The Reverse Ecology-Based Approach to Design a Bacterial Consortium as Soybean Bioinoculant.
Journal
Current microbiology
ISSN: 1432-0991
Titre abrégé: Curr Microbiol
Pays: United States
ID NLM: 7808448
Informations de publication
Date de publication:
22 Oct 2024
22 Oct 2024
Historique:
received:
26
07
2024
accepted:
24
09
2024
medline:
23
10
2024
pubmed:
23
10
2024
entrez:
22
10
2024
Statut:
epublish
Résumé
Bioinoculants traditionally rely on selecting efficient microbes from the soil with potential growth-enhancing traits for plants. However, such approaches often neglect microbe-microbe and microbe-plant interactions. In this study, we applied a reverse ecology framework to design and assess a bacterial consortium tailored for soybeans. Our analysis identified Paenibacillus polymyxa, Methylobacterium brachiatum, and Enterobacter sp. as key strains for their synergistic potential in promoting soybean growth. Computational analyses revealed that these selected strains exhibited low competitiveness and metabolic compatibility. Specifically, their complementary metabolic profiles suggested minimal competition for resources and potential for mutualistic interactions. In vitro experiments further supported these findings, demonstrating that the consortium maintained stable growth without inhibitory effects among strains. In addition, greenhouse validation experiments confirmed the efficacy of the microbial consortium in enhancing soybean growth such as root and shoot development and biomass production. Overall, this study underscores the potential of reverse ecology in optimizing microbial consortia design for bioinoculant applications.
Identifiants
pubmed: 39438288
doi: 10.1007/s00284-024-03926-z
pii: 10.1007/s00284-024-03926-z
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
421Subventions
Organisme : Fundação de Amparo à Pesquisa do Estado de Minas Gerais
ID : 402644/2021-2
Organisme : Conselho Nacional de Desenvolvimento Científico e Tecnológico
ID : APQ-02381-21
Informations de copyright
© 2024. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
Références
Kumar P, Singh S, Pranaw K et al (2022) Bioinoculants as mitigators of multiple stresses: a ray of hope for agriculture in the darkness of climate change. Heliyon 8:e11269. https://doi.org/10.1016/j.heliyon.2022.e11269
doi: 10.1016/j.heliyon.2022.e11269
pubmed: 36339753
pmcid: 9634370
Singh M, Bhasin S, Madan N et al (2021) Bioinoculants for agricultural sustainability. In: Soni R, Suyal DC, Bhargava P, Goel R (eds) Microbiological activity for soil and plant health management. Springer, Singapore, pp 629–641
Compant S, Clément C, Sessitsch A (2010) Plant growth-promoting bacteria in the rhizo- and endosphere of plants: their role, colonization, mechanisms involved and prospects for utilization. Soil Biol Biochem 42:669–678. https://doi.org/10.1016/j.soilbio.2009.11.024
doi: 10.1016/j.soilbio.2009.11.024
Souza R, Ambrosini A, Passaglia LMP et al (2015) Plant growth-promoting bacteria as inoculants in agricultural soils. Genet Mol Biol 38:401–419. https://doi.org/10.1590/S1415-475738420150053
doi: 10.1590/S1415-475738420150053
pubmed: 26537605
pmcid: 4763327
Glick BR (2012) Plant growth-promoting bacteria: mechanisms and applications. Scientifica (Cairo) 2012:963401. https://doi.org/10.6064/2012/963401
doi: 10.6064/2012/963401
pubmed: 24278762
Kavamura VN, Santos SN, da Silva JL et al (2013) Screening of Brazilian cacti rhizobacteria for plant growth promotion under drought. Microbiol Res 168:183–191. https://doi.org/10.1016/j.micres.2012.12.002
doi: 10.1016/j.micres.2012.12.002
pubmed: 23279812
