#PAGE_PARAMS# #ADS_HEAD_SCRIPTS# #MICRODATA#

A systems biology approach uncovers a gene co-expression network associated with cell wall degradability in maize


Autoři: Clément Cuello aff001;  Aurélie Baldy aff001;  Véronique Brunaud aff002;  Johann Joets aff004;  Etienne Delannoy aff002;  Marie-Pierre Jacquemot aff001;  Lucy Botran aff001;  Yves Griveau aff001;  Cécile Guichard aff002;  Ludivine Soubigou-Taconnat aff002;  Marie-Laure Martin-Magniette aff002;  Philippe Leroy aff006;  Valérie Méchin aff001;  Matthieu Reymond aff001;  Sylvie Coursol aff001
Působiště autorů: Institut Jean-Pierre Bourgin, INRA, AgroParisTech, CNRS, Université Paris-Saclay, Versailles, France aff001;  Institute of Plant Sciences Paris-Saclay, CNRS, INRA, Université Paris-Sud, Université Evry, Université Paris-Saclay, Gif-sur-Yvette, France aff002;  Institute of Plant Sciences Paris-Saclay, CNRS, INRA, Université Paris-Diderot, Sorbonne Paris-Cité, Gif-sur-Yvette, France aff003;  Génétique Quantitative et Evolution—Le Moulon, INRA, Université Paris-Sud, CNRS, AgroParisTech, Université Paris-Saclay, Gif-Sur-Yvette, France aff004;  UMR MIA-Paris, AgroParisTech, INRA, Université Paris-Saclay, Paris, France aff005;  GDEC, INRA, UCA, Clermont-Ferrand, France aff006
Vyšlo v časopise: PLoS ONE 14(12)
Kategorie: Research Article
prolekare.web.journal.doi_sk: https://doi.org/10.1371/journal.pone.0227011

Souhrn

Understanding the mechanisms triggering variation of cell wall degradability is a prerequisite to improving the energy value of lignocellulosic biomass for animal feed or biorefinery. Here, we implemented a multiscale systems approach to shed light on the genetic basis of cell wall degradability in maize. We demonstrated that allele replacement in two pairs of near-isogenic lines at a region encompassing a major quantitative trait locus (QTL) for cell wall degradability led to phenotypic variation of a similar magnitude and sign to that expected from a QTL analysis of cell wall degradability in the F271 × F288 recombinant inbred line progeny. Using DNA sequences within the QTL interval of both F271 and F288 inbred lines and Illumina RNA sequencing datasets from internodes of the selected near-isogenic lines, we annotated the genes present in the QTL interval and provided evidence that allelic variation at the introgressed QTL region gives rise to coordinated changes in gene expression. The identification of a gene co-expression network associated with cell wall-related trait variation revealed that the favorable F288 alleles exploit biological processes related to oxidation-reduction, regulation of hydrogen peroxide metabolism, protein folding and hormone responses. Nested in modules of co-expressed genes, potential new cell-wall regulators were identified, including two transcription factors of the group VII ethylene response factor family, that could be exploited to fine-tune cell wall degradability. Overall, these findings provide new insights into the regulatory mechanisms by which a major locus influences cell wall degradability, paving the way for its map-based cloning in maize.

Klíčová slova:

Gene expression – Genomics – Plant genomics – Maize – Genetic loci – Quantitative trait loci – Cell walls – Plant cell walls


Zdroje

1. Barrière Y. Brown-midrib genes in maize and their efficiency in dairy cow feeding. Perspectives for breeding improved silage maize targeting gene modifications in the monolignol and p-hydroxycinnamate pathways. Maydica. 2017;62: 1–19.

2. Barrière Y, Emile JC, Traineau R, Surault F, Briand M, Gallais A. Genetic variation for organic matter and cell wall digestibility in silage maize. Lessons from a 34-year long experiment with sheep in digestibility crates. Maydica. 2004;49: 115–126.

