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Aspergillus cvjetkovicii protects against phytopathogens through interspecies chemical signalling in the phyllosphere

Nature Microbiology (2024)Cite this article

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Abstract

Resident microbiota produces small molecules that influence the chemical microenvironments on leaves, but its signalling roles in pathogen defence are not yet well understood. Here we show that Aspergillus cvjetkovicii, enriched in rice leaf microbiota, subverts Rhizoctonia solani infections via small-molecule-mediated interspecies signalling. 2,4-Di-tert-butylphenol (2,4-DTBP), identified as a key signalling molecule within the Aspergillus-enriched microbiota, effectively neutralizes reactive oxygen species-dependent pathogenicity by switching off bZIP-activated AMT1 transcription in R. solani. Exogenous application of A. cvjetkovicii and 2,4-DTBP demonstrated varying degrees of protective effects against R. solani infection in diverse crops, including cucumber, maize, soybean and tomato. In rice field experiments, they reduced the R. solani-caused disease index to 19.7–32.2%, compared with 67.2–82.6% in the control group. Moreover, 2,4-DTBP showed activity against other rice phytopathogens, such as Fusarium fujikuroi. These findings reveal a defensive strategy against phytopathogens in the phyllosphere, highlighting the potential of symbiotic microbiota-driven neutralization of pathogenicity.

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Fig. 1: Leaf microbiome profiles of three representative rice cultivars with a gradient in disease resistance.
Fig. 2: R. solani-suppressing metabolites derived from the leaf microbiota member A. cvjetkovicii.
Fig. 3: Structural and biological characterization of the disease resistance-conferring molecule derived from A. cvjetkovicii.
Fig. 4: 2,4-DTBP-induced transcriptomic alteration and impact on the pathogenicity-governing key gene in R. solani.
Fig. 5: Elucidation and schematic visualization of a leaf microbiome-driven transcriptional switch in R. solani pathogenicity.

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Data availability

All raw sequencing data were deposited in the Sequence Read Archive (SRA) of NCBI (https://www.ncbi.nlm.nih.gov/sra) in FASTA format. Microbiome data were deposited under the accessions PRJNA913274 (rice leaves fungal community) and PRJNA913275 (rice leaves bacterial community). SILVA (release 138, https://www.arb-silva.de/no_cache/download/archive/release_138.1/ARB_files/) and the NCBI taxonomy database (https://www.ncbi.nlm.nih.gov/taxonomy) were used for analysis of bacterial 16S rRNA gene and fungal ITS sequences, respectively. Transcriptome data were deposited under the accession PRJNA535406. Source data are provided with this paper.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (U21A20219 to Z.M. and 32122074 to M.W.), National Key R&D Program of China (2021YFE0113700 to M.W. and T.C.), the Fundamental Research Funds for the Central Universities (226-2024-00070 and 2021FZZX001-31 to M.W.), Strategic Research on ‘Plant Microbiome and Agroecosystem Health’ (2020ZL008 to M.W., Cao Guangbiao High Science and Technology Foundation) and the Programme for High-Level Talents Cultivation of Zhejiang University to M.W. We thank Y. Liang (Zhejiang University), D. Li (Zhejiang University), Y. Ai (Zhejiang University), H. Fang (Zhejiang University), Z. Wang (Huazhong Agricultural University) and Z. Tang (Hainan University) for assistance with microscopic analysis as well as valuable suggestions; J. Pan (Zhejiang University) and J. Cheng (Zhejiang University) for assistance with NMR analysis; P. Shen (Office of Xiaoshan Agricultural Comprehensive Development Zone and Management Committee, Hangzhou, China) for support in field trials and greenhouse experiments; the sequencing provider Magigene and Personal Biotechnology for high-throughput sequencing services.

