Genome-wide investigation of superoxide dismutase (SOD) gene family and their regulatory miRNAs reveal the involvement in abiotic stress and hormone response in tea plant (Camellia sinensis)


Autoři: Chengzhe Zhou aff001;  Chen Zhu aff001;  Haifeng Fu aff001;  Xiaozhen Li aff001;  Lan Chen aff001;  Yuling Lin aff001;  Zhongxiong Lai aff001;  Yuqiong Guo aff001
Působiště autorů: College of Horticulture, Fujian Agriculture and Forestry University, Fuzhou, Fujian, China aff001;  Institute of Horticultural Biotechnology, Fujian Agriculture and Forestry University, Fuzhou, Fujian, China aff002;  Key Laboratory of Tea Science of Fujian Province, Fujian Agriculture and Forestry University, Fuzhou, Fujian, China aff003
Vyšlo v časopise: PLoS ONE 14(10)
Kategorie: Research Article
doi: 10.1371/journal.pone.0223609

Souhrn

Superoxide dismutases (SODs), as a family of metalloenzymes related to the removal of reactive oxygen species (ROS), have not previously been investigated at genome-wide level in tea plant. In this study, 10 CsSOD genes were identified in tea plant genome, including 7 Cu/Zn-SODs (CSDs), 2 Fe-SODs (FSDs) and one Mn-SOD (MSD), and phylogenetically classified in three subgroups, respectively. Physico-chemical characteristic, conserved motifs and potential protein interaction analyses about CsSOD proteins were carried out. Exon-intron structures and codon usage bias about CsSOD genes were also examined. Exon-intron structures analysis revealed that different CsSOD genes contained various number of introns. On the basis of the prediction of regulatory miRNAs of CsSODs, a modification 5’ RNA ligase-mediated (RLM)-RACE was performed and validated that csn-miR398a-3p-1 directly cleaves CsCSD4. By prediction of cis-acting elements, the expression patterns of 10 CsSOD genes and their regulatory miRNAs were detected under cold, drought, exogenous methyl jasmonate (MeJA) and gibberellin (GA3) treatments. The results showed that most of CsSODs except for CsFSD2 were induced under cold stress and CsCSDs may play primary roles under drought stress; exogenous GA3 and MeJA could also stimulated/inhibited distinct CsSODs at different stages. In addition, we found that csn-miR398a-3p-1 negatively regulated the expression of CsCSD4 may be a crucial regulatory mechanism under cold stress. This study provides a certain basis for the studies about stress resistance in tea plants, even provide insight into comprehending the classification, evolution, diverse functions and influencing factors of expression patterns for CsSOD genes.

Klíčová slova:

Arabidopsis thaliana – Gene expression – Introns – MicroRNAs – Plant resistance to abiotic stress – Sequence motif analysis – Tea – Thermal stresses


Zdroje

1. Zhao L, Jiang XL, Qian YM, Xie DY, Gao LP, Xia T. Metabolic Characterization of the Anthocyanidin Reductase Pathway Involved in the Biosynthesis of Flavan-3-ols in Elite Shuchazao Tea (Camellia sinensis) Cultivar in the Field. Molecules. 2017;22(12):2241. http://doi.org/10.3390/molecules22122241

2. Zhang Y, Zhu X, Chen X, Song C, Zou Z, Wang Y, et al. Identification and characterization of cold-responsive microRNAs in tea plant (Camellia sinensis) and their targets using high-throughput sequencing and degradome analysis. BMC Plant Biol. 2014;14:271. doi: 10.1186/s12870-014-0271-x 25330732

3. Guo YQ, Zhao S, Zhu C, Chang XJ, Yue C, Wang Z, et al. Identification of drought-responsive miRNAs and physiological characterization of tea plant (Camellia sinensis L.) under drought stress. BMC Plant Biol. 2017;17(1):211. http://doi.org/10.1186/s12870-017-1172-6 29157225

4. Zheng C, Zhao L, Wang Y, Shen JZ, Zhang YF, Jia SS, et al. Integrated RNA-Seq and sRNA-Seq Analysis Identifies Chilling and Freezing Responsive Key Molecular Players and Pathways in Tea Plant (Camellia sinensis). PLOS ONE 2015;10(4):e0125031. doi: 10.1371/journal.pone.0125031 25901577

