Bittner et al, Nature, August, 2000Saghatelian et al, PNAS, 2004
The most frequent chromosomal alterations in prostate cancer are deletions of parts of chromosome arms 6q, 8p, 10q, 13q, 16q and 17p, and amplification of 8q (Trapman et al. 1994; Van Alewijk et al. 1999 for chromosome arm 8p). Some chromosomal alterations can already be recognized in pre-cancerous lesions. However, chromosomal alterations are most frequent in tumor metastases. In a subset of endocrine-therapy resistant prostate cancers, amplification of the androgen receptor gene, which is located on the X chromosome, has been found (Koivisto et al. 1997). Out of the many known traditional oncogenes and tumor suppressor genes, inactivation of P53 at 17p and PTEN at 10q contribute most frequently to prostate cancer growth (Vlietstra et al., 1998).
J Clin Invest. 2004 March 15; 113 (6): 913–923DOI: 10.1172/JCI200420032
Gene expression profiling predicts clinical outcome of prostate cancerGennadi V. Glinsky,1 Anna B. Glinskii,1 Andrew J. Stephenson,2 Robert M. Hoffman,3 and William L. Gerald2
1Sidney Kimmel Cancer Center, San Diego, California, USA. 2Department of Pathology, Memorial Sloan-Kettering Cancer Center, New York, New York, USA. 3AntiCancer Inc., San Diego, California, USA.
12,625 transcripts in prostate tumors from patients with distinct clinical outcomes after therapy as well as metastatic human prostate cancer xenografts in nude mice
Hunt Nurse Hartwell
Orvosi Nobel-dij, 2001
After the gAfter the genome sequencing projectsenome sequencing projects
non-protein-coding RNAs can represent up to 97%–98% of all transcriptional output from the human genome.
much less than 1% (0.1-0.5%) of the sequence differences between individual humans and primates occurs in protein-coding sequences (versus appr 4% in non-coding when humans and apes compared) (Venter et al. 2001)
the majority of phenotypic variation between individuals (and species) results from differences in the control architecture, not the proteins themselves. phenotypic variation in complex organisms results from the differential use of a set of core components is becoming common (Gerhart and Kirschner 1997; Duboule and Wilkins 1998)
Repeat-associated siRNA(plants, Drosophila, yeast)histone és DNA-methylation→ Silencing selfish genetic elements
Piwi-interacting RNA(Drosophila, mouse, rat)histone és DNA-methylation→ Silencing selfish genetic elements in male germ cells
Non-coding RNA=ncRNA, make transcripts that function directly as RNA, rather than encoding proteins
microRNA(plants és animals)RNA-degradation and/or inhibition of translation→ wide spectrum of regulated processes,a new layer of gene expression regulation
epigenetics
Biogenesis and mechanism of actionBiogenesis and mechanism of action
The stem-loop structure is cleaved by the nuclear RNase III Drosha to release the precursor of miRNA (pre-miRNA)
Mature miRNAs are incorporated into the effector complexes, which are known as ‘miRNP’, ‘mirgonaute’ or, more generally, ‘miRISC’ (miRNA-containing RNA induced silencing complex). The effector complex containing siRNA, in distinction, is referred to as ‘RISC’,‘sirgonaute’ or siRISC’.
Dicer, is responsible for generating an approximately 21-nt, short, single stranded RNA that is the mature microRNA. Dicer was first recognized for its role in generating siRNAs that mediate RNA interference (RNAi) (Bernstein et al. 2001).
