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Root-secreted bitter triterpene modulates the rhizosphere microbiota to improve plant fitness

Nature Plants volume 8, pages 887–896 (2022)Cite this article

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Abstract

Underground microbial ecosystems have profound impacts on plant health1,2,3,4,5. Recently, essential roles have been shown for plant specialized metabolites in shaping the rhizosphere microbiome6,7,8,9. However, the potential mechanisms underlying the root-to-soil delivery of these metabolites remain to be elucidated10. Cucurbitacins, the characteristic bitter triterpenoids in cucurbit plants (such as melon and watermelon), are synthesized by operon-like gene clusters11. Here we report two Multidrug and Toxic Compound Extrusion (MATE) proteins involved in the transport of their respective cucurbitacins, a process co-regulated with cucurbitacin biosynthesis. We further show that the transport of cucurbitacin B from the roots of melon into the soil modulates the rhizosphere microbiome by selectively enriching for two bacterial genera, Enterobacter and Bacillus, and we demonstrate that this, in turn, leads to robust resistance against the soil-borne wilt fungal pathogen, Fusarium oxysporum. Our study offers insights into how transporters for specialized metabolites manipulate the rhizosphere microbiota and thereby affect crop fitness.

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Fig. 1: Genome mining for bitterness transporters in cucurbits.
Fig. 2: Characterization of in vitro and in vivo functions of the two cucurbit MATEs.
Fig. 3: Roles of CuB in shaping the rhizosphere microbiome of melon.
Fig. 4: Characteristics of CuB functional activity against wilt disease of melon.

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Data availability

All data supporting the findings of this study are available in the paper, the extended data and the Supplementary Information. The amplicon sequencing data and shotgun sequencing data were deposited at NCBI under Bioproject No. PRJNA756119 and are publicly accessible at https://www.ncbi.nlm.nih.gov/search/all/?term=PRJNA756119. Source data are provided with this paper.

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Acknowledgements

We thank Q. R. Shen (Nanjing Agricultural University), H. S. Wang and S. L. Wang (Institute of Vegetables and Flowers, CAAS) for providing experimental assistance. This work was supported by the Yunnan provincial, Shenzhen municipal and Dapeng district governments. This work was funded by the National Key Research and Development Program of China (grant no. 2018YFA0901800 to Y.S.), the Yunnan Science Fund (grant no. 202105AF150028, 202005AE160015 and 2019FJ004 to Y.S.), the Shenzhen Science and Technology Program (grant no. KQTD2016113010482651 to S.H.), the Special Funds for Science Technology Innovation and Industrial Development of Shenzhen Dapeng New District (grant no. RC201901-05 to S.H.), the BBSRC Institute Strategic Programme Grant ‘Molecules from Nature–Products and Pathways’ (grant no. BBS/E/J/000PR9790 to A.O.) and the John Innes Foundation.

Author information

Author notes

  1. These authors contributed equally: Yang Zhong, Weibing Xun, Xiaohan Wang.

Authors and Affiliations

  1. Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China

    Yang Zhong, Dawei Li, Xu Cheng, William J. Lucas, Sanwen Huang & Yongshuo Ma

  2. State Key Laboratory for Conservation and Utilization of Subtropical Agro-bioresources, College of Life Sciences, South China Agricultural University, Guangdong Laboratory for Lingnan Modern Agriculture, Guangzhou, China

    Yang Zhong & Yaoguang Liu

  3. Key Laboratory of Biology and Genetic Improvement of Horticultural Crops of the Ministry of Agriculture, Sino-Dutch Joint Laboratory of Horticultural Genomics, Institute of Vegetables and Flowers, Chinese Academy of Agricultural Sciences, Beijing, China

    Yang Zhong & Yuxuan Qin

  4. Jiangsu Provincial Key Lab of Solid Organic Waste Utilization, Jiangsu Collaborative Innovation Center of Solid Organic Wastes, Nanjing Agricultural University, Nanjing, China

    Weibing Xun

  5. College of Life Science, Capital Normal University, Beijing, China

    Xiaohan Wang & Legong Li

  6. National Watermelon and Melon Improvement Center, Beijing Academy of Agricultural and Forestry Sciences, Key Laboratory of Biology and Genetic Improvement of Horticultural Crops (North China), Beijing Key Laboratory of Vegetable Germplasm Improvement, Beijing, China

    Shouwei Tian

  7. Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA

    Yancong Zhang

  8. Hubei Key Laboratory of Natural Medicinal Chemistry and Resource Evaluation, School of Pharmacy, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China

    Yuan Zhou

  9. Yunnan Key Laboratory of Potato Biology, CAAS-YNNU-YINMORE Joint Academy of Potato Sciences, Yunnan Normal University, Kunming, China

