, Volume 143, Issue 6, pp 635–644 | Cite as

Identification of altered metabolic pathways of γ-irradiated rice mutant via network-based transcriptome analysis

  • Sun-Goo Hwang
  • Dong Sub Kim
  • Jung Eun Hwang
  • Hyeon Mi Park
  • Cheol Seong Jang


In order to develop rice mutants for crop improvement, we applied γ-irradiation mutagenesis and selected a rice seed color mutant (MT) in the M14 targeting-induced local lesions in genome lines. This mutant exhibited differences in germination rate, plant height, and root length in seedlings compared to the wild-type plants. We found 1645 different expressed probes of MT by microarray hybridization. To identify the modified metabolic pathways, we conducted integrated genomic analysis such as weighted correlation network analysis with a module detection method of differentially expressed genes (DEGs) in MT on the basis of large-scale microarray transcriptional profiling. These modules are largely divided into three subnetworks and mainly exhibit overrepresented gene ontology functions such as oxidation-related function, ion-binding, and kinase activity (phosphorylation), and the expressional coherences of module genes mainly exhibited in vegetative and maturation stages. Through a metabolic pathway analysis, we detected the significant DEGs involved in the major carbohydrate metabolism (starch degradation), protein degradation (aspartate protease), and signaling in sugars and nutrients. Furthermore, the accumulation of amino acids (asparagine and glutamic acid), sucrose, and starch in MT were affected by gamma rays. Our results provide an effective approach for identification of metabolic pathways associated with useful agronomic traits in mutation breeding.


Rice Gamma ray mutagenesis Weighted correlation network analysis (WGCNA) Microarray 



This work was carried out with the support of “Cooperative Research Program for Agriculture Science & Technology Development (Project No. PJ01115201)” Rural Development Administration, Republic of Korea, and a grant from the Nuclear R&D Program by the Ministry of Science, ICT and Future Planning (MSIP), Republic of Korea, and the 2014 Research Grant from Kangwon National University (No. 120140389).

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Supplementary material

10709_2015_9861_MOESM1_ESM.pptx (187 kb)
Supplementary material 1 (PPTX 187 kb)
10709_2015_9861_MOESM2_ESM.xlsx (32 kb)
Supplementary material 2 (XLSX 31 kb)


