Metabolomics

, Volume 1, Issue 2, pp 109–121 | Cite as

Correlative GC-TOF-MS-based metabolite profiling and LC-MS-based protein profiling reveal time-related systemic regulation of metabolite–protein networks and improve pattern recognition for multiple biomarker selection

  • Katja Morgenthal
  • Stefanie Wienkoop
  • Matthias Scholz
  • Joachim Selbig
  • Wolfram Weckwerth
Article

A novel approach is presented combining quantitative metabolite and protein data and multivariate statistics for the analysis of time-related regulatory effects of plant metabolism at a systems level. For the analysis of metabolites, gas chromatography coupled to a time-of-flight mass analyzer (GC-TOF-MS) was used. Proteins were identified and quantified using a novel procedure based on shotgun sequencing as described recently (Weckwerth et al., 2004b, Proteomics4, 78–83). For comparison, leaves of Arabidopsis thaliana wild type plants and starchless mutant plants deficient in phosphoglucomutase activity (PGM) were sampled at intervals throughout the day/night cycle. Using principal and independent components analysis, each dataset (metabolites and proteins) displayed discrete characteristics. Compared to the analysis of only metabolites or only proteins, independent components analysis (ICA) of the integrated metabolite/protein dataset resulted in an improved ability to distinguish between WT and PGM plants (first independent component) and, in parallel, to see diurnal variations in both plants (second independent component). Interestingly, levels of photorespiratory intermediates such as glycerate and glycine best characterized phases of diurnal rhythm, and were not influenced by high sugar accumulation in PGM plants. In contrast to WT plants, PGM plants showed an inversely regulated cluster of N-rich amino acid metabolites and carbohydrates, indicating a shift in C/N partitioning. This observation corresponds to altered utilization of urea cycle intermediates in PGM plants suggesting enhanced protein degradation and carbon utilization due to growth inhibition. Among the proteins chloroplastidic GAPDH (At3g26650) was the best discriminator between WT and PGM plants in contrast to the cytosolic isoform (At1g13440) according to the primary effect of mutation located in the chloroplast. The described method is applicable to all kinds of biological systems and enables the unbiased identification of biomarkers embedded in correlative metabolite–protein networks.

Key words:

metabolomics proteomics shotgun proteomics multivariate data analysis PCA ICA unsupervised methods systems biology diurnal rhythm 

Abbreviations

AA

ascorbic acid

Ala

alanine

Ara/Xyl

arabinose/xylose

Asn

asparagine

Asp

aspartic acid

BA

benzoic acid

b-Ala

beta-Alanine

CHO(1–12)

carbohydrate(1–12)

CitA

citric acid

Citn

citrulline

CMA

citramalic acid

Cys

cysteine

EA

ethanolamine

F6P

fructose 6-phosphate

Fru

fructose

Fuc

fucose

FumA

fumaric acid

G1P

glucose 1-phosphate

G6P

glucose 6-phosphate

GA

galactonic acid

GABA

4-aminobutyric acid

GalOH

galactinol

Glc

glucose

Gln

glutamine

Glu

glutamic acid

Gly

glycine

Glyc

glycerol

GlycA

glyceric acid

HA

hydroxylamine

HyPro

4-hydroxyproline

IAN

indole-3-acetonitrile

Ile

isoleucine

iso-SinA

iso-sinapinic acid

Leu

leucine

Lys

lysine

Mal

maltose

MalA

malic acid

Man

mannose

Met

methionine

myo-IN

myo-inositol

Orn/Arg

ornithine/arginine

P

phosphoric acid

PA

propylamine-2,3-diol

pGlu

pyroglutamic acid

Phe

phenylalanine

Pro

proline

Psi

psicose

Put

putrescine

PyrA

pyruvic acid

Raf

raffinose

Rib

ribose

RibA

ribonic acid

SalA

salicylic acid

Ser

serine

SinA

sinapinic acid

Spd

spermidine

Suc

sucrose

SucA

succinic acid

TAmam

tartronic acid 2-(methylaminomethyl)

