Abstract
The rhizosphere microbiome forms a first line of defense against soilborne pathogens. To date, most microbiome enhancement strategies have relied on bioaugmentation with antagonistic microorganisms that directly inhibit pathogens. Previous studies have shown that some root-associated bacteria are able to facilitate pathogen growth. We therefore hypothesized that inhibiting such pathogen helpers may help reduce pathogen densities. We examined tripartite interactions between a model pathogen, Ralstonia solanacearum, two model helper strains and a collection of 46 bacterial isolates recovered from the tomato rhizosphere. This system allowed us to examine the importance of direct (effects of rhizobacteria on pathogen growth) and indirect (effects of rhizobacteria on helper growth) pathways affecting pathogen growth. We found that the interaction between rhizosphere isolates and the helper strains was the major determinant of pathogen suppression both in vitro and in vivo. We therefore propose that controlling microbiome composition to prevent the growth of pathogen helpers may become part of sustainable strategies for pathogen control.
| Original language | English |
|---|---|
| Pages (from-to) | 868-875 |
| Number of pages | 8 |
| Journal | ISME Journal |
| Volume | 16 |
| Issue number | 3 |
| Early online date | 20 Oct 2021 |
| DOIs | |
| Publication status | Published - Mar 2022 |
Bibliographical note
Funding Information:This research was financially supported by the National Key Research and Development Program of China (SQ2021YFD1900024), National Natural Science Foundation of China (31972504, 42090060, 41922053, and 42007038), the Fundamental Research Funds for the Central Universities (KY2201719, KYT201802, KYXK202010, KJQN202116-KJQN202117), the Natural Science Foundation of Jiangsu Province (BK20190518, BK20180527 and BK20200533), and technically supported by the Bioinformatics Center of Nanjing Agricultural University. ML was supported by Chinese Scholarship Council (CSC).
Publisher Copyright:
© 2021, The Author(s).
Funding
This research was financially supported by the National Key Research and Development Program of China (SQ2021YFD1900024), National Natural Science Foundation of China (31972504, 42090060, 41922053, and 42007038), the Fundamental Research Funds for the Central Universities (KY2201719, KYT201802, KYXK202010, KJQN202116-KJQN202117), the Natural Science Foundation of Jiangsu Province (BK20190518, BK20180527 and BK20200533), and technically supported by the Bioinformatics Center of Nanjing Agricultural University. ML was supported by Chinese Scholarship Council (CSC).
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