1. helping parasitic disease management?
Metabolomics-based targets:
helping parasitic disease management?
Jia Li
Lecturer in Human Development and Microbial Signalling
Department of Surgery and Cancer
Centre for Digestive and Gut Health

2. Paradigm shift from Control to Elimination
85% prevalence
0.5% prevalence
Mass Drug Administration
Mass Diagnostic
Multi-diagnostic approaches
Field applicable
Sensitivity and specificity > present gold standard
Allows staging
Immediate confirmation of successful treatment

3. A new journey has started…
diagnosis
prevention
treatment
2004

4. Metabolomics, Metabonomics
Metabolomics: ANALYTICAL definition
“the quantitative measurement of the low molecular weight metabolites in a given sample, cell or tissue & the integration of the data in the context of gene function analysis.” (
Metabonomics: BIOLOGICAL definition
“the quantitative measurement of the multi-parametric (time-related) metabolic responses of complex systems to a patho-physiological stimulus or genetic modification.” (Nicholson et al., 1999, Xenobiotica, 29, )
Co-metabolite and co-metabolome:
“Compound or set of compounds derived from interactions of more than one genome in symbiotic systems.” (Holmes et al., 2008, Cell, 134, )

5. MULTIVARIATE STATISTICAL ANALYSIS &
Metabolic Profiling Strategy
NMR
APPLICATIONS
OTHER DATASETS
SAMPLES
biofluids
tissues
cells & media
CHEMICAL ANALYSIS
LC/GC-MS
MULTIVARIATE STATISTICAL ANALYSIS &
DATA VISUALIZATION
BIOMARKER RECOVERY
worm burden
clinical data
prognosis
diagnosis
mechanisms
VALIDATION
UPLC-MS
GC-MS CE

6. Diagnostic Biomarkers
diagnosis
prevention
treatment
Specific to infection
Stability over time
Reproducibility
Stability across co- and multiple infections
Transferability across host species
Stability across infection intensity

7. Li et al. 2011 Parasites & Vectors 4:179
Schistosoma mansoni infection in mice
Study Design:
10 mice infected with 80 S. mansoni cercariae each
10 uninfected mice
Urine, plasma and faeces were collected weekly up to 73 days post infection
PCA-trajectory plot Urine
PCA-trajectory plot Plasma
Li et al Parasites & Vectors 4:179

8. Li et al. 2011 Parasites & Vectors 4:179
Biomarker Stability
2-oxoadipate
phenylacetylglycine
hippurate
glycerol
leucine/isoleucine
Glycerophosphoryl-choline
N-acetyl-glycoprotein
5-aminovalerate
Li et al Parasites & Vectors 4:179

9. Biomarker Reproducibility

10. Biomarker Transferability
Balog C et al. Mol. BioSyst :

11. Plasmodium berghei infection in mice
1H NMR plasma spectra
Pre-infection
Experimental set up
Infection
∼20 million P. berghei-parasitized erythrocytes
Biofluids:
Plasma & Urine
Sampling:
Days -1 and 1-4 days post infection
Method:
1H NMR spectroscopy
4 days post-infection
Li et al J Proteome Res 7(9):3948

12. Plasmodium berghei infection in mice
PCA scores plot
D-1 control D1 D2 D3 D4
Experimental set up
Infection
∼20 million P. berghei-parasitized erythrocytes
Biofluids:
Plasma & Urine
Sampling:
Days -1 and 1-4 days post infection
Method:
1H NMR spectroscopy
Li et al J Proteome Res 7(9):3948

13. Identification of Pipecolic acid in urine
1-D STOCSY
Pipecolic acid is reported in patients with chronic liver disease, Dyggve-Melchior-Clausen (DMC) syndrome, pyridoxine-dependent epilepsy and Zellweger syndrome.
Pipecolic acid is derived from diet (e.g. dairy products).
pipecolic acid
Catabolism of lysine by intestinal microbiota
spiked sample
Pipecolic acid can act as a neuromodulator in the central nervous system.
Li et al J Proteome Res 7(9):3948