Thr

threonine

ThrA

threonic acid

ThrAL

threonic acid-1,4-lactone

Tre

trehalose

Tyr

tyrosine

UA

uric acid

Ura

uracil

Urea

urea

Val

valine

ADC

arginine decarboxylase

AIH

agmatine iminohydrolase

ARG

arginase

ASL

argininosuccinate lyase

ASS

argininosuccinate synthase

CPA

N-carbamoylputrescine amidohydrolase

CPS

carbamoylsynthetase

dSAM

decarboxylated S-adenosylmethionine

MTA

5′-methylthioadenosine

OCT

ornithine carbamoyltransferase

SST

spermidine synthase; PCA: principal components analysis; ICA: independent components analysis.

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References

  1. Blaschke, T., Wiskott, L. 2004CuBICA: independent component analysis by simultaneous third- and fourth-order cumulant diagonalizationIEEE Trans. Signal Process.5212501256CrossRefMathSciNetGoogle Scholar
  2. Boyes, D.C., Zayed, A.M., Ascenzi, R., McCaskill, A.J., Hoffman, N.E., Davis, K.R., Gorlach, J. 2001Growth stage-based phenotypic analysis of arabidopsis: a model for high throughput functional genomics in plantsPlant Cell1314991510CrossRefPubMedGoogle Scholar
  3. Camacho, D., Fuente, A.D.L. and Mendes, P. (2005). The origin of correlations in metabolomics data. Metabolomics (in press). Google Scholar
  4. Cao, Y., Williams, D.D., Williams, N.E. 1999Data transformation and standardization in the multivariate analysis of river water qualityEcol. Appl.9669677Google Scholar
  5. Caspar, T., Huber, S.C., Somerville, C. 1985Alterations in growth, photosynthesis, and respiration in a starchless mutant of Arabidopsis thaliana (L) deficient in chloroplast phosphoglucomutase activityPlant Physiol.791117Google Scholar
  6. Castrillo, J.O., Oliver, S.G. 2004Yeast as a touchstone in post-genomic research: strategies for integrative analysis in functional genomicsJ. Biochem. Mol. Biol.3793106PubMedGoogle Scholar
  7. Cichocki, A. and Amari, S. (2002). Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications. WileyGoogle Scholar
  8. Collister, J.W., Rieley, G., Stern, B., Eglinton, G., Fry, B. 1994Compound-specific delta-C-13 analyses of leaf lipids from plants with differing carbon-dioxide metabolismsOrganic Geochem.21619627CrossRefGoogle Scholar
  9. Cooper, M., Chapman, S., Podlich, D., Hammer, G. 2002The GP problem: quantifying gene-to-phenotype relationshipsIn Silico Biol.2151164PubMedGoogle Scholar
  10. Diamantaras, K., Kung, S. 1996Principal Component Neural NetworksWileyNew YorkGoogle Scholar
  11. Fiehn, O. 2002Metabolomics – the link between genotypes and phenotypesPlant Mol. Biol.48155171CrossRefPubMedGoogle Scholar
  12. Fiehn, O., Kopka, J., Dormann, P., Altmann, T., Trethewey, R.N., Willmitzer, L. 2000Metabolite profiling for plant functional genomicsNat. Biotechnol.1811571161CrossRefPubMedGoogle Scholar