mv
MicroRNAs and Their Regulatory Roles inAnimals and PlantsBAOHONG ZHANG,1,2* QINGLIAN WANG,2AND XIAOPING PAN1Three mechanisms have been described for miRNAmediated gene regulation: mRNA degradation, translational repression, and miRNA-mediated mRNA decay. No matter what kind of mechanisms, all miRNAs regulate gene expression at the posttranscriptional level.Translational repression and mRNA degradation are two common mechanisms for miRNA-mediated gene regulation. In most cases, it is governed by the complementarity between miRNAs and targeted mRNAs. When an miRNA perfectly or near-perfectlypairs to the targeted mRNAs, it was thought thatmRNA cleavage is the primary mechanism for miRNAmediated gene regulation (Rhoades et al., 2002; Bartel,2004). Otherwise, if a miRNA imperfectly pairs to its targeted mRNAs, translational repression is thought tobe occurred. The degree of repression is associated with the number of miRNA-binding sites in a targetedmRNA (Cuellar and McManus, 2005). In translational repression, a majority of miRNAs bind to their targeted mRNAs at the 30 UTRs; however, some miRNAs can also bind to the 50 UTRand/or theORF(Zeng et al., 2002;Doench and Sharp, 2004). Although the precise mechanism of miRNA-mediated translational repression has not been elucidated, studies suggest that miRNA may hamper ribosome movement along the mRNAs, and repress protein translation (Carrington and Ambros, 2003). However, not all miRNAs follow this role toregulation gene expression. For example, miR 172 regulates gene expression by repressing translation although it can perfectly complement to the targeted APETALA2 (AP2) mRNA (Aukerman and Sakai, 2003;Chen, 2004).A majority of miRNAs downregulate gene expression by translational repression in animals while by mRNA degradation in plants. However, some miRNAs downregulates gene expression by translational repression in plants. For example, miR 172 regulate AP2 through translational repression despite miR 172 can perfectlycomplement with AP2 mRNA (Aukerman and Sakai, 2003; Chen, 2004). In animals, there are also miRNAs which directly degrade their targeted mRNAs. For example, miR-196 directly cleaves themRNAof HOXB8 (Mansfield et al., 2004; Yekta et al., 2004; Hornstein et al., 2005), which play important role in animal development (van den Akker et al., 1999; Greer and Capecchi, 2002; Reilly, 2002; El-Mounayri et al., 2005).Recently studies suggest a third mechanism ofmiRNA-mediated gene expression. After mRNA transcription, a ploy(A) tail always is added to the 30 of the mRNA to keep mRNA more stable and avoid the occurrence of mRNA decay (Jacobson and Peltz, 1996;Coller and Parker, 2004). Recently, Wu et al. (2006) found that miRNAs are involved in mRNA decay by directing rapid deadenylation of mRNAs. Their finding indicates that miRNAs destabilize mRNAs by accelerating poly(A) tail removal as an initial step in mRNA degradation. Giraldez et al. (2006) also observed the same phenomena. In their study, they found that miR-430 accelerated the deadenylation of target mRNAs in zebrafish, and facilitated the deadenylation and clearance of maternal mRNAs during early embryogenesis and affected embryo development. This conclusion has been confirmed by several in vitro and in vivo studies (Bagga et al., 2005; Jing et al., 2005; Wu and Belasco,2005), in which the cellular concentrations of targetmRNAs were reduced by miRNAs that do not perfectly or near-perfectly complement to their targeted mRNAs.
mv
Genomics of microRNAV. Narry Kim and Jin-Wu NamTRENDS in Genetics Vol.22 No.3 March 2006Model for miRNA biogenesis. miRNA genes are transcribed by an RNA polymerase II (Pol II) to generate the primary transcripts (pri-miRNAs). The initiation step (cropping) is mediated by the Drosha–DGCR8 complex (also known as the microprocessor complex). Drosha and DGCR8 are located mainly in the nucleus.The product of the nuclear processing is 70-nt pre-miRNA, which possesses a short stem plus 2-nt 3' overhang. This structure can serve as a signature motif that is recognized by the nuclear export factor, Exportin-5 (Exp5). Pre-miRNAconstitutes a transport complex together with Exp5 and its cofactor Ran (the GTPboundform). Upon export, the cytoplasmic RNase III Dicer participates in the second processing step (dicing) to produce miRNA duplexes. The duplex is separated and usually one strand is selected as the mature miRNA, whereas the other strand is degraded. (In Drosophila, R2D2 forms a heterodimeric complex with Dicer and binds to one end of the siRNA duplex, thereby selecting one strand of the duplex. It is not known if miRNAs use the same machinery for strand selection. It is also unclear whether an R2D2 homolog functions in animals other than Drosophila.)
Structure of the hairpinStructure of the hairpin
Bartel DP. Cell 116:281, 2004
mature sequence
flanking sequence
stem-loop
MicroRNAs are named using the “miR” prefix and a unique identifying number (e.g., miR-1, miR-2, . . . miR-89, etc.). The identifying numbers are assigned sequentially, with identical miRNAs having the same number, regardless of organism. Nearly identical orthologs can also be given the same number, at the discretion of the researcher. Identical or very similar miRNA sequences within a species can also be given the same number, with their genes distinguished by letter and/or numeral suffixes, according to the convention of the organism (e.g., the
22-nt transcripts of ∼ Drosophila mir-13a and mir-13b are slightly different in sequence, whereas those of mir-6-1 and mir-6-2 are identical; Lagos-Quintana et al. 2001).Ambros et al.: A uniform system for microRNA annotation, RNA 2003.
