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Flax rust infection transcriptomics reveals a transcriptional profile that may be indicative for rust Avr genes


Autoři: Wenjie Wu aff001;  Adnane Nemri aff002;  Leila M. Blackman aff001;  Ann-Maree Catanzariti aff001;  Jana Sperschneider aff003;  Gregory J. Lawrence aff002;  Peter N. Dodds aff002;  David A. Jones aff001;  Adrienne R. Hardham aff001
Působiště autorů: Division of Plant Sciences, Research School of Biology, The Australian National University, Canberra, Australia aff001;  CSIRO Agriculture and Food, Canberra, Australia aff002;  Biological Data Science Institute, the Australian National University, Canberra, Australia aff003
Vyšlo v časopise: PLoS ONE 14(12)
Kategorie: Research Article
doi: https://doi.org/10.1371/journal.pone.0226106

Souhrn

Secreted effectors of fungal pathogens are essential elements for disease development. However, lack of sequence conservation among identified effectors has long been a problem for predicting effector complements in fungi. Here we have explored the expression characteristics of avirulence (Avr) genes and candidate effectors of the flax rust fungus, Melampsora lini. We performed transcriptome sequencing and real-time quantitative PCR (qPCR) on RNA extracted from ungerminated spores, germinated spores, isolated haustoria and flax seedlings inoculated with M. lini isolate CH5 during plant infection. Genes encoding two categories of M. lini proteins, namely Avr proteins and plant cell wall degrading enzymes (CWDEs), were investigated in detail. Analysis of the expression profiles of 623 genes encoding predicted secreted proteins in the M. lini transcriptome shows that the six known Avr genes (i.e. AvrM (avrM), AvrM14, AvrL2, AvrL567, AvrP123 (AvrP) and AvrP4) fall within a group of 64 similarly expressed genes that are induced in planta and show a peak of expression early in infection with a subsequent decline towards sporulation. Other genes within this group include two paralogues of AvrL2, an AvrL567 virulence allele, and a number of genes encoding putative effector proteins. By contrast, M. lini genes encoding CWDEs fall into different expression clusters with their distribution often unrelated to their catalytic activity or substrate targets. These results suggest that synthesis of M. lini Avr proteins may be regulated in a coordinated fashion and that the expression profiling-based analysis has significant predictive power for the identification of candidate Avr genes.

Klíčová slova:

Fungal spores – Gene expression – Haustoria – Leaves – Plant fungal pathogens – Transcriptome analysis – Flax – Pectins


Zdroje

1. Voegele RT, Hahn M, Mendgen K. The Uredinales: cytology, biochemistry, and molecular biology. In: Deising HB, editor. The Mycota. Plant relationships. V. 2nd ed: Springer-Verlag, Berlin; 2009. pp. 69–98.

2. Aime MC, Toome M, McLaughlin DJ. Pucciniomycotina. In: McLaughlin DJ, Spatafora JW, editors. Systematics and Evolution. VII. The Mycota (A Comprehensive Treatise on Fungi as Experimental Systems for Basic and Applied Research): Springer, Berlin, Heidelberg; 2014. pp. 271–294.

3. Kolmer JA, Ordonez ME, Groth JV. The Rust Fungi. In: Encyclopedia of Life Science. Chichester: John Wiley & Sons; 2009.

4. Duplessis S, Cuomo CA, Lin Y-C, Aerts A, Tisserant E, Veneault-Fourrey C, et al. Obligate biotrophy features unraveled by the genomic analysis of rust fungi. Proceedings of the National Academy of Sciences. 2011;108:9166–9171.

5. Singh RP, Hodson DP, Huerta-Espino J, Jin Y, Bhavani S, Njau P, et al. The emergence of Ug99 races of the stem rust fungus is a threat to world wheat production. Annu Rev Phytopathol. 2011;49:465–481. doi: 10.1146/annurev-phyto-072910-095423 21568701

6. Voegele RT. Uromyces fabae: development, metabolism, and interactions with its host Vicia faba. FEMS Microbiol Lett. 2006;259:165–173. doi: 10.1111/j.1574-6968.2006.00248.x 16734775

7. Kemen EM, Jones JD. Obligate biotroph parasitism: can we link genomes to lifestyles? Trends in Plant Science. 2012;17:448–457. doi: 10.1016/j.tplants.2012.04.005 22613788

8. Garnica DP, Nemri A, Upadhyaya NM, Rathjen JP, Dodds PN. The ins and outs of rust haustoria. PLoS Pathog. 2014;10:e1004329. doi: 10.1371/journal.ppat.1004329 25211126