  13. Geiger, D.R., Servaites, J.C. 1994Diurnal regulation of photosynthetic carbon metabolism in C-3 plantsAnn. Rev. Plant Physiol. – Plant Mol. Biol.45235256CrossRefGoogle Scholar
  14. Gerhardt, R., Stitt, M., Heldt, H.W. 1987Subcellular metabolite levels in spinach leaves – regulation of sucrose synthesis during diurnal alterations in photosynthetic partitioningPlant Physiol.83399407Google Scholar
  15. Gibon, Y., Blaesing, O., Hannemann, J., Carillo, P., Hohne, M., Hendriks, J., Palacios, N., Cross, J., Selbig, J., Stitt, M. 2004aA Robot-based platform to measure multiple enzyme activities in Arabidopsis using a set of cycling assays: comparison of changes of enzyme activities and transcript levels during diurnal cycles and in prolonged darknessPlant Cell1633043325CrossRefGoogle Scholar
  16. Gibon, Y., Blasing, O.E., Palacios-Rojas, N., Pankovic, D., Hendriks, J.H.M., Fisahn, J., Hohne, M., Gunther, M., Stitt, M. 2004bAdjustment of diurnal starch turnover to short days: depletion of sugar during the night leads to a temporary inhibition of carbohydrate utilization, accumulation of sugars and post-translational activation of ADP-glucose pyrophosphorylase in the following light periodPlant J.39847862CrossRefGoogle Scholar
  17. Glinski, M., Romeis, T., Witte, C., Wienkoop, S., Weckwerth, W. 2003Stable isotope labeling of phosphopeptides for multiparallel kinase target analysis and identification of phosphorylation sitesRapid Commun. Mass Spectrom.1715791584CrossRefPubMedGoogle Scholar
  18. Goodacre, R. 2003Explanatory analysis of spectroscopic data using machine learning of simple, interpretable rulesVibrat. Spectroscopy323345CrossRefGoogle Scholar
  19. Goodacre, R., Vaidyanathan, S., Dunn, W.B., Harrigan, G.G., Kell, D.B. 2004Metabolomics by numbers: acquiring and understanding global metabolite dataTrends Biotechnol.22245252CrossRefPubMedGoogle Scholar
  20. Halket, J.M., Przyborowska, A., Stein, S.E., Mallard, W.G., Down, S., Chalmers, R.A. 1999Deconvolution gas chromatography mass spectrometry of urinary organic acids – potential for pattern recognition and automated identification of metabolic disordersRapid Commun. Mass Spectrom.13279284CrossRefPubMedGoogle Scholar
  21. Hyvärinen, A., Karhunen, J. and Oja, E. (2001). Independent Component Analysis. J. WileyGoogle Scholar
  22. Ihmels, J., Levy, R., Barkai, N. 2004Principles of transcriptional control in the metabolic network of Saccharomyces cerevisiaeNat. Biotechnol.228692CrossRefPubMedGoogle Scholar
  23. Kell, D.B. 2002Metabolomics and machine learning: explanatory analysis of complex metabolome data using genetic programming to produce simple, robust rulesMol. Biol. Rep.29237241CrossRefPubMedGoogle Scholar
  24. Kell, D., Mendes, P. 2000

