LORENZA PUTIGNANI, UNITA' DI METAGENOMICA E DI PARASSITOLOGIA Children s Hospital and Research Institute, IRCCS, Rome, Italy

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1 Utilizzo di piattaforme metaomiche nella descrizione del microbiota intestinale di importanti patologie croniche: il caso della fibrosi cistica e della steatosi epatica MICROBIOTA TECHOLOGY AD DATA MAAGEMET LOREZA PUTIGAI, UITA' DI METAGEOMICA E DI PARASSITOLOGIA Children s Hospital and Research Institute, IRCCS, Rome, Italy

2 THE ISIDE STORY, Mullard, ature DIFFERETI LIVELLI DI ORGAIZAZZIOE BIOLOGICA Diet DESTRUTTURAZIOE DEI LIVELLI ITESTIE AS BIOREACTOR Microbiota Host TECHOLOGY AD DATA MAAGEMET Del Chierico et al., 2014, IJMS 41875R1-2014, Metagenomics in AD: tools and applications

3 3 OPBG OMICS and METAOMICS COSORTIUM THE SYSTEMS MEDICIE MODELS: MOUSE AD HUMA TOWARDS METABOTYPES.. TOWARDS ETEROTYPES.. Healthy nutrition Resistance to infection Oral tolerance Organ vitality Healthy ageing HEALTH DIET HOMEOSTASIS DYSFUCTIO DISEASE ITESTIE AS BIOREACTOR Obesity Metabolic syndrome Diabetes Infection Cancer IBD From Putignani et al., 2014, In press, Pediatric Research-ature

4 From Putignani et al., 2014, In press, Pediatric Research-ature 4

5 5 -OMICS PHEOTYPES AD CHARTS SYMBIOSIS I THE GUT DYSBIOSIS I THE GUT ITESTIAL DISEASES Age scale..critical to fill 0-24 months for fine programming plan Age scale..neonates and follow-up, phase and type of the disease DESCRIPTIO De Filippo et al., 2010, PAS; Dominguez-Bello et al., 2010, PAS; Palmer et al., 2007, PLOS Biology; Koenig et al., 2011, PAS; Meghan, et al., CMAJ; Jost et al., 2012 Yatsunenko et al., ature 486, (14 June 2012) CATEGORIES Arumugam et al., 2011, ature 473 (12 May 2012) Schloissnig et al., 2013, ature 493 (12 January 2013) From Putignani et al., 2014, In press, Pediatric Research-ature

6 GUT MICROBIOTA RELATED DISEASES 6 GUT DISEASE From Putignani et al., 2014, In press, Pediatric Research-ature

7 OPBG OMICS AD METAOMICS ACTIVITIES E COME SE PARLASSIMO DI UA DEFRAMMETAZIOE DI U COMPUTER 1. BARCODED SAMPLES 2. CULTUROMICS 3. GEOMICS 4. METAGEOMICS 5. METABOLOMICS 6. METAPROTEOMICS 4. METAGEOMICS DI U FC GUT

8 CULTUROMICS PROTEOMICS 8 Saliva Stool Sample collection Microbiological cultures Axenic cultures Del Chierico et al., 2013, IJMS 41875R Metagenomics in AD: tools and applications

9 HUMA ITESTIAL TRACTCHIP (HITChip) GEOMICS 9 DA extraction optimised for very earlylife samples Microbial community profiling HITChip Rajilic-Stojanovic et al., 2009 Microarray platform HITChip Agilent Technologies, 8 arrays, 15,000 probes, with over 4,800 tiling oligonucleotides targeting the V1 or the V6 region of the 16S rra gene from 1,140 microbial phylotypes present in the human gut. Italian Baby Trial Optimised procedures for meconium DA extraction: 95% Pearson correlation index Metagenomics of the human intestinal tract From Petrucca et al., 2014, Manuscript in preparation, American Journal of Gastroenterology

10 HUMA MICROBIOME (PYROSEQUECIG) 10 METAGEOMICS ext generation sequencing, GS DA EXTRACTIO TYPIG REAL-TIME PCR PYROSEQUECIG

11 METABOLOMICS: towards biomarker searching 11 Technological platforms Data analysis Statistical approach GC-MS GC-MS chromatogram PCA Heat map 1 H-MR 1 H-MR spectrum Sample collection: blood, plasma, faeces, urine and saliva Scatter plot LC-MS LC-MS spectrum AOVA Del Chierico et al., 2014, IJMS 41875R1-2014, Metagenomics in AD: tools and applications

