Indirect reduction of Ralstonia solanacearum via pathogen helper inhibition

  • Mei Li
  • , Thomas Pommier
  • , Yue Yin
  • , Jianing Wang
  • , Shaohua Gu
  • , Alexandre Jousset
  • , Joost Keuskamp
  • , Honggui Wang
  • , Zhong Wei*
  • , Yangchun Xu
  • , Qirong Shen
  • , George A. Kowalchuk
  • *Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

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 languageEnglish
Pages (from-to)868-875
Number of pages8
JournalISME Journal
Volume16
Issue number3
Early online date20 Oct 2021
DOIs
Publication statusPublished - 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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