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• W2613209216 abstract "Invasion is a hallmark of advanced head and neck squamous cell carcinoma (HNSCC). We previously determined that low relative miR-375 expression was associated with poor patient prognosis. HNSCC cells with increased miR-375 expression have lower invasive properties and impaired invadopodium activity. Using stable isotope labeling with amino acids in cell culture and reverse-phase liquid chromatography mass spectrometry, we assessed the impact of miR-375 expression on protein levels in UM-SCC-1 cells. Increased miR-375 expression was associated with down-regulation of proteins involved in cellular assembly and organization, death and survival, and movement. Two invasion-associated proteins, vimentin and L-plastin, were strongly down-regulated by miR-375. Luciferase reporter assays demonstrated that high miR-375 expression reduced vimentin promoter activity, suggesting that vimentin is an indirect target of miR-375. Runt-related transcription factor 1 (RUNX1) is a potential miR-375 direct target, and its knockdown reduced vimentin and L-plastin expression. Data in The Cancer Genome Atlas HNSCC database showed a significant inverse correlation between miR-375 expression and RUNX1, vimentin, and L-plastin RNA expression. These clinical correlations validate our in vitro model findings and support a mechanism in which miR-375 suppresses RUNX1 levels, resulting in reduced vimentin and L-plastin expression. Furthermore, knockdown of RUNX1, L-plastin, and vimentin resulted in significant reductions in cell invasion in vitro, indicating the functional significance of miR-375 regulation of specific proteins involved in HNSCC invasion. Invasion is a hallmark of advanced head and neck squamous cell carcinoma (HNSCC). We previously determined that low relative miR-375 expression was associated with poor patient prognosis. HNSCC cells with increased miR-375 expression have lower invasive properties and impaired invadopodium activity. Using stable isotope labeling with amino acids in cell culture and reverse-phase liquid chromatography mass spectrometry, we assessed the impact of miR-375 expression on protein levels in UM-SCC-1 cells. Increased miR-375 expression was associated with down-regulation of proteins involved in cellular assembly and organization, death and survival, and movement. Two invasion-associated proteins, vimentin and L-plastin, were strongly down-regulated by miR-375. Luciferase reporter assays demonstrated that high miR-375 expression reduced vimentin promoter activity, suggesting that vimentin is an indirect target of miR-375. Runt-related transcription factor 1 (RUNX1) is a potential miR-375 direct target, and its knockdown reduced vimentin and L-plastin expression. Data in The Cancer Genome Atlas HNSCC database showed a significant inverse correlation between miR-375 expression and RUNX1, vimentin, and L-plastin RNA expression. These clinical correlations validate our in vitro model findings and support a mechanism in which miR-375 suppresses RUNX1 levels, resulting in reduced vimentin and L-plastin expression. Furthermore, knockdown of RUNX1, L-plastin, and vimentin resulted in significant reductions in cell invasion in vitro, indicating the functional significance of miR-375 regulation of specific proteins involved in HNSCC invasion. Head and neck squamous cell carcinoma (HNSCC) is the sixth most common cancer worldwide.1Siegel R. Naishadham D. Jemal A. Cancer statistics, 2013.CA Cancer J Clin. 2013; 63: 11-30Crossref PubMed Scopus (11509) Google Scholar, 2Warnakulasuriya S. Global epidemiology of oral and oropharyngeal cancer.Oral Oncol. 