Letter | Published:

Resistance-gene-directed discovery of a natural-product herbicide with a new mode of action

Naturevolume 559pages415418 (2018) | Download Citation


Bioactive natural products have evolved to inhibit specific cellular targets and have served as lead molecules for health and agricultural applications for the past century1,2,3. The post-genomics era has brought a renaissance in the discovery of natural products using synthetic-biology tools4,5,6. However, compared to traditional bioactivity-guided approaches, genome mining of natural products with specific and potent biological activities remains challenging4. Here we present the discovery and validation of a potent herbicide that targets a critical metabolic enzyme that is required for plant survival. Our approach is based on the co-clustering of a self-resistance gene in the natural-product biosynthesis gene cluster7,8,9, which provides insight into the potential biological activity of the encoded compound. We targeted dihydroxy-acid dehydratase in the branched-chain amino acid biosynthetic pathway in plants; the last step in this pathway is often targeted for herbicide development10. We show that the fungal sesquiterpenoid aspterric acid, which was discovered using the method described above, is a sub-micromolar inhibitor of dihydroxy-acid dehydratase that is effective as a herbicide in spray applications. The self-resistance gene astD was validated to be insensitive to aspterric acid and was deployed as a transgene in the establishment of plants that are resistant to aspterric acid. This herbicide-resistance gene combination complements the urgent ongoing efforts to overcome weed resistance11. Our discovery demonstrates the potential of using a resistance-gene-directed approach in the discovery of bioactive natural products.

Access optionsAccess options

Rent or Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

Additional information

Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.


  1. 1.

    Dayan, F. E. & Duke, S. O. Natural compounds as next-generation herbicides. Plant Physiol. 166, 1090–1105 (2014).

  2. 2.

    Newman, D. J. & Cragg, G. M. Natural products as sources of new drugs from 1981 to 2014. J. Nat. Prod. 79, 629–661 (2016).

  3. 3.

    Clardy, J. & Walsh, C. Lessons from natural molecules. Nature 432, 829–837 (2004).

  4. 4.

    Ziemert, N., Alanjary, M. & Weber, T. The evolution of genome mining in microbes—a review. Nat. Prod. Rep. 33, 988–1005 (2016).

  5. 5.

    Clevenger, K. D. et al. A scalable platform to identify fungal secondary metabolites and their gene clusters. Nat. Chem. Biol. 13, 895–901 (2017).

  6. 6.

    Rutledge, P. J. & Challis, G. L. Discovery of microbial natural products by activation of silent biosynthetic gene clusters. Nat. Rev. Microbiol. 13, 509–523 (2015).

  7. 7.

    Tang, X. et al. Identification of thiotetronic acid antibiotic biosynthetic pathways by target-directed genome mining. ACS Chem. Biol. 10, 2841–2849 (2015).

  8. 8.

    Alanjary, M. et al. The antibiotic resistant target seeker (ARTS), an exploration engine for antibiotic cluster prioritization and novel drug target discovery. Nucleic Acids Res. 45, W42–W48 (2017).

  9. 9.

    Yeh, H.-H. et al. Resistance gene-guided genome mining: serial promoter exchanges in Aspergillus nidulans reveal the biosynthetic pathway for fellutamide B, a proteasome inhibitor. ACS Chem. Biol. 11, 2275–2284 (2016).

  10. 10.

    Amorim Franco, T. M. & Blanchard, J. S. Bacterial branched-chain amino acid biosynthesis: structures, mechanisms, and drugability. Biochemistry 56, 5849–5865 (2017).

  11. 11.

    Heap, I. Global perspective of herbicide-resistant weeds. Pest Manag. Sci. 70, 1306–1315 (2014).

  12. 12.

    Soltani, N. et al. Potential corn yield losses from weeds in North America. Weed Technol. 30, 979–984 (2016).

  13. 13.

    Swanton, C. J., Harker, K. N. & Anderson, R. L. Crop losses due to weeds in Canada. Weed Technol. 7, 537–542 (1993).

  14. 14.

    Gianessi, L. P. The increasing importance of herbicides in worldwide crop production. Pest Manag. Sci. 69, 1099–1105 (2013).

  15. 15.

    Flint, D. H. & Emptage, M. H. Dihydroxy acid dehydratase from spinach contains a [2Fe–2S] cluster. J. Biol. Chem. 263, 3558–3564 (1988).

  16. 16.

    Flint, D. H., Emptage, M. H., Finnegan, M. G., Fu, W. & Johnson, M. K. The role and properties of the iron–sulfur cluster in Escherichia coli dihydroxy-acid dehydratase. J. Biol. Chem. 268, 14732–14742 (1993).