9. Garnica DP, Upadhyaya NM, Dodds PN, Rathjen JP. Strategies for wheat stripe rust pathogenicity identified by transcriptome sequencing. PLoS One. 2013;8:e67150. doi: 10.1371/journal.pone.0067150 23840606

10. Barnabas L, Ashwin NMR, Kaverinathan K, Trentin AR, Pivato M, Sundar AR, et al. In vitro secretomic analysis identifies putative pathogenicity-related proteins of Sporisorium scitamineum—The sugarcan smut fungus. Fungal Biol. 2017;121:199–211. doi: 10.1016/j.funbio.2016.11.004 28215348

11. Mandelc S, Javornik B. The secretome of vascular wilt pathogen Verticillium albo-atrum in simulated xylem fluid. Proteomics. 2015;15:787–797. doi: 10.1002/pmic.201400181 25407791

12. Ben M'Barek S, Cordewener JHG, van der Lee TAJ, America AHP, Gohari AM, Mehrabi R, et al. Proteome catalog of Zymoseptoria tritici captured during pathogenesis in wheat. Fungal Genet Biol. 2015;79:42–53. doi: 10.1016/j.fgb.2015.04.018 26092789

13. Link TI, Lang P, Scheffler BE, Duke MV, Graham MA, Cooper B, et al. The haustorial transcriptomes of Uromyces appendiculatus and Phakopsora pachyrhizi and their candidate effector families. Mol Plant Pathol. 2014;15:379–393. doi: 10.1111/mpp.12099 24341524

14. Bozkurt TO, Schornack S, Banfield MJ, Kamoun S. Oomycetes, effectors, and all that jazz. Curr Opin Plant Biol. 2012;15:483–492. doi: 10.1016/j.pbi.2012.03.008 22483402

15. Petre B, Joly DL, Duplessis S. Effector proteins of rust fungi. Frontiers in Plant Science. 2014;5:416. doi: 10.3389/fpls.2014.00416 25191335

16. Saunders DGO, Win J, Cano LM, Szabo LJ, Kamoun S, Raffaele S. Using hierarchical clustering of secreted protein families to classify and rank candidate effectors of rust fungi. PLoS One. 2012;7:e29847. doi: 10.1371/journal.pone.0029847 22238666

17. Sperschneider J, Gardiner DM, Dodds PN, Tini F, Covarelli L, Singh KB, et al. EffectorP: Predicting fungal effector proteins from secretomes using machine learning. New Phytol. 2016;210:743–761. doi: 10.1111/nph.13794 26680733

18. Gan PHP, Rafiqi M, Hardham AR, Dodds PN. Effectors of biotrophic fungal plant pathogens. Funct Plant Biol. 2010;37:913–918.

19. Hardham AR, Cahill DM. The role of oomycete effectors in plant-pathogen interactions. Funct Plant Biol. 2010;37:919–925.

20. Hacquard S, Joly DL, Lin Y-C, Tisserant E, Feau N, Delaruelle C, et al. A comprehensive analysis of genes encoding small secreted proteins identifies candidate effectors in Melampsora larici-populina (poplar leaf rust). Mol Plant-Microbe Interact. 2012;25:279–293. doi: 10.1094/MPMI-09-11-0238 22046958

21. Hacquard S, Veneault-Fourrey C, Delaruelle C, Frey P, Martin F, Duplessis S. Validation of Melampsora larici-populina reference genes for in planta RT-quantitative PCR expression profiling during time-course infection of poplar leaves. Physiol Mol Plant Pathol. 2011;75:106–112.

22. Hacquard S, Delaruelle C, Frey P, Tisserant E, Kohler A, Duplessis S. Transcriptome analysis of poplar rust telia reveals overwintering adaptation and tightly coordinated karyogamy and meiosis processes. Frontiers in Plant Science. 2013;4:456. doi: 10.3389/fpls.2013.00456 24312107

23. Liu J-J, Sturrock RN, Sniezko RA, Williams H, Benton R, Zamany A. Transcriptome analysis of the white pine blister rust pathogen Cronartium ribicola: de novo assembly, expression profiling, and identification of candidate effectors. BMC Genomics. 2015;16:678. doi: 10.1186/s12864-015-1861-1 26338692

24. Fernandez D, Tisserant E, Talhinhas P, Azinheira H, Vieira A, Petitot A-S, et al. 454-pyrosequencing of Coffea arabica leaves infected by the rust fungus Hemileia vastatrix reveals in planta-expressed pathogen-secreted proteins and plant functions in a late compatible plant-rust interaction. Mol Plant Pathol. 2012;13:17–37. doi: 10.1111/j.1364-3703.2011.00723.x 21726390

25. Hrdlicková R, Toloue M, Tian B. RNA-Seq methods for transcriptome analysis. Wiley Interdisciplinary Reviews: RNA. 2017;8.

