US20120040851A1

US20120040851A1 - miRNA TARGETS

Info

Publication number: US20120040851A1
Application number: US13/063,156
Authority: US
Inventors: Judy Lieberman; Ashish Lal
Original assignee: Immune Disease Institute Inc
Current assignee: Childrens Medical Center Corp
Priority date: 2008-09-19
Filing date: 2009-09-18
Publication date: 2012-02-16
Also published as: WO2010033822A3; US9347089B2; US20160369274A1; US20120059043A1; WO2010033818A2; WO2010033822A2; WO2010033818A8; WO2010033818A3

Abstract

The present invention provides systems for identifying, isolating, and/or characterizing targets of micro RNAs.

Description

RELATED APPLICATIONS

The present application is related to U.S. Ser. No. 61/098,696, filed on Sep. 19, 2008, entitled “miRNA Targets”, and U.S. Ser. No. 61/098,707, filed on Sep. 19, 2008, entitled “Therapeutic and Diagnostic Strategies,” the entire contents of which are incorporated herein by reference.

BACKGROUND

microRNAs (miRNAs) regulate key steps of cell differentiation and development (1-3) by suppressing gene expression in a sequence-specific manner (4). In mammals, the active strand miRNA sequence (typically—22 base pairs) is partially complementary to binding sites in the 3′UTR of genes, often with full complementarity to 7 or 8 nucleotides in the “seed region” (residues 2-9) of the miRNA. Gene suppression in mammals is thought to occur primarily by inhibiting translation (5). However, miRNAs in mammals also cause mRNA decay (6, 7).
Current approaches to identify miRNA targets fall short of the task. The major tools that have been used are (1) bioinformatic algorithms that predict potential target genes that contain conserved complementary sequences in their 3′UTR to a seed region at the 5′-end of the miRNA active strand (12, 13), and (2) analysis of mRNAs that are down-regulated when a miRNA is over-expressed (14, 15).
The bioinformatic approach is hampered by the fact that the existing algorithms have a high margin of error (the majority of predicted genes are not real targets and some of the key targets, such as RAS for let-7, are not predicted). For many miRNAs, current algorithms predict hundreds or even thousands of potential targets, making it difficult to identify the most important targets.
Gene expression array analysis does not readily distinguish direct mRNA targets from mRNAs down-regulated through secondary effects and misses most target genes that are regulated by blocking translation rather than by mRNA degradation. Moreover, even when mRNA degradation occurs, changes in mRNA levels may be small (often less than 2-fold) and may be difficult to distinguish from background fluctuations, especially in genomewide surveys.
Even combining these 2 approaches still is not helpful in many situations. Recently mRNA targets of miRNAs have been identified by their enrichment in co-immunoprecipitates with tagged Argonaute proteins in Drosophila and human cell lines overexpressing the miRNA of interest (16-19). However these studies have not been shown to identify new miRNA targets. Argonaute over-expression globally increases miRNA levels, perhaps obscuring the effect of an individual over-expressed miRNA (20).

SUMMARY

The present invention provides an approach for identifying miRNA targets. In some embodiments, such target identification involves isolating mRNAs that bind to an miRNA active strand. In some embodiments, the miRNA active strand is labeled (e.g. biotinylated) to facilitate isolation of miRNA-target RNA complexes. This approach is exemplified by application to miR-24 (see Examples, which describe identification of genes regulated by miR-24). Combining additional analytical techniques (e.g. microarray analysis, bioinformatics analysis, etc.) has enabled us to overcome the hurdle of identifying miRNA-regulated genes.
Through its exemplary application to miR-24, the present invention, among other things, demonstrates that miR-24 regulates a network of genes that control cell cycle progression through G1, S and G2/M as well as key DNA repair genes. Over-expressing miR-24 increases the G1 population, reduces DNA replication and increases sensitivity to DNA damage (see U.S. Ser. No. 61/098,707, entitled “Therapeutic and Diagnostic Strategies” and filed on Sep. 19, 2008), while antagonizing miR-24 increases cell proliferation.

DESCRIPTION OF THE DRAWING

FIG. 1. miR-24 is up-regulated during hematopoietic cell differentiation into multiple lineages (B) miR-24 expression, measured using qRT-PCR relative to untreated cells, increases in K562 cells differentiated to megakaryocytes or erythrocytes and HL60 cells differentiated to macrophages, monocytes and granulocytes. Differentiation in all experiments was verified by cell surface phenotype. Both the primary transcript corresponding to the chromosome 19 miR-24 cluster (C, K562; D, HL60) and mature miR-24 (E, K562; F, HL60) increase rapidly and remain elevated when cells are differentiated with TPA. Mature miR-24 levels were determined by qRT-PCR and normalized to U6 whereas GAPDH was used as an internal control to measure changes in levels of pri-miR-24. Error bars in (B-F) represent standard deviation from 3 independent experiments (*, p<0.05; **, p<0.01; #, p<0.005; ##, p<0.001; ***, p<0.0001).

FIG. 2. Identification of mRNAs down-regulated by miR-24 over-expression (A) HepG2 cells express low levels of miR-24, assayed by qRT-PCR analysis normalized to U6, compared to HeLa, WI-38, HL60 and K562 cells. (B) Effective increase in miR-24 RNA in HepG2 cells 48 hr after transfection with miR-24 mimic compared to control cells transfected with cel-miR-67. #, p<0.005. (C) Venn diagram of genes down-regulated by miR-24 in HepG2 cells and genes predicted to be regulated by miR-24 using TargetScan 4.2. In the diagram, the 100 predicted genes, whose mRNA is also significantly down-regulated, have either conserved (20) or nonconserved (80) predicted miR-24 recognition sites. TargetScan 4.2 predicts 349 conserved miR-24 targets and many more that are not conserved. (D) Genes identified by microarray as downregulated by miR-24 overexpression were confirmed to be downregulated by qRT-PCR normalized to GAPDH. UBC is a housekeeping gene. Cells were transfected with cel-miR-67 (black) or miR-24 mimic (white). **p<0.01, #p<0.005, and ##p<0.001. The downregulated genes are graphed in order of their downregulation on the microarray; the Z ratio of the microarray analysis is shown below. Error bars represent SD from three independent experiments (A, B, and C). (E) Top 15 over-represented cellular processes for the 100 overlapping genes in (C). Histogram displays over-represented processes sorted by Score (−log [p-value]). A highly positive Score suggests that the subnetwork is highly saturated with genes identified experimentally and doesn't have many nodes not identified in the experiment. The complete list of 100 genes in the overlap and further statistical analysis is provided in Suppl. Table 3. The dotted line represents the statistically significant limit. (F) Direct interaction network of over-represented subnetworks (G) Sites complementary to the miR-24 seed are enriched in the 3′UTR of downregulated transcripts. The table shows the frequency of perfect hexamer (positions 2-7), heptamer (positions 2-8), and octamer (positions 2-9) miR-24 3′UTR seeds in the downregulated genes.

FIG. 3. Isolation of miRNA-bound target mRNAs (A) Biotin pull-down assay. (B) Activity of biotinylated miR-24 (Bi-miR-24) is similar to non-biotinylated miR-24 mimic. HepG2 cells were transfected with control miRNA or miR-24 mimics or 3′ biotin-miR-24 (Bi-miR-24). miRNA-transfected cells were cotransfected 48 hr later with pGL3-RL and pMIR-REPORT™ plasmid (black) or pMIR-REPORT™ containing a perfectly complementary target site for miR-24 (white) and assayed for luciferase expression after 24 hr. The firefly luciferase signal from pMIR-REPORT™ was normalized to the Renilla luciferase from pGL3-RL. #, p<0.005. (C) HepG2 cells were transfected with 30 nM Bi-miR-24 or Bi-cel-miR-67 miRNA, and RNA isolated from the streptavidin pull-down was analyzed by qRT-PCR for miR-24 and miR-16 (a control miRNA) after normalization to U6. miR-24 was −500-fold higher in miR-24 pull-down as compared to control pull-down (left panel). ##, p<0.001. miR-16 levels were similar in each pull-down (right panel). (D) H2AX and CDK6 mRNAs (encoding 2 and 3, 3′UTR miR-24 binding sites, respectively), assayed by qRT-PCR (normalized to GAPDH), are significantly enriched in miR-24 (white), compared to control (black) pull-downs from K562 cells. Enrichment was maximal when pull-down was done 24 hr after transfection. (E, F) Similar enrichment for miR-24 (E) and let-7 (F) regulated mRNAs was observed in HepG2 cells 24 hr after Bi-miR-24 or Bi-let-7 mimic transfection. The specific miRNA pull-down (white) is compared to cel-miR-67 pull-down (black). The housekeeping gene UBC is a negative control. Error bars represent standard deviation from 3 independent experiments (B, C, E, F). *, p<0.05 and *, p<0.01.

FIG. 4. Bioinformatic analysis of miR-24 pull-down genes suggests that miR-24 regulates cell cycle progression (A) Venn diagram of miR-24 target genes predicted by TargetScan 4.2 or experimentally identified by pull-down or by miR-24-dependent down-regulation in HepG2 cells. There is not much overlap between the genes identified by these methods. (B) Over-represented cellular processes of miR-24 pull-down genes. A histogram of the top 15 over-represented processes in the 269 gene pull-down set is ranked by Score (−log [p-value]). Dotted line indicates statistically significant score. More details are provided in Suppl. Table 6. (C) Direct interaction network of over-represented subnetworks, with non-connected nodes removed. Genes annotated as involved in any aspect of cell cycle regulation are indicated by a blue symbol; other genes are represented in gray (symbols as in FIG. 2F) Genes whose primary function is associated with a specific phase of the cell cycle are indicated [G1/S transition (G1/S), DNA replication and S phase (S), G2/M transition (G2/M) and mitosis (M)].

FIG. 5. miR-24 regulates MYC expression (A) MYC mRNA is selectively pulled-down from HepG2 cells with Bi-miR-24 (white) compared to control Bi-cel-miR-67 (black). For each condition, pulled down RNA was first normalized to GAPDH mRNA in the sample and then to relative input cellular RNA (#, p<0.001). The housekeeping gene UBC mRNA was not enriched in the pull-downs. (B) Predicted binding sites in the MYC 3′UTR for miR-24 (MRE1-6) by rna22. The miR-24 binding site (447-468) is in the 3′UTR of MYC mRNA. miR-24 over-expression in HepG2 (C) or K562 cells (D) decreases MYC mRNA, analyzed by qRTPCR and normalized to GAPDH (black, cel-miR-67; white, miR-24). (E) MYC protein is decreased in K562 cells upon overexpression of miR-24. Densitometry was used to quantify protein levels; a,-tubulin served as loading control. (F) miR-24 targets the MYC 3′UTR in a luciferase reporter assay. HepG2 cells were transfected with control miRNA (black) or miR-24 (white) mimic for 48 hr and then with MYC 3′UTR-luciferase reporter (MYC) or vector (V) for 24 hr. Mean±SD, normalized to vector control, of 3 independent experiments is shown (**, p<0.01). (G) (G-I) miR-24 regulates MRE3 and MRE6 by luciferase reporter assay. HepG2 cells were cotransfected with cel-miR-67 (black) or miR-24 (white) mimics and luciferase reporters containing the wild-type (wt) MRE1-6 in (B) and (H) or mutated (mt) MRE3 and MRE6 in (G) and (I) or vector (V). Luciferase activity was measured 48 hr after transfection. In (G), red letters denote point mutations that disrupt base pairing. Mean±SD, normalized to vector control, of three independent experiments is shown. *p<0.05, **p<0.01.

FIG. 6. miR-24 inhibits cell proliferation (A) Bi-miR-24 binds to mRNAs encoding E2F2, H2AX, PCNA, AURKB, CCNA2, BRCA1 and CHEK1, cell cycle and DNA repair genes identified in the pull-down microarray analysis, but not to E2F1, E2F3 or SDHA (a housekeeping gene), genes not enriched in the microarray. Streptavidin pull-down was performed in HepG2 cells transfected with Bi-miR-24 (white) or control Bi-miRNA (black). Data were normalized to GAPDH. The Z-ratios refer to the enrichment in the pull-down microarray analysis. (B) miR-24 silences the expression of luciferase genes engineered with the 3′UTR of E2F2, but not with E2F1 or E2F3 3′UTRs, suggesting that E2F2 is a direct miR-24 target but E2F1 and E2F3 are downregulated indirectly. Luciferase assays were performed in HepG2 cells overexpressing miR-24 (white) or control mimics (black). miR-24 targets the 3′UTR of E2F-regulated genes (AURKB, BRCA1, CCNA2, CDC2, and FEN1) and CDK4, a MYC-regulated gene. CHEK1 and PCNA 3′UTRs are not regulated by miR-24. HepG2 cells were cotransfected with a luciferase reporter containing the 3′UTR of the indicated gene and control miRNA (black) or synthetic miR-24 (white) for 48 hr. Expression of the unmodified luciferase vector (V) is unchanged by miR-24. (C) miR-24 significantly reduces mRNA levels of E2F1, E2F2, and E2F3 and of some E2F target genes (RRM2, CHEK1, CCNA2, FEN1, MCM4, MCM10, CDC2, and AURKB), but not BRCA1 and PCNA. CDK4, a key MYC target gene, is also downregulated. HepG2 cells were transfected with miR-24 (white) or control mimics (black) for 48 hr, and E2F and their target mRNAs were measured by qRT-PCR. Data normalized to GAPDH are expressed relative to control mimic-transfected cells. UBC is a control housekeeping gene. The Z ratios refer to the significantly downregulated mRNAs in the mRNA microarray in miR-24-overexpressing cells. (D) Protein expression of miR-24 target genes is substantially reduced in miR-24 mimic-transfected K562 cells 72 hr after transfection, relative to control miRNAtransfected cells. Densitometry was used to quantify protein relative to a-tubulin. HuR and a-tubulin are loading controls. (E) miR-24 knockdown in K562 cells specifically decreases miR-24 levels, assayed by qRT-PCR in cells transfected with miR-24 ASO (white) relative to control ASO (black). Expression relative to U6 snRNA is depicted normalized to control cells. (F) miR-24 knockdown with ASO increases K562 cell proliferation measured by thymidine uptake, both in the presence and absence of TPA. The decline in proliferation with TPA is completely restored by antagonizing miR-24. (G) miR-24 over-expression increases the G1 compartment in HepG2 cells. HepG2 cells transfected with miR-24 or control mimic for 48 hr were stained with propidium iodide and analyzed by flow cytometry. Representative analysis of three independent experiments is shown. Error bars represent standard deviation from 3 independent experiments (A-C, E, F). *, p<0.05; **, p<0.01; #, p<0.005.

FIG. 7. Endogenous miR-24 and Bi-miR-24 sediment with polysomes in K562 cell extracts. K562 cells were transfected with Bi-miR-24 (pink) or Bi-cel-miR-67 (blue) for 24 hr, fixed with formaldehyde and lysed by sonication. The lysates were fractionated on 10-50% sucrose gradients and RNA was isolated. The relative distribution of miR-24 (both endogenous and exogenous) in each fraction was determined by qRT-PCR analysis. The profiles of Bi-miR-24 and endogenous miR-24 in the control cells were similar with most miR-24 associated with the more dense polysome-containing fractions (6-10).

FIG. 8. Crosslinking or formaldehyde fixation enhances the enrichment for let-7 target HRAS and CDK6 mRNAs in the streptavidin pull-down of Bi-let-7. (A) let-7 RNA was selectively captured from HeLa cells transfected with Bi-let-7 compared to cells transfected with control miRNA. Enrichment was enhanced by crosslinking or by isolating polysomes. (B) let-7 target mRNAs (assayed by qRT-PCR) were also enriched by capturing let-7 vs control miRNA. Target mRNA capture was enhanced by crosslinking or polysome purification.

FIG. 9. miR-24 target mRNAs do not bind to exogenous miR-24 added at the time of K562 cell lysis. Streptavidin pull-downs were performed in the presence of 300 nM Bi-miR-24 or Bi-cel-miR-67. RNA bound to the beads was isolated and analyzed by qRT-PCR. miR-24 target mRNAs were not enriched in either Bi-cel-miR-67 (black) or Bi-miR-24 pull-down (white). In the manuscript we had already shown that the pull-down increases with time after transfection, presumably reflecting the time required for RISC incorporation of the exogenous Bi-miRNA.

FIG. 10. (B) miR-24 knockdown in K562 cells specifically decreases miR-24, assayed by qRT-PCR in cells transfected with miR-24 ASO (white) relative to control ASO (black). Expression relative to U6 snRNA is normalized to control cells. (C) miR-24 knockdown with ASO increases K562 cell proliferation measured by thymidine uptake, both in the presence and absence of TPA. The decline in proliferation with TPA is completely restored by antagonizing miR-24. (D and E) Knocking down miR-24 in WI-38 or IMR-90 cells also significantly increases proliferation as measured by thymidine incorporation 48 hr posttransfection. miR-24 knockdown by ASO measured by qRT-PCR is normalized to U6 snRNA. Error bars in (B-E) represent SD from three experiments. **p<0.01, ##p<0.001. (F) miR-24 overexpression increases the G1 compartment in HepG2 cells. HepG2 cells transfected with miR-24 or control mimic for 48 hr were stained with propidium iodide and analyzed by flow cytometry. Representative analysis of three independent experiments is shown. (G) K562 cells, synchronized in G2/M by nocodazole and then released, were analyzed by flow cytometry. A representative experiment is shown in the top panel, and the mean (±SD) percentage of cells in each phase of the cell cycle (from three independent experiments) is shown below (light gray, G1; dark gray, S; black, G2/M). (H) qRT-PCR analysis of miR-24 (normalized to U6 snRNA) from the partially synchronized K562 cells in (G) shows that miR-24 is most highly expressed in G1 and declines as cells progress to S and G2/M phase. #p<0.005.

FIG. 11. Bioinformatic Analysis of miR-24-Downregulated Genes Suggests that miR-24 Regulates Cell-Cycle Progression and DNA Repair. Major direct interaction network of the 248 genes significantly downregulated after transfection of HepG2 cells with miR-24 mimics. Nodes with at least five interactions (including autoregulation) are highlighted.

