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Effects of MCHM on yeast metabolism


Autoři: Amaury Pupo aff001;  Kang Mo Ku aff002;  Jennifer E. G. Gallagher aff001
Působiště autorů: Department of Biology, West Virginia University, Morgantown, West Virginia, United States of America aff001;  Division of Plant and Soil Sciences, West Virginia University, Morgantown, West Virginia, United States of America aff002;  Department of Horticulture, College of Agriculture and Life Sciences, Chonnam National University, Gwangju, Republic of Korea aff003
Vyšlo v časopise: PLoS ONE 14(10)
Kategorie: Research Article
doi: https://doi.org/10.1371/journal.pone.0223909

Souhrn

On January 2014 approximately 10,000 gallons of crude 4-Methylcyclohexanemethanol (MCHM) and propylene glycol phenol ether (PPH) were accidentally released into the Elk River, West Virginia, contaminating the tap water of around 300,000 residents. Crude MCHM is an industrial chemical used as flotation reagent to clean coal. At the time of the spill, MCHM's toxicological data were limited, an issue that has been addressed by different studies focused on understanding the immediate and long-term effects of MCHM on human health and the environment. Using S. cerevisiae as a model organism we study the effect of acute exposure to crude MCHM on metabolism. Yeasts were treated with MCHM 550 ppm in YPD for 30 minutes. Polar and lipid metabolites were extracted from cells by a chloroform-methanol-water mixture. The extracts were then analyzed by direct injection ESI-MS and by GC-MS. The metabolomics analysis was complemented with flux balance analysis simulations done with genome-scale metabolic network models (GSMNM) of MCHM treated vs non-treated control. We integrated the effect of MCHM on yeast gene expression from RNA-Seq data within these GSMNM. A total of 215 and 73 metabolites were identified by the ESI-MS and GC-MS procedures, respectively. From these 26 and 23 relevant metabolites were selected from ESI-MS and GC-MS respectively, for 49 unique compounds. MCHM induced amino acid accumulation, via its effects on amino acid metabolism, as well as a potential impairment of ribosome biogenesis. MCHM affects phospholipid biosynthesis, with a potential impact on the biophysical properties of yeast cellular membranes. The FBA simulations were able to reproduce the deleterious effect of MCHM on cellular growth and suggest that the effect of MCHM on ubiquinol:ferricytochrome c reductase reaction, caused by the under-expression of CYT1 gene, could be the driven force behind the observed effect on yeast metabolism and growth.

Klíčová slova:

Amino acid metabolism – Biosynthesis – Gas chromatography-mass spectrometry – Gene expression – Metabolites – Metabolomics – Saccharomyces cerevisiae – Nitrogen metabolism


Zdroje

1. Cooper WJ. Responding to Crisis: The West Virginia Chemical Spill. Environ Sci Technol. American Chemical Society; 2014;48: 3095–3095. doi: 10.1021/es500949g 24593286

2. Christie Richard D., Gross Anthony E., Fortin RJ. Process for coal flotation using 4-methyl cyclohexane methanol frothers [Internet]. 1989. Available: https://patents.google.com/patent/US4915825A/en

3. Thomasson ED, Scharman E, Fechter-Leggett E, Bixler D, Ibrahim S, Duncan MA, et al. Acute Health Effects After the Elk River Chemical Spill, West Virginia, January 2014. Public Health Rep. SAGE PublicationsSage CA: Los Angeles, CA; 2017;132: 196–202. doi: 10.1177/0033354917691257 28182515

4. Weidhaas JL, Dietrich AM, DeYonker NJ, Ryan Dupont R, Foreman WT, Gallagher D, et al. Enabling Science Support for Better Decision-Making when Responding to Chemical Spills. J Environ Qual. The American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America, Inc.; 2016;45: 1490. doi: 10.2134/jeq2016.03.0090 27695739

5. Eastman C. Eastman Crude MCHM Studies [Internet]. 2014. Available: http://www.eastman.com/Pages/Eastman-Crude-MCHM-Studies.aspx

