H-NMR METABOLOMIC ANALYSIS OF THE EFFECTS OF PROBIOTICS ADMINISTRATION ON HUMAN METABOLIC PHENOTYPE (on going) Probiota 2016 Dr.

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1 1 H-NMR METABOLOMIC ANALYSIS OF THE EFFECTS OF PROBIOTICS ADMINISTRATION ON HUMAN METABOLIC PHENOTYPE (on going) Probiota 2016 Dr. Marco Pane

Microbiota genome change over time Trends Microbiol. 2012 Aug;20(8):385-91. doi: 10.1016/j.tim.2012.05.003. Epub 2012 Jun 5.

3 Microbiota genome change over time Trends Microbiol Aug;20(8): doi: /j.tim Epub 2012 Jun 5. Intestinal microbiota is a plastic factor responding to environmental changes Candela M 1, Biagi E, Maccaferri S, Turroni S, Brigidi P. Human Host Genome stable over time A permanent lost of gut biodiversity can be associated with pathologic systemic outcome

but, more correctly, of the superorganism.

4 Superorganism Superorganisms metabotype Microbiota Human metabolic phenotype is very accurate and systemic representation of the healthy state, not only of the human organism but, more correctly, of the superorganism. Urine metabotype can be considered a metabolic snapshot of the superorganisms

5 NMR urine samples spectra for subject Enough to identify an individual well defined metabolic sub-space The individual discrimination accuracy is up to 99%

6 PCA-CA-KNN METABOLIC SPACE 98% 20 spectra per person >99% 40 spectra per person DAY TO DAY VARIABILITY AS A CONSEQUENCE OF THE ORGANISM ADAPTATION TO THE EXTERNAL STIUMULI AND STRESS CONDITIONS >99% 40 spectra per person >99% 40 spectra per person

7 98% 20 spectra per person INDIVIDUAL RECOGNITION (Pseudo-individual) >99% 40 spectra per person >99% 40 spectra per person >99% 40 spectra per person

8 In healthy subject Individual metabotype is stable over time Intra-individual differences are detected

9 Long-term recognition Confusion matrix heatmap for the individual prediction of the 2014 collection Healthy but breastfeeding Healthy Healthy Healthy Healthy Strong Antibiotic /Lactose intolerance Thyroidectomy Healthy Healthy Healthy Healthy but Oldest (?) Healthy but Youngest (?)

10 Resilience of Human Metabolic Phenotype Where they able to revert their metabotype? AW 16 urine samples in Jan 2015 long after the antibiotic therapy was finished and after dairy products were reintroduced in the diet. AG additional urine samples (only 7) in Jan 2015 after stopping breastfeeding Prediction accuracy increased by 90% independently of the data collection used for as training set. AG and AW returned to their unique metabotype!!!

CERM/CIRMMP Magnetic Resonance Center 800*

Biobank Lab (2014) 900* He Liquifier Conference

wb 950* Biobank 700 wb 600 Mass Spectrometry

11 CERM/CIRMMP Magnetic Resonance Center 800* Competence center Ivano Bertini (2014) 700* Biobank Lab (2014) 900* He Liquifier Conference Room Library Workstations * * 850 wb 950* Biobank 700 wb 600 Mass Spectrometry X-Ray Crystallography GENEXPRESS Genetic expression laboratory Relaxometer ( MHz) *equipped with cryoprobes Molecular biology, cellular biology and biophysics labs

12 Probiotics are able to modify the individual metabolic phenotypes acting through host microbiome? 1 H Nuclear Magnetic Resonance (NMR) spectroscopy has been used to explore the metabolic changes induced in urines and sera collected from healthy volunteers by the regular administration of probiotics. A + B A = Lactobacillus delbrueckii (1 MLD/dose) B = Bifidobacterium longum (1 MLD/dose) A B B + A More than 1300 urine sample

13 Influence of probiotic treatment on individual urine metabolic phenotype Fused data (Group 1 + Group 2) Separated data Phase I Individual discrimination 97% Was used as baseline reference in order to reduce the metabolic noise Phase II (4-6 week supplementation) Individual discrimination 94 % Phase III (4-6 week supplementation) Individual discrimination 90 % Phase I Phase II Phase III Group 1 BL 97% A 97% A+B 93% Group 2 BL 97% B 94% A+B 89% Score plot of PCA-CA-KNN individual discrimination. Each color in the score plot represents a different volunteer; each dot of the same color represents a different urine sample belonging to the same volunteer collected during: A) phase I B) phase II C) phase III. Fused data indicate that probiotics affect the urine host metabotype. Separate data indicate that the two probiotics have a different effect on the Individual Discrimination (not the same efficacy )

14 Collective Influence of probiotic treatment Multilevel PLS analysis comparison of urine samples collected during the three phases of the study to underline overall outcomes of probiotics assumption Phase I Phase 2 Phase 3