474 human miRNAs were tabulated in MicroRNA Registry until recentlyIt was estimated that there could be from 200 to 1000 microRNA genes in the mammalian genome (1%-3% of known genes are represented by microRNAs). Today the number of microRNAs, including those electronically cloned, is over 1000 and still growing.
microRNA: new members of the RNA-worldmicroRNA: new members of the RNA-world
The first miRNA was discovered in 1993 by Lee, Feinbaum, and Ambros in C. elegans. Hundreds of miRNAs have been identified in plants and animals, either through computational searches, RT-PCR-mediated cloning, or both.They were found in organisms ranging from nematodes to plants to humans. Many individual miRNAs are conserved across widely diverse phyla, indicating their physiological importance.
stable extended stem-loop structures, with continuous helical pairing and a few internal bulges.
Conservation filtering using genome sequences of phylogenetically closely related species.
Machine learning
algorythms
Northern blot, PCR, microarray
Training set: experimentally
validated miRNA genes
AGCTTCGGGTTGATC
mv
A módszer (in silico cloning) hiányosságai: a fajra specifikus miRNA-találatok száma csökken, magas álpozitív arányAz experimentális azonosítás: fajspecifikus, de az alacsony szinten, illetve kevés sejtben, vagy éppen csak adott körülmények között expresszálódók kimutatásának esélye alacsonyabb
The function: fine-tuning of gene expression
The ability to simultaneously regulate large sets of genes by a single microRNA appears to be at the heart of control of multiple pathways that include morphogenesis and cell fate decisions, response to infectious organisms, and centromeric heterochromatin structure.
It was found that different tissues have distinct miRNA expression profiles and that related tissues/organs (e.g., heart and muscle) have more similar profiles than more functionally distant tissues/organs.
Probing microRNAs with microarrays: Tissue specificity and functional inferenceBABAK T et al., RNA (2004), 10:1813–1819.
Mammalian microRNAs: a small world for fine-tuning geneexpressionCinzia Sevignani,1 George A. Calin,2 Linda D. Siracusa,1 Carlo M. Croce2
Tissue specificity
Shingara et al.: An optimized isolation and labeling platform for accurate microRNA expression profiling, RNA 2005
miRNA expression profile reflects on the developmental origin
Cell-type or differentiation stage specific expression?
In haemopoietic differentiation: the most pronounced similarities were observed among fully differentiated effector cells (Th1 and Th2 lymphocytes and mast cells) and
precursors at comparable stages of differentiation (double negative thymocytes and pro-B cells),
commitment to particular cellular lineages, miRNAs might have an important general role in the mechanism of cell differentiation and maintenance of cell identity.(Silvia Monticelli: MicroRNA profiling of the murine hematopoietic system, Genome Biology 2005)Seminars in Cancer Biology, 2007/8
In press (guest edited by A.Falus)
Distribution of putative function using computational approaches
Gaidatzis et al: Inference of miRNA targets using evolutionary conservation and pathway analysisBMC Bioinformatics 2007, 8:69
mv
Pathway analysis. Representation of individual pathways among the predicted targets of a given miRNA. Each column corresponds to a KEGG pathway and each row to a group of miRNAs with the same seed sequence. Red indicates overrepresentation of the targets of a specific miRNA among the genes in the corresponding pathway, whereas blue indicates depletion. The intensity of the color indicates the posterior probability of the dependent model (see Methods). Pathways have been grouped in larger functional categories according to the KEGG annotation. Only miRNAs with at least one significant association are shown.
It is now clear that miRNAs not only contribute in developmental processes related funtions, but also play an important role in the response of cells to outer signals, moreover miRNAs can be tools in the communication between different cell types. In a recent paper revealed that miRNAs are present in the by mast cells secreted microvesicles as part of the delivered “exosomal shuttled RNA”. Through this newly discovered form of cell-cell communication the donor cell may modulate the posttranscriptional system of target cells directly.
Valadi H et al.: Exosome-mediated transfer of mRNAs and microRNAs is a novel mechanism of genetic exchange between cells. Nat Cell Biol. 2007 Jun;9(6):654-9.
microRNAs in the “exosomal shuttled RNA”
molvik
Figure 4. Identification of exosomal microRNA.The median hybridization signals of miRNA in exosomes and cells analysed by miRCURY LNA array (n = 3) are shown. Exosomes carry approximately 121 miRNAs, found in all four experiments (see Supplementary Information, Table S6). Some miRNA were found to be expressed to a greater extent in the exosomes compared to their donor cells.
molvik
Figure 2. Exosomes contain multiple and heterogeneous RNA species.(a) RNA from MC/9 derived exosomes and their donor cells was detected using Bioanalyzer. The MC/9 exosomes contain a substantial amount of RNA, but no or very low amount of ribosomal RNA (18S- and 28S- rRNA) compared to their donor cells. Large amounts of small RNA (<30 nucleotides) were also detected in the exosomes and in their donor cells.
