Journal of Microbiology and Biotechnology

The Korean Society for Microbiology and Biotechnology (한국미생물·생명공학회)
권호 표지
DOI QR코드

DOI QR Code

Metabolomic Response of Chlamydomonas reinhardtii to the Inhibition of Target of Rapamycin (TOR) by Rapamycin

  • Lee, Do Yup (Department of Advanced Fermentation Fusion Science and Technology, Kookmin University) ;
  • Fiehn, Oliver (Genome Center, University of California)
  • Received : 2013.04.23
  • Accepted : 2013.05.02
  • Published : 2013.07.28
Download PDF
⟨ Previous Next ⟩

Abstract

Rapamycin, known as an inhibitor of Target of Rapamycin (TOR), is an immunosuppressant drug used to prevent rejection in organ transplantation. Despite the close association of the TOR signaling cascade with various scopes of metabolism, it has not yet been thoroughly investigated at the metabolome level. In our current study, we applied mass spectrometric analysis for profiling primary metabolism in order to capture the responsive dynamics of the Chlamydomonas metabolome to the inhibition of TOR by rapamycin. Accordingly, we identified the impact of the rapamycin treatment at the level of metabolomic phenotypes that were clearly distinguished by multivariate statistical analysis. Pathway analysis pinpointed that inactivation of the TCA cycle was accompanied by the inhibition of cellular growth. Relative to the constant suppression of the TCA cycle, most amino acids were significantly increased in a time-dependent manner by longer exposure to rapamycin treatment, after an initial down-regulation at the early stage of exposure. Finally, we explored the isolation of the responsive metabolic factors into the rapamycin treatment and the culture duration, respectively.

