Metabonomics analysis of urine samples of asthmatic children

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Metabonomics analysis of urine samples of asthmatic children Elia Mattarucchi 1, Fabiano Reniero 1, Giuseppe Giordano 1,2 Silvia Carraro 2,Eugenio Baraldi,2 & Claude Guillou 1 Joint

1 Metabonomics analysis of urine samples of asthmatic children Elia Mattarucchi 1, Fabiano Reniero 1, Giuseppe Giordano 1,2 Silvia Carraro 2,Eugenio Baraldi,2 & Claude Guillou 1 Joint Research Centre of the European Commission (JRC), Institute for Health and Consumer Protection, Unit of Systems Toxicology, Ispra, Italy 2 Department of Pediatrics, University of Padova, Italy

2 Systems Toxicology Unit Research: Metabonomics on Human Biofluids and on cells exposed to chemicals JRC ISPRA

Project on asthma in collaboration with Padova hospital Nuclear Magnetic Resonance of EBC Exhaled Breath Condensate (EBC) Collection HEALTHY ASTHMA ASTHMA Tidal breathing for 15 minutes condensate in

3 Project on asthma in collaboration with Padova hospital Nuclear Magnetic Resonance of EBC Exhaled Breath Condensate (EBC) Collection HEALTHY ASTHMA ASTHMA Tidal breathing for 15 minutes condensate in ice glass condenser oxidized compounds acetylated compounds Carraro et al, AMERICAN JOURNAL OF RESPIRATORY AND CRITICAL CARE MEDICINE VOL Metabolomics Applied to Exhaled Breath Condensate in Childhood Asthma

4 RESULTS PLS-DA analysis demonstrated that 3 PLS components give a classification ~95% successful with only one misclassified sample

5 THE BIONOMICS SCIENCES DNA GENOMICS RNA TRANSCRIPTOMICS PROTEIN PROTEOMICS METABOLITES METABOLOMICS METABONOMICS

6 Metabolomics: The last frontier flavors toxins colors anti-cancer drugs pesticides pheromones fragants antibiotics After Rainer Breitling, Metabomeeting 2009

7 Predisposition markers Onset of disease/effect Scheme of development of disease METABONOMICS Monitoring the homeostasis Context: Biomonitoring/surveillance programs Environmental policy Disease prevention Drug optimization Food optimization Early biomarkers of disease/effect Prognostic markers Nutrition Environmental Late biomarkers of disease/effect Diagnostic markers Pharma Disease Healthy Changes in pathway dynamics to maintain homeostasis Van der Greef J. et. al. Kluver Academic Publishers; 2003:

8 Metabonomics processing strategy ppm ppm ppm ppm ppm ppm ppm ppm ppm ppm ppm ppm Acquisition of metabolic data NMR Multivariate statistical processing METADATA: Exposure data Lifestyle, environmental stressors Spectrum Multidimensional data interpretation Biomarker identification Investigation of metabolic pathways Determination of risk factors Additional metabolic data Other techniques (MS) Histogram Bucketing Elaboration of statistical models Databases P C 2 PC 3 Classification Prediction Biomonitoring Risk assessment / management Clinical diagnosis Unsupervised methods (PCA, etc.) PC 1 Supervised methods (LDA, etc.)

Q-TOF Metabonomics Studies with MS FT-MS:

elemental composition of intact molecules,

9 Q-TOF Metabonomics Studies with MS FT-MS: LTQ Orbitrap accurate mass MS and MS n spectral data with scan cycles of one second at R = 60,000 or 1.6 second R >100,000. Accurate mass experiments yield the elemental composition of intact molecules, and MS n fragment ion provide additional structural confirmation. Scan speed: 20 spectra per second that is compatible with UPLC base separation. CID: MS/MS high/low energy collision Resolution: FWHM with W-Optics Mass accuracy: less 3 ppm in MS and MS/MS

10 interpretation samples samples samples MS - Metabolomic experiment flowchart Data extraction export align annotate metabolites Normalize and transform data metabolites m/z 204 m/z 133 m/z 131 m/z 129 N samples x ~ 25MB = ~ hundreds of GB hypothese s data experiment s Interpret findings Generate new hypotheses. Get significance and hidden data structures classes Structure data according to hypotheses or background data

11 Data processing flow chart

12 p(corr)[1] Correlation w the model p(corr)[1] dataset thousands of features (variables) x few samples 1.0 Multivariate analysis (supervised) OPLS-DA Overfitting Variables that fit the model by chance 02_NON-calcolata_NOR.M1 (OPLS/O2PLS-DA) p[comp. 1]/p(corr)[Comp. 1] Colored according to Var ID (Var. Sec. ID:1) A B C Mattarucchi E., Guillou C., Critical Aspects of Urine Profiling for the Selection of Potential Biomarkers Using UPLC-TOF-MS. Biomedical Chromatography (2012) 26, no. 4 : Urines spiked with known compounds = Expected markers _calcolata_media_80%.M2 (OPLS/O2PLS-DA), OPLS-DA_Par p[comp. 1]/p(corr)[Comp. 1] Colored according to Var ID (Spike) A B C p[1] p[1] R2X[1] = SIMCA-P :09:02 (UTC+1) R2X[1] = SIMCA-P :00:40 (UTC+1) Intensity Intensity

13 Asthma common heterogeneous disorder of the airways. physiological lung dysfunctions chronic inflammation and tissue remodeling. Allergic asthma typically is emerging as the most common chronic disease in children (Bisgaard and Szefler, 2007). Diagnosis usually based on medical history and manifestations, even if symptoms are frequently poorly recognized (Killian et al., 2000).