12 METAPROTEOMICS 12 METAPROTEOMICS An original metaproteomic pipeline to identify newborn mouse gut phylotypes BACTERIAL PURIFICATIO METAPROTEOMIC (LC-MS 2 ) EWBOR MOUSE GUT HOMOGEATE EBS Proxeon EASY-nLCTM AO-LC chromatogra m MUCOSAL AD FECAL BACTERIA BIOIFORMATIC WORKOW PROTEIS PEPTIDES AmaZon Ion Trap MS nanoflow ESI Sprayer MS and MS 2 SPECTRA MASCOT DATA Peptides to protein hits Protein hits to OTUs LIK TO FUCTIO OTUs linking to phyla and families F. Del Chierico, A. Petrucca, S. Levi Mortera, P. Vernocchi, M M Rosado; L. Pieroni, R. Carsetti, A. Urbani, L. Putignani. A metaproteomic pipeline to identify newborn mouse gut phylotypes. Journal of Proteomics, 2014 Jan 31;97: doi: /j.jprot

13 Relative abundance (%) Taxonomic classification of Balb/c and Rag2 ko gut microbiota characterized by axenic based MALDI- 13 TOF MS approach 100 Balb/c Rag2 ko age (days) Del Chierico et al., Journal of Proteomics, 2014 Jan 31;97:17-26

14 Taxonomic classification of Balb/c and Rag2ko gut microbiota characterized by metaproteomic pipeline 14 METAPROTEOMICS Del Chierico et al., Journal of Proteomics, 2014 Jan 31;97:17-26

15 COG functional category distribution associated to Firmicutes and Proteobacteria phyla: towards biomarker searching METAPROTEOMICS 15 Del Chierico et al., Journal of Proteomics, 2014 Jan 31;97:17-26

16 ORIGIAL BIOIFORMATIC PIPELIE 16 16S rra barcoded amplicons Statistical analysis Emulsion -PCR Database searching Principal component analysis Canonical Correlation analysis Pyrosequencing RAKIG Metadata analysis OTUs identification Raw sequences 1 CCCCAAGCGATACCATGCAATCGAACGGT CAGGAAGCCCGACTCGATCCAATTCCGGG 2 GGTCCAGACTCTACGAAAGGCAGCAGTGG GAATATTGCGGTTAAACTAGTAGCCATGCA 3 CTGGGAACTGATCTGATACTGGCAAGCTT GAGTCTCGTATTAAAACCCGTAGTCACTG SC OTUs grouping OTU phylogeny Denoising procedures Relative abundance of OTUs Del Chierico et al., 2013, IJMS 41875R Metagenomics in AD: tools and applications

17 Physiological status/disease 1 Patient (n ) Sample (n ) Years of project samples/year Project Priority Healthy subjects (reference cohort for all sub-projects, 1-18 years) (consortium OBG, RC) Early life vaginally-delivered neonates (0-1 years) (RF ) Juvenile Idiopathic Arthritis (MD Paedigree Project) Obeses (consortium OBG) Obeses (MD Paedigree Project) Cystic fibrosis (consortium OBG/Milan, Regione Lazio CF Grant, RC) OPBG microbiome Projects Obeses (consortium OBG, RC) ASH/AD (consortium OBG, RC) Williams Syndrome (consortium OBG, RC) EC/bowel syndromes/congenital gut malformations (RC) TBD TBD Mediterrean diet (HORIZO 2020) Mice Model (RC 2014) Autism spectrum disorder (ASD) TBD TBD GUT PROGRAMMIG GUT DISEASE GRADI OBESI TBD TBD ORAL COMMUITIES TBD TBD Idiopathic nephrotic syndrome (RC) 20 TBD 1-1 IBD/IBS (HORIZO 2020) TBD TBD Metabolic diseases TBD TBD Total sample number (minimum)

18 Gut microbiota in cystic fibrosis (CF) patients: a combined -omic translational workflow P. Vernocchi 1,2,3, M. Valerio 4, F. Del Chierico 1,2, L. Casadei 4, A. La Storia 5, F. De Filippis 5, G. Salerno 5, A. Petrucca 1,2,6, F. Majo 7, C. Rizzo 8, E. Fiscarelli 8, C. Manetti 4, M. Muraca 8, V. Lucidi 7, D. Ercolini 5, B. Dallapiccola 9 and L. Putignani 1,2 1. Unit of Parasitology, Bambino Gesù Children's Hospital, IRCCS, Rome, Italy 2. Units of Metagenomics, Bambino Gesù Children's Hospital, IRCCS, Rome, Italy 3. Interdipartimental Centre for Industrial Research-CIRI AGRIFOOD-Almat Mater, Bologna, Italy 4. Department of Chemistry, Sapienza, Università di Roma 5. Department of Agriculture, Università Federico II, aples, Italy 6. Department of Diagnostic Science, Sant Andrea Hospital, Rome, Italy 7. Unit of Cystic Fibrosis, Bambino Gesù Children's Hospital, IRCCS, Rome, Italy 8. Laboratory Medicine, Bambino Gesù Children's Hospital, IRCCS, Rome, Italy 9. Scientific Directorate, Bambino Gesù Children's Hospital, IRCCS, Rome, Italy

