Application of Multiplex Bisulfite PCR–Ligase Detection Reaction–Real-Time Quantitative PCR Assay in Interrogating Bioinformatically Identified, Blood-Based Methylation Markers for Colorectal Cancer
2020; Elsevier BV; Volume: 22; Issue: 7 Linguagem: Inglês
10.1016/j.jmoldx.2020.03.009
ISSN1943-7811
AutoresManny D. Bacolod, Aashiq H. Mirza, Jianmin Huang, Sarah F. Giardina, Philip B. Feinberg, Steven A. Soper, Francis Barany,
Tópico(s)RNA modifications and cancer
ResumoThe analysis of CpG methylation in circulating tumor DNA fragments has emerged as a promising approach for the noninvasive early detection of solid tumors, including colorectal cancer (CRC). The most commonly employed assay involves bisulfite conversion of circulating tumor DNA, followed by targeted PCR, then real-time quantitative PCR (alias methylation-specific PCR). This report demonstrates the ability of a multiplex bisulfite PCR–ligase detection reaction–real-time quantitative PCR assay to detect seven methylated CpG markers (CRC or colon specific), in both simulated (approximately 30 copies of fragmented CRC cell line DNA mixed with approximately 3000 copies of fragmented peripheral blood DNA) and CRC patient–derived cell-free DNAs. This scalable assay is designed for multiplexing and incorporates steps for improved sensitivity and specificity, including the enrichment of methylated CpG fragments, ligase detection reaction, the incorporation of ribose bases in primers, and use of uracil DNA glycosylase. Six of the seven CpG markers (located in promoter regions of PPP1R16B, KCNA3, CLIP4, GDF6, SEPT9, and GSG1L) were identified through integrated analyses of genome-wide methylation data sets for 31 different types of cancer. These markers were mapped to CpG sites at the promoter region of VIM; VIM and SEPT9 are established epigenetic markers of CRC. Additional bioinformatics analyses show that the methylation at these CpG sites negatively correlates with the transcription of their corresponding genes. The analysis of CpG methylation in circulating tumor DNA fragments has emerged as a promising approach for the noninvasive early detection of solid tumors, including colorectal cancer (CRC). The most commonly employed assay involves bisulfite conversion of circulating tumor DNA, followed by targeted PCR, then real-time quantitative PCR (alias methylation-specific PCR). This report demonstrates the ability of a multiplex bisulfite PCR–ligase detection reaction–real-time quantitative PCR assay to detect seven methylated CpG markers (CRC or colon specific), in both simulated (approximately 30 copies of fragmented CRC cell line DNA mixed with approximately 3000 copies of fragmented peripheral blood DNA) and CRC patient–derived cell-free DNAs. This scalable assay is designed for multiplexing and incorporates steps for improved sensitivity and specificity, including the enrichment of methylated CpG fragments, ligase detection reaction, the incorporation of ribose bases in primers, and use of uracil DNA glycosylase. Six of the seven CpG markers (located in promoter regions of PPP1R16B, KCNA3, CLIP4, GDF6, SEPT9, and GSG1L) were identified through integrated analyses of genome-wide methylation data sets for 31 different types of cancer. These markers were mapped to CpG sites at the promoter region of VIM; VIM and SEPT9 are established epigenetic markers of CRC. Additional bioinformatics analyses show that the methylation at these CpG sites negatively correlates with the transcription of their corresponding genes. Colorectal cancer (CRC) is the third most common cancer worldwide, with >1.8 million new cases in 2018.1Bray F. Ferlay J. Soerjomataram I. Siegel R.L. Torre L.A. Jemal A. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries.CA Cancer J Clin. 2018; 68: 394-424Crossref PubMed Scopus (11223) Google Scholar The global burden of CRC is projected to increase by 60% to >2.2 million new cases and 1.1 million deaths by 2030.1Bray F. Ferlay J. Soerjomataram I. Siegel R.L. Torre L.A. Jemal A. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries.CA Cancer J Clin. 2018; 68: 394-424Crossref PubMed Scopus (11223) Google Scholar The key to reducing mortality due to CRC is early detection, as it provides an opportunity to surgically remove premalignant lesions. Catching CRC at early stage saves lives, with 5-year survival rates (US data) for localized, regional, and distant CRC being 91%, 72%, and 13%, respectively.2Siegel R.L. Miller K.D. Fedewa S.A. Ahnen D.J. Meester R.G.S. Barzi A. Jemal A. Colorectal cancer statistics, 2017.CA Cancer J Clin. 2017; 67: 177-193Crossref PubMed Scopus (1736) Google Scholar Colonoscopy still remains the standard screening tool for CRC, and is recommended at the age of 50 years (with 5- to 10-year screening intervals).3Rex D.K. Boland C.R. Dominitz J.A. Giardiello F.M. Johnson D.A. Kaltenbach T. Levin T.R. Lieberman D. 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Such alternatives include procedures that detect heme (in case of fecal immunochemical test) or human globin (in case of fecal occult blood test) from blood released from advanced adenomas and adenocarcinomas into stool samples.5Das V. Kalita J. Pal M. Predictive and prognostic biomarkers in colorectal cancer: a systematic review of recent advances and challenges.Biomed Pharmacother. 2016; 87: 8-19Crossref PubMed Scopus (53) Google Scholar Unfortunately, fecal tests also have low specificity and sensitivity, low compliance, and variability in the test results because of many confounding factors.6Morikawa T. Kato J. Yamaji Y. Wada R. Mitsushima T. Shiratori Y. A comparison of the immunochemical fecal occult blood test and total colonoscopy in the asymptomatic population.Gastroenterology. 2005; 129: 422-428Abstract Full Text Full Text PDF PubMed Scopus (324) Google Scholar,7Oono Y. Iriguchi Y. Doi Y. Tomino Y. Kishi D. Oda J. Takayanagi S. Mizutani M. Fujisaki T. Yamamura A. Hosoi T. Taguchi H. Kosaka M. Delgado P. A retrospective study of immunochemical fecal occult blood testing for colorectal cancer detection.Clin Chim Acta. 2010; 411: 802-805Crossref PubMed Scopus (24) Google Scholar The limitations of the current tests used to detect CRC encourage persistent efforts toward developing noninvasive screening approaches, such as liquid biopsies. Indeed, many assays to detect CRC biomarkers, based on circulating tumor cells, proteins, cell-free DNA (cfDNA), and RNA, have been identified and validated in various clinical settings.8Yoruker E.E. Holdenrieder S. Gezer U. Blood-based biomarkers for diagnosis, prognosis and treatment of colorectal cancer.Clin Chim Acta. 2016; 455: 26-32Crossref PubMed Scopus (39) Google Scholar One of the underlying foundations of cancer liquid biopsy is the release of cfDNA from apoptotic cancer cells into the blood. The isolated cfDNA contains the same molecular aberrations as the solid tumors (mutations, hypermethylation or hypomethylation, copy number changes, or chromosomal rearrangements).9Ignatiadis M. Dawson S.J. Circulating tumor cells and circulating tumor DNA for precision medicine: dream or reality?.Ann Oncol. 2014; 25: 2304-2313Abstract Full Text Full Text PDF PubMed Scopus (103) Google Scholar Among the molecular markers present in cfDNA isolated from the blood of CRC patients are KRAS and BRAF mutations.10Spindler K.L. Pallisgaard N. Andersen R.F. Brandslund I. Jakobsen A. Circulating free DNA as biomarker and source for mutation detection in metastatic colorectal cancer.PLoS One. 2015; 10: e0108247Crossref PubMed Scopus (0) Google Scholar, 11Gonzalez-Cao M. Mayo-de-Las-Casas C. Molina-Vila M.A. De Mattos-Arruda L. Munoz-Couselo E. Manzano J.L. Cortes J. Berros J.P. Drozdowskyj A. Sanmamed M. Gonzalez A. Alvarez C. Viteri S. Karachaliou N. Martin Algarra S. Bertran-Alamillo J. 