14 Asthma asthma is not a single disease but a spectrum of phenotypes involving different cells types and mediators Distinguish between different inflammatory phenotypes is very important to: - Target anti-inflammatory therapy - Correlate the pathology with environmental exposure factors

15 Asthmatic children: Urine Analysis by UPLC-QTOF 41 atopic asthmatic children (median age 11) recruited among patients of the Dpt of Pediatrics "Salus Pueri" of the hospital of Padua, Italy: WCI : Well controlled asthma with therapy, WC : Well controlled asthma without therapy, PC : Poorly Controlled asthma with therapy 12 age-matched healthy subjects with no story of atopy or respiratory diseases were used as control H : Healthy Subjects.

16 treatment Asthma treatment: limited to control of the clinical manifestations. inhaled corticosteroids (ICS), the most effective controller medications in allergic asthma (Frois et al., 2009)

17 Effect of Drugs

FU41 FU42 FU43 FU44 FU45 HU16 HU15 HU13 HU12 HU11 HU10 HU9 HU8 HU7 HU6 HU5 HU4

18 FU1 FU2 FU3 FU4 FU5 FU6 FU7 FU8 FU9 FU11 FU13 FU14 FU15 FU16 FU17 FU18 FU19 FU20 FU21 FU22 FU23 FU24 FU25 FU26 FU27 FU28 FU29 FU30 FU31 FU32 FU33 FU34 FU35 FU36 FU37 FU38 FU39 FU41 FU42 FU43 FU44 FU45 HU16 HU15 HU13 HU12 HU11 HU10 HU9 HU8 HU7 HU6 HU5 HU4 Metabolites of drugs in urines Theophylline 1,3-dimethyluric acid

19 % A typical positive ionization chromatogram _A_DIL : TOF MS ES+ BPI 1.03e4 Saturation area (for this sample) Time

20 % Sulfadimetox D_50_DIL Sm (Mn, 2x2) 100 Sulfadimethoxine 1: TOF MS ES+ BPI 6.51e3 Quality controls (+) Erytromicyn 16 injections - overlaid plots Val-Tyr-Val (Leu-enk) Time Average Accuracy Error < 6 ppm Max Retention Time SD 0.05 min (0.6%)

% 10010028 URI_06_CAL_18 100 Dataset extraction 1: TOF MS ES+ BPI

50 6.00 6.50 7.00 7.50 8.00 8.50 9.00 9.50 10.

21 % URI_06_CAL_ Dataset extraction 1: TOF MS ES+ BPI 1.10e4 0 Time Extraction software (MarkerLynx or XCMS) Extraction parameters One size fits all

I injection raw I injection nor 4541 + 2203 variables extracted II injection raw III injection raw IV injection raw normalization II injection nor III injection nor IV injection nor Averaged dataset

22 I injection raw I injection nor variables extracted II injection raw III injection raw IV injection raw normalization II injection nor III injection nor IV injection nor Averaged dataset Statistical analysis Variable reduction* Final dataset Potential biomarkers * Variables in at least 80% of the samples within a single class (two classes at time). Further reduction according to the VIP

23 Comp[1]P Comp[1]P Comp[1]P DATASET-COMPLETO.M1 (OPLS/O2PLS-DA) R2Y(cum) Q2(cum) var Comp No. SIMCA-P :03:04 (UTC+1) Filtering: >80% of samples for each class 1.0 BCT-CS.M4 (OPLS/O2PLS-DA) R2Y(cum) Q2(cum) var Comp No. BCT_CS_Stocchero.M9 (OPLS/O2PLS-DA), OPLS-DA UV M8 SIMCA-P :32:03 (UTC+1) R2Y(cum) Q2(cum) Selection based on X,X and X,Y correlation and VIP filters var. Comp No. SIMCA-P :29:37 (UTC+1)

24 Box plots for the metabolites that showed the most relevant differences between patients taking, and not taking controller drugs (A) asthmatics and controls (B) levels of significance calculated using the Mann Whitney test.

25 m/z = Da RT =1.42 min p= Most significant difference between asthmatics and healthy controls. Fragmentation studies + reference compounds => fragment of urocanic acid after loss of H 2 O Mattarucchi, Elia, Eugenio Baraldi, and Claude Guillou. Biomedical Chromatography 26, no. 1 (January 2012): Metabolomics applied to urine samples in childhood asthma; differentiation between asthma phenotypes and identification of relevant metabolites intermediate in the catabolism of histidine, produced by the enzyme histidase, which is expressed mainly in liver and skin, but not in lung (Sano et al., 1997) Interestingly, cis urocanic acid (produced after UV radiation) is well known to be an immune suppressor (Gibbs et al., 2008)

27 Conclusions Discovery (untargeted Metabolomics): identification of biomarkers Good METADATA is essential Quality control of MS measurements Great attention to the pre-processing (extraction of variables) is critical for next stage of statistical evaluation (saturation, shifts, integration, normalisation.) Multivariate analysis should point at a few compounds to be identified and understood in the biological context. Our aim is not to build statistical models. Statistics is used only for filtering information

28 METABONOMICS: THE DREAM THE STAR TREK TRICORDER

29 JRC colleagues: Fabiano Reniero Giuseppe Giordano Elia Mattarucchi Thanks to: Dpt of Pediatrics Padova Thank you for your attention!

Discovery Metabolomics - Quantitative Profiling of the Metabolome using TripleTOF Technology

ANSWERS FOR SCIENCE. KNOWLEDGE FOR LIFE. Discovery Metabolomics - Quantitative Profiling of the Metabolome using TripleTOF Technology Baljit Ubhi Ph.D ASMS Baltimore, June 2014 What is Metabolomics? Also