19 CFTR-opathies : phenomics PHEOMICS 19 ID Age (years) Gender Sweat test (Cl, mmol/l) Genotype CFTR Pancreatic status Meconium ileus W/L or BMI (Z-score) Lung colonization Antibiotics Use of probiotics Disease severity P M 77 F508del/dele2,3 insufficient yes -1,5 Staphylococcus aureus no no severe P F 81 F508del/L1065P Insufficient no 2,1 Serratia marcescens yes no severe P F 95 F508del/F508del insufficient no -1 Stenotrophomonas maltophilia no no severe P F 125 F508del/F508del insufficient no 0,1 S. aureus no no severe P M 91 F508del/F508del insufficient yes -1 S. aureus no yes severe P F G>T/R553X insufficient yes -0,8 S. maltophilia no no severe P M 98 F508del/G1244E insufficient no 0,6 negative yes no severe P F G>T/1898+G> A insufficient no 2,5 Pseudomonas aeruginosa no no severe P F 94 F508del/1303K insufficient no 0 P. aeruginosa yes no severe P F 95 F508del/M1V insufficient no 0,3 S. maltophilia yes no severe P F 89 F508del/P5L sufficient no 1 negative no no mild P M 98 F508del/R1162X sufficient no 0,4 P. aeruginosa yes no severe P M 140 F508del/Unknown insufficient no 2,1 negative no yes mild P F 99 F508del/712-1G>T insufficient no -0,1 P. aeruginosa yes no severe P M 100 F508del/F508del insufficient no -1 S. aureus no no severe P F 95 F508del/R553X insufficient no -0,2 S. aureus yes yes severe P F K/2694 T>G sufficient no 0,44 negative no no mild P M K/2184m5A insufficient yes -1,27 P. aeruginosa yes no mild P M 96 F508del/F508del insufficient yes -0,48 Escherichia coli yes yes severe P M 90 F508del/R1070Q insufficient no 3,2 P. aeruginosa yes no severe P F 78 F508del/S1253R insufficient yes -0,4 S. maltophilia no yes severe P F 85 F508del/G542X insufficient no -0,1 P. aeruginosa yes no mild P M G>A/D1152H sufficient no -0,7 P. aeruginosa no no severe P M G>A/S1455X sufficient no 0,6 negative yes no mild P F 61 G542X/187K sufficient no 0,54 negative yes no mild P M 114 F508del/W1282X insufficient no 1,3 Haemophilus influentiae no no mild P F G>A/Unknown P F 84 F508del/3659delC insufficient no -1,2 S. aureus no no severe insufficient yes -3,1 S. aureus no no severe

20 RAKIG I FC GUT: PHYLUM 20 FUCTIOAL METAGEOMICS

21 RAKIG I FC GUT: CLASS 21

22 RAKIG I FC GUT: FAMILY 22

23 RAKIG I FC GUT: GEUS 23

24 RAKIG I FC GUT: SPECIES 24

25 UIFRAC: β-diversity analysis, UWEIGHTED and WEIGHTED 25

26 UIFRAC: β-diversity analysis, UWEIGHTED and WEIGHTED 26

27 Statistics at Phylum, Family, Genera and Species level (AOVA) 27 Tenericutes were more abundant in negative samples (avg. 14% in vs 1.8% in P) (p<0.001) Erysipelotrichaceae (avg. 14% in vs 1.8% in P), Ruminococcaceae (avg. 13% in vs 3.8% in P) and Lachnospiraceae (avg. 22% in vs 12% in P) families had a significantly lower occurrence in P, while Clostridiaceae (avg. 3% in vs 21% in P) was significantly higher (p<0.05). In P individuals it was found a lower occurrence of: Eggerthella spp. (avg. 2% in vs 0.19 in P) (p<0.001) Eggerthella lenta (avg. 1.1% in vs 0.05 in P) (p<0.05) Ruminococcus spp. (avg. 4% in vs. 0.2 in P) (p<0.01) Faecalibacterium praumitzii (avg. 2.5% in vs. 0.04% in P) (p<0.01) And an higher occurrence of: Escherichia spp. (avg. 0.2% in vs. 7.3% in P) (p<0.01) Clostridium spp. (avg. 2% in vs. 11% in P) (p<0.01) In particular, Clostridium difficile (avg. 0.01% in vs. 2.5% in P) (p<0.05) FUCTIOAL METAGEOMICS From Vernocchi et al., Manuscript in preparation, 2014

