 407-415. Fukao, T., Fukuda, Y., Kiga, K., Sharif, J., Hino, K., Enomoto, Y., Kawamura, A., Nakamura, K., Takeuchi, T., and Tanabe, M. (2007). An evolutionarily conserved mechanism for microRNA-223 expression revealed by microRNA gene profiling. Cell 129, 617-631. Garzon, R. (2009). MicroRNA profiling of megakaryocytes. Methods Mol Biol 496, 293-298. Garzon, R., Garofalo, M., Martelli, M. P., Briesewitz, R., Wang, L., FernandezCymering, C., Volinia, S., Liu, C. G., Schnittger, S., Haferlach, T., et al. (2008a). Distinctive microRNA signature of acute myeloid leukemia bearing cytoplasmic mutated nucleophosmin. Proc Natl Acad Sci U S A 105, 3945-3950. Garzon, R., Pichiorri, F., Palumbo, T., Iuliano, R., Cimmino, A., Aqeilan, R., Volinia, S., Bhatt, D., Alder, H., Marcucci, G., et al. (2006). MicroRNA fingerprints during human megakaryocytopoiesis. Proc Natl Acad Sci U S A 103, 5078-5083.  85  Garzon, R., Volinia, S., Liu, C. G., Fernandez-Cymering, C., Palumbo, T., Pichiorri, F., Fabbri, M., Coombes, K., Alder, H., Nakamura, T., et al. (2008b). MicroRNA signatures associated with cytogenetics and prognosis in acute myeloid leukemia. Blood, 111, 3183-3189. Georgantas, R. W., 3rd, Hildreth, R., Morisot, S., Alder, J., Liu, C. G., Heimfeld, S., Calin, G. A., Croce, C. M., and Civin, C. I. (2007). CD34+ hematopoietic stemprogenitor cell microRNA expression and function: a circuit diagram of differentiation control. Proc Natl Acad Sci U S A 104, 2750-2755. Girard, A., Sachidanandam, R., Hannon, G. J., and Carmell, M. A. (2006). A germline-specific class of small RNAs binds mammalian Piwi proteins. Nature 442, 199-202. Gramantieri, L., Fornari, F., Callegari, E., Sabbioni, S., Lanza, G., Croce, C. M., Bolondi, L., and Negrini, M. (2008). MicroRNA involvement in hepatocellular carcinoma. J Cell Mol Med 12, 2189-2204. Gregory, R. I., Chendrimada, T. P., Cooch, N., and Shiekhattar, R. (2005). Human RISC couples microRNA biogenesis and posttranscriptional gene silencing. Cell 123, 631-640. Griffiths-Jones, S., Grocock, R. J., van Dongen, S., Bateman, A., and Enright, A. J. (2006). miRBase: microRNA sequences, targets and gene nomenclature. Nucleic Acids Res 34, D140-144. Grimson, A., Srivastava, M., Fahey, B., Woodcroft, B. J., Chiang, H. R., King, N., Degnan, B. M., Rokhsar, D. S., and Bartel, D. P. (2008). Early origins and evolution of microRNAs and Piwi-interacting RNAs in animals. Nature 455, 1193-1197.  86  Grivna, S. T., Beyret, E., Wang, Z., and Lin, H. (2006). A novel class of small RNAs in mouse spermatogenic cells. Genes Dev 20, 1709-1714. Han, J., Lee, Y., Yeom, K. H., Kim, Y. K., Jin, H., and Kim, V. N. (2004). The Drosha-DGCR8 complex in primary microRNA processing. Genes Dev 18, 30163027. Hao, Y., Crenshaw, T., Moulton, T., Newcomb, E., and Tycko, B. (1993). Tumoursuppressor activity of H19 RNA. Nature 365, 764-767. He, L., Thomson, J. M., Hemann, M. T., Hernando-Monge, E., Mu, D., Goodson, S., Powers, S., Cordon-Cardo, C., Lowe, S. W., Hannon, G. J., and Hammond, S. M. (2005). A microRNA polycistron as a potential human oncogene. Nature 435, 828833. Houwing, S., Kamminga, L. M., Berezikov, E., Cronembold, D., Girard, A., van den Elst, H., Filippov, D. V., Blaser, H., Raz, E., Moens, C. B., et al. (2007). A role for Piwi and piRNAs in germ cell maintenance and transposon silencing in Zebrafish. Cell 129, 69-82. Humphreys, D. T., Westman, B. J., Martin, D. I., and Preiss, T. (2005). MicroRNAs control translation initiation by inhibiting eukaryotic initiation factor 4E/cap and poly(A) tail function. Proc Natl Acad Sci U S A 102, 16961-16966. Ibanez-Ventoso, C., Vora, M., and Driscoll, M. (2008). Sequence relationships among C. elegans, D. melanogaster and human microRNAs highlight the extensive conservation of microRNAs in biology. PLoS ONE 3, e2818. Isken, F., Steffen, B., Merk, S., Dugas, M., Markus, B., Tidow, N., Zuhlsdorf, M., Illmer, T., Thiede, C., Berdel, W. E., et al. (2008). Identification of acute myeloid leukaemia associated microRNA expression patterns. Br J Haematol 140, 153-161.  87  Jazdzewski, K., Murray, E. L., Franssila, K., Jarzab, B., Schoenberg, D. R., and de la Chapelle, A. (2008). Common SNP in pre-miR-146a decreases mature miR expression and predisposes to papillary thyroid carcinoma. Proc Natl Acad Sci U S A 105, 7269-7274. Jiang, J., Lee, E. J., Gusev, Y., and Schmittgen, T. D. (2005). Real-time expression profiling of microRNA precursors in human cancer cell lines. Nucleic Acids Res 33, 5394-5403. Johnnidis, J. B., Harris, M. H., Wheeler, R. T., Stehling-Sun, S., Lam, M. H., Kirak, O., Brummelkamp, T. R., Fleming, M. D., and Camargo, F. D. (2008). Regulation of progenitor cell proliferation and granulocyte function by microRNA-223. Nature 451, 1125-1129. Johnson, S. M., Grosshans, H., Shingara, J., Byrom, M., Jarvis, R., Cheng, A., Labourier, E., Reinert, K. L., Brown, D., and Slack, F. J. (2005). RAS is regulated by the let-7 microRNA family. Cell 120, 635-647. Jones, L. (2002). Revealing micro-RNAs in plants. Trends Plant Sci 7, 473-475. Jongen-Lavrencic, M., Sun, S. M., Dijkstra, M. K., Valk, P. J., and Lowenberg, B. (2008). MicroRNA expression profiling in relation to the genetic heterogeneity of acute myeloid leukemia. Blood. Ketting, R. F., Fischer, S. E., Bernstein, E., Sijen, T., Hannon, G. J., and Plasterk, R. H. (2001). Dicer functions in RNA interference and in synthesis of small RNA involved in developmental timing in C. elegans. Genes Dev 15, 2654-2659. Khvorova, A., Reynolds, A., and Jayasena, S. D. (2003). Functional siRNAs and miRNAs exhibit strand bias. Cell 115, 209-216.  88  Kuchenbauer, F., Morin, R. D., Argiropoulos, B., Petriv, O., Griffith, M., Heuser, M., Yung, E., Piper, J., Delaney, A., Prabhu, A. L., et al. (2008). In depth characterization of the microRNA transcriptome in a leukemia progression model. . Genome Research under Review. Lagos-Quintana, M., Rauhut, R., Yalcin, A., Meyer, J., Lendeckel, W., and Tuschl, T. (2002). Identification of tissue-specific microRNAs from mouse. Curr Biol 12, 735739. Lander, E. S., Linton, L. M., Birren, B., Nusbaum, C., Zody, M. C., Baldwin, J., Devon, K., Dewar, K., Doyle, M., FitzHugh, W., et al. (2001). Initial sequencing and analysis of the human genome. Nature 409, 860-921. Landgraf, P., Rusu, M., Sheridan, R., Sewer, A., Iovino, N., Aravin, A., Pfeffer, S., Rice, A., Kamphorst, A. O., Landthaler, M., et al. (2007). A mammalian microRNA expression atlas based on small RNA library sequencing. Cell 129, 1401-1414. Lau, N. C., Lim, L. P., Weinstein, E. G., and Bartel, D. P. (2001). An abundant class of tiny RNAs with probable regulatory roles in Caenorhabditis elegans. Science 294, 858-862. Lawrie, C. H., Soneji, S., Marafioti, T., Cooper, C. D., Palazzo, S., Paterson, J. C., Cattan, H., Enver, T., Mager, R., Boultwood, J., et al. (2007). Microrna expression distinguishes between germinal center B cell-like and activated B cell-like subtypes of diffuse large B cell lymphoma. Int J Cancer 121, 1156-1161. Lee, R. C., and Ambros, V. (2001). An extensive class of small RNAs in Caenorhabditis elegans. Science 294, 862-864.  89  Lee, R. C., Feinbaum, R. L., and Ambros, V. (1993). The C. elegans heterochronic gene lin-4 encodes small RNAs with antisense complementarity to lin-14. Cell 75, 843-854. Lee, Y., Han, J., Yeom, K. H., Jin, H., and Kim, V. N. (2006a). Drosha in primary microRNA processing. Cold Spring Harb Symp Quant Biol 71, 51-57. Lee, Y., Hur, I., Park, S. Y., Kim, Y. K., Suh, M. R., and Kim, V. N. (2006b). The role of PACT in the RNA silencing pathway. Embo J 25, 522-532. Lee, Y., Kim, M., Han, J., Yeom, K. H., Lee, S., Baek, S. H., and Kim, V. N. (2004). MicroRNA genes are transcribed by RNA polymerase II. Embo J 23, 4051-4060. Leighton, P. A., Ingram, R. S., Eggenschwiler, J., Efstratiadis, A., and Tilghman, S. M. (1995). Disruption of imprinting caused by deletion of the H19 gene region in mice. Nature 375, 34-39. Lewis, B. P., Burge, C. B., and Bartel, D. P. (2005). Conserved seed pairing, often flanked by adenosines, indicates that thousands of human genes are microRNA targets. Cell 120, 15-20. Liu, J., Rivas, F. V., Wohlschlegel, J., Yates, J. R., 3rd, Parker, R., and Hannon, G. J. (2005). A role for the P-body component GW182 in microRNA function. Nat Cell Biol 7, 1261-1266. Lu, J., Getz, G., Miska, E. A., Alvarez-Saavedra, E., Lamb, J., Peck, D., SweetCordero, A., Ebert, B. L., Mak, R. H., Ferrando, A. A., et al. (2005). MicroRNA expression profiles classify human cancers. Nature 435, 834-838.  90  Lu, J., Guo, S., Ebert, B. L., Zhang, H., Peng, X., Bosco, J., Pretz, J., Schlanger, R., Wang, J. Y., Mak, R. H., et al. (2008). MicroRNA-mediated control of cell fate in megakaryocyte-erythrocyte progenitors. Dev Cell 14, 843-853. Luciano, D. J., Mirsky, H., Vendetti, N. J., and Maas, S. (2004). RNA editing of a miRNA precursor. Rna 10, 1174-1177. Lund, E., Guttinger, S., Calado, A., Dahlberg, J. E., and Kutay, U. (2004). Nuclear export of microRNA precursors. Science 303, 95-98. Maniataki, E., and Mourelatos, Z. (2005). A human, ATP-independent, RISC assembly machine fueled by pre-miRNA. Genes Dev 19, 2979-2990. Marcucci, G., Radmacher, M. D., Maharry, K., Mrozek, K., Ruppert, A. S., Paschka, P., Vukosavljevic, T., Whitman, S. P., Baldus, C. D., Langer, C., et al. (2008). MicroRNA expression in cytogenetically normal acute myeloid leukemia. N Engl J Med 358, 1919-1928. Margulies, M., Egholm, M., Altman, W. E., Attiya, S., Bader, J. S., Bemben, L. A., Berka, J., Braverman, M. S., Chen, Y. J., Chen, Z., et al. (2005). Genome sequencing in microfabricated high-density picolitre reactors. Nature 437, 376-380. Mayr, C., Hemann, M. T., and Bartel, D. P. (2007). Disrupting the pairing between let-7 and Hmga2 enhances oncogenic transformation. Science 315, 1576-1579. Meister, G., Landthaler, M., Peters, L., Chen, P. Y., Urlaub, H., Luhrmann, R., and Tuschl, T. (2005). Identification of novel argonaute-associated proteins. Curr Biol 15, 2149-2155.  91  Monticelli, S., Ansel, K. M., Xiao, C., Socci, N. D., Krichevsky, A. M., Thai, T. H., Rajewsky, N., Marks, D. S., Sander, C., Rajewsky, K., et al. (2005). MicroRNA profiling of the murine hematopoietic system. Genome Biol 6, R71. Morin, R. D., O'Connor, M. D., Griffith, M., Kuchenbauer, F., Delaney, A., Prabhu, A. L., Zhao, Y., McDonald, H., Zeng, T., Hirst, M., et al. (2008). Application of massively parallel sequencing to microRNA profiling and discovery in human embryonic stem cells. Genome Res 18, 610-621. Neilson, J. R., Zheng, G. X., Burge, C. B., and Sharp, P. A. (2007). Dynamic regulation of miRNA expression in ordered stages of cellular development. Genes Dev 21, 578-589. Nottrott, S., Simard, M. J., and Richter, J. D. (2006). Human let-7a miRNA blocks protein production on actively translating polyribosomes. Nat Struct Mol Biol 13, 1108-1114. O'Connell, R. M., Rao, D. S., Chaudhuri, A. A., Boldin, M. P., Taganov, K. D., Nicoll, J., Paquette, R. L., and Baltimore, D. (2008). Sustained expression of microRNA155 in hematopoietic stem cells causes a myeloproliferative disorder. J Exp Med 205, 585-594. O'Donnell, K. A., Wentzel, E. A., Zeller, K. I., Dang, C. V., and Mendell, J. T. (2005). c-Myc-regulated microRNAs modulate E2F1 expression. Nature 435, 839-843. Okamura, K., Phillips, M. D., Tyler, D. M., Duan, H., Chou, Y. T., and Lai, E. C. (2008). The regulatory activity of microRNA* species has substantial influence on microRNA and 3' UTR evolution. Nat Struct Mol Biol 15, 354-363. Ota, A., Tagawa, H., Karnan, S., Tsuzuki, S., Karpas, A., Kira, S., Yoshida, Y., and Seto, M. (2004). Identification and characterization of a novel gene, C13orf25, as a  92  target for 13q31-q32 amplification in malignant lymphoma. Cancer Res 64, 30873095. Petersen, C. P., Bordeleau, M. E., Pelletier, J., and Sharp, P. A. (2006). Short RNAs repress translation after initiation in mammalian cells. Mol Cell 21, 533-542. Pillai, R. S., Bhattacharyya, S. N., Artus, C. G., Zoller, T., Cougot, N., Basyuk, E., Bertrand, E., and Filipowicz, W. (2005). Inhibition of translational initiation by Let-7 MicroRNA in human cells. Science 309, 1573-1576. Raymond, C. K., Roberts, B. S., Garrett-Engele, P., Lim, L. P., and Johnson, J. M. (2005). Simple, quantitative primer-extension PCR assay for direct monitoring of microRNAs and short-interfering RNAs. Rna 11, 1737-1744. Reid, J. G., Nagaraja, A. K., Lynn, F. C., Drabek, R. B., Muzny, D. M., Shaw, C. A., Weiss, M. K., Naghavi, A. O., Khan, M., Zhu, H., et al. (2008). Mouse let-7 miRNA populations exhibit RNA editing that is constrained in the 5'-seed/ cleavage/anchor regions and stabilize predicted mmu-let-7a:mRNA duplexes. Genome Res 18, 15711581. Rinn, J. L., Kertesz, M., Wang, J. K., Squazzo, S. L., Xu, X., Brugmann, S. A., Goodnough, L. H., Helms, J. A., Farnham, P. J., Segal, E., and Chang, H. Y. (2007). Functional demarcation of active and silent chromatin domains in human HOX loci by noncoding RNAs. Cell 129, 1311-1323. Ro, S., Park, C., Jin, J., Sanders, K. M., and Yan, W. (2006). A PCR-based method for detection and quantification of small RNAs. Biochem Biophys Res Commun 351, 756-763. Ro, S., Park, C., Young, D., Sanders, K. M., and Yan, W. (2007). Tissue-dependent paired expression of miRNAs. Nucleic Acids Res 35, 5944-5953.  93  Rodriguez, A., Vigorito, E., Clare, S., Warren, M. V., Couttet, P., Soond, D. R., van Dongen, S., Grocock, R. J., Das, P. P., Miska, E. A., et al. (2007). Requirement of bic/microRNA-155 for normal immune function. Science 316, 608-611. Rosa, A., Ballarino, M., Sorrentino, A., Sthandier, O., De Angelis, F. G., Marchioni, M., Masella, B., Guarini, A., Fatica, A., Peschle, C., and Bozzoni, I. (2007). The interplay between the master transcription factor PU.1 and miR-424 regulates human monocyte/macrophage differentiation. Proc Natl Acad Sci U S A 104, 1984919854. Rosenbauer, F., Koschmieder, S., Steidl, U., and Tenen, D. G. (2005). Effect of transcription-factor concentrations on leukemic stem cells. Blood 106, 1519-1524. Rouhi, A., Mager, D. L., Humphries, R. K., and Kuchenbauer, F. (2008). MiRNAs, epigenetics, and cancer. Mamm Genome. Ruby, J. G., Jan, C. H., and Bartel, D. P. (2007). Intronic microRNA precursors that bypass Drosha processing. Nature 448, 83-86. Schwarz, D. S., Hutvagner, G., Du, T., Xu, Z., Aronin, N., and Zamore, P. D. (2003). Asymmetry in the assembly of the RNAi enzyme complex. Cell 115, 199-208. Tagawa, H., and Seto, M. (2005). A microRNA cluster as a target of genomic amplification in malignant lymphoma. Leukemia 19, 2013-2016. Tam, O. H., Aravin, A. A., Stein, P., Girard, A., Murchison, E. P., Cheloufi, S., Hodges, E., Anger, M., Sachidanandam, R., Schultz, R. M., and Hannon, G. J. (2008). Pseudogene-derived small interfering RNAs regulate gene expression in mouse oocytes. Nature 453, 534-538.  