FIG. 12. (A) miR-24 levels significantly increased when K562 cells were transfected with 10 or 50 nM miR-24 mimics as measured by qRT-PCR analysis. Expression relative to U6 snRNA is depicted normalized to control cells. (B) A 4-fold increase in miR-24, obtained by transfecting 10 nM miR-24 mimic, reduces target proteins. Cell lysates of K562 cells, obtained 72 hr after transfection with indicated concentrations of control or miR-24 mimics, were analyzed by immunoblot. a-tubulin and HuR are loading controls. Error bars represent mean±SD from three independent experiments. *p<0.05, **p<0.01, and #p<0.005.

FIG. 13. (A) miR-24 downregulates luciferase activity of a reporter gene containing wild-type (wt) E2F2 MRE1. Mutations in the miR-24 pairing residues (mt) rescue luciferase expression (sequences and luciferase assays for candidate E2F2 WT 3′UTR MREs are shown in Figure S3). (B) Predicted binding sites in the 3′UTR of genes whose 3′UTR was repressed by miR-24 in (C) and binding site mutations tested (indicated in red). (C) Expression of reporter genes containing wild-type (wt) AURKB MRE1, BRCA1 MRE5, CDC2 MRE1, CDK4 MRE1, and FEN1 MRE1 is significantly reduced upon cotransfection of HepG2 cells with miR-24 mimics (white) and not the control mimic (black). Mutations in the miR-24 pairing residues (mt) rescue luciferase expression (sequences and luciferase assays for all tested wt MREs for these genes are shown in Figures S6 and S7). The CCNA2 MRE1 is not regulated. (D) However, miR-24 regulates a 181 nt region containing the CCNA2 MRE1 in the luciferase vector. Mutations in the binding residues of CCNA2 MRE1 within the extended sequence restore luciferase activity.

FIG. 14. E2F2 is a key miR-24 target gene. (A) Increased cell proliferation from antagonizing miR-24 in K562 cells is blocked by siRNA-mediated knockdown of E2F2, but not MYC. Knockdown is shown by immunoblot (Figure S5). K562 cells were cotransfected with or without miR-24 ASO plus control siRNA or siRNAs targeting E2F2 and/or MYC. The rate of cellular proliferation was determined 72 hr later by thymidine incorporation. Error bars represent mean±SD from three independent experiments. (B and C) Downregulation of E2F2 mRNA (B) and protein (C) during TPA-mediated differentiation of K562 cells to megakaryocytes is mediated by miR-24 and can be completely inhibited by antagonizing miR-24. K562 cells were transfected with miR-24 or a control (CTL) ASO for 72 hr and then treated with TPA for 6 hr. mRNA was assessed by qRT-PCR normalized to GAPDH and normalized to control cells transfected with CTL ASO. E2F2 protein was quantified by densitometry and normalized to a-tubulin. (D and E) Transfection of K562 cells with a miR-24 mimic reduces cell proliferation, which can be rescued by expressing miR-24-insensitive E2F2 lacking the 3′UTR. K562 cells were cotransfected with a vector expressing HA-tagged E2F2 or GFP and miR-24 or cel-miR-67 (CTL) mimics for 72 hr before measuring thymidine uptake. (E) Immunoblot probed for HA tag. Error bars represent mean±SD from three independent experiments. *p<0.05, **p<0.01, and #p<0.005. (F) Model of the miR-24, miR-17_—92, MYC, and E2F network of cell-cycle regulators. Here, we show that miR-24 directly suppresses expression of MYC and E2F2 (and indirectly suppresses E2F1 and E2F3) and thereby regulates the G1/S transition. Expression of the opposing miRNAs encoded by the miR-17_—92 and miR-106b _—25 clusters that promote cell proliferation is transcriptionally activated by the same transcription factors that miR-24 suppresses (O'Donnell et al., 2005; Petrocca et al., 2008). Therefore, miR-24 would be predicted to reduce expression of the proliferation-promoting miRNA clusters indirectly. These miRNAs also knock down the E2F genes but probably to fine-tune their proliferative effect. MYC may also suppress miR-24 transcription (Gao et al., 2009).

FIG. 15. (A) Direct interaction network of over-represented subnetworks built from the 100 genes down-regulated by miR-24 and also predicted to be miR-24 targets by TargetScan 4.2. (B) Direct gene interaction small subnetworks built from the 248 mRNAs that are down-regulated in miR-24 over-expressing cells.

FIG. 16. (A) Candidate miR-24 microRNA recognition elements (MRE) in the 3′UTR of E2F2 mRNA predicted by rna22. Numbers in parenthesis represent the location in the E2F2 3′UTR. (B) Only E2F2 MRE1 was found to be repressed by miR-24 by luciferase assay. Inserting the other E2F2 MREs (MRE2-5) in the 3′UTR of a luciferase gene had no significant effect on luciferase expression in HepG2 cells after over-expressing miR-24. Data are an average of two independent experiments. ** p<0.01

FIG. 17. miR-24 down-regulates luciferase expression from a reporter. gene containing the MYC and E2F2 MREs, before its effect on cell proliferation can be detected. (A) 24 hr after ectopic introduction of miR-24 mimic into HepG2 cells there is no significant change in cellular proliferation compared to control mimic transduced cells as measured by thymidine uptake. (B) Luciferase assays performed 24 hr post transfection of HepG2 with miR-24 (white) or cel-miR-67 (black) show Significant decreases in reporter expression from reporters encoding E2F2 MRE 1 or MYC MRE3 or MRE6 in miR-24 overexpressing cells (white). Luciferase activity was normalized. The mean and S.D. of 3 independent experiments is shown.

FIG. 18. miR-24 down-regulates target mRNAs when transfected at physiological levels. K562 cells were transfected with miR-24 or cel-miR-67 (CTL) mimics at 2 nM, 10 nM and 50 nM. miR-24 levels significantly increased when cells were transfected with 10 nM or 50 nM miR-24 (FIG. 5C). Target gene mRNA was assessed by qRT-PCR 72 hr later. E2F2, CCNA2, MYC, BRCA1 and H2AX mRNAs were down-regulated in cells transfected with 10 nM or 50 nM miR-24 mimic. miR-24 over-expression had no effect on PCNA mRNA levels as shown before (FIG. 5A). Light grey, dark grey and black bars correspond to miRNA concentrations of 2 nM, 10 nM and 50 nM, respectively. Expression is normalized to mRNA level in cells transfected with 2 nM control miRNA. Representative experiments are shown; each experiment was done twice with similar results.

FIG. 19. Candidate miR-24 microRNA recognition elements (MRE) in the 3′UTR of target mRNAs (AURKB, BRCA1, CCNA2, CDK4, CDC2 and FEN1) predicted by rna22 or PITA. Only BRCA1 MRE5 is predicted by TargetScan 4.2. Numbers in parenthesis represent the location in the 3′UTR of the target gene. The effect of inserting these MREs on luciferase expression is shown in FIG. 20.

FIG. 20. Inserting the miR-24 recognition elements present in the 3′UTR of AURKB (MRE1), BRCA1 (MRE5), CDK4 (MRE1), CDC2 (MRE1) and FEN1 (MRE1) in a luciferase gene significantly reduces luciferase expression in HepG2 cells transfected with miR-24 mimics (white) and not the control mimics (black). Sequences of these MREs are provided in Suppl. FIG. 6. Data are an average of 3 experiments. Error bars represent standard deviation. *p<0.05.

FIG. 21. siRNAs knockdown MYC and E2F2 expression. K562 cells were transfected with siRNAs targeting E2F2 or MYC or GFP (Ct1) for 48 hr before immunoblot analysis for E2F2 (A) or (B) MYC. a-Tubulin was probed as a loading control.

FIG. 22. (A) Inhibiting miR-24 partially rescues MYC mRNA expression in K562 cells treated with TPA. K562 cells were transfected with miR-24 ASO or a control (CTL) ASO for 72 hr and then treated with TPA for 6 hr. MYC mRNA levels were measured by qRTPCR analysis after normalizing to GAPDH mRNA. (B) Immunoblot analysis shows that antagonizing miR-24 in K562 cells increase MYC protein levels in untreated cells. However, MYC protein expression is still down-regulated after TPA treatment in cells transfected with miR-24 ASO (miR-24).

FIG. 23. Activity of Bi-miR-24 mimics is similar to non-biotinylated miR-24 mimics. (A) Effective increase in miR-24 (normalized to U6 SnRNA) in K562 cells 24 hr after transfection with 100 nM 3′-Biotinylated miR-24 compared to 3′-Biotinylated cel-miR-67 mimic transfected cells. (B) Activity of biotinylated miR-24 (Bi-miR-24) is similar to non-biotinylated miR-24 mimic. K562 cells were transfected with 100 nM control miRNA or miR-24 mimics or Bi-miR-24 for 24 hr and then cotransfected with 100 ng psiCHECK2 plasmid (black) or psiCHECK2 containing a perfectly complementary target site for miR-24 (white) and assayed for luciferase expression after 24 hr. The Renilla luciferase signal was normalized to the Firefly luciferase.

FIG. 24. Endogenous and Biotinylated miR-24 associate with RISC proteins. Both endogenous and Biotinylated-miR-24 specifically associate with RISC complex proteins such as HA-Ago1 and HA-Ago2 in K562 cells. K562 cells were co-transfected with 100 nM Bi-miR-24 or Bi-cel-miR-67 and 2 μg plasmids expressing HA-Ago1 or HA-Ago2 or Vector alone for 48 hr following which Streptavidin pull-downs were performed from cytoplasmic extracts for 16 hr. qRT-PCR analysis (normalized to U6 SnRNA) show that HA-Ago1 or HA-Ago2 pull-down endogenous or transfected Bi-miR-24.

FIG. 25. Endogenous miR-24 and Bi-miR-24 co-sediment with polysomes in K562 cell extracts. K562 cells were transfected with 100 nM Bi-cel-miR-67 (A) or Bi-miR-24 (B) for 24 hr and left untreated (▴) or treated with 200 uM puromycin (▪) before fixation with formaldehyde and lysis by sonication. The lysates were fractionated on 10-50% sucrose gradients and the relative distribution of miR-24 (both endogenous and exogenous) in each fraction was determined by qRT-PCR analysis. In the absence of puromycin, the profiles of Bi-miR-24 and endogenous miR-24 were similar with most miR-24 associated with the more dense polysome-containing fractions (7-9). Treatment with puromycin resulted in a decrease in the abundance of miR-24 or Bi-miR-24 in the heaviest fractions (8,9) with a subsequent increase in the less dense fractions (2,6).

FIG. 26. (A) K562 cells were transfected with 100 nM Bi-miR-24 or Bi-cel-miR-67 miRNA for 6, 12, 24, 48 and 60 hr. RNA was isolated from the Streptavidin pull-downs and analyzed by qRT-PCR for miR-24 after normalization to U6 SnRNA. miR-24 was specifically enriched in miR-24 pull-down and not the control pull-down. The enrichment of miR-24 in miR-24 pull-downs increased with time and was highest at 24 hr and later. (B) H2AX and E2F2 mRNAs (encoding 2 and 1,3′-UTR miR-24 binding sites, respectively), assayed by qRT-PCR (normalized to GAPDH), are significantly enriched in miR-24 and not the control pull-downs from K562 cells. Enrichment was maximal when pull-downs were performed 24 hr after transfection.

FIG. 27. (A) K562 cells were transfected with 100 nM Bi-miR-24 or Bi-cel-miR-67 mimics for 24 hr following which Streptavidin pull-downs were performed and the abundance of miR-24 target mRNAs was determined by qRT-PCR analysis normalized to GAPDH mRNA. H2AX, E2F2, MYC and AURKB mRNAs (containing 2, 1, 2 and 1 binding sites for miR-24) are specifically enriched in miR-24 pull-downs. The house-keeping mRNA UBC was not enriched. (B) mRNA targets of miR-34a (CDK4, CDK6 and MYB) are enriched specifically in Bi-miR-34a pull-downs performed from K562 cells transfected with 100 nM Biotinylated miR-34a mimics.

FIG. 28. Presents Supplementary Table 1. Down-regulated genes in over-expressing miR-24 cells with accession numbers, Z-ratio, p-value and number of TargetScan 4.2 predicted miR-24 binding sites in the 3′UTR.

FIG. 29. Presents Supplementary Table 2. Integrated gene list containing gene annotation, Gene Ontology (G0) processes, and miR-24 binding sites for all genes analyzed via miR-24 pull-down or microarray analysis.

FIG. 30. Presents Supplementary Table 3. Over-represented subnetworks amongst 100 genes that are miR-24 TargetScan 4.2 predicted targets and are also down-regulated in miR-24 over-expressing cells.

FIG. 31. Presents Supplementary Table 4. miR-24 pull-down genes with accession numbers, Z-ratio (enrichment value), p-value and number of TargetScan 4.2 predicted miR-24 binding sites in the 3′UTR.

FIG. 32. Presents Supplementary Table 5. Novel potential miR-24 gene targets based on miR-24 seed detection in coding region or 5′UTR.

FIG. 33. Presents Supplementary Table 6. Over-represented subnetworks within miR-24 pull-down genes.

FIG. 34. Presents Supplementary Table 7. Distribution of genes enriched by miR-24 pull-down across cell cycle phases.

FIG. 35. Presents Supplementary Table 8. Sequence of primers used for qRT-PCR.