28 ppm equivalent ppm equivalent ppm equivalent ppm equivalent GC-MS/SPME (solid phase microextraction) : VOLATILOME butanol 2-ethyl- 1hexanol Phenyl ethyl alcohol HC benzyl alcohol 2 ethyl 1 hexanol 1 octen 3 ol 2-dodecanol 1 hexanol 1-butanol 1-propanol ethanol benzyl alcohol 2 ethyl 1 hexanol 1 octen 3 ol 2-dodecanol 1 hexanol 1-butanol 1-propanol ethanol CF ethanol 1-propanol 1-pentanol HC CF P-06-8 P-11-7 P-11-4 P-10-7 P-10-4 P-10-2 P-09-4 P-09-2 P-08-6 P-08-4 P-08-2 P-07-7 P-07-6 P-07-5 P-07-1 P-06-2 P-11-3 P-06-6 P-11-9 P P P-07-9 P-08-8 P-07-4 P-06-7 P-08-3 P-09-6 P-09-7 P P-11-8 HC samples CF samples Median value and total amount (ppm) of Alcohols were higher in HC children but methyl acetate butyl acetate HC HC methyl pentanoate propyl decanoate ethyl decanoate ethyl octanoate butyl hexanoate propyl hexanoate iso amyl butyrate butyl butyrate propyl butyrate 2-methyl butanoate butyl acetate ethyl butyrate ethyl acetate methyl acetate methyl pentanoate propyl decanoate ethyl decanoate ethyl octanoate butyl hexanoate propyl hexanoate iso amyl butyrate butyl butyrate propyl butyrate 2-methyl butanoate butyl acetate ethyl butyrate ethyl acetate methyl acetate CF P-11-8 P P-09-7 HC samples CF samples P-11-3 P-06-6 P-11-9 P P P-07-9 P-08-8 P-07-4 P-06-7 P-08-3 P-09-6 P-06-8 P-11-7 P-11-4 P-10-7 P-10-4 P-10-2 P-09-4 P-09-2 P-08-6 P-08-4 P-08-2 P-07-7 P-07-6 P-07-5 P-07-1 P-06-2 CF ethyl acetate butyl butyrate 4-methyl pentanoate Median value and total amount (ppm) of Esters were higher in CF children From Vernocchi et al., Manuscript in preparation, 2014

29 ppm equivalent ppm equivalent ppm equivalent ppm equivalent GC-MS/SPME results 29 Butyric acid Valeric acid HC HC methyl butyric acid valeric acid 3 methyl butanoic acid butyric acid propionic acid acetic acid methyl butyric acid valeric acid 3 methyl butanoic acid butyric acid propionic acid acetic acid CF Acetic acid Propionic acid 3 methyl butanoic acid CF 0 P-06-8 P-11-7 P-11-4 P-10-7 P-10-4 P-10-2 P-09-4 P-09-2 P-08-6 P-08-4 P-08-2 P-07-7 P-07-6 P-07-5 P-07-1 P-06-2 P-11-3 P-06-6 P-11-9 P P P-07-9 P-08-8 P-07-4 P-06-7 P-08-3 P-09-6 P-09-7 P P-11-8 HC samples CF samples Median value and total amount (ppm) of SCFAs were higher CF children Phenol 4 methyl pehnol HC p-cresol phenol 2,6 bis phenol p-cresol phenol 2,6 bis phenol CF 2,6 phenol HC CF P-06-8 P-11-7 P-11-4 P-10-7 P-10-4 P-10-2 P-09-4 P-09-2 P-08-6 P-08-4 P-08-2 P-07-7 P-07-6 P-07-5 P-07-1 P-06-2 P-11-3 P-06-6 P-11-9 P P P-07-9 P-08-8 P-07-4 P-06-7 P-08-3 P-09-6 P-09-7 P P-11-8 HC samples CF samples Total amount (ppm) of Phenols was similar between HC and CF Median value (ppm) of Phenols was higher in HC children From Vernocchi et al., Manuscript in preparation, 2014

30 GC-MS/SPME results 30 Median values and ranges of the concentration (ppm) of volatile organic compounds (VOCs) (grouped in chemical class) of faecal samples from CF and HC determined by GC-MS/SPME Healthy children (HC) Cystic fibrosis patients (CF) p-value Chemical class IQR(25%-75%) IQR(25%-75%) median median acids alcohols alcanes alkenes aldehydes ketones sulphur compounds esters < eterocycles phenols furan furanones indoles piperidine pirazine pyridine pyrimidine terpenes *Data are the means of three independent experiments (n = 3) for each children From Vernocchi et al., Manuscript in preparation, 2014