  17. 17.

    Flint, D. H. & Nudelman, A. Studies on the active site of dihydroxy-acid dehydratase. Bioorg. Chem. 21, 367–385 (1993).

  18. 18.

    Pusztahelyi, T., Holb, I. J. & Pócsi, I. Secondary metabolites in fungus–plant interactions. Front. Plant Sci. 6, 573 (2015).

  19. 19.

    Kennedy, J. et al. Modulation of polyketide synthase activity by accessory proteins during lovastatin biosynthesis. Science 284, 1368–1372 (1999).

  20. 20.

    Regueira, T. B. et al. Molecular basis for mycophenolic acid biosynthesis in Penicillium brevicompactum. Appl. Environ. Microbiol. 77, 3035–3043 (2011).

  21. 21.

    Fischbach, M. A. & Walsh, C. T. Assembly-line enzymology for polyketide and nonribosomal peptide antibiotics: logic, machinery, and mechanisms. Chem. Rev. 106, 3468–3496 (2006).

  22. 22.

    Christianson, D. W. Structural biology and chemistry of the terpenoid cyclases. Chem. Rev. 106, 3412–3442 (2006).

  23. 23.

    Tang, M.-C. et al. Discovery of unclustered fungal indole diterpene biosynthetic pathways through combinatorial pathway reassembly in engineered yeast. J. Am. Chem. Soc. 137, 13724–13727 (2015).

  24. 24.

    Yoshisuke Tsuda et al. Aspterric acid, a new sesquiterpenoid of the carotane group, a metabolite from Aspergillus terreus IFO-6123. X-ray crystal and molecular structure of its p-bromobenzoate. J. Chem. Soc. Chem. Commun. 0, 160–161 (1978).

  25. 25.

    Shimada, A. et al. Aspterric acid and 6-hydroxymellein, inhibitors of pollen development in Arabidopsis thaliana, produced by Aspergillus terreus. Z. Naturforsch. C 57, 459–464 (2002).

  26. 26.

    Rahman, M. M. et al. The crystal structure of a bacterial l-arabinonate dehydratase contains a [2Fe–2S] cluster. ACS Chem. Biol. 12, 1919–1927 (2017).

  27. 27.

    Kirkwood, R. C. Use and mode of action of adjuvants for herbicides: a review of some current work. Pestic. Sci. 38, 93–102 (1993).

  28. 28.

    Hoerlein, G. Glufosinate (phosphinothricin), a natural amino acid with unexpected herbicidal properties. Rev. Environ. Contam. Toxicol. 138, 73–145 (1994).

  29. 29.

    McRae, D. H. in Plant Breeding Reviews (ed. Janick, J.) 169–191 (John Wiley & Sons, New Jersey, 1985).

  30. 30.

    Benbrook, C. M. Trends in glyphosate herbicide use in the United States and globally. Environ. Sci. Eur. 28, 3–19 (2016).

  31. 31.

    Liu, N. et al. Identification and heterologous production of a benzoyl-primed tricarboxylic acid polyketide intermediate from the zaragozic acid A biosynthetic pathway. Org. Lett. 19, 3560–3563 (2017).

  32. 32.

    Fang, F. et al. A vector set for systematic metabolic engineering in Saccharomyces cerevisiae. Yeast 28, 123–136 (2011).

  33. 33.

    Cool, L. G. ent-Daucane and acorane sesquiterpenes from ×Cupressocyparis leylandii foliage. Phytochemistry 58, 969–972 (2001).

  34. 34.

    Goldschmidt, L., Cooper, D. R., Derewenda, Z. S. & Eisenberg, D. Toward rational protein crystallization: a web server for the design of crystallizable protein variants. Protein Sci. 16, 1569–1576 (2007).

  35. 35.

    Otwinowski, Z., Minor, W. & Jr, W. Processing of X-ray diffraction data collected in oscillation mode. Methods Enzymol. 276, 307–326 (1997).

  36. 36.

    McCoy, A. J. et al. Phaser crystallographic software. J. Appl. Crystallogr. 40, 658–674 (2007).

  37. 37.

    Winn, M. D. et al. Overview of the CCP4 suite and current developments. Acta Crystallogr. D 67, 235–242 (2011).

  38. 38.

    Murshudov, G. N. et al. REFMAC5 for the refinement of macromolecular crystal structures. Acta Crystallogr. D 67, 355–367 (2011).

  39. 39.

    Emsley, P., Lohkamp, B., Scott, W. G. & Cowtan, K. Features and development of Coot. Acta Crystallogr. D 66, 486–501 (2010).