    Snapshots of systems: metabolic control analysis and biotechnology in the postgenomic era

    Cornish-Bowden, A.J.Cardenas, M.L. eds. Technological and Medical Implications of Metabolic Control AnalysisKluwer Academic PublishersNetherland225
    Google Scholar
  25. Kovats, E. 1958Gas-Chromatographische Charakterisierung Organischer Verbindungen. 1. Retentionsindices Aliphatischer Halogenide, Alkohole, Aldehyde Und KetoneHelv. Chim. Acta4119151932CrossRefGoogle Scholar
  26. Leonard, C., Sacks, R. 1999Tunable-column selectivity and time-of-flight detection for high-speed GC/MSAnal. Chem.7151775184CrossRefGoogle Scholar
  27. Lippincott, J., Apostol, I. 1999Carbamylation of cysteine: a potential artifact in peptide mapping of hemoglobins in the presence of ureaAnal. Biochem.2675764CrossRefPubMedGoogle Scholar
  28. Nicholson, J.K., Connelly, J., Lindon, J.C., Holmes, E. 2002Metabonomics: a platform for studying drug toxicity and gene functionNat. Rev. Drug Discovery1153161CrossRefGoogle Scholar
  29. Nicholson, J.K., Lindon, J.C., Holmes, E. 1999‘Metabonomics’: understanding the metabolic responses of living systems to pathophysiological stimuli via multivariate statistical analysis of biological NMR spectroscopic dataXenobiotica2911811189CrossRefPubMedGoogle Scholar
  30. Ott, K.H., Aranibar, N., Singh, B.J., Stockton, G.W. 2003Metabonomics classifies pathways affected by bioactive compounds. Artificial neural network classification of NMR spectra of plant extractsPhytochemistry62971985CrossRefPubMedGoogle Scholar
  31. Peng, J., Elias, J.E., Thoreen, C.C., Licklider, L.J., Gygi, S.P. 2003Evaluation of multidimensional chromatography coupled with tandem mass spectrometry (LC/LC-MS/MS) for large-scale protein analysis: the yeast proteomeJ. Proteome Res.24350CrossRefPubMedGoogle Scholar
  32. Roessner, U., Wagner, C., Kopka, J., Trethewey, R.N., Willmitzer, L. 2000Simultaneous analysis of metabolites in potato tuber by gas chromatography–mass spectrometryPlant J.23131142CrossRefPubMedGoogle Scholar
  33. Roessner-Tunali, U., Urbanczyk-Wochniak, E., Czechowski, T., Kolbe, A., Willmitzer, L., Fernie, A.R. 2003De novo amino acid biosynthesis in potato tubers is regulated by sucrose levelsPlant Physiol.133683692CrossRefPubMedGoogle Scholar
  34. Roweis, S.T. and Saul, L.K. (2000). Nonlinear dimensionality reduction by locally linear embedding. Science 290, 2323–2326Google Scholar
  35. Sauter, H., Lauer, M., Fritsch, H. 1991Metabolic profiling of plants – a new diagnostic-techniqueAcs Symp. Series443288299Google Scholar
  36. Scholz, M., Gatzek, S., Sterling, A., Fiehn, O., Selbig, J. 2004Metabolite fingerprinting: detecting biological features by independent component analysisBioinformatics2024472454CrossRefPubMedGoogle Scholar
  37. Scholz, M. and Vigário, R. (2002). Nonlinear PCA: a new hierarchical approach. Proc. ESANN. 439–444Google Scholar
  38. Steuer, R., Kurths, J., Fiehn, O., Weckwerth, W. 2003Observing and interpreting correlations in metabolomic networksBioinformatics1910191026CrossRefPubMedGoogle Scholar
  39. Sumner, L.W., Mendes, P., Dixon, R.A. 2003Plant metabolomics: large-scale phytochemistry in the functional genomics eraPhytochemistry62817836CrossRefPubMedGoogle Scholar
  40. Tabb, D.L., McDonald, W.H., Yates, J.R. 2002DTASelect and contrast: tools for assembling and comparing protein identifications from shotgun proteomicsJ. Proteome Res.12126CrossRefPubMedGoogle Scholar
  41. ter Kuile, B.H., Westerhoff, H.V. 2001Transcriptome meets metabolome: hierarchical and metabolic regulation of the glycolytic pathwayFEBS Lett.500169171CrossRefPubMedGoogle Scholar