31 31 GC-MS/SPME results CF children PC Healthy children PC1 GC-MS/SPME data by using Principal Component Analysis (PCA) carried out on samples from CF and HC children. The score plot shows a clear separation between the CF and healthy children on the PC1 (p= ) and PC2 (p= E-05). From Vernocchi et al., Manuscript in preparation, 2014

32 GC-MS/SPME results 32 PC acetic acid propyl butyrate ethyl butyrate amyl acetate propyl acetate 2,3 octanedione butyl butyrate ethyl octanoate 2 pentyl furan gamma terpinene dodecane heptanal AR curcumene octanal tetradecane Hexanal octanoic acid ethyl acetato nonanal octenal 2 heptanol Indole butyl acetato propionic acid alpha pinene 1-propanol 2-nonanone 2,4 octanedione 2-heptanone heptane ethanol 2-propanone 2,3 butanedione 2-pentanone gamma curcumene 2-octanol methyl pirazine 3 methyl indole linalool 2-nonanol 2,6 octadiene phenol 2,3 butanediol 2-dodecanol 2 ethyl hexanal 2-butanol 3-hexanone octyl acetate benzaldehyde decanol heptanoic acid pyridine 3-heptanol 2-hexanol HC CF Acetone CF children Overview of the PCA model built on the VOCs dataset of CF and HC faecal samples. The score and loading plots of the first two components (PC1 versus PC2) are shown superimposed. The score plot shows the differentiation between the CF and HC, while the loading plot highlights which metabolites are responsible in separating the samples Healthy children PC1 From Vernocchi et al., Manuscript in preparation, 2014

33 to[1] GC-MS/SPME results campioni FC tutti completo 28 GIUGO.M4 (OPLS/O2PLS-DA) t[comp. 1]/to[XSide Comp. 1] Colored according to classes in M METABOLOMICS 7 6 P-06-6 P Healthy children (2) P P-07-9 P-11-9 P P-06-7 P-11-3 P-08-6 P-09-7 P P-09-2 P-08-3 P-11-7 P-07-7 P-07-4 P-07-5 P-08-1 P-10-2 CF children P-10-7 P-08-2 P-07-6 P t[1] R2X[1] = R2X[XSide Comp. 1] = Ellipse: Hotelling T2 (0.95) SIMCA-P :54:51 (UTC+1) To eliminate these metabolic effects not due to CF disease, we analyzed the GC-MS/SPME data by using the orthogonal projections to latent structures-discriminant analysis (OPLS-DA). This analysis revealed a net separation between CF young patients and healthy children on first latent variable (p < ). From Vernocchi et al., Manuscript in preparation, 2014

34 1 H-MR results 34 PCA - Data set: 31 FC patients and 37 healthy children PC4 (5%) 5 0 P_09_2-5 P_7_ PC1 (12%) FC Controls MR data were investigated by using PCA carried out on samples from CF and healthy children. Five components are sufficient to explain 40% of total variability of the system. The score plot shows a clear separation between the CF and healthy children on the PC1 (p= 0.001) and PC4 (p< ). From Vernocchi et al., Manuscript in preparation, 2014

35 Comparison of clinical characteristics of patient s cohort and their corresponding metabolic profiles 35 Age FACTORS PATIETS PRICIPAL COMPOETS PC1 PC2 PC3 PC4 PC5 Relationship between Age or Sweat test values and the metabolic profiles of FC patients and healthy children. The table reports the Spearman's rank correlations between Age or Sweat test values with their corresponding metabolic profiles. All FC Healthy (0.04) ( ) Sweat test FC Sources of variability in metabolomic data of FC patients and healthy children as measured by analysis of variance (AOVA). The table reports the F-values for each factor (Gender, IP, Antibiotics and Probiotics) and, in parenthesis, the corresponding p-values. Significant values (p<0.05) are in bold. Gender All (0.898) (0.465) (0.859) (0.531) (0.288) IP All (0.001) (0.575) (0.871) (<0.0001) (0.031) Antibiotics All (0.133) (0.256) (<0.0001) (0.004) (0.389) From Vernocchi et al., Manuscript in preparation, 2014

36 Comparison of clinical characteristics of FC patient and their corresponding metabolic profiles 36 Sources of variability in metabolomic data of FC patients as measured by two-way analysis of variance. The table reports the F-values for each factor (Genotype, Lung and Age) and combination of factors (Genotype*Lung, Genotype*Age and Lung*Age) and, in parenthesis, the corresponding p-values. Significant values (p<0.05). PCs Factor Genotype Lung Age Genotype*Lung Genotype*Age Lung*Age PC (0.376) (0.768) (0.883) (0.481) (0.412) (0.216) PC (0.30) (0.647) (0.621) (0.307) (0.903) (0.701) PC (0.496) (0.395) (0.353) (0.60) (0.387) (0.198) PC (0.222) (0.705) (0.470) (0.995) (0.238) (0.825) PC (0.386) (0.577) (0.361) (0.997) (0.150) (0.762) From Vernocchi et al., Manuscript in preparation, 2014