6. Whelton AJ, McMillan LK, Connell M, Kelley KM, Gill JP, White KD, et al. Residential Tap Water Contamination Following the Freedom Industries Chemical Spill: Perceptions, Water Quality, and Health Impacts. Environ Sci Technol. ACS Publications; 2014;49: 813–823. doi: 10.1021/es5040969 25513829

7. Monnot AD, Novick RM, Paustenbach DJ. Crude 4-methylcyclohexanemethanol (MCHM) did not cause skin irritation in humans in 48-h patch test. Cutan Ocul Toxicol. 2017;36: 351–355. doi: 10.1080/15569527.2017.1296854 28277879

8. Han AA, Fabyanic EB, Miller J V., Prediger MS, Prince N, Mouch JA, et al. In vitro cytotoxicity assessment of a West Virginia chemical spill mixture involving 4-methylcyclohexanemethanol and propylene glycol phenyl ether. Environ Monit Assess. 2017;189: 190. doi: 10.1007/s10661-017-5895-5 28357716

9. Lan J, Hu M, Gao C, Alshawabkeh A, Gu AZ. Toxicity Assessment of 4-Methyl-1-cyclohexanemethanol and Its Metabolites in Response to a Recent Chemical Spill in West Virginia, USA. Environ Sci Technol. ACS Publications; 2015;

10. Horzmann KA, de Perre C, Lee LS, Whelton AJ, Freeman JL. Comparative analytical and toxicological assessment of methylcyclohexanemethanol (MCHM) mixtures associated with the Elk River chemical spill. Chemosphere. 2017;188: 599–607. doi: 10.1016/j.chemosphere.2017.09.026 28917212

11. Denoth Lippuner A, Julou T, Barral Y. Budding yeast as a model organism to study the effects of age. FEMS Microbiol Rev. Narnia; 2014;38: 300–325. doi: 10.1111/1574-6976.12060 24484434

12. Fruhmann G, Seynnaeve D, Zheng J, Ven K, Molenberghs S, Wilms T, et al. Yeast buddies helping to unravel the complexity of neurodegenerative disorders. Mech Ageing Dev. Elsevier; 2017;161: 288–305. doi: 10.1016/j.mad.2016.05.002 27181083

13. Braconi D, Bernardini G, Santucci A. Saccharomyces cerevisiae as a model in ecotoxicological studies: A post-genomics perspective. J Proteomics. Elsevier; 2016;137: 19–34. doi: 10.1016/J.JPROT.2015.09.001 26365628

14. Aliferis KA, Chrysayi-Tokousbalides M. Metabolomics in pesticide research and development: review and future perspectives. Metabolomics. Springer US; 2011;7: 35–53. doi: 10.1007/s11306-010-0231-x

15. Ibáñez C, Simó C, García-Cañas V, Cifuentes A, Castro-Puyana M. Metabolomics, peptidomics and proteomics applications of capillary electrophoresis-mass spectrometry in Foodomics: A review. Anal Chim Acta. Elsevier; 2013;802: 1–13. doi: 10.1016/j.aca.2013.07.042 24176500

16. Nicholson JK, Connelly J, Lindon JC, Holmes E. Metabonomics: a platform for studying drug toxicity and gene function. Nat Rev Drug Discov. Nature Publishing Group; 2002;1: 153–161. doi: 10.1038/nrd728 12120097

17. Allen J, Davey HM, Broadhurst D, Rowland JJ, Oliver SG, Kell DB. Discrimination of modes of action of antifungal substances by use of metabolic footprinting. Appl Environ Microbiol. American Society for Microbiology; 2004;70: 6157–65. doi: 10.1128/AEM.70.10.6157-6165.2004 15466562

18. Farrés M, Piña B, Tauler R. LC-MS based metabolomics and chemometrics study of the toxic effects of copper on Saccharomyces cerevisiae. Metallomics. The Royal Society of Chemistry; 2016;8: 790–798. doi: 10.1039/C6MT00021E 27302082