26. Patino LH, Ramírez JD. RNA-seq in kinetoplastids: A powerful tool for the understanding of the biology and host-pathogen interactions. Infect, Genet Evol. 2017;49:273–282.

27. Catanzariti A-M, Dodds PN, Lawrence GJ, Ayliffe MA, Ellis JG. Haustorially expressed secreted proteins from flax rust are highly enriched for avirulence elicitors. The Plant Cell. 2006;18:243–256. doi: 10.1105/tpc.105.035980 16326930

28. Kobayashi I, Kobayashi Y, Hardham AR. Dynamic reorganization of microtubules and microfilaments in flax cells during the resistance response to flax rust infection. Planta. 1994;195:237–247.

29. Murdoch LJ. Characterisation of host-pathogen interactions during the infection of flax by the flax-rust fungus, Melampsora lini. Ph.D Thesis, The Australian National University. 1997. Available from: https://openresearch-repository.anu.edu.au/handle/1885/144091.

30. Murdoch LJ, Hardham AR. Components in the haustorial wall of the flax rust fungus, Melampsora lini, are labelled by three anti-calmodulin monoclonal antibodies. Protoplasma. 1998;201:180–193.

31. Murdoch LJ, Kobayashi I, Hardham AR. Production and characterisation of monoclonal antibodies to cell wall components of the flax rust fungus. Eur J Plant Pathol. 1998;104:331–346.

32. Lin F, Zhao M, Baumann DD, Ping J, Sun L, Liu Y, et al. Molecular response to the pathogen Phytophthora sojae among ten soybean near isogenic lines revealed by comparative transcriptomics. BMC Genomics. 2014;15:18. doi: 10.1186/1471-2164-15-18 24410936

33. Sonah H, Zhang X, Deshmukh RK, Borhan MH, Fernando WGD, Bélanger RR. Comparative transcriptomic analysis of virulence factors in Leptosphaeria maculans during compatible and incompatible interactions with canola. Frontiers in Plant Science. 2016;7:1784. doi: 10.3389/fpls.2016.01784 27990146

34. Inglis DO, Voorhies M, Murray DRH, Sil A. Comparative transcriptomics of infectious spores from the fungal pathogen Histoplasma capsulatum reveals a core set of transcripts that specify infectious and pathogenic states. Eukaryot Cell. 2013;12:828–852. doi: 10.1128/EC.00069-13 23563482

35. Rinaldi C, Kohler A, Frey P, Duchaussoy F, Ningre N, Couloux A, et al. Transcript profiling of poplar leaves upon infection with compatible and incompatible strains of the foliar rust Melampsora larici-populina. Plant Physiol. 2007;144:347–366. doi: 10.1104/pp.106.094987 17400708

36. Bolton MD, Kolmer JA, Xu WW, Garvin DF. Lr34 -mediated leaf rust resistance in wheat: transcript profiling reveals a high energetic demand supported by transient recruitment of multiple metabolic pathways. Mol Plant-Microbe Interact. 2008;21:1515–1527. doi: 10.1094/MPMI-21-12-1515 18986248

37. Flor HH. The complmentary genic systems in flax and flax rust. Adv Genet. 1956;8:29–54.

38. Dodds PN, Lawrence GJ, Catanzariti A-M, Ayliffe MA, Ellis JG. The Melampsora lini AvrL567 avirulence genes are expressed in haustoria and their products are recognized inside plant cells. The Plant Cell 2004;16:755–768. doi: 10.1105/tpc.020040 14973158

39. Barrett LG, Thrall PH, Dodds PN, van der Merwe M, Linde CC, Lawrence GJ, et al. Diversity and evolution of effector loci in natural populations of the plant pathogen Melampsora lini. Mol Biol Evol. 2009;26:2499–2513. doi: 10.1093/molbev/msp166 19633228

40. Anderson C, Khan MA, Catanzariti A-M, Jack CA, Nemri A, Lawrence GJ, et al. Genome analysis and avirulence gene cloning using a high-density RADseq linkage map of the flax rust fungus, Melampsora lini. BMC Genomics. 2016;17:667. doi: 10.1186/s12864-016-3011-9 27550217

41. Lawrence GJ, Mayo GME, Shepherd KW. Interactions between genes-controlling pathogenicity in the flax rust fungus. Phytopathology. 1981;71:12–19.