37 1 H-MR results 37 OPLS-DA: FC patients without IP vs. FC with IP vs. Healthy children LV2 4 2 P_09_ MILD P_7_10-6 FC grading? LV1 FC patients without IP FC patients with IP Healthy children The metabolic FC effects is partially overwhelmed by IP and antibiotic consumption factors (AOVA: PC1 ( (p = 0.001)), PC4 ( (p <0.0001)) for IP and PC4 (8.828 (0.004)) for Antibiotic consumption). To eliminate these metabolic effects not due to CF disease, we analyzed the MR data by using the orthogonal projections to latent structures-discriminant analysis (OPLS-DA). This analysis revealed a net separation between CF patients and healthy children on first latent variable (p < ). From Vernocchi et al., Manuscript in preparation, 2014

38 1 H-MR results 38 Relative levels of the main metabolites which explain the differences across CF and healthy children according to O-PLS-DA analysis Spectral region Average Fold change Metabolites (ppm) a over control p value 0.82, Hydroxyisovalerate , 1.54, 2.14 Butyrate Isovalerate , 0.99 Ile Leu , 1.03 Val , 2.17 Propionate Ala Arg Acetate , 2.63 Methionine , Aminobutyrate 2.34 Glu < Sarcosine < Choline , 3.42, 3.45, Glucose , 4.63, Glycine , 7.52 Uracyl < , 7.19 Tyr , Phenyllactate , 2.79 Asp Phe From the analysis of O-PLS-DA loadings, the CF patients showed lower levels of: 1. SCFA (acetate, propionate, and butyrate), 2. amino acids (isoleucine, leucine, valine, alanine, arginine, methionine, glutamate, aspartate, glycine, tyrosine and phenylalanine) 3. isovalerates (2-hydroxy-isovalerate and isovalerate) 4. sarcosine, 5. glucose, 6. 3-phenyllactate, 7. uracyl than healthy children. On the other hand, the CF patients showed higher levels of: 1. 4-aminobutyrate, 2. choline. a Mid spectral integral region, i.e., 3.26 represents ppm 3.23 to 3.27; only spectral regions containing only one metabolite are reported.

39 1 H-MR results 39 1) The role of gender and age as potential confounding factors was investigated. o correlation between gender and metabolic profiles were found. For FC patients, no correlation between age and metabolic profiles were found. For healthy children, a negative correlation with PC1 (ρ = t-test 2006 vs p = 0.03; 2007 vs p = 0.04) as well as a positive correlation with PC4 were found (ρ = 0.448; t-test 2006 vs p = 0.02; 2006 vs p = 0.03; 2006 vs p = 0.003; 2007 vs p = 0.02) 2) o relationship of Sweat test values with metabolic profiles was found. 3) The metabolic variations due to IP is mainly accounted for by PC1 (12.738, p = 0.001), PC4 (37.148, p <0.0001) and PC5 (4.858, p = 0.031) 4) The metabolic variations due to antibiotic consumption is mainly accounted for by PC3 and PC4 components 5) o relationship between Genotype, Lung and age factors of FC patients and their metabolic profiles were found From Vernocchi et al., Manuscript in preparation, 2014

40 1 H-MR results 40 Graphical representation: comparison of clinical characteristics of the children cohort and their corresponding metabolic profiles I From Vernocchi et al., Manuscript in preparation, 2014

41 1 H-MR results 41 Graphical representation: comparison of clinical characteristics of the patient cohort and their corresponding metabolic profiles II Significant values (p<0.05) are in bold. From Vernocchi et al., Manuscript in preparation, 2014

42 Gut microbiota in AD-ASH patients: a combined -omic translational workflow F. Del Chierico 1,2, P. Vernocchi 1,2,3, Alisi, A. 4, V. Giorgio 5, A. Petrucca 1,2,6, C. Rizzo 7, B. Dallapiccola 8, V. obili 4,5 and L. Putignani 1,2 1. Unit of Parasitology, Bambino Gesù Children's Hospital, IRCCS, Rome, Italy 2. Units of Metagenomics, Bambino Gesù Children's Hospital, IRCCS, Rome, Italy 3. Interdipartimental Centre for Industrial Research-CIRI AGRIFOOD-Almat Mater, Bologna, Italy 4. Liver Research Unit, Bambino Gesù Children s Hospital, IRCCS, Rome, Italy 5. Hepato-Metabolic Disease Unit, Bambino Gesù Children s Hospital, IRCCS, Rome, Italy 6. Department of Diagnostic Science, Sant Andrea Hospital, Rome, Italy 7. Laboratory Medicine, Bambino Gesù Children's Hospital, IRCCS, Rome, Italy 8. Scientific Directorate, Bambino Gesù Children's Hospital, IRCCS, Rome, Italy