3. Grabber JH, Ralph J, Lapierre C, Barrière Y. Genetic and molecular basis of grass cell-wall degradability. I. Lignin-cell wall matrix interactions. Comptes Rendus Biologies. 2004;327: 455–465. doi: 10.1016/j.crvi.2004.02.009 15255476

4. Pauly M, Keegstra K. Plant cell wall polymers as precursors for biofuels. Curr Opin Plant Biol. 2010;13: 305–312. doi: 10.1016/j.pbi.2009.12.009 20097119

5. Zhao X, Zhang L, Liu D. Biomass recalcitrance. Part I: The chemical compositions and physical structures affecting the enzymatic hydrolysis of lignocellulose. Biofuels Bioproducts and Biorefining. 2012;6: 465–482.

6. Meng X, Ragauskas AJ. Recent advances in understanding the role of cellulose accessibility in enzymatic hydrolysis of lignocellulosic substrates. Curr Opin Biotechnol. 2014;27: 150–158. doi: 10.1016/j.copbio.2014.01.014 24549148

7. Méchin V, Argillier O, Rocher F, Hébert Y, Mila I, Pollet B, et al. In Search of a Maize Ideotype for Cell Wall Enzymatic Degradability Using Histological and Biochemical Lignin Characterization. J Agric Food Chem. 2005;53: 5872–5881. doi: 10.1021/jf050722f 16028968

8. Barrière Y, Méchin V, Riboulet C, Guillaumie S, Thomas J, Bosio M, et al. Genetic and genomic approaches for improving biofuel production from maize. Euphytica. 2009;170: 183–202.

9. Lübberstedt T, Melchinger AE, Klein D, Degenhardt H, Paul C. QTL mapping in testcrosses of European flint lines of maize: II. Comparison of different testers for forage quality traits. Crop science. 37: 1913–1922.

10. Bohn M, Schulz B, Kreps R, Klein D, Melchinger AE. QTL mapping for resistance against the European corn borer (Ostrinia nubilalis H.) in early maturing European dent germplasm. Theoretical and Applied Genetics. 101: 907–917.

11. Méchin V, Argillier O, Hébert Y, Guingo E, Moreau L, Charcosset A, et al. Genetic Analysis and QTL Mapping of Cell Wall Digestibility and Lignification in Silage Maize. Crop Science. 2001;41: 690. doi: 10.2135/cropsci2001.413690x

12. Roussel V, Gibelin C, Fontaine A, Barrière Y. Genetic analysis in recombinant inbred lines of early dent forage maize. 2002;47: 9–20.

13. Cardinal AJ, Lee M, Moore KJ. Genetic mapping and analysis of quantitative trait loci affecting fiber and lignin content in maize. Theor Appl Genet. 2003;106: 866–874. doi: 10.1007/s00122-002-1136-5 12647061

14. Fontaine A-S, Briand M, Barrière Y. Genetic variation and QTL mapping of para-coumaric and ferulic acid contents in maize stover at silage harvest. Maydica. 2003;48: 75–84.

15. Krakowsky M, Lee M, Beeghly H, Coors J. Characterization of quantitative trait loci affecting fiber and lignin in maize (Zea mays L.). Maydica. 2003;48: 283–292.

16. Riboulet C, Fabre F, Denoue D, Martinant JP, Lefevre B, Barriere Y. QTL mapping and candidate gene research from lignin content and cell wall digestibility in a top-cross of a flint maize recombinant inbred line progeny harvested at silage stage. Maydica. 2008;53: 1–9.

17. Barrière Y, Thomas J, Denoue D. QTL mapping for lignin content, lignin monomeric composition, p-hydroxycinnamate content, and cell wall digestibility in the maize recombinant inbred line progeny F838 x F286. Plant Science. 2008;175: 585–595.

18. Barrière Y, Méchin V, Denoue D, Bauland C, Laborde J. QTL for Yield, Earliness, and Cell Wall Quality Traits in Topcross Experiments of the F838 × F286 Early Maize RIL Progeny. Crop Science. 2010;50: 1761. doi: 10.2135/cropsci2009.11.0671

19. Barrière Y, Méchin V, Lefevre B, Maltese S. QTLs for agronomic and cell wall traits in a maize RIL progeny derived from a cross between an old Minnesota13 line and a modern Iodent line. Theor Appl Genet. 2012;125: 531–549. doi: 10.1007/s00122-012-1851-5 22437492