94  Tam, W., Hughes, S. H., Hayward, W. S., and Besmer, P. (2002). Avian bic, a gene isolated from a common retroviral site in avian leukosis virus-induced lymphomas that encodes a noncoding RNA, cooperates with c-myc in lymphomagenesis and erythroleukemogenesis. J Virol 76, 4275-4286. Thai, T. H., Calado, D. P., Casola, S., Ansel, K. M., Xiao, C., Xue, Y., Murphy, A., Frendewey, D., Valenzuela, D., Kutok, J. L., et al. (2007). Regulation of the germinal center response by microRNA-155. Science 316, 604-608. Tomari, Y., Matranga, C., Haley, B., Martinez, N., and Zamore, P. D. (2004). A protein sensor for siRNA asymmetry. Science 306, 1377-1380. Velu, C. S., Baktula, A. M., and Grimes, H. L. (2009). Gfi1 regulates miR-21 and miR-196b to control myelopoiesis. Blood. Ventura, A., Young, A. G., Winslow, M. M., Lintault, L., Meissner, A., Erkeland, S. J., Newman, J., Bronson, R. T., Crowley, D., Stone, J. R., et al. (2008). Targeted deletion reveals essential and overlapping functions of the miR-17 through 92 family of miRNA clusters. Cell 132, 875-886. Vigorito, E., Perks, K. L., Abreu-Goodger, C., Bunting, S., Xiang, Z., Kohlhaas, S., Das, P. P., Miska, E. A., Rodriguez, A., Bradley, A., et al. (2007). microRNA-155 regulates the generation of immunoglobulin class-switched plasma cells. Immunity 27, 847-859. Wakiyama, M., Takimoto, K., Ohara, O., and Yokoyama, S. (2007). Let-7 microRNAmediated mRNA deadenylation and translational repression in a mammalian cellfree system. Genes Dev 21, 1857-1862.  95  Wightman, B., Ha, I., and Ruvkun, G. (1993). Posttranscriptional regulation of the heterochronic gene lin-14 by lin-4 mediates temporal pattern formation in C. elegans. Cell 75, 855-862. Yekta, S., Shih, I. H., and Bartel, D. P. (2004). MicroRNA-directed cleavage of HOXB8 mRNA. Science 304, 594-596. Zhang, J., Jima, D. D., Jacobs, C., Fischer, R., Gottwein, E., Huang, G., Lugar, P. L., Lagoo, A. S., Rizzieri, D. A., Friedman, D. R., et al. (2009). Patterns of microRNA expression characterize stages of human B cell differentiation. Blood.  96  Chapter 3 Differential expression of miRNA*s in cancer and the contribution of miR-223* to the development of acute myeloid leukemia2  2  A version of this chapter will be submitted for publication. Kuchenbauer, F., Mah, S.M., McPherson, A., Heuser,  M., Argiropoulos, B., Morin, R.D., Berg, T., Lai, D., Muranyi, D.L., Hogge, D.E., Starczynowski, D.T., Karsan, A., O’Connor, M.D., Eaves, C.J., Watahiki, A., Wang, Y., Aparicio, S.A., Ganser, A., Krauter, J., Johnnidis, J.B., Marra, M., Carmargo, F.D. and Humphries, R.K.. Differential expression of miRNA* species in cancer and the contribution of miR-223* to the development of acute myeloid leukemia.  97  3.1 Introduction The canonical miRNA biogenesis pathway involves the stepwise processing of miRNA precursor transcripts containing hairpin structures in the nucleus as well as in the cytoplasm (Bartel, 2004). After processing through Drosha in the nucleus, miRNA containing hairpins are exported into the cytoplasm and cleaved by Dicer, resulting in a ~21-25nt miRNA duplex (Kim, 2005). Although both strands of miRNA duplexes are produced in equal amounts by transcription, their accumulation is mainly asymmetric at steady state. Depending on its frequency, the most abundant strand of a processed pre-miRNA is referred as “miRNA”, whereas the less abundant strand is known as “passenger strand” or miRNA* (Khvorova et al., 2003; Schwarz et al., 2003). Although the exact mechanisms of miRNA strand selection and RNA induced silencing complex (RISC) loading are still unclear, studies on siRNA duplexes indicated that the relative thermodynamic stability of the two ends of the duplex determines which strand is to be selected (Khvorova et al., 2003; Schwarz et al., 2003). The strand with relatively unstable base pairs at the 5′ end typically survives (Khvorova et al., 2003; Schwarz et al., 2003). Thermodynamic stability profiling studies on miRNA precursors suggested that the same rule might apply to most, although not all, miRNAs. Recent deep sequencing approaches from our group (Kuchenbauer et al., 2008; Morin et al., 2008) and others (Ruby et al., 2006) to detect and quantitate small RNA species demonstrated not only the presence of miRNA*s strands across species, but also their high abundance for certain miRNA duplexes. In addition, the incorporation of miRNA*s into the RISC was shown recently by Eric Lai’s group in D.  98  melanogaster (Okamura et al., 2008). Moreover, by using a dual-luciferase reporter assay in vitro (Ro et al., 2007) Ro et al. have provided evidence for the activity of both miRNA strands. Adding to this recent evidence are reports of SNPs within the pre-miR-146 hairpin (Jazdzewski et al., 2008), including one located in the seed region of miR-146a*, that potentially change its target range (Jazdzewski et al., 2009). Of further interest it was shown that miR-146* was expressed up to 2.6 fold higher in tumor samples compared to normal thyroid tissue, implying a tissue dependent strand selection as proposed by Ro et al. (Ro et al., 2007). Considering that every miRNA duplex consists of a miRNA and miRNA*, only 21% of miRNA*s (119 miRNA* out of 547 miRNAs, miRBase 13.0) in the mouse genome and 25% (182 miRNA* out of 706 miRNAs) of miRNA*s in the human genome have been annotated. This in part likely arises from the low expression levels of certain miRNA*s and that miRNA* sequences have only recently been added to miRNA microarrays and Taqman probe libraries. The advent of next generation sequencing has dramatically increased the ability to sensitively, comprehensively and quantitatively assess the pattern of miRNA and miRNA* species. To this end we examined 11 recently available deep sequencing libraries derived from different species, solid tumors as well leukemias and determined the expression levels of known and non-annotated miRNA*s compared to their related miRNA strands. This comprehensive approach allowed us to identify tissue and species independent patterns of miRNA/miRNA* expression, suggesting a novel classification for miRNA duplexes. These general findings pointed towards a strong differential expression of  99  miR-223*, which we further investigated in acute myeloid leukemia (AML) as well as normal hematopoiesis.  3.2 Materials and methods 3.2.1 Small RNA library preparation Previously published libraries for undifferentiated and differentiated H9 hESC cells as well as murine ND13 (mouse leukemia 1) and ND13+Meis1 (mouse leukemia 2) cell lines were re-annotated according the miRBase 13.0 (Kuchenbauer et al., 2008; Morin et al., 2008). Furthermore, 3 human leukemia cell lines (Leukemia 1-3), 2 libraries derived from human prostate cancer tissue (Prostate 1 and Prostate 2) and 2 libraries derived from human breast cancer tissue (Breast 1 and Breast 2) were generated as previously published (Morin et al., 2008). All libraries were annotated according to miRBase 13.0. MiRNA and miRNA* sequence counts were based on the most abundant miRNA sequence. Not-annotated miRNA* sequences were predicted and counted based on the most abundant complementary sequence to the known miRNA. All ratios were calculated according to the following formula: (miRNA counts+1):(miRNA* counts +1) to prevent division by 0.  3.2.2 Computational prediction of target genes MiR-223* target genes were predicted with Targetscan Custom (v5.0) using the hsamiR-223* seed sequence: GUGUAUU. 167 predicted conserved targets were analyzed with the Ingenuity Pathways Analysis software (http://www.ingenuity.com/) (Gusev and Brackett, 2007). 100  3.2.3 AML samples Diagnostic bone marrow (BM) or peripheral blood (PB) samples were analyzed from 93 adult patients (aged 16-60 years) with de novo or secondary AML (FrenchAmerican British [FAB] classification M0-M2, M4-M7) and normal cytogenetics who had been entered into the multicenter treatment trial AML SHG 01/99 and for whom RNA was available (ClinicalTrials Identifier NCT00209833, June 1999 to September 2004). Details of the treatment protocols have been previously reported (Heuser et al., 2006). All patients received intensive, response-adapted double induction and consolidation therapy. Written informed consent was obtained prior to therapy according to the Declaration of Helsinki, and the study was approved by the institutional review board of Hannover Medical School.  3.2.4 Statistical analysis Statistical analysis was performed as previously described (Heuser et al., 2006). The definition of complete remission (CR), relapse-free survival (RFS) and overall survival (OS) followed recommended criteria (Cheson et al., 2003). For hsa-miR-223 and hsa-miR-223* expression analysis, AML samples were dichotomized at the median expression value. Pairwise comparisons were performed by Student t test for continuous variables and by Chi-squared test for categorical variables. The Kaplan-Meier method and log-rank test were used to estimate the distribution of RFS and OS, and to compare differences between survival curves, respectively. 101  3.2.5 Isolation of hematopoietic stem and progenitor cells All bm cells were harvested from C57/B6 mice. For isolation of lineage-sca-1+c-kit+ (LSK) cells, bone marrow from 3 mice was pooled, lineage depleted with the EasySep™ negative selection mouse hematopoietic progenitor enrichment cocktail (StemCell Technologies, Vancouver, British Columbia), followed by a positive selection with allophycocyanin(APC)-conjugated anti-mouse c-kit (clone 2B8, BD Pharmingen; San Diego, CA), phycoerythrin(PE)-conjugated anti-mouse Sca-1 (clone D7, BD Pharmingen). Common myeloid progenitors (CMP), granulocytemacrophage progenitors (GMP), megakaryocyte-erythrocyte progenitors (MEP) were isolated with as previously published (Akashi et al., 2000). Other antibodies used were Gr1-PE, Mac1-APC (all obtained from Pharmingen, San Diego, CA, USA) (Argiropoulos et al., 2008). All antibodies can be found in (Kim et al., 2008) Supplementary Table 4.  3.2.6 Real-time PCR RNA was extracted using Trizol as previously described (Kuchenbauer et al., 2008). Reverse transcription of each miRNA or sno-202 was performed using the Taqman miRNA Reverse Transcription Kit (Applied Biosystems, Foster City, CA) according to the manufacturers instructions. Each patient RNA sample was measured using Nanodrop (Thermo Scientific) and equilibrated to a concentration of 5ng/ml. 20ng RNA were used for miR-223* specific reverse transcription. 5ng ND13 cell line RNA was used as standard and internal control within every run. MiR-92 was used as 102  housekeeping gene for all human samples (Isken et al., 2008) and ND13 cells, whereas sno-202 was used for all other murine samples. For determining DCT(miR223*-miR-223) and DCT(miR-21*-miR-21), 10ng RNA of each patient sample was reverse transcribed for miR-223 and miR-223* specific primers in separate reaction, but at the same time. Quantitative reverse transcription-polymerase chain reaction (RT-PCR) was performed using miR-223 (ABI#4395406), miR-223* (ABI#4395209), miR-92 (ABI#4373013) and sno-202 (ABI#4380914) ABI Taqman probes on an Applied Biosystems 7900HT Fast Real-Time PCR system in triplicates.  3.2.7 Retroviral vectors and cDNA All murine miR-223 constructs (miR-223, miR-223mut and miR-223*mut) were ordered from Integrated DNA Technologies (IDT, www.idtdna.com) and cloned into a murine stem cell virus (MSCV) construct as previously described (Pineault et al., 2003). In all constructs the published IRES cassette was replaced with a PGK promoter sequence, driving enhanced green fluorescent protein (eGFP) expression. The sequences for mmu-miR-223 are as follows: Native mmu-miR-223 sequence: GAATTCAAGATATAATCACCCTATTTTTTTCTCTTTCCAGTTGCACATCTTCCAGC ATGTTCTTGCTGCCCAGTGGAGGTTCCTGATCTGGCCATCTGCAGTGTCACGC TCCGTGTATTTGACAAGCTGAGTTGGACACTCTGTGTGGTAGAGTGTCAGTTTG TCAAATACCCCAAGTGTGGCTCATGCCTATCAGCTCCAGGTCCAGGACAGAGC  103  ACATAGCCTGCTGCCTACATATGAATGCTTATGAAACATGAAGCTCTCTGGTGT TATTCTCTCGAG. Mmu-miR-223mut (mutation in miR-223 strand bold): GAATTCAAGATATAATCACCCTATTTTTTTCTCTTTCCAGTTGCACATCTTCCAGC ATGTTCTTGCTGCCCAGTGGAGGTTCCTGATCTGGCCATCTGCAGTGTCACGC TCCGTGTATTTGACAAGCTGAGTTGGACACTCTGTGTGGTAGAGTGTCAACTTG TCAAATACCCCAAGTGTGGCTCATGCCTATCAGCTCCAGGTCCAGGACAGAGC ACATAGCCTGCTGCCTACATATGAATGCTTATGAAACATGAAGCTCTCTGGTGT TATTCTCTCGAG. Mmu-miR-223*mut (mutation in the miR-223* strand bold): GAATTCAAGATATAATCACCCTATTTTTTTCTCTTTCCAGTTGCACATCTTCCAGC ATGTTCTTGCTGCCCAGTGGAGGTTCCTGATCTGGCCATCTGCAGTGTCACGC TCCGTGGGTTTGACAAGCTGAGTTGGACACTCTGTGTGGTAGAGTGTCAGTTT GTCAAATACCCCAAGTGTGGCTCATGCCTATCAGCTCCAGGTCCAGGACAGAG CACATAGCCTGCTGCCTACATATGAATGCTTATGAAACATGAAGCTCTCTGGTG TTATTCTCTCGAG.  3.2.8 Retroviral infection and clonogenic progenitor assay Whole mouse bone marrow from miR-223 deficient mice as well as wild-type mice (Johnnidis et al., 2008) was extracted, lysed with NH4Cl and stimulated for 48 hours in DMEM supplemented with 15% FBS, 10 ng/ml hIL-6, 6 ng/ml mIL-3 and 100 ng/ml mSCF (StemCell Technologies Inc.; Vancouver, BC, Canada). The cells were transduced by co-cultivation with irradiated (4,000 cGy) viral producers in the  104  presence of 5µg/ml protamine sulfate (Sigma, Oakville, Canada; Cat. No. P4020) for 72 hours. GFP positive cells were analyzed and sorted into 15ml falcon tubes containing  3ml  methylcellulose  (Methocult  M3434;  StemCell  Technologies)  supplemented with cytokines as previously described (Pineault et al., 2005). The gene transfer ranged from 2%-5%. Colonies were evaluated and scored microscopically according to standard criteria 7 days after plating.  3.2.9 Sorting of AML subpopulations For cell sorting based on cell surface markers, freshly thawed cells were resuspended at 6.5x107 cells/mL in Hanks’ balanced salt solution modified (StemCell Technologies) with 2% FCS, and 0.04% sodium azide (HFN) with 5% human serum. From this 3x105 cells were placed into 4 separate 4 mL tubes in 100 µL HFN for control staining, with the remainder of cells being divided into 300 µL aliquots to which CD34-APC (clone 8G12, StemCell Technologies) and CD38-PE (clone HB-7, StemCell Technologies) antibodies were added. Cells were incubated on ice for 30 minutes, washed once with HFN, once with HFN and 2 µg/mL propidium iodide (PI), and resuspended in 1.5 mL HFN. Cells were sorted into 3 populations (CD34-, CD34+CD38+, and CD34+CD38-) and collected, using the FACSVantage SE or FACSVantage DiVa cell sorters, setting gates based on single antibody control stained cells. The patient samples were randomly chosen and showed the following characteristics: FAB M4 (normal karyotype), M1 (unknown karyotype), M5 (normal karyotype), M5b (normal karyotype), M1 (normal karyotype).  