Zhang et al. 10.1073/pnas.0508889103. |
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Supporting Methods
Fig. 5. miRNA gene copy number alteration analyzed by aCGH. (A) A high-resolution BAC aCGH was used in this study. BAC clones are ordered based on the University of California, Santa Cruz genome browser mapping positions and connected with lines. The clone collection has no gap >2 Mb and a mean spacing of <1 Mb. Tumor DNA was labeled with Cy3 and reference DNA with Cy5, or vice versa, and hybridized against BAC aCGH. (B) The circular binary segmentation (CBS) algorithm recursively identifies breakpoints based on a maximum t statistic. Solid lines are CBS calls, and dashed line is the diploid. (C) Two hundred eighty-three miRNAs located in autosomes were mapped and analyzed. The insert represents miRNAs mapped in chromosome 1 of the ovarian cancer data set. Asterisks represent miRNA genes. (D) Genomic profile of chromosome 6p in one ovarian cancer specimen. Green line represents the experiment in which tumor DNA was labeled with Cy3 and reference DNA with Cy5, whereas red line represents the experiment with the opposite dye labeling. Vertical axis represents the intensity ratio of Cy3 to Cy5. Thresholds for copy number gain or loss are 1.2 and 0.8, respectively. This amplicon contains mir-219-1, which was found amplified in 18.3% of ovarian cancers. (E) A display window of aCGHAnalyzer allows simultaneous visualization of chromosome 6p in all ovarian cancer samples: green and red indicate increased and decreased DNA copy number, respectively. The yellow line indicates the mir-219-1 amplicon.
Fig. 6. High frequency miRNA gene copy number alterations in breast cancer. aCGH frequency plots of breast cancer specimens. Green presents gains and red presents losses. Stars indicate miRNA genes.
Fig. 7. High-frequency miRNA gene copy number alterations in melanoma. aCGH frequency plots of ovarian melanoma specimens are shown. Green represents gains, and red represents losses. Stars indicate miRNA genes.
Fig. 8. Correlation analysis between DNA copy number alteration and miRNA expression in tumor specimens. PremiRNA expression of let-7a3, let-7f-2, mir-9-1, and mir-213 were quantified by real-time RT-PCR in ovarian tumors with or without copy number alteration.
Fig. 9. DNA copy number alterations of miRNA-associated genes in human cancer. (A–C) Protein-encoding genes associated with miRNA biogenesis and function and their DNA copy number alteration patterns in human cancer. (D) Genomic localization of miRNA-associated genes. (E) Correlation between Dicer1 mRNA expression and its DNA copy number alteration in ovarian cancer cell lines. (F) Correlation between Dicer1 protein expression and its DNA copy number alteration in ovarian cancer cell lines.
Supporting Methods
BAC Array Platforms
.BAC clones included in the "1-Mb array" platform were described in ref. 1. Briefly, 4,134 clones from the CalTech A/B and RPCI-11 libraries were collected from both commercial and private sources and were mapped to build 34 (June 2003) of the human genome (www.genome.ucsc.edu) by using either an sequence-tagged site (STS) marker (29%), end sequences (68%), or full sequences (3%). The mean spacing on the array was <1 Mb and contained no gaps in nonheterochromic regions >2 Mb. BAC clones were cultured in YT broth containing 12.5 mg/ml chloramphenicol. BAC DNA was extracted by using 96-well blocks (REAL prep kits; Qiagen, Valencia, CA). DNA was then amplified by degenerate oligonucleotide primer (DOP)-PCR and was resuspended to a final concentration of 200–300 ng/ml. Arrays were printed on Corning CMT Ultra-Gap slides by using a MicroGrid II spotter (Biorobotics, Cambridge, MA). A minimum of two replicates per clone were printed on each slide. A systematic protocol was used to analyze array comparative genomic hybridization (aCGH) data for copy number alterations. For quality control purposes, clones demonstrating an adjusted foreground to background intensity ratios of <0.8 in the reference channel were removed. With dye swap data merged as input, copy number breakpoints were estimated for each sample by the Circular Binary Segmentation (CBS) algorithm by using breakpoint significance based upon 10,000 permutations (2).TaqMan microRNA (miRNA) Assays.
Expression of 155 mature miRNAs in 18 human ovarian cancer cell lines were analyzed by TaqMan miRNA Assay (Applied Biosystems, Foster City, CA) under conditions defined by the supplier (3). Briefly, single-stranded cDNA was synthesized from 5.5 ng of total RNA in 15-ml reaction volume by using the TaqMan MicroRNA Reverse Transcription Kit (Applied Biosystems). The reactions were incubated first at 16°C for 30 min and then at 42°C for 30 min. The reactions were inactivated by incubation at 85°C for 5 min. Each cDNA generated was amplified by quantitative PCR by using sequence-specific primers from the TaqMan microRNA Assays Human Panel on an 7900HT Sequence Detection System (Applied Biosystems). The 20-ml PCR included 10 ml of 2× Universal PCR Master Mix (No AmpErase UNG), 2 ml of each 10× TaqMan MicroRNA Assay Mix and 1.5 ml of reverse transcription (RT) product. The reactions were incubated in a 384-well plate at 95°C for 10 min, followed by 40 cycles of 95°C for 15 s and 60°C for 1 min. The threshold cycle (CT) is defined as the fractional cycle number at which the fluorescence passes the fixed threshold (0.2). All signals with CT ³ 37.9 were manually set to undetermined. The relative quantity (RQ) of the target miRNAs was estimated by the DDCT study by using as reference (endogenous control) the expression of hsa-mir16 for each cell line and as calibrator sample a mixture of RNA from all of the cell lines that were tested in equimolar amounts.Low Molecular Weight (LMW) RNA Isolation and miRNA Microarray.