2009; 45: 309-316Abstract Full Text Full Text PDF PubMed Scopus (2118) Google Scholar HNSCC develops in the mucosal layer of the upper aerodigestive tract, arising in the squamous epithelium of the oral cavity, oropharynx, larynx, hypopharynx, and nasopharynx.3Leemans C.R. Braakhuis B.J. Brakenhoff R.H. The molecular biology of head and neck cancer.Nat Rev Cancer. 2011; 11: 9-22Crossref PubMed Scopus (1887) Google Scholar, 4Pai S.I. Westra W.H. Molecular pathology of head and neck cancer: implications for diagnosis, prognosis, and treatment.Annu Rev Pathol. 2009; 4: 49-70Crossref PubMed Scopus (307) Google Scholar Despite advances in multimodal treatment strategies, including surgery, radiotherapy, and chemotherapy, locoregional recurrence, lymph node metastases, and second primary tumors continue to contribute significantly to morbidity and mortality among HNSCC patients.5Ganci F. Sacconi A. Manciocco V. Covello R. Spriano G. Fontemaggi G. Blandino G. Molecular genetics and biology of head and neck squamous cell carcinoma: implications for diagnosis, prognosis and treatment. Head and Neck Cancer.in: Agulnik D.M. InTech. 2012: 73-122Crossref Google Scholar, 6Nisa L. Aebersold D.M. Giger R. Caversaccio M.D. Borner U. Medova M. Zimmer Y. Profiling invasiveness in head and neck cancer: recent contributions of genomic and transcriptomic approaches.Cancers (Basel). 2015; 7: 585-597Crossref PubMed Scopus (7) Google Scholar miRNAs are small noncoding single-stranded RNA molecules (approximately 22 nucleotides in length), which are post-transcriptional gene expression regulators.7Bartel D.P. MicroRNAs: genomics, biogenesis, mechanism, and function.Cell. 2004; 116: 281-297Abstract Full Text Full Text PDF PubMed Scopus (29453) Google Scholar, 8Yan J.W. Lin J.S. He X.X. The emerging role of miR-375 in cancer.Int J Cancer. 2014; 135: 1011-1018Crossref PubMed Scopus (188) Google Scholar, 9Lee Y.S. Dutta A. MicroRNAs in cancer.Annu Rev Pathol. 2009; 4: 199-227Crossref PubMed Scopus (779) Google Scholar Mature miRNAs usually bind to the 3′-untranslated region of the target mRNAs, leading to either mRNA degradation or translational repression.8Yan J.W. Lin J.S. He X.X. The emerging role of miR-375 in cancer.Int J Cancer. 2014; 135: 1011-1018Crossref PubMed Scopus (188) Google Scholar, 9Lee Y.S. Dutta A. MicroRNAs in cancer.Annu Rev Pathol. 2009; 4: 199-227Crossref PubMed Scopus (779) Google Scholar, 10Pencheva N. Tavazoie S.F. Control of metastatic progression by microRNA regulatory networks.Nat Cell Biol. 2013; 15: 546-554Crossref PubMed Scopus (266) Google Scholar Each miRNA targets hundreds of mRNA transcripts, and each mRNA transcript may possess multiple different miRNA binding sites.11Bargaje R. Gupta S. Sarkeshik A. Park R. Xu T. Sarkar M. Halimani M. Roy S.S. Yates J. Pillai B. Identification of novel targets for miR-29a using miRNA proteomics.PLoS One. 2012; 7: e43243Crossref PubMed Scopus (45) Google Scholar, 12Friedman R.C. Farh K.K. Burge C.B. Bartel D.P. Most mammalian mRNAs are conserved targets of microRNAs.Genome Res. 2009; 19: 92-105Crossref PubMed Scopus (6362) Google Scholar The ability of miRNAs to target diverse mRNAs accounts for their ability to control many cancer phenotypes, including cellular proliferation, differentiation, apoptosis, invasion, metastasis, and angiogenesis.9Lee Y.S. Dutta A. MicroRNAs in cancer.Annu Rev Pathol. 2009; 4: 199-227Crossref PubMed Scopus (779) Google Scholar, 13Singh R. Mo Y.Y. Role of microRNAs in breast cancer.Cancer Biol Ther. 2013; 14: 201-212Crossref PubMed Scopus (108) Google Scholar, 14Tu H.F. Lin S.C. Chang K.W. MicroRNA aberrances in head and neck cancer: pathogenetic and clinical significance.Curr Opin Otolaryngol Head Neck Surg. 2013; 21: 104-111Crossref PubMed Scopus (71) Google Scholar, 15Farazi T.A. Hoell J.I. Morozov P. Tuschl T. MicroRNAs in human cancer.Adv Exp Med Biol. 2013; 774: 1-20Crossref PubMed Scopus (93) Google Scholar Some miRNAs, including miR-375, have been identified as diagnostic and prognostic markers in human cancers.9Lee Y.S. Dutta A. MicroRNAs in cancer.Annu Rev Pathol. 