1. New algorithms for high-resolution metabolomics A case study on trypanosome parasites
Rainer Breitling – Groningen Bioinformatics Centre
University 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

2. 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
To test the technology, a simple experiment was done on the pathogen that causes sleeping sickness (historically the most important disease of tropical Africa, because it limits successful animal husbandry, and still one major cause of human morbidity and mortality). Drugs are very old, resistance is developing and the side effects are terribly bad. New drug targets are searched for. We wanted to find those metabolites that are taken up (scavenged) by the parasite from the medium. Also those that are produced specifically by the parasite. Both should lead to interesting drug targets, transporters in the first case, enzymes in the second.

3. FT-ICR mass spectrometry
High-resolution mass spectrometry. Detection/separation is done in an ion trap. Ions are cycling in a magnetic field and are excited by radio frequency pulses. The resonance frequency depends on the mass/charge ratio of an ion. The resulting detected signal is a convolution of the signals from all ions, it is deconcoluted by Fourier transformation and directly corresponds to a mass spectrum. The stronger the magnetic field, the better the resolution (similar to NMR) – almost unlimited.
 measurement of very small mass differences at very high accuracy in complex mixtures of biomolecules

4. The advantage of high resolution
The chemical composition of a metabolite can be estimated
Exact identification by mass may be possible (within limits)
CH6N2
Methylhydrazine
Mw =
C2H6O
Ethanol
Mw =
Only a limited number of molecular formulae can explain a given exact mass within acceptable limits of accuracy. The complexity of this task increases rapidly with the total mass (interesting computational challenge!!). And of course, it can’t discriminate between compounds with the same formula, but different connectivity.

5. High accuracy confirmed by standards
Compound
predicted mass
measured mass
ppm
average S/N
glutathione 1
438
oxidized glutathione
328
trypanothione 16
oxidized trypanothione
281
NADP 2
442
NAD
1229
ATP
289
ADP
118
AMP 14
berenil 9
pentamidine 67
DB75
115
melarsen oxide
113
spermine
[ ]
216 -
spermidine
[ ]
28466
putrescine
[100]
119000
ornithine
[ ]
31566
Strong signal to noise ratios (S/N) are detected for most compounds in the standard mixture – but some of them are not detectable at all (polyamines). Those that are found, are measured accurately up to the third decimal place.

6. Overview of experimental results
The global results are displayed in the form of a Venn diagram. Usually this is done for 3 sets at most, but there are solutions for 4 sets (like here) and even for 5. But they get increasingly difficult to interpret. The message is that (A) there is a large set of ubiquitous metabolites, (B) a smaller set of parasite-specific metabolites, and (C) some metabolites that are restricted to a single sample type. Also, the in vivo samples are consistently more complex.
 1251 mass peaks detected in total in the four sample types
Breitling et al., Metabolomics, 2006, 2:155

7. 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
The high accuracy limits the number of possible hits tremendously. Usually there is at most a single mass hit (the lists are still quite long, because there are usually many “isoforms”)
MetabolomeExplorer Classic (Breitling, unpubl.)

8. 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)

9. The phospholipid metabolome of trypanosomes
Even a small number of good hits allows further exploration.

10. Do mass differences contain additional information?
Cluster of common distances
Mass difference (all possible pairwise comparisons)
Breitling et al., Trends in Biotechnology, 2006, 24:543

11. Do mass differences contain additional information?
Real Masses (differences)
Frequency
Formula
exact mass
RANDOM masses
(differences)
382 H2 7
326
Na-H
284
13C isotope
260 C2 24 6
237
C2H2
218
C2H4
197 H4
164
H2-13C isotope
148
H2+13C isotope
140
C2-13C isotope
TOTAL
25370
115 (+/-22)
(in 2472 clusters of >5)
(in 19 +/- 4
clusters of >5)

12. 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)

13. Biochemically expected transformations
Frequency
Formula
exact mass
RANDOM
hydrogenation/
dehydrogenation
284 H2
Glycine 8
C2H2
211
cytosine (-H)
ethyl addition (-H2O)
191
C2H4
Threonine 7
hydroxylation (-H) 84 O
Serine
palmitoylation (-H2O) 57
C16H30O
isoprene addition (-H)
ketol group (-H2O)
C2H2O
condensation/dehydration
methanol (-H2O) 56
CH2
primary amine 6 40
H2O
Leucine
Formic Acid (-H2O) 28 CO
Carboxylation 25
CO2
carbamoyl P transfer
(-H2PO4)
TOTAL
1438
271 (+/- 25)
If masses are randomly distributed, their differences are not enriched in interesting transformations (right hand side), but in the real data, there are many of them, e.g. 284 pairs of metabolites differ by a mass of (+/- 1ppm), corresponding to a hydrogenation/dehydrogenation reaction

14. Visualization of “common” metabolic relationships
Based on the common “textbook transformations” one can find the metabolic neighbors of a certain mass…

15. Visualization of “common” metabolic relationships
“metabolic network” of masses that correlate with the amount of (C38:4) in trypanosome metabolism
…and this can be repeated iteratively, to build an entire network of interrelated metabolites. This corresponds to a biochemical pathway map, although not each step is necessarily catalyzed by an enzyme (some of the mass differences may refer to compounds with related formula, but without any metabolic relationship)

16. de novo network generation
In the end, a huge graph results from the de novo network building process – this is difficult to visualize, navigate and analyze – interesting challenges for bioinformatics

17. de novo network generation
In the end, a huge graph results from the de novo network building process – this is difficult to visualize, navigate and analyze – interesting challenges for bioinformatics
Does this network have a random structure, or are there certain patterns?

18. Degree distributions metabolites  exponential  random net
The distribution of “textbook transformations” in the trypanosome metabolome follows a power law (linear graph in a log-log plot) [right]. The distribution of “clusters of common distances” is closer to an exponential distribution [left]. The reason is simple: Many reactions involve small molecules, which are not detectable in the FTMS machine. These compounds would be hubs in the network. They are missing here, but are implicitly considered in the “textbook transformations”  lesson: Understand the limits of the data acquisition before trying an analysis
transformations  power-law  scale-free net
metabolites  exponential  random net
Power law:

19. 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

20. MetabolomeExplorer platform
Scheltema et al., submitted