In oncogenesis, some miRNAs expression is decreased in cancerous cells. These types of miRNAs are considered tumor suppressor genes
Those miRNAs whose expression is increased in tumors may be considered as oncogenes. These oncogene miRNAs, called “oncomirs”, usually promote tumor development by negatively inhibiting tumor suppressor genes and/or genes that control cell differentiation or apoptosis.
miRNAs as tumor suppressors and oncogenes
cytoplasm
nucleus
One of the strands incorporated to RISC
RISC
Translational repression
mRNA degradation
„haploidomics”
molvik
Developmental Biology Volume 302, Issue 1 , 1 February 2007, Pages 1-12Review microRNAs as oncogenes and tumor suppressors Baohong Zhang, a, , Xiaoping Pana, George P. Cobba and Todd A. Andersona
molvik
Nature Medicine 11, 712 - 714 (2005) doi:10.1038/nm0705-712 Sizing up miRNAs as cancer genesCarlos Caldas & James D Brenton
Other examples
Example: mir-17-92 polycistron
•mir-17–92 cluster is a miRNA polycistron located at chromosome 13q31, a genomic locus that is amplified in lung cancer and several kinds of lymphoma, including diffuse large B-cell lymphoma
•Overexpression of miR-17–92 using transgenic mice (hematopoietic stem cells) significantly accelerated the formation of lymphoid malignancies (He et al., 2005b).
•The expression of miR-17–92 is related to the expression of c-Myc gene; both miR-17–92 and c-Myc regulate the expression of cell cycle transcription factor gene E2F1 (O'Donnell et al., 2005)
These findings suggest that c-Myc regulated miR-17–92 modulates E2F1 expression (O'Donnell et al., 2005), that affects apoptosis-mediated cell death through the ARE-p53 pathway in which miR-17–92 inhibits Myc induced apoptosis (Hammond, 2006; O'Donnell et al., 2005).
molvik
Developmental Biology Volume 302, Issue 1 , 1 February 2007, Pages 1-12Review microRNAs as oncogenes and tumor suppressors Baohong Zhang, a, , Xiaoping Pana, George P. Cobba and Todd A. Andersonamir-17–92 is a good example for an oncogenic miRNA.mir-17–92 cluster is a miRNA polycistron located at chromosome 13q31, a genomic locus that is amplified in lung cancer and several kinds of lymphoma, including diffuse large B-cell lymphoma (Hayashita et al., 2005; He et al.,2005b). Compared with normal tissues, the expression of mir-17–92 is significantly increased in several cancer types,including lung cancer and lymphomas, especially in their most aggressive forms, such as small-cell lung cancer and humanB-cell lymphomas (Hayashita et al., 2005; He et al., 2005b).The miR-17–92 cluster also appears to enhance lung cancer cell growth (Hayashita et al., 2005). Overexpression of miR-17–92 using transgenic mice (hematopoietic stem cells)significantly accelerated the formation of lymphoid malignancies (He et al., 2005b). Co-expression of miR-17–19b, a truncated portion of miR-17–92, strongly accelerated ymphomagenesis (Hammond, 2006). All of these findings suggest that mir-17–92 functions as an oncogene in humans and other animal models.Bioinformatic studies indicate that numerous genes are the targets of miR-17–92: more than 600 for miR-19a and miR-20, two members of miR-17–92 cluster (Krek et al., 2005; Lewis etal., 2005). Two tumor suppressor genes PTEN (phosphatase and tensin homolog deleted on chromosome ten) and RB2 were predicted to be targeted by miR-17–92 cluster (Lewis et al.,2003). PTEN promotes apoptosis through the P13K-Akt-PKB pathway (Hammond, 2006). Unfortunately, no experiments have confirmed that PTEN and RB2 are truly targets of the miR-17–92 cluster. Whether the miR-17–92 cluster is directly involved in lung cancer development or controls lung cancer by targeting suppressor genes is still unknown.More recent studies indicate that the expression of miR-17–92 is related to the expression of c-Myc gene; both miR-17–92 and c-Myc regulate the expression of cell cycle transcription factor gene E2F1 (Fig. 2) (O'Donnell et al., 2005). c-Myc is one of the best-characterized oncogenes. It is a helix–loop–helix leucine zipper transcription factor that regulates cell proliferation, growth, and apoptosis-mediated cell death by targetingabout 10–15% of the genes in humans and other animals (Fernandez et al., 2003; Levens, 2002; Li et al., 2003; Orian et al., 2003). Misexpression or dysfunction of c-Myc usually causes human malignancy (Cole and McMahon, 1999).O'Donnell et al. (2005) demonstrated that c-Myc simultaneously activates the transcription of both E2F1 and miR-17–92 (O'Donnell et al., 2005). However, miR-17-5p and miR-20a, two miRNAs in the miR-17–92 cluster, repress E2F1 translation(O'Donnell et al., 2005). Transient overexpression of miR-17–92 in HeLa cells resulted in a 50% decrease in E2F1 proteinlevels without affecting E2F1 mRNA abundance. In addition,mutants of miR-20a also caused a four-fold increase in E2F1 protein levels without affecting E2F1 mRNA abundance (O'Donnell et al., 2005). These findings suggest that c-Mycregulated miR-17–92 modulates E2F1 expression (O'Donnell et al., 2005), that affects apoptosis-mediated cell death through the ARE-p53 pathway in which miR-17–92 inhibits Mycinduced apoptosis (Hammond, 2006; O'Donnell et al., 2005).