  1. Arulbalachandran D, Mullainathan L (2009) Changes on protein and methionine content of black gram (Vigna mungo (L.) Hepper) induced by gamma rays and EMS. Am-Eurasian J Sci Res 4:68–72Google Scholar
  2. Carter SL, Brechbuhler CM, Griffin M, Bond AT (2004) Gene co-expression network topology provides a framework for molecular characterization of cellular state. Bioinformatics 20:2242–2250. doi: 10.1093/bioinformatics/bth234 CrossRefPubMedGoogle Scholar
  3. Chang X, Liu S, Yu YT, Li YX, Li YY (2010) Identifying modules of coexpressed transcript units and their organization of Saccharopolyspora erythraea from time series gene expression profiles. PloS one 5:e12126. doi: 10.1371/journal.pone.0012126 PubMedCentralCrossRefPubMedGoogle Scholar
  4. Chen JJ et al (2008) A triallelic system of S5 is a major regulator of the reproductive barrier and compatibility of indica-japonica hybrids in rice. Proc Natl Acad Sci USA 105:11436–11441. doi: 10.1073/pnas.0804761105 PubMedCentralCrossRefPubMedGoogle Scholar
  5. Chen J, Ouyang Y, Wang L, Xie W, Zhang Q (2009) Aspartic proteases gene family in rice: gene structure and expression, predicted protein features and phylogenetic relation. Gene 442:108–118. doi: 10.1016/j.gene.2009.04.021 CrossRefPubMedGoogle Scholar
  6. Chow PS, Landhausser SM (2004) A method for routine measurements of total sugar and starch content in woody plant tissues. Tree Physiol 24:1129–1136CrossRefPubMedGoogle Scholar
  7. Chun JB et al (2012) Identification of mutations in OASA1 gene from a gamma-irradiated rice mutant population. Plant Breed 131:276–281. doi: 10.1111/j.1439-0523.2011.01933.x CrossRefGoogle Scholar
  8. Dandekar T, Snel B, Huynen M, Bork P (1998) Conservation of gene order: a fingerprint of proteins that physically interact. Trends Biochem Sci 23:324–328CrossRefPubMedGoogle Scholar
  9. Davidich MI, Bornholdt S (2008) Boolean network model predicts cell cycle sequence of fission yeast. PloS one 3:e1672. doi: 10.1371/journal.pone.0001672 PubMedCentralCrossRefPubMedGoogle Scholar
  10. Davies DR (1990) The structure and function of the aspartic proteinases. Annu Rev Biophys Biophys Chem 19:189–215. doi: 10.1146/ CrossRefPubMedGoogle Scholar
  11. Dunn BM (2002) Structure and mechanism of the pepsin-like family of aspartic peptidases. Chem Rev 102:4431–4458CrossRefPubMedGoogle Scholar
  12. Ficklin SP, Feltus FA (2011) Gene coexpression network alignment and conservation of gene modules between two grass species: maize and rice. Plant Physiol 156:1244–1256PubMedCentralCrossRefPubMedGoogle Scholar
  13. Ficklin SP, Luo F, Feltus FA (2010) The association of multiple interacting genes with specific phenotypes in rice using gene coexpression networks. Plant Physiol 154:13–24PubMedCentralCrossRefPubMedGoogle Scholar
  14. Forney RB, Hughes FW, Richards AB, Gates PW (1963) Toxicity and depressant action of ethanol and hexobarbital after pretreatment with asparagine. Toxicol Appl Pharmacol 5:790–793CrossRefPubMedGoogle Scholar
  15. Friedman N, Linial M, Nachman I, Peer D (2000) Using bayesian networks to analyze expression data. J Comput Biol 7:601–620CrossRefPubMedGoogle Scholar
  16. Hartwell LH, Hopfield JJ, Leibler S, Murray AW (1999) From molecular to modular cell biology. Nature 402:C47–C52. doi: 10.1038/35011540 CrossRefPubMedGoogle Scholar
  17. Hayashi T, Aoki S (1985) Effect of irradiation on the carbohydrate metabolism responsible for sucrose accumulation in potatoes. J Agric Food Chem 33:13–17Google Scholar
  18. Hruz T et al (2008) Genevestigator v3: a reference expression database for the meta-analysis of transcriptomes. Adv Bioinform 2008:420747. doi: 10.1155/2008/420747 CrossRefGoogle Scholar