  40. 40.

    Harder, E. et al. OPLS3: a force field providing broad coverage of drug-like small molecules and proteins. J. Chem. Theory Comput. 12, 281–296 (2016).

  41. 41.

    Søndergaard, C. R., Olsson, M. H. M., Rostkowski, M. & Jensen, J. H. Improved treatment of ligands and coupling effects in empirical calculation and rationalization of pKa values. J. Chem. Theory Comput. 7, 2284–2295 (2011).

  42. 42.

    Eswar, N. et al. Comparative protein structure modeling using MODELLER. Curr. Protoc. Bioinformatics 47, 5.6.1–5.6.32 (2006).

  43. 43.

    Greenwood, J. R., Calkins, D., Sullivan, A. P. & Shelley, J. C. Towards the comprehensive, rapid, and accurate prediction of the favorable tautomeric states of drug-like molecules in aqueous solution. J. Comput. Aided Mol. Des. 24, 591–604 (2010).

  44. 44.

    Friesner, R. A. et al. Glide: a new approach for rapid, accurate docking and scoring. 1. Method and assessment of docking accuracy. J. Med. Chem. 47, 1739–1749 (2004).

  45. 45.

    Yuan, S., Chan, H. C. S., Filipek, S. & Vogel, H. PyMOL and Inkscape bridge the data and the data visualization. Structure 24, 2041–2042 (2016).

  46. 46.

    Yuan, S., Chan, H. C. S. & Hu, Z. Using PyMOL as a platform for computational drug design. Wiley Interdiscip. Rev. Comput. Mol. Sci. 7, e1298 (2017).

  47. 47.

    Genheden, S. & Ryde, U. The MM/PBSA and MM/GBSA methods to estimate ligand-binding affinities. Expert Opin. Drug Discov. 10, 449–461 (2015).

  48. 48.

    Sirin, S. et al. A computational approach to enzyme design: predicting ω-aminotransferase catalytic activity using docking and MM-GBSA scoring. J. Chem. Inf. Model. 54, 2334–2346 (2014).

  49. 49.

    Clough, S. J. & Bent, A. F. Floral dip: a simplified method for Agrobacterium-mediated transformation of Arabidopsis thaliana. Plant J. 16, 735–743 (1998).

Download references


This work was supported by the NIH (1DP1GM106413 and 1R35GM118056) to Y.T. and CAS (XDB20000000) to J.Z. S.E.J. is an Investigator of the Howard Hughes Medical Institute. Q.L. is supported by the NIH (F32) Postdoctoral Fellowship. We thank Stanford Genome Technology Center for the S. cerevisiae DHY ΔURA3 strain. The diffraction data of holo-AthDHAD was collected at beamline BL19U1 of the Shanghai Synchrotron Radiation Facility (SSRF). The molecular modelling was performed at the Interdisciplinary Centre for Mathematical and Computational Modeling in Warsaw (GB70-3 & GB71-3). We thank W. Huang, L. Wu and R. Cheng for technical help with protein purification and crystallization.

Reviewer information

Nature thanks F. Dayan, E. Sattely and the other anonymous reviewer(s) for their contribution to the peer review of this work.

Author information

Author notes

  1. These authors contributed equally: Yan Yan, Qikun Liu, Xin Zang


  1. Department of Chemical and Biomolecular Engineering, University of California Los Angeles, Los Angeles, CA, USA

    • Yan Yan
    • , Undramaa Bat-Erdene
    •  & Yi Tang
  2. Department of Molecular, Cell, and Developmental Biology and Howard Hughes Medical Institute, University of California Los Angeles, Los Angeles, CA, USA

    • Qikun Liu
    • , Calvin Nguyen
    •  & Steven E. Jacobsen
  3. State Key Laboratory of Bio-organic and Natural Products Chemistry, Center for Excellence in Molecular Synthesis, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai, China

    • Xin Zang
    •  & Jiahai Zhou
  4. Department of Chemistry, Shanghai Normal University, Shanghai, China

    • Xin Zang
    •  & Jiahai Zhou
  5. Laboratory of Physical Chemistry of Polymers and Membranes, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland

    • Shuguang Yuan
  6. State Key Laboratory of Genetic Engineering, Collaborative Innovation Center of Genetics and Development, Department of Physiology and Biophysics, School of Life Sciences, Fudan University, Shanghai, China

    • Jianhua Gan
  7. Department of Chemistry and Biochemistry, University of California Los Angeles, Los Angeles, CA, USA

