Metabolite profiling of leaves of three Epilobium species

Cover Page

Cite item

Full Text

Open Access Open Access
Restricted Access Access granted
Restricted Access Subscription Access

Abstract

BACKGROUND: The ability of plants to adapt to oxygen deficiency is associated with the presence of various adaptations, many of which are mediated by significant changes of metabolism. These changes allow resistant wetland plants to grow even in oxygen-deficient environment.

AIM: The aim of the study was to carry out metabolic profiling of the leaves of the wetland species Epilobium palustre and Epilobium hirsutum, and the mesophyte species Epilobium angustifolium in order to identify the most characteristic metabolome traits of hypoxia-resistant plants.

MATERIALS AND METHODS: Metabolite profiling was performed by GC-MS. Statistical analysis of metabolomics data was processed using R 4.2.1 Funny-Looking Kid.

RESULTS: The resulting profile included about 360 compounds. 70 of these were identified and 50 compounds were determined to a class. Sugars (64) were the most widely represented in the obtained profiles. 16 amino and 20 carboxylic acids, lipids and secondary compounds have been identified. Significant differences were revealed between the profiles of leaf metabolomes of mesophyte E. angustifolium and hydrophytes E. hirsutum and E. palustre. The mesophyte was characterized by high levels of sugars. The metabolomes of wetland Epilobium species practically did not differ from each other and were characterized by the accumulation of amino acids, including GABA shunt intermediates, dicarboxylic acids of the Krebs cycle, and metabolites of glycolysis and lactic acid fermentation, which reflects the stimulation of anaerobic respiration, nitrogen metabolism, and alternative pathways of NAD(P)H reoxidation in wetland plants.

CONCLUSIONS: Traits of metabolic profiles detected in hydrophyte Epilobium species can be used to assess the degree of plant resistance to oxygen deficiency.

About the authors

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. (Biol.), Research Associate, Laboratory of Analytical Phytochemistry; Department of Plant Physiology and Biochemistry

Russian Federation, Saint Petersburg; Saint Petersburg

Pavel D. Smirnov

Saint Petersburg State University

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

Assistant Professor, Department of Botany

Russian Federation, Saint Petersburg

Sergey A. Vanisov

Saint Petersburg State University

Email: s.vanisov@mail.ru

Student, Department of Plant Physiology and Biochemistry

Russian Federation, Saint Petersburg

Maksim D. Dubrovskiy

Saint Petersburg State University

Email: max.d10@mail.ru

Student, Department of Plant Physiology and Biochemistry

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. (Biol.), Head of Laboratory of Analytical Phytochemistry; Center for Molecular and Cell Technologies

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. (Biol.), Professor, Department of Plant Physiology and Biochemistry

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

Cand. Sci. (Biol.), Associate Professor, Department of Genetics and Biotechnology

