Senescence metabolomics of Nicotiana tabacum L. VBI-0 heterotrophic suspension cultures

Cover Page

Cite item

Full Text

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

Abstract

BACKGROUND: Heterotrophic cell cultures are widely used as a model in plant biology. During a culture cycle the composition of the medium changes: the sucrose and other substrates are depleted, metabolism products are accumulated and the density increases. Finally, arrest of a growth is followed by cell death in a short time. These processes are accompanied with physiological alterations, corresponding to senescence.

AIM: To resolve metabolic features of tobacco cells in growing and stationary senescent suspension cultures VBI-0.

MATERIALS AND METHODS: Nicotiana tabacum VBI-0 cells were cultured in suspension MS medium supplied with 3% sucrose. Cells were sampled at 7th day, during intensive growth, and at 28th day, when the culture was in the stationary phase. The GC-MS method was used to profile the metabolites.

RESULTS: Sucrose depletion in media caused starvation of heterotrophic tobacco cell culture and was associated with a decrease in the accumulation of free amino acids. At the same time, the level of pentoses and complex sugars, including sucrose, increased, while the levels of glucose and fructose were not changed significantly and levels of hexose phosphates decreased. During culture senescence cells showed higher levels of accumulation of malate, pyruvate and some other carboxylates.

CONCLUSIONS: The metabolomic data indicate that culture senescence was associated with a drop in amino acids metabolism, a decrease in the activity of the upper part of glycolysis, and the accumulation of complex sugars, pentoses and carboxylates.

About the authors

Roman K. Puzanskiy

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

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

Cand. Sci. (Biology)

Russian Federation, Saint Petersburg; Saint Petersburg

Anastasiia A. Kirpichnikova

Saint Petersburg State University

Email: nastin1972@mail.ru
ORCID iD: 0000-0001-5133-5175
SPIN-code: 9960-9527
Russian Federation, Saint Petersburg

Alexey L. Shavarda

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

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

Cand. Sci. (Bioligy)

Russian Federation, Saint Petersburg; Saint Petersburg

Vladislav V. Yemelyanov

Saint Petersburg State University

Email: bootika@mail.ru
ORCID iD: 0000-0003-2323-5235
SPIN-code: 9460-1278

Cand. Sci. (Bioligy), Associate Professor

Russian Federation, Saint Petersburg

Maria F. Shishova

Saint Petersburg State University

Author for correspondence.
Email: mshishova@mail.ru
ORCID iD: 0000-0003-3657-2986
SPIN-code: 7842-7611
https://bio.spbu.ru/staff/id200_mfsh.php