2009; 4: 199-227Crossref PubMed Scopus (779) Google Scholar, 14Tu H.F. Lin S.C. Chang K.W. MicroRNA aberrances in head and neck cancer: pathogenetic and clinical significance.Curr Opin Otolaryngol Head Neck Surg. 2013; 21: 104-111Crossref PubMed Scopus (71) Google Scholar, 16Childs G. Fazzari M. Kung G. Kawachi N. Brandwein-Gensler M. McLemore M. Chen Q. Burk R.D. Smith R.V. Prystowsky M.B. Belbin T.J. Schlecht N.F. Low-level expression of microRNAs let-7d and miR-205 are prognostic markers of head and neck squamous cell carcinoma.Am J Pathol. 2009; 174: 736-745Abstract Full Text Full Text PDF PubMed Scopus (315) Google Scholar, 17Harris T. Jimenez L. Kawachi N. Fan J.B. Chen J. Belbin T. Ramnauth A. Loudig O. Keller C.E. Smith R. Prystowsky M.B. Schlecht N.F. Segall J.E. Childs G. Low-level expression of miR-375 correlates with poor outcome and metastasis while altering the invasive properties of head and neck squamous cell carcinomas.Am J Pathol. 2012; 180: 917-928Abstract Full Text Full Text PDF PubMed Scopus (100) Google Scholar We previously determined that low relative miR-375 expression in HNSCC tumors is associated with poor patient prognosis.17Harris T. Jimenez L. Kawachi N. Fan J.B. Chen J. Belbin T. Ramnauth A. Loudig O. Keller C.E. Smith R. Prystowsky M.B. Schlecht N.F. Segall J.E. Childs G. Low-level expression of miR-375 correlates with poor outcome and metastasis while altering the invasive properties of head and neck squamous cell carcinomas.Am J Pathol. 2012; 180: 917-928Abstract Full Text Full Text PDF PubMed Scopus (100) Google Scholar We also identified that when the level of miR-375 is stably increased in HNSCC cells, these cells exhibit impaired cell invasion and invadopodial function in vitro.17Harris T. Jimenez L. Kawachi N. Fan J.B. Chen J. Belbin T. Ramnauth A. Loudig O. Keller C.E. Smith R. Prystowsky M.B. Schlecht N.F. Segall J.E. Childs G. Low-level expression of miR-375 correlates with poor outcome and metastasis while altering the invasive properties of head and neck squamous cell carcinomas.Am J Pathol. 2012; 180: 917-928Abstract Full Text Full Text PDF PubMed Scopus (100) Google Scholar, 18Jimenez L. Sharma V.P. Condeelis J. Harris T. Ow T.J. Prystowsky M.B. Childs G. Segall J.E. MicroRNA-375 suppresses extracellular matrix degradation and invadopodial activity in head and neck squamous cell carcinoma.Arch Pathol Lab Med. 2015; 139: 1349-1361Crossref PubMed Scopus (20) Google Scholar miRNAs have the ability to regulate their mRNA targets by translational repression without necessarily causing mRNA degradation/cleavage.9Lee Y.S. Dutta A. MicroRNAs in cancer.Annu Rev Pathol. 2009; 4: 199-227Crossref PubMed Scopus (779) Google Scholar, 19Iorio M.V. Croce C.M. MicroRNA dysregulation in cancer: diagnostics, monitoring and therapeutics: a comprehensive review.EMBO Mol Med. 2012; 4: 143-159Crossref PubMed Scopus (1302) Google Scholar Stable isotope labeling of amino acids in cell culture (SILAC)–based proteomic analysis has emerged as a useful, unbiased method for the identification of miRNA-regulated proteins.11Bargaje R. Gupta S. Sarkeshik A. Park R. Xu T. Sarkar M. Halimani M. Roy S.S. Yates J. Pillai B. Identification of novel targets for miR-29a using miRNA proteomics.PLoS One. 2012; 7: e43243Crossref PubMed Scopus (45) Google Scholar, 20Lossner C. Meier J. Warnken U. Rogers M.A. Lichter P. Pscherer A. Schnolzer M. Quantitative proteomics identify novel miR-155 target proteins.PLoS One. 2011; 6: e22146Crossref PubMed Scopus (27) Google Scholar, 21Xiong Q. Zhong Q. Zhang J. Yang M. Li C. Zheng P. Bi L.J. Ge F. Identification of novel miR-21 target proteins in multiple myeloma cells by quantitative proteomics.J Proteome Res. 2012; 11: 2078-2090Crossref PubMed Scopus (63) Google Scholar, 22Yan G.R. Xu S.H. Tan Z.L. Liu L. He Q.Y. Global identification of miR-373-regulated genes in breast cancer by quantitative proteomics.Proteomics. 