• Hierarchical clustering of the samples using miRNA profiles paralleled the developmental origins of the tissues.
• miRNAs could be used to distinguish tumours from normal tissues
• Tumours of histologically uncertain cellular origin
Lu et al.:Nature, 2005
miRNA in tumor classification
High diagnostic relevance
molvik
LetterNature 435, 834-838 (9 June 2005) | doi:10.1038/nature03702; Received 2 February 2005; Accepted 5 May 2005MicroRNA expression profiles classify human cancersJun Lu1,4,7, Gad Getz1,7, Eric A. Miska2,7,8, Ezequiel Alvarez-Saavedra2, Justin Lamb1, David Peck1, Alejandro Sweet-Cordero3,4, Benjamin L. Ebert1,4, Raymond H. Mak1,4, Adolfo A. Ferrando4, James R. Downing5, Tyler Jacks2,3, H. Robert Horvitz2,6 and Todd R. Golub1,4,6
molvik
We then set out to determine the expression pattern of all known miRNAs across a large panel of samples representing diverse human tissues and tumour types. Although miRNA expression has been explored in small sets of tissues12, 14, 15, 16, 17, 18 or isolated cell types (for example, chronic lymphocytic leukaemia19), the extent of differential miRNA expression across cancers has not been determined. Indeed, we might not expect that miRNA expression patterns could be informative with respect to cancer diagnosis, because of the relatively small number of miRNAs encoded in the genome. However, we observed differential expression of nearly all miRNAs across cancer types (Fig. 2a). Moreover, hierarchical clustering of the samples using miRNA profiles paralleled the developmental origins of the tissues. For example, samples of epithelial origin were located on a single branch of the dendrogram, whereas the other major branch was predominantly populated with samples of haematopoietic malignancies.
molvik
We next turned to a more challenging diagnostic distinction: that of tumours of histologically uncertain cellular origin. It is estimated that 2–4% of all cancer diagnoses represent cancers of unknown origin or diagnostic uncertainty (see ref. 23). To address this, we analysed 17 poorly differentiated tumours, the histological appearance of which was non-diagnostic, but for which clinical diagnosis was established by anatomical context, either directly (for example, a primary tumour arising in the colon) or indirectly (a metastasis of a previously identified primary tumour). A training set of 68 more-differentiated tumours, representing 11 tumour types and for which both mRNA and miRNA profiles were available, was used to generate a classifier. This classifier was then used without modification to classify the 17 poorly differentiated test samples. As a group, poorly differentiated tumours had lower global levels of miRNA expression compared with the more-differentiated training set samples (see Supplementary Fig. 5), consistent with the notion that miRNA expression is closely linked to differentiation. Despite the overall low level of miRNA expression, the miRNA-based classifier established the correct diagnosis of the poorly differentiated samples with far greater accuracy than would be expected by chance for an 11-class classifier (12 out of 17 correct; P < 5 10-11). In contrast, the mRNA-based classifier was highly inaccurate (1 out of 17 correct; P = 0.47), as we previously reported4.
molvik
Having determined that miRNA expression distinguishes tumours of different developmental origin, we next asked whether miRNAs could be used to distinguish tumours from normal tissues. We have previously reported that there are no robust mRNA markers that show consistent differential expression between tumours and normal tissues of different lineages4. It was therefore striking to observe that despite the fact that some miRNA expression levels were upregulated or unchanged, most of the miRNAs (129 out of 217, P < 0.05 after correction for multiple hypothesis testing) had lower expression levels in tumours compared with normal tissues, irrespective of cell type (Fig. 3a).
