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New algorithms for high-resolution metabolomics

A case study on trypanosome parasites

Rainer Breitling – Groningen Bioinformatics CentreUniversity of Groningen

Michael P. Barrett – Infection & Immunity Division,University of Glasgow

Breitling et al., Ab initio prediction of metabolomic networks using FT-ICR MS, Metabolomics, 2006, 2:155

Breitling et al., Precision mapping of the metabolome, Trends in Biotechnology, 2006, 24:543

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The biological context – trypanosomiasis

• sleeping sickness is a major health problem in tropical Africa

• current drugs are becoming ineffective and are a health risk themselves (they kill up to 10% of patients, rather than healing them!)

• metabolite profiling in drug-treated and mutant parasites may identify new drug targets

• first pilot study: compare metabolome of in vivo and in vitro parasites to the composition of media – identify metabolite scavenging

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FT-ICR mass spectrometry

measurement of very small mass differences at very high accuracy in complex mixtures of biomolecules

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The advantage of high resolution

• The chemical composition of a metabolite can be estimated

• Exact identification by mass may be possible (within limits)

C2H6O

Ethanol

Mw = 46.0684

CH6N2

Methylhydrazine

Mw = 46.0718

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High accuracy confirmed by standardsCompound predicted mass measured mass ppm average S/N

glutathione 307.083807 307.0835 1 438

oxidized glutathione 612.152 612.1516 1 328

trypanothione 723.3044 723.3036 1 16

oxidized trypanothione 721.2887 721.2889 0 281

NADP 743.075458 743.0766 2 442

NAD 663.109125 663.1096 1 1229

ATP 506.99575 506.9945 2 289

ADP 427.029418 427.0293 0 118

AMP 347.063086 347.0633 1 14

berenil 281.138894 281.139 0 9

pentamidine 340.1899 340.1897 1 67

DB75 304.132411 304.1325 0 115

melarsen oxide 292.00538 292.0053 0 113

spermine 202.215747 [202.1721] 216 -

spermidine 145.157898 [141.1402] 28466 -

putrescine 88.100048 [100] 119000 -

ornithine 132.089878 [128.0479] 31566 -

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Overview of experimental results

1251 mass peaks detected in total in the four sample types

Breitling et al., Metabolomics, 2006, 2:155

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Can we use accuracy to get identities?• Searches against the PubChem database to identify putative molecular identities

• Few useful hits, indicating that many metabolites are novel

• But some hits reveal interesting clues – many are fatty acid related, and this can be used to guide further more targeted exploration

MetabolomeExplorer Classic (Breitling, unpubl.)

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Phospholipids of regular structure

Possible variations:

• Length of sidechain, in steps of 2C units (+C2H4)

• Degree of unsaturation (-H2)

• Type of headgroup (choline, ethanolamine, glycine…)

• connection via ester or ether bond (acyl or alkyl lipids)

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The phospholipid metabolome of trypanosomes

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Do mass differences contain additional information?

Mass difference (all possible pairwise comparisons)

Cluster of common distances

Breitling et al., Trends in Biotechnology, 2006, 24:543

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Do mass differences contain additional information?Real

Masses (differences) Frequency Formula exact mass

RANDOM masses

(differences) Frequency

2.015950785 382 H2 2.015650074 92.7097502 7

21.98312914 326 Na-H 21.98194466 205.304917 7

1.003209507 284 13C isotope 1.00335484 52.82462466 7

24.00000115 260 C2 24 193.6001474 6

26.01629789 237 C2H2 26.01565007 243.2921378 6

28.03188991 218 C2H4 28.03130015 254.7535545 6

4.032019289 197 H4 4.031300148 6.467240667 6

1.012596951 164 H2-13C isotope 1.012295234 52.69339973 6

3.019108784 148 H2+13C isotope 3.019004914 21.98649217 6

22.99695714 140 C2-13C isotope 22.99664516 22.12482588 6

TOTAL 25370 TOTAL 115 (+/-22)(in 2472

clusters of >5)(in 19 +/- 4

clusters of >5)

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Biochemically expected transformations

Not all kinds of mass differences are equally interesting

But some are particularly important, because they are expected:

(de)hydrogenation (de)amination (de)phosphorylation

…and many more (about 100 are really common)

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Biochemically expected transformations

Transformation Frequency Formulaexact mass RANDOM Frequency

hydrogenation/dehydrogenation 284 H2 2.015650074 Glycine 8

C2H2 211 C2H2 26.01565007 cytosine (-H) 8

ethyl addition (-H2O) 191 C2H4 28.03130015 Threonine 7

hydroxylation (-H) 84 O 15.99491464 Serine 7

palmitoylation (-H2O) 57 C16H30O 238.2296658 isoprene addition (-H) 7

ketol group (-H2O) 57 C2H2O 42.01056471 condensation/dehydration 7

methanol (-H2O) 56 CH2 14.01565007 primary amine 6

condensation/dehydration 40 H2O 18.01056471 Leucine 6

Formic Acid (-H2O) 28 CO 27.99491464 ketol group (-H2O) 6

Carboxylation 25 CO2 43.98982928carbamoyl P transfer (-H2PO4) 6

TOTAL 1438 TOTAL 271 (+/- 25)

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Visualization of “common” metabolic relationships

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Visualization of “common” metabolic relationships

“metabolic network” of masses that correlate with the amount of 809.5939 (C38:4) in trypanosome metabolism

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de novo network generation

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de novo network generation

Does this network have a random structure, or are there certain patterns?

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Degree distributions

transformations power-law scale-free net

metabolites exponential random net

Power law:

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Conclusions

• FT-ICR MS provides highly accurate measurements of metabolites in complex mixtures

• accuracy is sufficient to identify metabolites based on mass information

• mass differences are particularly informative• de novo metabolic network construction and

exploration are a distinct possibility• new analysis tools are necessary to make full

use of the available information

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MetabolomeExplorer platform

Scheltema et al., submitted