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Cross-platform metabolic profiling: application to the aquatic model organism Lymnaea stagnalis

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Abstract

The freshwater pond snail Lymnaea stagnalis is used in several studies on molecular and behavioral neurobiology and ecotoxicology showing its successful application as a model organism. In the present study, a cross-platform metabolomic approach has been evaluated to characterize the organ molecular phenotypes of L. stagnalis central nervous system (CNS), digestive gland (DG), and albumen gland (AG). Two types of tissue disruption methods were evaluated of which beads beating was the preferred method. To obtain a broad picture of the hydrophilic and lipophilic metabolome, two complementary analytical platforms have been employed: liquid chromatography (LC) and gas chromatography (GC) coupled to high-resolution mass spectrometry. Furthermore, to increase the power to separate small polar metabolites, hydrophilic interaction liquid chromatography (HILIC) was applied. The analytical platform performances have been evaluated based on the metabolome coverage, number of molecular features, reproducibility, and multivariate data analysis (MVDA) clustering. This multiplatform approach is a starting point for future global metabolic profiling applications on L. stagnalis.

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References

  1. Kell DB (2004) Metabolomics and systems biology: making sense of the soup. Curr Opin Microbiol 7:296–307. doi:10.1016/j.mib.2004.04.012

    Article  CAS  Google Scholar 

  2. Goodacre R, Vaidyanathan S, Dunn WB et al (2004) Metabolomics by numbers: acquiring and understanding global metabolite data. Trends Biotechnol 22:245–252. doi:10.1016/j.tibtech.2004.03.007

    Article  CAS  Google Scholar 

  3. Lindon JC, Holmes E, Bollard ME et al (2004) Metabonomics technologies and their applications in physiological monitoring, drug safety assessment and disease diagnosis. Biomark Biochem Indic Expo Response Susceptibility Chem 9:1–31. doi:10.1080/13547500410001668379

    CAS  Google Scholar 

  4. Sumner LW, Mendes P, Dixon RA (2003) Plant metabolomics: large-scale phytochemistry in the functional genomics era. Phytochemistry 62:817–836. doi:10.1016/S0031-9422(02)00708-2

    Article  CAS  Google Scholar 

  5. Rezzi S, Ramadan Z, Fay LB, Kochhar S (2007) Nutritional metabonomics: applications and perspectives. J Proteome Res 6:513–525. doi:10.1021/pr060522z

    Article  CAS  Google Scholar 

  6. Antignac J-P, Courant F, Pinel G et al (2011) Mass spectrometry-based metabolomics applied to the chemical safety of food. TrAC Trends Anal Chem 30:292–301. doi:10.1016/j.trac.2010.11.003

    Article  CAS  Google Scholar 

  7. Bundy JG, Davey MP, Viant MR (2009) Environmental metabolomics: a critical review and future perspectives. Metabolomics 5:3–21. doi:10.1007/s11306-008-0152-0

    Article  CAS  Google Scholar 

  8. Rittschof D, McClellan-Green P (2005) Molluscs as multidisciplinary models in environment toxicology. Mar Pollut Bull 50:369–373. doi:10.1016/j.marpolbul.2005.02.008

    Article  CAS  Google Scholar 

  9. Das S, Khangarot BS (2011) Bioaccumulation of copper and toxic effects on feeding, growth, fecundity and development of pond snail Lymnaea luteola L. J Hazard Mater 185:295–305. doi:10.1016/j.jhazmat.2010.09.033

    Article  CAS  Google Scholar 

  10. Desouky MMA (2006) Tissue distribution and subcellular localization of trace metals in the pond snail Lymnaea stagnalis with special reference to the role of lysosomal granules in metal sequestration. Aquat Toxicol 77:143–152. doi:10.1016/j.aquatox.2005.11.009

    Article  CAS  Google Scholar 

  11. Czech P, Weber K, Dietrich D (2001) Effects of endocrine modulating substances on reproduction in the hermaphroditic snail Lymnaea stagnalis L. Aquat Toxicol 53:103–114. doi:10.1016/S0166-445X(00)00169-7

    Article  CAS  Google Scholar 

  12. Clarac F, Pearlstein E (2007) Invertebrate preparations and their contribution to neurobiology in the second half of the 20th century. Brain Res Rev 54:113–161. doi:10.1016/j.brainresrev.2006.12.007

    Article  CAS  Google Scholar 

  13. Patel BA, Arundell M, Parker KH et al (2005) Simple and rapid determination of serotonin and catecholamines in biological tissue using high-performance liquid chromatography with electrochemical detection. J Chromatogr B 818:269–276. doi:10.1016/j.jchromb.2005.01.008

