GC-MS/SPME results

“Utilizzo di piattaforme metaomiche nella descrizione del
microbiota intestinale di
importanti patologie croniche:
il caso della fibrosi cistica e
della steatosi epatica
MICROBIOTA TECHNOLOGY AND DATA
MANAGEMENT
LORENZA PUTIGNANI, UNITA' DI METAGENOMICA E DI PARASSITOLOGIA
Children’s Hospital and Research Institute, IRCCS, Rome, Italy
2
THE INSIDE STORY, Mullard, Nature 2008……..
DIFFERENTI LIVELLI DI
ORGANIZAZZIONE BIOLOGICA
Diet
DESTRUTTURAZIONE DEI LIVELLI
INTESTINE AS
BIOREACTOR
Microbiota
Host
TECHNOLOGY AND DATA MANAGEMENT
Del Chierico et al., 2014, IJMS 41875R1-2014, Metagenomics in NAFLD: tools and applications
3
“OPBG –OMICS and METAOMICS CONSORTIUM”
THE SYSTEMS MEDICINE
MODELS: MOUSE AND HUMAN
TOWARDS METABOTYPES…..
TOWARDS ENTEROTYPES…..
DIET
Obesity
Healthy nutrition
Resistance to infection
HEALTH
HOMEOSTASIS
DYSFUNCTION
Metabolic syndrome
Diabetes
Oral tolerance
Organ vitality
DISEASE
INTESTINE AS
BIOREACTOR
Healthy ageing
Infection
Cancer
IBD
From Putignani et al., 2014, In press, “Pediatric Research-Nature
4
From Putignani et al., 2014, In press, “Pediatric Research-Nature
5
-OMICS PHENOTYPES AND CHARTS
SYMBIOSIS
IN THE GUT
DYSBIOSIS
IN THE GUT
INTESTINAL
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
DESCRIPTION
De Filippo et al., 2010, PNAS; Dominguez-Bello et al., 2010, PNAS; Palmer et al.,
2007, PLOS Biology; Koenig et al., 2011, PNAS; Meghan, et al., CMAJ; Jost et al., 2012
Yatsunenko et al., Nature 486, 222–227 (14 June 2012)
CATEGORIES
Arumugam et al., 2011, Nature 473 (12 May 2012)
Schloissnig et al., 2013, Nature 493 (12 January 2013)
From Putignani et al., 2014, In press, “Pediatric Research-Nature
6
GUT MICROBIOTA RELATED DISEASES
From Putignani et al., 2014, In press, “Pediatric Research-Nature
GUT DISEASE
“OPBG –OMICS AND METAOMICS
ACTIVITIES”
E’ COME SE PARLASSIMO DI UNA DEFRAMMENTAZIONE DI UN COMPUTER
1. BARCODED SAMPLES
2. CULTUROMICS
3. GENOMICS
4. METAGENOMICS
5. METABOLOMICS
6. METAPROTEOMICS
4. METAGENOMICS DI UN FC GUT
8
CULTUROMICS
Saliva
PROTEOMICS
Stool
Axenic cultures
Sample collection
Microbiological cultures
Del Chierico et al., 2013, IJMS 41875R1-2013
Metagenomics in NAFLD: tools and applications
9
HUMAN INTESTINAL TRACTCHIP (HITChip)
DNA
extraction
optimised for
“very earlylife samples”
Microbial
community
profiling
HITChip
Rajilic-Stojanovic´
et al., 2009
Italian Baby Trial
Metagenomics of the
human intestinal tract
From Petrucca et al., 2014, Manuscript in preparation,
“American Journal of Gastroenterology”
GENOMICS
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 rRNA gene from 1,140
microbial phylotypes present in
the human gut.