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Authors and Affiliations

  1. State Key Laboratory of Rice Biology and Breeding, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou, China

    Xiaoyan Fan, Haruna Matsumoto, Haorong Xu, Hongda Fang, Qianqian Pan, Tianxing Lv, Chengfang Zhan, Xiaoyu Liu, Danrui Su, Mengyuan Fan, Zhonghua Ma, Shaojia Li & Mengcen Wang

  2. Ministry of Agricultural and Rural Affairs Laboratory of Molecular Biology of Crop Pathogens and Insects, Zhejiang University, Hangzhou, China

    Xiaoyan Fan, Haruna Matsumoto, Haorong Xu, Hongda Fang, Qianqian Pan, Tianxing Lv, Chengfang Zhan, Xiaoyu Liu, Danrui Su, Mengyuan Fan, Zhonghua Ma & Mengcen Wang

  3. Key Laboratory of Biology of Crop Pathogens and Insects of Zhejiang Province, Institute of Pesticide and Environmental Toxicology, Zhejiang University, Hangzhou, China

    Xiaoyan Fan, Haruna Matsumoto, Haorong Xu, Hongda Fang, Qianqian Pan, Tianxing Lv, Chengfang Zhan, Xiaoyu Liu, Danrui Su & Mengcen Wang

  4. Institute of Environmental Biotechnology, Graz University of Technology, Graz, Austria

    Xiaoyan Fan, Gabriele Berg & Tomislav Cernava

  5. National Key Laboratory of Plant Molecular Genetics, CAS Center for Excellence in Molecular Plant Sciences, Institute of Plant Physiology and Ecology, Chinese Academy of Sciences, Shanghai, China

    Xiaoyan Fan

  6. Agricultural Experiment Station, Zhejiang University, Hangzhou, China

    Xiaoxiao Feng

  7. Australian Research Council Centre of Excellence in Plant Energy Biology, The University of Western Australia, Perth, Western Australia, Australia

    Xiaoyu Liu

  8. Leibniz Institute for Agricultural Engineering and Bioeconomy (ATB), Potsdam, Germany

    Gabriele Berg

  9. Institute for Biochemistry and Biology, University of Potsdam, Potsdam, Germany

    Gabriele Berg

  10. School of Biological Sciences, Faculty of Environmental and Life Sciences, University of Southampton, Southampton, UK

    Tomislav Cernava

  11. Global Education Program for AgriScience Frontiers, Graduate School of Agriculture, Hokkaido University, Sapporo, Japan

    Mengcen Wang

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Contributions

M.W., X. Fan, H.M., T.C. and S.L. conceived and designed the study. X. Fan, H.M., H.X., H.F., Q.P., T.L., C.Z., X. Feng, X.L., D.S., M.F. and S.L. performed the research as well as data analysis. Z.M. and G.B. gave valuable advice on the interpretation of the results, experimental design, as well as data analysis. X. Fan, H.M., M.W., T.C. and S.L. wrote the paper. All authors read and approved the final version of the paper.

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Correspondence to Shaojia Li, Tomislav Cernava or Mengcen Wang.

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Extended data

Extended Data Fig. 1 Bacterial diversity analysis among three rice cultivars with different disease resistances.

Rice cultivars Lemont (LM), Nipponbare (NB) and Teqing (TQ) show different disease resistance against R. solani. Alpha diversity including Chao1 index (a), Shannon index (b) and beta diversity based on Bray-Curtis dissimilarity (c). Significant differences among three cultivars were statistically tested through pairwise permutation multivariate analysis of variance (PERMANOVA) (P > 0.05). Different capital letters with error bars indicate a significant difference which was tested according to one-way analysis of variance (ANOVA) with Duncan’s multiple range tests (P < 0.05). None letter in the figure indicates no significance was observed among three cultivars in panel A. Values are means ± s.d. (shown as error bars; n = 10 samples).

Source data

Extended Data Fig. 2 Fungal community beta diversity analysis of three rice cultivars.