Gonçalves OS, Souza TS, Gonçalves GC et al (2023) Harnessing novel soil bacteria for beneficial interactions with soybean. Microorganisms. https://doi.org/10.3390/microorganisms11020300
doi: 10.3390/microorganisms11020300
pubmed: 38137994
pmcid: 10745812
Reuter JA, Spacek DV, Snyder MP (2015) High-throughput sequencing technologies. Mol Cell 58:586–597. https://doi.org/10.1016/j.molcel.2015.05.004
doi: 10.1016/j.molcel.2015.05.004
pubmed: 26000844
pmcid: 4494749
Imam J, Singh PK, Shukla P (2016) Plant microbe interactions in post genomic era: perspectives and applications. Front Microbiol 7:1488. https://doi.org/10.3389/fmicb.2016.01488
doi: 10.3389/fmicb.2016.01488
pubmed: 27725809
pmcid: 5035750
Bruto M, Prigent-Combaret C, Muller D, Moënne-Loccoz Y (2014) Analysis of genes contributing to plant-beneficial functions in plant growth-promoting rhizobacteria and related proteobacteria. Sci Rep 4:6261. https://doi.org/10.1038/srep06261
doi: 10.1038/srep06261
pubmed: 25179219
pmcid: 4151105
Levy R, Borenstein E (2012) reverse ecology: from systems to environments and back. Adv Exp Med Biol 751:329–345. https://doi.org/10.1007/978-1-4614-3567-9_15
doi: 10.1007/978-1-4614-3567-9_15
pubmed: 22821465
Freilich S, Kreimer A, Meilijson I et al (2010) The large-scale organization of the bacterial network of ecological co-occurrence interactions. Nucleic Acids Res 38:3857–3868. https://doi.org/10.1093/nar/gkq118
doi: 10.1093/nar/gkq118
pubmed: 20194113
pmcid: 2896517
Freilich S, Kreimer A, Borenstein E et al (2009) Metabolic-network-driven analysis of bacterial ecological strategies. Genome Biol 10:R61. https://doi.org/10.1186/gb-2009-10-6-r61
doi: 10.1186/gb-2009-10-6-r61
pubmed: 19500338
pmcid: 2718495
Parter M, Kashtan N, Alon U (2007) Environmental variability and modularity of bacterial metabolic networks. BMC Evol Biol 7:169. https://doi.org/10.1186/1471-2148-7-169
doi: 10.1186/1471-2148-7-169
pubmed: 17888177
pmcid: 2151768
Levy R, Borenstein E (2013) Metabolic modeling of species interaction in the human microbiome elucidates community-level assembly rules. Proc Natl Acad Sci USA 110:12804–12809. https://doi.org/10.1073/pnas.1300926110
doi: 10.1073/pnas.1300926110
pubmed: 23858463
pmcid: 3732988
Karpinets TV, Park BH, Syed MH et al (2014) metabolic environments and genomic features associated with pathogenic and mutualistic interactions between bacteria and plants. Mol Plant–Microbe Interact 27:664–677. https://doi.org/10.1094/MPMI-12-13-0368-R
doi: 10.1094/MPMI-12-13-0368-R
pubmed: 24580106
Ofaim S, Ofek-Lalzar M, Sela N et al (2017) Analysis of microbial functions in the rhizosphere using a metabolic-network based framework for metagenomics interpretation. Front Microbiol 8:1606
doi: 10.3389/fmicb.2017.01606
pubmed: 28878756
pmcid: 5572346
Bernstein DB, Dewhirst FE, Segrè D (2019) Metabolic network percolation quantifies biosynthetic capabilities across the human oral microbiome. Elife 8:1–33. https://doi.org/10.7554/eLife.39733.001
doi: 10.7554/eLife.39733.001
Michelini S, Balakrishnan B, Parolo S et al (2018) A reverse metabolic approach to weaning: in silico identification of immune-beneficial infant gut bacteria, mining their metabolism for prebiotic feeds and sourcing these feeds in the natural product space. Microbiome 6:1–18. https://doi.org/10.1186/s40168-018-0545-x
doi: 10.1186/s40168-018-0545-x
Gonçalves Silva O, Bonandi Barreiros R, Martins Tupy S, Ferreira Santana M (2022) A reverse-ecology framework to uncover the potential metabolic interplay among ‘Candidatus Liberibacter’ species, citrus hosts and psyllid vector. Gene 837:146679. https://doi.org/10.1016/j.gene.2022.146679
doi: 10.1016/j.gene.2022.146679
Gonçalves OS, Santana MF (2023) Uncovering the secrets of slow-growing bacteria in tropical savanna soil through isolation and genomic analysis. Microb Ecol 86:2687–2702. https://doi.org/10.1007/s00248-023-02275-x
