Discover the maze-like network for glabridin biosynthesis

1. Dvora, H. & Koffas, M. Microbial Production of Food Ingredients, Enzymes and Nutraceuticals, 234–261 (Elsevier, 2013).

2. Walsh, C. T. Tailoring enzyme strategies and functional groups in biosynthetic pathways. Nat. Prod. Rep. 40, 326–386 (2023).

   Google Scholar 

3. Nett, R. S. et al. Plant carbonic anhydrase-like enzymes in neuroactive alkaloid biosynthesis. Nature 624, 182–191 (2023).

   Google Scholar 

4. De La Peña, R. et al. Complex scaffold remodeling in plant triterpene biosynthesis. Science 379, 361–368 (2023).

   Google Scholar 

5. Robles, O. & Romo, D. Chemo-and site-selective derivatizations of natural products enabling biological studies. Nat. Prod. Rep. 31, 318–334 (2014).

   Google Scholar 

6. Wang, S., Alseekh, S., Fernie, A. R. & Luo, J. The structure and function of major plant metabolite modifications. Mol. Plant 12, 899–919 (2019).

   Google Scholar 

7. Kreis, W. & Munkert, J. Exploiting enzyme promiscuity to shape plant specialized metabolism. J. Exp. Bot. 70, 1435–1445 (2019).

   Google Scholar 

8. Notebaart, R. A., Kintses, B., Feist, A. M. & Papp, B. Underground metabolism: network-level perspective and biotechnological potential. Curr. Opin. Biotechnol. 49, 108–114 (2018).

   Google Scholar 

9. Zhang, J. et al. Review on the diverse biological effects of glabridin. Drug Des. Dev. Ther. 2023, 15–37 (2023).

   Google Scholar 

10. Simmler, C., Pauli, G. F. & Chen, S. N. Phytochemistry and biological properties of glabridin. Fitoterapia 90, 160–184 (2013).

    Google Scholar 

11. Ji, W. H. et al. Total synthesis of (±)-glabridin. Synth. Commun. 44, 540–546 (2014).

    Google Scholar 

12. Duigou, T., Du Lac, M., Carbonell, P. & Faulon, J.-L. RetroRules: a database of reaction rules for engineering biology. Nucleic Acids Res. 47, D1229–D1235 (2019).

    Google Scholar 

13. Sveshnikova, A., MohammadiPeyhani, H. & Hatzimanikatis, V. ARBRE: computational resource to predict pathways towards industrially important aromatic compounds. Metab. Eng. 72, 259–274 (2022).

    Google Scholar 

14. Koch, M., Duigou, T. & Faulon, J.-L. Reinforcement learning for bioretrosynthesis. ACS Synth. Biol. 9, 157–168 (2020).

    Google Scholar 

15. Song, W. et al. Biosynthesis-based quantitative analysis of 151 secondary metabolites of licorice to differentiate medicinal Glycyrrhiza species and their hybrids. Anal. Chem. 89, 3146–3153 (2017).

    Google Scholar 

16. Wang, X. et al. Crystal structure of isoflavone reductase from Alfalfa (Medicago sativa L.). J. Mol. Biol. 358, 1341–1352 (2006).

    Google Scholar 

17. Shao, H., Dixon, R. A. & Wang, X. Crystal structure of vestitone reductase from Alfalfa (Medicago sativa L.). J. Mol. Biol. 369, 265–276 (2007).

    Google Scholar 

18. Akashi, T., Koshimizu, S., Aoki, T. & Ayabe, S. Identification of cDNAs encoding pterocarpan reductase involved in isoflavan phytoalexin biosynthesis in Lotus japonicus by EST mining. FEBS Lett. 580, 5666–5670 (2006).

    Google Scholar 

19. Lieberman-Aiden, E. et al. Comprehensive mapping of long-range interactions reveals folding principles of the human genome. Science 326, 289–293 (2009).

    Google Scholar 

20. Lu, C. et al. Heterologous biosynthesis of medicarpin using engineered Saccharomyces cerevisiae. Synth. Syst. Biotechnol. 8, 749–756 (2023).

    Google Scholar 

21. Jurrus, E. et al. Improvements to the APBS biomolecular solvation software suite. Protein Sci. 27, 112–128 (2018).

    Google Scholar 

22. He, J. et al. Regio-specific prenylation of pterocarpans by a membrane-bound prenyltransferase from Psoralea corylifolia. Org. Biomol. Chem. 16, 6760–6766 (2018).

    Google Scholar 

23. He, B. et al. Enzymatic pyran formation involved in Xiamenmycin biosynthesis. ACS Catal. 9, 5391–5399 (2019).

    Google Scholar 

24. Huang, X. C. et al. The gradual establishment of complex coumarin biosynthetic pathway in Apiaceae. Nat. Commun. 15, 6864 (2024).