Optimised procedures for
meconium DNA extraction: 95%
Pearson correlation index
10
HUMAN MICROBIOME (PYROSEQUENCING)
METAGENOMICS
Next generation sequencing, NGS
DNA EXTRACTION
TYPING
REAL-TIME PCR
PYROSEQUENCING
11
METABOLOMICS: towards biomarker searching
Technological platforms
GC-MS
Data analysis
GC-MS chromatogram
Statistical approach
PCA
Heat map
1H-NMR
1H-NMR
spectrum
Sample
collection: blood,
plasma,
faeces, urine and
saliva
Scatter plot
ANOVA
LC-MS
LC-MS spectrum
Del Chierico et al., 2014, IJMS 41875R1-2014, Metagenomics in NAFLD: tools and applications
12
METAPROTEOMICS
METAPROTEOMICS
METAPROTEOMICS
An original metaproteomic pipeline to identify newborn mouse gut phylotypes
BACTERIAL
PURIFICATION
NEWBORN
MOUSE
GUT HOMOGENATE
METAPROTEOMIC (LC-MS2)
EBS
Proxeon EASY-nLCTM
MUCOSAL
AND FECAL
BACTERIA
PROTEINS
NANO-LC
chromatogra
m
PEPTIDES
AmaZon Ion Trap
MS
nanoFlow ESI
Sprayer
MS and MS2
SPECTRA
BIOINFORMATIC WORKFLOW
MASCOT DATA
Peptides to protein hits
Protein hits to OTUs
LINK TO FUNCTION
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;97:17-26. doi: 10.1016/j.jprot.2013.10.025
13
Taxonomic classification of Balb/c and Rag2ko gut microbiota characterized by axenic based MALDITOF MS approach
Balb/c
3
7
Rag2ko
14
100
90
Relative abundance (%)
80
70
60
50
40
30
20
10
Del Chierico et al., Journal of Proteomics, 2014 Jan 31;97:17-26
3
7
14
age (days)
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
Del Chierico et al., Journal of Proteomics, 2014 Jan 31;97:17-26
ORIGINAL BIOINFORMATIC PIPELINE
16S rRNA barcoded amplicons
Statistical analysis
Emulsion -PCR
Database searching
Principal component analysis
Pyrosequencing
RANKING
OTUs identification
Raw sequences
1
CCCCAAGCGATACCATGCAATCGAACGGT
CAGGAAGCCCGACTCGATCCAATTCCGGG
2
GGTCCAGACTCTACGAAAGGCAGCAGTGG
GAATATTGCGGTTAAACTAGTAGCCATGCA
OTUs grouping
OTU phylogeny
3
CTGGGAACTGATCTGATACTGGCAAGCTT
GAGTCTCGTATTAAAACCCGTAGTCACTG
SC
Denoising procedures
Relative abundance of OTUs
Del Chierico et al., 2013, IJMS 41875R1-2013 Metagenomics in NAFLD: tools and
applications
Canonical Correlation analysis
Metadata analysis
16
OPBG microbiome Projects
Physiological status/disease1
Healthy subjects
(reference cohort for all sub-projects, 1-18
years) (consortium OBG, RC)
Early life vaginally-delivered neonates (0-1
years) (RF-2011-02346886)
Juvenile Idiopathic Arthritis (MD Paedigree
Project)
Obeses (consortium OBG)
Obeses (MD Paedigree Project)
Patient (n°) Sample (n°) Years of project
17
samples/year Project Priority
200
200
2
100
1
30
210
3
105
1
GUT DISEASE
200
400
4
45
1
150
180
150
540
2
4
75
135
1
1
Cystic fibrosis (consortium OBG/Milan,
Regione Lazio CF Grant, RC)
70
703
2
35
1
Obeses (consortium OBG, RC)
NASH/NAFLD (consortium OBG, RC)
40
30
40
30
1
1
40
30
2
1
Williams Syndrome (consortium OBG, RC)
30
30
1
30
3
TBD
TBD
3
10
2
300
96
300
96
3
3
100
32
1
2
Autism spectrum disorder (ASD)
TBD
TBD
3
-
2
GRANDI OBESI
TBD
TBD
3
-
-
ORAL COMMUNITIES
TBD
TBD
3
-
-
Idiopathic nephrotic syndrome (RC)
IBD/IBS (HORIZON 2020)
Metabolic diseases
Total sample number (minimum)
20
TBD
TBD
1346
TBD
TBD
TBD
2066
1
4
300
1
1
-
-
-
NEC/bowel syndromes/congenital gut
malformations (RC)
Mediterrean diet (HORIZON 2020)
Mice Model (RC 2014)
GUT
PROGRAMMING
Gut microbiota in cystic fibrosis (CF) patients:
a combined -omic translational workflow
P. Vernocchi1,2,3, M. Valerio4, F. Del Chierico1,2, L. Casadei4, A. La Storia5, F. De Filippis5, G.
Salerno5, A. Petrucca1,2,6, F. Majo7, C. Rizzo8, E. Fiscarelli8, C. Manetti4, M. Muraca8, V. Lucidi7,
D. Ercolini5, B. Dallapiccola9 and L. Putignani1,2
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, Naples, 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
‘CFTR-opathies’: phenomics
ID
Sweat test
Age
Gender
(Cl,
(years)
mmol/l)
Genotype
CFTR
Pancreatic
status
W/L
Meconium or BMI
ileus
(Z-score)
PHENOMICS
19
Lung colonization
Antibiotics
Use of
probiotics
Disease
severity
severe
P-06-2
6
M
77
F508del/dele2,3
insufficient
yes
-1,5
Staphylococcus aureus
no
P-06-6
6
F
81
F508del/L1065P
Insufficient
no
2,1
yes
P-06-7
6
F
95
F508del/F508del
insufficient
no
-1
Serratia marcescens
Stenotrophomonas
maltophilia
no
no
no
no
severe
P-06-8
6
F
125
F508del/F508del
insufficient
0,1
S. aureus
no
no
severe
P-07-1
5
M
91
F508del/F508del
insufficient
no
yes
-1
S. aureus
no
severe
P-07-4
5
F
80
621+1G>T/R553X
insufficient
yes
-0,8
S. maltophilia
no
yes
no
insufficient
F508del/G1244E
621+1G>T/1898+G>
insufficient
A
insufficient
F508del/N1303K
no
0,6
negative
yes
no
severe
no
2,5
Pseudomonas aeruginosa
no
no
severe
no
0
P. aeruginosa
yes
no
severe
severe
severe
P-07-5
5
M
98
P-07-6
5
F
121
P-07-7
5
F
94
P-07-9
5
F
95
F508del/M1V
insufficient
no
0,3
S. maltophilia
yes
no
severe
P-07-10
5
F
89
F508del/P5L
sufficient
no
1
negative
no
no
mild
P-08-2
4
M
98
F508del/R1162X
no
0,4
P. aeruginosa
yes
no
severe
F508del/Unknown
sufficient
insufficient
no
2,1
negative
no
-0,1
P. aeruginosa
yes
yes
no
mild
no
severe
P-08-3
4
M
140
P-08-4
4
F
99
F508del/712-1G>T
insufficient
P-08-6
4
M
100
F508del/F508del
insufficient
no
-1
S. aureus
no
no
severe
P-08-8
4
F
95
F508del/R553X
insufficient
no
-0,2
S. aureus
yes
N1303K/2694 T>G
no
0,44
negative
no
yes
no
severe
sufficient
yes
-1,27
P. aeruginosa
mild
yes
-0,48
Escherichia coli
yes
yes
no
yes
severe
no
severe
severe
P-09-2
4
F
77
P-09-4
3
M
93
N1303K/2184m5A
insufficient
P-09-6
3
M
96
F508del/F508del
insufficient
F508del/R1070Q
insufficient
no
3,2
P. aeruginosa
yes
-0,4
S. maltophilia
no
-0,1
P. aeruginosa
yes
yes
no
P-09-7
3
M
90
mild
P-10-2
3
F
78
F508del/S1253R
insufficient
P-10-4
2
F
85
F508del/G542X
insufficient
yes
no
P-10-7
2
M
63
1717-1G>A/D1152H
sufficient
no
-0,7
P. aeruginosa
no
no
severe
P-10-13
P-11-3
2
2
M
F
81
61
1717-1G>A/S1455X
G542X/N187K
sufficient
no
no
mild
P-11-4
1
M
114
1,3
Haemophilus influentiae
yes
no
no
no
mild
mild
P-11-7
1
F
100
F508del/W1282X
17171G>A/Unknown
no
no
negative
negative
yes
sufficient
insufficient
0,6
0,54
insufficient
no
-1,2
S. aureus
no
no
severe
P-11-8
1
F
84
insufficient
yes
-3,1
S. aureus
no
no
severe
F508del/3659delC
mild
RANKING IN FC GUT: PHYLUM
20
FUNCTIONAL METAGENOMICS
RANKING IN FC GUT: CLASS
21
RANKING IN FC GUT: FAMILY
22
RANKING IN FC GUT: GENUS
23
RANKING IN FC GUT: SPECIES
24
UNIFRAC: β-diversity analysis, UNWEIGHTED and WEIGHTED
25
26
UNIFRAC: β-diversity analysis, UNWEIGHTED and WEIGHTED
27
Statistics at Phylum, Family, Genera and Species level (ANOVA)
