DOI QR코드

DOI QR Code

Creating Subnetworks from Transcriptomic Data on Central Nervous System Diseases Informed by a Massive Transcriptomic Network

  • Feng, Yaping (Development and Cell Biology, Department of Genetics, Iowa State University) ;
  • Syrkin-Nikolau, Judith A. (Macalester College) ;
  • Wurtele, Eve S. (Development and Cell Biology, Department of Genetics, Iowa State University)
  • Received : 2013.01.04
  • Accepted : 2013.01.14
  • Published : 2013.03.30

Abstract

High quality publicly-available transcriptomic data representing relationships in gene expression across a diverse set of biological conditions is used as a context network to explore transcriptomics of the CNS. The context network, 18367Hu-matrix, contains pairwise Pearson correlations for 22,215 human genes across18,637 human tissue samples1. To do this, we compute a network derived from biological samples from CNS cells and tissues, calculate clusters of co-expressed genes from this network, and compare the significance of these to clusters derived from the larger 18367Hu-matrix network. Sorting and visualization uses the publicly available software, MetaOmGraph (http://www.metnetdb.org/MetNet_MetaOm-Graph.htm). This identifies genes that characterize particular disease conditions. Specifically, differences in gene expression within and between two designations of glial cancer, astrocytoma and glioblastoma, are evaluated in the context of the broader network. Such gene groups, which we term outlier-networks, tease out abnormally expressed genes and the samples in which this expression occurs. This approach distinguishes 48 subnetworks of outlier genes associated with astrocytoma and glioblastoma. As a case study, we investigate the relationships among the genes of a small astrocytoma-only subnetwork. This astrocytoma-only subnetwork consists of SVEP1, IGF1, CHRNA3, and SPAG6. All of these genes are highly coexpressed in a single sample of anaplastic astrocytoma tumor (grade III) and a sample of juvenile pilocytic astrocytoma. Three of these genes are also associated with nicotine. This data lead us to formulate a testable hypothesis that this astrocytoma outlier-network provides a link between some gliomas/astrocytomas and nicotine.

Keywords

References

  1. Feng, Y., Hurst, J., Almeida-De-Macedo, M., Chen, X., Li, L., Ransom, N., and Wurtele, E. S. (2012). Massive human co-expression network and its medical applications. Chem Biodivers 9, 868-887. https://doi.org/10.1002/cbdv.201100355
  2. Behrends, C., Sowa, M. E., Gygi, S. P., and Harper, J. W. (2010). Network organization of the human autophagy system. Nature 466, 68-76. https://doi.org/10.1038/nature09204
  3. Mentzen, W. I., and Wurtele, E. S. (2008). Regulon organization of Arabidopsis. BMC Plant Biol 8, 99. https://doi.org/10.1186/1471-2229-8-99
  4. Tong, A. H., Lesage, G., Bader, G. D., Ding, H., Xu, H., Xin, X., Young, J., Berriz, G. F., Brost, R. L., Chang, M., et al. (2004). Global mapping of the yeast genetic interaction network. Science 303, 808-813. https://doi.org/10.1126/science.1091317
  5. Gieger, C., Radhakrishnan, A., Cvejic, A., Tang, W., Porcu, E., Pistis, G., Serbanovic-Canic, J., Elling, U., Goodall, A. H., Labrune, Y., et al. (2011). New gene functions in megakaryopoiesis and platelet formation. Nature 480, 201-208. https://doi.org/10.1038/nature10659
  6. Li, L., Foster, C. M., Gan, Q., Nettleton, D., James, M. G., Myers, A. M., and Wurtele, E. S. (2009). Identification of the novel protein QQS as a component of the starch metabolic network in Arabidopsis leaves. Plant J 58, 485-498. https://doi.org/10.1111/j.1365-313X.2009.03793.x
  7. Ngaki, M. N., Louie, G. V., Philippe, R. N., Manning, G., Pojer, F., Bowman, M. E., Li, L., Larsen, E., Wurtele, E. S., and Noel, J. P. (2012). Evolution of the chalcone-isomerase fold from fatty-acid binding to stereospecific catalysis. Nature 485, 530-533.
  8. Matthews, C. A., Shaw, J. E., Hooper, J. A., Young, I. G., Crouch, M. F., and Campbell, H. D. (2007). Expression and evolution of the mammalian brain gene Ttyh1. J Neurochem 100, 693-707. https://doi.org/10.1111/j.1471-4159.2006.04237.x
  9. Glait-Santar, C., and Benayahu, D. (2012). Regulation of SVEP1 gene expression by 17beta-estradiol and TNFalpha in pre-osteoblastic and mammary adenocarcinoma cells. J Steroid Biochem Mol Biol 130, 36-44. https://doi.org/10.1016/j.jsbmb.2011.12.015
  10. D'Souza, R. D., and Vijayaraghavan, S. (2012). Nicotinic receptor-mediated filtering of mitral cell responses to olfactory nerve inputs involves the alpha3beta4 subtype. J Neurosci 32, 3261-3266. https://doi.org/10.1523/JNEUROSCI.5024-11.2012
  11. Pakaski, M., and Kasa, P. (2003). Role of acetylcholinesterase inhibitors in the metabolism of amyloid precursor protein. Curr Drug Targets CNS Neurol Disord 2, 163-171. https://doi.org/10.2174/1568007033482869
  12. Paterson, D., and Nordberg, A. (2000). Neuronal nicotinic receptors in the human brain. Prog Neurobiol 61, 75-111. https://doi.org/10.1016/S0301-0082(99)00045-3
  13. Son, J. H., and Winzer-Serhan, U. H. (2009). Chronic neonatal nicotine exposure increases mRNA expression of neurotrophic factors in the postnatal rat hippocampus. Brain Res 1278, 1-14. https://doi.org/10.1016/j.brainres.2009.04.046
  14. Silvera, S. A., Miller, A. B., and Rohan, T. E. (2006). Cigarette smoking and risk of glioma: a prospective cohort study. Int J Cancer 118, 1848-1851. https://doi.org/10.1002/ijc.21569
  15. Rose, J. E., Behm, F. M., Drgon, T., Johnson, C., and Uhl, G. R. (2010). Personalized smoking cessation: interactions between nicotine dose, dependence and quit-success genotype score. Mol Med 16, 247-253.
  16. Nikolau, B. J., and Wurtele, E. S. (2007). Concepts in Plant Metabolomics, Dordrecht: Springer.