43 RAKIG I ASH GUT: CLASS 43

44 RAKIG I ASH GUT: ORDER 44

45 RAKIG I ASH GUT: FAMILY 45

46 RAKIG I ASH GUT: GEUS 46

47 RAKIG I ASH GUT: SPECIES

48 ppm ppm GC-MS/SPME results ASH3/98 ASH26/98 ASH30/98 ASH 4/99 1-heptanol 1 octen 3 ol 1 hexanol 1-pentanol iso amyl alcohol 1-butanol 1-propanol ethanol // ASH11/99 ASH20/99 ASH2/00 ASH17/00 ASH28/00 ASH16/01 ASH27/01 ASH19/02 ASH9/02 ASH10/ samples ASH7/02 ASH15/03 ASH14/03 ASH5/03 ASH25/04 ASH12/04 ASH8/05 ASH6/05 ASH29/05 ASH24/06 ASH23/ Alcohols in particular: 1-heptanol 1-octen 3 ol 1-hexanol 1-pentanol Iso amyl alcohol 1-propanol ethanol // Esters in particular: methyl acetate butyl acetate propyl butyrate butyl propanoate ethyl hexanoate butyl 2- methylbutyrate iso amyl butyrate hexyl acetate ASH3/98 ASH26/98 ASH30/98 ASH 4/99 ASH11/99 ASH20/99 ASH2/00 ASH17/00 ASH28/00 ASH16/01 ASH27/01 hexyl acetate iso amyl butyrate butyl 2-methylbutyrate ethyl hexanoate butyl propanoato propyl butyrate butyl acetato methyl acetato ASH19/02 ASH9/02 ASH10/02 samples ASH7/02 ASH15/03 ASH14/03 ASH5/03 ASH25/04 ASH12/ ASH8/05 ASH6/05 ASH29/05 ASH24/06 ASH23/06

49 ppm ppm GC-MS/SPME results ASH3/98 ASH26/98 ASH30/98 ASH 4/99 octanoic acid heptanoic acid pentanoic acid/valeric acid 2-methyl butyric acid butanoic/butyric acid propionic acid acetic acid ASH11/99 ASH20/99 ASH2/00 ASH17/00 ASH28/00 ASH16/01 ASH27/01 ASH19/02 ASH9/02 // // ASH10/02 samples ASH7/02 ASH15/03 ASH14/03 ASH5/03 ASH25/04 ASH12/04 ASH8/05 ASH6/ ASH29/05 ASH24/06 ASH23/06 Acids and SCFAs in particular: acetic acid propionic acid butyric acid 2-methyl butyric acid valeric acid heptanoic acid octanoic acid Aldehydes in particular: hexanal octanal nonanal benzaldehyde benzeneacetaldeh yde 4-methyl benzaldehyde ASH3/98 4-methyl benzaldehyde Benzeneacetaldehyde benzaldehyde nonanal octanal Hexanal/caproaldehyde ASH26/98 ASH30/98 ASH 4/99 ASH11/99 ASH20/99 From Del Chierico et al., manuscript in preparation, ASH2/00 ASH17/00 // ASH28/00 ASH16/01 ASH27/01 ASH19/02 ASH9/02 ASH10/02 samples ASH7/02 ASH15/03 ASH14/03 ASH5/03 ASH25/ // ASH12/04 ASH8/05 ASH6/05 ASH29/05 ASH24/06 ASH23/06

50 ppm ppm GC-MS/SPME results // // methyl phenol 2-methyl phenol 4 methyl phenol/p-cresol phenol Phenols in particular: phenol p-cresol 2-methyl phenol 3 methyl phenol ASH3/98 ASH26/98 ASH30/98 ASH 4/99 ASH11/99 ASH20/99 ASH2/00 ASH17/00 ASH28/00 ASH16/01 ASH27/01 ASH19/02 ASH9/02 ASH10/02 samples ASH7/02 ASH15/03 ASH14/03 ASH5/03 ASH25/04 ASH12/04 ASH8/05 ASH6/05 ASH29/05 ASH24/ ASH23/ // Ketons in particular: 2-propanone 2-butanone 2,3 butanedione 2-pentanone 4 methyl 2 pentanone 2-hexanone 2,6-dimethyl 4 heptanone 2-octanone 6 methyl 5 hepten 2 one 2-nonanone nonanone 6 methyl 5 hepten 2 one 2-octanone 2,6-dimethyl 4 heptanone 2-hexanone 4 methyl 2 pentanone 2-pentanone 2,3 butanedione 2-butanone 2-propanone/acetone ASH3/98 ASH26/98 ASH30/98 ASH 4/99 ASH11/99 ASH20/99 ASH2/00 ASH17/00 ASH28/00 ASH16/01 ASH27/01 ASH19/02 ASH9/02 ASH10/02 samples ASH7/02 ASH15/03 ASH14/03 ASH5/03 ASH25/04 ASH12/04 ASH8/05 ASH6/05 ASH29/05 ASH24/06 ASH23/06 From Del Chierico et al., manuscript in preparation,