molvik
Among the epithelial samples, those of the gastrointestinal tract were of particular interest. Samples from colon, liver, pancreas and stomach all clustered together (Fig. 2a), reflecting their common derivation from tissues of embryonic endoderm. We suggest that sample clustering in miRNA space is predominantly driven by developmental history. In contrast, when the same samples were profiled in the space of 16,000 mRNAs, the coherence of gut-derived samples was not observed in hierarchical clustering (Fig. 2c).
miRNA in tumor classification
MicroRNA expression changes have been described to correlate with the clinico-pathological features of the tumor in human cancers. e.g.:
•Calin, G. A et al: (2005) A microRNA signature associated with prognosis and progression in chronic lymphocytic leukemia. N. Engl. J. Med. 353:1793–1801.•Iorio, M. V et al: (2005) MicroRNA gene expression deregulation in human breast cancer. Cancer Res. 65:7065–7070. •Yanaihara et al: (2006) Unique microRNA molecular profiles in lung cancer diagnosis and prognosis. Cancer Cell 9:189–198.
major diferences in survival rate based on miRNA patterns
molvik
Yanaihara et al.:Figure 2. Kaplan-Meier survival curves for adenocarcinoma patientsAdenocarcinoma cases in which hybridization intensity was different from background (Experimental Procedures) were classified according to either hsa-mir-155 expression or hsa-let-7a-2 expression by miRNA microarray analysis. The survival data were compared using the log-rank test.A: hsa-mir-155 high expression (n = 27), group with the expression ratio ≥ mean expression ratio (1.42). The mean expression ratio is defined as mean expression ratio = mean of tumor expression/mean of noncancerous tissue expression. hsa-mir-155 low expression (n = 28), group with the expression ratio < mean expression ratio.B: hsa-let-7a-2 high expression (n = 34), group with the expression ratio ≥ mean expression ratio (0.95). hsa-let-7a-2 low expression (n = 18), group with the expression ratio < mean expression ratio.
miRNAs in fragile sites
Zhang et al.: microRNAs exhibit high frequency genomic alterations in human cancer PNAS 2006.
• more than half of miRNA genes were located in CAGRs (=cancer-associated genomic regions)
• miRNA genes were located in regions that exhibited DNA copy number abnormalities (CNV):
• ovarian cancer (37.1%)
• breast cancer (72.8%)
• melanoma (85.9%)
•Dicer1 and Argonaute 2 exhibited gains in DNA copy number by 24.8% and 51.5%, respectively, in ovarian tumors
aCGH
miR-coding locuses
Comparative genome hybridisation
molvik
Proc Natl Acad Sci U S A. 2006 June 13; 103(24): 9136–9141. Published online 2006 June 5. doi: 10.1073/pnas.0508889103.GeneticsmicroRNAs exhibit high frequency genomic alterations in human cancerLin Zhang,*†‡ Jia Huang,‡§ Nuo Yang,‡¶ Joel Greshock,‡§ Molly S. Megraw,‖ Antonis Giannakakis,*** Shun Liang,* Tara L. Naylor,§ Andrea Barchetti,* Michelle R. Ward,§ George Yao,* Angelica Medina,§ Ann O’Brien-Jenkins,* Dionyssios Katsaros,†† Artemis Hatzigeorgiou,‖ Phyllis A. Gimotty,‡‡ Barbara L. Weber,§ and George Coukos*†§§§
molvik
High frequency miRNA gene copy number alterations in ovarian cancer. aCGH frequency plots of ovarian cancer specimens are shown. Green represents gain, and red represents loss. Stars indicate miRNA genes
molvik
The results were surprising and suggested alarger than thought involvement of miRNAs in cancer.Overall, we found that more than half of miRNA genes were located in CAGRs, including 65 miRNAs located exactly in minimal regions of LOH, and 15 miRNAs in minimal regions of amplification, described in a variety of tumors, including lung, breast, ovarian, colon, gastric and hepatocellular carcinoma as well as leukemias and lymphomas. In addition, looking at 113 FRA scattered in human karyotype, we found that 61 miRNAs arelocated in the same cytogenetic positions with FRA (Figure 2) (Calin et al., 2004b).
molvik
These findings support the notion that copy number alterations of miRNAs may account partly for the frequent miRNA gene deregulation reported in several tumor types.....At this point, the mechanisms underlying the high frequency alteration of miRNA genes observed in cancer genome remain unclear. In light of a previous report that miRNAs are frequently located in fragile sites and genomic regions involved in cancers (30), one potential explanation is that genomic aberrations preferentially involve regions containing miRNA genes at a high density. Alternatively, clones with miRNA amplifications or deletions are selected because of the biological advantage that is afforded by these miRNA expression changes.
