Temporal change in chromatin accessibility predicts regulators of nodulation in Medicago truncatula.
Chromatin accessibility
Cis-regulatory elements
Gene regulatory network
Machine learning
Medicago
Nitrogen fixation
Nodulation
Symbiosis
Transcriptome and chromatin dynamics
Journal
BMC biology
ISSN: 1741-7007
Titre abrégé: BMC Biol
Pays: England
ID NLM: 101190720
Informations de publication
Date de publication:
09 11 2022
09 11 2022
Historique:
received:
16
05
2022
accepted:
25
10
2022
entrez:
10
11
2022
pubmed:
11
11
2022
medline:
15
11
2022
Statut:
epublish
Résumé
Symbiotic associations between bacteria and leguminous plants lead to the formation of root nodules that fix nitrogen needed for sustainable agricultural systems. Symbiosis triggers extensive genome and transcriptome remodeling in the plant, yet an integrated understanding of the extent of chromatin changes and transcriptional networks that functionally regulate gene expression associated with symbiosis remains poorly understood. In particular, analyses of early temporal events driving this symbiosis have only captured correlative relationships between regulators and targets at mRNA level. Here, we characterize changes in transcriptome and chromatin accessibility in the model legume Medicago truncatula, in response to rhizobial signals that trigger the formation of root nodules. We profiled the temporal chromatin accessibility (ATAC-seq) and transcriptome (RNA-seq) dynamics of M. truncatula roots treated with bacterial small molecules called lipo-chitooligosaccharides that trigger host symbiotic pathways of nodule development. Using a novel approach, dynamic regulatory module networks, we integrated ATAC-seq and RNA-seq time courses to predict cis-regulatory elements and transcription factors that most significantly contribute to transcriptomic changes associated with symbiosis. Regulators involved in auxin (IAA4-5, SHY2), ethylene (EIN3, ERF1), and abscisic acid (ABI5) hormone response, as well as histone and DNA methylation (IBM1), emerged among those most predictive of transcriptome dynamics. RNAi-based knockdown of EIN3 and ERF1 reduced nodule number in M. truncatula validating the role of these predicted regulators in symbiosis between legumes and rhizobia. Our transcriptomic and chromatin accessibility datasets provide a valuable resource to understand the gene regulatory programs controlling the early stages of the dynamic process of symbiosis. The regulators identified provide potential targets for future experimental validation, and the engineering of nodulation in species is unable to establish that symbiosis naturally.
Sections du résumé
BACKGROUND
Symbiotic associations between bacteria and leguminous plants lead to the formation of root nodules that fix nitrogen needed for sustainable agricultural systems. Symbiosis triggers extensive genome and transcriptome remodeling in the plant, yet an integrated understanding of the extent of chromatin changes and transcriptional networks that functionally regulate gene expression associated with symbiosis remains poorly understood. In particular, analyses of early temporal events driving this symbiosis have only captured correlative relationships between regulators and targets at mRNA level. Here, we characterize changes in transcriptome and chromatin accessibility in the model legume Medicago truncatula, in response to rhizobial signals that trigger the formation of root nodules.
RESULTS
We profiled the temporal chromatin accessibility (ATAC-seq) and transcriptome (RNA-seq) dynamics of M. truncatula roots treated with bacterial small molecules called lipo-chitooligosaccharides that trigger host symbiotic pathways of nodule development. Using a novel approach, dynamic regulatory module networks, we integrated ATAC-seq and RNA-seq time courses to predict cis-regulatory elements and transcription factors that most significantly contribute to transcriptomic changes associated with symbiosis. Regulators involved in auxin (IAA4-5, SHY2), ethylene (EIN3, ERF1), and abscisic acid (ABI5) hormone response, as well as histone and DNA methylation (IBM1), emerged among those most predictive of transcriptome dynamics. RNAi-based knockdown of EIN3 and ERF1 reduced nodule number in M. truncatula validating the role of these predicted regulators in symbiosis between legumes and rhizobia.
CONCLUSIONS
Our transcriptomic and chromatin accessibility datasets provide a valuable resource to understand the gene regulatory programs controlling the early stages of the dynamic process of symbiosis. The regulators identified provide potential targets for future experimental validation, and the engineering of nodulation in species is unable to establish that symbiosis naturally.
Identifiants
pubmed: 36352404
doi: 10.1186/s12915-022-01450-9
pii: 10.1186/s12915-022-01450-9
pmc: PMC9647978
doi:
Substances chimiques
Chromatin
0
Plant Proteins
0
Types de publication
Journal Article
Research Support, U.S. Gov't, Non-P.H.S.
Langues
eng
Sous-ensembles de citation
IM
Pagination
252Informations de copyright
© 2022. The Author(s).