Keywords

References

  1. Barbet N, Schneider U, Helliwell S, Stansfield I, Tuite M, Hall M. 1996. TOR controls translation initiation and early G1 progression in yeast. Molec. Biol. Cell 7: 25. https://doi.org/10.1091/mbc.7.1.25
  2. Benkeblia N, Shinano T, Osaki M. 2007. Metabolite profiling and assessment of metabolome compartmentation of soybean leaves using non-aqueous fractionation and GC-MS analysis. Metabolomics 3: 297-305. https://doi.org/10.1007/s11306-007-0078-y
  3. Brown EJ, Albers MW, Shin TB, Ichikawa K, Keith CT, Lane WS, et al. 1994. A mammalian protein targeted by G1-arresting rapamycin-receptor complex. Nature 369: 756-758. https://doi.org/10.1038/369756a0
  4. Caldana C, Li Y, Leisse A, Zhang Y, Bartholomaeus L, Fernie AR, et al. 2013. Systemic analysis of inducible target of rapamycin mutants reveal a general metabolic switch controlling growth in Arabidopsis thaliana. Plant J. 73: 897-909. https://doi.org/10.1111/tpj.12080
  5. Crespo JL, Diaz-Troya S, Florencio FJ. 2005. Inhibition of target of rapamycin signaling by rapamycin in the unicellular green alga Chlamydomonas reinhardtii. Plant Physiol. 139: 1736-1749. https://doi.org/10.1104/pp.105.070847
  6. Diaz-Troya S, Florencio FJ, Crespo JL. 2008. Target of rapamycin and LST8 proteins associate with membranes from the endoplasmic reticulum in the unicellular green alga Chlamydomonas reinhardtii. Eukaryotic Cell 7: 212-222. https://doi.org/10.1128/EC.00361-07
  7. Eisen MB, Spellman PT, Brown PO, Botstein D. 1998. Cluster analysis and display of genome-wide expression patterns. Proc. Natl. Acad. Sci. USA 95: 14863-14868. https://doi.org/10.1073/pnas.95.25.14863
  8. Fiehn O, Kopka J, Dormann P, Altmann T, Trethewey RN, Willmitzer L. 2000. Metabolite profiling for plant functional genomics. Nature Biotechnol. 18: 1157-1161. https://doi.org/10.1038/81137
  9. Goodacre R, Vaidyanathan S, Dunn WB, Harrigan GG, Kell DB. 2004. Metabolomics by numbers: Acquiring and understanding global metabolite data. Trends Biotechnol. 22: 245-252. https://doi.org/10.1016/j.tibtech.2004.03.007
  10. Guba M, von Breitenbuch P, Steinbauer M, Koehl G, Flegel S, Hornung M, et al. 2002. Rapamycin inhibits primary and metastatic tumor growth by antiangiogenesis: Involvement of vascular endothelial growth factor. Nature Med. 8: 128-135. https://doi.org/10.1038/nm0202-128
  11. Harris EH, Stern DB, Witman G. 1989. The Chlamydomonas Sourcebook. Cambridge University Press, UK.
  12. He Z, Li L, Luan S. 2004. Immunophilins and parvulins. Superfamily of peptidyl prolyl isomerases in Arabidopsis. Plant Physiol. 134: 1248-1267. https://doi.org/10.1104/pp.103.031005
  13. Heitman J, Movva NR, Hall MN. 1991. Targets for cell cycle arrest by the immunosuppressant rapamycin in yeast. Science 253: 905-909. https://doi.org/10.1126/science.1715094
  14. Hutschenreuther A, Kiontke A, Birkenmeier G, Birkemeyer C. 2012. Comparison of extraction conditions and normalization approaches for cellular metabolomics of adherent growing cells with GC-MS. Anal. Methods 4: 1953-1963. https://doi.org/10.1039/c2ay25046b
  15. Kind T, Wohlgemuth G, Lee DY, Lu Y, Palazoglu M, Shahbaz S, et al. 2009. FiehnLib: Mass spectral and retention index libraries for metabolomics based on quadrupole and time-of-flight gas chromatography/mass spectrometry. Anal. Chem. 81: 10038-10048. https://doi.org/10.1021/ac9019522
  16. Kofman AE, McGraw MR, Payne CJ. 2012. Rapamycin increases oxidative stress response gene expression in adult stem cells. Aging (Albany NY) 4: 279.
  17. Lee D, Park J, Barupal D, Fiehn O. 2012. System response of metabolic networks in Chlamydomonas reinhardtii to total available ammonium. Mol. Cell. Proteomics 11: 973-988. https://doi.org/10.1074/mcp.M111.016733
