Floral scent emission of Epiphyllum oxypetalum: discovery of its cytosol-localized geraniol biosynthesis

Yiyang Zhang , Yuhan Zhang , Andong Zhang , Qiurui Tian , Bin Yang , Likun Wei , Wei Wu , Ting Zhu , Zhiwei Zhou , Jiaqi Wang , Zhibin Liu , Wei Tang , Haijun Xiao , Mingchun Liu , Tao Li , Qun Sun

Horticulture Research ›› 2025, Vol. 12 ›› Issue (5) : 39

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Horticulture Research ›› 2025, Vol. 12 ›› Issue (5) :39 DOI: 10.1093/hr/uhaf039
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Floral scent emission of Epiphyllum oxypetalum: discovery of its cytosol-localized geraniol biosynthesis
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Abstract

Epiphyllum oxypetalum, a renowned ornamental species in Cactaceae, releases attractive fragrance during its infrequent, transient, and nocturnal blooms. However, the floral fragrance composition and biosynthesis remain largely unexplored. Employing volatilomics, transcriptomics, and biochemistry, we systematically characterized the composition, emission dynamics, and biosynthesis of the floral scent of E. oxypetalum. The floral scent composition of E. oxypetalum was highly dynamic. Starting after 8 p.m. local time, volatile emission increased 200-fold within 6 h. At full bloom, geraniol accounted for 72.54% of the total emission, followed by benzyl alcohol (12.96%) and methyl salicylate (3.75%). These scents predominantly originated from petals and sepals. Transcriptomic analysis and inhibition assays using pathway-specific inhibitors revealed that the mevalonate pathway was the precursor source for geraniol biosynthesis. Functionally characterized cytosol-localized geraniol synthase EoTPSa1 was the key enzyme responsible for geraniol biosynthesis. Together, these findings pinpoint a cytosolic biosynthetic route for the major scent volatile geraniol in E. oxypetalum. Our study provides new insights into the emission dynamics and biosynthesis of E. oxypetalum floral scents. In particular, we demonstrate a distinctive mevalonate pathway-based geraniol biosynthetic pathway, which may hold potential for the development of novel perfume products.

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Yiyang Zhang, Yuhan Zhang, Andong Zhang, Qiurui Tian, Bin Yang, Likun Wei, Wei Wu, Ting Zhu, Zhiwei Zhou, Jiaqi Wang, Zhibin Liu, Wei Tang, Haijun Xiao, Mingchun Liu, Tao Li, Qun Sun. Floral scent emission of Epiphyllum oxypetalum: discovery of its cytosol-localized geraniol biosynthesis. Horticulture Research, 2025, 12(5): 39 DOI:10.1093/hr/uhaf039

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Acknowledgements

We thank Yudong Ma, Rao Wu, and Jin Dai for their assistance in sample collection, and Bolei Jiao, Xinrui Liu, and Chuanfang Wu for sharing the N. benthamiana plants and pSuper1300 and pET-28a plastids. We further wish to thank Qijun Deng and Wei Ni from Sichuan Bowu Cultural Development Co. Ltd. for drawing the E. oxypetalum flowers.

Financial support was provided by the National Science and Technology Major Project (20250065), the National Nature Science Foundation of China (32271692, 32470009, 32100326,), and the Department of Science and Technology of Sichuan Province of China (2022ZHXC0009, 2022NSFSC0158, 2023YFSY0054).

Author Contributions

Y.Y.Z., Y.H.Z, L.T., and Q.S. designed the experiments. The top eight authors and W.T. jointly completed the collection and preparation of samples. Y.Y.Z. and Y.H.Z. quantified the VOC content via GC-MS. Y.Y.Z. analysed the GC-MS and transcriptome data. Y.Y.Z., Y.H.Z., A.D.Z, B.Y., and Q.R.T. performed the molecular cloning, enzymatic assays, and localization studies. L.K.W. performed the biochemical quantifications. Y.Y.Z. plotted the figures and wrote the first draft. Y.Y.Z., Y.H.Z., Q.S., T.L., H.J.X., Q.R.T., M.C.L., and Z.B.L. revised the manuscript. Y.Y.Z. and Y.H.Z. should be considered the joint first authors, whereas Q.S. and T.L. the joint corresponding authors.

Data availability

All the data needed to evaluate the conclusions in the paper are presented in the paper and/or the Supplementary Materials. The raw transcriptome reads for the nine individuals in this study have been deposited in the National Genomics Data Center (https://ngdc.cncb.ac.cn) under accession number PRJCA024038.

Conflict of interests

The authors declare that they have no conflicts of interest.

Supplementary Data

Supplementary data is available at Horticulture Research online.