Dr. Sci. (Biology), Professor

Russian Federation, Saint Petersburg

References

  1. Imseng N, Schillberg S, Schürch C, et al. Suspension culture of plant cells under heterotrophic conditions. In: Meyer H, Schmidhalter DR, editors. Industrial scale suspension culture of living cells. 1st edition. Wiley, 2014. P. 224–258. doi: 10.1002/9783527683321.ch07
  2. Loyola-Vargas VM, Vázquez-Flota F. An introduction to plant cell culture: back to the future. In: Loyola-Vargas VM, Vázquez-Flota F, editors. Plant Cell Culture Protocols. Methods in Molecular Biology™. Vol. 318. Humana Press, 2005. P. 3–8. doi: 10.1385/1-59259-959-1:003
  3. Mhlongo MI, Steenkamp PA, Piater LA, et al. Profiling of altered metabolomic states in Nicotiana tabacum cells induced by priming agents. Front Plant Sci. 2016;7:1527. doi: 10.3389/fpls.2016.01527
  4. Kariya K, Tsuchiya Y, Sasaki T, Yamamoto Y. Aluminium-induced cell death requires upregulation of NtVPE1 gene coding vacuolar processing enzyme in tobacco (Nicotiana tabacum L.). J Inorg Biochem. 2018;181:152–161. doi: 10.1016/j.jinorgbio.2017.09.008
  5. Bapat VA, Kavi Kishor PB, Jalaja N, et al. Plant cell cultures: Biofactories for the production of bioactive compounds. Agronomy. 2023;13(3):858. doi: 10.3390/agronomy13030858
  6. Zagorskaya AA, Deineko EV. Suspension-cultured plant cells as a platform for obtaining recombinant proteins. Russian Journal Plant Physiology. 2017;64(6):795–807. doi: 10.1134/S102144371705017X
  7. Roitsch T, Sinha AK. Application of photoautotrophic suspension cultures in plant science. Photosynt. 2002;40(4):481–492. doi: 10.1023/A:1024332430494
  8. Schenk RU, Hildebrandt AC. Medium and techniques for induction and growth of monocotyledonous and dicotyledonous plant cell cultures. Can J Bot. 1972;50(1):199–204. doi: 10.1139/b72-026
  9. Dominguez PG, Niittylä T. Mobile forms of carbon in trees: metabolism and transport. Tree Physiol. 2022;42(3):458–487. doi: 10.1093/treephys/tpab123
  10. Ruan YL. Sucrose metabolism: Gateway to diverse carbon use and sugar signaling. Annu Rev Plant Biol. 2014;65:33–67. doi: 10.1146/annurev-arplant-050213-040251
  11. Contento AL, Kim S-J, Bassham DC. Transcriptome profiling of the response of Arabidopsis suspension culture cells to Suc starvation. Plant Physiol. 2004;135(4):2330–2347. doi: 10.1104/pp.104.044362
  12. Journet EP, Bligny R, Douce R. Biochemical changes during sucrose deprivation in higher plant cells. J Biol Chem. 1986;261(7): 3193–3199. doi: 10.1016/S0021-9258(17)35767-8
  13. Wang H-J, Wan A-R, Hsu C–M, et al. Transcriptomic adaptations in rice suspension cells under sucrose starvation. Plant Mol Biol. 2007;63(4):441–463. doi: 10.1007/s11103-006-9100-4
  14. Gout E, Bligny R, Douce R, et al. Early response of plant cell to carbon deprivation: in vivo 31P-NMR spectroscopy shows a quasi-instantaneous disruption on cytosolic sugars, phosphorylated intermediates of energy metabolism, phosphate partitioning, and intracellular pHs. New Phytol. 2011;189(1):135–147. doi: 10.1111/j.1469–8137.2010.03449.x
  15. Roby C, Martin J-B, Bligny R, Douce R. Biochemical changes during sucrose deprivation in higher plant cells. Phosphorus-31 nuclear magnetic resonance studies. J Biol Chem. 1987;262(11):5000–5007. doi: 10.1016/S0021-9258(18)61145-7
  16. Inoue Y, Moriyasu Y. Degradation of membrane phospholipids in plant cells cultured in sucrose-free medium. Autophagy. 2006;2(3):244–246. doi: 10.4161/auto.2745
  17. Kim SW, Koo BC, Kim J, Liu JR. Metabolic discrimination of sucrose starvation from Arabidopsis cell suspension by 1H NMR based metabolomics. Biotechnol Bioprocess Eng. 2007;12(6):653–661. doi: 10.1007/BF02931082
  18. Binder S. Branched-chain amino acid metabolism in Arabidopsis thaliana. The Arabidopsis Book. 2010;2010(8): e0137. doi: 10.1199/tab.0137
