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Advanced material modulation of nutritional and phytohormone status alleviates damage from soybean sudden death syndrome

Nature Nanotechnology volume 15, pages 1033–1042 (2020)Cite this article

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Abstract

Customized Cu3(PO4)2 and CuO nanosheets and commercial CuO nanoparticles were investigated for micronutrient delivery and suppression of soybean sudden death syndrome. An ab initio thermodynamics approach modelled how material morphology and matrix effects control the nutrient release. Infection reduced the biomass and photosynthesis by 70.3 and 60%, respectively; the foliar application of nanoscale Cu reversed this damage. Disease-induced changes in the antioxidant enzyme activity and fatty acid profile were also alleviated by Cu amendment. The transcription of two dozen defence- and health-related genes correlates a nanoscale Cu-enhanced innate disease response to reduced pathogenicity and increased growth. Cu-based nanosheets exhibited a greater disease suppression than that of CuO nanoparticles due to a greater leaf surface affinity and Cu dissolution, as determined computationally and experimentally. The findings highlight the importance and tunability of nanomaterial properties, such as morphology, composition and dissolution. The early seedling foliar application of nanoscale Cu to modulate nutrition and enhance immunity offers a great potential for sustainable agriculture.

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Fig. 1: Modelled Cu2+ release.
Fig. 2: Physiological responses of FV infected soybean on foliar exposure to different types of Cu-based NMs.
Fig. 3: Cu and P content of FV infected soybean on foliar exposure to different types of Cu-based NMs.
Fig. 4: Cu-based NM distribution in FV-infected soybean foliar treated with 250 mg l–1 NM suspensions three times within a one-week interval.
Fig. 5: POD and PPO activity in FV-infected soybean on foliar exposure to different types of Cu-based NMs.
Fig. 6: Expression of disease-related genes in FV-infected soybean.

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Additional data related to this paper is available from the corresponding author upon reasonable request. Source data are provided with this paper.

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Acknowledgements

This material is based upon work supported by the National Science Foundation under grant no. CHE-2001611, the NSF Center for Sustainable Nanotechnology (CSN). The CSN is part of the Centers for Chemical Innovation Program. The ICP optical emission spectroscopy and ICP-MS work was supported by USDA-NIFA-AFRI 2016-67021-24985 and FDA 1U18FD005505-05, respectively. The computational portion of this work is was supported by computational resources of the University of Iowa and by the Extreme Science and Engineering Discovery Environment (XSEDE), which is supported by National Science Foundation through allocation ID TG-GEO160006: Density Functional Theory Calculations for Nanomaterials in Energy Applications and the Environment.

Author information

Authors and Affiliations

  1. The Center for Sustainable Nanotechnology, Department of Chemistry, University of Wisconsin, Madison, WI, USA

    Chuanxin Ma, Jaya Borgatta, Yu Shen & Robert John Hamers

  2. The Center for Sustainable Nanotechnology, Department of Analytical Chemistry, The Connecticut Agricultural Experiment Station, New Haven, CT, USA

    Chuanxin Ma, Roberto De La Torre-Roche, Nubia Zuverza-Mena, Yu Shen & Jason Christopher White

  3. The Center for Sustainable Nanotechnology, Department of Chemistry, University of Iowa, Iowa City, IA, USA

    Blake Geoffrey Hudson, Ali Abbaspour Tamijani & Sara Elizabeth Mason

  4. The Center for Sustainable Nanotechnology, Department of Plant Pathology and Ecology, The Connecticut Agricultural Experiment Station, New Haven, CT, USA

    Wade Elmer

  5. Stockbridge School of Agriculture, University of Massachusetts, Amherst, MA, USA

    Baoshan Xing

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  1. Chuanxin Ma
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Contributions

C.M. and J.C.W. designed the experiment. C.M. conducted the greenhouse experiment. J.B. and R.J.H. provided Cu3(PO4)2 and CuO NS. B.G.H., A.A.T. and S.E.M. conducted periodic DFT calculations. R.D.L.T.-R., N.Z.-M. and Y.S. helped with plant maintenance in the greenhouse. W.E. provided Fusarium millet and technical support for Fusarium infection. B.X. provided technical support for fatty acid analysis using gas chromatography–MS and provided greenhouse resources. C.M. and J.C.W. wrote the manuscript. C.M., R.J.H. and J.C.W revised the manuscript. All the authors discussed the results and commented on the manuscript.

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Correspondence to Jason Christopher White.

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Extended data

Extended Data Fig. 1 Phenotypic images of soybean seedlings foliar-treated with different types of Cu-based NMs w/ or w/o Fusarium infection.

Phenotypic images of soybean seedlings foliar-treated with different types of Cu-based NMs w/ or w/o Fusarium infection. Soybean seedlings were foliar-treated with 50 or 250 mg particles/L Cu-based NM suspensions. As shown in each panel, seedlings in red plates were grown in Promix infested with Fusarium virguliforme (2 g/L); seedlings in purple plates are the corresponding healthy NM controls.