References

[1]

Dudareva N, Klempien A, Muhlemann JK. et al. Biosynthesis, function and metabolic engineering of plant volatile organic compounds. New Phytol. 2013;198:16-32

[2]

Dötterl S, Gershenzon J. Chemistry, biosynthesis and biology of floral volatiles: roles in pollination and other functions. Nat Prod Rep. 2023;40:1901-37

[3]

Zhang W, Jiang Y, Chen F. et al. Dynamic regulation of volatile terpenoid production and emission from Chrysanthemummori-folium capitula. Plant Physiol Biochem. 2022;182:11-21

[4]

Kaiser R, Tollsten L. An introduction to the scent of cacti. Flavour Fragr J. 1995;10:153-64

[5]

Chen W, Viljoen AM. Geraniol — a review of a commercially important fragrance material. South Afr J Bot. 2010;76:643-51

[6]

Abbas F, Ke Y, Yu R. et al. Volatile terpenoids: multiple func-tions, biosynthesis, modulation and manipulation by genetic engineering. Planta. 2017;246:803-16

[7]

Muhlemann JK, Klempien A, Dudareva N. Floral volatiles: from biosynthesis to function. Plant Cell Environ. 2014;37:1936-49

[8]

Dudareva N, Andersson S, Orlova I. et al. The nonmevalonate pathway supports both monoterpene and sesquiterpene forma-tion in snapdragon flowers. Proc Natl Acad Sci. 2005;102:933-8

[9]

Vranová E, Coman D, Gruissem W. Structure and dynamics of the isoprenoid pathway network. Mol Plant. 2012;5:318-33

[10]

Chen F, Tholl D, Bohlmann J. et al. The family of terpene syn-thases in plants: a mid-size family of genes for specialized metabolism that is highly diversified throughout the kingdom: terpene synthase family. Plant J. 2011;66:212-29

[11]

Christianson DW. Structural and chemical biology of terpenoid cyclases. Chem Rev. 2017;117:11570-648

[12]

Abbas F, Ke Y, Yu R. et al. Functional characterization and expression analysis of two terpene synthases involved in floral scent formation in Lilium ‘Siberia’. Planta. 2019;249:71-93

[13]

Conart C, Bomzan DP, Huang XQ. et al. A cytosolic bifunctional geranyl/farnesyl diphosphate synthase provides MVA-derived GPP for geraniol biosynthesis in rose flowers. Proc Natl Acad Sci USA. 2023;120:e2221440120

[14]

Magnard J-L, Roccia A, Caissard JC. et al. Biosynthesis of monoter-pene scent compounds in roses. Science. 2015;349:81-3

[15]

Dong L, Jongedijk E, Bouwmeester H. et al. Monoterpene biosyn-thesis potential of plant subcellular compartments. New Phytol. 2016;209:679-90

[16]

Bartram S, Jux A, Gleixner G. et al. Dynamic pathway allocation in early terpenoid biosynthesis of stress-induced lima bean leaves. Phytochemistry. 2006;67:1661-72

[17]

Gutensohn M, Orlova I, Nguyen TTH. et al. Cytosolic monoter-pene biosynthesis is supported by plastid-generated geranyl diphosphate substrate in transgenic tomato fruits. Plant J. 2013;75:351-63

[18]

Opitz S, Nes WD, Gershenzon J. Both methylerythritol phosphate and mevalonate pathways contribute to biosynthesis of each of the major isoprenoid classes in young cotton seedlings. Phyto-chemistry. 2014;98:110-9

[19]

Mendoza-Poudereux I, Kutzner E, Huber C. et al. Metabolic cross-talk between pathways of terpenoid backbone biosynthesis in spike lavender. Plant Physiol Biochem. 2015;95:113-20

[20]

Kumar A, Patekar S, Mohapatra S. et al. Isoprenyl diphosphate synthases of terpenoid biosynthesis in rose-scented geranium (Pelargonium graveolens). Plant Physiol Biochem. 2024;210:108590

[21]

Mauseth JD. Structure-function relationships in highly modified shoots of Cactaceae. Ann Bot. 2006;98:901-26

[22]

Guerrero PC, Majure LC, Cornejo-Romero A. et al. Phylogenetic relationships and evolutionary trends in the cactus family. J Hered. 2019;110:4-21

[23]

Hernández-Hernández T, Brown JW, Schlumpberger BO. et al. Beyond aridification: multiple explanations for the elevated diversification of cacti in the New World succulent biome. New Phytol. 2014;202:1382-97

[24]

Schuurink RC, Haring MA, Clark DG. Regulation of volatile ben-zenoid biosynthesis in petunia flowers. Trends Plant Sci. 2006;11: 20-5