  19. Morkunas I, Borek S, Formela M, Ratajczak L. Plant responses to sugar starvation. In: Chang CF, editor. Carbohydrates — comprehensive studies on glycobiology and glycotechnology. InTech, 2012. doi: 10.5772/51569
  20. Tsuchiya Y, Nakamura T, Izumi Y, et al. Physiological role of aerobic fermentation constitutively expressed in an aluminum-tolerant cell line of tobacco (Nicotiana tabacum). Plant Cell Physiol. 2021;62(9):1460–1477. doi: 10.1093/pcp/pcab098
  21. Rontein D, Dieuaide-Noubhani M, Dufourc EJ, et al. The metabolic architecture of plant cells. J Biol Chem. 2002;277(46):43948–43960. doi: 10.1074/jbc.M206366200
  22. Peč J, Flores-Sanchez IJ, Choi YH, Verpoorte R. Metabolic analysis of elicited cell suspension cultures of Cannabis sativa L. by 1H-NMR spectroscopy. Biotechnol Lett. 2010;32(7):935–941. doi: 10.1007/s10529-010-0225-9
  23. Toyooka K, Sato M, Wakazaki M, Matsuoka K. Morphological and quantitative changes in mitochondria, plastids, and peroxisomes during the log-to-stationary transition of the growth phase in cultured tobacco BY-2 cells. Plant Signal Behav. 2016;11(3): e1149669. doi: 10.1080/15592324.2016.1149669
  24. Toyooka K, Sato M, Kutsuna N, et al. Wide-range high-resolution transmission electron microscopy reveals morphological and distributional changes of endomembrane compartments during log to stationary transition of growth phase in tobacco BY-2 cells. Plant Cell Physiol. 2014;55(9):1544–1555. doi: 10.1093/pcp/pcu084
  25. Voitsekhovskaja OV, Schiermeyer A, Reumann S. Plant peroxisomes are degraded by starvation-induced and constitutive autophagy in tobacco BY-2 suspension-cultured cells. Front Plant Sci. 2014;5:629. doi: 10.3389/fpls.2014.00629
  26. Zhao Z, Zhang J-W, Lu S-H, et al. Transcriptome divergence between developmental senescence and premature senescence in Nicotiana tabacum L. Sci Rep. 2020;10(1):20556. doi: 10.1038/s41598-020-77395-2
  27. Buchanan-Wollaston V, Page T, Harrison E, et al. Comparative transcriptome analysis reveals significant differences in gene expression and signalling pathways between developmental and dark/starvation-induced senescence in Arabidopsis. Plant J. 2005;42(4):567–585. doi: 10.1111/j.1365–313X.2005.02399.x
  28. Noor E, Eden E, Milo R, Alon U. Central carbon metabolism as a minimal biochemical walk between precursors for biomass and energy. Mol Cell. 2010;39(5):809–820. doi: 10.1016/j.molcel.2010.08.031
  29. Zakhartsev M, Medvedeva I, Orlov Y, et al. Metabolic model of central carbon and energy metabolisms of growing Arabidopsis thaliana in relation to sucrose translocation. BMC Plant Biol. 2016;16(1):262. doi: 10.1186/s12870-016-0868-3
  30. Fiehn O. Metabolomics by Gas chromatography–mass spectrometry: Combined targeted and untargeted profiling. Curr Prot Molecul Biol. 2016;114(1):30.4.1–30.4.32. doi: 10.1002/0471142727.mb3004s114
  31. Zažímalová E, Petrášek J, Morris DA. The dynamics of auxin transport in tobacco cells. Bulg J Plant Physiol. 2003;(S):207–224.
  32. Campanoni P, Blasius B, Nick P. Auxin transport synchronizes the pattern of cell division in a tobacco cell line. Plant Physiol. 2003;133(3):1251–1260. doi: 10.1104/pp.103.027953
  33. Zažimalová E, Opatrný Z, Březinová A, Eder J. The effect of auxin starvation on the growth of auxin-dependent tobacco cell culture: dynamics of auxin-binding activity and endogenous free IAA content. J Exp Bot. 1995;46(9):1205–1213. doi: 10.1093/jxb/46.9.1205
  34. 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
  35. 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. Berlin, Heidelberg: Springer, 2007. P. 75–95. doi: 10.1007/4735_2007_0229
  36. Hastie T, Tibshirani R, Narasimhan B, Chu G. impute: impute: Imputation for microarray data. Bioconductor. 2023; R package version 1.76.0. doi: 10.18129/B9.bioc.impute