15 Urine metabolite analysis 40 urine metabolites, with peaks in the spectra well defined and isolated Urine metabolites that shown significant concentration changes at least during one phase of the study All together phase I phase I phase II Metabolite phase II phase III phase III LEUCINA 0,020 0,222 0,656 ISOLEUCINA 0,057 0,004 0,351 VALINA 0,041 0,079 0,945 CITRATE 0,040 0,013 0,731 CREATINE+CREATININE 0,041 0,003 0,374 CREATININE 0,077 0,001 0,192 CHOLINE 0,228 0,013 0,521 1-METHYLNICOTINAMMIDE 0,185 6,32*10-5 0,083 MALONATE 0,001 1,27*10-5 0,351 VALERATE 0,040 0,079 0,858 2-HYDROXYISOBUTYRIC 0,358 0,019 0,351 ACETOACETATE 0,042 0,032 0,771 FUCOSIO 0,178 0,021 0,374 INDUXYLSULFATO 0,180 0,004 0,374 METANOLO 0,239 6,42*10-5 0,008 DIMETHILGLYCINE+X 0,013 0,008 0,848 ACETONE 2,71*10-5 0,013 0,351 GLYCOLATE 0,042 0,309 0,752 ORNITH.-ARGININE-ACETATE 0,021 0,001 0,666 4-HYDROXYHIPPURICACID 0,586 0,041 0,376 Group 1 Metabolite phase I phase II phase I phase III phase II phase III LEUCINA 0,026 0,359 0,622 VALINA 0,025 0,152 0,622 CITRATE 0,071 0,012 0,466 CREATINE+CREATININE 0,071 0,042 0,622 CHOLINE 0,135 0,001 0,466 1-METHYLNICOTINAMMIDE 0,386 0,012 0,466 MALONATE 0,059 0,043 0,925 INDUXYLSULFATO 0,984 0,043 0,466 DIMETHILGLYCINE+X 0,105 0,044 0,798 ACETONE 0,021 0,127 0,622 GLYCOLATE 0,026 0,273 0,466 ORNITH.-ARGININE-ACETATE 0,091 0,043 0,834 Group 2 ISOLEUCINA Metabolite CREATINE+CREATININE CREATININE FORMIATE 1-METHYLNICOTINAMMIDE MALONATE METANOLO ACETONE ERYTHRITOL ORNITH.-ARGININE-ACETATE 4-HYDROXYHIPPURICACID phase I phase II phase I phase III phase II phase III 0,282 0,033 0,282 0,281 0,036 0,282 0,281 0,009 0,282 0,860 0,033 0,860 0,517 0,009 0,517 0,038 0,0001 0,038 0,281 0,0001 0,282 0,005 0,086 0,005 0,158 0,009 0,158 0,236 0,032 0,236 0,302 0,033 0,302

16 Serum metabolite analysis 24 metabolites with peaks in the spectra well defined and isolated Discrimination 55% Discrimination 62% Serum metabolites that shown significant concentration changes at least during one phase of the study All together Metabolite phase I phase II phase I phase III phase II phase III PROLINE+GLU 0,018 0,014 0,886 PIRUVATE 0,078 0,0006 0,292 DIMETHYLGLYCINE 0,046 0,656 0,755 CREATINE+CREATININE 0,046 0,641 0,755 PHENYLALANINE 0,018 0,0006 0,806 FORMATE 0,042 0,989 0,292 Score plot of M-PLS discrimination of A) serum samples collected during phase I (black spheres) and phase II (red spheres) B) serum samples collected during phase I (black spheres) and phase III (green spheres).

17 Closing remarks 1 H Nuclear Magnetic Resonance (NMR) spectroscopy can been used to to define the metabolic sub-space of each single individual (20 urine samples identity 98%; 40 urine samples identity 99%); Healthy subjects are characterized by a stable metabolic space over time where their metabolic phenotypes are able to oscillate according to daily stressor and external stimuli (allostasis); NMR spectra of human urines indicates that although the genotype plays a role in defining the individual metabolic phenotype, the latter is modulated both by gut microflora and by response to external stimuli; Disruption of the intestinal microbiota affect greatly the individual metabolic subspace of the superorganism Probiotic supplementation alter the individual metabolic subspace of the superorganisms CONS Metabolic Analisys based only on well discriminated peaks and not for all the metabolites (> 100) Metabolic interpretation? General Predictive marker (health/disease)? Subpopulation and individual analysis?

18 1 H-NMR METABOLOMIC ANALYSIS OF THE EFFECTS OF PROBIOTICS ADMINISTRATION ON HUMAN METABOLIC PHENOTYPE (on going) Probiota 2016 Dr. Marco Pane

Metabolomics by NMR at CERM. Claudio Luchinat CERM/CIRMMP University of Florence

Metabolomics by NMR at CERM. Claudio Luchinat CERM/CIRMMP University of Florence Metabolomics by NMR at CERM Claudio Luchinat CERM/CIRMMP University of Florence CERM/CIRMMP Magnetic Resonance Center Main node of the Structural Biology Instruct-ERIC infrastructure Biobank Lab (2016)