Tenericutes were more abundant in negative samples (avg. 14% in N vs 1.8% in P) (p<0.001)
Erysipelotrichaceae (avg. 14% in N vs 1.8% in P), Ruminococcaceae (avg. 13% in N vs 3.8% in P)
and Lachnospiraceae (avg. 22% in N vs 12% in P) families had a significantly lower occurrence in P,
while Clostridiaceae (avg. 3% in N 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 N vs 0.19 in P) (p<0.001)
Eggerthella lenta (avg. 1.1% in N vs 0.05 in P) (p<0.05)
Ruminococcus spp. (avg. 4% in N vs. 0.2 in P) (p<0.01)
Faecalibacterium praumitzii (avg. 2.5% in N vs. 0.04% in P) (p<0.01)
And an higher occurrence of:
FUNCTIONAL METAGENOMICS
Escherichia spp. (avg. 0.2% in N vs. 7.3% in P) (p<0.01)
Clostridium spp. (avg. 2% in N vs. 11% in P) (p<0.01)
In particular, Clostridium difficile (avg. 0.01% in N vs. 2.5% in P) (p<0.05)
From Vernocchi et al., Manuscript in preparation, 2014
28
GC-MS/SPME (solid phase microextraction) : VOLATILOME
700
500
HC
400
300
CF
benzyl alcohol
600
2 ethyl 1 hexanol
1 octen 3 ol
2-dodecanol
500
•ethanol
•1-propanol
•1-pentanol
1 hexanol
ppm equivalent
HC
600
ppm equivalent
•1-butanol
•2-ethyl1hexanol
•Phenyl ethyl
alcohol
700
benzyl alcohol
2 ethyl 1 hexanol
1 octen 3 ol
2-dodecanol
1 hexanol
1-butanol
1-propanol
ethanol
1-butanol
400
1-propanol
ethanol
300
200
200
100
100
0
0
CF
P
-4
07
N-6
07
N-5
07
N-6
10
N-9
11
N-9
09
N-5
08
N-6
06
N-6
11
N-3
11
N-2
10
N-4
09
N-3
07
N-6
09
N-8
09
N-1
10
N-5
10
N-8
11
N-4
11
N-7
11
N-1
08
N-5
11
N-2
11
N-1
11
N-5
06
N-4
06
N-3
06
N-2
06
N-3
10
N-4
08
N-7
09
N-3
08
N-1
06
N4
-0
10
N-
-1
18
P
P
P
P
P
P
P
P
P
P
P
P
P
P
P
P
P
P
P
P
P
P
P
P
P
P
P
P
P
-1
-0
-1
-0
-0
-0
-0
-0
-0
-0
-1
-0
-1
-0
-0
-0
-0
-0
-0
-0
-0
-0
-0
-1
-1
-1
-1
-1
-0
1709986787161677778889900011610
10
13
7
6
3
7
4
8
9
9
6
3
2
1
5
6
7
2
4
6
2
4
2
4
7
4
7
8
CF samples
HC samples
Median value and total amount (ppm) of Alcohols were higher in HC children
but…..
1200
1000
HC
4 methyl pentanoate
1000
ethyl decanoate
ethyl decanoate
ethyl octanoate
butyl hexanoate
800
butyl hexanoate
800
butyl butyrate
propyl butyrate
600
2-methyl butanoate
butyl acetate
ethyl butyrate
400
ethyl acetate
ppm equivalent
propyl hexanoate
iso amyl butyrate
propyl hexanoate
iso amyl butyrate
butyl butyrate
600
propyl butyrate
2-methyl butanoate
ethyl butyrate
ethyl acetate
methyl acetate
200
200
CF
0
0
•ethyl
acetate
•butyl
butyrate
•4-methyl
pentanoate
butyl acetate
400
methyl acetate
HC
CF
propyl decanoate
propyl decanoate
ethyl octanoate
ppm equivalent
•methyl
acetate
•butyl
acetate
1200
4 methyl pentanoate
-4
07
N-6
07
N-5
07
N-6
10
N-9
11
N-9
09
N-5
08
N-6
06
N-6
11
N-3
11
N-2
10
N-4
09
N-3
07
N-6
09
N-8
09
N-1
10
N-5
10
N-8
11
N-4
11
N-7
11
N-1
08
N-5
11
N-2
11
N-1
11
N-5
06
N-4
06
N-3
06
N-2
06
N-3
10
N-4
08
N-7
09
N-3
08
N-1
06
N4
-0
10
N-
PPPPPPPPPPPPPPPPPPPPPPPPPPPPPP11
11
09
09
0
06
07
08
0
07
10
1
06
11
06
0
07
07
07
0
08
08
09
0
10
10
1
11
11
06
-8
-1
-7
-6 8 -3
-7
-4
-8 7 -9
-1
-1 1 -9
-6
-3
-2 7 -1
-5
-6
-7 8 -2
-4
-6
-2 9 -4
-2
-4 0 -7
-4
-7
-8
0
0
3
CF samples
HC samples
Median value and total amount (ppm) of Esters were higher in CF children
From Vernocchi et al., Manuscript in preparation, 2014
ppm equivalent
2000
1000
1000