LMW RNA was isolated from 107 cultured cells by using the mirVana miRNA Extraction Kit (Ambion, Austin, TX) and quantified by the RiboGreen kit (Molecular Probes). miRMAX miRNA microarray were described recently in ref. 4. One hundred nanograms of LMW RNA was used as input for the labeling reaction. miRNAs were labeled by the Array900 miRNA Direct or RT kits (Genisphere, Inc, Hatfield, PA) and hybridized to the miRMAX miRNA microarray chips (4) by using the Ventana Discovery System (Ventana Medical Systems, Tuscon, AZ). After initial hybridization, a mixture of Cy3- and Cy5-labeled 3DNA dendrimers was applied to each microarray chip and a second hybridization proceeded for 2 h at 45°C. Arrays were washed with 2× SSC (1´ SSC = 0.15 M sodium chloride/0.015 M sodium citrate, pH 7) at 42°C for 10 min and then removed from the hybridization system. Arrays were scanned by using a GenePix 4000B scanner (Axon Instruments, Union City, CA), and median spot intensities were collected by using GENEPIX 4.0 (Axon Instruments). Data analysis and manipulation were conducted in either GENESPRING 7.0 (Silicon Genetics, Redwood City, CA) or GENE TRAFFIC DUO (Stratagene, La Jolla, CA).Bioinformatic Analysis
. mRNA targets were predicted for 41 miRNAs of interest by using four well known miRNA target prediction programs: DIANA-MICROT (5), TARGETSCANS (6), MIRANDA (7), and PICTAR (8). DIANA-MICROT finds targets that are conserved in human and mouse; TARGETSCANS find targets which are conserved in human, mouse, rat, chicken, and dog; MIRANDA finds targets which are conserved in human, mouse, and rat; and PICTAR finds targets which are conserved in human, chimp, mouse, rat, and dog. We identified all miRNA-target gene pairs, as predicted by more than one program. In particular, we found that among our miRNAs of interest, 23 miRNA-target pairs were predicted by all four programs, and 1,045 pairs were predicted by three programs. Functional annotation for each target is provided for each miRNA-target pair.We did not place any additional score cutoffs or restrictions on the targets but simply took all of the top target candidates for each miRNA provided by the web site of each program. We also recognized that each program provides a different set of gene identifiers for predicted targets. For example, PICTAR reports targets by RefSeq ID, whereas MIRANDA reports Ensembl Transcript ID. To look for common miRNA-target gene pairs, we used the gene annotation identifier tables provided by the Ensembl BioMart application to associate other gene identifiers (Refseq; NCBI Entrez Gene Symbol) with an Ensembl Transcript ID. The vast majority of targets could be identified and compared in this way, although we recognize that this process is imperfect because not all gene ID symbols have an associated Ensembl Transcript ID. Web sites of miRNA targets prediction programs are as follows: DIANA-MICROT (http://diana.pcbi.upenn.edu/cgi-bin/micro_t.cgi; ref. 5); TARGETSCANS (http://genes.mit.edu/targetscan; ref. 6); MIRANDA (http://www.microrna.org; ref. 7); PICTAR (http://pictar.bio.nyu.edu; ref. 8).
Total RNA Isolation and Quantitative Real-time RT-PCR.
Total RNA was isolated from 100 to 500 mg of frozen tissue or 1 × 106 cultured cells with TRIzol reagent (Invitrogen). After treatment with RNase-free DNase (Invitrogen), total RNA was reverse-transcribed by using Superscript First-Strand Synthesis Kit for RT-PCR (Invitrogen) under the conditions defined by the supplier. cDNA was quantified by real-time PCR on the ABI Prism 7900 Sequence Detection System (Applied Biosystems) as described by others (9). Each sample was run in duplicate and each PCR experiment included two non-template control wells.Protein Isolation and Western Blot.
Cultured cells were lysed in 200 ml of lysis buffer containing 50 mM Tris·HCl (pH 7.4), 150 mM NaCl and 1% Triton X-100. Protein was separated by 7% SDS/PAGE under denaturing conditions and transferred to nitrocellulose membrane. Membranes were incubated with an anti-Dicer1 monoclonal antibody (1:250; Abcam, Cambridge, MA), followed by incubation in rabbit anti-mouse secondary antibody conjugated with horseradish peroxidase (1:5,000; Sigma). Immunoreactive proteins were visualized by using enhanced chemiluminescence detection system (Amersham Pharmacia Biosciences, Piscataway, NJ).1. Greshock, J., Naylor, T. L., Margolin, A., Diskin, S., Cleaver, S. H., Futreal, P. A., deJong, P. J., Zhao, S., Liebman, M. & Weber, B. L. (2004) Genome Res. 14, 179–187.
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