19. Wang X, Bai X, Chen D-F, Chen F-Z, Li B-Z, Yuan Y-J. Increasing proline and myo-inositol improves tolerance of Saccharomyces cerevisiae to the mixture of multiple lignocellulose-derived inhibitors. Biotechnol Biofuels. BioMed Central; 2015;8: 142. doi: 10.1186/s13068-015-0329-5 26379774

20. Ohta E, Nakayama Y, Mukai Y, Bamba T, Fukusaki E. Metabolomic approach for improving ethanol stress tolerance in Saccharomyces cerevisiae. J Biosci Bioeng. Elsevier; 2016;121: 399–405. doi: 10.1016/j.jbiosc.2015.08.006 26344121

21. Kim S, Kim J, Song JH, Jung YH, Choi I-S, Choi W, et al. Elucidation of ethanol tolerance mechanisms in Saccharomyces cerevisiae by global metabolite profiling. Biotechnol J. John Wiley & Sons, Ltd; 2016;11: 1221–1229. doi: 10.1002/biot.201500613 27313052

22. Fell DA, Small JR. Fat synthesis in adipose tissue. An examination of stoichiometric constraints. Biochem J. Portland Press Limited; 1986;238: 781–6. doi: 10.1042/bj2380781 3800960

23. Varma A, Palsson BO. Metabolic Capabilities of Escherichia coli: I. Synthesis of Biosynthetic Precursors and Cofactors. J Theor Biol. Academic Press; 1993;165: 477–502. doi: 10.1006/jtbi.1993.1202 21322280

24. Savinell JM, Palsson BO. Network analysis of intermediary metabolism using linear optimization: II. Interpretation of hybridoma cell metabolism. J Theor Biol. Academic Press; 1992;154: 455–473. doi: 10.1016/s0022-5193(05)80162-6 1593897

25. Varma A, Boesch BW, Palsson BO. Biochemical production capabilities ofescherichia coli. Biotechnol Bioeng. John Wiley & Sons, Ltd; 1993;42: 59–73. doi: 10.1002/bit.260420109 18609648

26. Yilmaz LS, Walhout AJ. Metabolic network modeling with model organisms. Curr Opin Chem Biol. Elsevier Current Trends; 2017;36: 32–39. doi: 10.1016/j.cbpa.2016.12.025 28088694

27. Heavner BD, Price ND. Comparative Analysis of Yeast Metabolic Network Models Highlights Progress, Opportunities for Metabolic Reconstruction. Ouzounis CA, editor. PLOS Comput Biol. Public Library of Science; 2015;11: e1004530. doi: 10.1371/journal.pcbi.1004530 26566239

28. Heavner BD, Smallbone K, Price ND, Walker LP. Version 6 of the consensus yeast metabolic network refines biochemical coverage and improves model performance. Database. Narnia; 2013;2013. doi: 10.1093/database/bat059 23935056

29. Colijn C, Brandes A, Zucker J, Lun DS, Weiner B, Farhat MR, et al. Interpreting Expression Data with Metabolic Flux Models: Predicting Mycobacterium tuberculosis Mycolic Acid Production. Papin JA, editor. PLoS Comput Biol. Public Library of Science; 2009;5: e1000489. doi: 10.1371/journal.pcbi.1000489 19714220

30. Guo W, Feng X. OM-FBA: Integrate Transcriptomics Data with Flux Balance Analysis to Decipher the Cell Metabolism. Singh PK, editor. PLoS One. Public Library of Science; 2016;11: e0154188. doi: 10.1371/journal.pone.0154188 27100883

31. Motamedian E, Mohammadi M, Shojaosadati SA, Heydari M. TRFBA: an algorithm to integrate genome-scale metabolic and transcriptional regulatory networks with incorporation of expression data. Bioinformatics. Narnia; 2017;33: btw772. doi: 10.1093/bioinformatics/btw772 28065897

32. Sánchez BJ, Zhang C, Nilsson A, Lahtvee P, Kerkhoven EJ, Nielsen J. Improving the phenotype predictions of a yeast genome‐scale metabolic model by incorporating enzymatic constraints. Mol Syst Biol. John Wiley & Sons, Ltd; 2017;13: 935. doi: 10.15252/msb.20167411 28779005