-8
06
P-7
11
P-4
11
P-7
10
P-4
10
P-2
10
P-4
09
P-2
09
P-6
08
P-4
08
P-2
08
P-7
07
P-6
07
P-5
07
P-1
07
P-2
06
P-3
11
P-6
06
P-9
11
P3
-1
10
P0
-1
07
P-9
07
P-8
08
P-4
07
P-7
06
P-3
08
P-6
09
P-7
09
P0
-1
11
P-8
11
P-
4000
3500
3000
2500
0
-4
07
N-6
07
N- 5
07
N-6
10
N- 9
11
N- 9
09
N-5
08
N- 6
06
N-6
11
N- 3
11
N-2
10
N- 4
09
N-3
07
N- 6
09
N- 8
09
N-1
10
N- 5
10
N-8
11
N- 4
11
N-7
11
N- 1
08
N- 5
11
N-2
11
N- 1
11
N- 5
06
N- 4
06
N-3
06
N-2
06
N-3
10
N- 4
08
N- 7
09
N-3
08
N- 1
06
N- 04
10
N-
6000
5000
0
0
CF samples
HC samples
-8
06
P-7
11
P-4
11
P-7
10
P-4
10
P-2
10
P-4
09
P-2
09
P-6
08
P-4
08
P-2
08
P-7
07
P-6
07
P-5
07
P-1
07
P-2
06
P-3
11
P-6
06
P-9
11
P3
-1
10
P0
-1
07
P-9
07
P-8
08
P-4
07
P-7
06
P-3
08
P-6
09
P-7
09
P0
-1
11
P-8
11
P-
-4
07
N-6
07
N-5
07
N-6
10
N-9
11
N-9
09
N-5
08
N-6
06
N-6
11
N-3
11
N-2
10
N-4
09
N-3
07
N-6
09
N-8
09
N-1
10
N-5
10
N-8
11
N-4
11
N-7
11
N-1
08
N-5
11
N-2
11
N-1
11
N-5
06
N-4
06
N-3
06
N-2
06
N-3
10
N-4
08
N-7
09
N-3
08
N-1
06
N4
-0
10
N-
From Vernocchi et al., Manuscript in preparation, 2014
CF samples
HC samples
ppm equivalent
propionic acid
ppm equivalent
1500
•2,6 phenol
4000
2000
acetic acid
2000
3 methyl butanoic acid
3000
CF
p-cresol
phenol
2,6 bis phenol
HC
4500
acetic acid
propionic acid
2500
butyric acid
2500
butyric acid
Total amount (ppm) of Phenols was similar between HC and CF
Median value (ppm) of Phenols was higher in HC children
•Acetic acid
•Propionic acid
•3 methyl
butanoic acid
3 methyl butanoic acid
CF
valeric acid
3000
5000
p-cresol
phenol
2,6 bis phenol
•Phenol
•4 methyl
pehnol
CF
1500
2000
HC
1000
CF
500
1000
HC
4000
4000
2-methyl butyric acid
3500
2-methyl butyric acid
HC
3500
valeric acid
•Butyric acid
•Valeric acid
3000
ppm equivalent
29
GC-MS/SPME results
1500
0
500
Median value and total amount (ppm) of SCFAs were higher CF children
500
30
GC-MS/SPME results
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
Chemical class
Healthy children (HC)
median
IQR(25%-75%)
Cystic fibrosis patients (CF)
median
IQR(25%-75%)
acids
101.8
10.8-951.9
357.4
0.0-1340
0.36
alcohols
19.61
5.1-112.0
9.5
0.0-38.7
0.08
alcanes
8.35
0.0-13.1
35.0
4.0-158.1
0.03
alkenes
1.8
0.0-12.8
5.8
2.0-27.3
0.49
aldehydes
32.8
1.4-140.5
95.3
0.0-247.8
0.77
ketones
58.2
5.7-366.1
63.2
6.4-485.8
0.55
sulphur compounds
251.9
151.2-352.6
118.9
94.1-143.7
1.00
esters
1.6
0.0-15.9
36.5
7.6-159.4
<0.0001
eterocycles
18.0
10.5-37.6
0.0
0.0-7.6
0.12
phenols
1614.0
26.4-11771.0
564.1
160.8-12558.0
1.00
furan
0.0
0.0-0.0
0.0
0.0-0.0
-
furanones
57.9
0.0-116.0
8.7
7.9-9.5
0.67
indoles
382.2
22.8-2290.0
312.1
0.0-58.6
0.56
piperidine
0.0
0.0-0.0
0.0
0.0-0.0
-
pirazine
0.0
0.0-0.0
0.0
0.0-0.0
-
pyridine
71.7
12.5-130.8
253.1
0.0-506.2
1.00
pyrimidine
0.0
0.0-0.0
0.0
0.0-0.0
-
terpenes
17.8
4.4-46.7
21.6
0.0-68.5
0.23
*Data are the means of three independent experiments (n = 3) for each children
From Vernocchi et al., Manuscript in preparation, 2014
p-value
31
GC-MS/SPME results
CF children
0.8
0.6
0.4
PC2
0.2
0.0
-0.2
-0.4
-0.6
-0.8
-0.6
-0.4
-0.2
Healthy children
0.0
0.2
0.4
0.6
0.8
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= 0.011266)
and PC2 (p= 1.91653E-05).