5. Cheruiyot EK, Mumera LM, Ng'Etich WK, Hassanali A, Wachira FN. HIGH FERTILIZER RATES INCREASE SUSCEPTIBILITY OF TEA TO WATER STRESS. J Plant Nutr. 2009;33(1):115–129. http://doi.org/10.1080/01904160903392659

6. Apel K, Hirt H. REACTIVE OXYGEN SPECIES: Metabolism, Oxidative Stress, and Signal Transduction. Annu Rev Plant Biol. 2004;55(1):373–399. http://doi.org/10.1146/annurev.arplant.55.031903.141701

7. Fink RC, Scandalios JG. Molecular Evolution and Structure-Function Relationships of the Superoxide Dismutase Gene Families in Angiosperms and Their Relationship to Other Eukaryotic and Prokaryotic Superoxide Dismutases. Arch Biochem Biophys. 2002;399(1):19–36. doi: 10.1006/abbi.2001.2739 11883900

8. Wang W, Xia MX, Chen J, Yuan R, Deng FN, Shen FF. Gene Expression Characteristics and Regulation Mechanisms of Superoxide Dismutase and Its Physiological Roles in Plants under Stress. Biochemistry (Mosc). 2016;81(5):465–80. http://doi.org/10.1134/S0006297916050047 27297897

9. Hu XX, Hao CY, Cheng ZM, Zhong Y. Genome-Wide Identification, Characterization, and Expression Analysis of the Grapevine Superoxide Dismutase (SOD) Family. Int J Genomics. 2019;2019:1–13. http://doi.org/10.1155/2019/7350414

10. Gill SS, Anjum NA, Gill R, Yadav S, Hasanuzzaman M, Fujita M, et al. Superoxide dismutase-mentor of abiotic stress tolerance in crop plants. Environ Sci Pollut R. 2015;22(14):10375–10394. http://doi.org/10.1007/s11356-015-4532-5

11. Suzuki N, Mittler R. Reactive oxygen species and temperature stresses: A delicate balance between signaling and destruction. Physiol Plantarum. 2006;126(1):45–51. http://doi.org/10.1111/j.0031-9317.2005.00582.x

12. Kliebenstein DJ, Monde RA, Last RL. Superoxide Dismutase in Arabidopsis: An Eclectic Enzyme Family with Disparate Regulation and Protein Localization1. Plant Physiol. 1998;118(2):637–650. doi: 10.1104/pp.118.2.637 9765550

13. Faize M, Burgos L, Faize L, Piqueras A, Nicolas E, Barba-Espin G, et al. Involvement of cytosolic ascorbate peroxidase and Cu/Zn-superoxide dismutase for improved tolerance against drought stress. J Exp Bot. 2011;62(8):2599–2613. doi: 10.1093/jxb/erq432 21239380

14. Molina-Rueda JJ, Tsai CJ, Kirby EG. The Populus Superoxide Dismutase Gene Family and Its Responses to Drought Stress in Transgenic Poplar Overexpressing a Pine Cytosolic Glutamine Synthetase (GS1a). PLOS ONE. 2013;8(2):e56421. doi: 10.1371/journal.pone.0056421 23451045

15. Lin YL, Lai ZX. Superoxide dismutase multigene family in longan somatic embryos: a comparison of CuZn-SOD, Fe-SOD, and Mn-SOD gene structure, splicing, phylogeny, and expression. Mol Breeding. 2013;32(3):595–615. http://doi.org/10.1007/s11032-013-9892-2

16. Gopavajhula VR, Chaitanya KV, Akbar AKP, Shaik JP, Reddy PN, Alanazi M. Modeling and analysis of soybean (Glycine max. L) Cu/Zn, Mn and Fe superoxide dismutases. Genet Mol Biol. 2013;36(2):225–36. doi: 10.1590/S1415-47572013005000023 23885205

17. FİLİZ E, TOMBULOĞLU H. Genome-wide distribution of superoxide dismutase (SOD) gene families in Sorghum bicolor. Turk J Biol. 2015;39:49–59. http://doi.org/10.3906/biy-1403-9

18. Feng X, Lai ZX, Lin YL, Lai GT, Lian CL. Genome-wide identification and characterization of the superoxide dismutase gene family in Musa acuminata cv. Tianbaojiao (AAA group). BMC genomics 2015;16(1):823. http://doi.org/10.1186/s12864-015-2046-7