105  3.2.10 Pre-miR-223 SNP analysis Genomic DNA from 95 AML patient samples was PCR amplified with the following primer  set:  FW:  AAGCTTGGGACCCCTAGAAA,  REV:  GGGAGGGGAATATGAAGGAA, using Hifi Taq Polymerase according to the manufacturer’s instructions (Invitrogen, Burlington, CA). The resulting 687bp PCR product was purified using the charge-switch PCR clean up kit according to manufacturer’s instructions (Invitrogen, Burlington, CA) and sequenced (McGill University,  Montreal,  CA)  using  GGAACCCTATGCCTATTTTGC,  REV:  Chromatograms  were  analysed  the  following  primers:  CCATCAGCACTCTCATGGTG. using  Chromas  FW: All pro  (http://www.technelysium.com.au/).  3.2.11 Luciferase assays Predicted (TargetScan custom v5.0, hsa-miR-223* seed: GUGUAUU) miR-223* binding regions of CUTL-1 (chr7:101,679,603-101,679,672) were cloned into pMIRREPORT (Ambion, Austin, Tx) and transfected with hsa-miR-223* (Thermo Scietific Dharmacon, Lafayette, USA), miR-155 (Ambion, Austin, Tx) or a negative control miRNA (Ambion, Austin, Tx) into 293T cells. For the 3’UTR-luciferase assays, 20 ng of pMirReport-3’UTR, 10pmol of miRNAs and 0.17 ng of thymidine kinase-renilla were cotransfected into 4x105 293T cells (48-well format) using the Lipofectamine 2000 transfection reagent (Invitrogen, Burlington, CA). The assays were read in the Lumat LB 9507 tube luminometer (EG&G BERTHOLD, Germany) and the  106  Luciferase/Renilla ratio calculated. Student’s t-test was used for statistical analysis and p<0.05 considered as significant.  3.3 Results 3.3.1 The abundance of miRNA*s is tissue dependent We investigated the expression and relationship of miRNAs and their corresponding miRNA*s in 11 Illumina sequencing libraries derived from murine and human cell lines and tissues. In detail, we analyzed the sequencing data of two murine hematopoietic libraries (mouse leukemia 1 [ND13] and mouse leukemia 2 [ND13+Meis1]) (Kuchenbauer et al., 2008), undifferentiated (ES undiff) and differentiated (ES diff) embryonic stem cell libraries (Morin et al., 2008), 3 human leukemia cell lines (Leukemia 1-3), 2 libraries derived from human prostate cancer tissue (Prostate 1 and Prostate 2) and 2 libraries derived from human breast cancer tissue (Breast 1 and Breast 2). All libraries were analyzed according to the sum of all sequences (Kuchenbauer et al., 2008; Morin et al., 2008) and only miRNA/miRNA* reads with a sampling higher than 100 sequences were considered. Across all libraries, the percentage of miRNA*s compared to all detected miRNAs ranged from 12.2% to 0.3% (Figure 3.1A), suggesting tissue dependent expression levels of miRNA*s. The highest percentage of miRNA* species was found in the ES undiff library (12.2%), whereas the lowest percentage was seen in the Prostate 2 library (0.3%) (Table 3.1). Calculating the ratios of miRNA and miRNA* species and separating them into ratio groups, revealed that on average ~50% of all miRNA duplexes (range: 40.7-61.2%) revealed high ratios (>100) consistent with strong  107  preferential processing of one dominant miRNA strand. A significant proportion, ~23.5% (range: 18.1-30.5%), had intermediate ratios (between 100 and 10). Strikingly ~13.5% (range: 8.6-25.2%) showed low ratios (between 10 and 1) and ~12.8% (range: 7-15.1%) even showed strongly inverted ratios (<1). This is in contrast to the general assumption that only one strand is highly dominant. Despite the relatively high percentages with intermediate and low ratios, mainly miRNA duplexes with intermediate to low abundance exhibit low ratios (Figure 3.1B). The fact that more than 10% of all miRNA duplexes displayed an inverse ratio might indicate incorrect annotations in miRBase (Supplementary Table 3.1 for all ratios, sequences and sequence counts from each library).  108  Figure 3.1 Distribution and Expression of several miRNA/miRNA* A Distribution of miRNA in a representative library. The range of miRNA*s within all libraries is indicated. B Distribution of miRNA/miRNA* ratios within 4 libraries. C Examples of miRNA/miRNA* ratios across all tissues D Ratios of dynamic miRNA duplexes. E Ratios of published miRNA*s.  109  110  Table 3.1 Percentage of miRNA* and top 5 of the highest expressed miRNA*s  3.3.2 MiRNA*s can be classified according to their abundance in relation to the corresponding miRNA Based on the finding that the distribution of miRNA/miRNA* ratios grouped similarly in all libraries, we wondered if the ratios for individual miRNA duplexes remained similar across all investigated tissues. Indeed, most miRNA duplexes preserved their miRNA/miRNA* distribution across the different libraries (Table 3.1, Figure 3.1C, Supplementary Table 3.2). Based on this, it is possible to classify miRNA duplexes into α-duplexes, having a dominant strand with a ratio >10 such as the let-7 family and into more balanced β-duplexes exhibiting ratios ≤10≥0.1 and spanning approximately one order of magnitude, such as miR-17 and miR-425 (Figure 3.1C, Table 3.2). However, 7 miRNAs were characterized by a dynamic arm expression with ratios >10 and <1 and therefore interchanging dominant miRNA arms between 111  the different tissues (Figure 3.1D, Table 3.3). This phenomenon was independent of conservation, as poorly conserved miRNA duplexes such as miR-1307 as well as broadly conserved duplexes, such as miR-223 oscillated in their miRNA arm expression between the investigated sequencing libraries. Interestingly, other recent miRNA profiling efforts have found miRNA*s, such as miR-9* (Packer et al., 2008), miR-199* (Kim et al., 2008; Lee et al., 2009), miR-126* (Kalscheuer et al., 2008; Musiyenko et al., 2008), miR-363* (Roccaro et al., 2009) and miR-18* (Tsang and Kwok, 2009), differentially expressed in the pathogenesis of Waldenstroem macroglobulinemia, lung cancer and a metastasis model as well as developmental processes such as organ adhesion. In our libraries, miR-378 (ratio: 675-4564) and miR-199 (ratio: 17-10366) showed features of an α-duplex, whereas miR-9 (ratio: 2.2-64) and miR-363 (0.21-38.7) displayed a more balanced expression (Figure 3.1E). These results are in contrast to published data suggesting that miR199* is expressed at detectable levels in fibroblasts (Kim et al., 2008), which might be attributable to differences in the methodologies and profiled cells.  112  Table 3.2 Examples of α- and β-duplexes  Table 3.3 Dynamically expressed miRNAs  113  3.3.2 MiR-223 and miR-223* arm accumulation is a dynamic process during leukemogenesis The surprising finding that certain miRNAs displayed tissue dependent miRNA arm selection (Figure 3.1D), raised the question whether selective accumulation of miRNA* strands might have functional implications. Of the reported miRNAs in Figure 3.1D, miR-223, a known regulator of myeloid differentiation (Chen et al., 2004; Fazi et al., 2005; Johnnidis et al., 2008), is the functionally best characterized miRNA in hematopoietic tissue. Lentiviral overexpression of hsa-miR-223 in NB4 cells, a leukemia cell line with features of myeloid differentiation derived from acute promyelocytic leukemia cells, led to further differentiation of these cells (Fazi et al., 2005), implying a leukemia-suppressor activity of this miRNA duplex (Fazi et al., 2007; Fazi et al., 2005). Interestingly, miR-223 and miR-223* were differentially expressed between the investigated tissues, especially in our ND13 (mouse leukemia 1) and ND13+Meis1 (mouse leukemia 2) leukemia progression model. Specifically, miR-223* was enriched by ~30 fold in the preleukemic ND13 line compared to its leukemic ND13+Meis1 counterpart (3366 vs. 115 counts) and higher expressed than its counter-strand miR-223 (Figure 3.2A). This dynamic pattern could be observed across all leukemia libraries, regardless if human or mouse (Figure 3.2A), implying that not only miR-223, but also miR-223* also might contribute to miR-223 functions. In contrast, other bone marrow specific miRNAs (Landgraf et al., 2007) showed consistent ratios (Figure 3.2A). Variable miR-223* levels and the fact that miR-223 as well as miR-223* remained broadly conserved in vertebrates (Figure 3.2B) pointed towards a miRNA duplex with two functional arms. Considering that ectopic expression of miR-223 involves overexpression of pri-miR-  114  223 (Chen et al., 2004; Fazi et al., 2005), including both arms of the miRNA duplex, it remains open that miR-223* contributes to the observed anti-leukemic phenotype. In addition, both arms were detectable in normal hematopoietic cells, following an increasing expression from primitive lineage negative sca1+ c-kit+ (LSK) to differentiated myeloid cells (Gr1+Mac1+) (Figure 3.2C).  115  Figure 3.2 miR-223 and miR-223* expression and characteristics A Bargraph indicating the distribution of miR-223 and miR-223* in a pre-leukemic (mouse leukemia 1) and leukemic (mouse leukemia 2) AML cell line. Plotted ratios of bone marrow specific miRNAs. B Sequences of pre-miR-223 across species. HsamiR-223 hairpin, miR-223 and miR-223* as well as their seed regions (red) are highlighted. C Relative quantification of miR-223 and miR-223* in sorted murine hematopoietic subpopulations. D Comparison of predicted miR-223 and miR-223* targets E Bioinformatics approach to predict targets of miR-223*. Luciferase assay on CUX1, a predicted target of miR-223*.  116  117  3.3.3 MiR-223* binds to the 3’UTR of the oncogene CUX1 in vitro In order to investigate a possible function of miR-223*, we used bioinformatics to predict 167 conserved targets with Targetscan custom (v5.0, www.targetscan.org) using the 7nt seed sequence (GUGUAUU) of hsa-miR-223* (Supplementary Table 3.3) as well as the 203 predicted targets of hsa-miR-223 (Figure 3.2C). With regards to predicted targets there was little overlap between hsa-miR-223* and hsa-miR-223 (Figure 3.2C). However, both arms are predicted to regulate the PTEN signalling, an important pathway involved in hematopoietic stem cell maintenance and lineage fate decisions (Zhang J, Nature 2006). Ingenuity Pathway Analysis also ranked cancer (p=1.13E-04 - 4.94E-02) as the highest miR-223* target related disease (Figure 3.2E). Strikingly, at least 12 (40%) of the listed 30 molecules are oncoproteins involved in hematological neoplasias (Figure 3.2D). These findings re-enforce a possible tumor-suppressor role for miR-223*. Of all predicted targets, CUX1 exhibited two conserved binding sites for miR-223* (chr7:101,679,642-101,679,648; 101,680,014-101,680,020) and is a known developmental regulator and oncogene in hematological neoplasias (Sansregret and Nepveu, 2008). Therefore, we cloned the miR-223* binding site containing 3’UTR region into a luciferase reporter and cotransfected 293T cells with miR-223*, miR-155, another predicted regulator of CUX1, or a negative control miRNA. Only miR-223* showed a significant downregulation (p=0.04) of CUX1 luciferase levels, consistent with miR-223* being a regulator of CUX1 (Figure 3.2E).  118  3.3.4 A higher relative abundance of miR-223* is associated with a trend towards a better overall survival in AML patients The observation of high miR-223* levels in preleukemic cells compared to leukemic cells made us wonder if the relative abundance of miR-223* compared to miR-223, defined by comparing absolute miR-223* and miR-223 abundance in a patient sample, correlates with survival of AML patients. To address this question, we determined the ΔCT value between miR-223* and miR-223 (ΔCT[miR-223*-miR-223] herein referred to as ΔCT) (Figure 3.3A top) in 43 AML patient samples, and correlated it with overall survival (OS) (Figure 3.3A). All patients were randomly chosen from the described patient group (see Material and Methods for details) and uniformly treated within treatment protocol AML SHG 01/99 (for more details, please refer to Materials and Methods). Dichotomizing the ΔCT levels above or below the median (50:50, n=21:21) did not show any differences in OS (p=0.78) (not shown). However, increasing the difference between the ΔCT by dichotomizing into the lowest 75% compared to the highest 25% (75:25, n=32:10) did show a trend towards a better OS (p=0.075) (Figure 3.3A). In contrast, a 25%:75% distribution (25:75, n=10:32) did not show any difference in OS (p=0.92) (not shown). This trend corroborates the assumption that not only the absolute abundance of a miRNA, but also the relationship between a miRNA and its miRNA* counterpart might be functionally and prognostically relevant in AML.  119  Figure 3.3 Relative miR-223* expression in AML patient samples A Experimental setup to measure miR-223 and miR-223* levels in 46 AML patient samples, using the same amount of RNA input and calculating the ΔCT(miR-223*miR-223) value. Correlation of the ΔCT value with overall survival using a 75%:25% dichotomy. B Correlation of miR-223* and miR-223 fold changes in 93 AML patients samples. C Relative quantification of miR-223 and miR-223* expression in 5 sorted AML patient samples.  120  121  3.3.5 MiR-223* and miR-223 correlate with different prognostic markers in AML patient samples Given the association of miR-223* and overall survival in AML patients, we asked if miR-223 and miR-223* expression correlated with known prognostic markers in AML patients. Real-time PCR analysis of miR-223* and miR-223 levels was carried out in 94 de-novo AML patient samples with normal karyotype under 60 years of age (Table 3.4). Indeed, miR-223* and miR-223 correlated inversely with unfavorable prognostic markers in AML. MiR-223 showed an inverse correlation with FLT3-ITDs (p=0.021) and a trend towards a positive correlation with CEBPa mutations (p=0.081), a known regulator of miR-223 expression (Fukao et al., 2007) (Table 3.5). MiR-223* correlated inversely with high CD34 expression (p=0.021), a marker for primitive hematopoietic cells (Table 3.5). This suggests that miR-223* and hence miR-223 are increasingly expressed along differentiated AML cells. In addition, we found that expression of miR-223* was only moderately correlated with expression of miR-223 (R2=0.3294) (Figure 3.3B), consistent with its variable expression in our sequencing libraries. We further sought to verify that miR-223* is actually more abundant in less primitive AML subpopulations. Therefore, we sorted 5 randomly chosen AML patient samples into CD34+CD38-, CD34+CD38+, CD34- fractions and measured miR-223* and miR-223 expression. Indeed, the differentiated CD34population showed increased miR-223* (2.1 fold) levels compared to the less differentiated  CD34+CD38-  subpopulation  (Figure  3.3C).  