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Journal of Genetics

, 98:9 | Cite as

Gene coexpression analysis reveals dose-dependent and type-specific networks responding to ionizing radiation in the aquatic model plant Lemna minor using public data

  • Lili Fu
  • Zehong DingEmail author
  • Anuwat Kumpeangkeaw
  • Xuepiao Sun
  • Jiaming ZhangEmail author
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Abstract

Ionizing radiations (IRs) are widespread damaging stresses to plant growth and development. However, the regulatory networks underlying the mechanisms of responses to IRs remains poorly understood. Here, a set of publicly available transcriptomic data (conducted by Van Hoeck et al. 2015a), in which Lemna minor plants were exposed to a series of doses of gamma, beta and uranium treatments was used to perform gene coexpression network analysis. Overall, the genes involved in DNA synthesis and chromatin structure, light signalling, photosynthesis, and carbohydrate metabolism were commonly responsive to gamma, beta and uranium treatments. Genes related to anthocyanin accumulation and trichome differentiation were specifically downregulated, and genes related to nitrogen and phosphate nutrition, cell vesicle transport, mitochondrial electron transport and ATP synthesis were specifically upregulated in response to uranium treatment. While genes involved in DNA damage and repair, RNA processing and RNA binding were specifically downregulated and genes involved in calcium signalling, redox and degradation of carbohydrate metabolism were specifically upregulated responding to gamma radiation. These findings revealed both dose-dependent and type-specific networks responding to different IRs in L. minor, and can be served as a useful resource to better understand the mechanisms of responses to different IRs in other plants.

Keywords

gamma radiation beta radiation uranium treatment coexpression network Lemna minor 

Notes

Acknowledgements

This project was funded by the International Science and Technology Co-operation Program of China (2010DFA62040) and Natural Science Foundation of Hainan Province (20164171), and the National Nonprofit Institute Research Grants (1630052016009).

Supplementary material

12041_2019_1063_MOESM1_ESM.docx (2.6 mb)
Supplementary material 1 (docx 2696 KB)
12041_2019_1063_MOESM2_ESM.xlsx (1.2 mb)
Supplementary material 2 (xlsx 1217 KB)
12041_2019_1063_MOESM3_ESM.xlsx (180 kb)
Supplementary material 3 (xlsx 180 KB)

