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

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

A new journey has started… diagnosis prevention treatment 2004

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.” (www.metabolomics.bbsrc.ac.uk) 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, 1181-89.) 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, 714-717)

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

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

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. 2011 Parasites & Vectors 4:179

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. 2011 Parasites & Vectors 4:179

Biomarker Reproducibility

Biomarker Transferability Balog C et al. Mol. BioSyst. 2011 7:1473-80

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. 2008 J Proteome Res 7(9):3948

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. 2008 J Proteome Res 7(9):3948

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. 2008 J Proteome Res 7(9):3948

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

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 2008 105: 6131

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 2008 105: 6131

sampling time point (Day) Trypanosoma brucei brucei Co-strain infection Li et al. 2011 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

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

Infection Progression Phenylpyruvate PCA scores plot Pyruvate Li et al. 2011 Am J Trop Med Hyg 84(1):91

Biomarker Specificity

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

Challenges diagnosis prevention treatment

Challenges Study Design Kafsack et al. 2010 Cell Host & Microbe 7:90

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

Evaluation and Application Careful evaluation Easy to apply Low cost

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.

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