Functional Analysis and Characterization of Differential Coexpression Networks

Scientific Reports

Volumn 5, Issue , 2015, Pages

  Hsu, Chia Lang ^a   Juan, Hsueh Fen ^a   Huang, Hsuan Cheng ^b  

^a NATIONAL TAIWAN UNIVERSITY   (Taiwan)

^b NATIONAL YANG MING UNIVERSITY   (Taiwan)

Author keywords

[No Author keywords available]

Indexed keywords

SACCHAROMYCES CEREVISIAE PROTEIN;

ALGORITHM; BIOLOGICAL MODEL; COMPUTER SIMULATION; GENE EXPRESSION PROFILING; GENE EXPRESSION REGULATION; METABOLISM; PHYSIOLOGY; PROCEDURES; SACCHAROMYCES CEREVISIAE; SIGNAL TRANSDUCTION;

ALGORITHMS; COMPUTER SIMULATION; GENE EXPRESSION PROFILING; GENE EXPRESSION REGULATION, FUNGAL; MODELS, BIOLOGICAL; SACCHAROMYCES CEREVISIAE; SACCHAROMYCES CEREVISIAE PROTEINS; SIGNAL TRANSDUCTION;

EID: 84939530361     PISSN: None     EISSN: 20452322     Source Type: Journal    
DOI: 10.1038/srep13295     Document Type: Article

References (63)