The  hierarchical  expression of both miRNAs points towards a role of miR-223* and miR-223 in the differentiation of AML cells.  122  Table 3.3 Clinical characteristics of 93 profiled AML patients  *  *  *  inverse correlation  Table 3.4 Correlation of miR-223/miR-223* expression with clinical parameters. P-values are as indicated, no Bonferroni's test for multiple correlations has been performed.  123  3.3.6 Inactivation of miR-223 decreases the colony forming capacity of normal bone marrow In an effort to functionally dissect the roles miR-223 and miR-223* in bone marrow cells, we had to overcome the problem of endogenous miR-223/miR-223* expression. Therefore, we exploited a murine miR-223 knockout (miR-223KO) model (Johnnidis et al., 2008) for functional dissection of the two strands in bone marrow (bm) cells. Previous analysis of miR-223KO bm revealed an enhanced number of myeloid progenitors as well as impaired differentiation of granulocytes (Johnnidis et al., 2008). To test the influence of miR-223* on myeloid progenitor cells in vitro, we rendered in separate retroviral constructs the seed region of each arm inactive (miR-223mut and miR-223*mut) (Figure 3.4A). Due to the limited ability to transfect bone marrow cells, we chose to use a retroviral approach, infected miR223KO bm (Johnnidis et al., 2008) and plated the cells in CFC cultures to assess their colony forming capacity (Figure 3.4A). CFC progenitor numbers were elevated in the miR-223 knockout background (p=0.0178). This elevation in CFC number was reversed by overexpression of the functional miR-223* species (p=0.008) but not the miR-223 species. (Figure 3.4B). Overexpression of miR-223, miR-223mut and miR223*mut in miR-223 wild-type bm did not lead to any changes in CFC counts (results not shown). These findings suggest that miR-223 and miR-223* might have separate functions that complement each other. Specific knockdown of the miR-223 strand has been shown to impair differentiation (Fazi et al., 2005), whereas our results imply a regulatory role of miR-223* in proliferation or self-renewal of myeloid progenitor cells. 124  Figure 3.4 Retroviral overexpression of miR-223 and miR-223* in myeloid precursor cells A Experimental setup to test the activity of each pre-miR-223 arm in CFC assays. B Colony counts for each experimental arm. Real-time PCR of miR-223 and miR-223* for each experimental arm.  125  3.4 Discussion Here we have exploited the recent availability of next generation deep sequencing data from 11 tissues to enable a comprehensive, quantitative analysis of miRNAs and the corresponding miRNA*s expression. Our results provide extensive evidence that relative miRNA and its corresponding miRNA* expression is conserved between various tissues and cell lines, allowing a novel classification of miRNA duplexes. Furthermore, we could show that certain miRNA did not retain their miRNA/miRNA* ratios across libraries, such as miR-223/miR-223*, which we further investigated. Besides the correlation of miR-223 and miR-223* with prognostic markers of AML, in vitro results suggested that miR-223* might also play a functional role in concert with miR-223 in myeloid cells. Between all libraries, the proportion of miRNA* within all miRNAs ranged between 0.3% and 3.2%, which is significantly lower than ~11% as recently published by Okamura et al. in an analysis of Drosophila (Okamura et al., 2008). These differences indicate that the overall miRNA* expression is tissue and organism dependent. However, variations in the sequencing protocols and platforms could also account for the observed differences. The comparison of all the sequencing libraries derived from different tissues revealed conserved patterns between miRNA and the corresponding miRNA* arms. Besides highly dominant miRNA duplexes, such as the strongly abundant let-7 family, we also found duplexes with a balanced expression such as miR-30e, confirming previous findings by Ro et al. (Ro et al., 2007). We termed miRNA duplexes with a dominant strand, α-duplexes and duplexes with a more balanced  126  strand expression, β-duplexes. In addition, we found several miRNAs where the annotated miRNA* arm is strongly dominant over the miRNA arm, pointing to a wrong annotation in miRBase, such as miR-129* and miR-517*. Furthermore, we are able to unambiguously annotate the miRNA* strand of miRNAs such as miR-423 and miR-371. Reviewing published miRNA* in the literature, only the miR-363 (Roccaro et al., 2009) and miR-9 duplex (Packer et al., 2008) showed a dynamic strand accumulation, similar to a β-duplex, whereas the miR-199 duplex (Kim et al., 2008) could be classified as a-duplex, with high miR-199 levels but barely any miR199*. It remains unclear if these discrepancies are due to differences in the methodology, investigated tissues or both. So far, the mechanisms by which strand selection occurs have not been completely resolved. Recent works by Khvorova et al. and Schwarz et al. indicate that the relative thermodynamic stability of the two ends of the duplex determines which strand is to be selected (Khvorova et al., 2003; Schwarz et al., 2003). In general, the strand with relatively unstable base pairs at the 5′ end does not get degraded. However, the works of Ro et al. as well as Okamura et al. describing tissue dependent paired expression of miRNAs, challenged this concept (Okamura et al., 2008; Ro et al., 2007). This could be due to tissue-dependent half-lives of miRNAs and miRNA*s as well as unknown extrinsic factors as Grimson et al recently reported a developmentally controlled arm switch between the embryonic (miR2015-5p) and adult (miR-2015-3p) stage of sponges for miR-2015 (Grimson et al., 2008). However, we could not verify such a mechanism comparing undifferentiated and differentiated ES cells. Another possibility involves the presence of a miRNA or  127  miRNA* target in a cell, leading to a target dependent strand selection or cell specific modification of RISC cofactors such as TRBP, which might influence selection of the active miRNA arm. However, only few miRNA duplexes exhibited an inconstant expression pattern, demonstrating that in general miRNA strand selection is a highly preserved mechanism. Analyzing the dynamics of miRNA strand accumulation, the abundance of miR-223* in a pre-leukemic cell line compared to its leukemic counterpart made us wonder if miR-223* might have a function within the development of acute myeloid leukemia. Based these findings, we further investigated its expression in human AML samples and its in vitro properties in colony forming assays. The relatively high abundance of miR-223* evident from our sequencing libraries, strongly argues that miR-223* is able to enter the RISC and therefore is functionally active. This possibility is reinforced by recent findings of Okamura et al. of frequent enrichment of even less abundant miRNA* strands in the RISC (Okamura et al., 2008). Interestingly, bioinformatic analysis of predicted miR-223* targets identified cancer as the highest ranked pathophysiological process. Indeed, almost half of the predicted oncoproteins have been shown to be functionally involved in hematological neoplasias. In contrast, predicted targets for miR-223 did not reveal a high ranking for cancer related processes. Only 5 predicted targets overlapped between miR-223 and miR-223*, indicating separate scopes of targeted pathways. However, a cooperative effect cannot be completely ruled out as shown for miR-9 and miR-9*, where both miRNAs target the same pathway in brain tissue (Packer et al., 2008). Besides predicted oncoproteins like CYBL, DEK and PTEN, CUX1, a member of the  128  homeodomain gene family, was the only known oncogene with two predicted miR223* binding sites. Several reports link CUX1 to regulation of gene expression, morphogenesis and differentiation in various tissues including bone marrow (Cadieux et al., 2008; Goulet et al., 2008; Sansregret and Nepveu, 2008). Therefore, the in vitro validation of CUX1 as miR-223* target might imply possible in vivo effects of miR-223* when overexpressed in bone marrow. Current approaches to overexpress a miRNA utilize miRNA mimics to produce a short-term effect in cell lines. In general, bone marrow cells are very difficult to transfect and changes are usually detected after a delay of several days. We therefore took advantage of a retroviral approach by creating retroviral vectors containing mutations in either the miR-223 or the miR-223* seed region. A vector in which the miR-223* seed region was expressed intact but miR-223 was mutated (inactive) was sufficient to at least partially, reverse (rescue) the enhanced CFC plating efficiency of miR223KO bm cells and thus a implicating functional role for miR-223*. Measuring miR-223 and miR-223* expression levels in normal karyotype AML patient samples revealed inverse correlations with unfavorable prognostic markers such as FLT3-ITDs for miR-223 and CD-34 for miR-223*. This indicates that miR223 and miR223* are more abundant in more differentiated AML cells and is supported by our findings that miR-223 and miR-223* are higher expressed in the more differentiated CD34-CD38+ AML cell populations, reflecting a similar hierarchy as seen in normal hematopoiesis for both miRNAs. Speculating that increased miR223 and miR-223* levels might activate different programs, miR-223 leading to  129  myeloid differentiation (Fazi et al., 2005) and miR-223* complementing miR-223 function by possibly activating apoptosis and/or inhibiting self-renewal and/or proliferation of progenitor cells (Figure 3.5). In addition, we found that a higher relative abundance of miR-223* shows a tendency towards a better OS In AML patients, consistent with a possible role of miR-223* as a tumor suppressor. However, in contrast to Jazdzewski et al. (Jazdzewski et al., 2009), we could not detect any miR-223* polymorphisms in 95 profiled AML patients. Our data reveal that miRNA arm accumulation underlies conserved patterns, but not exclusively the dominance of one miRNA duplex strand. Depending on the tissue, miRNA*s can be more abundant than previously assumed, implying a functional role for highly expressed miRNA*s. An important functional role for miRNA*s as illustrated by the results obtained for miR-223 in AML point towards a broader and thus more complicated interaction of miRNAs and disease related pathways, than previously reported.  130  Figure 3.5 Schematic model of the concerted action of miR-223 and miR-223*  131  3.5 Bibliography Akashi, K., Traver, D., Miyamoto, T., and Weissman, I. L. (2000). A clonogenic common myeloid progenitor that gives rise to all myeloid lineages. Nature 404, 193197. Argiropoulos, B., Palmqvist, L., Yung, E., Kuchenbauer, F., Heuser, M., Sly, L. M., Wan, A., Krystal, G., and Humphries, R. K. (2008). Linkage of Meis1 leukemogenic activity to multiple downstream effectors including Trib2 and Ccl3. Exp Hematol. 36, 845-859. Bartel, D. P. (2004). MicroRNAs: genomics, biogenesis, mechanism, and function. Cell 116, 281-297. Cadieux, C., Harada, R., Paquet, M., Cote, O., Trudel, M., Nepveu, A., and Bouchard, M. (2008). Polycystic kidneys caused by sustained expression of Cux1 isoform p75. J Biol Chem 283, 13817-13824. Chen, C. Z., Li, L., Lodish, H. F., and Bartel, D. P. (2004). MicroRNAs modulate hematopoietic lineage differentiation. Science 303, 83-86. Cheson, B. D., Bennett, J. M., Kopecky, K. J., Buchner, T., Willman, C. L., Estey, E. H., Schiffer, C. A., Doehner, H., Tallman, M. S., Lister, T. A., et al. (2003). Revised recommendations of the International Working Group for Diagnosis, Standardization of Response Criteria, Treatment Outcomes, and Reporting Standards for Therapeutic Trials in Acute Myeloid Leukemia. J Clin Oncol 21, 4642-4649. Fazi, F., Racanicchi, S., Zardo, G., Starnes, L. M., Mancini, M., Travaglini, L., Diverio, D., Ammatuna, E., Cimino, G., Lo-Coco, F., et al. (2007). Epigenetic Silencing of the Myelopoiesis Regulator microRNA-223 by the AML1/ETO Oncoprotein. Cancer Cell 12, 457-466.  132  Fazi, F., Rosa, A., Fatica, A., Gelmetti, V., De Marchis, M. L., Nervi, C., and Bozzoni, I. (2005). A minicircuitry comprised of microRNA-223 and transcription factors NFI-A and C/EBPalpha regulates human granulopoiesis. Cell 123, 819-831. Fukao, T., Fukuda, Y., Kiga, K., Sharif, J., Hino, K., Enomoto, Y., Kawamura, A., Nakamura, K., Takeuchi, T., and Tanabe, M. (2007). An evolutionarily conserved mechanism for microRNA-223 expression revealed by microRNA gene profiling. Cell 129, 617-631. Goulet, B., Markovic, Y., Leduy, L., and Nepveu, A. (2008). Proteolytic processing of cut homeobox 1 by neutrophil elastase in the MV4;11 myeloid leukemia cell line. Mol Cancer Res 6, 644-653. Gusev, Y., and Brackett, D. J. (2007). MicroRNA expression profiling in cancer from a bioinformatics prospective. Expert Rev Mol Diagn 7, 787-792. Heuser, M., Beutel, G., Krauter, J., Dohner, K., von Neuhoff, N., Schlegelberger, B., and Ganser, A. (2006). High meningioma 1 (MN1) expression as a predictor for poor outcome in acute myeloid leukemia with normal cytogenetics. Blood 108, 38983905. Isken, F., Steffen, B., Merk, S., Dugas, M., Markus, B., Tidow, N., Zuhlsdorf, M., Illmer, T., Thiede, C., Berdel, W. E., et al. (2008). Identification of acute myeloid leukaemia associated microRNA expression patterns. Br J Haematol 140, 153-161. Jazdzewski, K., Liyanarachchi, S., Swierniak, M., Pachucki, J., Ringel, M. D., Jarzab, B., and de la Chapelle, A. (2009). Polymorphic mature microRNAs from passenger strand of pre-miR-146a contribute to thyroid cancer. Proc Natl Acad Sci U S A 106, 1502-1505.  