20. Thomas J, Guillaumie S, Verdu C, Denoue D, Pichon M, Barrière Y. Cell wall phenylpropanoid-related gene expression in early maize recombinant inbred lines differing in parental alleles at a major lignin QTL position. Mol Breeding. 2010;25: 105–124. doi: 10.1007/s11032-009-9311-x

21. Courtial A, Thomas J, Reymond M, Méchin V, Grima-Pettenati J, Barrière Y. Targeted linkage map densification to improve cell wall related QTL detection and interpretation in maize. Theor Appl Genet. 2013;126: 1151–1165. doi: 10.1007/s00122-013-2043-7 23358861

22. Courtial A, Méchin V, Reymond M, Grima-Pettenati J, Barrière Y. Colocalizations Between Several QTLs for Cell Wall Degradability and Composition in the F288 × F271 Early Maize RIL Progeny Raise the Question of the Nature of the Possible Underlying Determinants and Breeding Targets for Biofuel Capacity. Bioenerg Res. 2014;7: 142–156. doi: 10.1007/s12155-013-9358-8

23. Torres AF, Noordam-Boot CMM, Dolstra O, van der Weijde T, Combes E, Dufour P, et al. Cell Wall Diversity in Forage Maize: Genetic Complexity and Bioenergy Potential. Bioenerg Res. 2015;8: 187–202. doi: 10.1007/s12155-014-9507-8

24. Li K, Wang H, Hu X, Ma F, Wu Y, Wang Q, et al. Genetic and Quantitative Trait Locus Analysis of Cell Wall Components and Forage Digestibility in the Zheng58 × HD568 Maize RIL Population at Anthesis Stage. Front Plant Sci. 2017;8: 1472. doi: 10.3389/fpls.2017.01472 28883827

25. Leng P, Ouzunova M, Landbeck M, Wenzel G, Eder J, Darnhofer B, et al. Quantitative trait loci mapping of forage stover quality traits in six mapping populations derived from European elite maize germplasm. Leon J, editor. Plant Breed. 2018;137: 139–147. doi: 10.1111/pbr.12572

26. Ralph J, Guillaumie S, Grabber JH, Lapierre C, Barrière Y. Genetic and molecular basis of grass cell-wall biosynthesis and degradability. III. Towards a forage grass ideotype. Comptes Rendus Biologies. 2004;327: 467–479. doi: 10.1016/j.crvi.2004.03.004 15255477

27. Truntzler M, Barrière Y, Sawkins MC, Lespinasse D, Betran J, Charcosset A, et al. Meta-analysis of QTL involved in silage quality of maize and comparison with the position of candidate genes. Theor Appl Genet. 2010;121: 1465–1482. doi: 10.1007/s00122-010-1402-x 20658277

28. Penning BW, Sykes RW, Babcock NC, Dugard CK, Held MA, Klimek JF, et al. Genetic Determinants for Enzymatic Digestion of Lignocellulosic Biomass Are Independent of Those for Lignin Abundance in a Maize Recombinant Inbred Population. Plant Physiol. 2014;165: 1475–1487. doi: 10.1104/pp.114.242446 24972714

29. Barrière Y, Courtial A, Chateigner-Boutin A-L, Denoue D, Grima-Pettenati J. Breeding maize for silage and biofuel production, an illustration of a step forward with the genome sequence. Plant Science. 2016;242: 310–329. doi: 10.1016/j.plantsci.2015.08.007 26566848

30. Yin X, Struik PC, Kropff MJ. Role of crop physiology in predicting gene-to-phenotype relationships. Trends Plant Sci. 2004;9: 426–432. doi: 10.1016/j.tplants.2004.07.007 15337492

31. Rafalski A, Morgante M. Corn and humans: recombination and linkage disequilibrium in two genomes of similar size. Trends Genet. 2004;20: 103–111. doi: 10.1016/j.tig.2003.12.002 14746992

32. Springer NM, Ying K, Fu Y, Ji T, Yeh C-T, Jia Y, et al. Maize inbreds exhibit high levels of copy number variation (CNV) and presence/absence variation (PAV) in genome content. PLoS Genet. 2009;5: e1000734. doi: 10.1371/journal.pgen.1000734 19956538