19. Wang W, Xia MX, Chen J, Deng FN, Yuan R, Zhang XP, et al. Genome-wide analysis of superoxide dismutase gene family in Gossypium raimondii and G. arboreum. Plant Gene. 2016;6:18–29. http://doi.org/10.1016/j.plgene.2016.02.002

20. Wang W, Zhang XP, Deng FN, Yuan R, Shen FF. Genome-wide characterization and expression analyses of superoxide dismutase (SOD) genes in Gossypium hirsutum. BMC Genomics. 2017;18(1):376. doi: 10.1186/s12864-017-3768-5 28499417

21. Zhou Y, Hu LF, Wu H, Jiang LW, Liu SQ. Genome-Wide Identification and Transcriptional Expression Analysis of Cucumber Superoxide Dismutase (SOD) Family in Response to Various Abiotic Stresses. Int J Genomics. 2017;2017:1–14. http://doi.org/10.1155/2017/7243973

22. Wang T, Song H, Zhang BH, Lu QW, Liu Z, Zhang SL, et al. Genome-wide identification, characterization, and expression analysis of superoxide dismutase (SOD) genes in foxtail millet (Setaria italica L.). 3 Biotech. 2018;8(12). http://doi.org/10.1007/s13205-018-1502-x

23. Han XM, Chen QX, Yang Q, Zeng QY, Lan T, Liu YJ. Genome-wide analysis of superoxide dismutase genes in Larix kaempferi. Gene. 2019;686:29–36. doi: 10.1016/j.gene.2018.10.089 30389562

24. Verma D, Lakhanpal N, Singh K. Genome-wide identification and characterization of abiotic-stress responsive SOD (superoxide dismutase) gene family in Brassica juncea and B. rapa. BMC Genomics. 2019;20(1):227. doi: 10.1186/s12864-019-5593-5 30890148

25. Zhang DY, Yang HL, Li XS, Li HY, Wang YC. Overexpression of Tamarix albiflonum TaMnSOD increases drought tolerance in transgenic cotton. Mol Breeding. 2014;34(1):1–11. http://doi.org/10.1007/s11032-014-0015-5

26. Wang FZ, Wang QB, Kwon SY, Kwak SS, Su WA. Enhanced drought tolerance of transgenic rice plants expressing a pea manganese superoxide dismutase. J Plant Physiol. 2005;162(4):465–472. doi: 10.1016/j.jplph.2004.09.009 15900889

27. Wang L, Yao LN, Hao XY, Li NN, Wang YC, Ding CQ, et al. Transcriptional and physiological analyses reveal the association of ROS metabolism with cold tolerance in tea plant. Environ Exp Bot. 2019;160:45–58. http://doi.org/10.1016/j.envexpbot.2018.11.011

28. Sunkar R. Novel and Stress-Regulated MicroRNAs and Other Small RNAs from Arabidopsis. THE PLANT CELL ONLINE. 2004;16(8):2001–2019. http://doi.org/10.1105/tpc.104.022830

29. Miura K, Furumoto T. Cold Signaling and Cold Response in Plants. Int J Mol Sci. 2013;14(3):5312–5337. doi: 10.3390/ijms14035312 23466881

30. Yan J, Zhao CZ, Zhou JP, Yang Y, Wang PC, Zhu XH, et al. The miR165/166 Mediated Regulatory Module Plays Critical Roles in ABA Homeostasis and Response in Arabidopsis thaliana. PLOS Genet. 2016;12(11):e1006416. doi: 10.1371/journal.pgen.1006416 27812104

31. Zhang JS, Zhang H, Srivastava AK, Pan YJ, Bai JJ, Fang JJ, et al. Knockdown of Rice MicroRNA166 Confers Drought Resistance by Causing Leaf Rolling and Altering Stem Xylem Development. Plant Physiol. 2018;176(3):2082–2094. http://doi.org/10.1104/pp.17.01432

32. Mukhopadhyay M, Mondal TK, Chand PK. Biotechnological advances in tea (Camellia sinensis [L.] O. Kuntze): a review. Plant Cell Rep. 2016, 35(2):255–287. doi: 10.1007/s00299-015-1884-8 26563347