133  Jazdzewski, K., Murray, E. L., Franssila, K., Jarzab, B., Schoenberg, D. R., and de la Chapelle, A. (2008). Common SNP in pre-miR-146a decreases mature miR expression and predisposes to papillary thyroid carcinoma. Proc Natl Acad Sci U S A 105, 7269-7274. Johnnidis, J. B., Harris, M. H., Wheeler, R. T., Stehling-Sun, S., Lam, M. H., Kirak, O., Brummelkamp, T. R., Fleming, M. D., and Camargo, F. D. (2008). Regulation of progenitor cell proliferation and granulocyte function by microRNA-223. Nature 451, 1125-1129. Kalscheuer, S., Zhang, X., Zeng, Y., and Upadhyaya, P. (2008). Differential expression of microRNAs in early-stage neoplastic transformation in the lungs of F344 rats chronically treated with the tobacco carcinogen 4-(methylnitrosamino)-1(3-pyridyl)-1-butanone. Carcinogenesis 29, 2394-2399. Khvorova, A., Reynolds, A., and Jayasena, S. D. (2003). Functional siRNAs and miRNAs exhibit strand bias. Cell 115, 209-216. Kim, S., Lee, U. J., Kim, M. N., Lee, E. J., Kim, J. Y., Lee, M. Y., Choung, S., Kim, Y. J., and Choi, Y. C. (2008). MicroRNA miR-199a* regulates the MET proto-oncogene and the downstream extracellular signal-regulated kinase 2 (ERK2). J Biol Chem 283, 18158-18166. Kim, V. N. (2005). MicroRNA biogenesis: coordinated cropping and dicing. Nat Rev Mol Cell Biol 6, 376-385. Kuchenbauer, F., Morin, R. D., Argiropoulos, B., Petriv, O., Griffith, M., Heuser, M., Yung, E., Piper, J., Delaney, A., Prabhu, A. L., et al. (2008). In depth characterization of the microRNA transcriptome in a leukemia progression model. . Genome Research, 18, 1787-1799.  134  Landgraf, P., Rusu, M., Sheridan, R., Sewer, A., Iovino, N., Aravin, A., Pfeffer, S., Rice, A., Kamphorst, A. O., Landthaler, M., et al. (2007). A mammalian microRNA expression atlas based on small RNA library sequencing. Cell 129, 1401-1414. Lee, D. Y., Shatseva, T., Jeyapalan, Z., Du, W. W., Deng, Z., and Yang, B. B. (2009). A 3'-untranslated region (3'UTR) induces organ adhesion by regulating miR199a* functions. PLoS ONE 4, e4527. Morin, R. D., O'Connor, M. D., Griffith, M., Kuchenbauer, F., Delaney, A., Prabhu, A. L., Zhao, Y., McDonald, H., Zeng, T., Hirst, M., et al. (2008). Application of massively parallel sequencing to microRNA profiling and discovery in human embryonic stem cells. Genome Res 18, 610-621. Musiyenko, A., Bitko, V., and Barik, S. (2008). Ectopic expression of miR-126*, an intronic product of the vascular endothelial EGF-like 7 gene, regulates prostein translation and invasiveness of prostate cancer LNCaP cells. J Mol Med 86, 313322. Okamura, K., Phillips, M. D., Tyler, D. M., Duan, H., Chou, Y. T., and Lai, E. C. (2008). The regulatory activity of microRNA* species has substantial influence on microRNA and 3' UTR evolution. Nat Struct Mol Biol 15, 354-363. Packer, A. N., Xing, Y., Harper, S. Q., Jones, L., and Davidson, B. L. (2008). The bifunctional microRNA miR-9/miR-9* regulates REST and CoREST and is downregulated in Huntington's disease. J Neurosci 28, 14341-14346. Pineault, N., Abramovich, C., and Humphries, R. K. (2005). Transplanf cell lines generated with NUP98-Hox fusion genes undergo leukemic progression by Meis1 independent of its binding to DNA. Leukemia 19, 636-643.  135  Pineault, N., Buske, C., Feuring-Buske, M., Abramovich, C., Rosten, P., Hogge, D. E., Aplan, P. D., and Humphries, R. K. (2003). Induction of acute myeloid leukemia in mice by the human leukemia-specific fusion gene NUP98-HOXD13 in concert with Meis1. Blood 101, 4529-4538. Ro, S., Park, C., Young, D., Sanders, K. M., and Yan, W. (2007). Tissue-dependent paired expression of miRNAs. Nucleic Acids Res 35, 5944-5953. Roccaro, A. M., Sacco, A., Chen, C., Runnels, J., Leleu, X., Azab, F., Azab, A. K., Jia, X., Ngo, H. T., Melhem, M. R., et al. (2009). microRNA expression in the biology, prognosis, and therapy of Waldenstrom macroglobulinemia. Blood 113, 4391-4402. Ruby, J. G., Jan, C., Player, C., Axtell, M. J., Lee, W., Nusbaum, C., Ge, H., and Bartel, D. P. (2006). Large-scale sequencing reveals 21U-RNAs and additional microRNAs and endogenous siRNAs in C. elegans. Cell 127, 1193-1207. Sansregret, L., and Nepveu, A. (2008). The multiple roles of CUX1: insights from mouse models and cell-based assays. Gene 412, 84-94. Schwarz, D. S., Hutvagner, G., Du, T., Xu, Z., Aronin, N., and Zamore, P. D. (2003). Asymmetry in the assembly of the RNAi enzyme complex. Cell 115, 199-208. Tsang, W. P., and Kwok, T. T. (2009). The miR-18a* microRNA functions as a potential tumor suppressor by targeting on K-Ras. Carcinogenesis.  136  Chapter 4 General discussion and conclusion 4.1 Summary of thesis findings In this thesis, I have, for the first time, given detailed insight into the changes of the miRNA transcriptome during the stepwise progression of leukemia as well as the expression of miRNA*s and in particular the role of miR-223* in leukemogenesis. In chapter 2, I investigated in great detail changes of the miRNA transcriptome in a murine leukemia progression model by using a combination of techniques such as next-generation sequencing, linker-based cloning and real-time PCR. This approach allowed a very global view of the miRNA transcriptome and at the same time a very detailed miRNA analysis (Kuchenbauer et al., 2008). We found more than 200 miRNA genes, giving rise to over 300 miRNA/miRNA* species, in the pre-leukemic ND13 and the leukemic ND13+Meis1 cell lines. Approximately, 1/5th (65) miRNAs were differentially expressed miRNAs with the majority undergoing rather downregulation than upregulation. Target prediction analysis coupled with in vitro validation for DEK and miR-23a and miR-155 revealed the potential for a miRNAmediated release of oncogenes that facilitates leukemic progression from the preleukemic to leukemia inducing state. Considering that just a few miRNAs with low expression levels were exclusive to each library, suggests that leukemic progression is dictated by the repertoire of shared, but differentially expressed miRNAs. The generation and analysis of our deep sequencing libraries also shed light on the expression of miRNA*s, which were considered as mere carriers that undergo rapid degradation. So far, only few studies addressed the abundance of miRNA*s, because only 50% have been annotated and therefore not been spotted on miRNA  137  microarrays. We found several miRNA*s expressed at intermediate to high levels, contradicting the general idea that miRNA* species are not abundant. Based on these findings, in chapter 3, I examined 9 deep sequencing libraries from various tissues and analyzed the amount and distribution of miRNA/miRNA* for each miRNA duplex. In contrast to previous assumptions that one strand is highly dominant, we found that only approximately 50% of the investigated miRNA duplexes exhibit high ratios with a dominating strand (ratio >100) and at least 10% have low ratios (ratio 110), indicating equal expression of both strands. In addition, most miRNA duplexes retain their miRNA/miRNA* distribution constant across tissues and species. However, certain ratios were inconsistent across all libraries, such as the ratio for miR-223/miR-223*, fluctuating between >10 and <1 in murine and human leukemia cell lines. Bioinformatics suggested a tumor suppressor-like role for miR-223* and in line with this, we found an inverse correlation of miR-223* with CD34 expression (p=0.018), a negative prognostic marker in AML, in 94 AML patients. In vitro analysis, showed that miR-223* is indeed able to bind to CUX1, a predicted target involved in hematological malignancies. A structure-functional analysis of pre-miR223 suggested that miR-223* has regulatory potential on the colony-forming ability of bone marrow progenitor cells. The findings in this chapter point towards an, up to now overlooked role of miRNA*s in pathophysiological processes, adding a new layer of complexity to the regulatory potential of miRNAs.  4.2 Evolution and pitfalls in the detection of miRNAs When I started my research project in 2005, miRNAs had just captured the attention  138  of researchers as potential regulators in physiological as well as pathophysiological processes. Depending on the scientific question, several methods had to be combined. A few years ago, expression profiling and the discovery of novel miRNAs became a major effort. In the following, newly established methods such as stemloop real-time PCR (Chen et al., 2005) and microarrays (Liu et al., 2008) were refined and the introduction of second generation sequencing platforms, such as the 454 and the Illumina platform made novel insights into the miRNA transcriptome possible (see Figure 4.1 for an overview of current methods to detect miRNAs). The depth of the sequencing libraries enabled us to get a more global view of miRNA expression in various cell types. Comparing our own as well as published sequencing libraries (Bar et al., 2008; Wyman et al., 2009) from cancer tissues with libraries generated from ES cells, similarities and differences became obvious. Within all libraries derived from malignancies, the let-7 family constituted for about 50% of all detectable miRNAs and miR-103 for about 10%. However, in ES cells the balance shifted towards an overrepresentation of miR-103 accounting for approximately 30% and the let-7 family for ca. 10%. So far, it is not clear why the let7 family and mIR-103 are so over-representative. It is very possible that they regulate critical cellular processes, such as cell division or cell aging. Let-7 is mainly known for its tumor suppressor functions by targeting the RAS family (Johnson et al., 2005) and HMGA2 (Mayr et al., 2007), an important protein in stem cell aging and development, implying important functions for cell homeostasis. About the role of miR-103 is not much known, except that its main predicted target (Targetscan v5.1) is DICER1 with 5 conserved 3’UTR binding sites, leaving room for speculation if  139  miR-103 is a master regulator of miRNA maturation and responsible for homeostasis of miRNA processing. Considering the important role of Dicer (Murchison et al., 2005) and hence miRNAs in embryonic development, as well as the role of several miRNAs in pluripotency (Tay et al., 2008; Xu et al., 2009), it is obvious that miRNAs have to under tight control in such a critical process. It would be very interesting to know if the balance of miR-103 and let-7 determines differentiation as in developed tissues miRNAs were considered as fine-tuners of differentiation, possibly requiring a less tight regulation of miRNA maturation. The ability to answer several scientific questions with one method instead of combining multiple technical approaches such as cloning and microarrays made small RNA deep sequencing so valuable. Since the first sequencing approach of David Bartel’s group in 2006 (Ruby et al., 2006), the number of new miRNAs doubled. However, most of them are species specific and not conserved. Sequencing also revealed to a larger extent, sequence variations within miRNAs (Morin et al., 2008; Ruby et al., 2006). Especially the 3’end, the less conserved part of a miRNA, showed multiple sequence variations deriving from the pre-miRNA. Despite a probably moderate impact on target recognition, future classifications of miRNAs might be based on tissue specific sequences as well as a miRBase reference sequence. Each method to detect miRNAs bears flaws, mainly recognized and improved through extensive use and distribution (Figure 4.1).  140  Figure 4.1 Current techniques of miRNA detection  Probably, the most unbiased method is Northern Blot analysis to detect and quantify miRNAs (Aravin and Tuschl, 2005). However, this method requires lots of starting material, involves radioactivity and takes a few days. The weakness and at the same time, the strength of stemloop based real-time PCR (Chen et al., 2005), such as the Taqman assay, lies in its initial non-covalent binding between miRNA and stemloop RT-primer, allowing to elongate the miRNA in the RT reaction (Figure 4.2). This step determines the sensitivity of the assay and recent unpublished results from our group clearly showed varying sensitivities between different Taqman assays. The sensitivity of miRNA microarrays depends on the composition of the array and a possible bias for next generation sequencing lies in the adapter ligation. Depending on the ligase and linkers, biases towards certain miRNA sequences could be favored (M. Hafner, personal communication, Keystone Meeting 2008). Future developments will try to decrease the starting amount for deep-sequencing miRNA  141  to, maybe, single cell level. In-situ hybridizations (Pena et al., 2009), a method to visualize the expression of miRNAs in tissues as well as assessing miRNAs with intra cellular flow cytometry, are still in its infancies and have not been routinely employed.  Figure 4.2 The principle of stemloop PCR In the initial step the miRNA forms a noncovalent complex with the stemloop primer and gets reverse transcribed, elongating the miRNA to 70-90 nts, allowing real-time PCR with a Taqman probe.  4.3 MiRNAs in cancer My initial idea was to exploit our well-defined leukemia progression model (Pineault 142  et al., 2005), based on a ND13 fusion gene and the addition of the Hox-cofactor Meis1, in order to identify miRNAs that are responsible for leukemic transformation. As discussed in chapter 2, to our surprise no exclusive, transformation specific miRNA could be detected. In general, the expression levels of the most differentially expressed miRNAs ranged in the low-mid range, indicating that transformation rather depends on relative levels than unique, tissue-specific miRNA expression. So far, only one study correlated a single miRNA with overall survival of leukemia patients (Garzon et al., 2008b). If statistics allowed a connection between miRNAs and cancers, especially leukemias, signatures consisting of up to a dozen miRNAs were reported (Jongen-Lavrencic et al., 2008; Marcucci et al., 2008). Despite many in vitro studies showing that miRNAs are deregulated in cancer, target tumor suppressor genes, accelerate cell cycle progression and impair differentiation, only miR-155 (Costinean et al., 2006) and miR-17-92 (He et al., 2005) have been shown to promote cancer in vivo. In this context, it would be interesting to generate vectors, expressing several miRNAs that have been associated with e.g. a certain AML subtype to test this hypothesis. However, the key question how important miRNAs in the pathogenesis of cancer really are can only be answered with in vivo strategies. In contrast to transient inhibition, a permanent in vivo knockdown of miRNAs has been posing a technical challenge over the last few years, as miRNAs are short and pre-miRNAs hard to access due to their hairpin structure. However, recent clever strategies such as miRNA sponges (Figure 4.3) (Ebert et al., 2007), paved the way for systemic miRNA screens in a variety of cells and, even more important, in animals. The advantage of  143  such an approach over traditional knock-out models lies in the redundancy of the seed region for members of the same miRNA family, such as the let-7 family, which are not organized in transcriptional units and hence distributed all over the genome. As previously mentioned, initial experiments to test the transforming potential of miRNAs used retroviral overexpression strategies. Besides considerations in the vector design, genomic flanking regions have to be added to the miRNA gene, overexpression works best for miRNAs that exhibit low endogenous expression levels. In chapter 3, I showed that reconstitution of miR-223 KO bone marrow with a retroviral miR-223 construct is not able to reach endogenous miR-223 levels. Interesting in this context is, that moderate overexpression of miRNAs, such as miR223 (1.7 fold) (Fazi et al., 2007) or miR-17-19b (2.5-5 fold) (He et al., 2007), induced differentiation of AML blasts or accelerated lymphoma development, respectively. Upcoming functional studies based on these techniques and the richness of the profiling efforts of miRNAs over the last few years will shed more light on the importance of miRNAs in the development of cancer.  