In plants, miRNA binding sites are usually contained in coding regions and have extensive complementarity
to the mature miRNA
Animal miRNA binding sites usually lie in 3' UTRs of target mRNAs and exhibit imperfect complementarity to the mature miRNA (estimated accuracy for targets is 50–85%)
Validation of predicted targetsValidation of predicted targets
miRNA knock-down or overexpression
microarrays to identify the genes that show expression changes and to correlate these changes with 3' UTR sequence motifs, for
example, by a linear regression model.
miRNAs recognize their targets at least partly on the basis of simple sequence complementarity=> pure knowledge of the sequence of a miRNA is sufficient to predict many targets. (not yet possible for transcription factors, for which large training data sets or other experimental information are needed to accurately identify targets computationally)
PredictionPrediction
molvik
PLoS Biol. 2004 November; 2(11): e363. Published online 2004 October 5. doi: 10.1371/journal.pbio.0020363.Human MicroRNA TargetsBino John,1 Anton J Enright,1,2 Alexei Aravin,3 Thomas Tuschl,3 Chris Sander,1 and Debora S Marks4
molvik
a direct approach to target discovery is to knockout or overexpress the miRNA and use microarrays to identify the genes that show expression changes (for example, Refs 18, 72, 73), and to correlate these changes with 3' UTR sequence motifs, for example, by a linear regression model
molvik
Most microRNA (miRNA) targets have been identified using bioinformatics methods (reviewed in Refs 88, 109, 121). In fact, miRNAs are one of the few classes of trans-acting regulatory factors for which computational approaches can, with reasonable confidence, successfully predict a large number of cis-regulatory binding sites. This is primarily owing to the fact that miRNAs recognize their targets at least partly on the basis of simple sequence complementarity between the miRNA and its binding site. In other words, pure knowledge of the sequence of a miRNA is sufficient to predict many targets. This is typically not yet possible for transcription factors, for which large training data sets or other experimental information are needed to accurately identify targets computationally122,123.
Validating targetsValidating targets
Expression data: miRNAs with different expression in experimental context
K:\work\molvik\mir_VEGF_principles.docHua Z, Lv Q, Ye W, Wong C-K, Cai G et al.: MiRNA-directed regulation of VEGF and other angiogenic factors under hypoxia (2006) PLoS ONEPrinciples of gene regulation by miRNAInitial data released by this investigation first reported miRNA-directed VEGF regulation. Through analysis of this data, we uncovered some new principles of gene regulation by miRNA and also further validated principles predicted by others [15], [20], [21] using experimental methods. The biological significance of these principles was also addressed in this investigation. The first is the coordinate principle. John and Marks [15] proposed that miRNAs may act cooperatively through multiple target sites in one gene. Krek's report proved this principle [20] and our results further indicate that miRNAs with independent binding sites in a gene can produce coordinate action, which increases the repressive effect of miRNAs on translation of this gene.The second is the co-regulatory principle. It is a common phenomenon that groups of functionally related genes act together to regulate a physiologically related function (e.g. angiogenesis). It therefore appears as if a common regulatory mechanism controls the multiple factors involved in these functions. MiRNA with the ability to target multiple genes could potentially regulate a group of functionally related genes. In this study, we determined that some miRNAs identified as VEGF regulators also regulated the expression of other angiogenic factors. We call this regulatory pattern a co-regulatory principle, meaning that one or a few miRNAs can co-regulate a group of functionally related genes. This is a convenient and efficient regulatory pattern but may cause unwanted cross-reaction between genes, if an miRNA is expressed for regulating one gene, but also targets other genes which need not be repressed under the same conditions. Therefore, there must be some mechanisms to prevent unwanted cross-reactions.The third is the principle of differential regulation. As an miRNA-targeted gene, VEGF might be regulated by many miRNAs, according to the number of binding sites in its 3′-UTR. However, only some of these miRNAs are involved in VEGF regulation or VEGF maintenance in CNE cells. The others might be reserved for the needs of other cells