From Vernocchi et al., Manuscript in preparation, 2014
32
GC-MS/SPME results
CF children
0.8
HC
CF
gamma curcumene
0.6
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.
propyl butyrate
acetic acid
ethyl
butyrate
2,3 octanedione
propyl acetate
amyl acetate
ethyl octanoate
butyl butyrate
2 pentyl furan
gamma terpinene
dodecane
heptanal
0.4
AR curcumene
octanoic acid
octenal
0.2
propionic acid
2-nonanone
2-heptanone
octanal
tetradecane
Hexanal
ethyl acetato
nonanal
2 heptanol
butyl acetato
Indole
alpha pinene
1-propanol
2,4 octanedione
ethanol
heptane
2-propanone
0.0
2,3 butanedione
PC2
pyridine
3 methyl indole
2-pentanone
-0.2
2-octanol
2-dodecanol
2 ethyl hexanal
2-nonanol
methyl pirazine
2,6 octadiene
-0.4
3-heptanol
2,3 butanediol
linalool
3-hexanone
The score plot shows the
differentiation between the
CF and HC , while the loading
plot highlights which
metabolites are responsible
in separating the samples.
Acetone
2-hexanol
benzaldehyde
decanol
octyl acetate
phenol
heptanoic acid
2-butanol
-0.6
-0.8
-0.8
-0.6
-0.4
-0.2
0.0
0.2
0.4
PC1
Healthy children
From Vernocchi et al., Manuscript in preparation, 2014
0.6
0.8
33
METABOLOMICS
GC-MS/SPME
results
campioni FC tutti completo 28 GIUGNO.M4
(OPLS/O2PLS-DA)
1
2
t[Comp. 1]/to[XSide Comp. 1]
Colored according to classes in M4
7
P-06-6
P-10-4
6
5
N-09-8
4
3
N-10-2
N-08-1
N-09-7
N-10-1
2
P-11-9
N-10-6
P-11-10
N-11-5
N-08-3 N-09-9
N-07-4
N-11-4
N-10-04
N-08-5
N-11-3
N-09-4
N-09-6
N-06-6
N-06-2(2)
N-06-3 N-11-6 N-06-1
N-07-3
N-11-2
N-08-4
N-07-6
N-10-3
N-11-9
to[1]
1
Healthy children
0
-1
P-08-6
CF children
P-09-2P-11-7
P-08-3
P-07-7P-07-4
P-10-2
P-08-1
P-07-5
N-10-5
P-10-7
N-11-1
-3
P-09-7
P-10-13
P-07-9
P-07-10
-2
P-11-3
P-06-7
P-08-2
P-07-6
N-06-4
N-11-7
P-07-1
-4
N-06-5
N-07-5
-5
-6
-7
-6
-5
-4
-3
-2
-1
0
1
2
3
4
5
6
7
t[1]
R2X[1] = 0.0356105
Ellipse: Hotelling T2 (0.95)
R2X[XSide Comp. 1] = 0.037364
SIMCA-P+ 12 - 2013-06-28 15:54:51 (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 <
0.0001).
From Vernocchi et al., Manuscript in preparation, 2014
34
1H-NMR results
PCA - Data set: 31 FC patients and 37 healthy children
15
PC4 (5%)
10
5
0
P_09_2
P_7_10
-5
FC
Controls
-10
-20
-15
-10
-5
0
5
10
15
PC1 (12%)
NMR 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< 0.0001).
From Vernocchi et al., Manuscript in preparation, 2014
35
Comparison of clinical characteristics of patient’s cohort and
their corresponding metabolic profiles
FACTORS
PATIENTS
PRINCIPAL COMPONENTS
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.
Age
All
-0.232
-0.231
-0.252
0.365
0.110
FC
-0.209
-0.132
-0.317
0.236
0.312
-0.442 (0.04)
-0.367
-0.160
0.448 (0.02-0.03)
-0.157
0.283
-0.159
-0.305
0.070
-0.101
Healthy
Sweat test
FC
Sources of variability in metabolomic data of FC patients and healthy children as measured by
analysis of variance (ANOVA). 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
0.017 (0.898)
0.540 (0.465)
0.032 (0.859)
0.396 (0.531)
1.150 (0.288)
IP
All
12.738 (0.001)
0.318 (0.575)
0.027 (0.871)
37.148 (<0.0001)
4.858 (0.031)
Antibiotics
All
2.310 (0.133)
1.312 (0.256)
18.757 (<0.0001)
8.828 (0.004)
0.753 (0.389)
From Vernocchi et al., Manuscript in preparation, 2014
36
Comparison of clinical characteristics of FC patient and
their corresponding metabolic profiles
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
PC1
1.013 (0.376)
0.381 (0.768)
0.340 (0.883)
0.934 (0.481)
1.086 (0.412)
1.605 (0.216)
PC2
1.26 (0.30)
0.559 (0.647)
0.711 (0.621)
1.287 (0.307)
0.402 (0.903)
0.711 (0.701)
PC3
0.72 (0.496)
1.034 (0.395)
1.169 (0.353)
0.703 (0.60)
1.146 (0.387)
1.693 (0.198)
PC4
1.595 (0.222)
0.472 (0.705)
0.945 (0.470)
0.024 (0.995)
1.486 (0.238)
0.513 (0.825)
PC5
0.987 (0.386)
0.674 (0.577)
1.153 (0.361)
0.039 (0.997)
1.782 (0.150)
0.633 (0.762)
From Vernocchi et al., Manuscript in preparation, 2014
37
1H-NMR results
OPLS-DA: FC patients without IP vs. FC with IP vs. Healthy children
14
12
10
8
LV2
6
4
P_09_2
2
0
MILD
-2
P_7_10
-4
-6
-6
-4
-2
0
2
FC grading?