144  Figure 4.3 The concept of miRNA sponges MiRNA sponges expressing a GFP construct containing binding sites for a miRNA, leading to disruption of the miRNA-target interaction.  4.4 Novel aspects of miRNAs As already mentioned, next generation sequencing delivers a general view of the miRNA transcriptome, facilitating the discovery of new aspects of miRNAs. A closer look at the miRNA transcriptome in our mouse leukemia libraries revealed that the expression of miRNA*s is more abundant than previously assumed. This lead to the data presented in chapter 3, representing a novel mode of action and possible classification for miRNA duplexes and contradicting current views of inactive miRNA*s. However, it still needs to be adressed how miRNA*s enter the RISC  145  complex and what other factors determine strand selection, besides the free enegry of the 5´ends. Another recent finding contradicted the general idea that miRNAs are merely posttranslational  repressors  (Vasudevan  et  al.,  2007).  The  variety  of  posttranscriptional silencing mechanisms, as pointed out in the introduction, indicates that miRNAs could have an unrecognized repertoire of functions. For example, Vasudevan et al. recently demonstrated that miRNAs are able to induce translation by interacting with Ago proteins, FXR1 after TNFa activation during cell cycle arrest (Vasudevan et al., 2007, 2008). In line with this, miR-10a has been shown to interact with the 5’ UTR of mRNAs encoding ribosomal proteins to enhance their translation. MiR-10a buffers translational repression of ribosomal protein mRNAs during amino acid starvation by binding in close proximity to the 5’TOP motif, a transcriptional regulatory site (Orom et al., 2008). Recent reports have linked miRNAs to epigenetic regulatory mechanisms (Rouhi et al., 2008). The transcription of a miRNAs can underlie extensive chromatin remodeling and in turn miRNAs can manipulate chromatin remodeling by targeting key enzymes such as histone acetyl modifiers and methyltransferases (DNMT) (Garzon et al., 2009). Interestingly, siRNAs have been shown to induce heterochromatin formation in S. pombe by associating with nascent RNA polymerase transcripts, leading to activation of a DNMT followed by H3Lys9 methylation and hence heterochromatin formation (Buhler et al., 2006). As siRNAs and miRNAs share many similarities, it is imaginable that this mechanism exists for both small RNA families. However, it is not clear of a perfect target complementary  146  is mandatory, which has been documented only for few miRNAs, such as miR-196a (Yekta et al., 2004).  4.5 Concluding remarks Within the last few years miRNAs have become a big research interest for many research groups spanning basic, clinical and plant research, demonstrating the big impact of small nc RNAs. In this thesis, I have endeavored to shed light on the expression of miRNAs in the development of leukemia and demonstrated the role of miRNA*s in AML, a previously unappreciated mechanism extending the regulatory range of a single miRNA duplex.  147  4.6 Bibliography Akao, Y., Nakagawa, Y., and Naoe, T. (2006). let-7 microRNA functions as a potential growth suppressor in human colon cancer cells. Biol Pharm Bull 29, 903906. Akashi, K., Traver, D., Miyamoto, T., and Weissman, I.L. (2000). A clonogenic common myeloid progenitor that gives rise to all myeloid lineages. Nature 404, 193197. Alizadeh, A.A., and Staudt, L.M. (2000). Genomic-scale gene expression profiling of normal and malignant immune cells. Curr Opin Immunol 12, 219-225. Ambros, V., Bartel, B., Bartel, D.P., Burge, C.B., Carrington, J.C., Chen, X., Dreyfuss, G., Eddy, S.R., Griffiths-Jones, S., Marshall, M., et al. (2003a). A uniform system for microRNA annotation. Rna 9, 277-279. Ambros, V., and Lee, R.C. (2004). Identification of microRNAs and other tiny noncoding RNAs by cDNA cloning. Methods Mol Biol 265, 131-158. Ambros, V., Lee, R.C., Lavanway, A., Williams, P.T., and Jewell, D. (2003b). MicroRNAs and other tiny endogenous RNAs in C. elegans. Curr Biol 13, 807-818. Aravin, A., Gaidatzis, D., Pfeffer, S., Lagos-Quintana, M., Landgraf, P., Iovino, N., Morris, P., Brownstein, M.J., Kuramochi-Miyagawa, S., Nakano, T., et al. (2006). A novel class of small RNAs bind to MILI protein in mouse testes. Nature 442, 203207. Aravin, A., and Tuschl, T. (2005). Identification and characterization of small RNAs involved in RNA silencing. FEBS Lett 579, 5830-5840.  148  Aravin, A.A., Lagos-Quintana, M., Yalcin, A., Zavolan, M., Marks, D., Snyder, B., Gaasterland, T., Meyer, J., and Tuschl, T. (2003). The small RNA profile during Drosophila melanogaster development. Dev Cell 5, 337-350. Argiropoulos, B., Palmqvist, L., Yung, E., Kuchenbauer, F., Heuser, M., Sly, L.M., Wan, A., Krystal, G., and Humphries, R.K. (2008). Linkage of Meis1 leukemogenic activity to multiple downstream effectors including Trib2 and Ccl3. Exp Hematol. Bandres, E., Agirre, X., Ramirez, N., Zarate, R., and Garcia-Foncillas, J. (2007). MicroRNAs as cancer players: potential clinical and biological effects. DNA Cell Biol 26, 273-282. Bar, M., Wyman, S.K., Fritz, B.R., Qi, J., Garg, K.S., Parkin, R.K., Kroh, E.M., Bendoraite, A., Mitchell, P.S., Nelson, A.M., et al. (2008). MicroRNA discovery and profiling in human embryonic stem cells by deep sequencing of small RNA libraries. Stem Cells 26, 2496-2505. Bartel, D.P. (2004). MicroRNAs: genomics, biogenesis, mechanism, and function. Cell 116, 281-297. Bartel, D.P. (2009). MicroRNAs: target recognition and regulatory functions. Cell 136, 215-233. Bentley, D.R., Balasubramanian, S., Swerdlow, H.P., Smith, G.P., Milton, J., Brown, C.G., Hall, K.P., Evers, D.J., Barnes, C.L., Bignell, H.R., et al. (2008). Accurate whole human genome sequencing using reversible terminator chemistry. Nature 456, 53-59. Berezikov, E., Cuppen, E., and Plasterk, R.H. (2006a). Approaches to microRNA discovery. Nat Genet 38 Suppl, S2-7.  149  Berezikov, E., Thuemmler, F., van Laake, L.W., Kondova, I., Bontrop, R., Cuppen, E., and Plasterk, R.H. (2006b). Diversity of microRNAs in human and chimpanzee brain. Nat Genet 38, 1375-1377. Berezikov, E., van Tetering, G., Verheul, M., van de Belt, J., van Laake, L., Vos, J., Verloop, R., van de Wetering, M., Guryev, V., Takada, S., et al. (2006c). Many novel mammalian microRNA candidates identified by extensive cloning and RAKE analysis. Genome Res 16, 1289-1298. Blow, M.J., Grocock, R.J., van Dongen, S., Enright, A.J., Dicks, E., Futreal, P.A., Wooster, R., and Stratton, M.R. (2006). RNA editing of human microRNAs. Genome Biol 7, R27. Borchert, G.M., Lanier, W., and Davidson, B.L. (2006). RNA polymerase III transcribes human microRNAs. Nat Struct Mol Biol 13, 1097-1101. Brannan, C.I., Dees, E.C., Ingram, R.S., and Tilghman, S.M. (1990). The product of the H19 gene may function as an RNA. Mol Cell Biol 10, 28-36. Brown, C.J., Hendrich, B.D., Rupert, J.L., Lafreniere, R.G., Xing, Y., Lawrence, J., and Willard, H.F. (1992). The human XIST gene: analysis of a 17 kb inactive Xspecific RNA that contains conserved repeats and is highly localized within the nucleus. Cell 71, 527-542. Bruchova, H., Yoon, D., Agarwal, A.M., Mendell, J., and Prchal, J.T. (2007). Regulated  expression  of  microRNAs  in  normal  and  polycythemia  vera  erythropoiesis. Exp Hematol 35, 1657-1667. Buhler, M., Verdel, A., and Moazed, D. (2006). Tethering RITS to a nascent transcript initiates RNAi- and heterochromatin-dependent gene silencing. Cell 125, 873-886.  150  Cadieux, C., Harada, R., Paquet, M., Cote, O., Trudel, M., Nepveu, A., and Bouchard, M. (2008). Polycystic kidneys caused by sustained expression of Cux1 isoform p75. J Biol Chem 283, 13817-13824. Calin, G.A., Dumitru, C.D., Shimizu, M., Bichi, R., Zupo, S., Noch, E., Aldler, H., Rattan, S., Keating, M., Rai, K., et al. (2002). Frequent deletions and downregulation of micro- RNA genes miR15 and miR16 at 13q14 in chronic lymphocytic leukemia. Proc Natl Acad Sci U S A 99, 15524-15529. Calin, G.A., Ferracin, M., Cimmino, A., Di Leva, G., Shimizu, M., Wojcik, S.E., Iorio, M.V., Visone, R., Sever, N.I., Fabbri, M., et al. (2005). A MicroRNA signature associated with prognosis and progression in chronic lymphocytic leukemia. N Engl J Med 353, 1793-1801. Calin, G.A., Liu, C.G., Sevignani, C., Ferracin, M., Felli, N., Dumitru, C.D., Shimizu, M., Cimmino, A., Zupo, S., Dono, M., et al. (2004a). MicroRNA profiling reveals distinct signatures in B cell chronic lymphocytic leukemias. Proc Natl Acad Sci U S A 101, 11755-11760. Calin, G.A., Sevignani, C., Dumitru, C.D., Hyslop, T., Noch, E., Yendamuri, S., Shimizu, M., Rattan, S., Bullrich, F., Negrini, M., and Croce, C.M. (2004b). Human microRNA genes are frequently located at fragile sites and genomic regions involved in cancers. Proc Natl Acad Sci U S A 101, 2999-3004. Chan, H.W., Kurago, Z.B., Stewart, C.A., Wilson, M.J., Martin, M.P., Mace, B.E., Carrington, M., Trowsdale, J., and Lutz, C.T. (2003). DNA methylation maintains allele-specific KIR gene expression in human natural killer cells. J Exp Med 197, 245-255.  151  Chan, H.W., Miller, J.S., Moore, M.B., and Lutz, C.T. (2005a). Epigenetic control of highly homologous killer Ig-like receptor gene alleles. J Immunol 175, 5966-5974. Chan, J.A., Krichevsky, A.M., and Kosik, K.S. (2005b). MicroRNA-21 is an antiapoptotic factor in human glioblastoma cells. Cancer Res 65, 6029-6033. Chen, C., Ridzon, D.A., Broomer, A.J., Zhou, Z., Lee, D.H., Nguyen, J.T., Barbisin, M., Xu, N.L., Mahuvakar, V.R., Andersen, M.R., et al. (2005). Real-time quantification of microRNAs by stem-loop RT-PCR. Nucleic Acids Res 33, e179. Chen, C.Z., Li, L., Lodish, H.F., and Bartel, D.P. (2004). MicroRNAs modulate hematopoietic lineage differentiation. Science 303, 83-86. Chendrimada, T.P., Gregory, R.I., Kumaraswamy, E., Norman, J., Cooch, N., Nishikura, K., and Shiekhattar, R. (2005). TRBP recruits the Dicer complex to Ago2 for microRNA processing and gene silencing. Nature 436, 740-744. Cheson, B.D., Bennett, J.M., Kopecky, K.J., Buchner, T., Willman, C.L., Estey, E.H., Schiffer, C.A., Doehner, H., Tallman, M.S., Lister, T.A., et al. (2003). Revised recommendations of the International Working Group for Diagnosis, Standardization of Response Criteria, Treatment Outcomes, and Reporting Standards for Therapeutic Trials in Acute Myeloid Leukemia. J Clin Oncol 21, 4642-4649. Chin, L.J., Ratner, E., Leng, S., Zhai, R., Nallur, S., Babar, I., Muller, R.U., Straka, E., Su, L., Burki, E.A., et al. (2008). A SNP in a let-7 microRNA complementary site in the KRAS 3' untranslated region increases non-small cell lung cancer risk. Cancer Res 68, 8535-8540. Cimmino, A., Calin, G.A., Fabbri, M., Iorio, M.V., Ferracin, M., Shimizu, M., Wojcik, S.E., Aqeilan, R.I., Zupo, S., Dono, M., et al. (2005). miR-15 and miR-16 induce apoptosis by targeting BCL2. Proc Natl Acad Sci U S A 102, 13944-13949.  152  Cobb, B.S., Nesterova, T.B., Thompson, E., Hertweck, A., O'Connor, E., Godwin, J., Wilson, C.B., Brockdorff, N., Fisher, A.G., Smale, S.T., and Merkenschlager, M. (2005). T cell lineage choice and differentiation in the absence of the RNase III enzyme Dicer. J Exp Med 201, 1367-1373. Coffin, J.M. (1979). Structure, replication, and recombination of retrovirus genomes: some unifying hypotheses. J Gen Virol 42, 1-26. Connolly, E., Melegari, M., Landgraf, P., Tchaikovskaya, T., Tennant, B.C., Slagle, B.L., Rogler, L.E., Zavolan, M., Tuschl, T., and Rogler, C.E. (2008). Elevated expression of the miR-17-92 polycistron and miR-21 in hepadnavirus-associated hepatocellular carcinoma contributes to the malignant phenotype. Am J Pathol 173, 856-864. Costa, F.F. (2007). Non-coding RNAs: lost in translation? Gene 386, 1-10. Costinean, S., Zanesi, N., Pekarsky, Y., Tili, E., Volinia, S., Heerema, N., and Croce, C.M. (2006). Pre-B cell proliferation and lymphoblastic leukemia/high-grade lymphoma in E(mu)-miR155 transgenic mice. Proc Natl Acad Sci U S A 103, 70247029. Crick, F. (1970). Central dogma of molecular biology. Nature 227, 561-563. Davison, T.S., Johnson, C.D., and Andruss, B.F. (2006). Analyzing micro-RNA expression using microarrays. Methods Enzymol 411, 14-34. Debernardi, S., Skoulakis, S., Molloy, G., Chaplin, T., Dixon-McIver, A., and Young, B.D. (2007). MicroRNA miR-181a correlates with morphological sub-class of acute myeloid leukaemia and the expression of its target genes in global genome-wide analysis. Leukemia 21, 912-916.  153  Dore, L.C., Amigo, J.D., Dos Santos, C.O., Zhang, Z., Gai, X., Tobias, J.W., Yu, D., Klein, A.M., Dorman, C., Wu, W., et al. (2008). A GATA-1-regulated microRNA locus essential for erythropoiesis. Proc Natl Acad Sci U S A 105, 3333-3338. Easow, G., Teleman, A.A., and Cohen, S.M. (2007). Isolation of microRNA targets by miRNP immunopurification. Rna 13, 1198-1204. Ebert, M.S., Neilson, J.R., and Sharp, P.A. (2007). MicroRNA sponges: competitive inhibitors of small RNAs in mammalian cells. Nat Methods 4, 721-726. Esquela-Kerscher, A., and Slack, F.J. (2006). Oncomirs - microRNAs with a role in cancer. Nat Rev Cancer 6, 259-269. Fazi, F., Racanicchi, S., Zardo, G., Starnes, L.M., Mancini, M., Travaglini, L., Diverio, D., Ammatuna, E., Cimino, G., Lo-Coco, F., et al. (2007). Epigenetic Silencing of the Myelopoiesis Regulator microRNA-223 by the AML1/ETO Oncoprotein. Cancer Cell 12, 457-466. Fazi, F., Rosa, A., Fatica, A., Gelmetti, V., De Marchis, M.L., Nervi, C., and Bozzoni, I. (2005). A minicircuitry comprised of microRNA-223 and transcription factors NFI-A and C/EBPalpha regulates human granulopoiesis. Cell 123, 819-831. Feinberg, A.P., Oshimura, M., and Barrett, J.C. (2002). Epigenetic mechanisms in human disease. Cancer Res 62, 6784-6787. Felli, N., Fontana, L., Pelosi, E., Botta, R., Bonci, D., Facchiano, F., Liuzzi, F., Lulli, V., Morsilli, O., Santoro, S., et al. (2005). MicroRNAs 221 and 222 inhibit normal erythropoiesis and erythroleukemic cell growth via kit receptor down-modulation. Proc Natl Acad Sci U S A 102, 18081-18086.  