Unveiling plant defense arsenal: metabolic strategies in Brassica oleracea during black rot disease

Abstract

Alterations in plant metabolism play a key role in the complex plant–pathogen interactions. However, there is still a lack of knowledge about the connection between changes in primary and specialized metabolism and the plant defense against diseases that impact crops. Thus, we aim to study the metabolic reprograming in Brassica oleracea plants upon infection by Xanthomonas campestris pv. campestris (Xcc). To accomplish this, we utilized a combination of untargeted and targeted metabolomics, through UPLC-Q-TOF-MS/MS and 1H-NMR, in two crop lines differing in resistance that were evaluated at two- and four-week intervals following inoculation (T1 and T2, respectively). Besides, to depict the physiological status of the plant during infection, enzymatic activities related to the carbohydrate pathway and oxidative stress were studied. Our results revealed different temporal dynamics in the responses of the susceptible vs. resistant crops lines. Resistant B. oleracea line suppresses carbohydrate metabolism contributing to limit nutrient supplies to the bacterium and prioritizes the induction of defensive compounds such as indolic glucosinolates, salicylic acid, phenylpropanoids and phytoalexins precursors at early infection stages. In contrast, the susceptible line invests in carbohydrate metabolism, including enzymatic activities related to the hexoses turnover, and activates defense signaling related to reactive oxygen species. Thus, each line triggers a different metabolic strategy that will affect how the plant overcomes the disease in terms of resistance and growth. This work provides first insights of a fine-tuned metabolic regulation during Xcc infection in B. oleracea that will contribute to develop new strategies for plant disease management.

Acknowledgements

This research was supported by the research projects PID2021-126472OB-I00 and RTI2018-094650-J-100 of the Ministry of Science and Innovation, the Government of Spain. Carmen Vega-Álvarez acknowledges a PFI fellowship from the Spanish Ministry of Science and Innovation. Marta Francisco acknowledges the Ramón y Cajal Research Program (RYC2019-027834-I) through the MCIN/AEI/10.13039/501100011033 and “ESF Investing in your future”. Thomas Roitsch would like to acknowledge funding by the Ministry of Education, Youth and Sports of Czech Republic within the National Sustainability Programme I (NPU I), grant number LO1415. The authors want to thank Juan Carlos Fernández, Victor Rodriguez, Rogelio Santiago, and Ana Carballeda at MBG and to Mohsina Ferdous and Chandana Pandey at the University of Copenhagen.

Author Contributions

M.F. and P.S. conceived and planned the experiments. C.V.A., M.F., and P.S. carried out the plant trials setup analyzed the data and wrote the manuscript. C.V.A. and R.A. carried out the laboratory work. T.R. supervised the enzymatic assays. P.V. contributed to the untargeted metabolomic analysis. All authors read and improved the final manuscript.

Data availability

Metabolomics data have been archived in the database EMBL-EBI MetaboLights (doi:10.1093/nar/gks1004; PubMed PMID: 23109552) with the identifier MTBLS7815. The other data will be available upon a reasonable request.

Conflict of interest statement

The authors declare no conflict of interest.

References

[1] Castro-Moretti FR, Gentzel IN, Mackey D. et al. Metabolomics as an emerging tool for the study of plant-pathogen interactions. Meta. 2020; 10: 1-23

[2] Mamun A, Islam T, Lee B. et al. Genotypic variation in resistance gene-mediated calcium signaling and hormonal signaling involved in effector-triggered immunity or disease susceptibility in the Xanthomonas campestris pv. campestris-Brassica napus pathosystem. Plan Theory. 2020; 9: 1-11

[3] Francisco M, Soengas P, Velasco P. et al. ‘Omics’ approach to identify factors involved in brassica disease resistance. Curr Issues Mol Biol. 2016; 19: 31-42

[4] Iglesias-Bernabé L, Madloo P, Rodríguez VM. et al. Dissecting quantitative resistance to Xanthomonas campestris pv. campestris in leaves of Brassica oleracea by QTL analysis. Sci Rep. 2019; 9: 1-11

[5] Pérez-Bueno ML, Pineda M, Barón M . Phenotyping plant responses to biotic stress by chlorophyll fluorescence imaging. Front Plant Sci. 2019; 10: 1-15

[6] Pastorczyk M, Bednarek P . The function of glucosinolates and related metabolites in plant innate immunity. Adv Bot Res. 2016; 80: 171-98

[7] Singh S, Dey SS, Bhatia R. et al. Molecular breeding for resistance to black rot [Xanthomonas campestris pv. campestris (Pammel) Dowson] in brassicas: recent advances. Euphytica. 2018; 214: 1-17

[8] Williams PH . Black rot: a continuing. Plant Dis. 1980; 64: 736

[9] Eicher T, Kinnebrew G, Patt A. et al. Metabolomics and multi-omics integration: a survey of computational methods and resources. Meta. 2020; 10: 1-35

[10] Misra BB . New software tools, databases, and resources in metabolomics: updates from 2020. Metabolomics. 2021; 17: 1-24