Russian Federation, Saint Petersburg

References

  1. 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
  2. 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
  3. Voesenek LACJ, Colmer TD, Pierik R, et al. How plants cope with complete submergence. New Phytol. 2006;170(2):213–226. doi: 10.1111/j.1469-8137.2006.01692.x
  4. 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
  5. Crawford RMM. Studies in plant survival. Anderson DJ, Greic-Smith P, Pitelka FA, editors. Ecological case histories of plant adaptation to adversity. Studies in ecology. Vol. 11. Oxford; London; Edinburgh; Boston; Palo Alto; Melbourne: Blackwell Scientific Publications, 1989. 296 p.
  6. Chirkova TV. Rastenie i anaehrobioz. Vestnik of Saint Petersburg University: Series 3: Biology. 1998;(2):41–52. (In Russ.)
  7. Vartapetian BB, Jackson MB. Plant adaptations to anaerobic stress. Ann Bot. 1997;79(S1):3–20. doi: 10.1093/oxfordjournals.aob.a010303
  8. Chirkova TV, Walter G, Leffer S, Novitskaya LO. Chloroplasts and mitochondria in the leaves of wheat and rice seedlings exposed to anoxia and long-term darkness: Some characteristics of organelle state. Russ J Plant Physiol. 1995;42(3):321–329.
  9. 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
  10. Blokhina OB, Chirkova TV, Fagerstedt KV. Anoxic stress leads to hydrogen peroxide formation in plant cells. J Exp Bot. 2001;52(359):1179–1190. doi: 10.1093/jexbot/52.359.1179
  11. 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
  12. Blokhina O, Fagerstedt KV. Reactive oxygen species and nitric oxide in plant mitochondria: Origin and redundant regulatory systems. Physiol Plant. 2010;138(4):447–462. doi: 10.1111/j.1399-3054.2009.01340.x
  13. Chirkova TV, Novitskaya LO, Blokhina OB. Lipid peroxidation and antioxidant systems under anoxia in plants differing in their tolerance to oxygen deficiency. Russ J Plant Physiol. 1998;45(1):55–62.
  14. Blokhina OB, Fagerstedt KV, Chirkova TV. Relationships between lipid peroxidation and anoxia tolerance in a range of species during post-anoxic reaeration. Physiol Plant. 1999;105(4):625–632. doi: 10.1034/j.1399-3054.1999.105405.x
  15. Shikov AE, Lastochkin VV, Chirkova TV, et al. Post-anoxic oxidative injury is more severe than oxidative stress induced by chemical agents in wheat and rice plants. Acta Physiol Plant. 2022;44(9):90. doi: 10.1007/s11738-022-03429-z
  16. Sweetlove LJ, Dunford R, Ratcliffe RG, Kruger NJ. Lactate metabolism in potato tubers deficient in lactate dehydrogenase activity. Plant Cell Environ. 2000;23(8):873–881. doi: 10.1046/j.1365-3040.2000.00605.x
  17. Licausi F, Perata P. Low oxygen signaling and tolerance in plants. Adv Bot Res. 2009;50:139–198. doi: 10.1016/S0065-2296(08)00804-5
  18. 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
  19. 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
  20. Barding GA Jr, Fukao T, Beni S, et al. Differential metabolic regulation governed by the rice SUB1A gene during submergence stress and identification of alanylglycine by 1H NMR spectroscopy. J Proteome Res. 2012;11(1):320–330. doi: 10.1021/pr200919b
  21. 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
  22. 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
  23. 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
  24. 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
  25. 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
  26. Coutinho ID, Henning LMM, Döpp SA, et al. Identification of primary and secondary metabolites and transcriptome profile of soybean tissues during different stages of hypoxia. Data in Brief. 2018;21:1089–1100. doi: 10.1016/j.dib.2018.09.122
  27. theplantlist.org [Internet]. The Plant List [cited 2022 Nov 20]. Available at: http://theplantlist.org/1.1/browse/A/Onagraceae/Epilobium/
  28. mobot.org [Internet]. Angiosperm phylogeny website, version 14 [cited 2022 Nov 20]. Available at: http://www.mobot.org/MOBOT/Research/APweb/orders/myrtalesweb2.htm#Onagraceae
  29. Maevskii PF. Flora srednei polosy evropeiskoi chasti Rossii. 11th edition. Moscow: Tovarishchestvo nauchnykh izdanii KMK, 2014. 635 p. (In Russ.)
  30. Ronzhina DA. Ecological differentiation between invasive and native species of the genus Epilobium in riparian ecosystems is associated with plant functional traits. Russ J Biol Invas. 2020;(1):38–51. (In Russ.)
  31. Chirkov YI. Osnovy agrometeorologii. Leningrad: Gidrometeoizdat, 1988. 248 p. (In Russ.)
  32. 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
  33. Lai Z, Tsugawa H, Wohlgemuth G, et al. Identifying metabolites by integrating metabolome databases with mass spectrometry cheminformatics. Nat Methods. 2018;15:53–56. doi: 10.1038/nmeth.4512
  34. Hummel J, Selbig J, Walther D, Kopka J. The Golm Metabolome Database: a Database for GC-MS based metabolite profiling. Nielsen J, Jewett MC, editors. Metabolomics. Vol. 18: Topics in Current Genetics. Berlin; Heidelberg: Springer. 2007. P. 75–95. doi: 10.1007/4735_2007_0229
  35. r-project.org [Internet]. R Core Team. R: The R Project for Statistical Computing [cited 2022 Nov 20]. Available at: https://www.r-project.org/
  36. CRAN.R-project.org [Internet]. Komsta L. outliers: Tests for Outliers. R package version 0.15, 2022 [cited 2022 Nov 20]. Available at: https://CRAN.R-project.org/package=outliers
  37. bioconductor.org [Internet]. Hastie T, Tibshirani R, Narasimhan B, Chu G. Impute: Imputation for microarray data. R package version 1.70.0. 2022. Available at: https://bioconductor.org/packages/release/bioc/html/impute.html
  38. 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
  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. Brereton RG, Lloyd GR. Partial least squares discriminant analysis: taking the magic away. J Chemom. 2013;28(4):213–225. doi: 10.1002/cem.2609
  41. Gu Z, Eils R, Schlesner M. Complex heatmaps reveal patterns and correlations in multidimensional genomic data. Bioinformatics. 2016;32(18):2847–2849. doi: 10.1093/bioinformatics/btw313
  42. Korotkevich G, Sukhov V, Sergushichev A. Fast gene set enrichment analysis. bioRxiv. 2019;1–40. doi: 10.1101/060012
  43. Kanehisa M, Furumichi M, Sato Y, et al. KEGG for taxonomy-based analysis of pathways and genomes. Nucleic Acids Res. 2022; gkac963. doi: 10.1093/nar/gkac963
  44. bioconductor.org [Internet]. 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. Available at: https://www.bioconductor.org/packages/release/bioc/html/KEGGREST.html
  45. Shannon P, Markiel A, Ozier O, et al. Cytoscape: A software environment for integrated models of biomolecular interaction networks. Genome Res. 2003;13(11):2498–2504. doi: 10.1101/gr.1239303
  46. 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
  47. 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
  48. Mustroph A, Barding GA Jr, Kaiser KA, et al. Characterization of distinct root and shoot responses to low-oxygen stress in Arabidopsis with a focus on primary C- and N-metabolism. Plant Cell Environ. 2014;37(10):2366–2380. doi: 10.1111/pce.12282
  49. Fukushima A, Kuroha T, Nagai K, et al. Metabolite and phytohormone profiling illustrates metabolic reprogramming as an escape strategy of deepwater rice during partially submerged stress. Metabolites. 2020;10(2):68. doi: 10.3390/metabo10020068
  50. Dacrema M, Sommella E, Santarcangelo C, et al. Metabolic profiling, in vitro bioaccessibility and in vivo bioavailability of a commercial bioactive Epilobium angustifolium L. extract. Biomed Pharmacother. 2020;131:110670. doi: 10.1016/j.biopha.2020.110670
  51. Ak G, Zengin G, Mahomoodally MF, et al. Shedding light into the connection between chemical components and biological effects of extracts from Epilobium hirsutum: Is it a potent source of bioactive agents from natural treasure? Antioxidants. 2021;10(9):1389. doi: 10.3390/antiox10091389
  52. Matysik J, Alia A, Bhalu B, Mohanty P. Molecular mechanisms of quenching of reactive oxygen species by proline under stress in plants. Curr Sci. 2002;82(5):525–532.
  53. Tamang BG, Fukao T. Plant adaptation to multiple stresses during submergence and following desubmergence. Int J Mol Sci. 2015;16(12):30164–30180. doi: 10.3390/ijms161226226
  54. 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
  55. Yemelyanov VV, Lastochkin VV, Prikazyuk EG, Chirkova TV. Activities of catalase and peroxidase in wheat and rice plants under conditions of anoxia and post-anoxic aeration. Russ J Plant Physiol. 2022;69(6):117. doi: 10.1134/S1021443722060036
  56. Shelp BJ, Bown AW, McLean MD. Metabolism and functions of gamma-aminobutyric acid. Trends Plant Sci. 1999;4(11): 446–452. doi: 10.1016/S1360-1385(99)01486-7