154  Fontana, L., Pelosi, E., Greco, P., Racanicchi, S., Testa, U., Liuzzi, F., Croce, C.M., Brunetti, E., Grignani, F., and Peschle, C. (2007). MicroRNAs 17-5p-20a-106a control monocytopoiesis through AML1 targeting and M-CSF receptor upregulation. Nat Cell Biol 9, 775-787. Friedlander, M.R., Chen, W., Adamidi, C., Maaskola, J., Einspanier, R., Knespel, S., and Rajewsky, N. (2008). Discovering microRNAs from deep sequencing data using miRDeep. Nat Biotechnol 26, 407-415. Fu, H., Tie, Y., Xu, C., Zhang, Z., Zhu, J., Shi, Y., Jiang, H., Sun, Z., and Zheng, X. (2005). Identification of human fetal liver miRNAs by a novel method. FEBS Lett 579, 3849-3854. Fukao, T., Fukuda, Y., Kiga, K., Sharif, J., Hino, K., Enomoto, Y., Kawamura, A., Nakamura, K., Takeuchi, T., and Tanabe, M. (2007). An evolutionarily conserved mechanism for microRNA-223 expression revealed by microRNA gene profiling. Cell 129, 617-631. Futreal, P.A., Coin, L., Marshall, M., Down, T., Hubbard, T., Wooster, R., Rahman, N., and Stratton, M.R. (2004). A census of human cancer genes. Nat Rev Cancer 4, 177-183. Garzon, R. (2009). MicroRNA profiling of megakaryocytes. Methods Mol Biol 496, 293-298. Garzon, R., Garofalo, M., Martelli, M.P., Briesewitz, R., Wang, L., FernandezCymering, C., Volinia, S., Liu, C.G., Schnittger, S., Haferlach, T., et al. (2008a). Distinctive microRNA signature of acute myeloid leukemia bearing cytoplasmic mutated nucleophosmin. Proc Natl Acad Sci U S A 105, 3945-3950.  155  Garzon, R., Liu, S., Fabbri, M., Liu, Z., Heaphy, C.E., Callegari, E., Schwind, S., Pang, J., Yu, J., Muthusamy, N., et al. (2009). MicroRNA -29b induces global DNA hypomethylation and tumor suppressor gene re-expression in acute myeloid leukemia by targeting directly DNMT3A and 3B and indirectly DNMT1. Blood. Garzon, R., Pichiorri, F., Palumbo, T., Iuliano, R., Cimmino, A., Aqeilan, R., Volinia, S., Bhatt, D., Alder, H., Marcucci, G., et al. (2006). MicroRNA fingerprints during human megakaryocytopoiesis. Proc Natl Acad Sci U S A 103, 5078-5083. Garzon, R., Volinia, S., Liu, C.G., Fernandez-Cymering, C., Palumbo, T., Pichiorri, F., Fabbri, M., Coombes, K., Alder, H., Nakamura, T., et al. (2008b). MicroRNA signatures associated with cytogenetics and prognosis in acute myeloid leukemia. Blood. Georgantas, R.W., 3rd, Hildreth, R., Morisot, S., Alder, J., Liu, C.G., Heimfeld, S., Calin, G.A., Croce, C.M., and Civin, C.I. (2007). CD34+ hematopoietic stemprogenitor cell microRNA expression and function: a circuit diagram of differentiation control. Proc Natl Acad Sci U S A 104, 2750-2755. Girard, A., Sachidanandam, R., Hannon, G.J., and Carmell, M.A. (2006). A germlinespecific class of small RNAs binds mammalian Piwi proteins. Nature 442, 199-202. Goulet, B., Markovic, Y., Leduy, L., and Nepveu, A. (2008). Proteolytic processing of cut homeobox 1 by neutrophil elastase in the MV4;11 myeloid leukemia cell line. Mol Cancer Res 6, 644-653. Gramantieri, L., Fornari, F., Callegari, E., Sabbioni, S., Lanza, G., Croce, C.M., Bolondi, L., and Negrini, M. (2008). MicroRNA involvement in hepatocellular carcinoma. J Cell Mol Med 12, 2189-2204.  156  Gregory, R.I., Chendrimada, T.P., Cooch, N., and Shiekhattar, R. (2005). Human RISC couples microRNA biogenesis and posttranscriptional gene silencing. Cell 123, 631-640. Griffiths-Jones, S. (2006). miRBase: the microRNA sequence database. Methods Mol Biol 342, 129-138. Griffiths-Jones, S., Grocock, R.J., van Dongen, S., Bateman, A., and Enright, A.J. (2006). miRBase: microRNA sequences, targets and gene nomenclature. Nucleic Acids Res 34, D140-144. Grimson, A., Srivastava, M., Fahey, B., Woodcroft, B.J., Chiang, H.R., King, N., Degnan, B.M., Rokhsar, D.S., and Bartel, D.P. (2008). Early origins and evolution of microRNAs and Piwi-interacting RNAs in animals. Nature 455, 1193-1197. Grivna, S.T., Beyret, E., Wang, Z., and Lin, H. (2006). A novel class of small RNAs in mouse spermatogenic cells. Genes Dev 20, 1709-1714. Gusev, Y., and Brackett, D.J. (2007). MicroRNA expression profiling in cancer from a bioinformatics prospective. Expert Rev Mol Diagn 7, 787-792. Han, J., Lee, Y., Yeom, K.H., Kim, Y.K., Jin, H., and Kim, V.N. (2004). The DroshaDGCR8 complex in primary microRNA processing. Genes Dev 18, 3016-3027. Hao, Y., Crenshaw, T., Moulton, T., Newcomb, E., and Tycko, B. (1993). Tumoursuppressor activity of H19 RNA. Nature 365, 764-767. He, L., He, X., Lim, L.P., de Stanchina, E., Xuan, Z., Liang, Y., Xue, W., Zender, L., Magnus, J., Ridzon, D., et al. (2007). A microRNA component of the p53 tumour suppressor network. Nature 447, 1130-1134.  157  He, L., Thomson, J.M., Hemann, M.T., Hernando-Monge, E., Mu, D., Goodson, S., Powers, S., Cordon-Cardo, C., Lowe, S.W., Hannon, G.J., and Hammond, S.M. (2005). A microRNA polycistron as a potential human oncogene. Nature 435, 828833. Held, W., and Raulet, D.H. (1997). Expression of the Ly49A gene in murine natural killer cell clones is predominantly but not exclusively mono-allelic. Eur J Immunol 27, 2876-2884. Hernando, E. (2007). microRNAs and cancer: role in tumorigenesis, patient classification and therapy. Clin Transl Oncol 9, 155-160. Heuser, M., Beutel, G., Krauter, J., Dohner, K., von Neuhoff, N., Schlegelberger, B., and Ganser, A. (2006). High meningioma 1 (MN1) expression as a predictor for poor outcome in acute myeloid leukemia with normal cytogenetics. Blood 108, 38983905. Houwing, S., Kamminga, L.M., Berezikov, E., Cronembold, D., Girard, A., van den Elst, H., Filippov, D.V., Blaser, H., Raz, E., Moens, C.B., et al. (2007). A role for Piwi and piRNAs in germ cell maintenance and transposon silencing in Zebrafish. Cell 129, 69-82. Humphreys, D.T., Westman, B.J., Martin, D.I., and Preiss, T. (2005). MicroRNAs control translation initiation by inhibiting eukaryotic initiation factor 4E/cap and poly(A) tail function. Proc Natl Acad Sci U S A 102, 16961-16966. Ibanez-Ventoso, C., Vora, M., and Driscoll, M. (2008). Sequence relationships among C. elegans, D. melanogaster and human microRNAs highlight the extensive conservation of microRNAs in biology. PLoS ONE 3, e2818.  158  Ibarra, I., Erlich, Y., Muthuswamy, S.K., Sachidanandam, R., and Hannon, G.J. (2007). A role for microRNAs in maintenance of mouse mammary epithelial progenitor cells. Genes Dev 21, 3238-3243. Isken, F., Steffen, B., Merk, S., Dugas, M., Markus, B., Tidow, N., Zuhlsdorf, M., Illmer, T., Thiede, C., Berdel, W.E., et al. (2008). Identification of acute myeloid leukaemia associated microRNA expression patterns. Br J Haematol 140, 153-161. Jay, C., Nemunaitis, J., Chen, P., Fulgham, P., and Tong, A.W. (2007). miRNA profiling for diagnosis and prognosis of human cancer. DNA Cell Biol 26, 293-300. Jazdzewski, K., Liyanarachchi, S., Swierniak, M., Pachucki, J., Ringel, M.D., Jarzab, B., and de la Chapelle, A. (2009). Polymorphic mature microRNAs from passenger strand of pre-miR-146a contribute to thyroid cancer. Proc Natl Acad Sci U S A 106, 1502-1505. Jazdzewski, K., Murray, E.L., Franssila, K., Jarzab, B., Schoenberg, D.R., and de la Chapelle, A. (2008). Common SNP in pre-miR-146a decreases mature miR expression and predisposes to papillary thyroid carcinoma. Proc Natl Acad Sci U S A 105, 7269-7274. Jiang, J., Lee, E.J., Gusev, Y., and Schmittgen, T.D. (2005). Real-time expression profiling of microRNA precursors in human cancer cell lines. Nucleic Acids Res 33, 5394-5403. Johnnidis, J.B., Harris, M.H., Wheeler, R.T., Stehling-Sun, S., Lam, M.H., Kirak, O., Brummelkamp, T.R., Fleming, M.D., and Camargo, F.D. (2008). Regulation of progenitor cell proliferation and granulocyte function by microRNA-223. Nature 451, 1125-1129.  159  Johnson, S.M., Grosshans, H., Shingara, J., Byrom, M., Jarvis, R., Cheng, A., Labourier, E., Reinert, K.L., Brown, D., and Slack, F.J. (2005). RAS is regulated by the let-7 microRNA family. Cell 120, 635-647. Jones, L. (2002). Revealing micro-RNAs in plants. Trends Plant Sci 7, 473-475. Jongen-Lavrencic, M., Sun, S.M., Dijkstra, M.K., Valk, P.J., and Lowenberg, B. (2008). MicroRNA expression profiling in relation to the genetic heterogeneity of acute myeloid leukemia. Blood. Kalscheuer, S., Zhang, X., Zeng, Y., and Upadhyaya, P. (2008). Differential expression of microRNAs in early-stage neoplastic transformation in the lungs of F344 rats chronically treated with the tobacco carcinogen 4-(methylnitrosamino)-1(3-pyridyl)-1-butanone. Carcinogenesis 29, 2394-2399. Ketting, R.F., Fischer, S.E., Bernstein, E., Sijen, T., Hannon, G.J., and Plasterk, R.H. (2001). Dicer functions in RNA interference and in synthesis of small RNA involved in developmental timing in C. elegans. Genes Dev 15, 2654-2659. Khvorova, A., Reynolds, A., and Jayasena, S.D. (2003). Functional siRNAs and miRNAs exhibit strand bias. Cell 115, 209-216. Kim, S., Lee, U.J., Kim, M.N., Lee, E.J., Kim, J.Y., Lee, M.Y., Choung, S., Kim, Y.J., and Choi, Y.C. (2008). MicroRNA miR-199a* regulates the MET proto-oncogene and the downstream extracellular signal-regulated kinase 2 (ERK2). J Biol Chem 283, 18158-18166. Kim, V.N. (2005). MicroRNA biogenesis: coordinated cropping and dicing. Nat Rev Mol Cell Biol 6, 376-385. Kuchenbauer, F., Morin, R.D., Argiropoulos, B., Petriv, O., Griffith, M., Heuser, M., Yung, E., Piper, J., Delaney, A., Prabhu, A.L., et al. (2008). In depth characterization  160  of the microRNA transcriptome in a leukemia progression model. . Genome Research under Review. Lagos-Quintana, M., Rauhut, R., Yalcin, A., Meyer, J., Lendeckel, W., and Tuschl, T. (2002). Identification of tissue-specific microRNAs from mouse. Curr Biol 12, 735739. Lander, E.S., Linton, L.M., Birren, B., Nusbaum, C., Zody, M.C., Baldwin, J., Devon, K., Dewar, K., Doyle, M., FitzHugh, W., et al. (2001). Initial sequencing and analysis of the human genome. Nature 409, 860-921. Landgraf, P., Rusu, M., Sheridan, R., Sewer, A., Iovino, N., Aravin, A., Pfeffer, S., Rice, A., Kamphorst, A.O., Landthaler, M., et al. (2007). A mammalian microRNA expression atlas based on small RNA library sequencing. Cell 129, 1401-1414. Lau, N.C., Lim, L.P., Weinstein, E.G., and Bartel, D.P. (2001). An abundant class of tiny RNAs with probable regulatory roles in Caenorhabditis elegans. Science 294, 858-862. Lawrie, C.H., Soneji, S., Marafioti, T., Cooper, C.D., Palazzo, S., Paterson, J.C., Cattan, H., Enver, T., Mager, R., Boultwood, J., et al. (2007). Microrna expression distinguishes between germinal center B cell-like and activated B cell-like subtypes of diffuse large B cell lymphoma. Int J Cancer 121, 1156-1161. Lee, D.Y., Shatseva, T., Jeyapalan, Z., Du, W.W., Deng, Z., and Yang, B.B. (2009). A 3'-untranslated region (3'UTR) induces organ adhesion by regulating miR-199a* functions. PLoS ONE 4, e4527. Lee, R.C., and Ambros, V. (2001). An extensive class of small RNAs in Caenorhabditis elegans. Science 294, 862-864.  161  Lee, R.C., Feinbaum, R.L., and Ambros, V. (1993). The C. elegans heterochronic gene lin-4 encodes small RNAs with antisense complementarity to lin-14. Cell 75, 843-854. Lee, Y., Han, J., Yeom, K.H., Jin, H., and Kim, V.N. (2006a). Drosha in primary microRNA processing. Cold Spring Harb Symp Quant Biol 71, 51-57. Lee, Y., Hur, I., Park, S.Y., Kim, Y.K., Suh, M.R., and Kim, V.N. (2006b). The role of PACT in the RNA silencing pathway. Embo J 25, 522-532. Lee, Y., Kim, M., Han, J., Yeom, K.H., Lee, S., Baek, S.H., and Kim, V.N. (2004). MicroRNA genes are transcribed by RNA polymerase II. Embo J 23, 4051-4060. Lee, Y.S., and Dutta, A. (2006). MicroRNAs: small but potent oncogenes or tumor suppressors. Curr Opin Investig Drugs 7, 560-564. Lee, Y.S., and Dutta, A. (2007). The tumor suppressor microRNA let-7 represses the HMGA2 oncogene. Genes Dev 21, 1025-1030. Leighton, P.A., Ingram, R.S., Eggenschwiler, J., Efstratiadis, A., and Tilghman, S.M. (1995). Disruption of imprinting caused by deletion of the H19 gene region in mice. Nature 375, 34-39. Lewis, B.P., Burge, C.B., and Bartel, D.P. (2005). Conserved seed pairing, often flanked by adenosines, indicates that thousands of human genes are microRNA targets. Cell 120, 15-20. Liu, C.G., Calin, G.A., Volinia, S., and Croce, C.M. (2008). MicroRNA expression profiling using microarrays. Nat Protoc 3, 563-578.  162  Liu, J., Rivas, F.V., Wohlschlegel, J., Yates, J.R., 3rd, Parker, R., and Hannon, G.J. (2005). A role for the P-body component GW182 in microRNA function. Nat Cell Biol 7, 1261-1266. Long, D., Lee, R., Williams, P., Chan, C.Y., Ambros, V., and Ding, Y. (2007). Potent effect of target structure on microRNA function. Nat Struct Mol Biol 14, 287-294. Looijenga, L.H., Gillis, A.J., Stoop, H., Hersmus, R., and Oosterhuis, J.W. (2007). Relevance of microRNAs in normal and malignant development, including human testicular germ cell tumours. Int J Androl. Lu, J., Getz, G., Miska, E.A., Alvarez-Saavedra, E., Lamb, J., Peck, D., SweetCordero, A., Ebert, B.L., Mak, R.H., Ferrando, A.A., et al. (2005). MicroRNA expression profiles classify human cancers. Nature 435, 834-838. Lu, J., Guo, S., Ebert, B.L., Zhang, H., Peng, X., Bosco, J., Pretz, J., Schlanger, R., Wang, J.Y., Mak, R.H., et al. (2008). MicroRNA-mediated control of cell fate in megakaryocyte-erythrocyte progenitors. Dev Cell 14, 843-853. Luciano, D.J., Mirsky, H., Vendetti, N.J., and Maas, S. (2004). RNA editing of a miRNA precursor. Rna 10, 1174-1177. Lui, W.O., Pourmand, N., Patterson, B.K., and Fire, A. (2007). Patterns of known and novel small RNAs in human cervical cancer. Cancer Res 67, 6031-6043. Lund, E., Guttinger, S., Calado, A., Dahlberg, J.E., and Kutay, U. (2004). Nuclear export of microRNA precursors. Science 303, 95-98. Ma, L., Teruya-Feldstein, J., and Weinberg, R.A. (2007). Tumour invasion and metastasis initiated by microRNA-10b in breast cancer. Nature 449, 682-688.  