33. Swanson-Wagner RA, Eichten SR, Kumari S, Tiffin P, Stein JC, Ware D, et al. Pervasive gene content variation and copy number variation in maize and its undomesticated progenitor. Genome Res. 2010;20: 1689–1699. doi: 10.1101/gr.109165.110 21036921

34. Hirsch CN, Foerster JM, Johnson JM, Sekhon RS, Muttoni G, Vaillancourt B, et al. Insights into the Maize Pan-Genome and Pan-Transcriptome. Plant Cell. 2014;26: 121–135. doi: 10.1105/tpc.113.119982 24488960

35. Jin M, Liu H, He C, Fu J, Xiao Y, Wang Y, et al. Maize pan-transcriptome provides novel insights into genome complexity and quantitative trait variation. Sci Rep. 2016;6: 18936. doi: 10.1038/srep18936 26729541

36. Darracq A, Vitte C, Nicolas S, Duarte J, Pichon J-P, Mary-Huard T, et al. Sequence analysis of European maize inbred line F2 provides new insights into molecular and chromosomal characteristics of presence/absence variants. BMC Genomics. 2018;19: 119. doi: 10.1186/s12864-018-4490-7 29402214

37. Baldauf JA, Marcon C, Lithio A, Vedder L, Altrogge L, Piepho H-P, et al. Single-Parent Expression Is a General Mechanism Driving Extensive Complementation of Non-syntenic Genes in Maize Hybrids. Current Biology. 2018;28: 431–437.e4. doi: 10.1016/j.cub.2017.12.027 29358068

38. Courtial A, Jourda C, Arribat S, Balzergue S. Comparative expression of cell wall related genes in four maize RILs and one parental line of variable lignin content and cell wall degradability. Maydica. 2012; 19.

39. Szalma SJ, Hostert BM, Ledeaux JR, Stuber CW, Holland JB. QTL mapping with near-isogenic lines in maize. Theor Appl Genet. 2007;114: 1211–1228. doi: 10.1007/s00122-007-0512-6 17308934

40. Feltus FA. Systems genetics: a paradigm to improve discovery of candidate genes and mechanisms underlying complex traits. Plant Sci. 2014;223: 45–48. doi: 10.1016/j.plantsci.2014.03.003 24767114

41. Baute J, Herman D, Coppens F, De Block J, Slabbinck B, Dell’Acqua M, et al. Combined Large-Scale Phenotyping and Transcriptomics in Maize Reveals a Robust Growth Regulatory Network. Plant Physiol. 2016;170: 1848–1867. doi: 10.1104/pp.15.01883 26754667

42. Wen W, Liu H, Zhou Y, Jin M, Yang N, Li D, et al. Combining Quantitative Genetics Approaches with Regulatory Network Analysis to Dissect the Complex Metabolism of the Maize Kernel. Plant Physiol. 2016;170: 136–146. doi: 10.1104/pp.15.01444 26556794

43. Fu Y-B, Yang M-H, Zeng F, Biligetu B. Searching for an Accurate Marker-Based Prediction of an Individual Quantitative Trait in Molecular Plant Breeding. Front Plant Sci. 2017;8: 1182. doi: 10.3389/fpls.2017.01182 28729875

44. Virlouvet L, Jacquemot M-P, Gerentes D, Corti H, Bouton S, Gilard F, et al. The ZmASR1 protein influences branched-chain amino acid biosynthesis and maintains kernel yield in maize under water-limited conditions. Plant Physiol. 2011;157: 917–936. doi: 10.1104/pp.111.176818 21852416

45. Kopylova E, Noé L, Touzet H. SortMeRNA: fast and accurate filtering of ribosomal RNAs in metatranscriptomic data. Bioinformatics. 2012;28: 3211–3217. doi: 10.1093/bioinformatics/bts611 23071270

46. Gagnot S, Tamby J-P, Martin-Magniette M-L, Bitton F, Taconnat L, Balzergue S, et al. CATdb: a public access to Arabidopsis transcriptome data from the URGV-CATMA platform. Nucleic Acids Res. 2008;36: D986–990. doi: 10.1093/nar/gkm757 17940091

47. Edgar R, Domrachev M, Lash AE. Gene Expression Omnibus: NCBI gene expression and hybridization array data repository. Nucleic Acids Res. 2002;30: 207–210. doi: 10.1093/nar/30.1.207 11752295