    Bo Zhang & Yi Shang

  10. Zhengzhou Fruit Research Institute, Chinese Academy of Agricultural Sciences, Zhengzhou, China

    Guangwei Zhao

  11. Hunan Vegetable Research Institute, Hunan Academy of Agricultural Sciences, Changsha, China

    Huiming Chen

  12. John Innes Centre, Norwich Research Park, Norwich, UK

    Anne Osbourn

  13. Department of Plant Biology, College of Biological Sciences, University of California, Davis, CA, USA

    William J. Lucas

  14. Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA

    Yongshuo Ma

Authors
  1. Yang Zhong
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Contributions

Y.S. and Y.M. conceived and designed the project. Y.M., Y. Zhong, X.W., S.T., D.L., Y. Zhou, Y.Q., B.Z., G.Z., X.C. and H.C. performed the experiments. W.X. and Y. Zhang performed the bioinformatics analysis on the microbiota sequencing results. Y.M., W.X., X.W., Y. Zhong and Y.S. analysed the data. Y.M., W.X., Y.L., L.L., A.O., W.J.L., Y.S. and S.H. wrote the paper.

Corresponding authors

Correspondence to Yongshuo Ma or Yi Shang.

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Nature Plants thanks Reuben Peters and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Distribution of cucurbitacin and expression profiles of the candidate genes in the cultivar and wild relative.

For melon, gene expression is presented by FPKM value of the candidates; for watermelon, gene expression was measured by qRT-PCR assay. Data are represented as mean ± SD (n = 3, biological independent samples). Relative gene expression level is shown with the same scale. Potential bitterness transporters are indicated in bold font.

Source data

Extended Data Fig. 2 Interaction between the promoter of two candidate cucurbitacin transporter genes and the bitterness regulators of melon and watermelon.

a and d, Yeast one-hybrid analysis of the interaction between the melon bitterness regulator, CmBl or CmBt, and the promoter of CmMATE1 (a), and the interaction between watermelon bitterness regulator, ClBl or ClBt, and the promoter of ClMATE1 (d). P, promoter; AD, activation domain; 3-AT, 3-amino-1,2,4-triazole; SD-L-T, synthetic dropout media W/O leucine and tryptophan; SD-L-T-H, synthetic dropout media W/O leucine, tryptophan, and histidine. b and e, CmBl or CmBt, binds to the promoter region of CmMATE1 (b) and activates a higher luciferase activity relative to the control (empty vector) in N. benthamiana leaves. Similar result was also observed for ClBl or ClBt, and ClMATE1 (e). LUC, luciferase. Renilla LUC was used as reference. Data are represented as mean ± SD (n = 3, biological independent experiments). Statistical differences were analysed using two-sided Student’s t-test. ** P < 0.01. *** P < 0.001. c and f, EMSA shows that CmBl or CmBt was recruited to the promoter of CmMATE1 (c). Similar result was also observed for ClBl and ClMATE1, or ClBt and ClMATE1 (f). Promoter region from -2,000 upstream from the start codon is schematically presented. Black vertical lines indicate the location of an E-box and red horizontal lines indicate the region amplified for EMSA. Comp, competitor (unlabeled probe); His, His tag; +/-, presence/absence of protein or competitor; closed triangle, increasing amount of protein or competitor.

Source data

Extended Data Fig. 3 Subcellular and tissue localization of the transport and biosynthetic genes.

a, Subcellular localization of the two MATEs in cucumber protoplasts. CmMATE1-GFP or ClMATE1-GFP fluorescence is co-localized with a known Arabidopsis plasma membrane marker PIP2A-RFP fluorescence. From left to right: GFP signal (green), RFP signal (red), Differential Interference Contrast (DIC), and overlay of three signals (green, red and DIC) of the same protoplast. b and c, In situ localization of CmBi (b) or ClBi (c) transcripts in the outer region of young root tips in melon and watermelon, respectively. Both transverse (above the broken line) and longitudinal (below the broken line) sections of the root tip are presented. d, Subcellular localization of CmMATE1-GFP or ClMATE1-GFP fusion protein in yeast. All MATE-GFP fusion proteins were localized to the yeast tonoplast. From left to right: GFP fluorescence (green), FM4-64 staining of the vacuolar membrane (red), Differential Interference Contrast (DIC), merged images with GFP, FM4-64 and DIC from the same sample. For a to d, Three independent experiments were repeated with similar results.