163  Maniataki, E., and Mourelatos, Z. (2005). A human, ATP-independent, RISC assembly machine fueled by pre-miRNA. Genes Dev 19, 2979-2990. Mansfield, J.H., Harfe, B.D., Nissen, R., Obenauer, J., Srineel, J., Chaudhuri, A., Farzan-Kashani, R., Zuker, M., Pasquinelli, A.E., Ruvkun, G., et al. (2004). MicroRNA-responsive  'sensor'  transgenes  uncover  Hox-like  and  other  developmentally regulated patterns of vertebrate microRNA expression. Nat Genet 36, 1079-1083. Marcucci, G., Radmacher, M.D., Maharry, K., Mrozek, K., Ruppert, A.S., Paschka, P., Vukosavljevic, T., Whitman, S.P., Baldus, C.D., Langer, C., et al. (2008). MicroRNA expression in cytogenetically normal acute myeloid leukemia. N Engl J Med 358, 1919-1928. Margulies, M., Egholm, M., Altman, W.E., Attiya, S., Bader, J.S., Bemben, L.A., Berka, J., Braverman, M.S., Chen, Y.J., Chen, Z., et al. (2005). Genome sequencing in microfabricated high-density picolitre reactors. Nature 437, 376-380. Mayr, C., Hemann, M.T., and Bartel, D.P. (2007). Disrupting the pairing between let7 and Hmga2 enhances oncogenic transformation. Science 315, 1576-1579. Meister, G., Landthaler, M., Peters, L., Chen, P.Y., Urlaub, H., Luhrmann, R., and Tuschl, T. (2005). Identification of novel argonaute-associated proteins. Curr Biol 15, 2149-2155. Monticelli, S., Ansel, K.M., Xiao, C., Socci, N.D., Krichevsky, A.M., Thai, T.H., Rajewsky, N., Marks, D.S., Sander, C., Rajewsky, K., et al. (2005). MicroRNA profiling of the murine hematopoietic system. Genome Biol 6, R71. Morin, R.D., O'Connor, M.D., Griffith, M., Kuchenbauer, F., Delaney, A., Prabhu, A.L., Zhao, Y., McDonald, H., Zeng, T., Hirst, M., et al. (2008). Application of  164  massively parallel sequencing to microRNA profiling and discovery in human embryonic stem cells. Genome Res 18, 610-621. Mullican, S.E., Zhang, S., Konopleva, M., Ruvolo, V., Andreeff, M., Milbrandt, J., and Conneely, O.M. (2007). Abrogation of nuclear receptors Nr4a3 and Nr4a1 leads to development of acute myeloid leukemia. Nat Med 13, 730-735. Murchison, E.P., Partridge, J.F., Tam, O.H., Cheloufi, S., and Hannon, G.J. (2005). Characterization of Dicer-deficient murine embryonic stem cells. Proc Natl Acad Sci U S A 102, 12135-12140. Musiyenko, A., Bitko, V., and Barik, S. (2008). Ectopic expression of miR-126*, an intronic product of the vascular endothelial EGF-like 7 gene, regulates prostein translation and invasiveness of prostate cancer LNCaP cells. J Mol Med 86, 313322. Negrini, M., Ferracin, M., Sabbioni, S., and Croce, C.M. (2007). MicroRNAs in human cancer: from research to therapy. J Cell Sci 120, 1833-1840. Neilson, J.R., Zheng, G.X., Burge, C.B., and Sharp, P.A. (2007). Dynamic regulation of miRNA expression in ordered stages of cellular development. Genes Dev 21, 578589. Nottrott, S., Simard, M.J., and Richter, J.D. (2006). Human let-7a miRNA blocks protein production on actively translating polyribosomes. Nat Struct Mol Biol 13, 1108-1114. O'Connell, R.M., Rao, D.S., Chaudhuri, A.A., Boldin, M.P., Taganov, K.D., Nicoll, J., Paquette, R.L., and Baltimore, D. (2008). Sustained expression of microRNA-155 in hematopoietic stem cells causes a myeloproliferative disorder. J Exp Med 205, 585594.  165  O'Donnell, K.A., Wentzel, E.A., Zeller, K.I., Dang, C.V., and Mendell, J.T. (2005). cMyc-regulated microRNAs modulate E2F1 expression. Nature 435, 839-843. Okamura, K., Phillips, M.D., Tyler, D.M., Duan, H., Chou, Y.T., and Lai, E.C. (2008). The regulatory activity of microRNA* species has substantial influence on microRNA and 3' UTR evolution. Nat Struct Mol Biol 15, 354-363. Orom, 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. Ota, A., Tagawa, H., Karnan, S., Tsuzuki, S., Karpas, A., Kira, S., Yoshida, Y., and Seto, M. (2004). Identification and characterization of a novel gene, C13orf25, as a target for 13q31-q32 amplification in malignant lymphoma. Cancer Res 64, 30873095. Packer, A.N., Xing, Y., Harper, S.Q., Jones, L., and Davidson, B.L. (2008). The bifunctional microRNA miR-9/miR-9* regulates REST and CoREST and is downregulated in Huntington's disease. J Neurosci 28, 14341-14346. Park, J.K., Liu, X., Strauss, T.J., McKearin, D.M., and Liu, Q. (2007). The miRNA pathway intrinsically controls self-renewal of Drosophila germline stem cells. Curr Biol 17, 533-538. Pena, J.T., Sohn-Lee, C., Rouhanifard, S.H., Ludwig, J., Hafner, M., Mihailovic, A., Lim, C., Holoch, D., Berninger, P., Zavolan, M., and Tuschl, T. (2009). miRNA in situ hybridization in formaldehyde and EDC-fixed tissues. Nat Methods 6, 139-141. Petersen, C.P., Bordeleau, M.E., Pelletier, J., and Sharp, P.A. (2006). Short RNAs repress translation after initiation in mammalian cells. Mol Cell 21, 533-542.  166  Pillai, R.S., Bhattacharyya, S.N., Artus, C.G., Zoller, T., Cougot, N., Basyuk, E., Bertrand, E., and Filipowicz, W. (2005). Inhibition of translational initiation by Let-7 MicroRNA in human cells. Science 309, 1573-1576. Pineault, N., Abramovich, C., and Humphries, R.K. (2005). Transplantable cell lines generated with NUP98-Hox fusion genes undergo leukemic progression by Meis1 independent of its binding to DNA. Leukemia 19, 636-643. Pineault, N., Buske, C., Feuring-Buske, M., Abramovich, C., Rosten, P., Hogge, D.E., Aplan, P.D., and Humphries, R.K. (2003). Induction of acute myeloid leukemia in mice by the human leukemia-specific fusion gene NUP98-HOXD13 in concert with Meis1. Blood 101, 4529-4538. Porkka, K.P., Pfeiffer, M.J., Waltering, K.K., Vessella, R.L., Tammela, T.L., and Visakorpi, T. (2007). MicroRNA expression profiling in prostate cancer. Cancer Res 67, 6130-6135. Raymond, C.K., Roberts, B.S., Garrett-Engele, P., Lim, L.P., and Johnson, J.M. (2005). Simple, quantitative primer-extension PCR assay for direct monitoring of microRNAs and short-interfering RNAs. Rna 11, 1737-1744. Reid, J.G., Nagaraja, A.K., Lynn, F.C., Drabek, R.B., Muzny, D.M., Shaw, C.A., Weiss, M.K., Naghavi, A.O., Khan, M., Zhu, H., et al. (2008). Mouse let-7 miRNA populations exhibit RNA editing that is constrained in the 5'-seed/ cleavage/anchor regions and stabilize predicted mmu-let-7a:mRNA duplexes. Genome Res 18, 15711581. Rinn, J.L., Kertesz, M., Wang, J.K., Squazzo, S.L., Xu, X., Brugmann, S.A., Goodnough, L.H., Helms, J.A., Farnham, P.J., Segal, E., and Chang, H.Y. (2007). Functional demarcation of active and silent chromatin domains in human HOX loci by noncoding RNAs. Cell 129, 1311-1323.  167  Ro, S., Park, C., Jin, J., Sanders, K.M., and Yan, W. (2006). A PCR-based method for detection and quantification of small RNAs. Biochem Biophys Res Commun 351, 756-763. Ro, S., Park, C., Young, D., Sanders, K.M., and Yan, W. (2007). Tissue-dependent paired expression of miRNAs. Nucleic Acids Res 35, 5944-5953. Roccaro, A.M., Sacco, A., Chen, C., Runnels, J., Leleu, X., Azab, F., Azab, A.K., Jia, X., Ngo, H.T., Melhem, M.R., et al. (2009). microRNA expression in the biology, prognosis, and therapy of Waldenstrom macroglobulinemia. Blood 113, 4391-4402. Rodriguez, A., Vigorito, E., Clare, S., Warren, M.V., Couttet, P., Soond, D.R., van Dongen, S., Grocock, R.J., Das, P.P., Miska, E.A., et al. (2007). Requirement of bic/microRNA-155 for normal immune function. Science 316, 608-611. Rosa, A., Ballarino, M., Sorrentino, A., Sthandier, O., De Angelis, F.G., Marchioni, M., Masella, B., Guarini, A., Fatica, A., Peschle, C., and Bozzoni, I. (2007). The interplay between the master transcription factor PU.1 and miR-424 regulates human monocyte/macrophage differentiation. Proc Natl Acad Sci U S A 104, 1984919854. Rosenbauer, F., Koschmieder, S., Steidl, U., and Tenen, D.G. (2005). Effect of transcription-factor concentrations on leukemic stem cells. Blood 106, 1519-1524. Rouhi, A., Mager, D.L., Humphries, R.K., and Kuchenbauer, F. (2008). MiRNAs, epigenetics, and cancer. Mamm Genome. Ruby, J.G., Jan, C., Player, C., Axtell, M.J., Lee, W., Nusbaum, C., Ge, H., and Bartel, D.P. (2006). Large-scale sequencing reveals 21U-RNAs and additional microRNAs and endogenous siRNAs in C. elegans. Cell 127, 1193-1207.  168  Ruby, J.G., Jan, C.H., and Bartel, D.P. (2007). Intronic microRNA precursors that bypass Drosha processing. Nature 448, 83-86. Sansregret, L., and Nepveu, A. (2008). The multiple roles of CUX1: insights from mouse models and cell-based assays. Gene 412, 84-94. Santourlidis, S., Trompeter, H.I., Weinhold, S., Eisermann, B., Meyer, K.L., Wernet, P., and Uhrberg, M. (2002). Crucial role of DNA methylation in determination of clonally distributed killer cell Ig-like receptor expression patterns in NK cells. J Immunol 169, 4253-4261. Schwarz, D.S., Hutvagner, G., Du, T., Xu, Z., Aronin, N., and Zamore, P.D. (2003). Asymmetry in the assembly of the RNAi enzyme complex. Cell 115, 199-208. Seitz, H., Ghildiyal, M., and Zamore, P.D. (2008). Argonaute loading improves the 5' precision of both MicroRNAs and their miRNA strands in flies. Curr Biol 18, 147-151. Sevignani, C., Calin, G.A., Nnadi, S.C., Shimizu, M., Davuluri, R.V., Hyslop, T., Demant, P., Croce, C.M., and Siracusa, L.D. (2007). MicroRNA genes are frequently located near mouse cancer susceptibility loci. Proc Natl Acad Sci U S A 104, 80178022. Shell, S., Park, S.M., Radjabi, A.R., Schickel, R., Kistner, E.O., Jewell, D.A., Feig, C., Lengyel, E., and Peter, M.E. (2007). Let-7 expression defines two differentiation stages of cancer. Proc Natl Acad Sci U S A 104, 11400-11405. Tagawa, H., and Seto, M. (2005). A microRNA cluster as a target of genomic amplification in malignant lymphoma. Leukemia 19, 2013-2016. Tam, O.H., Aravin, A.A., Stein, P., Girard, A., Murchison, E.P., Cheloufi, S., Hodges, E., Anger, M., Sachidanandam, R., Schultz, R.M., and Hannon, G.J. (2008).  169  Pseudogene-derived small interfering RNAs regulate gene expression in mouse oocytes. Nature 453, 534-538. Tam, W., Hughes, S.H., Hayward, W.S., and Besmer, P. (2002). Avian bic, a gene isolated from a common retroviral site in avian leukosis virus-induced lymphomas that encodes a noncoding RNA, cooperates with c-myc in lymphomagenesis and erythroleukemogenesis. J Virol 76, 4275-4286. Tang, F., Hajkova, P., Barton, S.C., O'Carroll, D., Lee, C., Lao, K., and Surani, M.A. (2006). 220-plex microRNA expression profile of a single cell. Nat Protoc 1, 11541159. Tang, F., Kaneda, M., O'Carroll, D., Hajkova, P., Barton, S.C., Sun, Y.A., Lee, C., Tarakhovsky, A., Lao, K., and Surani, M.A. (2007). Maternal microRNAs are essential for mouse zygotic development. Genes Dev 21, 644-648. 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. Thai, T.H., Calado, D.P., Casola, S., Ansel, K.M., Xiao, C., Xue, Y., Murphy, A., Frendewey, D., Valenzuela, D., Kutok, J.L., et al. (2007). Regulation of the germinal center response by microRNA-155. Science 316, 604-608. Thatcher, E.J., Flynt, A.S., Li, N., Patton, J.R., and Patton, J.G. (2007). MiRNA expression analysis during normal zebrafish development and following inhibition of the Hedgehog and Notch signaling pathways. Dev Dyn 236, 2172-2180. Tomari, Y., Matranga, C., Haley, B., Martinez, N., and Zamore, P.D. (2004). A protein sensor for siRNA asymmetry. Science 306, 1377-1380.  170  Tran, N., McLean, T., Zhang, X., Zhao, C.J., Thomson, J.M., O'Brien, C., and Rose, B. (2007). MicroRNA expression profiles in head and neck cancer cell lines. Biochem Biophys Res Commun 358, 12-17. Tsang, W.P., and Kwok, T.T. (2009). The miR-18a* microRNA functions as a potential tumor suppressor by targeting on K-Ras. Carcinogenesis. Valiante, N.M., Uhrberg, M., Shilling, H.G., Lienert-Weidenbach, K., Arnett, K.L., D'Andrea, A., Phillips, J.H., Lanier, L.L., and Parham, P. (1997). Functionally and structurally distinct NK cell receptor repertoires in the peripheral blood of two human donors. Immunity 7, 739-751. Vasudevan, S., Tong, Y., and Steitz, J.A. (2007). Switching from repression to activation: microRNAs can up-regulate translation. Science 318, 1931-1934. Vasudevan, S., Tong, Y., and Steitz, J.A. (2008). Cell-cycle control of microRNAmediated translation regulation. Cell Cycle 7, 1545-1549. Velu, C.S., Baktula, A.M., and Grimes, H.L. (2009). Gfi1 regulates miR-21 and miR196b to control myelopoiesis. Blood. Ventura, A., Young, A.G., Winslow, M.M., Lintault, L., Meissner, A., Erkeland, S.J., Newman, J., Bronson, R.T., Crowley, D., Stone, J.R., et al. (2008). Targeted deletion reveals essential and overlapping functions of the miR-17 through 92 family of miRNA clusters. Cell 132, 875-886. Vigorito, E., Perks, K.L., Abreu-Goodger, C., Bunting, S., Xiang, Z., Kohlhaas, S., Das, P.P., Miska, E.A., Rodriguez, A., Bradley, A., et al. (2007). microRNA-155 regulates the generation of immunoglobulin class-switched plasma cells. Immunity 27, 847-859.  171  Wakiyama, M., Takimoto, K., Ohara, O., and Yokoyama, S. (2007). Let-7 microRNAmediated mRNA deadenylation and translational repression in a mammalian cellfree system. Genes Dev 21, 1857-1862. Wang, Y., Medvid, R., Melton, C., Jaenisch, R., and Blelloch, R. (2007). DGCR8 is essential for microRNA biogenesis and silencing of embryonic stem cell selfrenewal. Nat Genet 39, 380-385. Wightman, B., Ha, I., and Ruvkun, G. (1993). Posttranscriptional regulation of the heterochronic gene lin-14 by lin-4 mediates temporal pattern formation in C. elegans. Cell 75, 855-862. Wyman, S.K., Parkin, R.K., Mitchell, P.S., Fritz, B.R., O'Briant, K., Godwin, A.K., Urban, N., Drescher, C.W., Knudsen, B.S., and Tewari, M. (2009). Repertoire of microRNAs in epithelial ovarian cancer as determined by next generation sequencing of small RNA cDNA libraries. PLoS ONE 4, e5311. Xu, N., Papagiannakopoulos, T., Pan, G., Thomson, J.A., and Kosik, K.S. (2009). MicroRNA-145 Regulates OCT4, SOX2, and KLF4 and Represses Pluripotency in Human Embryonic Stem Cells. Cell. Yekta, S., Shih, I.H., and Bartel, D.P. (2004). MicroRNA-directed cleavage of HOXB8 mRNA. Science 304, 594-596. Yu, F., Yao, H., Zhu, P., Zhang, X., Pan, Q., Gong, C., Huang, Y., Hu, X., Su, F., Lieberman, J., and Song, E. (2007a). let-7 Regulates Self Renewal and Tumorigenicity of Breast Cancer Cells. Cell 131, 1109-1123. Yu, J., Wang, F., Yang, G.H., Wang, F.L., Ma, Y.N., Du, Z.W., and Zhang, J.W. (2006). Human microRNA clusters: genomic organization and expression profile in leukemia cell lines. Biochem Biophys Res Commun 349, 59-68.  172  Yu, S.L., Chen, H.Y., Yang, P.C., and Chen, J.J. (2007b). Unique MicroRNA signature and clinical outcome of cancers. DNA Cell Biol 26, 283-292. Zhang, J., Jima, D.D., Jacobs, C., Fischer, R., Gottwein, E., Huang, G., Lugar, P.L., Lagoo, A.S., Rizzieri, D.A., Friedman, D.R., et al. (2009). Patterns of microRNA expression characterize stages of human B cell differentiation. Blood. Zuker, M. (2003). Mfold web server for nucleic acid folding and hybridization prediction. Nucleic Acids Res 31, 3406-3415.  173  Appendices  I Supplementary data The supplementary data for chapter 2 and chapter 3 can be downloaded from: www.mydrive.ch login: fkuchenbauer password: thesis  II UBC Research Ethics Board Certificates of Approval Animal Care Certificate, University of British Columbia Biohazard Approval Certificate, University of British Columbia  174  175  176  

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