33. Das A, Mondal TK. Computational identification of conserved microRNAs and their targets in tea (Camellia sinensis). American Journal of Plant Sciences. 2010, 01(02):77–86. http://doi.org/10.4236/ajps.2010.12010

34. Xia EH, Zhang HB, Sheng J, Li K, Zhang QJ, Kim CH, et al. The Tea Tree Genome Provides Insights into Tea Flavor and Independent Evolution of Caffeine Biosynthesis. Mol Plant. 2017;10(6):866–877. doi: 10.1016/j.molp.2017.04.002 28473262

35. Wei C, Yang H, Wang S, Zhao J, Liu C, Gao L, et al. Draft genome sequence of Camellia sinensis var. sinensis provides insights into the evolution of the tea genome and tea quality. Proc Natl Acad Sci USA. 2018;115(18):E4151–E4158. doi: 10.1073/pnas.1719622115 29678829

36. Letunic I, Bork P. 20 years of the SMART protein domain annotation resource. Nucleic Acids Res. 2018;46(D1):D493–D496. doi: 10.1093/nar/gkx922 29040681

37. Marchler-Bauer A, Bo Y, Han L, He J, Lanczycki CJ, Lu S, et al. CDD/SPARCLE: functional classification of proteins via subfamily domain architectures. Nucleic Acids Res. 2017;45(D1):D200–D203. doi: 10.1093/nar/gkw1129 27899674

38. Horton P, Park KJ, Obayashi T, Fujita N, Harada H, Adams-Collier CJ, et al. WoLF PSORT: protein localization predictor. Nucleic Acids Res. 2007;35(Web Server):W585–W587. doi: 10.1093/nar/gkm259 17517783

39. Wilkins MR, Gasteiger E, Bairoch A, Sanchez JC, Williams KL, Appel RD, et al. Protein identification and analysis tools in the ExPASy server. Methods Mol Biol. 1999;112:531–52. doi: 10.1385/1-59259-584-7:531 10027275

40. Edgar RC. MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res. 2004;32(5):1792–1797. doi: 10.1093/nar/gkh340 15034147

41. Tamura K, Peterson D, Peterson N, Stecher G, Nei M, Kumar S. MEGA5: Molecular Evolutionary Genetics Analysis Using Maximum Likelihood, Evolutionary Distance, and Maximum Parsimony Methods. Mol Biol Evol. 2011;28(10):2731–2739. doi: 10.1093/molbev/msr121 21546353

42. Letunic I, Bork P. Interactive tree of life (iTOL) v3: an online tool for the display and annotation of phylogenetic and other trees. Nucleic Acids Res. 2016;44(W1):W242–W245. doi: 10.1093/nar/gkw290 27095192

43. Chen C, Xia R, Chen H, He Y. TBtools, a Toolkit for Biologists integrating various biological data handling tools with a user-friendly interface. BioRxiv. 2018:289660. http://doi.org/10.1101/289660

44. Bailey TL, Boden M, Buske FA, Frith M, Grant CE, Clementi L, et al. MEME SUITE: tools for motif discovery and searching. Nucleic Acids Res. 2009;37(Web Server):W202–W208. doi: 10.1093/nar/gkp335 19458158

45. Szklarczyk D, Gable AL, Lyon D, Junge A, Wyder S, Huerta-Cepas J, et al. STRING v11: protein–protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res. 2019;47(D1):D607–D613. doi: 10.1093/nar/gky1131 30476243

46. Pan LL, Wang Y, Hu JH, Ding ZT, Li C. Analysis of codon use features of stearoyl-acyl carrier protein desaturase gene in Camellia sinensis. Journal of Theoretical Biology. 2013;334:80–86. doi: 10.1016/j.jtbi.2013.06.006 23774066

47. Lescot M, Déhais P, Thijs G, Marchal K, Moreau Y, Van de Peer Y, et al. PlantCARE, a database of plant cis-acting regulatory elements and a portal to tools for in silico analysis of promoter sequences. Nucleic acids res. 2002;30(1):325–327. doi: 10.1093/nar/30.1.325 11752327

48. Dai X, Zhuang Z, Zhao PX. psRNATarget: a plant small RNA target analysis server (2017 release). Nucleic Acids Res. 2018;46(W1):W49–W54. doi: 10.1093/nar/gky316 29718424