DEFINITIONS

Combination Therapy: The term “combination therapy”, as used herein, refers to those situations in which two or more different pharmaceutical agents are administered in overlapping regimens so that the subject is simultaneously exposed to both agents.
Expression: As used herein, “expression” of a nucleic acid sequence refers to one or more of the following events: (1) production of an RNA template from a DNA sequence (e.g., by transcription); (2) processing of an RNA transcript (e.g., by splicing, editing, and/or 3′ end formation); (3) translation of an RNA into a polypeptide or protein; (4) post-translational modification of a polypeptide or protein.
Gene: As used herein, the term “gene” has its meaning as understood in the art. It will be appreciated by those of ordinary skill in the art that the term “gene” may include gene regulatory sequences (e.g., promoters, enhancers, etc.) and/or intron sequences. It will further be appreciated that definitions of gene include references to nucleic acids that do not encode proteins but rather encode RNA molecules (e.g., functional RNA molecules, such as rRNAs and/or tRNAs). For the purpose of clarity we note that, as used in the present application, the term “gene” generally refers to a portion of a nucleic acid that encodes an rRNA or a sensitive fungal gene, as will be clear from context to those of ordinary skill in the art.
Gene product or expression product: As used herein, the term “gene product” or “expression product” generally refers to an RNA transcribed from the gene (pre- and/or post-processing) or a polypeptide (pre- and/or post-modification) encoded by an RNA transcribed from the gene.
Hybridize: As used herein, the term “hybridize” refers to the interaction between two complementary nucleic acid sequences. The phrase “hybridizes under high stringency conditions” describes an interaction that is sufficiently stable that it is maintained under art-recognized high stringency conditions. Guidance for performing hybridization reactions can be found, for example, in Current Protocols in Molecular Biology, John Wiley & Sons, N.Y., 6.3.1-6.3.6, 1989 (and in more recent updated editions), and in Sambrook et al., Molecular Cloning: A Laboratory Manual, 3^rded., Cold Spring Harbor Laboratory Press, Cold Spring Harbor, 2001. Aqueous and nonaqueous methods are described in these references, and either can be used. Typically, for nucleic acid sequences over approximately 50-100 nucleotides in length, various levels of stringency are defined, such as low stringency (e.g., 6× sodium chloride/sodium citrate (SSC) at about 45° C., followed by two washes in 0.2×SSC, 0.1% SDS at least at 50° C. (the temperature of the washes can be increased to 55° C. for medium-low stringency conditions)); 2) medium stringency hybridization conditions utilize 6×SSC at about 45° C., followed by one or more washes in 0.2×SSC, 0.1% SDS at 60° C.; 3) high stringency hybridization conditions utilize 6×SSC at about 45° C., followed by one or more washes in 0.2×SSC, 0.1% SDS at 65° C.; and 4) very high stringency hybridization conditions are 0.5M sodium phosphate, 0.1% SDS at 65° C., followed by one or more washes at 0.2×SSC, 1% SDS at 65° C.) Hybridization under high stringency conditions occurs between sequences with a very high degree of complementarity. One of ordinary skill in the art will recognize that the parameters for different degrees of stringency will generally differ based various factors such as the length of the hybridizing sequences, whether they comprise RNA or DNA, etc. For example, appropriate temperatures for high, medium, or low stringency hybridization will generally be lower for shorter sequences such as oligonucleotides than for longer sequences.
Identity: As used herein, the term “identity” refers to the overall relatedness between polymeric molecules, e.g. between nucleic acid molecules (e.g. DNA molecules and/or RNA molecules) and/or between polypeptide molecules. Calculation of the percent identity of two nucleic acid sequences, for example, can be performed by aligning the two sequences for optimal comparison purposes (e.g., gaps can be introduced in one or both of a first and a second nucleic acid sequences for optimal alignment and non-identical sequences can be disregarded for comparison purposes). In certain embodiments, the length of a sequence aligned for comparison purposes is at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 90%, at least 95% or 100% of the length of the reference sequence. The nucleotides at corresponding nucleotide positions are then compared. When a position in the first sequence is occupied by the same nucleotide as the corresponding position in the second sequence, then the molecules are identical at that position. The percent identity between the two sequences is a function of the number of identical positions shared by the sequences, taking into account the number of gaps, and the length of each gap, which needs to be introduced for optimal alignment of the two sequences. The comparison of sequences and determination of percent identity between two sequences can be accomplished using a mathematical algorithm. For example, the percent identity between two nucleotide sequences can be determined using the algorithm of Meyers and Miller (CABIOS, 1989, 4: 11-17), which has been incorporated into the ALIGN program (version 2.0) using a PAM120 weight residue table, a gap length penalty of 12 and a gap penalty of 4. The percent identity between two nucleotide sequences can, alternatively, be determined using the GAP program in the GCG software package using a NWSgapdna.CMP matrix.
microRNA (miRNA): As used herein, the term “microRNA” or “miRNA” refers to an RNAi agent that is approximately 21-23 nucleotides (nt) in length. miRNAs can range between 18-26 nucleotides in length. Typically, miRNAs are single-stranded. However, in some embodiments, miRNAs may be at least partially double-stranded. In certain embodiments, miRNAs may comprise an RNA duplex (referred to herein as a “duplex region”) and may optionally further comprises one or two single-stranded overhangs. In some embodiments, an RNAi agents comprises a duplex region ranging from 15 to 29 bp in length and optionally further comprising one or two single-stranded overhangs. An miRNA may be formed from two RNA molecules that hybridize together, or may alternatively be generated from a single RNA molecule that includes a self-hybridizing portion. In general, free 5′ ends of miRNA molecules have phosphate groups, and free 3′ ends have hydroxyl groups. The duplex portion of an miRNA usually, but does not necessarily, comprise one or more bulges consisting of one or more unpaired nucleotides. One strand of an miRNA includes a portion that hybridizes with a target RNA. In certain embodiments of the invention, one strand of the miRNA is not precisely complementary with a region of the target RNA, meaning that the miRNA hybridizes to the target RNA with one or more mismatches. In other embodiments of the invention, one strand of the miRNA is precisely complementary with a region of the target RNA, meaning that the miRNA hybridizes to the target RNA with no mismatches. Typically, miRNAs are thought to mediate inhibition of gene expression by inhibiting translation of target transcripts. However, in some embodiments, miRNAs may mediate inhibition of gene expression by causing degradation of target transcripts.
MicroRNA Agent: A “microRNA agent” as that term is used herein, refers to an entity whose nucleotide sequence is substantially identical to that of a natural miRNA. As will be appreciated by those of ordinary skill in the art, naturally-occurring miRNAs are comprised of RNA. As will be further appreciated by those of ordinary skill in the art, RNA is a particularly labile chemical. Furthermore, a variety of strategies are known for preparing molecules that are structural mimics of RNA (and therefore have a “sequence” in the same sense as RNA) but that may, for example, have greater stability and/or somewhat altered hybridization characteristics. For example, in some embodiments, such structural mimics include one or more backbone modifications (e.g., substitution of phosphorothioate backbone structures for phosphodiester structures found in RNA) and/or one or more base modifications (e.g., 2′-OMe modifications). In some embodiments, such structural mimics are encompassed within “microRNA agent” as that term is used herein.
miRNA target regulating factor: As used herein, the term “miRNA target regulating factor” in its broadest sense, refers to any agent that, when administered to a cell, alters level and/or activity of an RNA that is also the target of an miRNA. In some embodiments, miRNA target regulating factors alters the level/activity to be higher in presence of agent than in absence. In some embodiments, the level or activity is at least about 1.1, about 1.2, about 1.3, about 1.4, about 1.5, about 1.6, about 1.7, about 1.8, about 1.9, about 2, about 2.5, about 3, about 3.5, about 4, about 4.5, about 5, about 6, about 7, about 8, about 9, about 10, about 11, about 12, about 13, about 14, about 15, about 16, about 17, about 18, about 19, about 20, to about 200 fold or even higher in the cell as to regulate the effect of this target as compared to not administering the siRNA. In some embodiments, miRNA target regulating factors alters the level/activity to be lower in presence of agent than in absence. In some embodiments, the level or activity is at least about 1.1, about 1.2, about 1.3, about 1.4, about 1.5, about 1.6, about 1.7, about 1.8, about 1.9, about 2, about 2.5, about 3, about 3.5, about 4, about 4.5, about 5, about 6, about 7, about 8, about 9, about 10, about 11, about 12, about 13, about 14, about 15, about 16, about 17, about 18, about 19, about 20, to about 200 fold or even lower in the cell as to regulate the effect of this target as compared to not administering the siRNA. In some embodiments, exemplary miRNA target regulating factors can include siRNA, shRNA, and/or miRNA. In some embodiments the siRNA, shRNA and/or miRNA targets RNA. Generally, miRNA target regulating factors include a portion that is substantially complementary to a target RNA. In some embodiments, miRNA target regulating factors are at least partly double-stranded. In some embodiments, miRNA target regulating factors are single-stranded. In some embodiments, miRNA target regulating factors may be composed entirely of natural RNA nucleotides (i.e., adenine, guanine, cytosine, and uracil). In some embodiments, miRNA target regulating factors may include one or more non-natural RNA nucleotides (e.g., nucleotide analogs, DNA nucleotides, etc.). Inclusion of non-natural RNA nucleic acid residues may be used to make the miRNA target regulating factors more resistant to cellular degradation than RNA. In some embodiments, the term “miRNA target regulating factor” may refer to any RNA, RNA derivative, and/or nucleic acid encoding an RNA that induces an RNAi effect (e.g., degradation of target RNA and/or inhibition of translation). In some embodiments, the miRNA target regulating factors may comprise a blunt-ended (i.e., without overhangs) dsRNA that can act as a Dicer substrate. For example, such an miRNA target regulating factor may comprise a blunt-ended dsRNA which is >25 base pairs length, which may optionally be chemically modified to abrogate an immune response.
Nucleic acid: As used herein, the term “nucleic acid,” in its broadest sense, refers to any compound and/or substance that can be incorporated into an oligonucleotide chain. In some embodiments, “nucleic acid” encompasses RNA as well as single and/or double-stranded DNA and/or cDNA. Furthermore, the terms “nucleic acid,” “DNA,” “RNA,” and/or similar terms include nucleic acid analogs, i.e. analogs having other than a phosphodiester backbone. For example, the so-called “peptide nucleic acids,” which are known in the art and have peptide bonds instead of phosphodiester bonds in the backbone, are considered within the scope of the present invention. The term “nucleotide sequence encoding an amino acid sequence” includes all nucleotide sequences that are degenerate versions of each other and/or encode the same amino acid sequence. Nucleotide sequences that encode proteins and/or RNA may include introns.
RNA interference (RNAi): As used herein, the term “RNA interference” or “RNAi” refers to sequence-specific inhibition of gene expression and/or reduction in target RNA levels mediated by an at least partly double-stranded RNA, which RNA comprises a portion that is substantially complementary to a target RNA. Typically, at least part of the substantially complementary portion is within the double stranded region of the RNA. In some embodiments, RNAi can occur via selective intracellular degradation of RNA. In some embodiments, RNAi can occur by translational repression.
RNAi agent: As used herein, the term “RNAi agent” refers to an RNA, optionally including one or more nucleotide analogs or modifications, having a structure characteristic of molecules that can mediate inhibition of gene expression through an RNAi mechanism. In some embodiments, RNAi agents mediate inhibition of gene expression by causing degradation of target transcripts. In some embodiments, RNAi agents mediate inhibition of gene expression by inhibiting translation of target transcripts. Generally, an RNAi agent includes a portion that is substantially complementary to a target RNA. In some embodiments, RNAi agents are at least partly double-stranded. In some embodiments, RNAi agents are single-stranded. In some embodiments, exemplary RNAi agents can include siRNA, shRNA, and/or miRNA. In some embodiments, RNAi agents may be composed entirely of natural RNA nucleotides (i.e., adenine, guanine, cytosine, and uracil). In some embodiments, RNAi agents may include one or more non-natural RNA nucleotides (e.g., nucleotide analogs, DNA nucleotides, etc.). Inclusion of non-natural RNA nucleic acid residues may be used to make the RNAi agent more resistant to cellular degradation than RNA. In some embodiments, the term “RNAi agent” may refer to any RNA, RNA derivative, and/or nucleic acid encoding an RNA that induces an RNAi effect (e.g., degradation of target RNA and/or inhibition of translation). In some embodiments, an RNAi agent may comprise a blunt-ended (i.e., without overhangs) dsRNA that can act as a Dicer substrate. For example, such an RNAi agent may comprise a blunt-ended dsRNA which is ≧25 base pairs length, which may optionally be chemically modified to abrogate an immune response.
RNAi-inducing entity: As used herein, the term “RNAi-inducing entity” encompasses any entity that delivers, regulates, and/or modifies the activity of an RNAi agent. In some embodiments, RNAi-inducing entities may include vectors (other than naturally occurring molecules not modified by the hand of man) whose presence within a cell results in RNAi and leads to reduced expression of a transcript to which the RNAi-inducing entity is targeted. In some embodiments, RNAi-inducing entities are RNAi-inducing vectors. In some embodiments, RNAi-inducing entities are compositions comprising RNAi agents and one or more pharmaceutically acceptable excipients and/or carriers.
RNAi-inducing vector: As used herein, the term “RNAi-inducing vector” refers to a vector whose presence within a cell results in production of one or more RNAs that self-hybridize or hybridize to each other to form an RNAi agent (e.g. siRNA, shRNA, and/or miRNA). In various embodiments of the invention this term encompasses plasmids, e.g., DNA vectors (whose sequence may comprise sequence elements derived from a virus), or viruses (other than naturally occurring viruses or plasmids that have not been modified by the hand of man), whose presence within a cell results in production of one or more RNAs that self-hybridize or hybridize to each other to form an RNAi agent. In general, the vector comprises a nucleic acid operably linked to expression signal(s) so that one or more RNAs that hybridize or self-hybridize to form an RNAi agent are transcribed when the vector is present within a cell. Thus the vector provides a template for intracellular synthesis of the RNA or RNAs or precursors thereof. For purposes of inducing RNAi, presence of a viral genome in a cell (e.g., following fusion of the viral envelope with the cell membrane) is considered sufficient to constitute presence of the virus within the cell. In addition, for purposes of inducing RNAi, a vector is considered to be present within a cell if it is introduced into the cell, enters the cell, or is inherited from a parental cell, regardless of whether it is subsequently modified or processed within the cell. An RNAi-inducing vector is considered to be targeted to a transcript if presence of the vector within a cell results in production of one or more RNAs that hybridize to each other or self-hybridize to form an RNAi agent that is targeted to the transcript, i.e., if presence of the vector within a cell results in production of one or more RNAi agents targeted to the transcript.
Short, interfering RNA (siRNA): As used herein, the term “short, interfering RNA” or “siRNA” refers to an RNAi agent comprising an RNA duplex (referred to herein as a “duplex region”) that is approximately 19 basepairs (bp) in length and optionally further comprises one or two single-stranded overhangs. In some embodiments, an RNAi agents comprises a duplex region ranging from 15 to 29 bp in length and optionally further comprising one or two single-stranded overhangs. An siRNA may be formed from two RNA molecules that hybridize together, or may alternatively be generated from a single RNA molecule that includes a self-hybridizing portion. In general, free 5′ ends of siRNA molecules have phosphate groups, and free 3′ ends have hydroxyl groups. The duplex portion of an siRNA may, but typically does not, comprise one or more bulges consisting of one or more unpaired nucleotides. One strand of an siRNA includes a portion that hybridizes with a target RNA. In certain embodiments of the invention, one strand of the siRNA is precisely complementary with a region of the target RNA, meaning that the siRNA hybridizes to the target RNA without a single mismatch. In other embodiments of the invention one or more mismatches between the siRNA and the targeted portion of the target RNA may exist. In some embodiments of the invention in which perfect complementarity is not achieved, any mismatches are generally located at or near the siRNA termini. In some embodiments, siRNAs mediate inhibition of gene expression by causing degradation of target transcripts.
Short hairpin RNA (shRNA): As used herein, the term “short hairpin RNA” or “shRNA” refers to an RNAi agent comprising an RNA having at least two complementary portions hybridized or capable of hybridizing to form a double-stranded (duplex) structure sufficiently long to mediate RNAi (typically at least approximately 19 bp in length), and at least one single-stranded portion, typically ranging between approximately 1 and 10 nucleotides (nt) in length that forms a loop. In some embodiments, an shRNA comprises a duplex portion ranging from 15 to 29 bp in length and at least one single-stranded portion, typically ranging between approximately 1 and 10 nt in length that forms a loop. The duplex portion may, but typically does not, comprise one or more bulges consisting of one or more unpaired nucleotides. In some embodiments, siRNAs mediate inhibition of gene expression by causing degradation of target transcripts. shRNAs are thought to be processed into siRNAs by the conserved cellular RNAi machinery. Thus shRNAs may be precursors of siRNAs. Regardless, siRNAs in general are capable of inhibiting expression of a target RNA, similar to siRNAs.
Small molecule: In general, a “small molecule” is understood in the art to be an organic molecule that is less than about 5 kilodaltons (Kd) in size. In some embodiments, the small molecule is less than about 3 Kd, 2 Kd, or 1 Kd. In some embodiments, the small molecule is less than about 800 daltons (D), 600 D, 500 D, 400 D, 300 D, 200 D, or 100 D. In some embodiments, small molecules are non-polymeric. In some embodiments, small molecules are not proteins, peptides, or amino acids. In some embodiments, small molecules are not nucleic acids or nucleotides. In some embodiments, small molecules are not saccharides or polysaccharides.
Vector: As used herein, “vector” refers to a nucleic acid molecule capable of mediating entry of (e.g., transferring, transporting, etc.) a second nucleic acid molecule into a cell. The transferred nucleic acid is generally linked to (e.g., inserted into) the vector nucleic acid molecule. A vector may include sequences that direct autonomous replication, or may include sequences sufficient to allow integration into cellular DNA. Useful vectors include, for example

Description of Certain Embodiments

The present invention provides, among other things, a discovery that a combination of analyses—biochemical interaction assays (herein referred to as “pull-down assays”) and a second analysis—together create a powerful system that allows ready identification of targets of microRNAs. Although some elegant examples of miRNA gene regulation pathways have emerged by thoughtful mining of miRNA target prediction algorithms and differential mRNA expression profiling (2, 48), the unpublished examples of failures using this approach are probably much more common. The present invention encompasses the recognition that one reason for such failures may be that differential expression profiling often does not reveal miRNA effects (which may well occur primarily at the level of translation). Furthermore, the present invention demonstrates unexpectedly that true targets of miRNAs are often not predicted by available algorithms and other techniques. The present invention also provides kits for the detection of miRNAs and identification of drug targets, as well as drug screening and therapeutic applications.

MicroRNAs

The present invention provides systems that allow identification of targets of microRNAs. As will be appreciated by those of ordinary skill, the inventive methods can be applied to identify targets of any of a variety of microRNAs. Representative such miRNAs include, for example, miR-22, miR-125a, miR-24 (e.g., miR-24-1; miR-24-2), miR-23 (e.g., miR-23a, miR-23B), miR-27 (e.g., miR-27a, miR-27b), miR-17, miR-18, miR-19, miR-20, miR-34a, miR-92, miR-125, miR-146a, miR-155, miR-181a, 200a, miR-48, miR-84, and miR-241.
In some embodiments, the miRNA whose targets are identified in accordance with the present invention is one whose expression level increases or decreases during a particular developmental stage of interest or in response to a particular trigger or event of interest. For example, in some embodiments, the miRNA is one whose expression level changes during terminal differentiation. To give but one specific example, in some embodiments, the miRNA is up-regulated during terminal differentiation of hematopoeitic cells.
In some embodiments, an miRNA whose expression changes during a particular developmental stage of interest, or in response to a particular trigger or event of interest, increases or decreases at least about 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100 fold or more.
In some embodiments, the miRNA whose targets are identified in accordance with the present invention is one that regulates cell cycle progression. In some embodiments, the miRNA suppresses the expression of cell cycle regulator genes. In some embodiments, the miRNA is characterized in that its overexpression increases the number of cells in the G1 phase; in some embodiments, the miRNA is characterized in that its inhibition causes differentiating cells to keep proliferating.
In some embodiments, the miRNA targets genes that initiate pathways such as synthesis of DNA building blocks; DNA replication; DNA damage recognition; expression, transcriptional regulation, and/or post-translational modification of cyclins, cyclin-dependent kinases, and/or other cell cycle regulators. In some embodiments, the miRNA targets MYC, E2F, and/or their targets.
In some embodiments, the miRNA targets genes that are implicated in progression through the cell cycle, for example, through G1, the G1/S checkpoint, S, and/or G2/M.
In some embodiments, the miRNA targets genes that are involved in DNA repair, including for example, genes (e.g., H2AX) that sensitize cells to DNA damaging agents.
In some embodiments, the miRNA is selected from the group consisting of miR-24 and/or other miRNAs in the same cluster. In some embodiments, the miRNA is miR-22 or miR-125a. For example, in some embodiments, the miRNA is selected from the group consisting of miR-24 (e.g., miR-24-1; miR-24-2), miR-23 (e.g., miR-23a, miR-23B), and miR-27 (e.g., miR-27a, miR-27b), etc. In some embodiments, the miRNA is a member of the let-7 family of miRNAs. In some embodiments, the miRNA is selected from the group consisting of miR-48, miR-84, and miR-241. In some embodiments, the miRNA is selected from the group consisting of miR-17, miR-18, miR-19, miR-20, miR-34a, miR-92, miR-125, miR-146a, miR-155, miR-181a, 200a. In some embodiments, the miRNA is one that is found on chromosome 9, or on chromosome 19. In some embodiments, the miRNA is one that is found in an intergenic region of a chromosome (e.g., chromosome 19). In some embodiments, the miRNA is a viral miRNA. In some embodiments, the miRNA is a member of the Herpes virus family. In some embodiments, the miRNA is miR-K12-11. The present Examples exemplify the invention with respect to miR-24.