33. Bordbar A, Yurkovich JT, Paglia G, Rolfsson O, Sigurjónsson ÓE, Palsson BO. Elucidating dynamic metabolic physiology through network integration of quantitative time-course metabolomics. Sci Rep. Nature Publishing Group; 2017;7: 46249. doi: 10.1038/srep46249 28387366

34. Brachmann CB, Davies A, Cost GJ, Caputo E, Li J, Hieter P, et al. Designer deletion strains derived from Saccharomyces cerevisiae S288C: a useful set of strains and plasmids for PCR-mediated gene disruption and other applications. Yeast. Department of Molecular Biology and Genetics, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA.; 1998;14: 115–132. doi: 10.1002/(SICI)1097-0061(19980130)14:2<115::AID-YEA204>3.0.CO;2-2 9483801

35. Bourque SD, Titorenko VI. A Quantitative Assessment of The Yeast Lipidome using Electrospray Ionization Mass Spectrometry. J Vis Exp. 2009; e1513. doi: 10.3791/1513 19701157

36. Smith CA, Want EJ, O’Maille G, Abagyan R, Siuzdak G, Colin A. Smith, et al. XCMS:  Processing Mass Spectrometry Data for Metabolite Profiling Using Nonlinear Peak Alignment, Matching, and Identification.—Anal Chem.—American Chemical Society; 2006;78: 79. doi: 10.1021/ac051437y 16448051

37. Du P, Kibbe WA, Lin SM. Improved peak detection in mass spectrum by incorporating continuous wavelet transform-based pattern matching. Bioinformatics. Narnia; 2006;22: 2059–2065. doi: 10.1093/bioinformatics/btl355 16820428

38. Zhou B, Wang J, Ressom HW. MetaboSearch: Tool for Mass-Based Metabolite Identification Using Multiple Databases. Fernandez-Fuentes N, editor. PLoS One. Public Library of Science; 2012;7: e40096. doi: 10.1371/journal.pone.0040096 22768229

39. DATTA A, Kamthan A, Kamthan M, Chakraborty N, Chakraborty S, Datta A. A simple protocol for extraction, derivatization, and analysis of tomato leaf and fruit lipophilic metabolites using GC-MS. Protoc Exch. 2012; doi: 10.1038/protex.2012.061

40. Xue Z, Duan L-X, Qi X. Gas Chromatography Mass Spectrometry Coupling Techniques. Plant Metabolomics. Dordrecht: Springer Netherlands; 2015. pp. 25–44. doi: 10.1007/978-94-017-9291-2_2

41. Chong J, Soufan O, Li C, Caraus I, Li S, Bourque G, et al. MetaboAnalyst 4.0: towards more transparent and integrative metabolomics analysis. Nucleic Acids Res. Narnia; 2018;46: W486–W494. doi: 10.1093/nar/gky310 29762782

42. King ZA, Dräger A, Ebrahim A, Sonnenschein N, Lewis NE, Palsson BO. Escher: A Web Application for Building, Sharing, and Embedding Data-Rich Visualizations of Biological Pathways. Gardner PP, editor. PLOS Comput Biol. Public Library of Science; 2015;11: e1004321. doi: 10.1371/journal.pcbi.1004321 26313928

43. Pupo A, Ayers MC, Sherman ZN, Vance RJ, Cumming JR, Gallagher JEG. MCHM Acts as a Hydrotrope, Altering the Balance of Metals in Yeast. Biol Trace Elem Res. Springer US; 2019; 1–12. doi: 10.1007/s12011-019-01850-z 31392542

44. Rong-Mullins XX, Ayers MC, Summers M, Gallagher JEG. Transcriptional Profiling of Saccharomyces cerevisiae Reveals the Impact of Variation of a Single Transcription Factor on Differential Gene Expression in 4NQO, Fermentable, and Nonfermentable Carbon Sources. G3 Genes, Genomes, Genet. 2018;8. doi: 10.1534/g3.117.300138 29208650