Extended Data Fig. 2 Physiological responses of FV-infected soybean upon exposure to different types of Cu-based NMs at 50 and 250 mg/L in a second trial.

Physiological responses of FV-infected soybean upon exposure to different types of Cu-based NMs at 50 and 250 mg/L. Figure a and b represents shoot and root biomass of soybean, respectively; Figure c is chla/chlb ratio; Figure d to f represent net photosynthetic rate (Pn), stomatal conductance (Sc), and transpiration rate (Tr), respectively. Green bars and red bars represent the healthy control and the diseased control, respectively. For the fresh weight, error bars correspond to standard error of mean (n = 6); for the chla/chlb ratio, error bars correspond to standard error of mean (n = 4); for the Pn, Sc and Tr, error bars correspond to standard error of mean (n = 6). Values followed by different letters are significantly different at p < 0.05. Single asterisk ‘*’ indicates significant difference between the diseased control and each Cu-based NM treatment at p < 0.05; double asterisks ‘**’ indicate significant difference between the diseased control and each Cu-based NM treatment at p < 0.01 using a Student t-test.

Source data

Extended Data Fig. 3 Element accumulation in FV-infected soybean shoots and roots upon exposure to different types of Cu-based NMs at 50 and 250 mg/L in a second trial.

Element accumulation in FV-infected soybean shoots and roots upon exposure to different types of Cu-based NMs at 50 and 250 mg/L. Figure a and b represents the Cu content in shoots and roots, respectively; Figure c and d represents the P content in shoots and roots, respectively. Green bars and red bars represent the healthy control and the diseased control, respectively. Error bars correspond to standard error of mean (n = 4). Values followed by different letters are significantly different at p < 0.05. Single asterisk ‘*’ indicates significant difference between the diseased control and each Cu-based NM treatment at p < 0.05; double asterisks ‘**’ indicate significant difference between the diseased control and each Cu-based NM treatment at p < 0.01; triple asterisks ‘***’ indicate significant difference between the diseased control and each Cu-based NM treatment at p < 0.001 using a Student t-test.

Source data

Extended Data Fig. 4 POD and PPO activity in FV-infected soybean shoots and roots upon exposure to different types of Cu-based NMs at 50 and 250 mg/L in a second trial.

POD and PPO activity in FV-infected soybean shoots and roots upon exposure to different types of Cu-based NMs at 50 and 250 mg/L. Figure a and b represents the POD activity in shoots and roots, respectively; Figure c and d represents the PPO activity in shoots and roots, respectively. Red bars represent the healthy control and diseased control, respectively. Error bars correspond to standard error of mean (n = 4). Values followed by different letters are significantly different at p < 0.05.

Source data

Extended Data Fig. 5 Fatty acids profile in FV infected soybean shoots and roots upon exposure to different types of Cu-based NMs at 50 and 250 mg/L in a second trial.

Fatty acids profile in FV infected soybean shoots and roots upon exposure to different types of Cu-based NMs at 50 and 250 mg/L. Figure a and d represents the fatty acids content in shoots and roots, respectively; Figure b and e represents the SFA/UFA ratio in shoots and roots, respectively; Figure c and f represents the C18:3/(C18:0+C18:2) ratio in shoots and roots, respectively. Red bar represents healthy control and diseased control, respectively. Error bars correspond to standard error of mean (n = 3). Values followed by different letters are significantly different at p < 0.05.

Source data

Supplementary information

Supplementary Information

Supplementary Figs. 1–26 and Tables 1–5.

Source data

Source Data Fig. 1

The ΔGtot values of Cu-OH removals (red) relative to Cu3(PO4)2-4.

Source Data Fig. 2

Fresh weight and photosynthesis.

Source Data Fig. 3

Cu and P content of FV infected soybean.

Source Data Fig. 5

POD and PPO activity in FV infected soybean.

Source Data Fig. 6

Heatmap and PCA analysis of the relative expression of disease-related genes in FV infected shoots and roots.

Source Data Extended Data Fig. 2

Fresh biomass of soybean shoots and roots, chlorophyll a/b ratio, photosynthetic efficiency.

Source Data Extended Data Fig. 3

Cu and P contents in soybean shoots and roots.

Source Data Extended Data Fig. 4

POD and PPO activity in soybean shoots and roots.

Source Data Extended Data Fig. 5

Fatty acids content, the SFA/UFA ratio, and the C18:3/(C18:0+C18:2) ratio in soybean shoots and roots.

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Ma, C., Borgatta, J., Hudson, B.G. et al. Advanced material modulation of nutritional and phytohormone status alleviates damage from soybean sudden death syndrome. Nat. Nanotechnol. 15, 1033–1042 (2020). https://doi.org/10.1038/s41565-020-00776-1

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