Pull-Down Technologies

The present invention combines use of interaction assays, or pull-down assays, with the analyses (e.g. bioinformatic analyses), to identify targets of microRNAs.
According to the present invention, a pull-down assay tests direct, physical interaction between an miRNA of interest and its target(s). In some embodiments, pull-down technologies for use in accordance with the present invention isolate RNAs (e.g. mRNAs) that are specifically bound to a miRNA of interest.
For example, in some embodiments, interacting RNAs are isolated by increasing levels of the miRNA of interest in a cell, and then identifying RNAs (or other factors) associated with the overexpressed miRNA. An increase in miRNA level can be achieved by any of a variety of available means including, for example, transfection, injection, induction, etc. Those of ordinary skill in the art will appreciate that pull-down assays may be performed utilizing a natural miRNA molecule, but will further appreciate that a variety of strategies are known for preparing molecules that are structural mimics of RNA (and therefore have a “sequence” in the same sense as RNA) but that may, for example, have greater stability and/or somewhat altered hybridization characteristics.
For example, in some embodiments, such structural mimics include one or more backbone modifications (e.g., substitution of phosphorothioate backbone structures for phosphodiester structures found in RNA) and/or one or more base modifications (e.g., 2′-OMe modifications). In some embodiments, such structural mimics are locked nucleic acids (LNAs; see, for example, U.S. Pat. No. 6,977,295). Use of such miRNA mimics is encompassed by the present invention; those of ordinary skill will readily appreciate when discussions of miRNAs herein can relate to such mimics. In some embodiments, such miRNAs mimics have increased stability as compared with the natural miRNA. In some embodiments, miRNA mimics bind with greater affinity and/or specificity to the same target(s) bound by the natural miRNA. In some embodiments, such greater affinity and/or specificity is at least about 1.5, 2, 2.5, 3, 3.5, 4, 4.5, 5, 5.5, 6, 6.5, 7, 7.5, 8, 8.5, 9, 9.5, 10 or more (e.g. 15, 20, 25, 30, 35, 40, 45, 50, 75, 100, 200, 300, 400, 500, 1000 or more) fold higher than that observed with the natural miRNA.
In some embodiments, an increased level of miRNA is about at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, or more (e.g. 20, 30, 40, 50, 60, 70, 80, 90, 100 or more) fold compared with levels of endogenous miRNA. Those of ordinary skill in the art will readily appreciate that different target RNAs (and/or different amounts of a given target RNA) may be found in (and therefore identified and/or isolated from as described herein) different cell types.
In some embodiments, interacting RNAs are isolated by increasing levels of the miRNA of interest in a cell that itself underexpresses the miRNA of interest (e.g. has a low endogenous expression level as compared with other cells).
In some embodiments, cells containing overexpressed miRNA are fixed prior to isolation of miRNA-target RNA complexes. Those of ordinary skill in the art will be aware of a variety of techniques for cell fixation. In some embodiments, the cells are fixed by formaldehyde treatment. In one aspect, the present invention encompasses the recognition that such fixation can improve accuracy of miRNA target identification (see FIG. 8).
In some embodiments, miRNA-target complexes are isolated. In some embodiments, such isolation includes isolation of a cellular fraction. In some embodiments, the cellular fraction is or comprises a polysome fraction. In some embodiments, the present invention encompasses the recognition that isolation of a cellular fraction (e.g. a polysome fraction) can improve accuracy of target identification. In some embodiments, the present invention provides the recognition that the miRNA profile maybe greater than 80% in the dense polysome fractions. In some embodiments, the miRNA profile in polysome fractions may be indistinguishable from the profile of endogenous miRNA in the same cells (see FIG. 7).
In some embodiments, the overexpressed miRNA is labeled (e.g. directly or indirectly). In some embodiments, an miRNA sense strand is labeled; in some embodiments, and miRNA anti-sense strand is labeled; in some embodiments, both sense and antisense strands are labeled. In some embodiments, a label is covalently associated with the miRNA. In some embodiments, a label is covalently or non-covalently associated with the 5′ end of an miRNA strand. In some embodiments a label is covalently or non-covalently associated with the 3′ end of an miRNA strand.
It may be desirable to utilize a label that does not interfere with activity of the miRNA. For example, in some embodiments, labeled miRNA is still incorporated into RISC. Ability to be incorporated into RISC can be assayed by any of a variety of means including, for example, by (1) microscopy to show colocalization of the labeled miRNA with processing bodies (P bodies), and (2) immunoblot analysis of Ago1 and Ago2 enrichment in pull-down fractions. In some embodiments, labeled miRNA retains silencing activity, for example when tested on a model silencing construct. As will be clear to those of ordinary skill in the art, any of a variety of labels may be utilized in accordance with the present invention. In some embodiments, the label is one that facilitates isolation of the miRNA, for example when complexed with one or more target RNAs. According to the present invention, as illustrated in the Examples, biotin represents an appropriate and useful label. In some embodiments of the present invention, pull-down assays enrich target RNAs by a factor of 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 120, 140, 160, 180, 200, 250, 300, 350, 400, 450, 500, 550, 600, 650, 700, 750, 800, 850, 900, 950, 1000, or more as compared with their cellular expression level. Among other things, the present invention encompasses the recognition that normalization of pulled-down target RNA levels to cellular levels of the same RNA can materially facilitate determination of true targets. Those of ordinary skill in the art will be aware of a variety of strategies for performing such normalization including, for example, comparison with any of a variety of controls.
In some embodiments, specificity of pull-down assays is assessed. In some embodiments, miRNA-target RNA complex formation within an intact cell is assessed. In some embodiments, exogenous miRNAs in excess of that which is overexpressed in the cell are added to the pull-down assay (e.g., after cell lysis). In some embodiments, quantitative analysis (e.g. quantitative RT-PCR) is used to determine the amount of target RNA bound by the miRNA. As can be evidenced in FIG. 9, the present invention provides the recognition that a reduction of miRNA-target RNA binding may occur when exogenous miRNA is added upon lysis.
The present invention specifically establishes that pull-down assays without normalization may well be uninformative. Without wishing to be bound by any particular theory, the present inventors note that non-normalized pull-down studies often identify overlapping sets of highly abundant transcripts, such as ribosomal protein mRNAs, that can be pulled down using any of a variety of different miRNAs (often including non-specific control miRNAs). Moreover, the present invention demonstrates that true specific targets often are not identified in such non-normalized assays. Specifically, miR-24 validated targets H2AX and CDK6, or let-7 validated targets CDK6, DICER1, MCM4, and CDC20 were not enriched in non-normalized pull-down experiments. Similarly, a recent study (40) looking at miR-10a pulled-down-genes, which normalized only to a control miRNA pull-down and did not take into account cellular expression, identified 100 putative enriched target genes, most of which were also highly abundant ribosomal proteins. In fact, 35 of those 100 genes were pulled down with miR-24 relative to cel-miR-67, raising questions of specificity.
In some embodiments of the present invention, target RNAs are identified in pull-down assays as those that are enriched with a Z-ratio of at least 1.5. In some embodiments, target RNAs are identified as those that are enriched with a Z-ratio of at least 2.0. Indeed, in one aspect, the present invention encompasses the finding that, despite common acceptance of a Z-ratio of 1.5, increasing the stringency of analysis by requiring a Z-ratio of 2.0 significantly improves accuracy of target identification.
In some embodiments, pull-down assays utilized in accordance with the present invention simultaneously or sequentially assess interaction with at least one factor other than the relevant miRNA. According to the present invention, such approaches can increase specificity of assay results as compared with miRNA-only pull-down assays. For example, in some embodiments, the second factor comprises a cellular component (other than a target RNA) with which the miRNA interacts. In some embodiments, the second factor comprises a cellular component with which all miRNAs interact. For example, in some embodiments, the second factor comprises one or more components of RISC. To give but a few specific examples, those of ordinary skill in the art will appreciate that the RISC complex can be pulled down using an antibody such as Ago antibody.
In some embodiments, a second factor is pulled down using a pull-down reagent that differs from the pull-down reagent used to pull down the miRNA. For example, in some embodiments, an antibody is used for one and a different category of binding agent (e.g., biotin/streptavidin) is used for the other. In some embodiments, multiple factors are pulled down.

Analysis of Targets

Targets (e.g., target RNAs) identified and/or isolated as described herein may be characterized by any of a variety of means. In some embodiments, for example, RNAs are subjected to reverse transcription (RT) and/or polymerase chain reaction (PCR). In some embodiments, RT and/or PCR are performed under conditions that permit quantification of the target RNA (quantitative reverse-transcription-polymerase-chain-reaction, qRT-PCR).
Target RNAs identified and/or isolated as described herein may be characterized through sequence analysis (e.g. deep sequencing). In some embodiments, presence or absence of a canonical miRNA binding site in the 5′UTR of a putative target RNA is determined. Among other things, the present invention demonstrates that not all miRNA targets in fact have canonical miRNA binding sites in their 5′UTRs. For example, as specifically exemplified herein, only 39 of the pulled-down genes are predicted miR-24 target genes by TargetScan 4.2, and most of those do not have evolutionarily conserved recognition sites. Other similar algorithms based on evolutionary conservation and seed region pairing give similar results, although the predicted gene sets are not identical. This number might be increased by adding 19 genes that have exact seed matches in their 5′UTR or coding region. Nonetheless, they still represent a small fraction of the pulled-down genes. Our high degree of experimental validation of a subset of pulled-down target genes suggests that most of our set are true targets. The implication is that a relaxation of the identical seed pairing requirement (for instance taking into account G:U wobbles or extensive pairing elsewhere in the sequence) and/or the requirement of evolutionary conservation for each particular site built into these algorithms might be desirable. Analysis of the set of pull-down genes for miR-24 and other miRNAs might provide a useful data set for training and developing alternate prediction algorithms
Target RNAs identified and/or isolated as described herein may be characterized through analysis of the extent to which their expression level is affected by expression of the miRNA with which they interact. The present invention encompasses the finding that target RNAs often are only modestly affected by levels of their cognate miRNAs. That is, according to the present invention, the expression level of many target RNAs is not significantly affected in response to increases or decreases in expression levels of the miRNA of interest. For example, it is common for target RNA levels to remain substantially unchanged, for example within a factor of two, despite significant changes in miRNA level. Without wishing to be bound by any particular theory, the present inventors propose that this common insensitivity to miRNA levels may have contributed to difficulties encountered in the past by others attempting to identify and/or validate miRNA targets using gene expression analysis. The present invention specifically demonstrates that broad gene expression analysis technologies (e.g., arrays) may be particularly poorly suited for the identification of miRNA target RNAs.
In some embodiments, targets of miRNAs identified and/or isolated as described herein are cloned (e.g. introduced into a vector) as is known in the art.
To give a specific example, in the analysis of miR-24 targets described in the Examples, only 16% of the 269 pull-down genes were significantly down-regulated by mRNA microarray analysis after overexpressing miR-24, while 9 of 11 genes in the set had significantly decreased mRNA by qRT-PCR. This suggests that even when mRNA levels may be regulated by miRNAs, the degree of mRNA down-regulation may not be substantial enough and the sensitivity of mRNA microarrays may not be high enough for this to be an efficient way of identifying miRNA target genes. Without wishing to be bound by any particular theory, we note that, since the miR-24 pull-down set includes at least 18 transcription factors or co-factors (notably E2F2, MYC, MYB, KLF2, VHL), regulated mRNAs may decline indirectly because of reduced transcription and/or directly by miRNA-mediated accelerated mRNA decay. It seems likely that global analysis of differential protein expression, may be preferable in the analysis of miRNA targets.
The foregoing notwithstanding, in some embodiments, one or more target RNAs show expression levels that respond to changes in level of miRNA of interest. In some embodiments, target RNA levels are increased or decreased by at least 2 fold or more (e.g., 3, 4, 5, 6, 7, 8, 9, or even 10 fold or more) in response to corresponding changes in miRNA levels.
As described below in the Examples, a variety of different approaches were used to characterize targets of miR-24 as exemplified herein. For example, potential targets were characterized by microarray analysis, by qRT-PCR, by responsiveness to the miRNA in a model gene (luciferase) assay, by protein expression analysis, etc. Of 269 pull-down genes that were identified by microarray analysis, 8 of 8 were quantitatively enriched. The 3′UTRs of 5 of 5 genes (only 2 predicted to be TargetScan 4.2 targets) were shown by luciferase assay to be regulated by miR-24; protein expression of 9 of 9 genes (8 in this manuscript plus H2AX in the accompanying patent application Ser. No. 61/098,707, entitled “Therapeutic and Diagnostic Strategies”, filed on Sep. 19, 2008) was reduced by at least 2-fold and 9 of 11 genes had significantly reduced mRNA expression in cells over-expressing miR-24. These confirmatory experiments suggest that enrichment in the pull-down is very specific and that the overwhelming majority of identified genes are likely to be true targets of miR-24.
In some embodiments of the present invention, target RNAs identified using pull-down technologies are subjected to network analysis to identify biological processes that are represented (or over-represented) among pulled-down RNAs.
For example, as exemplified herein, genes involved in various aspects of cell cycle regulation are over-represented among RNAs enriched in pull-down analyses with miR-24.
Kits and/or Compositions
The present invention also provides kits or compositions including components useful in the identification of miRNA target RNAs and/or drug targets as described herein. Such kits may be of particular use in both academic and commercial research applications.
For example, in some embodiments, inventive such kits include one or more control miRNAs and/or reagents for labeling miRNAs and/or for quantification of degree of target RNA enrichment relative to cellular expression levels. In some embodiments, such kits include one or more reagents useful in performing reverse transcription, polymerase chain reaction, nucleic acid sequencing, analysis of RNA expression levels, etc. In some embodiments, such kits include one or more antibodies. In some embodiments, such kits include one or more nucleic acid standards (e.g., size standards, known miRNAs, etc.). In some embodiments, such kits include nucleotide analogs useful in preparation of miRNA mimics.
To give but a few examples, in some embodiments, inventive kits include, for example, biotin and/or streptavidin reagents suitable for labeling miRNAs. In some embodiments, such reagents achieve covalent attachment of the label (e.g., biotin or streptavidin) to an miRNA. In some embodiments, such reagents achieve non-covalent attachment of the label to an miRNA. For example, a labeling reagent may associate a label with an miRNA via hybridization. To give but one example, in some embodiments, inventive kits comprise a means for attaching a standard sequence element to an miRNA (e.g., via expression of the miRNA in a vector containing the sequence element, direct linkage of a nucleic acid fragment containing the sequence element, etc.), and further comprise a label attached to the complement of the sequence element.
In some embodiments, inventive kits comprise one or more of a reverse transcriptase enzyme, deoxyribonucleotides, DNA polymerase (e.g., thermostable DNA polymerase), chain-terminating nucleotides, detectable (e.g., fluorescent, radioactive) nucleotides, one or more buffers, distilled water, etc.
In some embodiments, the present invention provides kits or compositions containing one or more agents that regulates mRNA levels; in some such embodiments, the present invention provides kits or compositions containing one or more agents that regulate levels of one or more miRNA targets. In some embodiments, such a provided kit or compositions will include one or more siRNA, for example targeting a specific miRNA target. The present invention therefore provides systems (including methods and compositions) for regulating an miRNA target RNA through administration of a miRNA target regulating factor. In some embodiments, miRNA target regulating factors alters the level/activity to be higher in presence of agent than in absence. In some embodiments, the level or activity is at least about 1.1, about 1.2, about 1.3, about 1.4, about 1.5, about 1.6, about 1.7, about 1.8, about 1.9, about 2, about 2.5, about 3, about 3.5, about 4, about 4.5, about 5, about 6, about 7, about 8, about 9, about 10, about 11, about 12, about 13, about 14, about 15, about 16, about 17, about 18, about 19, about 20, to about 200 fold or even higher in the cell as to regulate the effect of this target as compared to not administering the siRNA. In some embodiments, miRNA target regulating factors alters the level/activity to be lower in presence of agent than in absence. In some embodiments, the level or activity is at least about 1.1, about 1.2, about 1.3, about 1.4, about 1.5, about 1.6, about 1.7, about 1.8, about 1.9, about 2, about 2.5, about 3, about 3.5, about 4, about 4.5, about 5, about 6, about 7, about 8, about 9, about 10, about 11, about 12, about 13, about 14, about 15, about 16, about 17, about 18, about 19, about 20, to about 200 fold or even lower in the cell as to regulate the effect of this target as compared to not administering the siRNA. In some embodiments, exemplary miRNA target regulating factors can include siRNA, shRNA, and/or miRNA. In some embodiments the siRNA, shRNA and/or miRNA targets RNA. In some embodiments, the mixture comprises a RNA mimic, which is a sequence that is analogous to another RNA sequence. In some embodiments the mixture comprises a small molecule agent or moiety that regulates the levels of the target miRNA. In some embodiments, these components will be administered together. In some embodiments, these components will be administered separately.
In certain embodiments, inventive such kits contain all of the components necessary to perform a relevant assay (e.g., detection assay, regulation assay, . . . ), including all controls, directions for performing assays, and any necessary software for analysis and presentation of results.
In some embodiments, components of inventive kits are provided in individual containers and multiple such containers are provided together in a common housing.

Exemplification

Materials and Methods

Cell Culture and Differentiation

HepG2 cells were grown in DMEM supplemented with 10% FCS. HL60 and K562 cells were grown in RPMI-1640 supplemented with 10% FCS. K562 cells (0.5×10⁶cells/ml) were treated with TPA (16 nM, 2 or 4 d) or hemin (100 μM, 5 d) for differentiation into megakaryocytes or erythrocytes, respectively. To induce macrophage or monocyte differentiation, HL60 cells (0.5×10⁶cells/ml) were treated with TPA (16 nM, 3 d) or vitamin D3 (25 nM, 5 d), respectively. Differentiation was verified by flow cytometry staining for CD41a, CD61, and CD14 (antibodies from BD Biosciences), by benzidine staining for hemoglobin and by microscopic analysis of morphology.

RNA Isolation and Quantitative RT-PCR

Total RNA was isolated using Trizol reagent (Invitrogen) and reverse transcribed using random hexamers and superscript II reverse transcriptase (Invitrogen). qRT-PCR was performed in triplicate samples using the SYBR Green master mix (Applied Biosystems) and the BioRad iCycler. Results were normalized to GAPDH. miRNA quantitative PCR was done in triplicate using the TaqMan MicroRNA Assay from Applied Biosystems as per the manufacturer's instructions and normalized to U6. Sequences of primers are listed in Supplementary Table 8. HepG2 cells were reverse transfected with miRNA mimics using Neofx (Ambion, Inc) as per manufacturer's instructions. K562 cells were transfected with miRNA mimics or antisense oligonucleotides using Amaxa nucleofection following the manufacturer's protocol.
Transfection of miRNA Mimics, Antisense Oligonucleotides, siRNAs, and Expression Plasmids
HepG2, WI-38 and IMR-90 cells were reverse transfected using Neofx (Ambion, Inc) as per the manufacturer's instructions. K562 cells were transfected using Amaxa nucleofection following the manufacturer's protocol. siRNAs targeting GFP (D-001940-01-05), E2F2 (On-targetplus SMARTpool L-003260-00-005) or MYC (On-targetplus SMARTpool L-003282-00-0005) were purchased from Dharmacon and transfected into K562 cells (1×10⁶cells) for 48 hr using Amaxa. In some experiments K562 cells (1×10⁶cells) were transfected with miR-24 or cel-miR-67 miRNA mimics (100 nM or indicated concentrations, Dharmacon), with or without a plasmid expressing HA-tagged E2F2 or eGFP (5 μg) for 48 hr using Amaxa. To determine the effect of miR-24 knockdown on E2F2 and MYC expression, K562 cells (1×10⁶cells/well) were transfected in triplicate wells with miR-24 or control ASO (100 nM, Ambion) using Amaxa nucleofection following the manufacturer's protocol and 72 hr later treated with TPA (16 nM) for 6 hr. The cells were then harvested followed by qRT-PCR and Western blot analysis for MYC and E2F2.