[11] Mehari TG, Xu Y, Umer MJ. et al. Genome-wide identification and expression analysis elucidates the potential role of PFK gene family in drought stress tolerance and sugar metabolism in cotton. Front Genet. 2022; 13: 922024

[12] Moormann J, Heinemann B, Hildebrandt TM . News about amino acid metabolism in plant-microbe interactions. Trends Biochem Sci. 2022; 47: 839-50

[13] Delplace F, Huard-Chauveau C, Berthomé R. et al. Network organization of the plant immune system: from pathogen perception to robust defense induction. Plant J. 2022; 109: 447-70

[14] Shaw RK, Shen Y, Wang J. et al. Advances in multi-omics approaches for molecular breeding of black rot resistance in Brassica oleracea L. Front Plant Sci. 2021; 12: 1-34

[15] Tortosa M, Cartea ME, Rodríguez VM. et al. Unraveling the metabolic response of Brassica oleracea exposed to Xanthomonas campestris pv. Campestris. J Sci Food Agric. 2018; 98: 3675-83

[16] Marin VR, Ferrarezi JH, Vieira G. et al. Recent advances in the biocontrol of Xanthomonas spp. World J Microbiol Biotechnol. 2019; 35: 1-11

[17] Vega-Álvarez C, Francisco M, Cartea ME. et al. The growth-immunity tradeoff in Brassica oleracea-Xanthomonas campestris pv. campestris pathosystem. Plant Cell Environ. 2022; 16: 1-13

[18] Vicente J, Conway J, King G. et al. Resistance to Xanthomonas campestris pv. campestris in Brassica spp. Acta Hortic. 2000; 110: 61-7

[19] Tortosa M, Velasco P, Rodríguez VM. et al. Changes in Brassica oleracea leaves infected with Xanthomonas campestris pv. campestris by proteomics analysis. Front Plant Sci. 2022; 12: 1-12

[20] Kanwar P, Jha G . Alterations in plant sugar metabolism: signatory of pathogen attack. Planta. 2019; 249: 305-18

[21] Khare S, Singh NB, Singh A. et al. Plant secondary metabolites synthesis and their regulations under biotic and abiotic constraints. J Plant Biol. 2020; 63: 203-16

[22] Bahaji A, Sánchez-López ÁM, De Diego N. et al. Plastidic phosphoglucose isomerase is an important determinant of starch accumulation in mesophyll cells, growth, photosynthetic capacity, and biosynthesis of plastidic cytokinins in Arabidopsis. PLoS One. 2015; 10: 1-35

[23] Ehness R, Ecker M, Godt DE . Glucose and stress independently regulate source and sink metabolism and defense mechanisms via signal transduction pathways involving protein phosphorylation. Plant Cell. 1997; 9: 1825-41

[24] Roitsch T. Source-sink regulation by sugar and stress. Curr Opin Plant Biol. 1999; 2: 198-206

[25] Berger S, Sinha AK, Roitsch T . Plant physiology meets phytopathology: plant primary metabolism and plant-pathogen interactions. J Exp Bot. 2007; 58: 4019-26

[26] Rojas CM, Senthil-Kumar M, Tzin V. et al. Regulation of primary plant metabolism during plant-pathogen interactions and its contribution to plant defense. Front Plant Sci. 2014; 5: 1-12

[27] Quijano C, Trujillo M, Castro L. et al. Interplay between oxidant species and energy metabolism. Redox Biol. 2016; 8: 28-42

[28] Yusuf M, Saeed Almehrzi AS, Nasir Alnajjar AJ. et al. Glucose modulates copper induced changes in photosynthesis, ion uptake, antioxidants and proline in Cucumis sativus plants. Carbohydr Res. 2021; 501: 108271-10

[29] Couée I, Sulmon C, Gouesbet G. et al. Involvement of soluble sugars in reactive oxygen species balance and responses to oxidative stress in plants. J Exp Bot. 2006; 57: 449-59

[30] Killiny N, Hijaz F . Amino acids implicated in plant defense are higher in candidatus liberibacter asiaticus-tolerant citrus varieties. Plant Signal Behav. 2016; 11: 1-10

[31] Vogt T . Phenylpropanoid biosynthesis. Mol Plant. 2010; 3: 2-20

[32] Kulbat K . The role of phenolic compounds in plant resistance. Biotechnology and Food Sciences. 2016; 80: 97-108

[33] Sun Q, Xu Z, Huang W. et al. Integrated metabolome and transcriptome analysis reveals salicylic acid and flavonoid pathways’ key roles in cabbage’s defense responses to Xanthomonas campestris pv. campestris. Front Plant Sci. 2022; 13: 1-20

[34] Lu L, Monakhos SG, Lim YP. et al. Early defense mechanisms of Brassica oleracea in response to attack by Xanthomonas campestris pv. Campestris. Plan Theory. 2021; 10: 2705

[35] Di X, Gomila J, Takken FLW . Involvement of salicylic acid, ethylene and jasmonic acid signalling pathways in the susceptibility of tomato to fusarium oxysporum. Mol Plant Pathol. 2017; 18: 1024-35