45. Yu G, Wang L-G, Han Y, He Q-Y. clusterProfiler: an R Package for Comparing Biological Themes Among Gene Clusters. Omi A J Integr Biol. Mary Ann Liebert, Inc. 140 Huguenot Street, 3rd Floor New Rochelle, NY 10801 USA; 2012;16: 284–287. doi: 10.1089/omi.2011.0118 22455463

46. Sánchez B, Li F, Lu H, Kerkhoven E, Nielsen J. SysBioChalmers/yeast-GEM: yeast 8.3.4. 2019; doi: 10.5281/ZENODO.3353593

47. Ebrahim A, Lerman JA, Palsson BO, Hyduke DR. COBRApy: COnstraints-Based Reconstruction and Analysis for Python. BMC Syst Biol. BioMed Central; 2013;7: 74. doi: 10.1186/1752-0509-7-74 23927696

48. Babbio F, Farinacci M, Saracino F, Carbone MLA, Privitera E. Expression and Localization Studies of hSDA, the Human Ortholog of the Yeast SDA1 Gene. Cell Cycle. Taylor & Francis; 2004;3: 484–488. doi: 10.4161/cc.3.4.792

49. Horsey EW, Jakovljevic J, Miles TD, Harnpicharnchai P, Woolford JL. Role of the yeast Rrp1 protein in the dynamics of pre-ribosome maturation. RNA. Cold Spring Harbor Laboratory Press; 2004;10: 813–27. doi: 10.1261/rna.5255804 15100437

50. Peng W-T, Krogan NJ, Richards DP, Greenblatt JF, Hughes TR. ESF1 is required for 18S rRNA synthesis in Saccharomyces cerevisiae. Nucleic Acids Res. Narnia; 2004;32: 1993–1999. doi: 10.1093/nar/gkh518 15056729

51. Perez-Fernandez J, Roman A, Rivas JD Las, Bustelo XR, Dosil M, Pérez-Fernández J, et al. The 90S preribosome is a multimodular structure that is assembled through a hierarchical mechanism. Mol Cell Biol. 2007;27: 5414–5429. doi: 10.1128/MCB.00380-07 [pii] 17515605

52. Jin S-B, Zhao J, Bjork P, Schmekel K, Ljungdahl PO, Wieslander L. Mrd1p is required for processing of pre-rRNA and for maintenance of steady-state levels of 40 S ribosomal subunits in yeast. J Biol Chem. American Society for Biochemistry and Molecular Biology; 2002;277: 18431–9. doi: 10.1074/jbc.M112395200 11884397

53. Sun C, Woolford JL. The yeast NOP4 gene product is an essential nucleolar protein required for pre-rRNA processing and accumulation of 60S ribosomal subunits. EMBO J. John Wiley & Sons, Ltd; 1994;13: 3127–3135. doi: 10.1002/j.1460-2075.1994.tb06611.x 8039505

54. Miles TD, Jakovljevic J, Horsey EW, Harnpicharnchai P, Tang L, Woolford JL. Ytm1, Nop7, and Erb1 form a complex necessary for maturation of yeast 66S preribosomes. Mol Cell Biol. American Society for Microbiology Journals; 2005;25: 10419–32. doi: 10.1128/MCB.25.23.10419-10432.2005 16287855

55. Buscemi G, Saracino F, Masnada D, Carbone ML. The Saccharomyces cerevisiae SDA1 gene is required for actin cytoskeleton organization and cell cycle progression. J Cell Sci. 2000;113 (Pt 7): 1199–211. Available: http://www.ncbi.nlm.nih.gov/pubmed/10704371

56. Zimmerman ZA, Kellogg DR. The Sda1 Protein Is Required for Passage through Start. Hunt T, editor. Mol Biol Cell. 2001;12: 201–219. doi: 10.1091/mbc.12.1.201 11160833

57. Iraqui I, Vissers S, Cartiaux M, Urrestarazu A. Characterisation of Saccharomyces cerevisiae ARO8 and ARO9 genes encoding aromatic aminotransferases I and II reveals a new aminotransferase subfamily. Mol Gen Genet MGG. Springer-Verlag; 1998;257: 238–248. doi: 10.1007/s004380050644 9491083