  19. Huang DW, Sherman BT, Lempicki RA (2009) Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc 4:44–57. doi: 10.1038/nprot.2008.211 CrossRefGoogle Scholar
  20. Hwang JE et al (2013) Transcriptome profiling in response to different types of ionizing radiation and identification of multiple radio marker genes in rice. Physiol Plant. doi: 10.1111/ppl.12121 Google Scholar
  21. Hwang SG, Hwang JG, Kim DS, Jang CS (2014) Genome-wide DNA polymorphism and transcriptome analysis of an early-maturing rice mutant. Genetica 142:73–85. doi: 10.1007/s10709-013-9755-0 CrossRefPubMedGoogle Scholar
  22. Iancu OD et al (2010) Genetic diversity and striatal gene networks: focus on the heterogeneous stock-collaborative cross (HS-CC) mouse. BMC Genom 11:585. doi: 10.1186/1471-2164-11-585 CrossRefGoogle Scholar
  23. Ivliev AE, 't Hoen PA, Sergeeva MG (2010) Coexpression network analysis identifies transcriptional modules related to proastrocytic differentiation and sprouty signaling in glioma. Cancer Res 70:10060–10070. doi: 10.1158/0008-5472.CAN-10-2465 CrossRefPubMedGoogle Scholar
  24. Jain SM, Ahloowalia BS, Veilleux RE (1998) Somaclonal variation and induced mutation in crop improvement. Curr Plant Sci Biotechnol Agric 32:203–218CrossRefGoogle Scholar
  25. Kim DS, Lee IS, Jang CS, Hyun DY, Seo YW, Lee YI (2004a) Selection of 5-methyltryptophan resistant rice mutants from irradiated calli derived from embryos. Euphytica 135:9–19CrossRefGoogle Scholar
  26. Kim DS, Lee IS, Jang CS, Lee SJ, Song HS, Lee YI, Seo YW (2004b) AEC resistant rice mutants induced by gamma-ray irradiation may include both elevated lysine production and increased activity of stress related enzymes. Plant Sci 167:305–316CrossRefGoogle Scholar
  27. Kim DS, Lee IS, Jang CS, Kang SY, Seo YW (2005) Characterization of the altered anthranilate synthase in 5-methyltryptophan-resistant rice mutants. Plant Cell Rep 24:357–365. doi: 10.1007/s00299-005-0943-y CrossRefPubMedGoogle Scholar
  28. Kim DS et al (2011) Antioxidant response of Arabidopsis plants to gamma irradiation: genome-wide expression profiling of the ROS scavenging and signal transduction pathways. J Plant Physiol 168:1960–1971. doi: 10.1016/j.jplph.2011.05.008 CrossRefPubMedGoogle Scholar
  29. Kim SH et al (2012) Genome-wide transcriptome profiling of ROS scavenging and signal transduction pathways in rice (Oryza sativa L.) in response to different types of ionizing radiation. Mol Biol Rep 39:11231–11248. doi: 10.1007/s11033-012-2034-9 CrossRefPubMedGoogle Scholar
  30. Kovacs E, Keresztes A (2002) Effect of gamma and UV-B/C radiation on plant cells. Micron 33:199–210CrossRefPubMedGoogle Scholar
  31. Langfelder P, Horvath S (2008) WGCNA: an R package for weighted correlation network analysis. BMC Bioinform 9:559. doi: 10.1186/1471-2105-9-559 CrossRefGoogle Scholar
  32. Lansford EM Jr, Hill ID, Shive W (1962) Effects of asparagine and other related nutritional supplements upon alcohol-induced rat liver triglyceride elevation. J Nutr 78:219–222PubMedGoogle Scholar
  33. Lea PJ, Miflin BJ (2003) Glutamate synthase and the synthesis of glutamate in plants. Plant Physiol Biochem 41:555–564CrossRefGoogle Scholar
  34. Lea PJ, Sodek L, Parry MAJ, Shewry PR, Halford NG (2007) Asparagine in plants. Ann Appl Biol 150:1–26CrossRefGoogle Scholar
  35. Mansfeld J, Gebauer S, Dathe K, Ulbrich-Hofmann R (2006) Secretory phospholipase A2 from Arabidopsis thaliana: insights into the three-dimensional structure and the amino acids involved in catalysis. Biochemistry 45:5687–5694. doi: 10.1021/bi052563z CrossRefPubMedGoogle Scholar
  36. Movahedi S, Van de Peer Y, Vandepoele K (2011) Comparative network analysis reveals that tissue specificity and gene function are important factors influencing the mode of expression evolution in arabidopsis and rice. Plant Physiol 156:1316–1330. doi: 10.1104/pp.111.177865 PubMedCentralCrossRefPubMedGoogle Scholar