42. Dudler R. The single-copy actin gene of Phytophthora megasperma encodes a protein considerably diverged from any other known actin. Plant Mol Biol. 1990;14:415–422. doi: 10.1007/bf00028777 2102822

43. Vandesompele J, De Preter K, Pattyn F, Poppe B, Van Roy N, De Paepe A, et al. Accurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple internal control genes. Genome Biol. 2002;3:research0034.0031–0034.0011.

44. Pfaffl MW, Tichopad A, Prgomet C, Neuvians TP. Determination of stable housekeeping genes, differentially regulated target genes and sample integrity: BestKeeper—Excel-based tool using pair-wise correlations. Biotechnol Lett. 2004;26:509–515. doi: 10.1023/b:bile.0000019559.84305.47 15127793

45. Andersen CL, Jensen JL, Ørntoft TF. Normalization of real-time quantitative reverse transcription-PCR data: a model-based variance estimation approach to identify genes suited for normalization, applied to bladder and colon cancer data sets. Cancer Res. 2004;64:5245–5250. doi: 10.1158/0008-5472.CAN-04-0496 15289330

46. Ruijter JM, Ramakers C, Hoogaars WMH, Karlen Y, Bakker O, Van den Hoff MJB, et al. Amplification efficiency: linking baseline and bias in the analysis of quantitative PCR data. Nucleic Acids Res. 2009;37:e45. doi: 10.1093/nar/gkp045 19237396

47. Tong Z, Gao Z, Wang F, Zhou J, Zhang Z. Selection of reliable reference genes for gene expression studies in peach using real-time PCR. BMC Mol Biol. 2009;10:71. doi: 10.1186/1471-2199-10-71 19619301

48. Veazey KJ, Golding MC. Selection of stable reference genes for quantitative RT-PCR comparisons of mouse embryonic and extra-embryonic stem cells. PLoS One. 2011;6:e27592. doi: 10.1371/journal.pone.0027592 22102912

49. Smeds L, Künstner A. ConDeTri—a content dependent read trimmer for Illumina data. PLoS One. 2011;6:e26314. doi: 10.1371/journal.pone.0026314 22039460

50. Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics. 2014;30:2114–2120. doi: 10.1093/bioinformatics/btu170 24695404

51. Nemri A, Saunders DGO, Anderson C, Upadhyaya NM, Win J, Lawrence GJ, et al. The genome sequence and effector complement of the flax rust pathogen Melampsora lini. Frontiers in Plant Science. 2014;5:98. doi: 10.3389/fpls.2014.00098 24715894

52. Langmead B, Salzberg SL. Fast gapped-read alignment with Bowtie 2. Nat Methods. 2012;9:357–359. doi: 10.1038/nmeth.1923 22388286

53. Wang Z, Hobson N, Galindo L, Zhu S, Shi D, McDill J, et al. The genome of flax (Linum usitatissimum) assembled de novo from short shotgun sequence reads. The Plant Journal. 2012;72:461–473. doi: 10.1111/j.1365-313X.2012.05093.x 22757964

54. Trapnell C, Williams BA, Pertea G, Mortazavi A, Kwan G, van Baren MJ, et al. Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation. Nat Biotechnol. 2010;28:511–515. doi: 10.1038/nbt.1621 20436464

55. Soneson C, Delorenzi M. A comparison of methods for differential expression analysis of RNA-seq data. BMC Bioinformatics. 2013;14:91. doi: 10.1186/1471-2105-14-91 23497356

56. Sperschneider J, Dodds PN, Gardiner DM, Singh KB, Taylor JM. Improved prediction of fungal effector proteins from secretomes with EffectorP 2.0. Mol Plant Pathol. 2018;19:2094–2110. doi: 10.1111/mpp.12682 29569316

57. Zhang H, Yohe T, Huang L, Entwistle S, Wu P, Yang Z, et al. dbCAN2: a meta server for automated carbohydrate-active enzyme annotation. Nucleic Acids Research. 2018;46:W95–W101. doi: 10.1093/nar/gky418 29771380