4
6
8
LV1
FC patients without IP
FC patients with IP
Healthy children
The metabolic FC effects is partially overwhelmed by IP and antibiotic consumption factors (ANOVA: PC1 (12.738 (p = 0.001)), PC4 (37.148
(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 NMR 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 < 0.0001).
From Vernocchi et al., Manuscript in preparation, 2014
1H-NMR 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
(ppm)a
0.82, 0.94
0.88, 1.54, 2.14
0.89
0.92, 0.99
0.95
0.97, 1.03
1.04, 2.17
1.46
1.73
1.90
2.12, 2.63
2.28, 3.01
2.34
2.74
3.19
3.39, 3.42, 3.45,
3.47, 4.63, 5.21
3.54
5.78, 7.52
6.89, 7.19
7.30, 7.36
2.66, 2.79
7.43
Metabolitesb,c
2-Hydroxyisovalerate
Butyrate
Isovalerate
Ile
Leu
Val
Propionate
Ala
Arg
Acetate
Methionine
4Aminobutyrate
Glu
Sarcosine
Choline
Glucose
Average Fold change
over control
0.597
p value
0.039
0.610
0.624
0.602
0.680
0.751
0.625
0.672
0.916
0.672
0.653
1.968
0.008
0.064
0.002
0.018
0.086
0.002
0.006
0.819
0.001
0.004
0.010
0.377
0.475
3.179
0.531
<0.0001
<0.0001
0.017
0.003
Glycine
Uracyl
Tyr
3-Phenyllactate
0.883
0.323
0.571
0.694
0.383
<0.0001
0.006
0.076
Asp
Phe
0.688
1.024
0.046
0.907
From the analysis of O-PLS-DA loadings, the
CF patients showed lower levels of:
1.
SCFA (acetate, propionate, and butyrate),
2.
amino
alanine,
(isoleucine,
arginine,
aspartate,
leucine,
methionine,
glycine,
valine,
glutamate,
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.
Mid spectral integral region, i.e., 3.26 represents ppm 3.23 to 3.27; only spectral
regions containing only one metabolite are reported.
a
acids
39
1H-NMR results
1) The role of gender and age as potential confounding factors was investigated.
No 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 = -0.442 t-test 2006 vs. 2011 p = 0.03;
2007 vs. 2011 p = 0.04) as well as a positive correlation with PC4 were found (ρ = 0.448; t-test
2006 vs. 2009 p = 0.02; 2006 vs. 2010 p = 0.03; 2006 vs. 2011 p = 0.003; 2007 vs. 2011 p =
0.02)
2) No 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) No 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
1H-NMR results
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
1H-NMR results
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
Gut microbiota in NAFLD-NASH patients:
a combined -omic translational workflow
F. Del Chierico1,2, P. Vernocchi1,2,3, Alisi, A.4, V. Giorgio5, A. Petrucca1,2,6, C. Rizzo7, B.
Dallapiccola8, V. Nobili4,5 and L. Putignani1,2
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
2005 N
2005
N
2000
FL
2002
N
2002 N
2000
FL
1999
N
2005
N
1999
N
2002
FL
2004
FL
1998
FL
2002
FL
1999 N
2005 N
1999
N
2002
FL
2004
FL
1998
FL
2002
FL
RANKING IN NASH GUT: CLASS
43
2002
FL
1998
FL
2004
FL
2002
FL
1999
N
2005
N
1999
N
2002
N
2000
FL
2005
N
2002
FL
1998
FL
2004
FL
2002
FL
1999
N
2005
N
1999
N
2002
N
2000
FL
2005
N
RANKING IN NASH GUT: ORDER
44
2002
FL
1998
FL
2004
FL
2002
FL