doi: 10.1007/s00248-023-02275-x
pubmed: 37507488
Aramaki T, Blanc-Mathieu R, Endo H et al (2020) KofamKOALA: KEGG Ortholog assignment based on profile HMM and adaptive score threshold. Bioinformatics 36:2251–2252. https://doi.org/10.1093/bioinformatics/btz859
doi: 10.1093/bioinformatics/btz859
pubmed: 31742321
Cao Y, Wang Y, Zheng X et al (2016) RevEcoR: an R package for the reverse ecology analysis of microbiomes. BMC Bioinform 17:294. https://doi.org/10.1186/s12859-016-1088-4
doi: 10.1186/s12859-016-1088-4
Levy R, Carr R, Kreimer A et al (2015) NetCooperate: a network-based tool for inferring host–microbe and microbe–microbe cooperation. BMC Bioinform 16:164. https://doi.org/10.1186/s12859-015-0588-y
doi: 10.1186/s12859-015-0588-y
Caspi R, Billington R, Keseler IM et al (2020) The MetaCyc database of metabolic pathways and enzymes—a 2019 update. Nucleic Acids Res 48:D445–D453. https://doi.org/10.1093/nar/gkz862
doi: 10.1093/nar/gkz862
pubmed: 31586394
Timmusk S, Grantcharova N, Wagner EGH (2005) Paenibacillus polymyxa invades plant roots and forms biofilms. Appl Environ Microbiol 71:7292–7300. https://doi.org/10.1128/AEM.71.11.7292-7300.2005
doi: 10.1128/AEM.71.11.7292-7300.2005
pubmed: 16269771
pmcid: 1287669
Soni R, Rawal K, Keharia H (2021) Genomics assisted functional characterization of Paenibacillus polymyxa HK4 as a biocontrol and plant growth promoting bacterium. Microbiol Res 248:126734. https://doi.org/10.1016/j.micres.2021.126734
doi: 10.1016/j.micres.2021.126734
pubmed: 33690069
Pandey AK, Barbetti MJ, Lamichhane JR (2023) Paenibacillus polymyxa. Trends Microbiol 31:657–659. https://doi.org/10.1016/j.tim.2022.11.010
doi: 10.1016/j.tim.2022.11.010
pubmed: 36564337
Singh RR, Wesemael WML (2022) Endophytic Paenibacillus polymyxa LMG27872 inhibits Meloidogyne incognita parasitism, promoting tomato growth through a dose-dependent effect. Front Plant Sci 13:961085
doi: 10.3389/fpls.2022.961085
pubmed: 36186028
pmcid: 9516289
Koch AL (2001) Oligotrophs versus copiotrophs. BioEssays 23:657–661. https://doi.org/10.1002/bies.1091
doi: 10.1002/bies.1091
pubmed: 11462219
Macabuhay A, Arsova B, Walker R et al (2022) Modulators or facilitators? Roles of lipids in plant root microbe interactions. Trends Plant Sci 27:180–190. https://doi.org/10.1016/j.tplants.2021.08.004
doi: 10.1016/j.tplants.2021.08.004
pubmed: 34620547
Abanda-Nkpwatt D, Müsch M, Tschiersch J et al (2006) Molecular interaction between Methylobacterium extorquens and seedlings: growth promotion, methanol consumption, and localization of the methanol emission site. J Exp Bot 57:4025–4032. https://doi.org/10.1093/jxb/erl173
doi: 10.1093/jxb/erl173
pubmed: 17043084
MacDonald RC, Fall R (1993) Detection of substantial emissions of methanol from plants to the atmosphere. Atmos Environ Part A Gen Top 27:1709–1713. https://doi.org/10.1016/0960-1686(93)90233-O
doi: 10.1016/0960-1686(93)90233-O
Nemecek-Marshall M, MacDonald RC, Franzen JJ et al (1995) Methanol emission from leaves (enzymatic detection of gas-phase methanol and relation of methanol fluxes to stomatal conductance and leaf development). Plant Physiol 108:1359–1368
doi: 10.1104/pp.108.4.1359
pubmed: 12228547
pmcid: 157513
Bradáčová K, Florea A, Bar-Tal A et al (2019) Microbial consortia versus single-strain inoculants: an advantage in PGPM-assisted tomato production? Agronomy 9:105. https://doi.org/10.3390/agronomy9020105
doi: 10.3390/agronomy9020105
Jansson JK, Hofmockel KS (2020) Soil microbiomes and climate change. Nat Rev Microbiol 18:35–46. https://doi.org/10.1038/s41579-019-0265-7
doi: 10.1038/s41579-019-0265-7
pubmed: 31586158