[25]

Foster And SP, Harris MO. Behavioral manipulation methods for insect pest-management. Annu Rev Entomol. 1997;42:123-46

[26]

Raguso RA. Wake up and smell the roses: the ecology and evolution of floral scent. Annu Rev Ecol Evol Syst. 2008;39:549-69

[27]

Bergman ME, Bhardwaj M, Phillips MA. Cytosolic geraniol and citronellol biosynthesis require a Nudix hydrolase in rose-scented geranium (Pelargonium graveolens). Plant J Cell Mol Biol. 2021;107:493-510

[28]

Aharoni A, Giri AP, Verstappen FWA. et al. Gain and loss of fruit flavor compounds produced by wild and cultivated strawberry species. Plant Cell. 2004;16:3110-31

[29]

Dong L, Miettinen K, Goedbloed M. et al. Characterization of two geraniol synthases from Valeriana officinalis and Lippia dulcis: similar activity but difference in subcellular localization. Metab Eng. 2013;20:198-211

[30]

Wu S, Schalk M, Clark A. et al. Redirection of cytosolic or plastidic isoprenoid precursors elevates terpene production in plants. Nat Biotechnol. 2006;24:1441-7

[31]

Davidovich-Rikanati R, Lewinsohn E, Bar E. et al. Overexpression of the lemon basil alpha-zingiberene synthase gene increases both mono- and sesquiterpene contents in tomato fruit. Plant J Cell Mol Biol. 2008;56:228-38

[32]

Zhou F, Pichersky E. The complete functional characterisation of the terpene synthase family in tomato. New Phytol. 2020;226: 1341-60

[33]

Hampel D, Swatski A, Mosandl A. et al. Biosynthesis of monoter-penes and norisoprenoids in raspberry fruits (Rubus idaeus L.): the role of cytosolic mevalonate and plastidial methylerythritol phosphate pathway. J Agric Food Chem. 2007;55:9296-304

[34]

Weng J-K. The evolutionary paths towards complexity: a metabolic perspective. New Phytol. 2014;201:1141-9

[35]

Johnsen LG, Skou PB, Khakimov B. et al. Gas chromatography -mass spectrometry data processing made easy. JChromatogrA. 2017;1503:57-64.

[36]

Kita T, Brown MS, Goldstein JL. Feedback regulation of 3-hydroxy-3-methylglutaryl coenzyme A reductase in livers of mice treated with mevinolin, a competitive inhibitor of the reductase. J Clin Invest. 1980;66:1094-100

[37]

Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics. 2014;30:2114-20

[38]

Grabherr MG, Haas BJ, Yassour M. et al. Trinity: reconstructing a full-length transcriptome without a genome from RNA-Seq data. Nat Biotechnol. 2011;29:644-52

[39]

Haas BJ, Papanicolaou A, Yassour M. et al. De novo transcript sequence reconstruction from RNA-Seq: reference generation and analysis with Trinity. Nat Protoc. 2013;8:1494-512

[40]

Altschul SF, Gish W, Miller W. et al. Basic local alignment search tool. J Mol Biol. 1990;215:403-10

[41]

Eddy SR. Accelerated profile HMM searches. PLoS Comput Biol. 2011;7:e1002195

[42]

Duarte GT, Volkova PY, Geras’kin SA.A pipeline for non-model organisms for de novo transcriptome assembly, annotation, and gene ontology analysis using open tools: case study with Scots pine. Bio Protoc. 2021;11:e3912

[43]

Li B, Dewey CN. RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome. BMC Bioinfor-matics. 2011;12:323

[44]

Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15:550

[45]

Young MD, Wakefield MJ, Smyth GK. et al.Gene ontology anal-ysis for RNA-seq: accounting for selection bias. Genome Biol. 2010;11:R14

[46]

Edgar RC. MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res. 2004;32:1792-7

[47]

Darriba D, Posada D, Kozlov AM. et al. ModelTest-NG: a new and scalable tool for the selection of DNA and protein evolutionary models. Mol Biol Evol. 2020;37:291-4

[48]

Kozlov AM, Darriba D, Flouri T. et al. RAxML-NG: a fast, scalable and user-friendly tool for maximum likelihood phylogenetic inference. Bioinformatics. 2019;35:4453-5

[49]

Jumper J, Evans R, Pritzel A. et al. Highly accurate protein struc-ture prediction with AlphaFold. Nature. 2021;596:583-9

[50]

Morris GM, Huey R, Lindstrom W. et al. AutoDock4 and AutoDockTools4: automated docking with selective receptor flexibility. JComputChem. 2009;30:2785-91

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