58. Lombard V, Golaconda Ramulu H, Drula E, Coutinho PM, Henrissat B. The carbohydrate-active enzymes database (CAZy) in 2013. Nucleic Acids Res. 2014;42:D490–D495. doi: 10.1093/nar/gkt1178 24270786

59. Petersen TN, Brunak S, Heijne GV, Nielsen H. SignalP 4.0: discriminating signal peptides from transmembrane regions. Nat Methods. 2011;8:785–786. doi: 10.1038/nmeth.1701 21959131

60. Bendtsen JD, Jensen LJ, Blom N, Von Heijne G, Brunak S. Feature-based prediction of non-classical and leaderless protein secretion. Protein Eng Des Sel. 2004;17:349–356. doi: 10.1093/protein/gzh037 15115854

61. Bholowalia P, Kumar A. EBK-means: a clustering technique based on elbow method and k-means in WSN. International Journal of Computer Applications. 2014;105:17–24.

62. Guo G, Huss M, Tong GQ, Wang C, Sun LL, Clarke ND, et al. Resolution of cell fate decisions revealed by single-cell gene expression analysis from zygote to blastocyst. Developmental Cell. 2010;18:675–685. doi: 10.1016/j.devcel.2010.02.012 20412781

63. Obayashi T, Okegawa T, Sasaki-Sekimoto Y, Shimada H, Masuda T, Asamizu E, et al. Distinctive features of plant organs characterized by global analysis of gene expression in Arabidopsis. DNA Research. 2004;11:11–25. doi: 10.1093/dnares/11.1.11 15141942

64. Venglat P, Xiang D, Qiu S, Stone SL, Tibiche C, Cram D, et al. Gene expression analysis of flax seed development. BMC Plant Biol. 2011;11:74. doi: 10.1186/1471-2229-11-74 21529361

65. Danna CH, Sacco F, Ingala LR, Saione HA, Ugalde RA. Cloning and mapping of genes involved in wheat-leaf rust interaction through gene-expression analysis using chromosome-deleted near-isogenic wheat lines. Theor Appl Genet. 2002;105:972–979. doi: 10.1007/s00122-002-0990-5 12582923

66. Marioni JC, Mason CE, Mane SM, Stephens M, Gilad Y. RNA-seq: An assessment of technical reproducibility and comparison with gene expression arrays. Genome Res. 2008;18:1509–1517. doi: 10.1101/gr.079558.108 18550803

67. Wang Z, Gerstein M, Snyder M. RNA-Seq: a revolutionary tool for transcriptomics. Nature Reviews Genetics. 2009;10:57–63. doi: 10.1038/nrg2484 19015660

68. Raffaele S, Kamoun S. Genome evolution in filamentous plant pathogens: why bigger can be better. Nat Rev Microbiol. 2012;10:417–430. doi: 10.1038/nrmicro2790 22565130

69. Chen J, Upadhyaya NM, Ortiz D, Sperschneider J, Li F, Bouton C, et al. Loss of AvrSr50 by somatic exchange in stem rust leads to virulence for Sr50 resistance in wheat. Science. 2017;358:1607–1610. doi: 10.1126/science.aao4810 29269475

70. Salcedo A, Rutter W, Wang S, Akhunova A, Bolus S, Chao S, et al. Variation in the AvrSr35 gene determines Sr35 resistance against wheat stem rust race Ug99. Science. 2017;358:1604–1606. doi: 10.1126/science.aao7294 29269474

71. Duplessis S, Hacquard S, Delaruelle C, Tisserant E, Frey P, Martin F, et al. Melampsora larici-populina transcript profiling during germination and timecourse infection of poplar leaves reveals dynamic expression patterns associated with virulence and biotrophy. Mol Plant-Microbe Interact. 2011;24:808–818. doi: 10.1094/MPMI-01-11-0006 21644839

72. Schwessinger B, Sperschneider J, Cuddy WS, Garnica DP, Miller ME, Taylor JM, et al. A near complete haplotype-phased genome of the dikaryotic wheat stripe rust fungus Puccinia striiformis f. sp. tritici reveals high inter-haplome diversity. mBio. 2018;9:e02275–02217. doi: 10.1128/mBio.02275-17 29463659

73. Miller ME, Zhang Y, Omidvar V, Sperschneider J, Schwessinger B, Raley C, et al. De novo assembly and phasing of dikaryotic genomes from two isolates of Puccinia coronata f. sp. avenae, the causal agent of oat crown rust. mBio. 2018;9:e01650–01617. doi: 10.1128/mBio.01650-17 29463655