    Article  CAS  Google Scholar 

  14. Bouétard A, Noirot C, Besnard A-L et al (2012) Pyrosequencing-based transcriptomic resources in the pond snail Lymnaea stagnalis, with a focus on genes involved in molecular response to diquat-induced stress. Ecotoxicology 21:2222–2234. doi:10.1007/s10646-012-0977-1

    Article  Google Scholar 

  15. Sadamoto H, Takahashi H, Okada T et al (2012) De novo sequencing and transcriptome analysis of the central nervous system of mollusc Lymnaea stagnalis by deep RNA sequencing. PLoS ONE 7:e42546. doi:10.1371/journal.pone.0042546

    Article  CAS  Google Scholar 

  16. Feng Z-P, Zhang Z, van Kesteren R et al (2009) Transcriptome analysis of the central nervous system of the mollusc Lymnaea stagnalis. BMC Genomics 10:451. doi:10.1186/1471-2164-10-451

    Article  Google Scholar 

  17. Silverman-Gavrila LB, Lu TZ, Prashad RC et al (2009) Neural phosphoproteomics of a chronic hypoxia model—Lymnaea stagnalis. Neuroscience 161:621–634. doi:10.1016/j.neuroscience.2009.03.043

    Article  CAS  Google Scholar 

  18. Silverman-Gavrila LB, Senzel AG, Charlton MP, Feng Z-P (2011) Expression, phosphorylation, and glycosylation of CNS proteins in aversive operant conditioning associated memory in Lymnaea stagnalis. Neuroscience 186:94–109. doi:10.1016/j.neuroscience.2011.04.027

    Article  CAS  Google Scholar 

  19. Fiehn O (2002) Metabolomics—the link between genotypes and phenotypes. Plant Mol Biol 48:155–171. doi:10.1023/A:1013713905833

    Article  CAS  Google Scholar 

  20. Villas-Bôas SG, Rasmussen S, Lane GA (2005) Metabolomics or metabolite profiles? Trends Biotechnol 23:385–386. doi:10.1016/j.tibtech.2005.05.009

    Article  Google Scholar 

  21. Creek DJ, Jankevics A, Breitling R et al (2011) Toward global metabolomics analysis with hydrophilic interaction liquid chromatography–mass spectrometry: improved metabolite identification by retention time prediction. Anal Chem 83:8703–8710. doi:10.1021/ac2021823

    Article  CAS  Google Scholar 

  22. Phua LC, Koh PK, Cheah PY et al (2013) Global gas chromatography/time-of-flight mass spectrometry (GC/TOFMS)-based metabonomic profiling of lyophilized human feces. J Chromatogr B 937:103–113. doi:10.1016/j.jchromb.2013.08.025

    Article  CAS  Google Scholar 

  23. Loftus NJ, Lai L, Wilkinson RW et al (2013) Global metabolite profiling of human colorectal cancer xenografts in mice using HPLC–MS/MS. J Proteome Res 12:2980–2986. doi:10.1021/pr400260h

    Article  CAS  Google Scholar 

  24. Lisacek F (2007) Metabolome analysis: an introduction. By Silas G. Villas-Bôas, Ute Roessner, Michael A. E. Hansen, Jørn Smedsgaard, Jens Nielsen. Proteomics 7:3634–3634. doi:10.1002/pmic.200790078

  25. Calingacion MN, Boualaphanh C, Daygon VD et al (2012) A genomics and multi-platform metabolomics approach to identify new traits of rice quality in traditional and improved varieties. Metabolomics 8:771–783. doi:10.1007/s11306-011-0374-4

    Article  CAS  Google Scholar 

  26. Ruiz-Aracama A, Peijnenburg A, Kleinjans J et al (2011) An untargeted multi-technique metabolomics approach to studying intracellular metabolites of HepG2 cells exposed to 2,3,7,8-tetrachlorodibenzo-p-dioxin. BMC Genomics 12:251. doi:10.1186/1471-2164-12-251

    Article  CAS  Google Scholar 

  27. Zonneveld C, Kooijman SALM (1989) Application of a dynamic energy budget model to Lymnaea stagnalis (L.). Funct Ecol 3:269–278. doi:10.2307/2389365

    Article  Google Scholar 

  28. Aleksic M, Feng Z-P (2012) Identification of the role of C/EBP in neurite regeneration following microarray analysis of a L. stagnalis CNS injury model. BMC Neurosci 13:2. doi:10.1186/1471-2202-13-2

    Article  CAS  Google Scholar 

  29. Bradford MM (1976) A rapid and sensitive method for the quantitation of microgram quantities of protein utilizing the principle of protein-dye binding. Anal Biochem 72:248–254. doi:10.1016/0003-2697(76)90527-3

    Article  CAS  Google Scholar 

  30. Ward JH (1963) Hierarchical grouping to optimize an objective function. J Am Stat Assoc 58:236–244. doi:10.1080/01621459.1963.10500845