49. Wang PJ, Chen D, Zheng YC, Jin S, Yang JF, Ye NX. Identification and Expression Analyses of SBP-Box Genes Reveal Their Involvement in Abiotic Stress and Hormone Response in Tea Plant (Camellia sinensis). Int J Mol Sci. 2018;19:3404. http://doi.org/10.3390/ijms19113404

50. Chen W, Xu Y, Mao J, Hao W, Liu Y, Ni D, et al. Cloning and expression patterns of VQ-motif-containing proteins under abiotic stress in tea plant. Plant Growth Regul 2019;87(2):277–286. http://doi.org/10.1007/s10725-018-0469-2

51. Guo YQ, Chang XJ, Zhu C, Zhang ST, Li XZ, Fu HF, et al. De novo transcriptome combined with spectrophotometry and gas chromatography-mass spectrometer (GC-MS) reveals differentially expressed genes during accumulation of secondary metabolites in purple-leaf tea (Camellia sinensis cv Hongyafoshou). Journal of Horticultural Science&Biotechnology. 2019;94(3):349–367. http://doi.org/10.1080/14620316.2018.1521708

52. Xia EH, Li FD, Tong W, Li PH, Wu Q, Zhao HJ, et al. Tea Plant Information Archive: a comprehensive genomics and bioinformatics platform for tea plant. Plant Biotechnol J. 2019. http://doi.org/10.1111/pbi.13111

53. Lin YL, Lai ZX. Reference gene selection for qPCR analysis during somatic embryogenesis in longan tree. Plant Science. 2010;178(4):359–365. https://doi.org/10.1016/j.plantsci.2010.02.005

54. Guruprasad K, Reddy BV, Pandit MW. Correlation between stability of a protein and its dipeptide composition: a novel approach for predicting in vivo stability of a protein from its primary sequence. Protein Eng. 1990;4(2):155–61. doi: 10.1093/protein/4.2.155 2075190

55. Moran JF. Functional Characterization and Expression of a Cytosolic Iron-Superoxide Dismutase from Cowpea Root Nodules. Plant Physiol. 2003;133(2):773–782. doi: 10.1104/pp.103.023010 14512518

56. Modrek B, Lee C. A genomic view of alternative splicing. Nat Genet. 2002;30(1):13. doi: 10.1038/ng0102-13 11753382

57. Casareno RLB, Waggoner D, Gitlin JD. The copper chaperone CCS directly interacts with copper/zinc superoxide dismutase. J Biol Chem. 1998;273(37):23625–23628. doi: 10.1074/jbc.273.37.23625 9726962

58. Hiraoka Y, Kawamata K, Haraguchi T, Chikashige Y. Codon usage bias is correlated with gene expression levels in the fission yeast Schizosaccharomyces pombe. Genes Cells. 2009;14(4):499–509. doi: 10.1111/j.1365-2443.2009.01284.x 19335619

59. Wright F. The 'effective number of codons' used in a gene. Gene 1990;87(1):23–29. http://doi.org/10.1016/0378-1119(90)90491-9 2110097

60. Lauressergues D, Couzigou J, San Clemente H, Martinez Y, Dunand C, Bécard G, et al. Primary transcripts of microRNAs encode regulatory peptides. Nature. 2015;520(7545):90. doi: 10.1038/nature14346 25807486

61. Sakharkar MK, Chow VTK, Kangueane P. Distributions of exons and introns in the human genome. In silico biology. 2004;4(4):387–393. 15217358

62. Rogozin IB, Sverdlov AV, Babenko VN, Koonin EV. Analysis of evolution of exon-intron structure of eukaryotic genes. Brief Bioinform. 2005;6(2):118–134. doi: 10.1093/bib/6.2.118 15975222

63. Deutsch M, Long M. Intron-exon structures of eukaryotic model organisms. Nucleic Acids Res. 1999;27(15):3219–28. doi: 10.1093/nar/27.15.3219 10454621

64. Xu G, Guo C, Shan H, Kong H. Divergence of duplicate genes in exon-intron structure. Proc Natl Acad Sci USA. 2012;109(4):1187–1192. doi: 10.1073/pnas.1109047109 22232673