References

  1. Albrecht V., Simkova K., Carrie C., Delannoy E., Giraud E., Whelan J. et al. 2010 The cytoskeleton and the peroxisomal-targeted snowy cotyledon3 protein are required for chloroplast development in Arabidopsis. Plant Cell 22, 3423–3438.CrossRefGoogle Scholar
  2. Alexa A., Rahnenfuhrer J. and Lengauer T. 2006 Improved scoring of functional groups from gene expression data by decorrelating GO graph structure. Bioinformatics 22, 1600–1607.CrossRefGoogle Scholar
  3. Anderson H. J., Vonarx E. J., Pastushok L., Nakagawa M., Katafuchi A., Gruz P. et al. 2008 Arabidopsis thaliana Y-family DNA polymerase eta catalyses translesion synthesis and interacts functionally with PCNA2. Plant J. 55, 895–908.CrossRefGoogle Scholar
  4. Bachrati C. Z. and Hickson I. D. 2008 RecQ helicases: guardian angels of the DNA replication fork. Chromosoma 117, 219–233.CrossRefGoogle Scholar
  5. Badri H., Monsieurs P., Coninx I., Wattiez R. and Leys N. 2015 Molecular investigation of the radiation resistance of edible cyanobacterium Arthrospira sp. PCC 8005. Microbiologyopen 4, 187–207.CrossRefGoogle Scholar
  6. Cantero A., Barthakur S., Bushart T. J., Chou S., Morgan R. O., Fernandez M. P. et al. 2006 Expression profiling of the Arabidopsis annexin gene family during germination, de-etiolation and abiotic stress. Plant Physiol. Biochem. 44, 13–24.CrossRefGoogle Scholar
  7. Chen C. N., Chu C. C., Zentella R., Pan S. M. and Ho T. H. 2002 AtHVA22 gene family in Arabidopsis: phylogenetic relationship, ABA and stress regulation, and tissue-specific expression. Plant Mol. Biol. 49, 633–644.PubMedGoogle Scholar
  8. Cheng X. F. and Wang Z. Y. 2005 Overexpression of COL9, a CONSTANS-LIKE gene, delays flowering by reducing expression of CO and FT in Arabidopsis thaliana. Plant J. 43, 758–768.CrossRefGoogle Scholar
  9. Conesa A., Gotz S., Garcia-Gomez J. M., Terol J., Talon M. and Robles M. 2005 Blast2GO: a universal tool for annotation, visualization and analysis in functional genomics research. Bioinformatics 21, 3674–3676.CrossRefGoogle Scholar
  10. Culligan K. M., Robertson C. E., Foreman J., Doerner P. and Britt A. B. 2006 ATR and ATM play both distinct and additive roles in response to ionizing radiation. Plant J. 48, 947–961.CrossRefGoogle Scholar
  11. Ding Z., Weissmann S., Wang M., Du B., Huang L., Wang L. et al. 2015 Identification of photosynthesis-associated C4 candidate genes through comparative leaf gradient transcriptome in multiple lineages of C3 and C4 species. PLoS One 10, e0140629.CrossRefGoogle Scholar
  12. Ding Z., Zhang Y., Xiao Y., Liu F., Wang M., Zhu X. et al. 2016 Transcriptome response of cassava leaves under natural shade. Sci. Rep. 6, 31673.CrossRefGoogle Scholar
  13. Doskocilova A., Plihal O., Volc J., Chumova J., Kourova H., Halada P. et al. 2011 A nodulin/glutamine synthetase-like fusion protein is implicated in the regulation of root morphogenesis and in signalling triggered by flagellin. Planta 234, 459–476.CrossRefGoogle Scholar
  14. Fu L., Ding Z., Han B., Hu W., Li Y. and Zhang J. 2016 Physiological investigation and transcriptome analysis of polyethylene glycol (PEG)-induced dehydration stress in cassava. Int. J. Mol. Sci. 17, 283.CrossRefGoogle Scholar
  15. Hayashi G., Shibato J., Imanaka T., Cho K., Kubo A., Kikuchi S. et al. 2014 Unraveling low-level gamma radiation-responsive changes in expression of early and late genes in leaves of rice seedlings at Iitate Village, Fukushima. J. Hered. 105, 723–738.CrossRefGoogle Scholar
  16. Hwang J. E., Hwang S. G., Kim S. H., Lee K. J., Jang C. S., Kim J. B. et al. 2014 Transcriptome profiling in response to different types of ionizing radiation and identification of multiple radio marker genes in rice. Physiol. Plant. 150, 604–619.CrossRefGoogle Scholar
  17. Johnson W. E., Li C. and Rabinovic A. 2007 Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics 8, 118–127.CrossRefGoogle Scholar
  18. Kim S. H., Song M., Lee K. J., Hwang S. G., Jang C. S., Kim J. B. 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.CrossRefGoogle Scholar
  19. Langfelder P. and Horvath S. 2008 WGCNA: an R package for weighted correlation network analysis. BMC Bioinf. 9, 559.CrossRefGoogle Scholar
  20. Pierrugues O., Brutesco C., Oshiro J., Gouy M., Deveaux Y., Carman G. M. et al. 2001 Lipid phosphate phosphatases in Arabidopsis. Regulation of the AtLPP1 gene in response to stress. J. Biol. Chem. 276, 20300–20308.CrossRefGoogle Scholar
  21. Sahr T., Voigt G., Schimmack W., Paretzke H. G. and Ernst D. 2005 Low-level radiocaesium exposure alters gene expression in roots of Arabidopsis. New Phytol. 168, 141–148.CrossRefGoogle Scholar
  22. Sharma S., Stumpo D. J., Balajee A. S., Bock C. B., Lansdorp P. M., Brosh R. M. Jr and Blackshear P. J. 2007 RECQL, a member of the RecQ family of DNA helicases, suppresses chromosomal instability. Mol. Cell. Biol. 27, 1784–1794.CrossRefGoogle Scholar
  23. Smoot M. E., Ono K., Ruscheinski J., Wang P. L. and Ideker T. 2011 Cytoscape 2.8: new features for data integration and network visualization. Bioinformatics 27, 431–432.CrossRefGoogle Scholar
  24. Thimm O., Blasing O., Gibon Y., Nagel A., Meyer S., Kruger P. et al. 2004 MAPMAN: a user-driven tool to display genomics data sets onto diagrams of metabolic pathways and other biological processes. Plant J. 37, 914–939.CrossRefGoogle Scholar
  25. Trapnell C., Pachter L. and Salzberg S. L. 2009 TopHat: discovering splice junctions with RNA-Seq. Bioinformatics 25, 1105–1111.CrossRefGoogle Scholar
  26. Trapnell C., Roberts A., Goff L., Pertea G., Kim D., Kelley D. R. et al. 2012 Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks. Nat. Protoc. 7, 562–578.CrossRefGoogle Scholar
  27. Van Hoeck A., Horemans N., Monsieurs P., Cao H. X., Vandenhove H. and Blust R. 2015a The first draft genome of the aquatic model plant Lemna minor opens the route for future stress physiology research and biotechnological applications. Biotechnol. Biofuels 8, 188.CrossRefGoogle Scholar
  28. Van Hoeck A., Horemans N., Van Hees M., Nauts R., Knapen D., Vandenhove H. and Blust R. 2015b Beta-radiation stress responses on growth and antioxidative defense system in plants: a study with Strontium-90 in Lemna minor. Int. J. Mol. Sci. 16, 15309–15327.CrossRefGoogle Scholar
  29. Van Hoeck A., Horemans N., Van Hees M., Nauts R., Knapen D., Vandenhove H. and Blust R. 2015c Characterizing dose response relationships: chronic gamma radiation in Lemna minor induces oxidative stress and altered polyploidy level. J. Environ. Radioact. 150, 195–202.CrossRefGoogle Scholar
  30. Van Hoeck A., Horemans N., Nauts R., Van Hees M., Vandenhove H. and Blust R. 2017 Lemna minor plants chronically exposed to ionising radiation: RNA-seq analysis indicates a dose rate dependent shift from acclimation to survival strategies. Plant Sci. 257, 84–95.CrossRefGoogle Scholar
  31. Wang H., Ma L. G., Li J. M., Zhao H. Y. and Deng X. W. 2001 Direct interaction of Arabidopsis cryptochromes with COP1 in light control development. Science 294, 154–158.CrossRefGoogle Scholar

Copyright information

© Indian Academy of Sciences 2019

Authors and Affiliations

  1. 1.Institute of Tropical Bioscience and Biotechnology, MOA Key Laboratory of Tropical Crops Biology and Genetic Resources, Hainan Bioenergy CenterChinese Academy of Tropical Agricultural SciencesHaikouPeople’s Republic of China

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