A framework for the targeted recruitment of crop-beneficial soil taxa based on network analysis of metagenomics data.
Biostimulants
Compound
Differential abundance
Disease suppressive soils
Functional annotation
MAG
Metagenomics
Microbial community
Microbiome
Network
Pathway
Rhizosphere
Rootstock
Shotgun sequencing
Journal
Microbiome
ISSN: 2049-2618
Titre abrégé: Microbiome
Pays: England
ID NLM: 101615147
Informations de publication
Date de publication:
12 01 2023
12 01 2023
Historique:
received:
14
04
2022
accepted:
28
11
2022
entrez:
12
1
2023
pubmed:
13
1
2023
medline:
17
1
2023
Statut:
epublish
Résumé
The design of ecologically sustainable and plant-beneficial soil systems is a key goal in actively manipulating root-associated microbiomes. Community engineering efforts commonly seek to harness the potential of the indigenous microbiome through substrate-mediated recruitment of beneficial members. In most sustainable practices, microbial recruitment mechanisms rely on the application of complex organic mixtures where the resources/metabolites that act as direct stimulants of beneficial groups are not characterized. Outcomes of such indirect amendments are unpredictable regarding engineering the microbiome and achieving a plant-beneficial environment. This study applied network analysis of metagenomics data to explore amendment-derived transformations in the soil microbiome, which lead to the suppression of pathogens affecting apple root systems. Shotgun metagenomic analysis was conducted with data from 'sick' vs 'healthy/recovered' rhizosphere soil microbiomes. The data was then converted into community-level metabolic networks. Simulations examined the functional contribution of treatment-associated taxonomic groups and linked them with specific amendment-induced metabolites. This analysis enabled the selection of specific metabolites that were predicted to amplify or diminish the abundance of targeted microbes functional in the healthy soil system. Many of these predictions were corroborated by experimental evidence from the literature. The potential of two of these metabolites (dopamine and vitamin B This research demonstrates how genomic-based algorithms can be used to formulate testable hypotheses for strategically engineering the rhizosphere microbiome by identifying specific compounds, which may act as selective modulators of microbial communities. Applying this framework to reduce unpredictable elements in amendment-based solutions promotes the development of ecologically-sound methods for re-establishing a functional microbiome in agro and other ecosystems. Video Abstract.
Sections du résumé
BACKGROUND
The design of ecologically sustainable and plant-beneficial soil systems is a key goal in actively manipulating root-associated microbiomes. Community engineering efforts commonly seek to harness the potential of the indigenous microbiome through substrate-mediated recruitment of beneficial members. In most sustainable practices, microbial recruitment mechanisms rely on the application of complex organic mixtures where the resources/metabolites that act as direct stimulants of beneficial groups are not characterized. Outcomes of such indirect amendments are unpredictable regarding engineering the microbiome and achieving a plant-beneficial environment.
RESULTS
This study applied network analysis of metagenomics data to explore amendment-derived transformations in the soil microbiome, which lead to the suppression of pathogens affecting apple root systems. Shotgun metagenomic analysis was conducted with data from 'sick' vs 'healthy/recovered' rhizosphere soil microbiomes. The data was then converted into community-level metabolic networks. Simulations examined the functional contribution of treatment-associated taxonomic groups and linked them with specific amendment-induced metabolites. This analysis enabled the selection of specific metabolites that were predicted to amplify or diminish the abundance of targeted microbes functional in the healthy soil system. Many of these predictions were corroborated by experimental evidence from the literature. The potential of two of these metabolites (dopamine and vitamin B
CONCLUSIONS
This research demonstrates how genomic-based algorithms can be used to formulate testable hypotheses for strategically engineering the rhizosphere microbiome by identifying specific compounds, which may act as selective modulators of microbial communities. Applying this framework to reduce unpredictable elements in amendment-based solutions promotes the development of ecologically-sound methods for re-establishing a functional microbiome in agro and other ecosystems. Video Abstract.
Identifiants
pubmed: 36635724
doi: 10.1186/s40168-022-01438-1
pii: 10.1186/s40168-022-01438-1
pmc: PMC9835355
doi:
Substances chimiques
Soil
0
Types de publication
Video-Audio Media
Journal Article
Research Support, Non-U.S. Gov't
Research Support, U.S. Gov't, Non-P.H.S.
Langues
eng
Sous-ensembles de citation
IM
Pagination
8Informations de copyright
© 2023. The Author(s).