[36] Caarls L, Pieterse CMJ, Van Wees SCM . How salicylic acid takes transcriptional control over jasmonic acid signaling. Front Plant Sci. 2015; 6: 1-11

[37] Rubel MH, Abuyusuf M, Nath UK. et al. Glucosinolate profile and glucosinolate biosynthesis and breakdown gene expression manifested by black rot disease infection in cabbage. Plan Theory. 2020; 9: 1-16

[38] Madloo P, Lema M, Francisco M. et al. Role of major Glucosinolates in the defense of kale against Sclerotinia sclerotiorum and Xanthomonas campestris pv. campestris. Phytopathology. 2019; 109: 1246-56

[39] Tortosa M, Cartea ME, Velasco P. et al. Calcium-signaling proteins mediate the plant transcriptomic response during a well-established Xanthomonas campestris pv. campestris infection. Hortic Res. 2019; 6: 1-10

[40] Velasco P, Lema M, Francisco M. et al. In vivo and in vitro effects of secondary metabolites against Xanthomonas campestris pv. campestris. Molecules. 2013; 18: 11131-43

[41] Agerbirk N, De Vos M, Kim JH. et al. Indole glucosinolate breakdown and its biological effects. Phytochem Rev. 2009; 8: 101-20

[42] Zhang X, Dong J, Liu H. et al. Transcriptome sequencing in response to salicylic acid in salvia miltiorrhiza. PLoS One. 2016; 11: 1-27

[43] Zhao Y, Wang J, Liu Y. et al. Classic myrosinase-dependent degradation of indole glucosinolate attenuates fumonisin B1-induced programmed cell death in Arabidopsis. Plant J. 2015; 81: 920-33

[44] Yang L, Wang X, Chang N. et al. Cytosolic glucose-6-phosphate dehydrogenase is involved in seed germination and root growth under salinity in Arabidopsis. Front Plant Sci. 2019; 10: 1-14

[45] Stampft H, Fritz M, Santo SD. et al. The GSK3/shaggy-like kinase ASKα contributes to pattern-triggered immunity in Arabidopsis thaliana. Plant Physiol. 2016; 171: 1366-77

[46] Hu Y, You J, Li J. et al. Loss of cytosolic glucose-6-phosphate dehydrogenase increases the susceptibility of Arabidopsis thaliana to root-knot nematode infection. Ann Bot. 2019; 123: 37-46

[47] Das K, Roychoudhury A . Reactive oxygen species (ROS) and response of antioxidants as ROS-scavengers during environmental stress in plants. Front Environ Sci. 2014; 2: 1-13

[48] Doghri M, Rodríguez VM, Kliebenstein DJ. et al. Plant responses underlying timely specialized metabolites induction of Brassica crops. Front Plant Sci. 2022; 12: 1-11

[49] Rodriguez VM, Velasco P, Abilleira R. et al. Metabolomic fingerprint of cabbage resistance to Mamestra brassicae L. (Lepidoptera: Noctuidae). Pest Manag Sci. 2022; 79: 1-25

[50] Kessler N, Neuweger H, Barsch A. et al. What are we eating? MetaboScape® software; enabling the de-replication and identification of unknowns in food metabolomics. Application Note LCMS-116. 2016; 1-2

[51] Pang Z, Chong J, Zhou G. et al. MetaboAnalyst 5.0: narrowing the gap between raw spectra and functional insights. Nucleic Acids Res. 2021; 49: 388-96

[52] Van Berg RA, Hoefsloot HCJ, Westerhuis JA. et al. Centering, scaling, and transformations: improving the biological information content of metabolomics data. BMC Genomics. 2006; 15: 1-15

[53] Dührkop K, Fleischauer M, Ludwig M. et al. SIRIUS 4: a rapid tool for turning tandem mass spectra into metabolite structure information. Nat Methods. 2019; 16: 299-302

[54] Choi H, Hae Y, Verberne M. et al. Metabolic fingerprinting of wild type and transgenic tobacco plants by 1H NMR and multivariate analysis technique. Phytochemistry. 2004; 65: 857-64

[55] SAS institute . Statistical Analysis Software© (SAS). 2008. https://support.sas.com/software/92/deployment.html.

[56] Jammer A, Gasperl A, Luschin-Ebengreuth N. et al. Simple and robust determination of the activity signature of key carbohydrate metabolism enzymes for physiological phenotyping in model and crop plants. J Exp Bot. 2015; 66: 5531-42

[57] Pandey C, Großkinsky DK, Westergaard JC. et al. Identification of a bio-signature for barley resistance against Pyrenophora teres infection based on physiological, molecular and sensor-based phenotyping. Plant Sci. 2021; 313: 111072-11

[58] Fimognari L, Dölker R, Kaselyte G. et al. Simple semi-high throughput determination of activity signatures of key antioxidant enzymes for physiological phenotyping. Plant Methods. 2020; 16: 1-19