Gene Discovery Analysis from Mouse Embryonic Stem Cells Based on Time Course Microarray Data

  • Suh, Young Ju (Division of Biological Science, The Research Institute of Natural Sciences, Sookmyung Women's University) ;
  • Cho, Sun A (Department of Biological Science, Sookmyung Women's University) ;
  • Shim, Jung Hee (Department of Biological Science, Sookmyung Women's University) ;
  • Yook, Yeon Joo (Department of Biological Science, Sookmyung Women's University) ;
  • Yoo, Kyung Hyun (Department of Biological Science, Sookmyung Women's University) ;
  • Kim, Jung Hee (Department of Biological Science, Sookmyung Women's University) ;
  • Park, Eun Young (Department of Biological Science, Sookmyung Women's University) ;
  • Noh, Ji Yeun (Division of Biological Science, The Research Institute of Natural Sciences, Sookmyung Women's University) ;
  • Lee, Seong Ho (Division of Biological Science, The Research Institute of Natural Sciences, Sookmyung Women's University) ;
  • Yang, Moon Hee (Division of Biological Science, The Research Institute of Natural Sciences, Sookmyung Women's University) ;
  • Jeong, Hyo Seok (Division of Biological Science, The Research Institute of Natural Sciences, Sookmyung Women's University) ;
  • Park, Jong Hoon (Division of Biological Science, The Research Institute of Natural Sciences, Sookmyung Women's University)
  • 투고 : 2007.11.16
  • 심사 : 2008.07.07
  • 발행 : 2008.10.31

초록

An embryonic stem cell is a powerful tool for investigation of early development in vitro. The study of embryonic stem cell mediated neuronal differentiation allows for improved understanding of the mechanisms involved in embryonic neuronal development. We investigated expression profile changes using time course cDNA microarray to identify clues for the signaling network of neuronal differentiation. For the short time course microarray data, pattern analysis based on the quadratic regression method is an effective approach for identification and classification of a variety of expressed genes that have biological relevance. We studied the expression patterns, at each of 5 stages, after neuronal induction at the mRNA level of embryonic stem cells using the quadratic regression method for pattern analysis. As a result, a total of 316 genes (3.1%) including 166 (1.7%) informative genes in 8 possible expression patterns were identified by pattern analysis. Among the selected genes associated with neurological system, all three genes showing linearly increasing pattern over time, and one gene showing decreasing pattern over time, were verified by RT-PCR. Therefore, an increase in gene expression over time, in a linear pattern, may be associated with embryonic development. The genes: Tcfap2c, Ttr, Wnt3a, Btg2 and Foxk1 detected by pattern analysis, and verified by RT-PCR simultaneously, may be candidate markers associated with the development of the nervous system. Our study shows that pattern analysis, using the quadratic regression method, is very useful for investigation of time course cDNA microarray data. The pattern analysis used in this study has biological significance for the study of embryonic stem cells.