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Beare D. Gunasekaran P. Leung K. Bindal N. Boutselakis H. Ding M. Bamford S. Cole C. Ward S. Kok C.Y. Jia M. De T. Teague J.W. Stratton M.R. McDermott U. Campbell P.J. COSMIC: exploring the world's knowledge of somatic mutations in human cancer.Nucleic Acids Res. 2015; 43: D805-D811Crossref PubMed Scopus (1460) Google Scholar Furthermore, these mutations are commonly found in many different solid tumors, confounding the ability to determine where the cancer originated. According to the Catalogue of Somatic Mutations in Cancer,13Forbes S.A. Beare D. Gunasekaran P. Leung K. Bindal N. Boutselakis H. Ding M. Bamford S. Cole C. Ward S. Kok C.Y. Jia M. De T. Teague J.W. Stratton M.R. McDermott U. Campbell P.J. COSMIC: exploring the world's knowledge of somatic mutations in human cancer.Nucleic Acids Res. 2015; 43: D805-D811Crossref PubMed Scopus (1460) Google Scholar BRAF mutations frequently occur in melanoma (44%) and thyroid cancer (52%), whereas KRAS is also highly mutated in lung (15%) and pancreatic cancers (63%). In the most common type of pancreatic cancer (ductal adenocarcinoma), KRAS mutation occurs at a much higher rate (>90%).14Wood L.D. Hruban R.H. Pathology and molecular genetics of pancreatic neoplasms.Cancer J. 2012; 18: 492-501Crossref PubMed Scopus (0) Google Scholar Tumor-specific CpG methylations have been detected in the plasma from patients from a variety of solid tumors,15Pratt V.M. Are we ready for a blood-based test to detect colon cancer?.Clin Chem. 2014; 60: 1141-1142Crossref PubMed Scopus (0) Google Scholar,16Warton K. Samimi G. Methylation of cell-free circulating DNA in the diagnosis of cancer.Front Mol Biosci. 2015; 2: 13Crossref PubMed Google Scholar and detected by techniques involving bisulfite conversion of unmethylated cytosines, methylation-sensitive enzymes, or immunoprecipitation of 5-methylcytosines.17Jorda M. Peinado M.A. Methods for DNA methylation analysis and applications in colon cancer.Mutat Res. 2010; 693: 84-93Crossref PubMed Scopus (23) Google Scholar Methylation signatures are promising as blood-based biomarkers as they demonstrate better specificity toward a particular cancer type because methylation patterns are highly tissue specific.18Issa J.P. DNA methylation as a therapeutic target in cancer.Clin Cancer Res. 2007; 13: 1634-1637Crossref PubMed Scopus (207) Google Scholar The best-studied blood-based methylation markers for CRC detection are located in the promoter region of the SEPT9 gene.19Church T.R. Wandell M. Lofton-Day C. Mongin S.J. Burger M. Payne S.R. Castanos-Velez E. 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Samowitz W.S. Heichman K.A. Septin 9 methylated DNA is a sensitive and specific blood test for colorectal cancer.BMC Med. 2011; 9: 133Crossref PubMed Scopus (231) Google Scholar The clinical adaptation of any cancer diagnostic test is determined by several factors, foremost of which is whether it can perform within acceptable performance metrics, such as sensitivity, specificity, and utility, along with cost-effectiveness. For any CpG methylation-interrogating, noninvasive assay for early cancer detection, sound performance metrics may then largely depend on how it addresses biological realities regarding the CRC-specific methylation markers contained in cfDNAs isolated from patient plasma: that they are present in minuscule quantities (down to several