Biotin Pull-Down Experiments

HepG2 cells (2.5×10⁵/well) were reverse transfected with 3′-biotinylated miR-24 (Dharmacon) or 3′-biotinylated control miRNA (cel-miR-67) at a final concentration of 30 nM using Neofx (Ambion, Inc) in six-well plates in sextuplicate wells following the manufacturer's protocol. Twenty-four hours later, the cells were trypinized and pelleted at 500×g. After washing twice with PBS and resuspension in 0.5 ml lysis buffer (20 mM Tris (pH 7.5), 100 mM KCl, 5 mM MgCl₂, 0.3% NP-40, 50 U of RNase OUT (Invitrogen), complete mini-protease inhibitor cocktail (Roche Applied Science)), and incubation on ice for 5 min, the cytoplasmic extract was isolated by centrifugation at 10,000×g for 10 min. Streptavidin-coated magnetic beads (50 μA, Invitrogen) were blocked for 2 hr at 4° C. in lysis buffer containing 1 mg/ml yeast tRNA and 1 mg/ml BSA (Ambion) and washed twice with 1 ml lysis buffer. Cytoplasmic extract was then added to the beads and incubated for 4 h at 4° C., following which the beads were washed five times with 1 ml lysis buffer. RNA bound to the beads (pull-down RNA) or from 10% of the extract (input RNA), was isolated using Trizol LS reagent (Invitrogen). The level of mRNA in the miR-24 or control pull-down was quantified by qRT-PCR and normalized to its abundance in the input RNA.
miRNA Microrray
miRNA microarrays were performed as described in (S1).

Microarray Analysis

HepG2 cells (2.5×10⁵/well) were reverse transfected in triplicate in six-well plates with either miR-24 mimics or control miRNA mimics (cel-miR-67) at a final concentration of 30 nM using NeoFx (Ambion). Total RNA isolated 48 hr post-transfection (independently for two experiments) was amplified, labeled and hybridized to Illumina arrays (Refseq-8). Raw hybridization intensity data were log-transformed and normalized to yield Z-ratios, which in turn were used to calculate a Z-ratio value for each gene. The Z-ratio was calculated as the difference between the observed gene Z-ratios for the experimental and the control comparisons, divided by the standard deviation associated with the distribution of these differences (S2). Z-ratio values of >1.5 or 5-1.5 were chosen as cut-off values, defining increased and decreased expression, respectively. To identify the mRNAs directly bound to miR-24, biotin pull-down assays were performed from two independent experiments (as described above) and the isolated RNA was subjected to microarray analysis as above. For each pull-down, Z-ratios>2.0 were chosen as cut-off. The complete microarray data set is available at: http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE17828.
TargetScan Analysis of miR-24 Target Genes
To determine whether a gene was also a predicted target of miR-24, the presence of miR-24 binding sites was analyzed using TargetScan 4.2 (http://www.targetscan.org). To determine whether a gene is a predicted target of miR-24, the presence of miR-24 binding sites was analyzed using TargetScan 4.2 (http://www.targetscan.org) (Lewis et al., 2003) or rna22 (http://cbcsrv.watson.ibm.com/rna22_targets.html (Miranda et al., 2006). miR-24 binding sites in the miR-24 down-regulated mRNAs (Z-ratio>1.5) that had a sequence complementary to the miR-24 seed were identified by using PITA (http://132.77.150.113/pubs/mir07/mir07_prediction.html). The miR-24 mature miRNA sequence was obtained from miRBase (http://microrna.sanger.ac.uk/sequences/). The 3′ UTR sequences in FASTA format were obtained from the UCSC Genome Browser [1] using RefSeq version (Release 34, [2]). UTR coordinate intervals were filtered through a Perl script to remove redundant UTRs from transcript variants and non-reference genomic sequences yielding a final set of 22,231 sequences (background set) after filtering. Occurrence and frequencies of the target nucleotide sequences (UGAGCC, CUGAGCC, and ACUGAGCC) were established for both the background set as well as the subset of 3′UTR sequences present in the miR-24-target gene set. For each target sequence we compared the number of matches in the UTR sequences of both the target and background set to the number of all possible N-mer matches of the same size as the target sequence. The number of matches in the target UTR sequences was contrasted to their background distribution using a chi-square test in the R environment. Of the 249 target genes, 219 genes had an annotated, non-redundant UTR sequence.

Gene Ontology (GO) Analysis

The Gene Ontology (GO) project provides structured controlled vocabularies, or ontologies, to describe genes relative to their biological processes. Each biological process consists of a series of events achieved by one or more molecular functions. The ontologies are stored in directed acyclic graphs where each node represents a biological process and each subsequent node corresponds to a more specialized term. Over-represented Gene Ontology (GO) biological processes were determined using a MetaCore tool which utilizes the hypergeometric distribution to calculate the statistical significance (p-value) for a subset of genes showing enrichment in a biological process. The value is equivalent to the probability of a subset of genes from a specific experiment (ie, miR-24 pulldown) to arise by chance given the number of total genes associated with the biological process.

Network Visualization and Analysis

We developed a graphical representation of the molecular relationships between proteins from miR-24 pulldown and miR-24 TargetScan 4.2 targets down-regulated in miR-24 over-expressing cells. Network analysis of miR-24 pulldown genes and miR-24 TargetScan 4.2 targets down-regulated in miR-24 over-expressing cells was performed using the MetaCore Analytical Suite (GeneCo Inc., St Joseph, Mich., http://www.genego.com) and Ingenuity Pathways Analysis (Ingenuity Inc. www.ingenuity.com). Proteins are represented as nodes, and the biological relationship between two nodes is represented as an edge (line). Nodes are displayed using shapes that represent the functional class of the gene product. The tools were used for functional and pathway analysis utilizing support for edges in the networks from at least 1 reference from the literature, from a textbook, or from canonical information stored in the network generation software database—manually curated for human protein-protein interactions, protein-DNA, and protein-compound interactions.
To identify unconventional miR-24 binding sites, the coding and 5′-UTR regions were downloaded from UCSC (http://genome.ucsc.edu), for genes satisfying the following criteria: (1) down-regulation after miR-24 over-expression (Z-ratio>1.5) or enrichment by miR-24 pulldown (Z-ratio>2.0); and (2) not identified as a miR-24 target by TargetScan 4.2. The miR-24 mature miRNA sequence was obtained from miRBase www.mirbase.com). The seed region was defined as any 7-mer contained within positions 2-9 of the sequence (5′-TACGATCGA-3′). Using Perl scripts, we enumerated instances of this seed region in any of the 5′UTR and/or coding regions. We analyzed all alternative splice variants and enumerated potential miR-24 recognition sites using the isoform that yielded maximum seed regions.

Cell Cycle Analysis

HepG2 cells were reverse transfected with miR-24 mimics or control miRNA mimics as described above and 2 days later, treated with nocodazole (100 ng/ml) to synchronize cells in G2/M phase of the cell cycle. After 16 hr, cells were stained with propidium iodide and analyzed by flow cytometry using a FACScaliber instrument (Becton Dickinson) and Cellquest Pro software following the manufacturer's protocol. To analyze changes in miR-24 expression with cell cycle progression, K562 cells were arrested in G2/M phase by treatment with nocodazole (100 ng/ml) for 16 hr, and then washed to remove nocodazole and grown in complete medium in the absence of nocodazole. Cells were collected at indicated times and analyzed for cell cycle distribution by propidium iodide staining and flow cytometry using FlowJo software and by qRT-PCR for miR-24 expression.

Luciferase Assay

HepG2 cells were reverse transfected (as above) in triplicate with 30 nM miR-24 mimic or control miRNA mimic. Two days later, cells were transfected using Lipofectamine 2000 (Invitrogen) with psiCHECK2 (Promega) vector (0.5 μg/well) containing a single copy of the predicted MREs or the full-length 3′UTR of indicated genes cloned into the multiple cloning site (NotI and XhoI) of Renilla luciferase or control. After 24 hr luciferase activities were measured using the Dual Luciferase Assay System (Promega) and Top count NXT microplate reader (Perkin Elmer) per manufacturer's instructions. Data were normalized to Firefly luciferase. The 3′UTR of miR-24 target genes was PCR amplified using human genomic DNA as template and primers containing the NotI and XhoI restriction enzyme sites at the 5′ end. The PCR products were digested with NotI and XhoI and cloned into the 3′UTR of Renilla luciferase of pSICHECK2. Individual wild-type and mutant MREs were cloned into pSICHECK2 by annealing the forward and reverse oligonucleotides containing NotI and XhoI sticky ends, followed by phosphorylation (using T4 polynucleotide kinase (New England Biolabs)) and ligation (quick DNA ligase (New England Biolabs)). The wild-type short fragment in CCNA2 3′UTR (containing WT CCNA2 MRE1) was cloned by PCR and the mutant short CCNA2 3′UTR fragment (containing MT CCNA2 MRE1) was cloned by oligonucleotide annealing as mentioned above.

Thymidine Incorporation Assay

To measure the effects of miR-24 on cell proliferation, K562 cells (1×10⁶cells/well) were transfected with miR-24 ASO (100 nM) or control ASO using Amaxa nucleofection following the manufacturer's protocol and 36 hr later treated with TPA (16 nM) for 2 hr. The cells in duplicate wells were then incubated with ³H-Thymidine (2 pCi/well) for 2 hr and [³H]-incorporation measured using a liquid scintillation counter (Beckman). The ratio of [³H]-incorporation in miR-24 ASO-transfected cells relative to that in cells treated with control ASO from 3 independent experiments was compared.

Immunoblot

K562 cells (1×10) were transfected with miR-24 mimics or control miRNA mimics (cel-miR-67) as above and 48 h later whole cell lysates were prepared using RIPA buffer (150 mM NaCl, 1% NP-40, 0.5% sodium deoxycholate, 0.1% SDS, 50 mM Tris pH 8.0). Protein samples were quantified using Bradford reagent (BioRad) and resolved on 10% SDS-PAGE gels and analyzed by immunoblot probed with antibodies to MYC (Santa Cruz Biotechnology), E2F2 (Sigma), Cyclin A (Santa Cruz Biotechnology), Chek1, PCNA, BRCA1, AURKB and CDK4 (Cell Signaling Technology). a-Tubulin and HuR (Santa Cruz Biotechnology) served as internal controls. All antibodies were used at a dilution of 1:500. Western blots were quantified by densitometry relative to a-tubulin.