Références
Science. 2003 Oct 24;302(5645):630-3
pubmed: 12947035
Nat Methods. 2017 Jul;14(7):687-690
pubmed: 28581496
KDD. 2012;2012:1095-1103
pubmed: 25309808
Bioinformatics. 2012 Sep 15;28(18):2297-303
pubmed: 22730432
Plant Cell. 2012 Sep;24(9):3838-52
pubmed: 23023168
Mol Cell. 2010 May 28;38(4):576-89
pubmed: 20513432
Proc Natl Acad Sci U S A. 2017 Apr 25;114(17):4543-4548
pubmed: 28404731
Trends Plant Sci. 2016 Feb;21(2):159-167
pubmed: 26616195
Plant Cell. 2006 Oct;18(10):2680-93
pubmed: 17028204
Plant Cell Physiol. 2019 Mar 1;60(3):575-586
pubmed: 30476329
Nat Commun. 2019 Aug 16;10(1):3703
pubmed: 31420535
Nat Rev Microbiol. 2013 Apr;11(4):252-63
pubmed: 23493145
Plant Cell. 2008 Oct;20(10):2681-95
pubmed: 18931020
Nat Biotechnol. 2016 May;34(5):525-7
pubmed: 27043002
Nucleic Acids Res. 1985 Aug 26;13(16):5965-76
pubmed: 2994020
Nat Plants. 2018 Dec;4(12):1017-1025
pubmed: 30397259
Plant Physiol. 2014 Apr 10;165(2):747-758
pubmed: 24722550
Methods Mol Biol. 2018;1675:183-201
pubmed: 29052193
Plant Physiol. 2008 Aug;147(4):2030-40
pubmed: 18567832
Proc Natl Acad Sci U S A. 2010 Jan 5;107(1):478-83
pubmed: 20018678
Mol Plant Microbe Interact. 2021 Aug;34(8):904-921
pubmed: 33819071
BMC Genomics. 2018 Mar 1;19(1):169
pubmed: 29490630
J Exp Bot. 2014 Jun;65(10):2633-43
pubmed: 24474807
Gigascience. 2021 Feb 16;10(2):
pubmed: 33590861
Plant Cell. 2001 Aug;13(8):1835-49
pubmed: 11487696
Cell. 2014 Sep 11;158(6):1431-1443
pubmed: 25215497
Plant Cell. 2020 Jan;32(1):15-41
pubmed: 31649123
Proc Natl Acad Sci U S A. 2011 Aug 23;108(34):14348-53
pubmed: 21825141
Science. 2004 Feb 27;303(5662):1361-4
pubmed: 14963335
Plant J. 2014 Mar;77(6):817-37
pubmed: 24483147
Nat Biotechnol. 2014 Feb;32(2):171-178
pubmed: 24441470
Nucleic Acids Res. 2017 Apr 7;45(6):e41
pubmed: 27903897
Genome Biol. 2010;11(10):R106
pubmed: 20979621
Annu Rev Genet. 2011;45:119-44
pubmed: 21838550
Science. 2019 Nov 22;366(6468):1021-1023
pubmed: 31754003
J Mol Biol. 2008 Jun 13;379(4):772-86
pubmed: 18485363
Plant Physiol. 2006 Sep;142(1):265-79
pubmed: 16844829
Plant Cell. 2000 Sep;12(9):1647-66
pubmed: 11006338
Nat Methods. 2013 Dec;10(12):1213-8
pubmed: 24097267
Genome Biol. 2008;9(9):R137
pubmed: 18798982
Nat Methods. 2012 Mar 04;9(4):357-9
pubmed: 22388286
Plant Physiol. 2015 Sep;169(1):233-65
pubmed: 26175514
Mol Plant Microbe Interact. 2001 Jun;14(6):695-700
pubmed: 11386364
Plant Cell. 2019 Jan;31(1):68-83
pubmed: 30610167
EMBO J. 2010 Oct 20;29(20):3496-506
pubmed: 20834229
Plant Physiol. 2012 Aug;159(4):1671-85
pubmed: 22652128
Mol Cell Proteomics. 2012 Sep;11(9):724-44
pubmed: 22683509
BMC Bioinformatics. 2010 May 11;11:237
pubmed: 20459804
Plant Physiol. 2017 Jul;174(3):1795-1806
pubmed: 28550207
Plant Cell. 2018 Jan;30(1):15-36
pubmed: 29229750
Ann Bot. 2013 Jun;111(6):1021-58
pubmed: 23558912
Front Plant Sci. 2016 Feb 16;7:96
pubmed: 26909085
Proc Natl Acad Sci U S A. 2012 Feb 28;109(9):3576-81
pubmed: 22323601
Nat Plants. 2016 Oct 31;2(11):16166
pubmed: 27797357
Science. 2014 Jan 31;343(6170):1248559
pubmed: 24482483
Plant Physiol. 2018 Feb;176(2):1764-1772
pubmed: 29187569
Carbohydr Res. 2016 Nov 3;434:83-93
pubmed: 27623438
Bioinformatics. 2010 Mar 15;26(6):841-2
pubmed: 20110278
Genome Res. 2022 Jul;32(7):1367-1384
pubmed: 35705328
PLoS Comput Biol. 2013;9(10):e1003252
pubmed: 24146602
Brief Bioinform. 2013 Mar;14(2):178-92
pubmed: 22517427
Development. 2015 Sep 1;142(17):2941-50
pubmed: 26253408
Cell Syst. 2019 Aug 28;9(2):167-186.e12
pubmed: 31302154
Curr Opin Plant Biol. 2013 Feb;16(1):118-27
pubmed: 23246268