키워드

과제정보

연구 과제 주관 기관 : Korea Research Foundation

참고문헌

  1. Alberts, R., Fu, J., Swertz, M.A., Lubbers, L.A., Albers, C.J., and Jansen, R.C. (2005). Combining microarrays and genetic analysis. Brief Bioiform. 6, 135-145 https://doi.org/10.1093/bib/6.2.135
  2. Antonson, P., Schuster, G.U., Wang, L., Rozell, B., Holter, E., Flodby, P., Treuter, E., Holmgren, L., and Gustafsson, J.A. (2003). Inactivation of the neuclear receptor coactivator RAP250 in mice results in placental vascular dysfuction. Mol. Cell. Biol. 23, 1260-1268 https://doi.org/10.1128/MCB.23.4.1260-1268.2003
  3. Bain, G., Kitchens, D., Yao, M., Huettner, J.E., and Gottlieb, D.I. (1995). Embryonic stem cells express neuronal properties in vitro. Dev. Biol. 168, 342-357 https://doi.org/10.1006/dbio.1995.1085
  4. Benjamini, Y., and Hochberg, Y. (1995). Controlling the false discovery rate: A practical and powerful approach to multiple testing. J. Roy. Stat. Soc. B 57, 289-300
  5. Eckel, J.E., Gennings, C., Chinchilli, V.M., Burgoon, L.D., and Zacharewski, T.R. (2004). Empirical bayes gene screening tool for time-course or dose-response microarray data. J. Biopharm. Stat. 14, 647-670 https://doi.org/10.1081/BIP-200025656
  6. El-Ghissassi, F., Valsesia-Wittmann, S., Falette, N., Duriez, C., Walden, P.D., and Puisieux, A. (2002). $BTG2^{TIS21/PC3}$ induces neuronal differentiation and prevents apoptosis of terminally differentiated PC12 cells. Oncogene 21, 6772-6778 https://doi.org/10.1038/sj.onc.1205888
  7. Kermer, P., Krajewska, M., Zapata, J.M., Takayama, S., Mai, J., Krajewski, S., and Reed, J.C. (2002). Bag1 is a regulator and marker of neuronal differentiation. Cell Death Differ. 9, 405-413 https://doi.org/10.1038/sj.cdd.4400972
  8. Kim, C.G., Lee, J.J., Jung, D.Y., Jeon, J., Heo, H.S., Kang, H.C., Shin, J.H., Cho, Y.S., Cha, K.J., Kim, C.G., et al. (2006). Profiling of differentially expressed genes in human stem cells by cDNA microarray. Mol. Cells 21, 343-355
  9. Liu, H., Tarima, S., Borders, A.S., Getchell, T.V., Getchell, M.L., and Stromberg, A.J. (2005). Quadratic regression analysis for gene discovery and pattern recognition for non-cyclic short timecourse microarray experiments. BMC Bioinformatics 6, 106 https://doi.org/10.1186/1471-2105-6-106
  10. Lu, W.G., Yamamoto, V., Ortega, B., and Baltimore, D. (2004). Mammalian Ryk is a Wnt coreceptor required for stimulation of neurite outgrowth. Cell 119, 97-108 https://doi.org/10.1016/j.cell.2004.09.019
  11. Martin, D., Brun, C., Remy, E., Mouren, P., Thieffry, D., and Jacq, B. (2004). GOToolBox : functional investigation of gene datasets based on gene ontology. Genome Biol. 5, R101 https://doi.org/10.1186/gb-2004-5-12-r101
  12. Muroyama, Y., Kondoh, H., and Takada, S. (2004). Wnt proteins promote neuronal differentiation in neural stem cell culture. Biochem. Biophys. Res. Commun. 313, 915-921
  13. Park, T., Yi, S.G., Lee, S., Lee, S.Y., Yoo, D.H., Ahn, J.I., and Lee, Y.S. (2003). Statistical tests for identifying differentially expressed genes in time-course microarray experiments. Bioinformatics 19, 694-703 https://doi.org/10.1093/bioinformatics/btg068
  14. Peddada, S.D., Lobenhofer, E.K., Li, L., Afshari, C.A., Weinberg, C.R., and Umbach, D.M. (2003). Gene selection and clustering for time-course and dose-response microarray experiments using order-restricted inference. Bioinformatics 19, 834-841 https://doi.org/10.1093/bioinformatics/btg093
  15. Puente, L.G., Borris, D.J., Carriere, J.F., Kelly, J.F., and Megeney, L.A. (2006). Identification of candidate regulators of embryonic stem cell differentiation by comparative phosphoprotein affinity profiling. Mol. Cell. Proteomics 5, 57-67 https://doi.org/10.1074/mcp.M500166-MCP200
  16. Rouse, R., and Hardiman, G. (2003). Microarray technology-- intellectual property retrospective. Pharmacogenomics 4, 623- 632 https://doi.org/10.1517/phgs.4.5.623.23792
  17. Santos, E., Monzon-Mayor, M., Romero-Aleman, M.M., and Yanes, C. (2008). Distribution of neurotrophin-3 during the ontogeny and regeneration of the lizard (Gallotia galloti) visual system. Dev. Neurobiol. 68, 31-44 https://doi.org/10.1002/dneu.20566
  18. Sekkai, D., Gruel, G., Herry, M., Moucadel, V., Constantinescu, S.N., Albagli, O., Tronik-Le Roux, D., Vainchenker, W., and Bennaceur-Griscelli, A. (2005). Microarray analysis of LIF/Stat3 transcriptional targets in embryonic stem cells. Stem Cells 23, 1634-1642 https://doi.org/10.1634/stemcells.2005-0182
  19. Soltani, M.H., Pichardo, R., Song, Z., Sangha, N., Camacho, F., Satyamoorthy, K., Sangueza, O.P., and Setaluri, V. (2005). Microtubule-associated protein 2, a marker of neuronal differentiation, induces mitotic defects, inhibits growth of melanoma cells, and predicts metastatic potential of cutaneous melanoma. Am. J. Pathol. 166, 1841-1850 https://doi.org/10.1016/S0002-9440(10)62493-5
  20. Stein, T.D., Anders, N.J., DeCarli, C., Chan, S.L., Mattson, M.P., and Johnson, J.A. (2004). Neutralization of transthyretin reverses the neuroprotective effects of secreted amyloid precursor protein (APP) in APPSW mice resulting in tau phosphorylation and loss of hippocampal neurons: support for the amyloid hypothesis. J. Neurosci. 27, 7707-7717
  21. Storey, J.D., Xiao, W., Leek, J.T., Tompkins, R.G., and Davis, R.W. (2005). Significance analysis of time course microarray experiments. Proc. Natl. Acad. Sci. USA 102, 12837-12842
  22. Troyanskaya, O., Cantor, M., Sherlock, G., Brown, P., Hastie, T., Tibshirani, R., Botstein, D., and Altman, R.B. (2001). Missing value estimation methods for DNA microarrays. Bioinformatics 17, 520-525 https://doi.org/10.1093/bioinformatics/17.6.520
  23. Werling, U., and Schorle, H. (2002). Transcription factor gene AP- 2gamma essential for early murine development. Mol. Cell. Biol. 22, 3149-3156 https://doi.org/10.1128/MCB.22.9.3149-3156.2002
  24. Wijchers, P.J., Hoekman, M.F., Burbach, J.P., and Smidt, M.P. (2006). Identification of forkhead transcription factors in cortical and dopaminergic areas of the adult murine brain. Brain Res. 1068, 23-33 https://doi.org/10.1016/j.brainres.2005.11.022
  25. Wolfinger, R.D., Gibson, G., Wolfinger, E.D., Bennett, L., Hamadeh, H., Bushel, P., Afshari, C., and Paules, R.S. (2001). Assessing gene significance from cDNA microarray expression data via mixed models. J. Comput. Biol. 8, 625-637 https://doi.org/10.1089/106652701753307520
  26. Xu, X.L., Olson, J.M., and Zhao, L.P. (2002). A regression-based method to identify differentially expressed genes in microarray time course studies and its application in an inducible Huntington's disease transgenic model. Hum. Mol. Genet. 10, 1977-1985
  27. Yamazoe, H., and Iwata, H. (2005). Cell microarray for screening feeder cells for differentiation of embryonic stem cells. J. Biosci. Bioeng. 100, 292-296 https://doi.org/10.1263/jbb.100.292