Rice cultivars Lemont (LM, red), Nipponbare (NB, green) and Teqing (TQ, blue) were included. The fungal communities from the same rice cultivar are enclosed by an ellipse in the corresponding color. Distances shown in the PCoA plot are based on Bray-Curtis dissimilarity. Significant differences among three cultivars were statistically tested through pairwise permutation multivariate analysis of variance (PERMANOVA) (P = 0.001). Each rice cultivar is represented with 10 replicates.

Extended Data Fig. 3 Bacterial community structures of three rice cultivars with different disease resistances.

Top 10 bacterial phyla (a), top 30 families (b) and top 30 genera (c) were assigned. LM, NB and TQ indicate the rice cultivars Lemont, Nipponbare and Teqing. The y-axis in each panel represents the relative abundance of each assigned taxon in the respective rice cultivar. In panel c, the taxonomy was assessed at genus level whennever possible; ‘f_’ in front of the Latin name indicates the taxon could not be assigned at genus level but only at family level. Each rice cultivar is represented by 10 replicates.

Extended Data Fig. 4 Fungal community structures at family level of three rice cultivars with different disease resistances.

LM, NB and TQ indicate the rice cultivars Lemont, Nipponbare and Teqing. The y-axis indicates the relative abundance of each assigned taxon in the respective rice cultivar. The taxonomy was assessed at family level whennever possible; ‘o_’, ‘c_’, and ‘p_’ indicate that specific taxa could not be assigned at family level but only at order, class, and phylum level, respectively. Each rice cultivar is represented by 10 replicates.

Extended Data Fig. 5 Phylogenetic analysis of the CaM gene sequence of the antagonistic Aspergillus strain ZJ-45 that was enriched in the Teqing rice cultivar.

The phylogenetic tree was constructed based on the Maximum Likelihood method using CaM gene sequences of strain ZJ-45 and other closely-related Aspergillus cvjetkovicii isolates. The scale indicates 0.002 substitutions per nucleotide position.

Extended Data Fig. 6 1H spectrum of the active molecule obtained from Aspergillus sp. ZJ-45.

Multiplicity of protons is shown in Supplementary Table 2.

Extended Data Fig. 7 13C-NMR spectrum of the active molecule obtained from Aspergillus sp. ZJ-45.

Multiplicity of carbon is shown in Supplementary Table 2.

Extended Data Fig. 8 DEPT and HSQC spectrum of the active signaling molecule obtained from Aspergillus sp. ZJ-45.

(a) DEPT (Distortionless Enhancement by Polarization Transfer) shows distinguishable primary carbons, secondary carbons, tertiary carbons and quaternary carbons by comparison with 13C-NMR. (b) C-H correlation deduced from HSQC (Heteronuclear Single Quantum Correlation) is shown in Supplementary Table 2.

Extended Data Fig. 9 HMBC and NOESY spectrum of the active signaling molecule obtained from Aspergillus sp. ZJ-45.

HMBC (Heteronuclear Multiple Bond Correlation, a) shows a remote coupling of carbon with protons, which is indicated by the blue arrows. NOESY (Nuclear Overhauser Effect Spectroscopy, b) shows a close relationship between all protons spatially, which is indicated by the red arrows.

Extended Data Fig. 10 Construction and identification of the FfAMT1 gene deletion mutants.

(a) The gene knockout strategy for FfAMT1 in the Fusarium fujikuroi genome. PCR primers used for verification were marked with small arrows. HPH, Hygromycin B phosphotransferase gene. Scale bar = 500 bp. (b-c) Deletion mutants were identified by PCR assays with two primer pairs including FfAMT1-ID-F/R (b) and FfAMT1-IN-F/R (c), representative images from the experiments repeated three times.

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Supplementary Information

Supplementary Figs. 1–25 and Tables 1–7.

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Supplementary Data

Supplementary Data 1–7.

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Fan, X., Matsumoto, H., Xu, H. et al. Aspergillus cvjetkovicii protects against phytopathogens through interspecies chemical signalling in the phyllosphere. Nat Microbiol (2024). https://doi.org/10.1038/s41564-024-01781-z

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