Références
mSystems. 2019 May 28;4(3):
pubmed: 31138719
mSystems. 2016 Jan-Feb;1(1):
pubmed: 27239563
Front Microbiol. 2017 Jun 06;8:1025
pubmed: 28634478
Microorganisms. 2021 Aug 30;9(9):
pubmed: 34576734
Curr Opin Microbiol. 2015 Oct;27:37-44
pubmed: 26207681
Front Microbiol. 2017 Aug 23;8:1606
pubmed: 28878756
Bioinformatics. 2012 Mar 1;28(5):734-5
pubmed: 22219204
Nucleic Acids Res. 2008 Jan;36(Database issue):D480-4
pubmed: 18077471
Front Microbiol. 2018 Dec 10;9:2933
pubmed: 30619106
Sci Rep. 2018 Apr 17;8(1):6116
pubmed: 29666454
ISME J. 2017 Jun;11(6):1483-1499
pubmed: 28106881
mSystems. 2019 Apr 9;4(2):
pubmed: 30984871
Bioinformatics. 2015 May 15;31(10):1674-6
pubmed: 25609793
Nucleic Acids Res. 2020 Jan 8;48(D1):D626-D632
pubmed: 31728526
BMC Genomics. 2018 May 25;19(1):402
pubmed: 29801436
Nat Plants. 2018 May;4(5):247-257
pubmed: 29725101
PLoS Comput Biol. 2010 Feb 26;6(2):e1000690
pubmed: 20195496
Int J Syst Evol Microbiol. 2007 Jul;57(Pt 7):1435-1441
pubmed: 17625171
Life Sci. 1992;50(3):203-12
pubmed: 1731173
ISME J. 2008 Aug;2(8):805-14
pubmed: 18615117
Bioinformatics. 2014 Aug 1;30(15):2114-20
pubmed: 24695404
Microb Genom. 2018 Apr;4(4):
pubmed: 29620507
Sci Rep. 2016 Oct 11;6:35046
pubmed: 27725750
Sci Total Environ. 2018 Mar;616-617:107-116
pubmed: 29107775
Appl Microbiol Biotechnol. 2002 Mar;58(3):275-85
pubmed: 11935176
FEMS Microbiol Ecol. 2021 Sep 6;97(9):
pubmed: 34379764
Phytopathology. 1998 Sep;88(9):930-8
pubmed: 18944871
ISME J. 2019 Feb;13(2):494-508
pubmed: 30291327
Front Microbiol. 2022 Jul 25;13:949404
pubmed: 35958152
Sci Rep. 2016 Feb 22;6:21928
pubmed: 26898409
Annu Rev Phytopathol. 2018 Aug 25;56:1-20
pubmed: 29768137
Nat Commun. 2011 Dec 13;2:589
pubmed: 22158444
Curr Opin Biotechnol. 2013 Aug;24(4):810-20
pubmed: 23623295
Genome Inform. 2004;15(1):35-45
pubmed: 15712108
Microb Pathog. 1999 Feb;26(2):85-91
pubmed: 10090855
Front Plant Sci. 2017 Sep 19;8:1617
pubmed: 28974956
Nat Commun. 2019 Jan 9;10(1):103
pubmed: 30626871
Trends Biotechnol. 2019 May;37(5):532-547
pubmed: 30447878
Nature. 2020 Nov;587(7832):103-108
pubmed: 32999461
Appl Environ Microbiol. 2019 Mar 6;85(6):
pubmed: 30658982
Appl Environ Microbiol. 2011 Dec;77(24):8509-15
pubmed: 21984248
Phytopathology. 2015 Apr;105(4):460-9
pubmed: 25412009
Annu Rev Phytopathol. 2012;50:45-65
pubmed: 22559069
Plant Dis. 2009 Jan;93(1):51-57
pubmed: 30764268
Phytopathology. 2019 Aug;109(8):1378-1391
pubmed: 30887889
ISME J. 2018 Jun;12(7):1861-1866
pubmed: 29523891
Phytopathology. 2000 Feb;90(2):114-9
pubmed: 18944598
J Mol Biol. 2005 Jun 17;349(4):745-63
pubmed: 15896806
ISME J. 2020 Jan;14(1):53-66
pubmed: 31492962
Environ Microbiome. 2019 Nov 7;14(1):8
pubmed: 33902732
Phytopathology. 2017 Mar;107(3):256-263
pubmed: 27898265
Nat Methods. 2015 Jan;12(1):59-60
pubmed: 25402007
Microorganisms. 2020 Jun 03;8(6):
pubmed: 32503277
Microbiome. 2020 Sep 22;8(1):137
pubmed: 32962766
Anim Microbiome. 2020 Aug 18;2(1):30
pubmed: 33499981
Genome Res. 2007 Mar;17(3):377-86
pubmed: 17255551
Microbiome. 2021 Oct 12;9(1):202
pubmed: 34641955
PLoS One. 2013;8(2):e56329
pubmed: 23457551
BMC Bioinformatics. 2010 Mar 08;11:119
pubmed: 20211023
Front Plant Sci. 2018 Oct 23;9:1473
pubmed: 30405652
Sci Rep. 2020 Aug 3;10(1):13019
pubmed: 32747737
Bioprocess Biosyst Eng. 2016 Oct;39(10):1527-37
pubmed: 27282166
Antonie Van Leeuwenhoek. 2015 Feb;107(2):467-85
pubmed: 25481407
Trends Biotechnol. 2019 Feb;37(2):123-125
pubmed: 30477738
Bioinformatics. 2013 Dec 1;29(23):3100-1
pubmed: 24021386
Proc Natl Acad Sci U S A. 2006 Dec 5;103(49):18389-94
pubmed: 17130454
Plant Dis. 2010 Jul;94(7):835-842
pubmed: 30743552
J Biol Chem. 2008 Mar 28;283(13):8183-9
pubmed: 18234666
Phytopathology. 1999 Oct;89(10):920-7
pubmed: 18944736
ISME J. 2018 Jun;12(7):1729-1742
pubmed: 29476143
ISME J. 2016 Nov;10(11):2557-2568
pubmed: 27022995
Philos Trans R Soc Lond B Biol Sci. 2020 May 11;375(1798):20190250
pubmed: 32200747
Life Sci. 2000 Nov 10;67(25):3075-85
pubmed: 11125844
Annu Rev Phytopathol. 2016 Aug 4;54:25-54
pubmed: 27215969