    • Yi Tang


  1. Search for Yan Yan in:

  2. Search for Qikun Liu in:

  3. Search for Xin Zang in:

  4. Search for Shuguang Yuan in:

  5. Search for Undramaa Bat-Erdene in:

  6. Search for Calvin Nguyen in:

  7. Search for Jianhua Gan in:

  8. Search for Jiahai Zhou in:

  9. Search for Steven E. Jacobsen in:

  10. Search for Yi Tang in:


Y.Y., Q.L., J.Z., S.E.J. and Y.T. developed the hypothesis and designed the study. Y.Y. performed all in vivo and in vitro experiments. Q.L., Y.Y. and C.N. performed plant experiments. X.Z. and J.Z. performed crystallography experiments. J.G. determined the protein structure. S.Y. performed computational experiments. Y.Y. and U.B.-E. performed yeast experiments. Y.Y., Q.L., J.Z., S.E.J. and Y.T. prepared the manuscript.

Competing interests

A PCT patent application related to this manuscript has been filed by UCLA.

Corresponding authors

Correspondence to Jiahai Zhou or Steven E. Jacobsen or Yi Tang.

Extended data figures and tables

  1. Extended Data Fig. 1 The rationale of resistance-gene-directed discovery of a natural herbicide with a new mode of action.

    a, Phylogenetic tree of DHAD among bacteria, fungi and plants. The evolutionary history was inferred by using the neighbour-joining method (MEGA7). Scale-bar units represent the number of amino acid substitutions per site. b, Representatives of small molecules that inhibit DHAD in vitro, but fail to inhibit plant growth. c, Examples of co-localization of biosynthetic gene clusters (BGCs) and targets. The biosynthetic core genes are shown in blue and the self-resistance enzymes (SREs) are shown in red. The blockbuster cholesterol-lowering lovastatin drug targets HMG-CoA reductase (HMGR) in eukaryotes. In the fungus A. terreus that produces lovastatin, a second copy of HMGR encoded by ORF8 is present in the gene cluster (top). The BGC of the immunosuppressant mycophenolic acid from Penicillium sp. contains a second copy of inosine monophosphate dehydrogenase (IMPDH), which represents the SRE to this cluster (bottom).

  2. Extended Data Fig. 2 Biochemical assays of DHAD functions.

    a, Assaying DHAD activities in the conversion of the dihydroxyacid 4 into the α-ketoacid 5. Formation of 5 can be detected with HPLC by chemical derivatization using phenylhydrazine (PHH) to yield 6. b, LC–MS traces of the biochemical assays of AthDHAD (plant DHAD, pDHAD). EIC of positive ion mass of [M + H]+ = 207 is shown in red. Panels i–iv in b: i, the derivatization reaction was validated by using the authentic 5; ii, the bioactivity of AthDHAD in converting 4 into 5 was validated; iii, addition of DMSO to AthDHAD enzymatic reaction mixture has no effect; and iv, addition of 10 μM aspterric acid to the reaction mixture abolished AthDHAD activity. The experiments were repeated independently three times with similar results.

  3. Extended Data Fig. 3 Inhibition assay of different DHADs using aspterric acid.

    ac, Three DHAD enzymes were assayed, including AthDHAD (plant DHAD, pDHAD), AteDHAD (fungal housekeeping DHAD from A. terreus, fDHAD) and AstD (DHAD homologue within ast cluster). IC50 and Ki values of aspterric acid were measured on the basis of inhibition percentage at different aspterric acid concentrations. Data are mean ± s.d. from three biologically independent experiments. a, Plot of the inhibition percentage of 0.5 μM AteDHAD as a function of aspterric acid concentration. b, Plot of the inhibition percentage of 0.5 μM AthDHAD as a function of aspterric acid concentration. c, Plot of the inhibition percentage of 0.5 μM AstD as a function of aspterric acid concentration. d, Analysis of inhibitory kinetics of aspterric acid on AthDHAD using the Lineweaver–Burk method at different concentrations of aspterric acid (left). Linear fitting of the apparent Michaelis constant (KM,app) as a function of aspterric acid concentration yields the Ki of aspterric acid on AthDHAD (right). Source data

  4. Extended Data Fig. 4 Growth curve of S. cerevisiaeILV3 expressing AstD and AteDHAD.

    ad, The genome copy of DHAD encoded by ILV3 was first deleted from S. cerevisiae strain DHY ΔURA3 to give UB02. UB02 was then either chemically complemented by growth on ILV (leucine, isoleucine and valine)-containing medium or genetically by expressing of AteDHAD (fungal housekeeping DHAD from A. terreus, fDHAD) or AstD episomally (TY06 or TY07, respectively). The empty vector pXP318 was also transformed into UB02 to generate a control strain TY05. Cell growth (optical density) under different conditions was plotted as a function of time. Data are mean ± s.d. from three biologically independent experiments. a, Growth curve in ILV dropout medium with no aspterric acid. b, Growth curve in ILV dropout medium with 125 μM aspterric acid. c, Growth curve in ILV supplemented medium. d, Growth curve in ILV supplemented medium with 250 μM aspterric acid.