48. Leroy P, Guilhot N, Sakai H, Bernard A, Choulet F, Theil S, et al. TriAnnot: A Versatile and High Performance Pipeline for the Automated Annotation of Plant Genomes. Front Plant Sci. 2012;3: 5. doi: 10.3389/fpls.2012.00005 22645565

49. Choulet F, Alberti A, Theil S, Glover N, Barbe V, Daron J, et al. Structural and functional partitioning of bread wheat chromosome 3B. Science. 2014;345: 1249721. doi: 10.1126/science.1249721 25035497

50. Altschul SF, Madden TL, Schäffer AA, Zhang J, Zhang Z, Miller W, et al. Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res. 1997;25: 3389–3402. doi: 10.1093/nar/25.17.3389 9254694

51. Lowe TM, Eddy SR. tRNAscan-SE: a program for improved detection of transfer RNA genes in genomic sequence. Nucleic Acids Res. 1997;25: 955–964. doi: 10.1093/nar/25.5.955 9023104

52. Lagesen K, Hallin P, Rødland EA, Staerfeldt H-H, Rognes T, Ussery DW. RNAmmer: consistent and rapid annotation of ribosomal RNA genes. Nucleic Acids Res. 2007;35: 3100–3108. doi: 10.1093/nar/gkm160 17452365

53. Slater GSC, Birney E. Automated generation of heuristics for biological sequence comparison. BMC Bioinformatics. 2005;6: 31. doi: 10.1186/1471-2105-6-31 15713233

54. Stanke M, Waack S. Gene prediction with a hidden Markov model and a new intron submodel. Bioinformatics. 2003;19 Suppl 2: ii215–225. doi: 10.1093/bioinformatics/btg1080 14534192

55. Amano N, Tanaka T, Numa H, Sakai H, Itoh T. Efficient plant gene identification based on interspecies mapping of full-length cDNAs. DNA Res. 2010;17: 271–279. doi: 10.1093/dnares/dsq017 20668003

56. Sammut SJ, Finn RD, Bateman A. Pfam 10 years on: 10,000 families and still growing. Brief Bioinformatics. 2008;9: 210–219. doi: 10.1093/bib/bbn010 18344544

57. Finn RD, Mistry J, Tate J, Coggill P, Heger A, Pollington JE, et al. The Pfam protein families database. Nucleic Acids Res. 2010;38: D211–222. doi: 10.1093/nar/gkp985 19920124

58. Zdobnov EM, Apweiler R. InterProScan—an integration platform for the signature-recognition methods in InterPro. Bioinformatics. 2001;17: 847–848. doi: 10.1093/bioinformatics/17.9.847 11590104

59. Sigrist CJA, Cerutti L, de Castro E, Langendijk-Genevaux PS, Bulliard V, Bairoch A, et al. PROSITE, a protein domain database for functional characterization and annotation. Nucleic Acids Res. 2010;38: D161–166. doi: 10.1093/nar/gkp885 19858104

60. Letunic I, Doerks T, Bork P. SMART 6: recent updates and new developments. Nucleic Acids Res. 2009;37: D229–232. doi: 10.1093/nar/gkn808 18978020

61. Abeel T, Van Parys T, Saeys Y, Galagan J, Van de Peer Y. GenomeView: a next-generation genome browser. Nucleic Acids Res. 2012;40: e12. doi: 10.1093/nar/gkr995 22102585

62. Robinson MD, McCarthy DJ, Smyth GK. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics. 2010;26: 139–140. doi: 10.1093/bioinformatics/btp616 19910308

63. McCarthy DJ, Chen Y, Smyth GK. Differential expression analysis of multifactor RNA-Seq experiments with respect to biological variation. Nucleic Acids Res. 2012;40: 4288–4297. doi: 10.1093/nar/gks042 22287627

64. Thimm O, Bläsing O, Gibon Y, Nagel A, Meyer S, Krüger P, et al. MAPMAN: a user-driven tool to display genomics data sets onto diagrams of metabolic pathways and other biological processes. Plant J. 2004;37: 914–939. doi: 10.1111/j.1365-313x.2004.02016.x 14996223