    Article  Google Scholar 

  31. Eriksson L, Trygg J, Wold S (2008) CV-ANOVA for significance testing of PLS and OPLS® models. J Chemom 22:594–600. doi:10.1002/cem.1187

    Article  CAS  Google Scholar 

  32. Pingret D, Fabiano-Tixier A-S, Chemat F (2013) Degradation during application of ultrasound in food processing: a review. Food Control 31:593–606. doi:10.1016/j.foodcont.2012.11.039

    Article  Google Scholar 

  33. Eh AL-S, Teoh S-G (2012) Novel modified ultrasonication technique for the extraction of lycopene from tomatoes. Ultrason Sonochem 19:151–159. doi:10.1016/j.ultsonch.2011.05.019

    Article  CAS  Google Scholar 

  34. Sun Y, Ma G, Ye X et al (2010) Stability of all-trans-β-carotene under ultrasound treatment in a model system: effects of different factors, kinetics and newly formed compounds. Ultrason Sonochem 17:654–661. doi:10.1016/j.ultsonch.2009.12.005

    Article  CAS  Google Scholar 

  35. Geier FM, Want EJ, Leroi AM, Bundy JG (2011) Cross-platform comparison of Caenorhabditis elegans tissue extraction strategies for comprehensive metabolome coverage. Anal Chem 83:3730–3736. doi:10.1021/ac2001109

    Article  CAS  Google Scholar 

  36. Saric J, Want EJ, Duthaler U et al (2012) Systematic evaluation of extraction methods for multiplatform-based metabotyping: application to the Fasciola hepatica metabolome. Anal Chem 84:6963–6972. doi:10.1021/ac300586m

    Article  CAS  Google Scholar 

  37. Ikegami T, Horie K, Saad N et al (2008) Highly efficient analysis of underivatized carbohydrates using monolithic-silica-based capillary hydrophilic interaction (HILIC) HPLC. Anal Bioanal Chem 391:2533–2542. doi:10.1007/s00216-008-2060-6

    Article  CAS  Google Scholar 

  38. Guo Y, Gaiki S (2005) Retention behavior of small polar compounds on polar stationary phases in hydrophilic interaction chromatography. J Chromatogr A 1074:71–80. doi:10.1016/j.chroma.2005.03.058

    Article  CAS  Google Scholar 

  39. Cubbon S, Antonio C, Wilson J, Thomas-Oates J (2010) Metabolomic applications of HILIC–LC–MS. Mass Spectrom Rev 29:671–684. doi:10.1002/mas.20252

    Article  CAS  Google Scholar 

  40. Buszewski B, Noga S (2012) Hydrophilic interaction liquid chromatography (HILIC)—a powerful separation technique. Anal Bioanal Chem 402:231–247. doi:10.1007/s00216-011-5308-5

    Article  CAS  Google Scholar 

  41. Dejaegher B, Vander Heyden Y (2010) HILIC methods in pharmaceutical analysis. J Sep Sci 33:698–715. doi:10.1002/jssc.200900742

    Article  CAS  Google Scholar 

  42. Dallet P, Labat L, Kummer E, Dubost JP (2000) Determination of urea, allantoin and lysine pyroglutamate in cosmetic samples by hydrophilic interaction chromatography. J Chromatogr B Biomed Sci App 742:447–452. doi:10.1016/S0378-4347(00)00196-1

    Article  CAS  Google Scholar 

  43. Yang Y, Boysen RI, Hearn MTW (2009) Hydrophilic interaction chromatography coupled to electrospray mass spectrometry for the separation of peptides and protein digests. J Chromatogr A 1216:5518–5524. doi:10.1016/j.chroma.2009.05.085

    Article  CAS  Google Scholar 

  44. Matthews SB, Santra M, Mensack MM et al (2012) Metabolite profiling of a diverse collection of wheat lines using ultraperformance liquid chromatography coupled with time-of-flight mass spectrometry. PLoS ONE 7:e44179. doi:10.1371/journal.pone.0044179

    Article  CAS  Google Scholar 

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Acknowledgments

This study was carried out within the Marie Curie Research Training Network EDA-EMERGE (www.eda-emerge.eu) supported by the EU (MRTN-CT-2012-290100).

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Authors and Affiliations

  1. Institute for Environmental Studies (IVM), VU University Amsterdam, De Boelelaan 1087, 1081 HV, Amsterdam, The Netherlands

    Sara Tufi, Marja H. Lamoree, Jacob De Boer & Pim E. G. Leonards

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  1. Sara Tufi
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Correspondence to Sara Tufi.

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Tufi, S., Lamoree, M.H., De Boer, J. et al. Cross-platform metabolic profiling: application to the aquatic model organism Lymnaea stagnalis . Anal Bioanal Chem 407, 1901–1912 (2015). https://doi.org/10.1007/s00216-014-8431-2

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