  42. Thimm, O., Blasing, O., Gibon, Y., Nagel, A., Meyer, S., Kruger, P., Selbig, J., Muller, L.A., Rhee, S.Y., Stitt, M. 2004MAPMAN: a user-driven tool to display genomics data sets onto diagrams of metabolic pathways and other biological processesPlant J.37914939CrossRefPubMedGoogle Scholar
  43. Trethewey, R.N., Krotzky, A.J., Willmitzer, L. 1999Metabolic profiling: a Rosetta Stone for genomics?Curr. Opin. Plant Biol.28385CrossRefPubMedGoogle Scholar
  44. Urbanczyk-Wochniak, E., Luedemann, A., Kopka, J., Selbig, J., Roessner-Tunali, U., Willmitzer, L., Fernie, A.R. 2003Parallel analysis of transcript and metabolic profiles: a new approach in systems biologyEMBO Rep.4989993CrossRefPubMedGoogle Scholar
  45. Veriotti, T., Sacks, R. 2001High-speed GC and GC/time-of-flight MS of lemon and lime oil samplesAnal. Chem.7343954402CrossRefPubMedGoogle Scholar
  46. Viant, M.R. 2003Improved methods for the acquisition and interpretation of NMR metabolomic dataBiochem. Biophys. Res. Commun.310943948CrossRefPubMedGoogle Scholar
  47. Wagner, A. 1997Causality in complex systemsBiol. Philos.1483101CrossRefGoogle Scholar
  48. Watson, J.T., Schultz, G.A., Tecklenburg, R.E., Allison, J. 1990Renaissance of Gas-chromatography time-of-flight mass-spectrometry – meeting the challenge of capillary columns with a beam deflection instrument and time array detectionJ. Chromatogr.518283295CrossRefPubMedGoogle Scholar
  49. Webb, J.W., Gates, S.C., Comiskey, J.P. and Weber, D.F. (1986). Metabolic profiling of corn plants using HPLC and GC/MS. Abstracts of Papers of the American Chemical Society 191, 70-ANYLGoogle Scholar
  50. Weckwerth, W. 2003Metabolomics in systems biologyAnn. Rev. Plant Biol.54669689CrossRefGoogle Scholar
  51. Weckwerth, W., Fiehn, O. 2002Can we discover novel pathways using metabolomic analysis?Curr. Opin. Biotechnol.13156160CrossRefPubMedGoogle Scholar
  52. Weckwerth, W., Loureiro, M.E., Wenzel, K., Fiehn, O. 2004aDifferential metabolic networks unravel the effects of silent plant phenotypesProc. Natl. Acad. Sci. USA10178097814CrossRefGoogle Scholar
  53. Weckwerth, W., Tolstikov, V. and Fiehn, O. (2001). Metabolomic characterization of transgenic potato plants using GC/TOF and LC-MS analysis reveals silent metabolic phenotypes. Proceedings of the 49th ASMS Conference on Mass spectrometry and Allied Topics, American Society of Mass Spectrometry, Chicago, pp. 1–2Google Scholar
  54. Weckwerth, W., Wenzel, K., Fiehn, O. 2004bProcess for the integrated extraction identification, and quantification of metabolites, proteins and RNA to reveal their co-regulation in biochemical networksProteomics47883CrossRefGoogle Scholar
  55. Wienkoop, S., Glinski, M., Tanaka, N., Tolstikov, V., Fiehn, O., Weckwerth, W. 2004aLinking protein fractionation with multidimensional monolithic RP peptide chromatography/mass spectrometry enhances protein identification from complex mixtures even in the presence of abundant proteinsRapid Commun. Mass Spectrom.18643650CrossRefGoogle Scholar
  56. Wienkoop, S., Zoeller, D., Ebert, B., Simon-Rosin, U., Fisahn, J., Glinski, M., Weckwerth, W. 2004bCell-specific protein profiling in Arabidopsis thaliana trichomes: identification of trichome-located proteins involved in sulfur metabolism and detoxificationPhytochemistry6516411649CrossRefGoogle Scholar
  57. Winter, H., Huber, S.C. 2000Regulation of sucrose metabolism in higher plants: localization and regulation of activity of key enzymesCrit. Rev. Biochem. Mol. Biol.35253289PubMedGoogle Scholar

Copyright information

© Springer Science+Business Media, Inc. 2005

Authors and Affiliations

  • Katja Morgenthal
    • 1
  • Stefanie Wienkoop
    • 1
  • Matthias Scholz
    • 1
  • Joachim Selbig
    • 1
  • Wolfram Weckwerth
    • 1
  1. 1.Max Planck Institute of Molecular Plant PhysiologyPotsdamGermany

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