  18. Lee DY, Fiehn O. 2008. High quality metabolomic data for Chlamydomonas reinhardtii. Plant Methods 4: 7. https://doi.org/10.1186/1746-4811-4-7
  19. Luo F, Khan L, Bastani F, Yen I-L, Zhou J. 2004. A dynamically growing self-organizing tree (DGSOT) for hierarchical clustering gene expression profiles. Bioinformatics 20: 2605-2617. https://doi.org/10.1093/bioinformatics/bth292
  20. Mahfouz MM, Kim S, Delauney AJ, Verma DPS. 2006. Arabidopsis target of rapamycin interacts with raptor, which regulates the activity of S6 kinase in response to osmotic stress signals. Plant Cell 18: 477-490. https://doi.org/10.1105/tpc.105.035931
  21. Marobbio CM, Pisano I, Porcelli V, Lasorsa FM, Palmieri L. 2012. Rapamycin reduces oxidative stress in frataxin-deficient yeast cells. Mitochondrion 12: 156-161. https://doi.org/10.1016/j.mito.2011.07.001
  22. Menand B, Desnos T, Nussaume L, Berger F, Bouchez D, Meyer C, et al. 2002. Expression and disruption of the Arabidopsis TOR (target of rapamycin) gene. Proc. Natl. Acad. Sci. USA 99: 6422-6427. https://doi.org/10.1073/pnas.092141899
  23. Messac A, Ismail-Yahaya A, Mattson CA. 2003. The normalized normal constraint method for generating the Pareto frontier. Struct. Multidiscip. Opt. 25: 86-98. https://doi.org/10.1007/s00158-002-0276-1
  24. Perez-Perez ME, Florencio FJ, Crespo JL. 2010. Inhibition of target of rapamycin signaling and stress activate autophagy in Chlamydomonas reinhardtii. Plant Physiol. 152: 1874-1888. https://doi.org/10.1104/pp.109.152520
  25. Qualley AV, Widhalm JR, Adebesin F, Kish CM, Dudareva N. 2012. Completion of the core ${\beta}$-oxidative pathway of benzoic acid biosynthesis in plants. Proc. Natl. Acad. Sci. USA 109: 16383-16388. https://doi.org/10.1073/pnas.1211001109
  26. Saunders RN, Metcalfe MS, Nicholson ML. 2001. Rapamycin in transplantation: A review of the evidence. Kidney Int. 59: 3-16. https://doi.org/10.1046/j.1523-1755.2001.00460.x
  27. Saxena D, Kannan K, Brandriss MC. 2003. Rapamycin treatment results in GATA factor-independent hyperphosphorylation of the proline utilization pathway activator in Saccharomyces cerevisiae. Eukaryotic Cell 2: 552-559. https://doi.org/10.1128/EC.2.3.552-559.2003
  28. Sengupta A, Ghosh M. 2012. Comparison of native and capric acid-enriched mustard oil effects on oxidative stress and antioxidant protection in rats. Br. J. Nutr. 107: 845. https://doi.org/10.1017/S0007114511003874
  29. Sturn A, Quackenbush J, Trajanoski Z. 2002. Genesis: Cluster analysis of microarray data. Bioinformatics 18: 207-208. https://doi.org/10.1093/bioinformatics/18.1.207
  30. Toronen P, Kolehmainen M, Wong G, Castren E. 1999. Analysis of gene expression data using self-organizing maps. FEBS Lett. 451: 142-146. https://doi.org/10.1016/S0014-5793(99)00524-4
  31. Tataranni T, Biondi G, Cariello M, Mangino M, Colucci G, Rutigliano M, et al. 2011. Rapamycin-induced hypophosphatemia and insulin resistance are associated with mTORC2 activation and klotho expression. Am. J. Transplant. 11: 1656-1664. https://doi.org/10.1111/j.1600-6143.2011.03590.x
  32. Vezina C, Kudelski A, Sehgal S. 1975. Rapamycin (AY-22,989), a new antifungal antibiotic. I. Taxonomy of the producing streptomycete and isolation of the active principle. J. Antibiotics 28: 721. https://doi.org/10.7164/antibiotics.28.721
  33. Wullschleger S, Loewith R, Hall MN. 2006. TOR signaling in growth and metabolism. Cell 124: 471-484. https://doi.org/10.1016/j.cell.2006.01.016
  34. Xia J, Mandal R, Sinelnikov IV, Broadhurst D, Wishart DS. 2012. MetaboAnalyst 2.0 - a comprehensive server for metabolomic data analysis. Nucleic Acids Res. 40: W127-W133. https://doi.org/10.1093/nar/gks374