2011; 11: 912-920Crossref PubMed Scopus (70) Google Scholar, 23Yang Y. Chaerkady R. Kandasamy K. Huang T.C. Selvan L.D. Dwivedi S.B. Kent O.A. Mendell J.T. Pandey A. Identifying targets of miR-143 using a SILAC-based proteomic approach.Mol Biosyst. 2010; 6: 1873-1882Crossref PubMed Scopus (54) Google Scholar, 24Huang T.C. Renuse S. Pinto S. Kumar P. Yang Y. Chaerkady R. Godsey B. Mendell J.T. Halushka M.K. Civin C.I. Marchionni L. Pandey A. Identification of miR-145 targets through an integrated omics analysis.Mol Biosyst. 2015; 11: 197-207Crossref PubMed Google Scholar The incorporation of nonradioactive, stable isotopes of amino acids (ie, lysine and arginine) into proteins22Yan G.R. Xu S.H. Tan Z.L. Liu L. He Q.Y. Global identification of miR-373-regulated genes in breast cancer by quantitative proteomics.Proteomics. 2011; 11: 912-920Crossref PubMed Scopus (70) Google Scholar, 25Bauer K.M. Hummon A.B. Effects of the miR-143/-145 microRNA cluster on the colon cancer proteome and transcriptome.J Proteome Res. 2012; 11: 4744-4754Crossref PubMed Scopus (44) Google Scholar in combination with quantitative mass spectrometry allows for the detection of differences in protein levels between biological samples.20Lossner C. Meier J. Warnken U. Rogers M.A. Lichter P. Pscherer A. Schnolzer M. Quantitative proteomics identify novel miR-155 target proteins.PLoS One. 2011; 6: e22146Crossref PubMed Scopus (27) Google Scholar, 26Ong S.E. Blagoev B. Kratchmarova I. Kristensen D.B. Steen H. Pandey A. Mann M. Stable isotope labeling by amino acids in cell culture, SILAC, as a simple and accurate approach to expression proteomics.Mol Cell Proteomics. 2002; 1: 376-386Crossref PubMed Scopus (4569) Google Scholar This study focuses on the identification of miR-375–regulated proteins using a SILAC-based proteomic strategy. We quantitated 2188 proteins in four subcellular fractions, subsets of which were down-regulated or up-regulated in UM-SCC-1 cells with increased miR-375 expression. Among the down-regulated proteins were two previously published miR-375 targets, metadherin and lactate dehydrogenase B.8Yan J.W. Lin J.S. He X.X. The emerging role of miR-375 in cancer.Int J Cancer. 2014; 135: 1011-1018Crossref PubMed Scopus (188) Google Scholar Vimentin and L-plastin emerged as the most down-regulated proteins. As vimentin and L-plastin are not predicted to be direct targets of miR-375, we went on to identify runt-related transcription factor 1 (RUNX1) as a potential target of miR-375 that regulates vimentin and L-plastin expression. These results are supported by negative correlations between miR-375 and RUNX1, vimentin, and L-plastin mRNA levels in the HNSCC database from The Cancer Genome Atlas (TCGA). The generation of stable UM-SCC-1 empty vector control (Control) and precursor miR-375–expressing (Pre375) lentiviral transductant cell lines was previously described.17Harris T. Jimenez L. Kawachi N. Fan J.B. Chen J. Belbin T. Ramnauth A. Loudig O. Keller C.E. Smith R. Prystowsky M.B. Schlecht N.F. Segall J.E. Childs G. Low-level expression of miR-375 correlates with poor outcome and metastasis while altering the invasive properties of head and neck squamous cell carcinomas.Am J Pathol. 