Volatile DMNT directly protects plants against Plutella xylostella by disrupting peritrophic matrix barrier in midgut

  1. Chen Chen
  2. Hongyi Chen
  3. Shijie Huang
  4. Taoshan Jiang
  5. Chuanhong Wang
  6. Zhen Tao
  7. Chen He
  8. Qingfeng Tang
  9. Peijin Li  Is a corresponding author
  1. Anhui Agricultual University, China
  2. Anhui Agricultural University, China

Abstract

Insect pests negatively affect crop quality and yield; identifying new methods to protect crops against insects therefore has important agricultural applications. Our analysis of transgenic Arabidopsis thaliana plants showed that overexpression of PENTACYCLIC TRITERPENE SYNTHASE 1 (PEN1), encoding the key biosynthetic enzyme for the natural plant product (3E)-4,8-dimethyl-1,3,7-nonatriene (DMNT), led to significant resistance against a major insect pest, Plustella xylostella. DMNT treatment severely damaged the peritrophic matrix (PM), a physical barrier isolating food and pathogens from the midgut wall cells. DMNT repressed the expression of PxMucin in midgut cells and knocking down PxMucin resulted in PM rupture and P. xylostella death. A 16S RNA survey revealed that DMNT significantly disrupted midgut microbiota populations and that midgut microbes were essential for DMNT-induced killing. Therefore, we propose that the midgut microbiota assists DMNT in killing P. xylostella. These findings may provide a novel approach for plant protection against P. xylostella.

Data availability

All data generated or analysed during this study are included in the manuscript and supporting files. Source data files have been provided for Figure 1-6, Figure 1-figure supplement 1, 3-5, Figure 4-figure supplement 2, Figure 5-figure supplement 2, and Figure 6-figure supplement 2-3.

Article and author information

Author details

  1. Chen Chen

    The School of Life Sciences, Anhui Agricultual University, Hefei, China
    Competing interests
    The authors declare that no competing interests exist.
  2. Hongyi Chen

    The School of Life Sciences, Anhui Agricultual University, Hefei, China
    Competing interests
    The authors declare that no competing interests exist.
  3. Shijie Huang

    The School of Life Sciences, Anhui Agricultual University, Hefei, China
    Competing interests
    The authors declare that no competing interests exist.
  4. Taoshan Jiang

    The School of Life Sciences, Anhui Agricultual University, Hefei, China
    Competing interests
    The authors declare that no competing interests exist.
  5. Chuanhong Wang

    The School of Life Sciences, Anhui Agricultual University, Hefei, China
    Competing interests
    The authors declare that no competing interests exist.
  6. Zhen Tao

    The School of Life Sciences, Anhui Agricultual University, Hefei, China
    Competing interests
    The authors declare that no competing interests exist.
  7. Chen He

    The School of Life Sciences, Anhui Agricultual University, Hefei, China
    Competing interests
    The authors declare that no competing interests exist.
  8. Qingfeng Tang

    The School of Plant Protection, Anhui Agricultural University, Hefei, China
    Competing interests
    The authors declare that no competing interests exist.
  9. Peijin Li

    The School of Life Sciences, Anhui Agricultual University, Hefei, China
    For correspondence
    Peijin.li@ahau.edu.cn
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-1579-7553

Funding

National Key Research and Development Program of China (2017YFD0301301)

  • Peijin Li

National Key Research and Development Program of China (2016YFD0101803)

  • Peijin Li

Natural Science Foundation of China (31670264)

  • Peijin Li

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Copyright

© 2021, Chen et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

Metrics

  • 2,845
    views
  • 672
    downloads
  • 40
    citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Open citations (links to open the citations from this article in various online reference manager services)

Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)

  1. Chen Chen
  2. Hongyi Chen
  3. Shijie Huang
  4. Taoshan Jiang
  5. Chuanhong Wang
  6. Zhen Tao
  7. Chen He
  8. Qingfeng Tang
  9. Peijin Li
(2021)
Volatile DMNT directly protects plants against Plutella xylostella by disrupting peritrophic matrix barrier in midgut
eLife 10:e63938.
https://doi.org/10.7554/eLife.63938

Share this article

https://doi.org/10.7554/eLife.63938

Further reading

    1. Ecology
    Mercury Shitindo
    Insight

    Tracking wild pigs with GPS devices reveals how their social interactions could influence the spread of disease, offering new strategies for protecting agriculture, wildlife, and human health.

    1. Ecology
    2. Neuroscience
    Ralph E Peterson, Aman Choudhri ... Dan H Sanes
    Research Article

    In nature, animal vocalizations can provide crucial information about identity, including kinship and hierarchy. However, lab-based vocal behavior is typically studied during brief interactions between animals with no prior social relationship, and under environmental conditions with limited ethological relevance. Here, we address this gap by establishing long-term acoustic recordings from Mongolian gerbil families, a core social group that uses an array of sonic and ultrasonic vocalizations. Three separate gerbil families were transferred to an enlarged environment and continuous 20-day audio recordings were obtained. Using a variational autoencoder (VAE) to quantify 583,237 vocalizations, we show that gerbils exhibit a more elaborate vocal repertoire than has been previously reported and that vocal repertoire usage differs significantly by family. By performing gaussian mixture model clustering on the VAE latent space, we show that families preferentially use characteristic sets of vocal clusters and that these usage preferences remain stable over weeks. Furthermore, gerbils displayed family-specific transitions between vocal clusters. Since gerbils live naturally as extended families in complex underground burrows that are adjacent to other families, these results suggest the presence of a vocal dialect which could be exploited by animals to represent kinship. These findings position the Mongolian gerbil as a compelling animal model to study the neural basis of vocal communication and demonstrates the potential for using unsupervised machine learning with uninterrupted acoustic recordings to gain insights into naturalistic animal behavior.