Extended Data Fig. 4 Phenotypic analysis of melon and watermelon seedlings.

a, CuB level accumulated in the rhizosphere of soils planted with Cm104 or Cm70 treated with different levels of CuB solution. Data are represented as mean ± SD (n = 3). DW, dry weight of soil; ND, not detected. b and c, The growth of Cm70 and Cm104 was normal when planted in pots containing the steriled conducive soil after 8 days (b). However, in the non-sterile conducive soil, most of the Cm70 seedlings exhibited wilting symptoms and eventually died, whereas most Cm104 seedlings grew normally (c). Cm104, the melon with a bitter root; Cm70, the melon line with a non-bitter root. d, Progress of wilt disease on the Cm70 plants treated with CuC or CuE. Data are represented as mean ± SD for three independent experiments (n = 48 in total for each treatment). e, The growth of ClMATE1-edited and its wild-type (WT) watermelon plants was normal when planted on the same non-sterile conducive soil, after 8 days. For b, c, e, Three independent experiments were repeated with similar results.

Source data

Extended Data Fig. 5 Shannon diversity of the root-associated microbiome.

a and b, The bacterial (a) and fungal (b) diversity index of the soil, rhizosphere and endosphere communities of healthy and diseased seedlings (means ± SD, n = 3). The bacterial Shannon diversity was significantly lower in the healthy lines than the diseased lines, in the rhizosphere, but not in the endosphere communities, whereas the fungal Shannon diversity showed no significant changes between the two groups, regardless of rhizosphere or endosphere. Statistical differences were analysed using two-sided Student’s t-test. ***, significant difference (P < 0.001). n.s., Not significant.

Source data

Extended Data Fig. 6 Principal coordinates analysis of bacterial communities from the rhizosphere of healthy and diseased seedlings.

The three genera were selected as indicators correlating to plant disease susceptibility, and their significance was detected by the Mantel test.

Extended Data Fig. 7 The relationship between relative abundance of Fusarium and Bacillus (a) as well as Fusarium and Enterobacter (b).

The grey area along the solid lines shows the 95% confidence bands of the linear regression.

Extended Data Fig. 8 The relative abundance of detected Bacillus (a) and Enterobacter (b) species and F. oxysporum (c) in the rhizosphere of the Cm104, healthy and diseased Cm70 seedlings as determined by shotgun sequencing (n = 3).

Unpaired two-tail Student’s T-test was used to detect the difference of the relative abundance of species in the rhizosphere between group levels. *P < 0.05, **P < 0.01, *** P < 0.001. ns: not significant. The box plots show the first and third quartiles (boxes) and the median (middle line); whiskers show inner fences (that is 1.5 times the interquartile range below the first quartile and above the third quartile); points in the boxes show the mean values.

Source data

Extended Data Fig. 9 Growth profiles of the Bacillus (a) or Enterobacter (b) strains cultivated in TSB medium supplemented with CuB.

Note that growth of the Bacillus strains could be significantly inhibited in TSB medium supplemented with 150 ppm CuB, whereas CuB had little effect on the growth of the Enterobacter strains E-126 and E-128. Data are represented as mean ± SD (n = 6 for a and n = 8 for b).

Source data

Extended Data Fig. 10 CuB potentially provide a carbon source for promotion of Enterobacter growth.

a, The profiles of CuB content in TSB medium after 3 days of culture with the indicated Enterobacter strains. A significant decrease in CuB level was observed in the medium cultured with Enterobacter strains, compared to that of TSB medium without Enterobacter strain (CK). Data are represented as mean ± SD (n = 3). b and c, HPLC-QTOF-MS analysis of the E-159 medium supplemented with CuB. b, Metabolic profiling analysis of extracts prepared from E-159 medium. The medium supplemented with only CuB (CuB + medium) or without CuB but containing E-159 strain (E-159 + medium) was considered as negative control. CuB was dissolved in methanol, and equal-volume methanol was added for each treatment. Two specific peaks (indicated by CuB derivate), at retention time of 2.64 and 2.72 min, respectively, were detected in the E-159 medium supplemented with CuB. TIC, total ion chromatograms; EIC 558.3421, extracted a characteristic fragmental ion of the CuB derivates at m/z of 558.3421. c, Comparison of the mass spectrum between CuB derivates and CuB standard. Although the mass spectra of CuB derivates and CuB shared the same quasi-molecular ion peak at m/z 576, the ion signal in CuB derivates were deciphered as a hydron plus CuB derivate ([M + H]+), while the ion signal in CuB it was suggested to be an ammonium cation plus CuB ([M + NH4]+).

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Zhong, Y., Xun, W., Wang, X. et al. Root-secreted bitter triterpene modulates the rhizosphere microbiota to improve plant fitness. Nat. Plants 8, 887–896 (2022). https://doi.org/10.1038/s41477-022-01201-2

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