Cited by

  1. Rationales and Approaches for Studying Metabolism in Eukaryotic Microalgae vol.4, pp.2, 2013, https://doi.org/10.3390/metabo4020184
  2. Similar local, but different systemic, metabolomic responses of closely related pine subspecies to folivory by caterpillars of the processionary moth vol.18, pp.3, 2013, https://doi.org/10.1111/plb.12422
  3. Are the metabolomic responses to folivory of closely related plant species linked to macroevolutionary and plant–folivore coevolutionary processes? vol.6, pp.13, 2013, https://doi.org/10.1002/ece3.2206
  4. Topsoil depth substantially influences the responses to drought of the foliar metabolomes of Mediterranean forests vol.21, pp.None, 2013, https://doi.org/10.1016/j.ppees.2016.06.001
  5. Elucidation of ethanol tolerance mechanisms in Saccharomyces cerevisiae by global metabolite profiling vol.11, pp.9, 2013, https://doi.org/10.1002/biot.201500613
  6. Close and distant: Contrasting the metabolism of two closely related subspecies of Scots pine under the effects of folivory and summer drought vol.7, pp.21, 2013, https://doi.org/10.1002/ece3.3343
  7. Highly Time-Resolved Metabolic Reprogramming toward Differential Levels of Phosphate in Chlamydomonas reinhardtii vol.27, pp.6, 2013, https://doi.org/10.4014/jmb.1701.01060
  8. The TOR Signaling Network in the Model Unicellular Green Alga Chlamydomonas reinhardtii vol.7, pp.3, 2017, https://doi.org/10.3390/biom7030054
  9. Physiological and Metabolomic Analysis ofIssatchenkia orientalisMTY1 With Multiple Tolerance for Cellulosic Bioethanol Production vol.12, pp.11, 2013, https://doi.org/10.1002/biot.201700110
  10. Quantitative in vivo phosphoproteomics reveals reversible signaling processes during nitrogen starvation and recovery in the biofuel model organism Chlamydomonas reinhardtii vol.10, pp.None, 2013, https://doi.org/10.1186/s13068-017-0949-z
  11. Quantitative Phosphoproteomic and System-Level Analysis of TOR Inhibition Unravel Distinct Organellar Acclimation in Chlamydomonas reinhardtii vol.9, pp.None, 2013, https://doi.org/10.3389/fpls.2018.01590
  12. The target of rapamycin kinase affects biomass accumulation and cell cycle progression by altering carbon/nitrogen balance in synchronized Chlamydomonas reinhardtii cells vol.93, pp.2, 2013, https://doi.org/10.1111/tpj.13787
  13. Coping with iron limitation: a metabolomic study of Synechocystis sp. PCC 6803 vol.40, pp.2, 2013, https://doi.org/10.1007/s11738-018-2603-1
  14. The potential effect of low cell osmolarity on cell function through decreased concentration of enzyme substrates vol.69, pp.20, 2013, https://doi.org/10.1093/jxb/ery254
  15. Atmo-ecometabolomics: a novel atmospheric particle chemical characterization methodology for ecological research vol.191, pp.2, 2013, https://doi.org/10.1007/s10661-019-7205-x
  16. Evolution of TOR-SnRK dynamics in green plants and its integration with phytohormone signaling networks vol.70, pp.8, 2013, https://doi.org/10.1093/jxb/erz107
  17. Inhibition of TOR in Chlamydomonas reinhardtii Leads to Rapid Cysteine Oxidation Reflecting Sustained Physiological Changes vol.8, pp.10, 2019, https://doi.org/10.3390/cells8101171
  18. TOR inhibition interrupts the metabolic homeostasis by shifting the carbon-nitrogen balance in Chlamydomonas reinhardtii vol.14, pp.11, 2013, https://doi.org/10.1080/15592324.2019.1670595
  19. Targeting TOR signaling for enhanced lipid productivity in algae vol.169, pp.None, 2013, https://doi.org/10.1016/j.biochi.2019.06.016
  20. Microalgal Target of Rapamycin (TOR): A Central Regulatory Hub for Growth, Stress Response and Biomass Production vol.61, pp.4, 2013, https://doi.org/10.1093/pcp/pcaa023
  21. Alteration in the Expression of Genes Encoding Primary Metabolism Enzymes and Plastid Transporters during the Culture Growth of Chlamydomonas reinhardtii vol.54, pp.4, 2013, https://doi.org/10.1134/s0026893320040147
  22. The Plant Target of Rapamycin: A Conduc TOR of Nutrition and Metabolism in Photosynthetic Organisms vol.11, pp.11, 2013, https://doi.org/10.3390/genes11111285
  23. Development and characterization of a Nannochloropsis mutant with simultaneously enhanced growth and lipid production vol.13, pp.None, 2013, https://doi.org/10.1186/s13068-020-01681-4
  24. Shedding Light on the Dynamic Role of the “Target of Rapamycin” Kinase in the Fast-Growing C4 Species Setaria viridis, a Suitable Model for Biomass Crops vol.12, pp.None, 2013, https://doi.org/10.3389/fpls.2021.637508