  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. Thévenot 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
  39. Korotkevich G, Sukhov V, Budin N, et al. Fast gene set enrichment analysis. BioRxiv. 2021;60012. doi: 10.1101/060012
  40. Kanehisa M, Furumichi M, Sato Y, et al. KEGG for taxonomy-based analysis of pathways and genomes. Nucleic Acids Res. 2023;51(D1):D587–D592. doi: 10.1093/nar/gkac963
  41. Tenenbaum D, Maintainer B. KEGGREST: Client-side REST access to the Kyoto Encyclopedia of genes and genomes (KEGG). Bioconductor. 2022; R package version 1.36.2. doi: 10.18129/B9.bioc.KEGGREST
  42. 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
  43. Yemelyanov VV, Puzanskiy RK, Burlakovskiy MS, et al. Metabolic profiling of transgenic tobacco plants synthesizing bovine interferon-gamma. In: Zhan X, editor. Metabolomics — methodology and applications in medical sciences and life sciences. IntechOpen, 2021. doi: 10.5772/intechopen.96862
  44. Li D, Heiling S, Baldwin IT, Gaquerel E. Illuminating a plant’s tissue-specific metabolic diversity using computational metabolomics and information theory. PNAS USA. 2016;113(47): E7610–E7618. doi: 10.1073/pnas.1610218113
  45. Bartnik M, Facey PC. Glycosides. In: Badal S, Delgoda R, editors. Pharmacognosy. Elsevier, 2017. P. 101–161. doi: 10.1016/B978-0-12-802104-0.00008-1
  46. Tugizimana F, Steenkamp PA, Piater LA, Dubery IA. Multi-platform metabolomic analyses of ergosterol-induced dynamic changes in Nicotiana tabacum cells. PLoS ONE. 2014;9(1):e87846. doi: 10.1371/journal.pone.0087846
  47. Petrova NV, Sazanova KV, Medvedeva NA, Shavarda AL. Features of metabolomic profiles in different stages of ontogenesis in Prunella vulgaris (Lamiaceae) grown in a climate chamber. Russian Journal Bioorganic Chemistry. 2019;45(7):906–912. doi: 10.1134/S1068162019070100
  48. Yurkov AP, Puzanskiy RK, Avdeeva GS, et al. Mycorrhiza-induced alterations in metabolome of Medicago lupulina leaves during symbiosis development. Plants. 2021;10(11):2506. doi: 10.3390/plants10112506
  49. Liu Y, Li M, Xu J, et al. Physiological and metabolomics analyses of young and old leaves from wild and cultivated soybean seedlings under low-nitrogen conditions. BMC Plant Biol. 2019;19(1):389. doi: 10.1186/s12870-019-2005-6
  50. Shtark OY, Puzanskiy RK, Avdeeva GS, et al. Metabolic alterations in pea leaves during arbuscular mycorrhiza development. PeerJ. 2019;7:e7495. doi: 10.7717/peerj.7495
  51. Smith EN, Ratcliffe RG, Kruger NJ. Isotopically non-stationary metabolic flux analysis of heterotrophic Arabidopsis thaliana cell cultures. Front Plant Sci. 2023;13:1049559. doi: 10.3389/fpls.2022.1049559
  52. Shameer S, Vallarino JG, Fernie AR, et al. Flux balance analysis of metabolism during growth by osmotic cell expansion and its application to tomato fruits. Plant J. 2020;103(1):68–82. doi: 10.1111/tpj.14707
  53. Shameer S, Vallarino JG, Fernie AR, et al. Predicting metabolism during growth by osmotic cell expansion. BiolRxiv. 2019;731232. doi: 10.1101/731232
  54. Li W, Zhang H, Li X, et al. Intergrative metabolomic and transcriptomic analyses unveil nutrient remobilization events in leaf senescence of tobacco. Sci Rep. 2017;7(1):12126. doi: 10.1038/s41598-017-11615-0
  55. Tohge T, Ramos MS, Nunes-Nesi A, et al. Toward the storage metabolome: Profiling the barley vacuole. Plant Physiol. 2011;157(3):1469–1482. doi: 10.1104/pp.111.185710
  56. Ohnishi M, Anegawa A, Sugiyama Y, et al. Molecular components of Arabidopsis intact vacuoles clarified with metabolomic and proteomic analyses. Plant Cell Physiol. 2018;59(7):1353–1362. doi: 10.1093/pcp/pcy069
  57. Beauvoit BP, Colombié S, Monier A, et al. Model-assisted analysis of sugar metabolism throughout tomato fruit development reveals enzyme and carrier properties in relation to vacuole expansion. Plant Cell. 2014;26(8):3224–3242. doi: 10.1105/tpc.114.127761