Results

As described herein, the role of miRNAs during terminal hematopoietic cell differentiation, was analyzed by microarray in 2 human leukemia cell lines—K562 cells differentiated to megakaryocytes using 12-O-tetradecanoylphorbol-13-acetate (TPA) or to erythrocytes with hemin, and HL60 cells differentiated to macrophages using TPA or to monocytes using vitamin D3. Only a few miRNAs were consistently up-regulated (by at least 40%) in all 4 systems of terminal differentiation—miR-22, miR-125a, and members of the two miR-24 clusters—miR-24, miR-23a, miR-23b and miR-27a. Consistent with other information (including, for example, as described herein), miR-24 stood out as the most up-regulated miRNA. The only member of the 2 miR-24 clusters that was not consistently up-regulated was miR-27b, whose hybridization signal was substantially lower for all conditions than the other cluster members, suggesting that hybridization to that probe was inefficient.
qRT-PCR analysis established that miR-24 transcript was in fact upregulated, and further showed a 2- to 8-fold increase during differentiation into megakaryocytes, erythrocytes, macrophages and monocytes. miR-24 also increased 3-fold in HL60 cells differentiated to granulocytes using DMSO. Expression of the chromosome 19 miR-24 cluster primary transcript, encoding miR-23a, miR-27a and miR-24, increased in both cell lines within 6 h of TPA treatment, peaked at −12 hr and remained elevated for at least 2 d, suggesting that the observed increase in mature miR-24 was due to increased transcription. The Dicer-cleaved miRNA showed a slightly delayed increase following TPA induction of K562 or HL60 cells, becoming significant at 12-16 hr. Mature miR-24 levels remained elevated for as long as was measured (4 d).
Because cessation of cell proliferation is a hallmark of terminal differentiation, we first examined whether proliferation is altered by either inhibiting or enhancing miR-24 function by transfecting cells with miR-24 2′-OMe antisense oligonucleotide (ASO) or miRNA mimics, respectively. When K562 cells were transfected with miR-24 ASO, miR-24 was dramatically and specifically reduced by qRT-PCR 36 hr later (FIG. 10B). DNA replication, measured by thymidine incorporation, doubled in cells transfected with miR-24 ASO compared to cells transfected with control ASO (FIG. 10C). When K562 cells were differentiated with TPA for 4 hr, thymidine incorporation declined by 60%. However, in cells transfected with miR-24 ASO and treated with TPA, thymidine uptake was indistinguishable from that of the control ASO-transfected, but TPA-untreated, cells (FIG. 10C). Therefore, miR-24 ASO fully restored proliferation to differentiating K562 cells. To examine whether miR-24 also inhibits cell proliferation in nontransformed cells, we next antagonized miR-24 in early passage WI-38 and IMR-90 normal diploid fibroblasts. Antagonizing miR-24 in WI-38 and IMR-90 cells dramatically reduced miR-24 (FIG. 10D) and increased thymidine uptake>2-fold 48 hr after transfection (FIG. 10E). Conversely, overexpressing miR-24 in HepG2 cells synchronized with nocodazole, which typically leads to mitotic arrest and only 8% of cells in G1, increased G1 cells 3-fold (22%; miR-24 versus cel-miR-67, p<0.001) (FIG. 10F).
We next analyzed how miR-24 expression changes during normal cell-cycle progression using K562 cells released at various times from nocodazole treatment, which synchronized them in G2/M (Bar-Joseph et al., 2008; O'Donnell et al., 2005) (FIGS. 10G and 10H). Before release, 90% of cells were in G2/M; 8 hr later, 65% were in G1; and 12 hr after removing nocodazole, 45% were in S phase. miR-24 was low in G2/M, increased >3-fold by 8 hr when most cells were in G1, and then declined by 12 hr as cells progressed into S phase. These results suggest that miR-24 is most highly expressed in G1. Taken together with our finding that cells transduced with miR-24 mimics accumulate in G1, these results suggest that miR-24 regulates cell-cycle progression mostly by blocking or delaying the G1/S transition.
The conventional approach to identify potential miRNA targets is to analyze gene expression following miRNA over-expression or knockdown (14, 15, 21). We therefore transfected HepG2 cells, which have low endogenous miR-24 expression (FIG. 2A), with a dsRNA that mimics Dicer-cleaved miR-24 increasing miR-24 expression-80-fold compared with cells transfected with control miRNA (FIG. 2B). Total RNA, isolated 48 hr later from cells transfected with miR-24 or control miRNA (cel-miR-67), mimic, was amplified, labeled and hybridized to Illumina mRNA microarrays. 248 genes were down-regulated at least 2-fold by miR-24 over-expression (Z-ratio>1.5), of which 100 were also predicted TargetScan 4.2 targets (22) (FIG. 2C, Suppl. Table 1,2). Amongst these 100 targets, only 20 genes have predicted miR-24 3′UTR binding sites that are conserved in human, mouse, rat and dog, while 80 genes have poorly conserved sites. The microarray data was validated by performing qRT-PCR for 9 randomly chosen down-regulated genes (TOP1, MBD6, H2AFX UBD, CNDP2, PER2, BCL2L12, STX16, ZNF317) that spanned the range of significantly down-regulated genes (Z-ratios, 1.6-6.1). Six genes (CNDP2, PER2, STX16, UBD, BCL2L12 and ZNF317) were predicted miR-24 targets by TargetScan 4.2 and 3 were not. All 9 genes were significantly down-regulated and the extent of down-regulation correlated well with the degree of reduced expression in the microarray data (FIG. 2D).
To determine what fraction of the downregulated genes are likely direct targets of miR-24, we used two approaches. First, we compared our experimental list of downregulated transcripts with TargetScan predictions. One hundred downregulated genes were also predicted by TargetScan 4.2 (Lewis et al., 2003) (FIG. 2C). Among these 100 targets, however, only 20 have predicted miR-24 3′UTR miRNA recognition sites (MRE) conserved in human, mouse, rat, and dog. Second, we examined the frequency of a 3′UTR sequence perfectly complementary to the miR-24 seed (hexamer [positions 2-7], heptamer [positions 2-8], and octamer [positions 2-9]). The down regulated genes were highly enriched for miR-24 seed matches (FIG. 2G). Just more than half of the 219 miR-24-down-regulated transcripts that have an annotated 3′UTR contain a 3′UTR complementary hexamer sequence (53%, p=2×10⁻¹⁶relative to the background frequency of the seed in the known transcriptome); 32% have a heptamer match (p=7×10⁻¹⁵); and 8% have an octamer seed match (p=0.0002). This significant enrichment of predicted miR-24 target genes and the high frequency of genes containing perfect seed matches suggest that a substantial proportion of the downregulated genes may be direct miR-24 targets.
We next looked at whether the set of 100 down-regulated genes, which were also predicted miR-24 targets, are enriched for specific biological processes. A functional enrichment and network analysis (GeneGo Inc) revealed statistically significant enrichment for 49 processes, many of which are overlapping (FIG. 2E,F; Suppl. Table 3). The top 3 most enriched GO processes involve DNA repair (DNA damage checkpoint, double strand break repair by homologous recombination, and recombinational repair; each enriched with significance of p=0.0001). Also amongst the most significantly enriched processes were categories involved in cell cycle regulation (regulation of cell cycle, p=0.0002; cell cycle arrest, p=0.0007; cell cycle, p=0.001; DNA recombination, p=0.001).
We previously found that miR-24 interferes with the DNA damage response in terminally differentiated hematopoietic cells, predominantly by reducing expression of the histone variant H2AFX, which recruits and retains DNA repair factors at double-strand breaks (Lal et al., 2009). In addition, multiple GO processes involved in cell-cycle regulation were also highly enriched (regulation of cell cycle, p=0.0002; DNA integrity checkpoint, p=0.0003; cell-cycle arrest, p=0.0007; cell cycle, p=0.001; DNA recombination, p=0.001). This was not surprising based on the effect of miR-24 on cell-cycle progression. When networks were developed to identify known directly interacting proteins from these overrepresented biological processes, there was one cluster of six genes centered around MYC (c-myc) and three other small clusters involving two or three genes (FIG. 15A); the other 87 genes in the data set lack any previously annotated direct interactions. Because the TargetScan algorithm might miss some important miRNA-regulated genes, we also constructed a direct interaction network from the 248 downregulated mRNAs. The direct interaction network constructed from all significantly downregulated mRNAs was a highly interactive set of 68 interacting genes, many of which are important in cell-cycle regulation. The major connected network of miR-24-downregulated genes is shown in FIG. 3B; there were also some smaller networks (Figure S2B). Key nodes of the major network are MYC (22 interactions), E2F2 (six interactions), VHL (six interactions), CDC2 (six interactions), CCNB1 (five interactions), and CDKN1B (five interactions). The MYC and E2F2 transcription factors play a central role in regulating G1/S transition and progression through S. They inhibit cell differentiation and apoptosis and promote cellular transformation (Bracken et al., 2004; Lebofsky and Walter, 2007). MYC regulates the transcription of other genes in the network, including E2F2, CDKN1B, CCNB1, CDC2, CDCA7, and RRM2. E2F2 also regulates the transcription of other genes in the network, including CDC2, MYC, RRM2, and the mini-chromosome maintenance proteins MCM4 and MCM1 0 that are essential for initiating DNA replication. These analyses support our experimental findings that miR-24 regulates cell-cycle progression and DNA repair.
When network diagrams were developed to identify directly interacting genes from these over-represented biological processes, one cluster of genes centered around MYC (c-myc) involving 14 genes was identified (FIG. 2F); the other 86 genes in the data set lack any previously published direct interactions. MYC is a transcription factor that plays a central role in cell cycle progression, apoptosis and cellular transformation (23, 24). In particular MYC regulates the transcription of other genes in the network, including CDKNIB (p27KIP1), a cyclin dependent kinase (CDK) inhibitor that inhibits progression through G1 (25). Therefore this analysis suggests that miR-24 may regulate cell cycle progression and DNA repair during terminal differentiation and its effect on MYC may play an important role in these processes.
To validate the role of miR-24 in regulating the mRNAs that were enriched in the pull-down, we began with MYC, since it was a key node of both networks (FIG. 2F, 4C). To validate the association of MYC mRNA and miR-24, streptavidin pull-downs were performed from HepG2 cells transfected for 24 hr with Bi-miR-24 or control Bi-miRNA (cel-miR-67) (FIG. 5A). MYC mRNA, but not mRNA encoding the housekeeping gene UBC, was enriched −2.5 fold by qRTPCR in the Bi-miR-24 streptavidin pull-down. The MYC 3′UTR is 488 nucleotides long and contains a poorly conserved 7-mer exact match to the miR-24 seed, at positions 462-468 (FIG. 5B). To determine the effect of miR-24 on MYC expression, we transfected HepG2 and K562 cells with miR-24 mimics and 48 hr later, measured MYC mRNA by qRT-PCR normalized to GAPDH. Over-expression of miR-24 decreased MYC mRNA by -2-4 fold in HepG2 and K562 cells (FIG. 5C,D). UBC mRNA, a negative control gene, did not change significantly. The decrease in MYC mRNA was associated with a similar decrease (85%) in MYC protein when miR-24 was over-expressed for 72 hr in K562 cells (FIG. 5E). Further evidence that the decrease in MYC expression by over-expressing miR-24 was direct was provided by measuring changes in luciferase activity upon miR-24 co-transfection from a reporter containing the MYC 3′UTR. Luciferase activity was unchanged from control reporters, but was reduced 2.2-fold by miR-24 expression (FIG. 5F). Collectively, these findings suggest that miR-24 binds to the MYC 3′UTR and down-regulates its expression.
We next sought to identify miR-24 MREs in the MYC 3′UTR. TargetScan 4.2 predicts a single MRE containing a poorly conserved 7-mer exact seed match at positions 462-468 (although the recent TargetScan 5.0 algorithm does not list MYC as a miR-24 target), whereas rna22, an algorithm that does not require a seed match (Miranda et al., 2006), identifies six potential miR-24 MREs in the 488 nt MYC 3′UTR, including the TargetScan 4.2-predicted MRE (MRE6) (FIG. 5B). miR-24 overexpression specifically and significantly reduced luciferase activity by 1.9- and 3.9-fold for MRE3 and MRE6, respectively, but the other MREs had no significant effect on luciferase activity (FIG. 5H). MRE3 has no seed but has extensive complementarity with the 3′ end of miR-24. Point mutations of MRE3 and MRE6 that disrupted miR-24 binding restored luciferase activity (FIGS. 5G and 5I). These findings suggest that miR-24 binds to two partially complementary sites (MRE3 and MRE6) in the MYC 3′UTR.
To gain a better understanding of miR-24 regulated genes, we developed a direct approach to isolate miRNA-bound mRNAs. We initially tried to optimize the biochemical method developed by S. Cohen and colleagues (18) to pull-down mRNAs bound to HA-tagged Ago1 in RISC in HepG2 and HeLa cells. However, we were unable to obtain more than 2-fold enrichment for miR-24 in cells over-expressing miR-24, compared to cells transfected with cel-miR-67 (data not shown).
We next modified a method (26) of capturing miRNA-mRNA complexes using streptavidin-coated beads from cells transfected with miR-24 biotinylated at the 3′-end of the antisense strand or control biotinylated miRNA (FIG. 3A). Biotinylated (Bi-)miR-24 had unimpaired gene silencing activity in a luciferase assay in which a fully complementary miR-24 sequence was engineered into the luciferase 3′UTR (FIG. 3B), suggesting that the biotinylated active strand was incorporated into RISC and bound to target genes as well as the unmodified miRNA mimic. Moreover, in miR-24-transfected HepG2 cells harvested 24 hr after transfection, miR-24 was enriched-500-fold in the miR-24 pull-down compared to pull-down from control biotinylated miRNA-transfected cells, as assessed by qRT-PCR (FIG. 3C), suggesting that the pull-down conditions were specific.
To improve the assay conditions, qRT-PCR was used to quantify pulled-down mRNAs in K562 cells transfected with miR-24 or cel-miR-67 harvested at different times after transfection. Two target genes, CDK6, a previously validated miR-24 target with 3 predicted miR-24 binding sites (27), and H2AX, which has 2 predicted evolutionarily conserved miR-24 binding sites and is down-regulated 2.3-fold in miR-24 over-expressing cells (Suppl. Table 1), were chosen as likely positive controls, while the housekeeping gene UBC was used as a negative control. Both H2AX and CDK6 mRNAs, but not UBC mRNA, were enriched in miR-24 vs cel-miR-67 transfected cells at all times tested (6, 12 and 24 hr) (FIG. 3D). The enrichment was greatest (2-3 fold) at 24 hr, which was chosen for subsequent experiments. The specificity of the pull-down was verified in HepG2 cells where H2AX and CDK6 mRNAs were reproducibly enriched 4- and 2.5-fold, respectively, in cells transfected with biotinylated miR-24 compared to control cells, and 2 housekeeping genes (SDHA and UBC) were unchanged (FIG. 3E). The general applicability of the pull-down to enrich for miRNA target genes was verified for another miRNA, let-7. Streptavidin pull-down also enriched for 2 well validated let-7 targets, HRAS and CDK6 (15, 28, 29), in let-7a-transfected HepG2 cells (FIG. 3F).
With the pull-down method validated, we next analyzed by mRNA microarray the enrichment for mRNAs pulled down from duplicate samples of biotinylated miR-24-transfected HepG2 cells, normalized to total cellular RNA. 269 mRNAs were enriched >2-fold (Z-ratio>2) in the miR-24 pull-downs (FIG. 4A, Suppl. Table 2,4). Although a Z-ratio of 1.5 is generally considered significant, we excluded genes with Z-ratios of 1.5-2 from further analysis because we were unable to validate by qRT-PCR analysis 3 of 3 randomly chosen genes with this level of enrichment. All 7 of the genes we tested with a Z-ratio>2 were validated (FIG. 6A). Of note, only a minority of pulled down mRNAs were predicted TargetScan 4.2 targets (39; 14%) and only 8 genes had conserved binding sites. An additional 19 genes had a perfect seed match potential binding site in the coding region and 4 of these also had a miR-24 binding site in their 5′UTR (Suppl. Table 5). Also only a small minority of the pulled down mRNAs decreased by >2-fold (42; 16%) when miR-24 was over-expressed, suggesting that many of the mRNAs bound by miR-24 may be regulated to a greater extent by inhibiting translation rather than by substantially altering mRNA stability. 22 genes satisfied all 3 criteria.
An enrichment and network analysis of the 269 genes enriched by at least 2-fold in the miR-24 pull-down experiment found a highly significant over-representation of genes annotated as involved in various aspects of cell cycle regulation (FIG. 4B,C; Suppl. Table 6). Roles in cell cycle regulation could be attributed to the 9 most over-represented GO processes, which were each significantly over-represented with p-values ranging from 8E-18 to 6E-13; the 10^thmost significantly overrepresented GO process was DNA damage response (p=6E-13). Therefore both approaches strongly suggested a role for miR-24 in regulating both cell cycle progression and DNA repair.
Two trends emerged when comparing the over-represented processes identified by miR-24 target prediction and mRNA down-regulation (‘set 1’), with the processes prominent in the miR-24 pull-down genes (‘set 2’). First, the miR-24 pulldown genes are highly enriched for a coherent set of processes that involve interrelated pathways associated with cellular proliferation. Second, for each cell cycle or DNA repair process identified, the significance was overwhelmingly greater in set 2 of pull-down genes than in set 1. A high score in the analysis (defined as −log [p-value]) suggests that the network is highly saturated with identified genes and that there are few nodes in the network not identified in the experiment. For example for DNA replication, 12% of all genes annotated to this GO process (31 of 267 annotated genes) were in the pull-down data set 2 (P=8E-18, score 17). The most significant GO process in set 1, DNA damage checkpoint, included 4 of 44 genes (9%) and had a score of 4. Interestingly this GO process was the 44^thmost significant process over-represented in set 1, but the pulldown captured more of the checkpoint genes (7 of 44, 16%) and hence had higher significance (p=7E-06, score 5).
This analysis, particularly of the genes that were pulled down with miR-24, suggests that miR-24 might act as a master regulator of cell proliferation, suppressing many key genes that control various aspects of cell cycle progression. Regulation likely occurs at multiple levels. For example, not only is the transcription factor E2F2 a miR-24 pulled-down gene target, but so are many of the genes whose transcription it regulates (CCNA2, PCNA, MCM2, MCM3, MYC, MYB, AURKB, RRM2, BRCA1, CHEK1). In fact, 28 of the miR-24 pull-down genes (−10%) are genes known to be regulated by E2F transcription factors, representing 22% of the 130 known E2F-dependent genes compiled in a recent review (30). Similarly, MYC is in the pull-down set of genes and so are many genes known to be transcriptionally regulated by MYC (including AURKB, TYMS, CDCA7, CEBPB, CDK4).
A recent genome-wide survey used microarray analysis of gene expression in synchronized cells to identify 480 of 18,400 transcripts (2.6%), whose expression varies with the cell cycle in primary fibroblasts (31). In line with the postulated role of miR-24 in regulating the cell cycle, an analysis of the pull-down genes shows that a large fraction (62/269, 23%) are periodic—8 are preferentially expressed in M/G1, 25 in G1/S and 29 in G2/M (Suppl. Table 7). This highly significant selective enrichment for cycling genes, many of which control cell cycle transitions, supports the idea that miR-24 regulates cell proliferation.
A direct interaction network, constructed from miR-24 pull-down genes, revealed a highly interactive network of genes whose products regulate cell cycle checkpoints, transition through G1, S and G2/M, and DNA repair (FIG. 4C). The interaction network of the pull-down genes contains 67 genes and is much more connected than the similar analysis of ‘set l’ genes in which only 14 genes demonstrate previously published direct interactions and the network only has one node centered around MYC (FIG. 2F). The genes in the pull-down network that have the most annotated interactions with other pull-down genes are a “who's who” of genes involved in cell cycle regulation: MYC (29 interactions), PCNA (12), BRCA1 (12), CDKN1B (p27KIP1, 8), CCNB 1 (cyclin B1, 7), E2F2 (6), CDK4 (6) and CCNA2 (cyclin A2, 6). Key nodes of the miR-24 pull-down network include important genes that regulate multiple stage of cell cycle progression. These include a CDK active in regulating G1 to S progression (CDK4) and cyclin A2 and cyclin B1, which regulate progression through S and G2/M transitions, and the CDK2 phosphatase and activator CDC25A (32). It should be noted that although the mRNA for CDK6, the other CDK that can substitute for CDK4 and regulate G1 to S transition, was not significantly enriched in the pull-down microarray analysis, CDK6 mRNA was enriched in the pull-down by qRT-PCR (FIG. 3E) and is regulated by miR-24 (27). Other genes that regulate progression through G1 and S include the transcription factors MYC and E2F2 that participate in regulating G1/S transition and progression through S (33).
Important genes required for DNA replication were also pulled-down with miR-24, including ORC1L, which binds to origins of replication to recruit the pre-replication complex. Of note 5 of the 6 MCM genes in the pre-replication complex were also pulled down, as was the licensing factor CDT1, required to initiate replication (34), and PCNA, which forms a moving platform to recruit replication enzymes to the replication fork, and 2 genes that make up its RFC chaperone complex (RFC4 and RFC5) (35). In addition the pulldown captured genes encoding a key enzyme required for nucleotide synthesis (thymidylate synthetase, TYMS) and many DNA replication enzymes, the primase PRIM 1, the DNA polymerases POLA2 and POLE, the topoisomerase TOP2A required to relieve torsional stress generated during DNA replication, and the repair genes needed to remove Okazaki fragments, FEN 1 and EXO 1.
In addition to inhibiting transition to mitosis by targeting the A and B cyclins, genes associated with key steps in mitosis were also pulled-down. These included mRNAs for genes involved in chromosomal attachment to the mitotic spindle (CENPA), mitotic spindle formation, stability and regulation (CDCA8, aurora B kinase that regulates the kinetochore and chromosomal segregation (36)), microtubule dynamics (STMN1), and the anaphase promoting complex (APC) adaptor CDC20.
Silencing the genes mentioned above would all be expected to inhibit cell division. In addition to these genes, miR-24 also interacted with mRNAs for genes that encode for cell cycle progression inhibitors. Prominent in the network is the cyclin D inhibitor CDKN 1 B (p27KIP 1), an inhibitor of the CDK required for progression through G1 (25). Previous work also showed that p161NK4A, an inhibitor of the cyclin D CDKs, is regulated by miR-24, although it was not enriched in the pull-down microarray analysis.
Prominent in the network are other genes involved in arresting the cell cycle, especially in response to DNA damage, notably CHEK1, which participates in the G2/M checkpoint and is activated by ATR in response to unresolved DNA damage (37); BRCA1, which participates in a surveillance complex that activates double-strand break repair (38); PCNA involved in repairing replication-mediated DNA damage; FEN1, a flap endonuclease (39) involved in base excision repair (BER). In addition to these checkpoint proteins, many of the miR-24-bound genes are key players in multiple DNA repair pathways, including BER (UNG, FEN1, EXO1), H2AFX, a histone variant phosphorylated at sites of double-stranded DNA damage (40); and XBP1, a transcription factor that up-regulates DNA repair genes (41). There are other examples of regulating both a gene required for cell cycle progression and its inhibitor—CDT1 and GMNN; CDC20 and both MAD2L1 and the F box gene FBXO5, and another F box gene SKP1 and its target p27KIP1 (42). This suggests that the role of miR-24 in regulating the cell cycle may be complex. However if cells are unable to exit G1 and replicate their DNA, it may be economical to suppress the expression of the inhibitory genes that guard the genome from propagating damaged DNA.