65. Rohart F, Gautier B, Singh A, Lê Cao K-A. mixOmics: An R package for ‘omics feature selection and multiple data integration. PLoS Comput Biol. 2017;13: e1005752. doi: 10.1371/journal.pcbi.1005752 29099853

66. Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 2003;13: 2498–2504. doi: 10.1101/gr.1239303 14597658

67. Assenov Y, Ramírez F, Schelhorn S-E, Lengauer T, Albrecht M. Computing topological parameters of biological networks. Bioinformatics. 2008;24: 282–284. doi: 10.1093/bioinformatics/btm554 18006545

68. Bernard V, Lecharny A, Brunaud V. Improved detection of motifs with preferential location in promoters. Genome. 2010;53: 739–752. doi: 10.1139/g10-042 20924423

69. Mejía-Guerra MK, Li W, Galeano NF, Vidal M, Gray J, Doseff AI, et al. Core Promoter Plasticity Between Maize Tissues and Genotypes Contrasts with Predominance of Sharp Transcription Initiation Sites. Plant Cell. 2015;27: 3309–3320. doi: 10.1105/tpc.15.00630 26628745

70. Higo K, Ugawa Y, Iwamoto M, Higo H. PLACE: a database of plant cis-acting regulatory DNA elements. Nucleic Acids Res. 1998;26: 358–359. doi: 10.1093/nar/26.1.358 9399873

71. Davuluri RV, Sun H, Palaniswamy SK, Matthews N, Molina C, Kurtz M, et al. AGRIS: Arabidopsis gene regulatory information server, an information resource of Arabidopsis cis-regulatory elements and transcription factors. BMC Bioinformatics. 2003;4: 25. doi: 10.1186/1471-2105-4-25 12820902

72. Ye J, Coulouris G, Zaretskaya I, Cutcutache I, Rozen S, Madden TL. Primer-BLAST: a tool to design target-specific primers for polymerase chain reaction. BMC Bioinformatics. 2012;13: 134. doi: 10.1186/1471-2105-13-134 22708584

73. Riboulet C, Guillaumie S, Méchin V, Bosio M, Pichon M, Goffner D, et al. Kinetics of Phenylpropanoid Gene Expression in Maize Growing Internodes: Relationships with Cell Wall Deposition. Crop Science. 2009;49: 211. doi: 10.2135/cropsci2008.03.0130

74. Zhang Q, Cheetamun R, Dhugga KS, Rafalski J, Tingey SV, Shirley NJ, et al. Spatial gradients in cell wall composition and transcriptional profiles along elongating maize internodes. BMC Plant Biol. 2014;14: 27. doi: 10.1186/1471-2229-14-27 24423166

75. Paschold A, Jia Y, Marcon C, Lund S, Larson NB, Yeh C-T, et al. Complementation contributes to transcriptome complexity in maize (Zea mays L.) hybrids relative to their inbred parents. Genome Research. 2012;22: 2445–2454. doi: 10.1101/gr.138461.112 23086286

76. Sengupta D, Naik D, Reddy AR. Plant aldo-keto reductases (AKRs) as multi-tasking soldiers involved in diverse plant metabolic processes and stress defense: A structure-function update. J Plant Physiol. 2015;179: 40–55. doi: 10.1016/j.jplph.2015.03.004 25840343

77. Yu Y, Zhang H, Li W, Mu C, Zhang F, Wang L, et al. Genome-wide analysis and environmental response profiling of the FK506-binding protein gene family in maize (Zea mays L.). Gene. 2012;498: 212–222. doi: 10.1016/j.gene.2012.01.094 22366304

78. Verger S, Chabout S, Gineau E, Mouille G. Cell adhesion in plants is under the control of putative O-fucosyltransferases. Development. 2016;143: 2536–2540. doi: 10.1242/dev.132308 27317803

79. Barghetti A, Sjögren L, Floris M, Paredes EB, Wenkel S, Brodersen P. Heat-shock protein 40 is the key farnesylation target in meristem size control, abscisic acid signaling, and drought resistance. Genes Dev. 2018;31: 2282–2295. doi: 10.1101/gad.301242.117 29269486

80. Yao Y, Chen X, Wu A-M. ERF-VII members exhibit synergistic and separate roles in Arabidopsis. Plant Signaling & Behavior. 2017;12: e1329073. doi: 10.1080/15592324.2017.1329073 28537474