    Google Scholar 

25. Zou, J. et al. Complete biosynthetic pathway of furochromones in Saposhnikovia divaricata and its evolutionary mechanism in Apiaceae plants. Nat. Commun. 16, 3109 (2025).

    Google Scholar 

26. Akashi, T., Uchida, K. & Aoki, T. CYP71D8 and CYP82A2 catalyze the last committed step in biosynthesis of glyceollin isomers in soybean. Plant Biotechnol. 42, 51–56 (2025).

    Google Scholar 

27. Xie, J. et al. Glyceollin biosynthesis in a plant chassis engineered for isoflavone production. Nat. Chem. Biol. 21, 1497–1508 (2025).

28. Sun, Y. et al. Elucidation and de novo reconstitution of glyceollin biosynthesis. Mol. Plant 18, 820–832 (2025).

    Google Scholar 

29. Ding, Q. et al. The evolutionary origin of naturally occurring intermolecular Diels-Alderases from Morus alba. Nat. Commun. 15, 2492 (2024).

    Google Scholar 

30. Nag, A. et al. Spatial transcriptional dynamics of geographically separated genotypes revealed key regulators of podophyllotoxin biosynthesis in. Ind. Crops Prod. 147, 112247 (2020).

    Google Scholar 

31. Ikezawa, N., Iwasa, K. & Sato, F. Molecular cloning and characterization of CYP80G2, a cytochrome P450 that catalyzes an intramolecular C-C phenol coupling of (S)-reticuline in magnoflorine biosynthesis, from cultured Coptis japonica cells. J. Biol. Chem. 283, 8810–8821 (2008).

    Google Scholar 

32. Qiu, S. et al. Functional evolution and diversification of CYP82D subfamily members have shaped flavonoid diversification in the genus Scutellaria. Plant Commun. 6, 101134 (2025).

    Google Scholar 

33. Maloney, A. P. & VanEtten, H. D. A gene from the fungal plant pathogen Nectria haematococca that encodes the phytoalexin-detoxifying enzyme pisatin demethylase defines a new cytochrome P450 family. Mol. Gen. Genet. 243, 506–514 (1994).

    Google Scholar 

34. Tolleson, W. H., Doerge, D. R., Churchwell, M. I., Marques, M. M. & Roberts, D. W. Metabolism of biochanin A and formononetin by human liver microsomes in vitro. J. Agric. Food Chem. 50, 4783–4790 (2002).

    Google Scholar 

35. Nagayoshi, H. et al. Preference for O-demethylation reactions in the oxidation of 2′-, 3′-, and 4′-methoxyflavones by human cytochrome P450 enzymes. Xenobiotica 50, 1158–1169 (2020).

    Google Scholar 

36. Chen, S. et al. Vps13 is required for the packaging of the ER into autophagosomes during ER-phagy. Proc. Natl. Acad. Sci. USA 117, 18530–18539 (2020).

    Google Scholar 

37. Bar-Peled, L. & Kory, N. Principles and functions of metabolic compartmentalization. Nat. Metab. 4, 1232–1244 (2022).

    Google Scholar 

38. Kistler, H. C. & Broz, K. Cellular compartmentalization of secondary metabolism. Front. Microbiol. 6, 68 (2015).

    Google Scholar 

39. Roze, L. V., Chanda, A. & Linz, J. E. Compartmentalization and molecular traffic in secondary metabolism: a new understanding of established cellular processes. Fungal Genet. Biol. 48, 35–48 (2011).

    Google Scholar 

40. Liang, F. et al. Elucidation of the final steps in Taxol biosynthesis and its biotechnological production. Nat. Synth. 4, 1212–1222 (2025).

41. Narasimha, S. M., Malpani, T., Mohite, O. S., Nath, J. S. & Raman, K. Understanding flux switching in metabolic networks through an analysis of synthetic lethals. NPJ Syst. Biol. Appl. 10, 104 (2024).

    Google Scholar 

42. Glasner, M. E., Truong, D. P. & Morse, B. C. How enzyme promiscuity and horizontal gene transfer contribute to metabolic innovation. FEBS J. 287, 1323–1342 (2020).

    Google Scholar 

43. Guzmán, G. I. et al. Enzyme promiscuity shapes adaptation to novel growth substrates. Mol. Syst. Biol. 15, e8462 (2019).

    Google Scholar 

44. Bowie, J. U. Stabilizing membrane proteins. Curr. Opin. Struct. Biol. 11, 397–402 (2001).

    Google Scholar 

45. Kitagawa, I. et al. Chemical studies of Chinese licorice-roots. I. Elucidation of five new flavonoid constituents from the roots of Glycyrrhiza glabra L. collected in Xinjiang. Chem. Pharm. Bull. 42, 1056–1062 (1994).