58. Gallagher JEG. Proteins and RNA sequences required for the transition of the t-Utp complex into the SSU processome. FEMS Yeast Res. 2019;19. doi: 10.1093/femsyr/foy120 30445532

59. Panaretou B, Siligardi G, Meyer P, Maloney A, Sullivan JK, Singh S, et al. Activation of the ATPase activity of hsp90 by the stress-regulated cochaperone aha1. Mol Cell. Elsevier; 2002;10: 1307–18. doi: 10.1016/S1097-2765(02)00785-2

60. Garay-Arroyo A, Covarrubias AA. Three genes whose expression is induced by stress inSaccharomyces cerevisiae. Yeast. John Wiley & Sons, Ltd; 1999;15: 879–892. doi: 10.1002/(SICI)1097-0061(199907)15:10A<879::AID-YEA428>3.0.CO;2-Q 10407268

61. Delaveau T, Delahodde A, Carvajal E, Subik J, Jacq C. PDR3, a new yeast regulatory gene, is homologous toPDR1 and controls the multidrug resistance phenomenon. MGG Mol Gen Genet. Springer-Verlag; 1994;244: 501–511. doi: 10.1007/bf00583901 8078477

62. Rogers B, Decottignies A, Kolaczkowski M, Carvajal E, Balzi E, Goffeau A. The pleitropic drug ABC transporters from Saccharomyces cerevisiae. J Mol Microbiol Biotechnol. Phytera Inc, Worcester, MA, USA.; 2001;3: 207–214. Available: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=11321575 11321575

63. Tenreiro S, Vargas RC, Teixeira MC, Magnani C, Sá-Correia I. The yeast multidrug transporter Qdr3 (Ybr043c): localization and role as a determinant of resistance to quinidine, barban, cisplatin, and bleomycin. Biochem Biophys Res Commun. Academic Press; 2005;327: 952–959. doi: 10.1016/j.bbrc.2004.12.097 15649438

64. Philpott CC, Protchenko O, Kim YW, Boretsky Y, Shakoury-Elizeh M. The response to iron deprivation in Saccharomyces cerevisiae: expression of siderophore-based systems of iron uptake. Biochem Soc Trans. Portland Press Limited; 2002;30: 698–702. doi: 10.1042/bst0300698 12196168

65. Krüger A, Vowinckel J, Mülleder M, Grote P, Capuano F, Bluemlein K, et al. Tpo1-mediated spermine and spermidine export controls cell cycle delay and times antioxidant protein expression during the oxidative stress response. EMBO Rep. John Wiley & Sons, Ltd; 2013;14: 1113–1119. doi: 10.1038/embor.2013.165 24136413

66. Ghosh AK, Ramakrishnan G, Rajasekharan R. YLR099C (ICT1) Encodes a Soluble Acyl-CoA-dependent Lysophosphatidic Acid Acyltransferase Responsible for Enhanced Phospholipid Synthesis on Organic Solvent Stress in Saccharomyces cerevisiae. J Biol Chem. American Society for Biochemistry and Molecular Biology; 2008;283: 9768–9775. doi: 10.1074/jbc.M708418200 18252723

67. Alberti S, Gladfelter A, Mittag T. Considerations and Challenges in Studying Liquid-Liquid Phase Separation and Biomolecular Condensates. Cell. Cell Press; 2019;176: 419–434. doi: 10.1016/j.cell.2018.12.035 30682370

68. Oechsner U, Hermann H, Zollner A, Haid A, Bandlow W. Expression of yeast cytochrome C1 is controlled at the transcriptional level by glucose, oxygen and haem. MGG Mol Gen Genet. Springer-Verlag; 1992;232: 447–459. doi: 10.1007/bf00266250 1316998

69. Nilsson A, Nielsen J. Metabolic Trade-offs in Yeast are Caused by F1F0-ATP synthase. Sci Rep. Nature Publishing Group; 2016;6: 22264. doi: 10.1038/srep22264 26928598


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