copies), thus the assay should be highly sensitive, and that they can be identified in large excesses of genomic DNAs (gDNAs) from peripheral blood, as well as cfDNAs released by other tissues (including other types of cancer). The latter therefore requires the assay to be designed for methylation markers predicted to be negative in peripheral blood, as well as most other cancer types. Approaches to address such concerns are presented in the current study. The described technique employs multiplexed amplification of the CpG cancer markers (relative to normal DNA), such that the marker can be confidently detected even at the single-molecule level. Moreover, the novel methylation markers described herein were identified through comprehensive bioinformatic approaches, integrating genome-wide methylation data sets for CRC, peripheral blood, immune infiltrating cells, and other cancer types. The identification of candidate CRC-specific, plasma-based CpG methylation sites entailed integration of various publicly available genome-wide methylation data sets, including genome-wide methylation (Illumina 450K methylation array; Illumina, San Diego, CA) and expression (RNA-sequencing) data sets for colorectal adenocarcinoma (COADREAD), generated by The Cancer Genome Atlas (TCGA) project,26Kaiser J. National Institutes of Health: NCI gears up for cancer genome project.Science. 2005; 307: 1182Crossref PubMed Google Scholar and previously compiled (and processed) in the University of California, Santa Cruz, Cancer Genomics (now known as University of California, Santa Cruz, Xena) website (https://xena.ucsc.edu/welcome-to-ucsc-xena, last accessed January 23, 2020).27Zhu J. Sanborn J.Z. Benz S. Szeto C. Hsu F. Kuhn R.M. Karolchik D. Archie J. Lenburg M.E. Esserman L.J. Kent W.J. Haussler D. Wang T. 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Immune regulation by low doses of the DNA methyltransferase inhibitor 5-azacitidine in common human epithelial cancers.Oncotarget. 2014; 5: 587-598Crossref PubMed Scopus (212) Google Scholar and GSE68379.37Iorio F. Knijnenburg T.A. Vis D.J. Bignell G.R. Menden M.P. Schubert M. et al.A landscape of pharmacogenomic interactions in cancer.Cell. 2016; 166: 740-754Abstract Full Text Full Text PDF PubMed Scopus (424) Google Scholar All statistical analyses (ie, comparative statistics, normalization, correlation, and regression analyses, multivariate analyses, and hierarchical clustering) were performed using JMP Pro 13.2.1/JMP Genomics 9.0 software (SAS, Cary, NC), Gene-E (Broad Institute, Cambridge, MA), and Microsoft Excel 2016 Office Analysis ToolPak (Microsoft Corp., Redmond, WA). Genomic sequence extraction and alignment were performed through the University of California, Santa Cruz, Genome Browser (https://genome.ucsc.edu, last accessed January 23, 2020).38Kent W.J. Sugnet C.W. Furey T.S. Roskin K.M. Pringle T.H. Zahler A.M. Haussler D. The human genome browser at UCSC.Genome Res. 2002; 12: 996-1006Crossref PubMed Scopus (5199) Google Scholar The OligoAnalyzer Tool from Integrated DNA Technologies Inc. (Coralville, IA) aided primer designs. The primary task was to identify CpG markers whose high degree of methylation in blood cfDNA may be indicative of primary CRC tumors, but not of peripheral blood, or other cancer types (overlap with methylation signals from normal colon was not preferred, but not as stringently selected against). Methylation marker prediction starts with defining the metric associated with every CpG marker P for a given cohort C (eg, TCGA–breast invasive adenocarcinoma, TCGA-COADREAD, GSE77871), and cohort subset S (eg, primary tumors and solid normal tissues). In the current analysis, this particular metric is the statistical value V, such as %UM, %IM, %LM, %HM, %UM + %IM, and %LM + %HM, where UM, IM, LM, and HM refer to UnMethylated (βP ≤ 0.15), Indeterminately Methylated (0.15 < βP ≤ 0.3), Lowly Methylated (0.3 < βP ≤ 0.6), and Highly Methylated (βP > 0.6), respectively. The candidate markers (Ps) are dynamically identified by isolating CpG sites that satisfy multiple criteria in the general form: VP(C,S) ≥ n; {0 ≤ n ≤ 100} (explained further in Results). Accompanying TCGA COADREAD data sets are clinicopathologic data, such as sample type (primary tumor or normal), primary tumor pathologic stage (I to III), microsatellite stability status [microsatellite (MS) stable, high MS instability (MSI-H), and low MSI], and anatomic origin (colon or rectal), sex, and age. The colon adenocarcinoma cell lines HT29, LoVo, and SW1116 would serve as sources of cancer gDNAs. All cell lines were seeded in 60-cm2 culture dishes, kept in a humidified atmosphere containing 5% CO2, and grown in recommended media: HT29 cells in McCoy's 5a medium containing 4.5 g/L glucose, supplemented with 10% fetal bovine serum; SW1116 cells in Leibovitz's L-15 Medium containing 10% fetal bovine serum; and LoVo in ATCC (Manassas, VA)–formulated F-12K Medium, supplemented with 10% fetal bovine serum. Once cells reached 80% to 90% confluence, they were washed with phosphate-buffered saline (×3) and collected by centrifugation (500 × g). The gDNAs from these cell lines were isolated using the DNeasy Blood & Tissue Kit (Qiagen, Valencia, CA). Roche (Indianapolis, IN) DNA, which is pooled gDNA (>50-kb size) isolated from the blood (buffy coat) of approximately 80 healthy individuals was purchased from Roche. gDNAs were then fragmented (50 bp to 1 kb size) through nonrandom sonication method, using ultrasonicator from Covaris (Woburn, MA). The fragmentation size was assessed using Agilent Bioanalyzer system (Agilent Technologies, Santa Clara, CA). DNA concentrations were determined using Quant-iT Picogreen Assay (Life Technologies/Thermo Fisher Scientific, Waltham, MA). The isolation of human plasma samples from both CRC and healthy patients was performed by the MT Group, Inc. (Van Nuys, CA). In the isolation procedure, 10 mL of blood was drawn into a Streck Cell-Free DNA BCT Tube (Streck, Omaha, NE) by venipuncture. The subsequent steps toward plasma isolation carefully adhered to the Streck Tube manufacturer's instructions to prevent the release of gDNA from nucleated blood cells. The cfDNAs were subsequently extracted from the plasma samples (5 mL) using the QIAamp Circulating Nucleic Acid Kit (Qiagen, Valencia, CA), and quantified with the Quant-iT Picogreen Assay. Processing and handling of patient specimens were in accordance with the approved institutional review board protocols for our group at Weill Cornell Medicine (Cancer Serum Detection Project; institutional review board identifier 1308014272), as well as that of the MT Group, Inc. (institutional review board identifier 3764). Two approaches were employed to enrich the population of genomic and cfDNA fragments containing the desired methylated CpG markers. In one approach, the gDNAs (500 ng) from the cell lines were digested with 10 units of the restriction enzyme Bsh1236I (BstUI) in 20 μL of reaction mixture containing 1× CutSmart buffer (50 mmol/L potassium acetate, 20 mmol/L Tris-acetate, 10 mmol/L magnesium acetate, and 100 μg/mL bovine serum albumin, pH 7.9 at 25°C). Bsh1236I is a methylation-sensitive restriction enzyme that specifically recognizes the sequence 5′CGCG if the Cs are unmethylated. The digestion reactions were performed at 37°C for 1 hour with subsequent enzyme inactivation by heating to 80°C for 20 minutes. An alternative enrichment strategy was the use of EpiMark Methylated DNA