1. Vidal, M., Cusick, M. E. & Barabasi, A. L. Interactome networks and human disease. Cell 144, 986-98 (2011).
2. Mitra, K., Carvunis, A. R., Ramesh, S. K. & Ideker, T. Integrative approaches for finding modular structure in biological networks. Nat Rev Genet 14, 719-32 (2013).
3. Harrold, J. M., Ramanathan, M. & Mager, D. E. Network-based approaches in drug discovery and early development. Clin Pharmacol Ther 94, 651-8 (2013).
4. Robin, X. et al. Personalized network-based treatments in oncology. Clin Pharmacol Ther 94, 646-50 (2013).
5. Chen, B., Fan, W., Liu, J. & Wu, F. X. Identifying protein complexes and functional modules-from static PPI networks to dynamic PPI networks. Brief Bioinform 15, 177-94 (2014).
6. Ideker, T. & Krogan, N. J. Differential network biology. Mol Syst Biol 8, 565 (2012).
7. Bisson, N. et al. Selected reaction monitoring mass spectrometry reveals the dynamics of signaling through the GRB2 adaptor. Nat Biotechnol 29, 653-8 (2011).
8. Bandyopadhyay, S. et al. Rewiring of genetic networks in response to DNA damage. Science 330, 1385-9 (2010).
9. Stuart, J. M., Segal, E., Koller, D. & Kim, S. K. A gene-coexpression network for global discovery of conserved genetic modules. Science 302, 249-55 (2003).
10. Wyrick, J. J. & Young, R. A. Deciphering gene expression regulatory networks. Curr Opin Genet Dev 12, 130-6 (2002).
11. Reverter, A. et al. Simultaneous identification of differential gene expression and connectivity in inflammation, adipogenesis and cancer. Bioinformatics 22, 2396-404 (2006).
12. Choi, J. K., Yu, U., Yoo, O. J. & Kim, S. Differential coexpression analysis using microarray data and its application to human cancer. Bioinformatics 21, 4348-55 (2005).
13. Zhang, B. & Horvath, S. A general framework for weighted gene co-expression network analysis. Stat Appl Genet Mol Biol 4, Article17 (2005).
14. Yu, H. et al. Link-based quantitative methods to identify differentially coexpressed genes and gene pairs. BMC Bioinformatics 12, 315 (2011).
15. Amar, D., Safer, H. & Shamir, R. Dissection of regulatory networks that are altered in disease via differential co-expression. PLoS Comput Biol 9, e1002955 (2013).
16. Tesson, B. M., Breitling, R. & Jansen, R. C. DiffCoEx: a simple and sensitive method to find differentially coexpressed gene modules. BMC Bioinformatics 11, 497 (2010).
17. de la Fuente, A. From 'differential expression' to 'differential networking'-identification of dysfunctional regulatory networks in diseases. Trends Genet 26, 326-33 (2010).
18. Southworth, L. K., Owen, A. B. & Kim, S. K. Aging mice show a decreasing correlation of gene expression within genetic modules. PLoS Genet 5, e1000776 (2009).
19. van Nas, A. et al. Elucidating the role of gonadal hormones in sexually dimorphic gene coexpression networks. Endocrinology 150, 1235-49 (2009).
20. Walley, A. J. et al. Differential coexpression analysis of obesity-associated networks in human subcutaneous adipose tissue. Int J Obes (Lond) 36, 137-47 (2012).
21. Edgar, R., Domrachev, M. & Lash, A. E. Gene Expression Omnibus: NCBI gene expression and hybridization array data repository. Nucleic Acids Res 30, 207-10 (2002).
22. Ronen, M. & Botstein, D. Transcriptional response of steady-state yeast cultures to transient perturbations in carbon source. Proc Natl Acad Sci USA 103, 389-94 (2006).
23. Pramila, T., Miles, S., GuhaThakurta, D., Jemiolo, D. & Breeden, L. L. Conserved homeodomain proteins interact with MADS box protein Mcm1 to restrict ECB-dependent transcription to the M/G1 phase of the cell cycle. Genes Dev 16, 3034-45 (2002).
24. Barabasi, A. L. & Oltvai, Z. N. Network biology: understanding the cell's functional organization. Nat Rev Genet 5, 101-13 (2004).
25. Lin, C. C., Lee, C. H., Fuh, C. S., Juan, H. F. & Huang, H. C. Link clustering reveals structural characteristics and biological contexts in signed molecular networks. PLoS One 8, e67089 (2013).
26. Filkov, V., Saul, Z. M., Roy, S., D'Souza, R. M. & Devanbu, P. T. Modeling and verifying a broad array of network properties. Europhys Lett 86, 28003 (2009).
27. Roy, S. & Filkov, V. Strong associations between microbe phenotypes and their network architecture. Phys Rev E 80, 040902 (R) (2009).
28. Stark, C. et al. BioGRID: a general repository for interaction datasets. Nucleic Acids Res 34, D535-9 (2006).
29. Tucker, C. L. & Fields, S. Lethal combinations. Nat Genet 35, 204-5 (2003).
30. Costanzo, M., Baryshnikova, A., Myers, C. L., Andrews, B. & Boone, C. Charting the genetic interaction map of a cell. Curr Opin Biotechnol 22, 66-74 (2011).
31. Kelley, R. & Ideker, T. Systematic interpretation of genetic interactions using protein networks. Nat Biotechnol 23, 561-6 (2005).
32. Jona, G., Choder, M. & Gileadi, O. Glucose starvation induces a drastic reduction in the rates of both transcription and degradation of mRNA in yeast. Biochim Biophys Acta 1491, 37-48 (2000).
33. Haurie, V., Boucherie, H. & Sagliocco, F. The Snf1 protein kinase controls the induction of genes of the iron uptake pathway at the diauxic shift in Saccharomyces cerevisiae. J Biol Chem 278, 45391-6 (2003).
34. Rutherford, J. C. & Bird, A. J. Metal-responsive transcription factors that regulate iron, zinc, and copper homeostasis in eukaryotic cells. Eukaryot Cell 3, 1-13 (2004).