  37. Mumford JA, Horvath S, Oldham MC, Langfelder P, Geschwind DH, Poldrack RA (2010) Detecting network modules in fMRI time series: a weighted network analysis approach. NeuroImage 52:1465–1476. doi: 10.1016/j.neuroimage.2010.05.047 PubMedCentralCrossRefPubMedGoogle Scholar
  38. Murashige T, Skoog F (1962) A revised medium for rapid growth and bio-assays with tobacco tissue cultures. Physiol Plant 15:473–497CrossRefGoogle Scholar
  39. Nakagawa H (2009) Induced mutations in plant breeding and biological researches in japan, In: Induced plant mutations in the genomics era proceedings of an international joint FAO/IAEA symposium, International Atomic Energy Agency, Vienna, Austria, pp 48–54Google Scholar
  40. Parry MAJ, Flexas J, Medrano H (2005) Prospects for crop production under drought: research priorities and future directions. Ann Appl Biol 147:211–226CrossRefGoogle Scholar
  41. Perry J et al (2009) TILLING in lotus japonicus identified large allelic series for symbiosis genes and revealed a bias in functionally defective ethyl methanesulfonate alleles toward glycine replacements. Plant Physiol 151:1281–1291. doi: 10.1104/pp.109.142190 PubMedCentralCrossRefPubMedGoogle Scholar
  42. Song JY et al (2012) Physiological characterization of gamma-ray induced salt tolerant rice mutants. AJCS 6:421–429Google Scholar
  43. Stuart JM, Segal E, Koller D, Kim SK (2003) A gene-coexpression network for global discovery of conserved genetic modules. Science 302:249–255. doi: 10.1126/science.1087447 CrossRefPubMedGoogle Scholar
  44. Studt F, Tuczek F (2005) Energetics and mechanism of a room-temperature catalytic process for ammonia synthesis (Schrock cycle): comparison with biological nitrogen fixation. Angew Chem Int Ed Engl 44:5639–5642. doi: 10.1002/anie.200501485 CrossRefPubMedGoogle Scholar
  45. Tegner J, Yeung M, Hasty J, Collins J (2003) Reverse engineering gene networks: integrating genetic perturbations with dynamical modeling. Proc Natl Acad Sci USA 100:5944–5949PubMedCentralCrossRefPubMedGoogle Scholar
  46. Thimm O et al (2004) MAPMAN: a user-driven tool to display genomics data sets onto diagrams of metabolic pathways and other biological processes. Plant J Cell Mol Biol 37:914–939CrossRefGoogle Scholar
  47. Verdoy D, Coba de la Pen˜ a T, Redono FJ, Luca MM, Pueyo JJ (2006) Transgenic Medicago truncatula plants that accumulate proline display nitrogen-fixing activity with enhanced tolerance to osmotic stress. Plant Cell Physiol 29:1913–1923Google Scholar
  48. Wolfe CJ, Kohane IS, Butte AJ (2005) Systematic survey reveals general applicability of “guilt-by-association” within gene coexpression networks. BMC Bioinform 6:227. doi: 10.1186/1471-2105-6-227 CrossRefGoogle Scholar
  49. Xu F (2005) Application of oxidoreductases: recent progress. Ind Biotechnol 1:38–50CrossRefGoogle Scholar
  50. Yook SH, Oltvai ZN, Barabasi AL (2004) Functional and topological characterization of protein interaction networks. Proteomics 4:928–942. doi: 10.1002/pmic.200300636 CrossRefPubMedGoogle Scholar
  51. Zhang B, Horvath S (2005) A general framework for weighted gene co-expression network analysis. Stat Appl Genet Mol Biol 4(1). doi:  10.2202/1544-6115.1128

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Sun-Goo Hwang
    • 1
  • Dong Sub Kim
    • 2
  • Jung Eun Hwang
    • 2
  • Hyeon Mi Park
    • 1
  • Cheol Seong Jang
    • 1
  1. 1.Plant Genomics Lab, Department of Applied Plant SciencesKangwon National UniversityChuncheonSouth Korea
  2. 2.Advanced Radiation Technology InstituteKorea Atomic Energy Research InstituteJeongeupSouth Korea

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