Metabolic profiling of leaves of four Ranunculus species

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Abstract

BACKGROUND: Plant ability to survive oxygen deficiency is associated with the presence of various adaptations, majority of which are mediated by significant changes of metabolism. These alterations allow resistant wetland plants to grow even in an oxygen-depleted environment.

AIM: To compare metabolic profiles of the leaves of the wetland species Ranunculus lingua, R. repens and R. sceleratus, and the mesophyte species R. acris growing in their natural habitat in order to identify the most characteristic metabolic traits of hypoxia-resistant plants.

MATERIALS AND METHODS: Metabolite profiling was performed by GC-MS. Statistical analysis of metabolomics data was processed using R 4.3.1 Beagle Scouts.

RESULTS: The resulting profile included 360 compounds. 74 of these were identified and 114 compounds were determined to a class. Sugars (114) were the most widely represented in the obtained profiles. 10 amino and 23 carboxylic acids, lipids and phenolic compounds have been identified. Significant differences were revealed between the profiles of leaf metabolomes of all tested species, which were clustered according to phylogenetic relation. The hydrophytic R. sceleratus, growing under submergence, showed the most unique metabolome, in which the level of sugars was reduced and intermediates of anaerobic metabolism, nitrogen metabolism, and alternative pathways of NAD(P)H reoxidation were accumulated. The profile of mesophytic R. acris was markedly different by decreased levels of amino acids, fatty acids and sterols. The metabolite profiles of waterlogged hydrophytes R. lingua and R. repens occupied an intermediate position.

CONCLUSIONS: The identified differences of metabolomes of Ranunculus species are due to genetic determinants, ecological niche and direct impact of a stressor.

About the authors

Pavel D. Smirnov

Saint Petersburg State University

Email: p.d.smirnov@gmail.com
ORCID iD: 0000-0002-4663-8398
SPIN-code: 4273-1520
Russian Federation, Saint Petersburg

Roman K. Puzanskiy

Saint Petersburg State University; Komarov Botanical Institute of the Russian Academy of Sciences

Email: puzansky@yandex.ru
ORCID iD: 0000-0002-5862-2676
SPIN-code: 6399-2016

Cand. Sci (Biology)

Russian Federation, Saint Petersburg; Saint Petersburg

Sergey A. Vanisov

Saint Petersburg State University

Email: s.vanisov@mail.ru
Russian Federation, Saint Petersburg

Maksim D. Dubrovskiy

Saint Petersburg State University

Email: max.d10@mail.ru
Russian Federation, Saint Petersburg

Alexey L. Shavarda

Saint Petersburg State University; Komarov Botanical Institute of the Russian Academy of Sciences

Email: stachyopsis@gmail.com
ORCID iD: 0000-0003-1778-2814
SPIN-code: 5637-5122

Cand. Sci. (Biology)

Russian Federation, Saint Petersburg; Saint Petersburg

Maria F. Shishova

Saint Petersburg State University

Email: mshishova@mail.ru
ORCID iD: 0000-0003-3657-2986
SPIN-code: 7842-7611

Dr. Sci. (Biology), Professor

Russian Federation, Saint Petersburg

Vladislav V. Yemelyanov

Saint Petersburg State University

Author for correspondence.
Email: bootika@mail.ru
ORCID iD: 0000-0003-2323-5235
SPIN-code: 9460-1278
http://www.bio.spbu.ru/staff/id179_evv.php

