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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

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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, Proteomics 4, 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.

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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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  1. Max Planck Institute of Molecular Plant Physiology, 14424, Potsdam, Germany

    Katja Morgenthal, Stefanie Wienkoop, Matthias Scholz, Joachim Selbig & Wolfram Weckwerth

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  1. Katja Morgenthal
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  2. Stefanie Wienkoop
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  3. Matthias Scholz
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Morgenthal, K., Wienkoop, S., Scholz, M. et al. 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. Metabolomics 1, 109–121 (2005). https://doi.org/10.1007/s11306-005-4430-9

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