35. Philpott, C. C., Protchenko, O., Kim, Y. W., Boretsky, Y. & Shakoury-Elizeh, M. The response to iron deprivation in Saccharomyces cerevisiae: expression of siderophore-based systems of iron uptake. Biochem Soc Trans 30, 698-702 (2002).
36. Horak, C. E. et al. Complex transcriptional circuitry at the G1/S transition in Saccharomyces cerevisiae. Genes Dev 16, 3017-33 (2002).
37. Levin, D. E. Regulation of cell wall biogenesis in Saccharomyces cerevisiae: the cell wall integrity signaling pathway. Genetics 189, 1145-75 (2011).
38. Levin, D. E. Cell wall integrity signaling in Saccharomyces cerevisiae. Microbiol Mol Biol Rev 69, 262-91 (2005).
39. Thiel, G., Lietz, M. & Hohl, M. How mammalian transcriptional repressors work. Eur J Biochem 271, 2855-62 (2004).
40. Yang, J. et al. DCGL v2.0: an R package for unveiling differential regulation from differential co-expression. PLoS One 8, e79729 (2013).
41. Young, E. T., Dombek, K. M., Tachibana, C. & Ideker, T. Multiple pathways are co-regulated by the protein kinase Snf1 and the transcription factors Adr1 and Cat8. J Biol Chem 278, 26146-58 (2003).
42. Haurie, V. et al. The transcriptional activator Cat8p provides a major contribution to the reprogramming of carbon metabolism during the diauxic shift in Saccharomyces cerevisiae. J Biol Chem 276, 76-85 (2001).
43. Kratzer, S. & Schuller, H. J. Transcriptional control of the yeast acetyl-CoA synthetase gene, ACS1, by the positive regulators CAT8 and ADR1 and the pleiotropic repressor UME6. Mol Microbiol 26, 631-41 (1997).
44. Wiatrowski, H. A. & Carlson, M. Yap1 accumulates in the nucleus in response to carbon stress in Saccharomyces cerevisiae. Eukaryot Cell 2, 19-26 (2003).
45. Lewis, J. A. & Gasch, A. P. Natural variation in the yeast glucose-signaling network reveals a new role for the Mig3p transcription factor. G3 (Bethesda) 2, 1607-12 (2012).
46. Rutherford, J. C., Jaron, S. & Winge, D. R. Aft1p and Aft2p mediate iron-responsive gene expression in yeast through related promoter elements. J Biol Chem 278, 27636-43 (2003).
47. Stadler, J. A. & Schweyen, R. J. The yeast iron regulon is induced upon cobalt stress and crucial for cobalt tolerance. J Biol Chem 277, 39649-54 (2002).
48. Protchenko, O. et al. Three cell wall mannoproteins facilitate the uptake of iron in Saccharomyces cerevisiae. J Biol Chem 276, 49244-50 (2001).
49. Qi, J., Han, A., Yang, Z. & Li, C. Metal-sensing transcription factors Mac1p and Aft1p coordinately regulate vacuolar copper transporter CTR2 in Saccharomyces cerevisiae. Biochem Biophys Res Commun 423, 424-8 (2012).
50. Hou, J., Osterlund, T., Liu, Z., Petranovic, D. & Nielsen, J. Heat shock response improves heterologous protein secretion in Saccharomyces cerevisiae. Appl Microbiol Biotechnol 97, 3559-68 (2013).
51. Bean, J. M., Siggia, E. D. & Cross, F. R. High functional overlap between MluI cell-cycle box binding factor and Swi4/6 cell-cycle box binding factor in the G1/S transcriptional program in Saccharomyces cerevisiae. Genetics 171, 49-61 (2005).
52. Koch, C., Moll, T., Neuberg, M., Ahorn, H. & Nasmyth, K. A role for the transcription factors Mbp1 and Swi4 in progression from G1 to S phase. Science 261, 1551-7 (1993).
53. Bastajian, N., Friesen, H. & Andrews, B. J. Bck2 acts through the MADS box protein Mcm1 to activate cell-cycle-regulated genes in budding yeast. PLoS Genet 9, e1003507 (2013).
54. Iyer, V. R. et al. Genomic binding sites of the yeast cell-cycle transcription factors SBF and MBF. Nature 409, 533-8 (2001).
55. Kumar, R. et al. Forkhead transcription factors, Fkh1p and Fkh2p, collaborate with Mcm1p to control transcription required for M-phase. Curr Biol 10, 896-906 (2000).
56. Darieva, Z. et al. A competitive transcription factor binding mechanism determines the timing of late cell cycle-dependent gene expression. Mol Cell 38, 29-40 (2010).
57. Hudson, N. J., Reverter, A. & Dalrymple, B. P. A differential wiring analysis of expression data correctly identifies the gene containing the causal mutation. PLoS Comput Biol 5, e1000382 (2009).
58. Alstott, J., Bullmore, E. & Plenz, D. powerlaw: A Python Package for Analysis of Heavy-Tailed Distributions. PLoS One 9, e85777 (2014).
59. Schlicker, A., Domingues, F. S., Rahnenfuhrer, J. & Lengauer, T. A new measure for functional similarity of gene products based on Gene Ontology. BMC Bioinformatics 7, 302 (2006).
60. Resnik, P. Using information content to evaluate semantic similarity in a taxonomy. in Proceedings of the 14th international joint conference on Artificial intelligence-Volume 1 448-453 (Morgan Kaufmann Publishers Inc., Montreal, Quebec, Canada, 1995).
61. Lin, D. An Information-Theoretic Definition of Similarity. in Proceedings of the Fifteenth International Conference on Machine Learning 296-304 (Morgan Kaufmann Publishers Inc., 1998).
62. Wu, H.-M., Tien, Y.-J. & Chen, C.-h. GAP: A graphical environment for matrix visualization and cluster analysis. Comput Stat Data An 54, 767-778 (2010).
63. Teixeira, M. C. et al. The YEASTRACT database: an upgraded information system for the analysis of gene and genomic transcription regulation in Saccharomyces cerevisiae. Nucleic Acids Res 42, D161-6 (2014).