Supplementary files

Supplementary Files
Action
1. JATS XML
Download
2. Fig. 1. Heatmap of mean normalized content identified metabolites. Barplots — VIPs from OPLS-DA models for comparison: above — hydrophytes and mesophyte, under — E. hirsutum and E. palustre. In metabolite names: RI — retention index, -P — phosphate, compsug-complex sugars or molecules with sugar parts, FA — fatty acid, MG — monoacylglycerol

Download (429KB)
Indexing metadata
3. Fig. 2. Unsupervised analysis of metabolite profiles from three Epilobium species sampled at two years. a, b — PCA score plots, ellipses — 95% CI; c — dendrogram of hierarchical clustering [with Pearson distance (1-r), Ward method]

Download (281KB)
Indexing metadata
4. Fig. 3. Metabolite sets enrichment analysis based on loadings from OPLS-DA classification of hydrophytes and mesophyte. Nodes are the paths extracted from KEGG. If the paths share metabolites, then they are connected by edge. Nodes attract with each other in dependence of number of common metabolites. Color — significance of influence on this pathway, size — strength of influence (|NES|). NES — normalized enrichment score. FDR — false discovery rate

Download (324KB)
Indexing metadata
5. Fig. 4. Comparison of differences between hydrophytes and mesophyte and between two hydrophytes. SUS (shared and unique structures) plot in the space of the loadings from two OPLS-DA models. Positive loadings correspond to a higher content in mesophyte and in E. palustre

Download (177KB)
Indexing metadata
6. Fig. 5. Comparison of annual changes in metabolite profiles of three Epilobium species: a — comparison E. palustre vs E. hirsutum; b — comparison E. angustifolium vs E. palustre; с — comparison E. angustifolium vs E. hirsutum. SUS plots in the loadings space of the corresponding OPLS-DA models. Positive loadings correspond to a higher level in the second year of observations

Download (242KB)
Indexing metadata

Copyright (c) 2022 Puzanskiy R.K., Smirnov P.D., Vanisov S.A., Dubrovskiy M.D., Shavarda A.L., Shishova M.F., Yemelyanov V.V.

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
 


This website uses cookies

You consent to our cookies if you continue to use our website.

About Cookies