14. Biomarker Transferability
Urinary pipecolic acid level is higher in P. vivax-infected patients compared with health people

15. Trypanosoma brucei brucei infection in mice
Experimental set up
Infection
~20,000 T.b.b i.p.
Biofluids:
Plasma & Urine
Sampling:
Days -2 and 1, 7, 14, 21, 28, 33 post infection
Method:
1H NMR spectroscopy
plasma
urine
Wang et al. PNAS : 6131

16. Early biomarkers in plasma
Metabolites
Day 1
R2=0.90
Qy2=0.61
Day 7
R2=0.94
Qy2=0.72
Day 14
R2=0.95
Qy2=0.75
Day 21
R2=0.96
Qy2=0.82
Day 28
Qy2=0.74
Day 33
Lactate (21)
+0.65
+0.76
+0.83
+0.74
+0.77
Glutamine (30)
-0.50
-0.80
-0.72
Oxaloacetate (29)
+0.69
+0.66
+0.51
Lysine (35)
+0.62
+0.79
+0.82
Glucose (34)
+0.56
-0.58
-0.59
Isoleucine (28)
-0.86
-0.82
-0.88
-0.83
-0.78
Valine (26)
-0.81
-0.76
Leucine (27)
-0.84
-0.64
Acetate (6)
+0.68
+0.54
+0.55
O-acetyl-glycoprotein (33)
+0.81
+0.75
+0.80
Choline (31)
+0.61
+0.89
+0.67
Phospotidyl-choline (32)
-0.70
-0.79
-0.85
Creatine (13)
+0.72
Lipoprotein (25)
-0.61
-0.75
Unknown
+0.50
+0.63
+0.87
Disease stages and treatment
Stratify patients
Wang et al. PNAS : 6131

17. sampling time point (Day)
Trypanosoma brucei brucei Co-strain infection
Li et al Am J Trop Med Hyg 84(1):91
5 mice
STIB777AE-G, 1x107
5 mice
STIB246BA-R, 1x107
5 mice
uninfected
R, 1x107
G, 1x105
5 mice
G, 1x107
R, 1x107
5 mice
sampling time point (Day)
urine/faeces
D-1
D0 infection D1 D3 D4

18. Trypanosoma brucei brucei Co-strain infection
Control Day 4
Co-infected
Day 4

19. Infection Progression
Phenylpyruvate
PCA scores plot
Pyruvate
Li et al Am J Trop Med Hyg 84(1):91

20. Biomarker Specificity

21. Biomarker patterns for each parasite-animal model
Specific biomarker patterns hold potential in parasitic disease diagnosis

22. Challenges
diagnosis
prevention
treatment

23. Challenges
Study Design
Kafsack et al Cell Host & Microbe 7:90

24. Challenges – Biomarker Identification
Database
2-D NMR experiments
no ID
Metabolic profiling of biofluids
Multivariate statistical analysis and modelling
Unknown biomarkers
Isolate compounds
Candidate
Mass spectrometry
Spike in standard compound
2-D NMR experiments

25. Evaluation and Application
Careful evaluation
Easy to apply
Low cost

26. Strategy
diagnosis
prevention
treatment
Standardization of the sample collection, preparation and analysis protocols.
Large-scale population-based studies will be needed to identify robust diagnostic markers and to further understand molecular mechanism of parasitic diseases.
Building metabolite database
Cross-disciplinary collaborations are needed to discover the new methods and technologies for disease diagnosis.

27. Acknowledgements Prof. Elaine Holmes Prof. Juerg Utzinger
Prof. Jennifer Keiser
Dr. Oliver Balmer
Prof. Elaine Holmes
Prof. Jeremy Nicholson
Dr. Jasmina Saric
University of Florida
Prof. Burton H. Singer
Physics and Mathematics Institute, China
Prof. Yulan Wang