Cand. Sci. (Biology), Assistant Professor

Russian Federation, Saint Petersburg

References

  1. Dalgliesh CE, Horning EC, Horning MG, et al. A gas-liquid-chromatographic procedure for separating a wide range of metabolites occurring in urine or tissue extracts. Biochem J. 1966;101(3):792–810. doi: 10.1042/bj1010792
  2. Tweeddale H, Notley-McRobb L, Ferenci T. Effect of slow growth on metabolism of Escherichia coli, as revealed by global metabolite pool (“Metabolome”) analysis. J Bacteriol. 1998;180(19):5109–5116. doi: 10.1128/jb.180.19.5109-5116.1998
  3. Holmes E, Wilson ID, Nicholson JK. Metabolic phenotyping in health and disease. Cell. 2008;134(5):714–717. doi: 10.1016/j.cell.2008.08.026
  4. Sauter H, Lauer M, Fritsch H. Metabolic profiling of plants a new diagnostic technique. ACS Symp Ser. 1991;443:288–299. doi: 10.1021/bk-1991-0443.ch024
  5. Ghatak A, Chaturvedi P, Weckwerth W. Metabolomics in plant stress physiology. Varshney R, Pandey M, Chitikineni A, editors. Plant genetics and molecular biology. Advances in biochemical engineering/biotechnology. Vol. 164. Springer, Cham, 2018. P. 187–236. doi: 10.1007/10_2017_55
  6. Xu Y, Fu X. Reprogramming of plant central metabolism in response to abiotic stresses: A metabolomics view. Int J Mol Sci. 2022;23(10):5716. doi: 10.3390/ijms23105716
  7. Chirkova T, Yemelyanov V. The study of plant adaptation to oxygen deficiency in Saint Petersburg University. Biol Commun. 2018;63(1):17–31. doi: 10.21638/spbu03.2018.104
  8. Fukao T, Barrera-Figueroa BE, Juntawong P, Peña-Castro JM. Submergence and waterlogging stress in plants: A review highlighting research opportunities and understudied aspects. Front Plant Sci. 2019;10:340. doi: 10.3389/fpls.2019.00340
  9. Dennis ES, Dolferus R, Ellis M, et al. Molecular strategies for improving waterlogging tolerance in plants. J Exp Bot. 2000;51(342):89–97. doi: 10.1093/jexbot/51.342.89
  10. Bailey-Serres J, Voesenek LACJ. Flooding stress: Acclimations and genetic diversity. Annu Rev Plant Biol. 2008;59:313–339. doi: 10.1146/annurev.arplant.59.032607.092752
  11. van Dongen JT, Frohlich A, Ramirez-Aguilar SJ, et al. Transcript and metabolite profiling of the adaptive response to mild decreases in oxygen concentration in the roots of Arabidopsis plants. Ann Bot. 2009;103(2):269–280. doi: 10.1093/aob/mcn126
  12. Rocha M, Licausi F, Araujo WL, et al. Glycolysis and the tricarboxylic acid cycle are linked by alanine aminotransferase during hypoxia induced by waterlogging of Lotus japonicas. Plant Physiol. 2010;152(3):1501–1513. doi: 10.1104/pp.109.150045
  13. Shingaki-Wells RN, Huang S, Taylor NL, et al. Differential molecular responses of rice and wheat coleoptiles to anoxia reveal novel metabolic adaptations in amino acid metabolism for tissue tolerance. Plant Physiol. 2011;156(4):1706–1724. doi: 10.1104/pp.111.175570
  14. Locke AM, Barding GA Jr, Sathnur S, et al. Rice SUB1A constrains remodelling of the transcriptome and metabolome during submergence to facilitate post-submergence recovery. Plant Cell Environ. 2018;41(4):721–736. doi: 10.1111/pce.13094
  15. Herzog M, Fukao T, Winkel A, et al. Physiology, gene expression, and metabolome of two wheat cultivars with contrasting submergence tolerance. Plant Cell Environ. 2018;41(7):1632–1644. doi: 10.1111/pce.13211
  16. Andrzejczak OA, Havelund JF, Wang W-Q, et al. The hypoxic proteome and metabolome of barley (Hordeum vulgare L.) with and without phytoglobin priming. Int J Mol Sci. 2020;21(4):1546. doi: 10.3390/ijms21041546
  17. Antonio C, Päpke C, Rocha M, et al. Regulation of primary metabolism in response to low oxygen availability as revealed by carbon and nitrogen isotope redistribution. Plant Physiol. 2016;170(1):43–56. doi: 10.1104/pp.15.00266
  18. Hasler-Sheetal H, Fragner L, Holmer M, Weckwerth W. Diurnal effects of anoxia on the metabolome of the seagrass Zostera marina. Metabolomics. 2015;11(5):1208–1218. doi: 10.1007/s11306-015-0776-9