To validate the miR-24 pull-down genes further, we analyzed the streptavidin pull-down samples from HepG2 cells transfected with either biotinylated miR-24 or cel-miR-67 by qRT-PCR for 6 genes enriched in the pull-down microarrays (FIG. 4C) that are important in cell cycle progression and/or DNA repair (E2F2, H2AX, PCNA, AURKB, CCNA2, BRCA1 and CHEK1) and 3 genes not enriched (SDHA and the E2F2 homologs E2F1 and E2F3). The qRT-PCR analysis replicated the microarray results (FIG. 6A). Of note, of the 6 miR-24-associated genes studied, only E2F2 and H2AX mRNAs were significantly reduced (e.g., at least about 2-fold) by microarray analysis after miR-24 over-expression.
To validate that these genes are direct targets of miR-24, the 3′UTR of E2F1, E2F2, E2F3, CCNA2, CDK4 and BRCA1 were cloned downstream of luciferase, and luciferase activity was measured following co-transfection with miR-24 or the control miRNA mimic. The 3′UTR of E2F2, CCNA2, CDK4 and BRCA1, but not of the control genes (E2F1 and E2F3), significantly repressed luciferase activity in a miR-24-dependent manner (FIG. 6B). mRNA expression of 10 pull-down genes and 3 control genes was also compared by qRT-PCR in miR-24 or cel-miR-67 over-expressing cells. Eight of the 10 pull-down genes had significantly reduced mRNA coincident with miR-24 over-expression, but the difference was >2-fold only for E2F2, CDK4, AURKB and TOP1 (FIG. 6C). A different, but overlapping, set of 4 genes in this group (E2F2, CDC2, FEN1, TOP1) were identified as significantly down-regulated by miR-24 by microarray (Suppl. Tables 1, 2). If these results are typical of the extent of mRNA down-regulation by miRNA over-expression, they suggest that mRNA microarray is not sensitive enough to identify most miRNA target genes, consistent with the low overlap between pull-down and down-regulated genes (FIG. 4A). Of note, qRT-PCR analysis identified all 3 E2F paralogs as significantly down-regulated by over-expressing miR-24, although the two E2F genes that are not miR-24 target genes were not identified by the less sensitive microarray analysis. The down-regulation of E2F1 and E2F3 may be secondary to E2F2 down-regulation since the E2F-family of transcription factors are known to regulate each other (30, 43, 44) or may be mediated by MYC, since MYC and E2F1 have been shown to activate each other's transcription (45).
Although target gene mRNA levels might not be altered by miRNA over-expression, reduced protein levels are expected. Protein expression of all seven pull-down genes examined (E2F2, CCNA2, PCNA, CHEK1, BRCA1, AURKB, CDK4), quantified by densitometry of immunoblots, decreased by 53-98% in cells transfected with miR-24 mimics, compared to cells transfected with the control mimics (FIG. 6D). H2AX protein was also reduced by 88% (accompanying manuscript). These analyses demonstrate that the miR-pulldown specifically identifies miR-24 target genes. Moreover, most target genes show some reduction in mRNA levels. However, these modest changes in mRNA may be difficult to capture by current microarray technology.
If miR-24 inhibits cell cycle progression and DNA replication as postulated, then antagonizing miR-24 should enhance cell proliferation, while overexpressing miR-24 should cause cell cycle arrest. To test this hypothesis, K562 cells, which express higher levels of endogenous miR-24 than HepG2 cells (FIG. 2A), were transfected with miR-24 2′-OMe antisense oligonucleotide (ASO) to knock down miR-24. miR-24, but not control miRNAs, was dramatically reduced 36 hr later (FIG. 6E). DNA replication, measured by thymidine incorporation, doubled in cells transfected with miR-24 ASO compared to cells transfected with a control ASO (FIG. 6F). When K562 cells were treated with terminal differentiation-inducing TPA for 4 hr, thymidine incorporation declined by 60%. However, in cells transfected with miR-24 ASO and treated with TPA, thymidine uptake was indistinguishable from that of the control ASO-transfected but TPA untreated cells. Therefore miR-24 ASO fully restored replicative capacity to differentiating K562 cells. Conversely, over-expression of miR-24 in synchronized HepG2 cells led to a substantial increase in G1 cells (22.4% in miR-24 transfected cells vs 8.2% in cel-miR-67-controls, p<0.001) (FIG. 6G). These results suggest that miR-24 is a major regulator of cell cycle progression, operating mostly to block or delay the G1/S transition. Although miR-24 regulates genes involved in progression through G1, S and G2/M, its effect on progression through G1 and S may be more important.
We next examined the effect of miR-24 on E2F2 because E2F2 is downregulated by miR-24 overexpression by microarray, is a key node in the gene interaction network (FIG. 11), and plays a crucial role in regulating progression through G1, where miR24-overexpressing cells pile up (Polager and Ginsberg, 2008). qRT-PCR analysis confirmed that E2F2 mRNA was significantly downregulated by overexpressing miR-24 (FIG. 6C). In addition, the related E2F family members, E2F1 and E2F3, were also significantly decreased, although these two genes were not identified by the less sensitive microarray analysis. E2F1 and E2F3 downregulation may be secondary to E2F2 downregulation because the E2F family of transcription factors regulates each other (Bracken et al., 2004; Vernell et al., 2003) or may be mediated by MYC, given that MYC and E2F1 have been shown to transactivate each other (Fernandez et al., 2003). As expected, E2F2 protein (FIG. 6D) was also substantially reduced (9-fold).
The E2F transcription factors activate the transcription of many genes essential for DNA replication, cell-cycle progression, and DNA repair. If miR-24 overexpression downregulates E2F2, E2F2 target gene mRNAs would also be expected to decline after ectopic miR-24 expression. The effect of ectopic miR-24 expression in HepG2 cells on transcripts of 10 E2F targets that are important for cell-cycle progression and DNA repair (AURKB, BRCA1, CCNA2, CDC2, CHEK1, FEN1, PCNA, RRM2, MCM4, and MCM10) was analyzed 48 hr later. Eight of the ten transcripts, with the exception of BRCA1 and PCNA, were significantly reduced (>40%) (FIG. 6C). A subset of these genes (CDC2, MCM4, MCM10, RRM2, and FEN1) was also significantly downregulated by miR-24 overexpression by microarray (Table S1). mRNA microarray may not be sensitive enough to identify some genes whose expression is suppressed, either directly or indirectly, by a miRNA. Protein levels of E2F2 and all seven miR-24 target genes examined (AURKB, BRCA1, CCNA2, CDC2, CHEK1, FEN1, and PCNA), quantified by densitometry of immunoblots, decreased by at least 2-fold (FIG. 6D). A possible explanation for the lack of correlation between the mRNA and protein levels of BRCA1 and PCNA is that mRNA levels were measured 48 hr after transfection of one cell type (HepG2), whereas protein levels were assayed 72 hr posttransfection in K562 cells. In fact, BRCA1, but not PCNA, mRNA was reduced by 2-fold when K562 cells were transfected with miR-24 for 3 days (FIG. 18).
Because miR-24 overexpression reduced MYC protein by 85% (FIG. 5E), MYC target genes should also be downregulated. Consistent with this hypothesis, 11 known MYC-regulated genes (A TAD3A, ACTL6A , ARHGEF7, CCNB1, CDCA7, EXOSC8, E2F2, METAP2, N-PAC, RRM2, and UBE2C) were significantly downregulated in miR-24-overexpressing HepG2 cells by mRNA microarray (Table S1). Among MYC-regulated genes, CDK4 is an important mediator of MYC's effects on cellular proliferation (Hermeking et al., 2000). Although CDK4 mRNA was not significantly altered by microarray, CDK4 mRNA declined 4-fold after ectopic expression of miR-24 in HepG2 cells by more sensitive qRT-PCR assay (FIG. 6C), and CDK4 protein became undetectable (FIG. 6C). Therefore, miR-24 overexpression decreases the levels of many genes that are important in cell-cycle progression.
In these experiments, we overexpressed miR-24 80-fold above the level in undifferentiated K562 cells, whereas the physiological increase after TPA treatment of K562 cells is only 8-fold. To determine whether these genes are regulated by a physiological increase in miR-24, these experiments were repeated by transfecting K562 cells with varying miR-24 mimic concentrations (2-50 nM). Transfection of 2 nM miR-24 did not significantly alter miR-24, whereas 10 and 50 nM miR-24 increased miR-24 levels by 4- and 28-fold, respectively (FIG. 12A). E2F2, MYC, and three of four other E2F2-regulated mRNAs (AURKB, CCNA2, and H2AX, but not PCNA) (FIG. 18) and protein levels of all seven genes tested (E2F2, AURKB, CHEK1, CCNA2, CDK4, MYC, and PCNA) were all significantly reduced by a 4-fold increase in miR-24 (FIG. 12B). Therefore, the genes that were identified by mRNA microarray as down regulated after ectopic miR-24 expression are likely physiologically relevant direct and/or indirect miR-24 targets.
None of the three E2F paralogs (E2F1, E2F2, and E2F3) are a predicted target of miR24, and their 3′UTRs do not contain a miR-24 seed match sequence. We nonetheless tested whether the E2F 3′UTRs might be directly regulated by miR-24 by luciferase assay. The E2F2, but not the E2F1 or E2F3, 3′UTR significantly repressed luciferase activity in a miR-24-dependent manner (FIG. 6B), suggesting that E2F2 is a direct miR-24 target. rna22 identified five candidate E2F2 3′UTR miR-24 MREs (FIGS. 13A and 16). miR-24 significantly suppressed luciferase activity of a reporter gene containing the E2F2 MRE1. E2F2 MRE1 does not have a seed match in the 3′UTR, even if G:U wobbles are allowed, but has extensive complementarity to miR-24 elsewhere. Point mutations that disrupt base pairing between miR-24 and E2F2 MRE1 rescued luciferase expression, verifying that miR24 specifically recognizes the E2F2 MRE1.
To verify further that MYC and E2F2 are direct targets of miR-24, we also looked at changes in expression of luciferase reporter genes 24 hr after transduction of HepG2 cells with miR-24 mimic. At this early time, thymidine uptake of HepG2 cells does not significantly change (FIG. 17A), but ectopic miR-24 still suppresses luciferase reporters encoding MYC MRE3 or MRE6 or E2F2 MRE1 (FIG. 17B). The identification of seedless E2F2 and MYC MREs confirms previous studies showing that MREs lacking a seed with good downstream complementarity can contribute to miRNA gene regulation (Didiano and Hobert, 2006, 2008; Vella et al., 2004).
Because seedless MREs contributed to the regulation of E2F2 and MYC by miR-24, we next investigated whether some of the E2F2- and MYC-regulated genes, whose transcripts declined in response to miR-24, might also be direct miR-24 targets even though they might lack a predicted MRE. We selected eight genes (AURKB, BRCA1, CCNA2, CHEK1, CDC2, CDK4, FEN1, and PCNA) that play important roles in cell-cycle progression and cloned their entire 3′UTRs into the luciferase reporter. The 3′UTR of six out of eight genes (AURKB, BRCA1, CCNA2, CDC2, CDK4, and FEN1, but not CHEK1 or PCNA) was significantly repressed by miR-24, suggesting that these genes may be direct targets (FIG. 6B). To confirm that these genes are direct miR-24 targets, we next sought to identify the miR-24 MREs that regulate their expression. Among the six genes whose 3′UTR was specifically repressed by miR-24, BRCA1 is the only gene that is predicted by TargetScan. The BRCA1 3′UTR contains a nonconserved perfect 7-mer seed match sequence that functions as a miR-24 MRE by luciferase assay (FIGS. 13B and 13C). To identify potential miR-24 MREs in these E2F2- and MYC-regulated genes, we used the rna22 or PITA algorithms, which allow G:U wobbles or seed mismatches. These algorithms identified one candidate MRE for AURKB; five for BRCA1 (which included the TargetScan BRCA1 site); and three sites each for CCNA2, CDC2, CDK4, and FEN1 (FIG. 19). miR-24 significantly repressed luciferase activity of one MRE for five out of six of these reporter genes (AURKB MRE1, BRCA1 MRE5, CDC2 MRE1, CDK4 MRE1, and FEN1 MRE1) (FIGS. 13B, 13D, and 20). A1 though CCNA2 MRE1 appeared to be inactive, a longer fragment (181 nucleotides) from the CCNA2 3′UTR (that included only CCNA2 MRE1) significantly repressed luciferase expression in a miR-24-dependent manner when cloned into the luciferase vector 3′UTR (FIG. 13D). Point mutations that disrupt base pairing between miR-24 and the five minimal MREs and the CCNA2 MRE within the extended sequence rescued luciferase expression, verifying that these MREs are regulated by miR-24 (FIGS. 13B-13D). Therefore, we have identified and verified by mutation seven seedless miR-24 MREs in genes important in cell-cycle progression.
Because both MYC and E2F2 are important cell-cycle progression regulators, we next examined their contributions to the increased cellular proliferation from antagonizing miR-24 by knocking down MYC and/or E2F2 in K562 cells cotransfected with miR-24 ASO (FIGS. 14A and 21). Introducing miR-24 ASO into K562 cells doubled thymidine incorporation (as in FIG. 1C). E2F2 knockdown completely abrogated the proliferative effect of miR-24 ASO, but MYC knockdown had no significant effect. Moreover, E2F2 downregulation by miR-24 is physiologically relevant. When K562 cells were terminally differentiated to megakaryocytes with TPA, the decrease in E2F2 mRNA and protein was completely blocked by inhibiting miR-24 (FIGS. 14B and 14C). Conversely, ectopic expression of miR-24-insensitive E2F2 lacking the 3′UTR restored proliferation to miR-24-treated K562 cells (FIGS. 14D and 14E). Therefore, the miR-24 antiproliferative effect is largely mediated by its downregulation of E2F2.
Antagonizing miR-24 elevated MYC protein levels in untreated K562 cells, and the downregulation of MYC mRNA in TPA-treated K562 cells could be partially rescued by antagonizing miR-24 (FIGS. 22A and 22B). However, antagonizing miR-24 did not restore MYC protein to differentiating cells, suggesting that, although miR-24 suppresses MYC expression, downregulation of MYC protein during postmitotic differentiation is also controlled by miR-24-independent changes in protein stability. This may help to explain why MYC siRNAs had no significant effect on proliferation of cells transduced with miR-24 ASO (FIG. 14A).
miR-24 and its clustered miRNAs are among only a handful of miRNAs consistently up-regulated during hematopoietic terminal differentiation. Here, we show that miR-24 suppresses expression of several key genes that regulate cell-cycle progression. Overexpressing miR-24 increases the percentage of cells in the G1 phase, whereas antagonizing it causes differentiating cells to keep proliferating. The antiproliferative effect of miR-24 is not restricted to tumor cells (HepG2 and K562 cells) but also occurs in human diploid fibroblasts.
miRNAs can regulate expression of hundreds of genes. Genomewide analysis of miRNA target genes has been assessed following miRNA overexpression or knockdown for only a handful of miRNAs (Chang et al., 2007; Johnson et al., 2007; Lim et al., 2005). Using this approach for miR-24 enabled us to identify 248 candidate genes that might be either directly or indirectly regulated by miR-24. Of these downregulated genes, 40% are predicted miR-24-regulated genes by TargetScan, and 53% have a 3′UTR hexamer sequence complementary to the miR-24 seed, suggesting that a large proportion of miR-24-downregulated genes may be direct targets.
To make sense of the set of 248 genes downregulated by miR-24 overexpression, we used bioinformatics to identify overrepresented processes and direct interacting protein networks within this gene set. This type of analysis, which surprisingly does not seem to have been applied to understanding miRNA regulation, led to the hypothesis that miR-24 might regulate cell-cycle progression during postmitotic differentiation by targeting MYC and/or E2F2, given that they constituted nodes of the major interaction network of the downregulated gene set. Both MYC and E2F2 are directly regulated by miR-24, but neither of these genes is a predicted miR-24 target. MYC, which has a 3′UTR hexamer seed sequence, is regulated both by a seed-containing MRE and a noncanonical seedless MRE. E2F2 lacks any miR-24 seed match. However, E2F2 turned out to be the key gene for miR-24 inhibition of the cell cycle because overexpressing miR-24-insensitive E2F2 completely restored proliferation.
The GO analysis of miR-24-downregulated genes also suggested that miR-24 might regulate DNA repair. We recently verified this prediction by showing that overexpression of miR-24 enhances sensitivity to DNA damage (Lal et al., 2009). The key miR-24 target for this biological effect is H2AFX, which has two seed-bearing predicted MREs.
An unbiased analysis, which did not filter out genes whose 3′UTR lack seed binding sites, was critical for enabling us to identify E2F2 as the key miR-24 target gene for cell-cycle regulation. In addition to MYC and E2F2, we found five other miR-24-downregulated genes whose 3′UTR was inhibited by miR-24 through seedless MREs. These genes (AURKB, CCNA2, CDK4, CDC2, and FEN1) are also transcriptionally regulated by E2Fs or MYC and play crucial roles in cell-cycle progression. Our results suggest that, in addition to genes containing miR-24 perfect seed matches, seedless MREs are also important. Indeed, seedless MREs are critical for miR-24 function because the antiproliferative effect of miR-24 can be recapitulated by silencing or obliterated by overexpressing the seedless E2F2 gene. However, the importance of recognition of seedless versus seed-bearing MREs could vary between miRNAs. An assessment of this question could be determined by experimental testing of a large set of randomly chosen genes, whose protein or mRNA is downregulated by miRNA overexpression or increased by miRNA inhibition. In addition to seedless 3′UTR MREs, we previously identified a coding region miR-24 MRE in p161NK4A (Lal et al., 2008). Other recent studies also identified coding region MREs (Duursma et al., 2008; Tay et al., 2008). Taken together, these results suggest that target gene identification might be improved by not disregarding noncanonical MREs.
miR-24 directly regulates not only critical nodes of the interactome of cell-cycle regulatory genes, but also genes downstream of these nodes. This multitiered gene regulation may guarantee that cell-cycle arrest is not easily evaded. In fact, we have preliminary data suggesting that miR-24 may directly regulate many additional periodic genes, including others that lack a canonical seed-bearing MRE. Because expression of many of these genes is suppressed in nondividing cells, we were careful to show that miR-24-mediated gene suppression occurs before miR-24-transduced cells have stopped dividing (FIG. 17), so their downregulation is a cause, not consequence, of cell-cycle arrest.
MYC and E2F regulate progression through G1. Regulating the transition to S phase may be the major site of miR-24 action because miR-24-treated cells accumulate in G1. Because MYC and E2F2 promote each other's transcription, miR-24 may prevent the reciprocal activation of these genes by regulating both of them. The dramatic downregulation of both proteins in miR-24-overexpressing cells could, therefore, be a combined effect of posttranscriptional and transcriptional regulation. Other miR-24 targets that are also transcriptionally regulated by MYC or E2F2 are implicated in controlling progression through G1, the G1/S checkpoint, S, and G2/M. For example, overexpressing miR-24 downregulated the mRNA and protein levels of the E2F regulated genes, CCNA2 and CDC2, which act together to promote G1/S and G2/M transitions. CCNA2 binds to and activates CDC2, thereby promoting G1/S and G2/M transition. Although the mRNA for CDK6, which regulates G1-to-S transition, was not significantly changed in our microarrays, we previously showed that CDK6 is directly regulated by miR-24 (Lal et al., 2008). CDK6 may be an example of a target gene regulated primarily by translational inhibition. p161NK4A is another direct target of miR-24 that is translationally regulated by miR-24 and, therefore, not downregulated in the microarrays (Lal et al., 2008). In addition to genes, which act at the G1/S transition, cells transfected with miR-24 mimics also have decreased expression of genes that principally act at other phases of the cell cycle. Important genes required for DNA replication in S phase were also downregulated by miR-24, including MCM4 and MCM 10 in the prereplication complex; RRM2, a ribonucleotide reductase that catalyzes deoxyribonucleotide synthesis from ribonucleotides; PCNA, which forms a moving platform to recruit replication enzymes to the replication fork; and FEN1, a flap endonuclease involved in rejoining Okasaki fragments. Other downregulated genes act principally to facilitate mitosis, including AURKB and CCNB 1. Therefore, miR-24 may put the breaks on cell division at multiple steps in cell-cycle progression.
Silencing the genes mentioned above would be expected to inhibit cell division. However, suppressing other miR-24-downregulated genes would promote cell-cycle progression, especially in the context of DNA damage. These genes include CHEK1, which participates in the G2/M checkpoint and is activated by ATR in response to unresolved DNA damage, and BRCA1, which is in a surveillance complex that activates double-strand break repair. Prominent in the downregulated gene interaction network are the cyclin D inhibitor CDKN1B (p27KIP1) and VHL, a tumor suppressor protein. In addition, p161NK4A, a CDK inhibitor, is a validated direct miR-24 target (Lal et al., 2008). Thus, the role of miR-24 in regulating the cell cycle may be complex. If cells are unable to exit G1 and replicate their DNA, it may be economical to suppress the inhibitory genes that guard the genome from propagating damaged DNA. However, in some contexts, depending on the transcripts expressed in a particular cell, miR-24 might actually promote cell proliferation by suppressing these cell-cycle inhibitory genes. In fact, inhibiting miR-24 decreases proliferation of A549 lung cancer cells but has the opposite effect on HeLa cells (Cheng et al., 2005).
miR-24 is most highly expressed in G1. This is consistent with our finding that miR-24 regulates the G1/S transition. The E2F family of transcription factors regulates progression through this checkpoint. It therefore makes sense that miR-24 acts, in large part, by directly targeting E2F2 (and thereby indirectly suppressing E2F1 and E2F3 expression). The pattern of miR-24 expression is consistent with the known cell-cycle variation of E2F family members (Sears et al., 1997). When miR-24 is high in G1, E2F1 and E2F2 are low; the E2F family begins to be expressed in late G1 and peaks in S phase when miR-24 is turned down. The E2Fs continue to be expressed in G2 and M (where they also have important functions) when miR-24 levels remain low.
miR-24 is not the only miRNA that regulates the cell cycle and targets the E2F family (FIG. 14F). For instance, the miR-1792 cluster directly downregulates the E2F family (O'Donnell et al., 2005; Petrocca et al., 2008). However, unlike miR-24, whose expression varies inversely with E2F expression, miR-1792 appears to be expressed uniformly except in quiescent cells. Moreover, E2F2 downregulation should antagonize the dominant effect of these miRNAs to promote cell proliferation. Thus, E2F downregulation is likely not a defining effect of miR-1 792 but, rather, a secondary effect that fine-tunes its major proliferative effect. Because miR-24 suppresses MYC and E2F expression and both MYC and the E2F family activate miR-1 792 and miR-1 06b25 transcription (O'Donnell et al., 2005; Petrocca et al., 2008), miR-24 also likely inhibits proliferation by indirectly suppressing transcription of these cell-cycle-promoting miRNAs. A recent paper also suggests another layer of complexity to the miR-24, miR-1792, MYC, and E2F network (Gao et al., 2009). MYC suppresses the transcription of miR-23b, which is encoded with miR-24. Although one recent study suggests that miR-24 and miR-23b are independently transcribed (Sun et al., 2009), MYC might also regulate miR-24 transcription. It is worth noting that E2F1 mRNA and protein do not correlate during the cell cycle, consistent with posttranscriptional regulation by miRNAs (O'Donnell et al., 2005). Because E2F2 mRNA has kinetics similar to E2F1 (Sears et al., 1997), miRNA-dependent regulation of translation may be operating, possibly for all E2Fs that promote G1/S transition.
The integrated effect of miR-24 on a highly interacting set of key genes acts as a switch to stop cell division, rather than as a fine-tuning rheostat. It will be interesting to understand how expression of these two miR-24 gene clusters is regulated and to understand the function of the clustered miRNAs (miR-23 and miR-27). The only other miRNAs consistently up-regulated during terminal differentiation are miR-22 and miR-125a (a mammalian ortholog of lin-4) (Lal et al., 2009). There are suggestions in the literature that these genes might also regulate important pathways of cell differentiation (Choong et al., 2007; Wu and Belasco, 2005).
miR-24 directly regulates both cell proliferation and DNA repair Enhancing miR-24 function in cancer cells by introducing miR-24 mimics might be an attractive therapeutic, given that it could potentially block dysregulated cell proliferation and also sensitize cancer cells to DNA damage from chemo- and radiotherapy.