2012; 180: 917-928Abstract Full Text Full Text PDF PubMed Scopus (100) Google Scholar, 18Jimenez L. Sharma V.P. Condeelis J. Harris T. Ow T.J. Prystowsky M.B. Childs G. Segall J.E. MicroRNA-375 suppresses extracellular matrix degradation and invadopodial activity in head and neck squamous cell carcinoma.Arch Pathol Lab Med. 2015; 139: 1349-1361Crossref PubMed Scopus (20) Google Scholar For the SILAC labeling, the SILAC Protein Identification and Quantitation Media Kit (catalog number MS10030; Invitrogen, Waltham, MA) was used. UM-SCC-1 transductant lines were grown in Dulbecco's modified Eagle's medium supplemented with 10% dialyzed fetal bovine serum, penicillin-streptomycin (100 U/mL penicillin, 100 μg/mL streptomycin) (catalog number 15140-122; Gibco/Life Technologies, Waltham, MA), and 2 mmol/L l-glutamine (catalog number 25030-081; Gibco/Life Technologies). The heavy medium was supplemented with 13C6-lysine and 13C6, 15N4-arginine. The light medium was supplemented with 12C6-lysine and 12C6, 14N4-arginine. To prevent the conversion of heavy arginine to heavy proline, 200 mg/mL l-proline was also added to the heavy SILAC growth media.27Bendall S.C. Hughes C. Stewart M.H. Doble B. Bhatia M. Lajoie G.A. Prevention of amino acid conversion in SILAC experiments with embryonic stem cells.Mol Cell Proteomics. 2008; 7: 1587-1597Crossref PubMed Scopus (154) Google Scholar The UM-SCC-1 Control and Pre375 cells were grown for 7 days in the heavy and light SILAC growth medium, respectively. Cell lines were maintained in a 37°C incubator with 5% CO2. A total of 5 × 106 cells each of the light-grown and heavy-grown UM-SCC-1 cells were combined, and subcellular fractionation was performed to improve the dynamic range of detection. Subcellular fractionation of the combined cells was conducted with the QProteome Cell Compartment kit (catalog number 37502; Qiagen, Germantown, MD), according to the manufacturer's suggested protocol. In brief, the combined cells were centrifuged at 500 × g for 10 minutes at 4°C. The combined cells were washed twice with 2 mL ice-cold phosphate-buffered saline and centrifuged after each wash. The pellet was resuspended in 1 mL ice-cold Extraction Buffer CE1 (containing 1× protease inhibitor) and incubated for 10 minutes at 4°C on an end-over-end shaker. The lysate was centrifuged at 1000 × g for 10 minutes at 4°C. The supernatant was designated the cytosolic fraction. The pellet was resuspended in 1 mL ice-cold Extraction Buffer CE2 (containing 1× protease inhibitor) and incubated for 30 minutes at 4°C on an end-over-end shaker. The suspension was centrifuged at 6000 × g for 10 minutes at 4°C. The supernatant was designated the membrane fraction. To the pellet, 7 μL of benzonase nuclease and 13 μL of distilled water were added and resuspended by gently flicking the bottom of the tube. The suspension was incubated for 15 minutes at room temperature. Ice-cold Extraction Buffer CE3 (500 μL; containing 1× protease inhibitor) was added into the tube and incubated for 10 minutes at 4°C on an end-over-end shaker. The suspension was centrifuged at 6800 × g for 10 minutes at 4°C. The supernatant was designated the nuclear fraction. The pellet was resuspended in 500 μL of Extraction Buffer CE4 and designated the cytoskeletal fraction. To concentrate the subcellular fractions, the protein concentrations of the fractions were quantitated with the Pierce BCA Protein Assay (catalog number 23225; Thermo Scientific, Waltham, MA). To 70 μg of cytosol, nucleus, membrane, or cytoskeleton fractions, four times the volume of ice-cold acetone was added. After a 20-minute incubation on ice, each suspension was centrifuged at 12,000 × g for 10 minutes at 4°C. After removing the supernatant, each pellet was air dried and resuspended with 35 μL of Laemmli buffer containing 5 mmol/L of Tris (2-carboxyethyl) phosphine and incubated at 40°C for 30 minutes with 600 rpm agitation. The