74. Lanver D, Müller AN, Happel P, Schweizer G, Haas FB, Franitza M, et al. The biotrophic development of Ustilago maydis studied by RNA-Seq analysis. The Plant Cell. 2018;30:300–323. doi: 10.1105/tpc.17.00764 29371439

75. Chen X-R, Zhang B-Y, Xing Y-P, Li Q-Y, Li Y-P, Tong Y-H, et al. Transcriptomic analysis of the phytopathogenic oomycete Phytophthora cactorum provides insights into infection-related effectors. BMC Genomics. 2014;15:980. doi: 10.1186/1471-2164-15-980 25406848

76. Stam R, Jupe J, Howden AJM, Morris JA, Boevink PC, Hedley PE, et al. Identification and characterisation CRN effectors in Phytophthora capsici shows modularity and functional diversity. PLoS One. 2013;8:e59517. doi: 10.1371/journal.pone.0059517 23536880

77. Guyon K, Balagué C, Roby D, Raffaele S. Secretome analysis reveals effector candidates associated with broad host range necrotrophy in the fungal plant pathogen Sclerotinia sclerotiorum. BMC Genomics. 2014;15:336. doi: 10.1186/1471-2164-15-336 24886033

78. Marton K, Flajšman M, Radišek S, Košmelj K, Jakše J, Javornik B, et al. Comprehensive analysis of Verticillium nonalfalfae in silico secretome uncovers putative effector proteins expressed during hop invasion. PLoS One. 2018;13:e0198971. doi: 10.1371/journal.pone.0198971 29894496

79. Rudd JJ, Kanyuka K, Hassani-Pak K, Derbyshire M, Andongabo A, Devonshire J, et al. Transcriptome and metabolite profiling of the infection cycle of Zymoseptoria tritici on wheat reveals a biphasic interaction with plant immunity involving differential pathogen chromosomal contributions and a variation on the hemibiotrophic lifestyle definition. Plant Physiol. 2015;167:1158–1185. doi: 10.1104/pp.114.255927 25596183

80. Glazebrook J. Contrasting mechanisms of defense against biotrophic and necrotrophic pathogens. Annu Rev Phytopathol. 2005;43:205–227. doi: 10.1146/annurev.phyto.43.040204.135923 16078883

81. Lawrence GJ, Dodds PN, Ellis JG. Rust of flax and linseed caused by Melampsora lini. Mol Plant Pathol. 2007;8:349–364. doi: 10.1111/j.1364-3703.2007.00405.x 20507505

82. Rutter WB, Salcedo A, Akhunova A, He F, Wang S, Liang H, et al. Divergent and convergent modes of interaction between wheat and Puccinia graminis f. sp. tritici isolates revealed by the comparative gene co-expression network and genome analyses. BMC Genomics. 2017;18:291. doi: 10.1186/s12864-017-3678-6 28403814

83. Lionetti V, Francocci F, Ferrari S, Volpi C, Bellincampi D, Galletti R, et al. Engineering the cell wall by reducing de-methyl-esterified homogalacturonan improves saccharification of plant tissues for bioconversion. Proceedings of the National Academy of Sciences. 2010;107:616–621.

84. Blackman LM, Cullerne DP, Torreña P, Taylor J, Hardham AR. RNA-Seq analysis of the expression of genes encoding cell wall degrading enzymes during infection of lupin (Lupinus angustifolius) by Phytophthora parasitica. PLoS One. 2015;10:e0136899. doi: 10.1371/journal.pone.0136899 26332397

85. Horowitz BB, Ospina-Giraldo MD. The pectin methylesterase gene complement of Phytophthora sojae: structural and functional analyses, and the evolutionary relationships with its oomycete homologs. PLoS One. 2015;10:e0142096. doi: 10.1371/journal.pone.0142096 26544849

86. Fernández JA, Moigne NL, Caro-Bretelle AS, Hage RE, Duc AL, Lozachmeurc M, et al. Role of flax cell wall components on the microstructure and transversemechanical behaviour of flax fabrics reinforced epoxy biocomposites. Industrial Crops and Products. 2016;85:93–108.

87. Wojtasik W, Kulma A, Dymińska L, Hanuza J, Czemplik M, Szopa J. Evaluation of the significance of cell wall polymers in flax infected with a pathogenic strain of Fusarium oxysporum. BMC Plant Biol. 2016;16:75. doi: 10.1186/s12870-016-0762-z 27005923


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