    Google Scholar 

46. Chin, Y. W. et al. Anti-oxidant constituents of the roots and stolons of licorice (Glycyrrhiza glabra). J. Agric. Food Chem. 55, 4691–4697 (2007).

    Google Scholar 

47. Lu, H. Z. et al. A consensus S. cerevisiae metabolic model Yeast8 and its ecosystem for comprehensively probing cellular metabolism. Nat. Commun. 10, 3586 (2019).

48. Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–U354 (2012).

    Google Scholar 

49. Li, H. New strategies to improve minimap2 alignment accuracy. Bioinformatics 37, 4572–4574 (2021).

    Google Scholar 

50. Chen, C. J. et al. TBtools-II: A one for all, all for one bioinformatics platform for biological big-data mining. Mol. Plant 16, 1733–1742 (2023).

    Google Scholar 

51. Danecek, P. et al. Twelve years of SAMtools and BCFtools. Gigascience 10, https://doi.org/10.1093/gigascience/giab008 (2021).

52. Emms, D. M. & Kelly, S. OrthoFinder: phylogenetic orthology inference for comparative genomics. Genome Biology 20, 238 (2019).

53. Edgar, R. C. High-accuracy alignment ensembles enable unbiased assessments of sequence homology and phylogeny. 13, 6968 (2022).

54. Minh, B. Q. et al. IQ-TREE 2: new models and efficient methods for phylogenetic inference in the genomic era. Mol. Biol. Evol. 37, 1530–1534 (2020).

    Google Scholar 

55. Hoang, D. T., Chernomor, O., von Haeseler, A., Minh, B. Q. & Vinh, L. S. UFBoot2: improving the ultrafast bootstrap approximation. Mol. Biol. Evol. 35, 518–522 (2017).

    Google Scholar 

56. Kim, D., Paggi, J. M., Park, C., Bennett, C. & Salzberg, S. L. Graph-based genome alignment and genotyping with HISAT2 and HISAT-genotype. Nat. Biotechnol. 37, 907–915 (2019).

    Google Scholar 

57. Keilwagen, J., Hartung, F. & Grau, J. GeMoMa: Homology-based gene prediction utilizing intron position conservation and RNA-seq data. Methods Mol. Biol. 1962, 161–177 (2019).

    Google Scholar 

58. Pertea, M. et al. StringTie enables improved reconstruction of a transcriptome from RNA-seq reads. Nat. Biotechnol. 33, 290–295 (2015).

    Google Scholar 

59. Zhang, Y. et al. A gRNA-tRNA array for CRISPR-Cas9 based rapid multiplexed genome editing in Saccharomyces cerevisiae. Nat. Commun. 10, 1053 (2019).

    Google Scholar 

60. Gietz, R. & Schiestl, R. High-efficiency yeast transformation using the LiAc/SS carrier DNA/PEG method. Nat. Protoc. 2, 31–34 (2007).

    Google Scholar 

61. Abramson, J. et al. Accurate structure prediction of biomolecular interactions with AlphaFold 3. Nature 630, 493–500 (2024).

    Google Scholar 

62. Cummins, P. L., Ramnarayan, K., Singh, U. & Gready, J. E. Molecular dynamics/free energy perturbation study on the relative affinities of the binding of reduced and oxidized NADP to dihydrofolate reductase. J. Am. Chem. Soc. 113, 8247–8256 (1991).

    Google Scholar 

63. Neese, F. Software update: The ORCA program system—Version 5.0. WIREs Comput. Mol. Sci. 12, e1606 (2022).

    Google Scholar 

64. Zhao, Y. & Truhlar, D. G. The M06 suite of density functionals for main group thermochemistry, thermochemical kinetics, noncovalent interactions, excited states, and transition elements: two new functionals and systematic testing of four M06-class functionals and 12 other functionals. Theor. Chem. Acc. 120, 215–241 (2008).

    Google Scholar 

65. Grimme, S., Antony, J., Ehrlich, S. & Krieg, H. A consistent and accurate ab initio parametrization of density functional dispersion correction (DFT-D) for the 94 elements H-Pu. J. Chem. Phys. 132, 154104 (2010).

66. Wang, J., Wolf, R. M., Caldwell, J. W., Kollman, P. A. & Case, D. A. Development and testing of a general amber force field. J. Comput. Chem. 25, 1157–1174 (2004).

    Google Scholar 

67. Wang, J., Wang, W., Kollman, P. A. & Case, D. A. Automatic atom type and bond type perception in molecular mechanical calculations. J. Mol. Graph. Model. 25, 247–260 (2006).

    Google Scholar 

68. Kroll, A., Engqvist, M. K. M., Heckmann, D. & Lercher, M. J. Deep learning allows genome-scale prediction of Michaelis constants from structural features. Plos Biol. 19, https://doi.org/10.1371/journal.pbio.3001402 (2021).

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