65. Shabalina SA, Ogurtsov AY, Spiridonov AN, Novichkov PS, Spiridonov NA, Koonin EV. Distinct patterns of expression and evolution of intronless and intron-containing mammalian genes. Mol Biol Evol. 2010;27(8):1745–9. doi: 10.1093/molbev/msq086 20360214

66. Jain M, Khurana P, Tyagi AK, Khurana JP. Genome-wide analysis of intronless genes in rice and Arabidopsis. Funct Integr Genomic. 2008;8(1):69–78. http://doi.org/10.1007/s10142-007-0052-9

67. Feng K, Yu JH, Cheng Y, Ruan MY, Wang RQ, Ye QJ, et al. The SOD Gene Family in Tomato: Identification, Phylogenetic Relationships, and Expression Patterns. Front Plant Sci. 2016;7. http://doi.org/10.3389/fpls.2016.01279

68. Chiang CM, Kuo WS, Lin KH. Cloning and gene expression analysis of sponge gourd ascorbate peroxidase gene and winter squash superoxide dismutase gene under respective flooding and chilling stresses. Horticulture, Environment, and Biotechnology. 2014;55(2):129–137. http://doi.org/10.1007/s13580-014-0116-4

69. Liu ZB, Zhang WJ, Gong XD, Zhang Q, Zhou LR. A Cu/Zn superoxide dismutase from Jatropha curcas enhances salt tolerance of Arabidopsis thaliana. Genet Mol Res. 2015;14(1):2086–98. doi: 10.4238/2015.March.20.19 25867355

70. Raja V, Majeed U, Kang H, Andrabi KI, John R. Abiotic stress: Interplay between ROS, hormones and MAPKs. Environ Exp Bot. 2017;137:142–157. http://doi.org/10.1016/j.envexpbot.2017.02.010

71. Colebrook E.H.; Thomas S.G.; Phillips A.L.; Hedden P. The role of gibberellin signalling in plant responses to abiotic stress. J Exp Biol. 2013, 217, 67–75. doi: http://doi.org/10.1242/jeb.089938

72. Wang F, Wang C, Yan Y, Jia H, Guo X. Overexpression of Cotton GhMPK11 Decreases Disease Resistance through the Gibberellin Signaling Pathway in Transgenic Nicotiana benthamiana. Front Plant Sci. 2016;7:689. doi: 10.3389/fpls.2016.00689 27242882

73. Jiang D, Yan SC. MeJA is more effective than JA in inducing defense responses in Larix olgensis. Arthropod-Plant Inte. 2018;12(1):49–56. http://doi.org/10.1007/s11829-017-9551-3

74. Zhu C, Ding Y, Liu H. MiR398 and plant stress responses. Physiol Plant. 2011;143(1):1–9. doi: 10.1111/j.1399-3054.2011.01477.x 21496029

75. Song X, Li Y, Cao X, Qi Y. MicroRNAs and Their Regulatory Roles in Plant-Environment Interactions. Annu Rev Plant Biol. 2019. http://doi.org/10.1146/annurev-arplant-050718-100334

76. Chen Y, Jiang J, Song A, Chen S, Shan H, Luo H, et al. Ambient temperature enhanced freezing tolerance of Chrysanthemum dichrum CdICE1 Arabidopsis via miR398. BMC Biol 2013;11:121. doi: 10.1186/1741-7007-11-121 24350981

77. Wang B, Sun YF, Song N, Wei JP, Wang XJ, Feng H, et al. MicroRNAs involving in cold, wounding and salt stresses in Triticum aestivum L. Plant Physiol Bioch. 2014;80:90–96. http://doi.org/10.1016/j.plaphy.2014.03.020

78. Xu S, Jiang YL, Cui WT, Jin QJ, Zhang YH, Bu D, et al. Hydrogen enhances adaptation of rice seedlings to cold stress via the reestablishment of redox homeostasis mediated by miRNA expression. Plant and Soil. 2017;414(1–2):53–67. http://doi.org/10.1007/s11104-016-3106-8

79. Srivastava P.K., Moturu T.R., Pandey P., Baldwin I.T., Pandey S.P. A comparison of performance of plant miRNA target prediction tools and the characterization of features for genome-wide target prediction. Bmc Genomics 2014; 348(15):15348. http://doi.org/10.1186/1471-2164-15-348


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