  19. Parveen M, Miyagi A, Kawai-Yamada M, et al. Metabolic and biochemical responses of Potamogeton anguillanus Koidz. (Potamogetonaceae) to low oxygen conditions. J Plant Physiol. 2019;232:171–179. doi: 10.1016/j.jplph.2018.11.023
  20. Yemelyanov VV, Puzanskiy RK, Shishova MF. Plant life with and without oxygen: A metabolomics approach. Int J Mol Sci. 2023;24(22):16222. doi: 10.3390/ijms242216222
  21. Jethva J, Schmidt RR, Sauter M, Selinski J. Try or die: Dynamics of plant respiration and how to survive low oxygen conditions. Plants. 2022;11(2):205. doi: 10.3390/plants11020205
  22. Puzanskiy RK, Smirnov PD, Vanisov SA, et al. Metabolite profiling of leaves of three Epilobium species. Ecological genetics. 2022;20(4):279–293. doi: 10.17816/ecogen114743
  23. wfoplantlist.org [Internet]. The WFO Plant list [cited 2023 Nov 15]. Available at: https://wfoplantlist.org/plant-list/
  24. catalogueoflife.org [Internet]. The catalogue of life [cited 2023 Nov 15]. Available at: https://www.catalogueoflife.org/
  25. Bobrov EG, Bulavkina AA, Komarov VL, et al. Flora of the USSR. Vol. 7. Moscow: Izdatel’stvo AN SSSR, 1937. 793 p. (In Russ.)
  26. Averyanov LV, Budantsev AL, Geltman DV, et al. Illustrated identifier of plants of the Leningrad region. Budantsev AL, Yakovlev GP, editors. Moscow: Tovarishchestvo nauchnykh izdanii KМK, 2006. 799 p. (In Russ.)
  27. Emadzade K, Gehrke B, Linder HP, Hörandl E. The biogeographical history of the cosmopolitan genus Ranunculus L. (Ranunculaceae) in the temperate to meridional zones. Mol Phylogenet Evol. 2011;58(1):4–21. doi: 10.1016/j.ympev.2010.11.002
  28. Bakhmatova KA, Vasilieva VA, Vershinina OM, et al. Sergievka Park — a complex natural monument. Saint Petersburg: Tipografiya OOO SPb SRP “Pavel”, 2005. 144 p. (In Russ.)
  29. Maevsky PF. Flora of the middle zone of the European part of Russia. 11th ed. Moscow: Tovarishchestvo nauchnykh izdanii KMK, 2014. 635 p. (In Russ.)
  30. Puzanskiy RK, Yemelyanov VV, Shavarda AL, et al. Age- and organ-specific differences of potato (Solanum phureja) plants metabolome. Russ J Plant Physiol. 2018;65(6):813–823. doi: 10.1134/S1021443718060122
  31. Johnsen LG, Skou PB, Khakimov B, Bro R. Gas chromatography — Mass spectrometry data processing made easy. J Chromatogr A. 2017;1503:57–64. doi: 10.1016/j.chroma.2017.04.052
  32. Hummel J, Selbig J, Walther D, Kopka J. The golm metabolome database: a database for GC-MS based metabolite profiling. In: Nielsen J, Jewett MC, editors. Metabolomics. Topics in current genetics. Vol. 18. Springer, Berlin, Heidelberg, 2007. P. 75–95. doi: 10.1007/4735_2007_0229
  33. r-project.org [Internrt]. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. 2023 [cited 2023 Nov 15]. Available at: https://www.r-project.org/
  34. Hastie T, Tibshirani R, Narasimhan B, Chu G. impute: Imputation for microarray data. R package version 1.70.0. 2022. doi: 10.18129/B9.bioc.impute
  35. Gu Z. Complex heatmap visualization. iMeta. 2022;1(3):e43. doi: 10.1002/imt2.43
  36. Liaw A, Wiener M. Classification and regression by randomForest. R News. 2002;2(3):18–22.
  37. Stacklies W, Redestig H, Scholz M, et al. pcaMethods — a bioconductor package providing PCA methods for incomplete data. Bioinformatics. 2007;23(9):1164–1167. doi: 10.1093/bioinformatics/btm069
  38. Vaughan TG. IcyTree: Rapid browser-based visualization for phylogenetic trees and networks. Bioinformatics. 2017;33(15): 2392–2394. doi: 10.1093/bioinformatics/btx155
  39. Thevenot EA, Roux A, Xu Y, et al. Analysis of the human adult urinary metabolome variations with age, body mass index and gender by implementing a comprehensive workflow for univariate and OPLS statistical analyses. J Proteome Res. 2015;14(8):3322–3335. doi: 10.1021/acs.jproteome.5b00354