REFERENCES

S1. O. Barad et al., Genome Res 14, 2486 (2004).
S2. C. Cheadle, M. P. Vawter, W. J. Freed, K. G. Becker, J Mol Diagn 5, 73 (2003).
S3. B. P. Lewis, I. H. Shih, M. W. Jones-Rhoades, D. P. Bartel, C. B. Burge, Cell 115, 787 (2003).
1. V. Ambros, Nature 431, 350 (2004).
2. Y. Zhao, D. Srivastava, Trends Biochem Sci 32, 189 (2007).
3. J. Lu et al., Dev Cell 14, 843 (2008).
4. D. P. Bartel, C. Z. Chen, Nat Rev Genet. 5, 396 (2004).
5. P. H. Olsen, V. Ambros, Dev Biol 216, 671 (1999).
6. A. J. Giraldez et al., Science 312, 75 (2006).
7. S. Yekta, I. H. Shih, D. P. Bartel, Science 304, 594 (2004).
8. J. R. Neilson, G. X. Zheng, C. B. Burge, P. A. Sharp, Genes Dev 21, 578 (2007).
9. Q. Sun et al., Nucleic Acids Res 36, 2690 (2008).
10. Q. Wang et al., Blood 111, 588 (2008).
11. G. A. Calin et al., N Engl J Med 353, 1793 (2005).
12. J. G. Doench, P. A. Sharp, Genes Dev 18, 504 (2004).
13. J. Brennecke, A. Stark, R. B. Russell, S. M. Cohen, PLoS Biol 3, e85 (2005).
14. L. P. Lim et al., Nature 433, 769 (2005).
15. C. D. Johnson et al., Cancer Res 67, 7713 (2007).
16. L. Zhang et al., Mol Cell 28, 598 (2007).
17. M. Beitzinger, L. Peters, J. Y. Zhu, E. Kremmer, G. Meister, RNA Biol 4, 76 (2007).
18. G. Easow, A. A. Teleman, S. M. Cohen, Rna 13, 1198 (2007).
19. F. V. Karginov et al., Proc Natl Acad Sci USA 104, 19291 (2007).
20. S. Diederichs, D. A. Haber, Cell 131, 1097 (2007).
21. J. Krutzfeldt et al., Nature 438, 685 (2005).
22. B. P. Lewis, I. H. Shih, M. W. Jones-Rhoades, D. P. Bartel, C. B. Burge, Cell 115, 787 (2003).
23. J. Secombe, S. B. Pierce, R. N. Eisenman, Cell 117, 153 (2004).
24. R. Lebofsky, J. C. Walter, Cancer Cell 12, 102 (2007).
25. R. K. Assoian, Dev Cell 6, 458 (2004).
26. U. A. Orom, A. H. Lund, Methods 43, 162 (2007).
27. A. Lal et al., PLoS ONE 3, e1864 (2008).
28. F. Yu et al., Cell 131, 1109 (2007).
29. S. M. Johnson et al., Cell 120, 635 (2005).
30. A. P. Bracken, M. Ciro, A. Cocito, K. Helin, Trends Biochem Sci 29, 409 (2004).
31. Z. Bar-Joseph et al., Proc Natl Acad Sci USA 105, 955 (2008).
32. D. Ray, H. Kiyokawa, Cancer Res 68, 1251 (2008).
33. L. Wu et al., Nature 414, 457 (2001).
34. M. Fujita, Cell Div 1, 22 (2006).
35. G. L. Moldovan, B. Pfander, S. Jentsch, Cell 129, 665 (2007).
36. J. R. Bischoff, G. D. Plowman, Trends Cell Biol 9, 454 (1999).
37. E. Petermann, K. W. Caldecott, Cell Cycle 5, 2203 (2006).
38. K. Gudmundsdottir, A. Ashworth, Oncogene 25, 5864 (2006).
39. Y. Liu, H. I. Kao, R. A. Bambara, Annu Rev Biochem 73, 589 (2004).
40. N. Motoyama, K. Naka, Curr Opin Genet Dev 14, 11 (2004).
41. A. Nekrutenko, J. He, Trends Genet. 22, 645 (2006).
42. M. Pagano, Mol Cell 14, 414 (2004).
43. H. Muller et al., Genes Dev 15, 267 (2001).
44. R. Vernell, K. Helin, H. Muller, J Biol Chem 278, 46124 (2003).
45. P. C. Fernandez et al., Genes Dev 17, 1115 (2003).
46. L. Wu, J. G. Belasco, Mol Cell Biol 25, 9198 (2005).
47. M. L. Choong, H. H. Yang, I. McNiece, Exp Hematol 35, 551 (2007).
48. J. F. Chen et al., Nat Genet. 38, 228 (2006).
49. U. A. Orom, F. C. Nielsen, A. H. Lund, Mol Cell 30, 460 (2008).
Baek, D., Villen, J., Shin, C., Camargo, F. D., Gygi, S. P., and Bartel, D. P. (2008). The impact of microRNAs on protein output. Nature 455, 64-71.
Bar-Joseph, Z., Siegfried, Z., Brandeis, M., Brors, B., Lu, Y., Eils, R., Dynlacht, B. D., and Simon, I. (2008). Genome-wide transcriptional analysis of the human cell cycle identifies genes differentially regulated in normal and cancer cells. Proc. Natl. Acad. Sci. USA 105, 955-960.
Bartel, D. P. (2009). MicroRNAs: target recognition and regulatory functions. Cell 136, 215-233.
Bracken, A. P., Ciro, M., Cocito, A., and Helin, K. (2004). E2F target genes: unraveling the biology. Trends Biochem. Sci. 29, 409-417.
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, 1793-1 801.
Chang, T. C., Wentzel, E. A., Kent, O. A., Ramachandran, K., Mullendore, M., Lee, K. H., Feldmann, G., Yamakuchi, M., Ferlito, M., Lowenstein, C. J., et al. (2007). Transactivation of miR-34a by p53 broadly influences gene expression and promotes apoptosis. Mol. Cell. 26, 745-752.
Cheng, A. M., Byrom, M. W., Shelton, J., and Ford, L. P. (2005). Antisense inhibition of human miRNAs and indications for an involvement of miRNA in cell growth and apoptosis. Nucleic Acids Res. 33, 1290-1297.
Choong, M. L., Yang, H. H., and McNiece, I. (2007). MicroRNA expression profiling during human cord blood-derived CD34 cell erythropoiesis. Exp. Hematol. 35, 551-564.
Didiano, D., and Hobert, O. (2006). Perfectseed pairing is not a generally reliable predictor for miRNA-target interactions. Nat. Struct. Mol. Biol. 13, 849-851.
Didiano, D., and Hobert, O. (2008). Molecular architecture of a miRNA-regulated 3⁰UTR. RNA 14, 1297-1317.
Diederichs, S., and Haber, D. A. (2007). Dual role for argonautes in microRNA processing and posttranscriptional regulation of microRNA expression. Cell 131, 1097-1108.
Doench, J. G., and Sharp, P. A. (2004). Specificity of microRNA target selection in translational repression. Genes Dev. 18, 504-511.
Duursma, A. M., Kedde, M., Schrier, M., leSage, C., and Agami, R. (2008). miR148 targets human DNMT3b protein coding region. RNA 14, 872-877.
Easow, G., Teleman, A. A., and Cohen, S. M. (2007). Isolation of microRNA targets by miRNP immunopurification. RNA 13, 1198-1204.
Fernandez, P. C., Frank, S. R., Wang, L., Schroeder, M., Liu, S., Greene, J., Cocito, A., and Amati, B. (2003). Genomic targets of the human c-Myc protein. Genes Dev. 17, 1115-1129.
Fukuda, Y., Kawasaki, H., and Taira, K. (2005). Exploration of human miRNA target genes in neuronal differentiation. Nucleic Acids Symp. Ser. (Oxf.) 49, 341-342.
Gao, P., Tchernyshyov, I., Chang, T. C., Lee, Y. S., Kita, K., Ochi, T., Zeller, K. I., De Marzo, A. M., Van Eyk, J. E., Mendell, J. T., and Dang, C. V. (2009). c-Myc suppression of miR-23a/b enhances mitochondrial glutaminase expression and glutamine metabolism. Nature 458, 762-765.
Hermeking, H., Rago, C., Schuhmacher, M., Li, Q., Barrett, J. F., Obaya, A. J., O'Connell, B. C., Mateyak, M. K., Tam, W., Kohlhuber, F., et al. (2000). Identification of CDK4 as a target of c-MYC. Proc. Natl. Acad. Sci. USA 97, 2229-2234.
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.
Johnson, C. D., Esquela-Kerscher, A., Stefani, G., Byrom, M., Kelnar, K., Ovcharenko, D., Wilson, M., Wang, X., Shelton, J., Shingara, J., et al. (2007). The let-7 microRNA represses cell proliferation pathways in human cells. Cancer Res. 67, 7713-7722.
Lal, A., Kim, H. H., Abdelmohsen, K., Kuwano, Y., Pullmann, R., Jr., Srikantan, S., Subrahmanyam, R., Martindale, J. L., Yang, X., Ahmed, F., et al. (2008). p16(INK4a) translation suppressed by miR-24. PLoS ONE 3, e1864.
Lal, A., Pan, Y., Navarro, F., Dykxhoorn, D., Moreau, L., Meire, E., Bentwich, Z., Lieberman, J., and Chowdhury, D. (2009). miR-24-mediated down-regulation of H2AX suppresses DNA repair in terminally differentiated blood cells. Nat. Struct. Mol. Biol. 16, 492-498.
Lebofsky, R., and Walter, J. C. (2007). New Myc-anisms for DNA replication and tumorigenesis? Cancer Cell 12, 102-103.
Lewis, B. P., Shih, I. H., Jones-Rhoades, M. W., Bartel, D. P., and Burge, C. B. (2003). Prediction of mammalian microRNA targets. Cell 115, 787-798.
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.
Lim, L. P., Lau, N. C., Garrett-Engele, P., Grimson, A., Schelter, J. M., Castle, J., Bartel, D. P., Linsley, P. S., and Johnson, J. M. (2005). Microarray analysis shows that some microRNAs downregulate large numbers of target mRNAs. Nature 433, 769-773.
Miranda, K. C., Huynh, T., Tay, Y., Ang, Y. S., Tam, W. L., Thomson, A. M., Lim, B., and Rigoutsos, I. (2006). A pattern-based method for the identification of MicroRNA binding sites and their corresponding heteroduplexes. Cell 126, 1203-121 7.
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.
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.
Olsen, P. H., and Ambros, V. (1999). The lin-4 regulatory RNAcontrols developmental timing in Caenorhabditis elegans by blocking LIN-14 protein synthesis after the initiation of translation. Dev. Biol. 216, 671-680.
Petrocca, F., Visone, R., Onelli, M. R., Shah, M. H., Nicoloso, M. S., de Martino, I., Iliopoulos, D., Pilozzi, E., Liu, C. G., Negrini, M., et al. (2008). E2F1-regulated microRNAs impair TGFbeta-dependent cell-cycle arrest and apoptosis in gastric cancer. Cancer Cell 13, 272-286.
Polager, S., and Ginsberg, D. (2008). E2F—at the crossroads of life and death. Trends Cell Biol. 18, 528-535.
Sears, R., Ohtani, K., and Nevins, J. R. (1997). Identification of positively and negatively acting elements regulating expression of the E2F2 gene in response to cell growth signals. Mol. Cell. Biol. 17, 5227-5235.
Selbach, M., Schwanhausser, B., Thierfelder, N., Fang, Z., Khanin, R., and Rajewsky, N. (2008). Widespread changes in protein synthesis induced by microRNAs. Nature 455, 58
Sun, Q., Zhang, Y., Yang, G., Chen, X., Cao, G., Wang, J., Sun, Y., Zhang, P., Fan, M., Shao, N., and Yang, X. (2008). Transforming growth factor-beta-regulated miR-24 promotes skeletal muscle differentiation. Nucleic Acids Res. 36, 2690-2699.
Sun, F., Wang, J., Pan, Q., Yu, Y., Zhang, Y., Wan, Y., Wang, J., Li, X., and Hong, A. (2009). Characterization of function and regulation of miR-24-1 and miR-31. Biochem. Biophys. Res. Commun. 380, 660-665.
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.
Vella, M. C., Choi, E. Y., Lin, S. Y., Reinert, K., and Slack, F. J. (2004). The C. elegans microRNA let-7 binds to imperfect let-7 complementary sites from the lin-41 3′UTR. Genes Dev. 18, 132-137.
Vernell, R., Helin, K., and Muller, H. (2003). Identification of target genes of the p 161NK4A-pRB-E2F pathway. J. Biol. Chem. 278, 46124-46137.
Wu, L., and Belasco, J. G. (2005). Micro-RNA regulation of the mammalian lin-28 gene during neuronal differentiation of embryonal carcinoma cells. Mol. Cell. Biol. 25, 91 98-9208.

Zhang, L., Ding, L., Cheung, T. H., Dong, M. Q., Chen, J., Sewell, A. K., Liu, X., Yates, J. R., III, and Han, M. (2007). Systematic identification of C. elegans miRISC proteins, miRNAs, and mRNA targets by their interactions with GW182 proteins AIN-1 and AIN-2. Mol. Cell. 28, 598-613.

EQUIVALENTS

Having now described some illustrative embodiments of the invention, it will be apparent to those skilled in the art that the foregoing is merely illustrative and not limiting, having been presented by way of example only. Numerous modifications and other illustrative embodiments are within the scope of one of ordinary skill in the art and are contemplated as falling within the scope of the invention. In particular, although many of the examples presented herein involve specific combinations of method acts or system elements, it should be understood that those acts and those elements may be combined in other ways to accomplish the same objectives. Acts, elements and features discussed only in connection with one embodiment are not intended to be excluded from a similar role in other embodiments.

Claims

We claim:

1. A method comprising steps of:

providing a cell that contains an miRNA of interest; and

identifying one or more RNAs that interact with the miRNA in the cell.

2. The method of claim 1, wherein the step of identifying comprises isolating miRNA-RNA complexes.

3. The method of claim 1, wherein the miRNA is miR-24.

4. The method of claim 1, wherein the step of identifying comprises determining that the one or more interacting RNAs is enriched in miRNA-RNA complexes as compared with its cellular expression level.

5. The method of claim 1, further comprising a step of:

analyzing the one or more interacting RNAs by a technique selected from the group consisting of: reverse transcription (RT), polymerase chain reaction (PCR), sequence analysis, expression level analysis, network analysis, and combinations thereof.

6. A target RNA identified according to the method of claim 1.

7. A kit comprising components for performing the method of claim 1.

8. The kit of claim 6, comprising one or more components selected from the group consisting of: nucleic acid standards, reagents for labeling miRNAs, reagents for quantifying degree of target RNA enrichment relative to cellular expression levels, and combinations thereof.

9. The kit of claim 6, which kit further contains one or more components selected from the group consisting of: nucleic acid polymerases, nucleotides, nucleotide analogs, buffers, antibodies, labels, and combinations thereof.

10. The kit of claim 6, which kit further contains one or more components that regulate the concentration or the downstream effects of the one or more interacting RNAs.