81. Giuntoli B, Perata P. Group VII Ethylene Response Factors in Arabidopsis: Regulation and Physiological Roles. Plant Physiol. 2018;176: 1143–1155. doi: 10.1104/pp.17.01225 29269576

82. Tran L-SP, Nakashima K, Sakuma Y, Simpson SD, Fujita Y, Maruyama K, et al. Isolation and functional analysis of Arabidopsis stress-inducible NAC transcription factors that bind to a drought-responsive cis-element in the early responsive to dehydration stress 1 promoter. Plant Cell. 2004;16: 2481–2498. doi: 10.1105/tpc.104.022699 15319476

83. Kim HJ, Park J-H, Kim J, Kim JJ, Hong S, Kim J, et al. Time-evolving genetic networks reveal a NAC troika that negatively regulates leaf senescence in Arabidopsis. Proc Natl Acad Sci USA. 2018;115: E4930–E4939. doi: 10.1073/pnas.1721523115 29735710

84. Després C, Chubak C, Rochon A, Clark R, Bethune T, Desveaux D, et al. The Arabidopsis NPR1 disease resistance protein is a novel cofactor that confers redox regulation of DNA binding activity to the basic domain/leucine zipper transcription factor TGA1. Plant Cell. 2003;15: 2181–2191. doi: 10.1105/tpc.012849 12953119

85. Solano R, Stepanova A, Chao Q, Ecker JR. Nuclear events in ethylene signaling: a transcriptional cascade mediated by ETHYLENE-INSENSITIVE3 and ETHYLENE-RESPONSE-FACTOR1. Genes Dev. 1998;12: 3703–3714. doi: 10.1101/gad.12.23.3703 9851977

86. Brown RL, Kazan K, McGrath KC, Maclean DJ, Manners JM. A role for the GCC-box in jasmonate-mediated activation of the PDF1.2 gene of Arabidopsis. Plant Physiol. 2003;132: 1020–1032. doi: 10.1104/pp.102.017814 12805630

87. Li K, Wang H, Hu X, Liu Z, Wu Y, Huang C. Genome-Wide Association Study Reveals the Genetic Basis of Stalk Cell Wall Components in Maize. PLoS ONE. 2016;11: e0158906. doi: 10.1371/journal.pone.0158906 27479588

88. Paschold A, Larson NB, Marcon C, Schnable JC, Yeh C-T, Lanz C, et al. Nonsyntenic genes drive highly dynamic complementation of gene expression in maize hybrids. Plant Cell. 2014;26: 3939–3948. doi: 10.1105/tpc.114.130948 25315323

89. Marcon C, Paschold A, Malik WA, Lithio A, Baldauf JA, Altrogge L, et al. Stability of Single-Parent Gene Expression Complementation in Maize Hybrids upon Water Deficit Stress. Plant Physiol. 2017;173: 1247–1257. doi: 10.1104/pp.16.01045 27999083

90. Vélez-Bermúdez I-C, Salazar-Henao JE, Fornalé S, López-Vidriero I, Franco-Zorrilla J-M, Grotewold E, et al. A MYB/ZML Complex Regulates Wound-Induced Lignin Genes in Maize. Plant Cell. 2015;27: 3245–3259. doi: 10.1105/tpc.15.00545 26566917

91. Weits DA, Giuntoli B, Kosmacz M, Parlanti S, Hubberten H-M, Riegler H, et al. Plant cysteine oxidases control the oxygen-dependent branch of the N-end-rule pathway. Nat Commun. 2014;5: 3425. doi: 10.1038/ncomms4425 24599061

92. White MD, Klecker M, Hopkinson RJ, Weits DA, Mueller C, Naumann C, et al. Plant cysteine oxidases are dioxygenases that directly enable arginyl transferase-catalysed arginylation of N-end rule targets. Nat Commun. 2017;8: 14690. doi: 10.1038/ncomms14690 28332493

93. Mustroph A, Lee SC, Oosumi T, Zanetti ME, Yang H, Ma K, et al. Cross-kingdom comparison of transcriptomic adjustments to low-oxygen stress highlights conserved and plant-specific responses. Plant Physiol. 2010;152: 1484–1500. doi: 10.1104/pp.109.151845 20097791