under different conditions. To ensure that VEGF expression is regulated by distinct miRNAs in different cells under different conditions, we analyzed the relationship between VEGF expression and the miRNA expression profile in five VEGF-expressing cell lines: CNE, HeLa [34], MCF-7, HL60, and K562 [35]. In these cell lines, 31–104 miRNAs were detected and 9–31 of those miRNAs were predicted as putative regulators of VEGF. Through comparison of these five cell lines, we found that not only does each cell line express different numbers of miRNAs, they also employ discrete combinations of VEGF-related miRNAs (Table 5). Since angiogenesis is crucial for a wide variety of physiological and pathological processes including development, wound healing, inflammation, and tumor formation, many molecules have been implicated as positive regulators of angiogenesis. Being a pivotal factor, VEGF expression is complex and needs to be well regulated [22], [23]. Various miRNA binding sites provide a number of choices which not only meet different needs under various processes but also avoid unwanted cross-reactions. We call this pattern differential regulation, which means that a gene with multiple binding sites for a number of miRNAs can be regulated by discrete miRNAs in differing cells under different conditions. However, the pattern of differential regulation may not be enough to prevent unwanted cross-reactions between genes, since some miRNAs have too many target genes.Competitive action was recognized as the final principle of gene regulation by miRNAs, and it can be seen when multiple miRNAs compete with each other for a common binding site (called a multi-miRNA binding site) or a functional miRNA competes with a false positive miRNA for the same binding site. The biological role of competitive action is not very clear. It might be related to prevention of unwanted cross-reactions between genes. In this investigation, we found a multi-miRNA binding site located in nt162–185 of the VEGF 3′-UTR, which functioned as a specially designed ruse for the prevention of unwanted cross-reactions between miRNA-targeting genes. This binding site is shared by 12 different miRNAs, according to the bioinformatics prediction, but only miR-17-5p, miR-20a, miR-20b, miR-106a, and miR-106b were detected in DFOM-untreated CNE cells. Among these 5 miRNAs, miR-20a and miR-20b were validated as regulatory miRNAs in hypoxia-induced VEGF expression, whereas the other three could be expressed for the regulation of other genes without over-repressing VEGF expression due to competitive action. Thus, the unwanted cross-reaction of VEGF with other genes could be prevented. In some circumstances, if VEGF expression needs no repression, but some regulatory miRNAs of VEGF are expressed for regulation of other genes, the unwanted cross-reaction of VEGF can be decreased through the expression of false positive miRNAs to compete with the functionally effective miRNAs. The competitive principle, differential regulation, multi-miRNA binding sites, and false positive miRNAs might be useful strategies to avoid unwanted cross actions between targeting genes of miRNAs with multiple targets. The patterns of miRNA-directed VEGF regulation demonstrate several principles of miRNA-mediated gene regulation, and these principles will be helpful for us to understand the highly complex pattern of gene regulation by miRNAs.
Intervention to miRNA-pathway
miRNA gene (vector)
miRNA-precursor mimicking shRNA-
vector
mature miRNA mimicking
siRNA(pre-mir és anti-mir)
human melanocytes
HT168HT168
SCID
SCIDliver-
metastasis
human melanoma
HT168
HT168-M1
In vitro melanoma-model Pos et al, Cancer Res, 2005, 2007 in press
Some miRNAs expressed only or predominantly in tissue samples (not only melanocyte-origin: other cells are present eg. endothel, lymphocyte etc.) miRNAs with
low expression orabsent signal
abundantmiRNAs
Lin
ka
ge
Dis
tan
ce
8 4 0 -4 -8 -12
miR
16
let7
a miR
23
bm
iR9
9a
miR
10
0m
iR1
30
bm
iR1
40
miR
29
am
iR2
11
miR
14
6m
iR2
21
miR
17
3p
miR
31
miR
10
7m
iR1
26
miR
20
0c
miR
19
9s
miR
14
23
pm
iR2
18
miR
29
6m
iR2
13
miR
18
5m
iR1
81
cm
iR3
4a
miR
21
5m
iR1
55
miR
18
7m
iR1
24
bm
iR1
39
miR
14
1m
iR1
33
am
iR2
24
miR
19
8m
iR1
82
*m
iR3
68
miR
18
3m
iR1
54
miR
37
3*
miR
15
4*
miR
37
0m
iR3
23
miR
12
7m
iR2
99
miR
13
7m
iR2
19
miR
13
5a
miR
18
4m
iR1
89
miR
19
0m
iR3
67
miR
33
7ce
lmiR
2ce
llin4
miR
10
4
m el1m el2HT_1HT_2M1_1M1_2clin _1clin _2
Application possibilities
new layer of gene expression regulation miRNAs: new factors in tumor biology complexity is getting increased?
•High-troughput systems•Bioinformatics•Conditional knock-out és transgenic animal models