51 ppm ppm GC-MS/SPME results // // ASH3/98 ASH26/98 ASH30/98 ASH 4/99 ASH11/99 ASH20/99 ASH2/00 ASH17/00 ASH28/00 ASH16/01 ASH27/01 ASH19/02 ASH9/02 // ASH10/02 samples beta caryophyllene trans alpha bergamotene alpha terpinolene para cimene gamma terpinene delta 3-carene beta pinene ASH7/02 ASH15/03 ASH14/03 ASH5/03 ASH25/04 ASH12/04 ASH8/05 ASH6/05 ASH29/05 ASH24/ ASH23/06 Terpenes in particular: beta pinene delta 3-carene gamma terpinene para cimene alpha terpinolene trans alpha bergamotene beta caryophyllene Indoles in particular: 5-methyl- 2phenyl-1H-Indole 1H-indole Indole 7-methyl indole 3 methyl 1H indole 2 methyl 1H indole 3 methyl indole ASH3/98 ASH26/98 From Del Chierico et al., manuscript in preparation, methyl indole 2 methyl 1H indole 3 methyl 1H indole 7-methyl indole Indole 1H-indole 5-methyl-2phenyl-1H-Indole ASH30/98 ASH 4/99 ASH11/99 ASH20/99 ASH2/00 ASH17/00 ASH28/00 ASH16/01 ASH27/01 ASH19/02 ASH9/02 ASH10/02 samples // ASH7/02 ASH15/03 ASH14/03 ASH5/03 ASH25/04 ASH12/04 ASH8/05 ASH6/05 ASH29/05 ASH24/06 ASH23/

52 COCLUSIOS 52 BIOMARKER SEARCHIG WITHI MICROBIOME OF HARMFUL AD HARMELESS BACTERIA BIOMARKER SEARCHIG OF PROTEIS AD COGS BIOMARKERS SEARCHIG OF METABOLITES SYMBIOSIS I THE GUT DYSBIOSIS I THE GUT ITESTIAL DISEASES LABORATORY MEDICIE AP TECHOLOGY PUBLIC HEALTH

53 LOCAL AD PUBLIC DATA REPOSITORY 53

54 ACKOWLEDGEMETS Grant X "La proteomica in microbiologia: dallo studio dei singoli patogeni alla biologia dei sistemi complessi to L. Putignani/OPBG 2. Ricerca Corrente (201402G003251) Bambino Gesù Hospital and Research Institute (OBG) to L. Putignani/OPBG 3. Ricerca Corrente (RC P002991) Bambino Gesù Hospital and Research Institute (OBG) to L. Putignani/OPBG 4. Ricerca Corrente (RC G003050) Bambino Gesù Hospital and Research Institute (OBG) to L. Putignani/OPBG 5. Italian Proteomics Association, ItPA, Mobility fellowship, 2011, Studio proteomico dell'effetto modulante dell allattamento materno sul gut microbiota del neonato to P. Vernocchi, UIBO/OPBG 6. OPBG-DICOFARM: The protein high mobility group box1, HMGB1, as new intestinal marker in neonates affected by EC 7. COTO CAPITALE 2012 Studio integrato del metagenoma e del metaproteoma in patologie pediatriche associate ad alterazioni del microbiota intestinale to Dallapiccola/Putignani OPBG 8. SEVETH FRAMEWORK PROGRAMME, ICT , MD-PEDIGREE, GRAT UMBER , to Dallapiccola/Putignani FODAZIOE LUCA BARBARESCHI

55 THE OPBG SCIETIFIC AD CLIICAL COSORTIUM

56 META-OMICS COLLABORATIOS GEOMICS GROUP ADREA URBAI AXEL KARGER PROTEOMICS GROUP DAILO ERCOLII CAROLI KOLMEDER WILLEM DE VOS STEFAO LEVI MORTERA METABOLOMICS GROUP CESARE MAETTI ME GUERZOI CRISTIA VALERIO ROSALBA LACIOTTI LUCA CASADEI ALFREDO MICCHELI

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