samples were placed in a prechilled TLA100 rotor and spun down at 70,000 × g for 30 minutes at 4°C in the Beckman TL-100 centrifuge and supernatants (60 μg in 30 μL) loaded on a 4% to 20% gradient gel with protein ladders. The gel was stained with GelCode Blue Stain Reagent (catalog number 24590; Thermo Scientific) for 30 minutes and destained overnight. Complete lanes were excised and sliced to produce 24 bands sized 10 × 1 × 1 mm3 from each subcellular fraction. Band slices were placed in wells of 96-well plates and in-gel digested using an Ettan Digester (GE Healthcare, Chicago, IL). Gel pieces were reduced with dithiothreitol and alkylated with iodoacetamide, then digested with trypsin, as described previously.28Shevchenko A. Tomas H. Havlis J. Olsen J.V. Mann M. In-gel digestion for mass spectrometric characterization of proteins and proteomes.Nat Protoc. 2006; 1: 2856-2860Crossref PubMed Scopus (3531) Google Scholar A nano Acquity ultra performance liquid chromatograph interfaced to an Orbitrap Velos mass spectrometer (Thermo Scientific) was used for analysis. Digested peptides were loaded onto a Symmetry C18 nanoACQUITY trap column (100 Å, 5 μm, 180 μm × 20 mm) at a flow rate of 8 μL/minute with 0.1% formic acid in water and delivered to an ACQUITY UPLC PST C18 nanoACQUITY Column (300 Å, 1.7 μm, 75 μm × 150 mm; Waters, Milford, MA). Peptides were eluted from the nanocolumn at a flow rate of 270 nL/minute with 0.1% formic acid in water (solvent A) and 0.1% formic acid in acetonitrile (solvent B) gradient. The gradient used was as follows: 0 minute, 1% B; 1 minute, 2% B; 2 minutes, 5% B; 42 minutes, 42% B; 52 minutes, 65% B; 57 minutes, 70% B; 60 minutes, 80% B; 65 minutes, 85% B; 70 minutes, 85% B; 72 minutes, 1% B; 90 minutes, 1% B. The 20 most intense ions with charge states between 2 and 4 from an initial survey scan between 300 and 1650 m/z, were selected for tandem mass spectrometry (MS/MS). MS/MS was performed using an isolation width of 2 m/z, normalized collision energy of 35%, and a minimum signal intensity of 1000 counts. Dynamic exclusion was enabled, so once a certain ion was selected twice for MS/MS within 30 seconds, the ion was excluded from being selected again for MS/MS during the next 120 seconds. Once obtained, peak lists were generated from MS/MS spectra using Proteome Discoverer (version 1.3.0.339; Thermo Scientific) and searched against the IPI-Human database (version 3.73) concatenated with a reverse decoy, using Mascot (version 2.3; Matrix Science, Boston, MA). Fixed modification of cysteines to S-carbamidomethyl derivatives and variable ones of methionine oxidation, 13C6-lysine, and 13C615N4-arginine were defined for the database search. Mass tolerance was set to 5 ppm for precursor ions and 0.6 Da for fragment ions. Search results were exported as DAT files and grouped to protein matches using ProteoIQ (version 2.6.03; Nusep, Sydney, Australia). Protein hits were filtered to include only those that were identified with <1% false discovery rate and two peptides detection. Raw files were converted to mzXML with Trans Proteomic Pipeline (version 4.4).29Deutsch E.W. Mendoza L. Shteynberg D. Farrah T. Lam H. Tasman N. Sun Z. Nilsson E. Pratt B. Prazen B. Eng J.K. Martin D.B. Nesvizhskii A.I. Aebersold R. A guided tour of the trans-proteomic pipeline.Proteomics. 