94. Gonzali S, Loreti E, Cardarelli F, Novi G, Parlanti S, Pucciariello C, et al. Universal stress protein HRU1 mediates ROS homeostasis under anoxia. Nature Plants. 2015;1: 15151. doi: 10.1038/nplants.2015.151 27251529

95. Napoleão TA, Soares G, Vital CE, Bastos C, Castro R, Loureiro ME, et al. Methyl jasmonate and salicylic acid are able to modify cell wall but only salicylic acid alters biomass digestibility in the model grass Brachypodium distachyon. Plant Science. 2017;263: 46–54. doi: 10.1016/j.plantsci.2017.06.014 28818383

96. Zhang X-C, Millet YA, Cheng Z, Bush J, Ausubel FM. Jasmonate signalling in Arabidopsis involves SGT1b-HSP70-HSP90 chaperone complexes. Nat Plants. 2015;1. doi: 10.1038/nplants.2015.49 27054042

97. Wang R, Zhang Y, Kieffer M, Yu H, Kepinski S, Estelle M. HSP90 regulates temperature-dependent seedling growth in Arabidopsis by stabilizing the auxin co-receptor F-box protein TIR1. Nat Commun. 2016;7: 10269. doi: 10.1038/ncomms10269 26728313

98. Thieme CJ, Rojas-Triana M, Stecyk E, Schudoma C, Zhang W, Yang L, et al. Endogenous Arabidopsis messenger RNAs transported to distant tissues. Nat Plants. 2015;1: 15025. doi: 10.1038/nplants.2015.25 27247031

99. Wang W-W, Ma Q, Xiang Y, Zhu S-W, Cheng B-J. Genome-wide analysis of immunophilin FKBP genes and expression patterns in Zea mays. Genet Mol Res. 2012;11: 1690–1700. doi: 10.4238/2012.June.25.2 22782589

100. Kulich I, Pečenková T, Sekereš J, Smetana O, Fendrych M, Foissner I, et al. Arabidopsis exocyst subcomplex containing subunit EXO70B1 is involved in autophagy-related transport to the vacuole. Traffic. 2013;14: 1155–1165. doi: 10.1111/tra.12101 23944713

101. Simpson PJ, Tantitadapitak C, Reed AM, Mather OC, Bunce CM, White SA, et al. Characterization of two novel aldo-keto reductases from Arabidopsis: expression patterns, broad substrate specificity, and an open active-site structure suggest a role in toxicant metabolism following stress. J Mol Biol. 2009;392: 465–480. doi: 10.1016/j.jmb.2009.07.023 19616008

102. Suekawa M, Fujikawa Y, Inada S, Murano A, Esaka M. Gene expression and promoter analysis of a novel tomato aldo-keto reductase in response to environmental stresses. J Plant Physiol. 2016;200: 35–44. doi: 10.1016/j.jplph.2016.05.015 27337067

103. Sharma D, Cukras AR, Rogers EJ, Southworth DR, Green R. Mutational analysis of S12 protein and implications for the accuracy of decoding by the ribosome. J Mol Biol. 2007;374: 1065–1076. doi: 10.1016/j.jmb.2007.10.003 17967466

104. Clasen SJ, Shao W, Gu H, Espenshade PJ. Prolyl dihydroxylation of unassembled uS12/Rps23 regulates fungal hypoxic adaptation. eLife. 2017;6: e28563. doi: 10.7554/eLife.28563 29083304


Článok vyšiel v časopise

PLOS One


2019 Číslo 12
Najčítanejšie tento týždeň
Najčítanejšie v tomto čísle
Kurzy

Zvýšte si kvalifikáciu online z pohodlia domova

Aktuální možnosti diagnostiky a léčby litiáz
nový kurz
Autori: MUDr. Tomáš Ürge, PhD.

Všetky kurzy
Prihlásenie
Zabudnuté heslo

Zadajte e-mailovú adresu, s ktorou ste vytvárali účet. Budú Vám na ňu zasielané informácie k nastaveniu nového hesla.

Prihlásenie

Nemáte účet?  Registrujte sa

#ADS_BOTTOM_SCRIPTS#