Supplementary files

Supplementary Files
Action
1. JATS XML
Download
2. Fig. 1. Growth of heterotrophic suspension cell culture N. tabacum VBI-0: fresh weight density mg per ml

Download (73KB)
Indexing metadata
3. Fig. 2. Score plot from PCA of metabolite profiles extracted from heterotrophic suspension cell culture N. tabacum VBI-0 at growth and senescence. Eclipses — are the 95% confidence intervals, % — percent of variation

Download (97KB)
Indexing metadata
4. Fig. 3. Visualization of differentially accumulated metabolites (DAMs) in suspension cell culture N. tabacum VBI-0 at growth and senescence stages. DAMs were selected by rule: VIP > 1. Increase refers to higher level at senescence. FC — fold changes

Download (340KB)
Indexing metadata
5. Fig. 4. Metabolite sets enrichment analysis based on loadings from OPLS-DA classification. Nodes of graph are KEGG pathways for N. tabacum, edges, contracting nodes, are presence of common metabolites in profiles. Size — strength of influence (NES, normalized enrichment score), color — FDR (false discovery rate), up triangles refer to accumulation of pathway intermediates at senescence

Download (305KB)
Indexing metadata

Copyright (c) 2024 Eco-Vector

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


Согласие на обработку персональных данных с помощью сервиса «Яндекс.Метрика»

1. Я (далее – «Пользователь» или «Субъект персональных данных»), осуществляя использование сайта https://journals.rcsi.science/ (далее – «Сайт»), подтверждая свою полную дееспособность даю согласие на обработку персональных данных с использованием средств автоматизации Оператору - федеральному государственному бюджетному учреждению «Российский центр научной информации» (РЦНИ), далее – «Оператор», расположенному по адресу: 119991, г. Москва, Ленинский просп., д.32А, со следующими условиями.

2. Категории обрабатываемых данных: файлы «cookies» (куки-файлы). Файлы «cookie» – это небольшой текстовый файл, который веб-сервер может хранить в браузере Пользователя. Данные файлы веб-сервер загружает на устройство Пользователя при посещении им Сайта. При каждом следующем посещении Пользователем Сайта «cookie» файлы отправляются на Сайт Оператора. Данные файлы позволяют Сайту распознавать устройство Пользователя. Содержимое такого файла может как относиться, так и не относиться к персональным данным, в зависимости от того, содержит ли такой файл персональные данные или содержит обезличенные технические данные.

3. Цель обработки персональных данных: анализ пользовательской активности с помощью сервиса «Яндекс.Метрика».

4. Категории субъектов персональных данных: все Пользователи Сайта, которые дали согласие на обработку файлов «cookie».

5. Способы обработки: сбор, запись, систематизация, накопление, хранение, уточнение (обновление, изменение), извлечение, использование, передача (доступ, предоставление), блокирование, удаление, уничтожение персональных данных.

6. Срок обработки и хранения: до получения от Субъекта персональных данных требования о прекращении обработки/отзыва согласия.

7. Способ отзыва: заявление об отзыве в письменном виде путём его направления на адрес электронной почты Оператора: info@rcsi.science или путем письменного обращения по юридическому адресу: 119991, г. Москва, Ленинский просп., д.32А

8. Субъект персональных данных вправе запретить своему оборудованию прием этих данных или ограничить прием этих данных. При отказе от получения таких данных или при ограничении приема данных некоторые функции Сайта могут работать некорректно. Субъект персональных данных обязуется сам настроить свое оборудование таким способом, чтобы оно обеспечивало адекватный его желаниям режим работы и уровень защиты данных файлов «cookie», Оператор не предоставляет технологических и правовых консультаций на темы подобного характера.

9. Порядок уничтожения персональных данных при достижении цели их обработки или при наступлении иных законных оснований определяется Оператором в соответствии с законодательством Российской Федерации.

10. Я согласен/согласна квалифицировать в качестве своей простой электронной подписи под настоящим Согласием и под Политикой обработки персональных данных выполнение мною следующего действия на сайте: https://journals.rcsi.science/ нажатие мною на интерфейсе с текстом: «Сайт использует сервис «Яндекс.Метрика» (который использует файлы «cookie») на элемент с текстом «Принять и продолжить».