2010; 10: 1150-1159Crossref PubMed Scopus (601) Google Scholar Intensities of peptide peaks were extracted from mzXML using deisotoped accurate mass retention time pairs, which were replicated throughout the experiments. Windows of the mass tolerance and retention time were 5 ppm and 0 seconds each. Intensity measurements were matched to the redundant, tryptic peptides identified from each protein to determine an average, relative protein fold change using ProteoIQ. Nonproteotypic peptides with significantly different ratios from proteotypic peptides of the same protein were excluded from quantitation. Systematic bias was corrected using intensity normalization. Presence of an interfering precursor that overlaps with the distribution of interest was removed from quantitation using the correlation coefficient measured between the theoretical and experimental isotopic distributions for the quantitative precursors. We screened all of the SILAC-identified proteins for those that had at least a 20% reduction or increase in protein levels in UM-SCC-1 Pre375 (high miR-375) compared to the UM-SCC-1 control (low miR-375). The lists of down-regulated and up-regulated proteins were compared to the theoretical direct target lists based on seed matches in miRWalk.30Dweep H. Sticht C. Pandey P. Gretz N. miRWalk–database: prediction of possible miRNA binding sites by “walking” the genes of three genomes.J Biomed Inform. 2011; 44: 839-847Crossref PubMed Scopus (1376) Google Scholar miRWalk has a combined list of the putative target lists from 10 different prediction algorithms, including miRWalk,30Dweep H. Sticht C. Pandey P. Gretz N. miRWalk–database: prediction of possible miRNA binding sites by “walking” the genes of three genomes.J Biomed Inform. 2011; 44: 839-847Crossref PubMed Scopus (1376) Google Scholar TargetScan,31Lewis B.P. Burge C.B. Bartel D.P. Conserved seed pairing, often flanked by adenosines, indicates that thousands of human genes are microRNA targets.Cell. 2005; 120: 15-20Abstract Full Text Full Text PDF PubMed Scopus (9835) Google Scholar DIANA-mT,32Vlachos I.S. Kostoulas N. Vergoulis T. Georgakilas G. Reczko M. Maragkakis M. Paraskevopoulou M.D. Prionidis K. Dalamagas T. Hatzigeorgiou A.G. DIANA miRPath v.2.0: investigating the combinatorial effect of microRNAs in pathways.Nucleic Acids Res. 2012; 40: W498-W504Crossref PubMed Scopus (442) Google Scholar miRanda,33Betel D. Wilson M. Gabow A. Marks D.S. Sander C. The microRNA.org resource: targets and expression.Nucleic Acids Res. 2008; 36: D149-D153Crossref PubMed Scopus (2037) Google Scholar RNA22,34Miranda K.C. Huynh T. Tay Y. Ang Y.S. Tam W.L. Thomson A.M. Lim B. Rigoutsos I. A pattern-based method for the identification of microRNA binding sites and their corresponding heteroduplexes.Cell. 2006; 126: 1203-1217Abstract Full Text Full Text PDF PubMed Scopus (1567) Google Scholar and RNAhybrid.35Kruger J. Rehmsmeier M. RNAhybrid: microRNA target prediction easy, fast and flexible.Nucleic Acids Res. 2006; 34: W451-W454Crossref PubMed Scopus (1213) Google Scholar We confirmed some of the down-regulated and up-regulated proteins by microarray analysis. We compared the average UM-SCC-1 Pre375/Control ratios of mRNA abundance in three independent replicates (the procedure for the microarray analysis is described below). The lists of SILAC-identified down-regulated and up-regulated proteins and the SILAC Pre375/Control log ratio values of the proteins were uploaded into Ingenuity Pathway Analysis (IPA; Qiagen, Redwood City, CA). For the data set, a Core Analysis was run in IPA. The top molecular and cellular functions that the down-regulated and up-regulated proteins were associated with were identified. The list of down-regulated and up-regulated proteins that were recognized in IPA analysis as associated with cellular movement was plugged to STRING version 10 (http://string-db.org, last accessed November 2015).36Szklarczyk D. Franceschini A. Wyder S. Forslund K. Heller D. Huerta-Cepas J. Simonovic M. Roth A. Santos A. Tsafou K.P. Kuhn M. Bork P. Jensen L.J. von Mering C. STRING v10: protein-protein interaction networks, integrated over the tree of life.Nucleic Acids Res. 2015; 43: D447-D452Crossref