Enrichment Kit (New England BioLabs, Ipswich, MA). This approach uses selective binding of double-stranded methyl-CpG DNA to the methyl-CpG binding domain of human methyl-CpG-binding domain protein 2 (MBD2) fused to the Fc tail of human IgG1 (MBD2-Fc).39Gebhard C. Schwarzfischer L. Pham T.H. Andreesen R. Mackensen A. Rehli M. Rapid and sensitive detection of CpG-methylation using methyl-binding (MB)-PCR.Nucleic Acids Res. 2006; 34: e82Crossref PubMed Scopus (0) Google Scholar The fused IgG1 (MBD2-Fc) antibody is coupled to paramagnetic hydrophilic protein A magnetic beads. Epimark enrichment was performed according to the manufacturer's instructions. Bisulfite conversion of cytosine bases was accomplished using the EZ DNA Methylation-Lightning kit from Zymo Research Corp. (Irvine, CA). In brief, 130 μL of Lightning Conversion Reagent (Zymo Research Corp.) was added to 20 μL of previously enriched gDNA fragments (or cfDNA). Subsequent protocol steps (per manufacturer's instructions) led to elution of bisulfite converted DNA fragments in 10 μL of elution buffer. The assay developed for detection of CRC-specific, plasma-based methylation markers is divided into several steps described in following subsections. All of the necessary primers (Table 1) were purchased from Integrated DNA Technologies Inc.Table 1List of Primers Used for the Multiplex PCR-LDR-qPCR AssayMarkerPrimer typeSequencem_SEPT9F (PCR)5′-TCCTCCGACGACTAACTCTACACrUACAG-C3-3′m_SEPT9R (PCR)5′-GGTGTCGTGAGGTAGCGGCGAGGAAGCrGTTTC-C3-3′m_SEPT9U (LDR)5′-TAGGAACACGGAGGACATCAACGACGACTAACTCTACACTACAAAAATGCrGAATA-C3-3′m_SEPT9D (LDR)5′-GAACGCGACGCCCCAACCAAC TTGTGGGTGGGTATAGGTCAGA-3′m_SEPT9P (qPCR)5′-FAM-TTAAAATGC-ZEN-GAACGCGACGCCC-IABkFQ-3′m_SEPT9F (qPCR)5′-TAGGAACACGGAGGACATCAA-3′m_SEPT9R (qPCR)5′-TCTGACCTATACCCACCCACAA-3′m_GSG1LF (PCR)5′-AACCGAAACCGAACTAACCGCrCGCCT-C3-3′m_GSG1LR (PCR)5′-GGTGTCGTGGGAATTTTTATATCGGTATTTGGTATTCGTGCrGAGGG-C3-3′m_GSG1LU (LDR)5′-TTCGTCCCTGCACGCTAACCGAACTAACCGCCGTCCrGCGTA-C3-3′m_GSG1LD (LDR)5′-GCGCGCACTCACCAAACCCGGTTCCATCACCGTTAGGCCA-3′m_GSG1LP (qPCR)5′-FAM-TTCCGTCCG-ZEN-CGCGCA-IABkFQ-3′m_GSG1LF (qPCR)5′-TTCGTCCCTGCACGCTAAC-3′m_GSG1LR (qPCR)5′-TGGCCTAACGGTGATGGAAC-3′m_PP1R16BF (PCR)5′-CTATTCCGAAACCTAACCACGTCCrCAACT-C3-3′m_PP1R16BR (PCR)5′-GGTGTCGTGGAGGTGGGCGCGTTTAATTTTATTCrGGTTC-C3-3′m_PP1R16BU (LDR)5′-TTCAGCAGCCTGGCATCACGAAACCTAACCACGTCCCAGCCrGATCC-C3-3′m_PP1R16BD (LDR)5′-GATTTCAACTTCCTACAACTCAAAAAAAAAATCCCCACCGTGGAGCGCTAAGGTTGCA-3′m_PP1R16BP (qPCR)5′-FAM-TTCGTCCCA-ZEN-GCCGATTTCAACTTCCTACAA-IABkFQ-3′m_PP1R16BF (qPCR)5′-TTCAGCAGCCTGGCATCAC-3′m_PP1R16BR (qPCR)5′-TGCAACCTTAGCGCTCCAC-3′m_KCNA3F (PCR)5′-GACTCGTAACGATCGCAACCGrCCGCT-C3-3′m_KCNA3R (PCR)5′-GGTGTCGTGGCGGTTACGCGGAGTTCGTCrGTAGA-C3-3′m_KCNA3U (LDR)5′-TCACAGAGACTTGCCGATCACGATCGCAACCGCCACCrGCCGT-C3-3′m_KCNA3D (LDR)5′-GCCACAACCGCCTTAAAACGAAACCCGTGTGTAGCTTAGACATGGCCA-3′m_KCNA3P (qPCR)5′-FAM-TTCGCCACC-ZEN-GCCACAACC-IABkFQ-3′m_KCNA3F (qPCR)5′-TCACAGAGACTTGCCGATCAC-3′m_KCNA3R (qPCR)5′-TGGCCATGTCTAAGCTACACAC-3′m_CLIP4F (PCR)5′-CGCGAGGTTGAGGGTTGTGrAAGGT-C3-3′m_CLIP4R (PCR)5′-GGTGTCGTGGGTCTACGAAATATCGCAATATTACCTCCrCCCGT-C3-3′m_CLIP4U (LDR)5′-TTCAGAGCACCTGCGTACCGAGGTTGAGGGTTGTGAAAGCrGGTAA-C3-3′m_CLIP4D (LDR)5′-GGTGGGTACGTACGGCGTGTCGGGTTCTTCGGCTGGCTCAA-3′m_CLIP4P (qPCR)5′-FAM-CCTTGTGAA-ZEN-AGCGGTGGGTACGTAC-IABkFQ-3′m_CLIP4F (qPCR)5′-TTCAGAGCACCTGCGTACC-3′m_CLIP4R (qPCR)5′-TTGAGCCAGCCGAAGAACC-3′m_GDF6F (PCR)5′-AACGCAAAAACCAACGAAAAACCrCGCGT-C3-3′m_GDF6R (PCR)5′-GGTGTCGTGGTGGAAAGTTTTGGGTAAAGTCGGTArUTAGA-C3-3′m_GDF6U (LDR)5′-TCTTACGCCCAGGGAATGTAACCGCCAAAACCAAAAAACTACCCAACrGCCAT-C3-3′m_GDF6D (LDR)5′-GCCGCTCGCGAACTAATTCCTCAAACTATAAAACGTTGTCCGGCTGTGGTTACA-3′m_GDF6P (qPCR)5′-FAM-TGTACCCAA-ZEN-CGCCGCTCGC-IABkFQ-3′m_GDF6F (qPCR)5′
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