  5. Extended Data Fig. 5 X-ray structure of holo-AthDHAD and homology model of AstD.

    a, Superimpositions of the monomer of holo-AthDHAD (PDB: 5ZE4, 2.11 Å) and RlArDHT (PDB: 5J84). The holo structure containing the 2Fe–2S cofactor and Mg2+ ion in the active site. The structure of holo-AthDHAD is in white; the crystal structure of RlArDHT is in cyan. b, Superimpositions of holo-AthDHAD and homology-modelled AstD. The structure of AstD was constructed by homology modelling on the basis of the structure of holo-AthDHAD. The structure of holo-AthDHAD is in white; the crystal structure of AstD is in green. c, The electron density map of cofactors in the holo structure of AthDHAD. White mesh indicates the 2Fo − Fc map at the 1.2σ level; green mesh indicates the Fo − Fc positive map at the 3.2σ level; cyan sticks represent the acetic acid molecule. d, Comparison of the active sites in the crystal structure of AthDHAD and the modelled structure of AstD. The cartoon represents superimposed binding sites of AthDHAD (white) and AstD (green). The shift of a loop in AstD, where L518 (corresponding to V496 in AthDHAD) is located, coupled with a larger L198 residue (corresponding to I177 in AthDHAD) leads to a smaller hydrophobic pocket in AstD than in AthDHAD. e, The surface of binding sites of AstD (left) and AthDHAD (right). The smaller hydrophobic channel in the modelled AstD cannot accommodate the aspterric acid molecule (yellow ball and stick model).

  6. Extended Data Fig. 6 Sequence alignment between AthDHAD and AstD.

    The sequence identity between AthDHAD and AstD is 56.8%, whereas the similarity between them is 75.0%. Residues were coloured according to their property and similarity.

  7. Extended Data Fig. 7 Spray assay of aspterric acid on A. thaliana.

    Glufosinate-resistant A. thaliana was treated with (right) or without (left) aspterric acid in the solvent, which is a commercial glufosinate-based herbicide marketed as Finale. To improve the wetting and penetration, aspterric acid was first dissolved in ethanol and then added to the solvent (0.06 g l−1 Finale (Bayer) with 20 g l−1 ethanol) to make 250 μM aspterric acid spraying solution. The control plants were treated with solvent containing ethanol only. Spraying treatments began upon seed germination, and were repeated once every two days with approximately 0.4 ml aspterric acid solution per time per pot for four weeks. The picture shown is taken after one month of treatment. The application rate of aspterric acid is approximately 1.6 lb per acre, which is comparable to the commonly used herbicide glyphosate (0.75–1.5 lb per acre). The experiments were repeated independently three times with similar results.

  8. Extended Data Fig. 8 Specific inhibition of anther development in A. thaliana.

    af, Comparison of flower organs between the aspterric acid-treated (a–c) and non-treated (d–f) Arabidopsis. a, d, The aspterric acid-treated flower shows abnormal pistil elongation owing to the lack of pollination. b, e, The aspterric acid treated flower is missing one stamen. c, f, The aspterric acid treated anther is depleted of healthy and mature pollen. The experiments were performed twice with similar results.

  9. Extended Data Fig. 9 Schematic of results from the cross experiment.

    a, Wild-type A. thaliana treated with 250 μM aspterric acid was pollinated with pollen from the un-treated plant that carries the glufosinate-resistance gene. Offspring was obtained, and inherited the glufosinate resistance from the pollen donor. b, As in a, except that the pollen donor was also treated with 250 μM aspterric acid. No offspring was obtained from this cross. Similar results were obtained after treatment with 100 μM aspterric acid.

  10. Extended Data Table 1 Data collection and refinement statistics (molecular replacement)

Supplementary information

  1. Supplementary Information

    This file contains Supplementary Figures 1-8, Supplementary Tables 1-6 and Supplementary References.

  2. Reporting Summary

  3. Supplementary Data

    This file contains source data for Supplementary Figure 5.

Source Data

About this article

Publication history




Issue Date



Further reading


By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.