PubMed Scopus (6709) Google Scholar The confidence view was selected to show the STRING network view of these dysregulated proteins. Total RNA from three biological replicates from the UM-SCC-1 transductant cells was isolated using TRIzol (catalog number 15596-026; Life Technologies), according to the manufacturer's protocol. The quality of the RNA samples was monitored using a 2100 Bioanalyzer (Agilent Technologies, Santa Clara, CA). For each RNA sample (total of six samples), linear amplification and biotin labeling of 500 ng of total RNA were performed using the Illumina TotalPrep RNA Amplification Kit (Ambion, Waltham, MA), according to the suggested manufacturer's protocol. The amplified RNA was hybridized to Illumina HumanHT-12 v3 Expression BeadChips (Illumina, San Diego, CA). BeadChips were scanned using an Illumina BeadArray Reader and the Bead Scan software GenomeStudio version 2011.1 (Illumina). The raw data were quantile normalized and uploaded to ArrayExpress. Microarray data are available in the ArrayExpress database (http://www.ebi.ac.uk/arrayexpress; accession number E-MTAB-5405). The average signal of each mRNA from the independent replicates of UM-SCC-1 Control and Pre375 cells was determined. Unpaired t-tests were calculated for each of the mRNAs comparing the signals from the three replicates of the UM-SCC-1 Control and Pre375 cells. Vimentin (catalog number sc-6260) and zinc finger E-box binding homeobox (ZEB) 2 (catalog number sc-271984) antibodies were purchased from Santa Cruz Biotechnology, Inc. (Dallas, TX). L-plastin (catalog number 5350), metadherin (catalog number 9596), RUNX1/AML1 (catalog number 4334), JunD (catalog number 5000), c-Jun (catalog number 9165), and phospho-c-Jun (Ser 63) (catalog number 9261) antibodies were purchased from Cell Signaling Technology (Danvers, MA). Lactate dehydrogenase B (catalog number 14824-1-AP) was purchased from ProteinTech (Rosemont, IL). B-tubulin (catalog number T4026) and B-actin (catalog number A5441) antibodies were purchased from Sigma-Aldrich (St. Louis, MO). IRDye 680RD anti-mouse (catalog number 926-68072) and IRDye 800CW anti-rabbit (catalog number 926-32213) secondary antibodies were purchased from LI-COR Biosciences (Lincoln, NE). The UM-SCC-1 cell lysates were prepared in two ways: the cells were lysed in radioimmunoprecipitation assay buffer containing 1× protease inhibitors (catalog number 11836170-001; Roche, New York, NY). Cell lysates were sonicated for 15 seconds at 4°C. Quantitation of total protein concentration was performed with the Pierce BCA Protein Assay (catalog number 23225; Thermo Scientific). SDS-PAGE sample loading buffer was added to 40 μg of total protein and heated at 95°C for 5 minutes, and the cells were lysed in 2× sample buffer with 2× protease inhibitors. The cell lysates were diluted down to 1× and sheared with a 28-gauge syringe needle. The sheared lysates were heated at 95°C for 5 minutes. The prepared lysates were loaded in polyacrylamide gels and run at 120 V for approximately 1.5 hours. Gels were transferred to nitrocellulose membranes at 100 V for 1 hour. Membranes were blocked in LI-COR Odyssey blocking buffer (catalog number 927-40000; LI-COR Biosciences) for 1 hour at room temperature and incubated with primary antibodies overnight at 4°C. The membranes were washed three times with 1× Tris-buffered saline–Tween and incubated with secondary antibodies (IRDye 680RD anti-mouse and IRDye 800CW anti-rabbit; 1:5000) for 1 hour at room temperature. The membranes were then washed again thre" @default.
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• W2613209216 title "miR-375 Regulates Invasion-Related Proteins Vimentin and L-Plastin" @default.
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