  40. Zyla J, Marczyk M, Domaszewska T, et al. Gene set enrichment for reproducible science: Comparison of CERNO and eight other algorithms. Bioinformatics. 2019;35(24):5146–5154. doi: 10.1093/bioinformatics/btz447
  41. Kanehisa M, Furumichi M, Sato Y, et al. KEGG for taxonomy-based analysis of pathways and genomes. Nucleic Acids Res. 2023;51(D1):gkac963. doi: 10.1093/nar/gkac963
  42. Tenenbaum D, Maintainer B. KEGGREST: Client-side REST access to the Kyoto Encyclopedia of Genes and Genomes (KEGG). 2022. R package version 1.36.2. doi: 10.18129/B9.bioc.KEGGREST
  43. Michl J, Modarai M, Edwards S, Heinrich M. Metabolomic analysis of Ranunculus spp. as potential agents involved in the etiology of equine grass sickness. J Agric Food Chem. 2011;59(18): 10388–10393. doi: 10.1021/jf201430k
  44. Labarrere B, Prinzing A, Dorey T, et al. Variations of secondary metabolites among natural populations of sub-Antarctic Ranunculus species suggest functional redundancy and versatility. Plants. 2019;8(7):234. doi: 10.3390/plants8070234
  45. Carillo P, Dell’Aversana E, Modarelli GC, et al. Metabolic profile and performance responses of Ranunculus asiaticus L. hybrids as affected by light quality of photoperiodic lighting. Front Plant Sci. 2020;11:597823. doi: 10.3389/fpls.2020.597823
  46. Modarelli GC, Arena C, Pesce G, et al. The role of light quality of photoperiodic lighting on photosynthesis, flowering and metabolic profiling in Ranunculus asiaticus L. Physiol Plant. 2020;170(2): 187–201. doi: 10.1111/ppl.13122
  47. Fusco GM, Carillo P, Nicastro R, et al. Metabolic profiling in tuberous roots of Ranunculus asiaticus L. as influenced by vernalization procedure. Plants. 2023;12(18):3255. doi: 10.3390/plants12183255
  48. Gargallo-Garriga A, Ayala-Roque M, Sardans J, et al. Impact of soil warming on the plant metabolome of Icelandic grasslands. Metabolites. 2017;7(3):44. doi: 10.3390/metabo7030044
  49. Blokhina O, Virolainen E, Fagerstedt KV. Antioxidants, oxidative damage and oxygen deprivation stress: A review. Ann Bot. 2003;91(2):179–194. doi: 10.1093/aob/mcf118
  50. Ohkama-Ohtsu N, Oikawa A, Zhao P, et al. A gamma-glutamyl transpeptidase-independent pathway of glutathione catabolism to glutamate via 5-oxoproline in Arabidopsis. Plant Physiol. 2008;148(3):1603–1613. doi: 10.1104/pp.108.125716.
  51. Shikov AE, Chirkova TV, Yemelyanov VV. Post-anoxia in plants: reasons, consequences, and possible mechanisms. Russ J Plant Physiol. 2020;67(1):45–59. doi: 10.1134/S1021443720010203
  52. He JB, Bögemann GM, van de Steeg HM, et al. Survival tactics of Ranunculus species in river floodplains. Oecologia. 1999;118(1):1–8. doi: 10.1007/s004420050696

Supplementary files

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2. Fig. 1. Heatmap of mean normalized content identified metabolites. Barplots — Mean Decrease Accuracy from Random Forest. In metabolite names: RI — retention index, compsug — complex sugars or molecules with sugar parts, FA — fatty acid, MG — monoacylglycerol

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3. Fig. 2. Unsupervised analysis of metabolite profiles from four Ranunculus species: a — PCA score plots; b — dendrogram of hierarchical clustering of metabolic profiles, with Pearson distance (1 – rho), Ward method; c — dendrogram of the phylogenetic relationships of studied Ranunculus species of the combined plastid and ITS dataset based on Maximum Parsimony analyses (after: [27], with modifications)

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4. Fig. 3. PLS-DA classification of four Ranunculus species: a — PLS-DA score plots; b — PLS-DA loading plots, colors and symbols correspond to chemical classes; c — metabolite set enrichment analysis (U-test) on loadings of first three components and sets of metabolites representing chemical groups. q — False discovery rate

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