1999
N
2005
N
1999
N
2002
N
2000
FL
2005
N
2002
FL
1998
FL
2004
FL
2002
FL
1999
N
2005
N
1999
N
2002
N
2000
FL
2005
N
RANKING IN NASH GUT: FAMILY
45
2004
FL
2002
FL
1999
N
2005
N
1999
N
2002
N
2004
FL
2002
FL
1999
N
2005
N
1999
N
2002
N
2005
N
2005
N
2000
FL
1998
FL
1998
FL
2000
FL
2002
FL
2002
FL
RANKING IN NASH GUT: GENUS
46
2005
N
2000
FL
2002
N
1999
N
2005
N
1999
N
2002
FL
2004
FL
1998
FL
2002
FL
2005
N
2000
FL
2002
N
1999
N
2005
N
2002
FL
2004
FL
1998
FL
2002
FL
RANKING IN NASH GUT: SPECIES
48
GC-MS/SPME results
//
2500
1-heptanol
1 octen 3 ol
2000
ppm
1500
1 hexanol
1-pentanol
iso amyl alcohol
Alcohols in
particular:
•1-heptanol
•1-octen 3 ol
•1-hexanol
•1-pentanol
•Iso amyl alcohol
•1-propanol
•ethanol
1-butanol
1-propanol
ethanol
1000
500
N
N
A
S
H
3
A /9
S 8
H
N 26
A /9
S 8
H
3
N 0/
A 98
S
H
N 4
A /9
S 9
H
N 11
A /9
S 9
H
2
N 0/9
A 9
S
H
N 2
A /0
S 0
H
N 17/
A 0
S 0
H
N 28
A /0
S 0
H
N 16
A /0
S 1
H
N 27
A /0
S 1
H
1
N 9/0
A 2
S
H
N 9
A /0
S 2
H
1
N 0/0
A 2
S
H
N 7
A /0
S 2
H
N 15
A /0
S 3
H
1
N 4/0
A 3
S
H
N 5
A /0
S 3
H
N 25
A /0
S 4
H
1
N 2/0
A 4
S
H
N 8/0
A 5
S
H
N 6
A /0
S 5
H
N 29
A /0
S 5
H
N 24
A /0
S 6
H
23
/0
6
0
samples
2006
1998
//
300
250
hexyl acetate
iso amyl butyrate
butyl 2-methylbutyrate
ethyl hexanoate
butyl propanoato
propyl butyrate
200
butyl acetato
150
100
50
8
30
N /98
A
S
H
4/
9
N
9
A
S
H
11
/9
N
9
A
S
H
20
/9
N
A 9
S
H
2/
00
N
A
S
H
17
/0
N
0
A
S
H
28
/0
N
0
A
S
H
16
/0
N
1
A
S
H
27
/0
N
1
A
S
H
19
/0
N
2
A
S
H
9/
02
N
A
S
H
10
/
N 02
A
S
H
7/
N
02
A
S
H
15
/0
N
3
A
S
H
14
/
N 03
A
S
H
5/
03
N
A
S
H
25
/0
N
4
A
S
H
12
/0
N
4
A
S
H
8/
N 05
A
S
H
6/
05
N
A
S
H
29
/0
N
5
A
S
H
24
/0
N
6
A
S
H
23
/0
6
26
/9
S
H
A
N
N
A
S
H
S
H
3/
98
0
A
ppm
methyl acetato
N
Esters in particular:
•methyl acetate
•butyl acetate
•propyl butyrate
•butyl propanoate
•ethyl hexanoate
•butyl 2methylbutyrate
•iso amyl butyrate
•hexyl acetate
samples
1998
2006
49
GC-MS/SPME results
//
//
10000
octanoic acid
heptanoic acid
9000
pentanoic acid/valeric acid
2-m ethyl butyric acid
butanoic/butyric acid
8000
propionic acid
acetic acid
7000
ppm
6000
5000
4000
3000
2000
1000
/0
6
/0
6
H
23
N
A
S
H
24
N
A
S
H
29
/0
5
H
6/
05
S
S
A
A
N
S
A
N
N
/0
4
H
8/
05
/0
4
H
12
H
25
S
A
S
A
N
N
/0
3
H
5/
03
N
A
S
H
14
H
15
S
A
N
S
A
N
N
/0
3
H
7/
02
/0
2
S
H
10
A
A
S
S
A
N
N
/0
2
H
9/
02
/0
1
N
A
S
H
19
/0
1
N
A
S
H
27
/0
0
N
A
S
H
16
/0
0
H
28
H
17
S
A
S
A
A
S
N
N
/9
9
H
2/
00
/9
9
H
20
H
11
S
A
N
N
A
S
S
H
A
N
N
/9
8
4/
99
/9
8
H
30
N
A
S
H
26
S
A
N
N
A
S
H
3/
98
0
Acids and SCFAs in
particular:
•acetic acid
•propionic acid
•butyric acid
•2-methyl butyric
acid
•valeric acid
•heptanoic acid
•octanoic acid
samples
2006
1998
//
//
8000
4-m ethyl benzaldehyde
Benzeneacetaldehyde
benzaldehyde
6000
nonanal
octanal
5000
Hexanal/caproaldehyde
4000
3000
2000
1000
N
A
S
H
3
A /9
S
8
H
2
N 6
A /9
S
H 8
3
N 0/
A 98
S
H
N 4
A /9
S
H 9
N 11
A /9
S
H 9
2
N 0/9
A
S 9
H
N 2
A /0
S
0
H
N 17
A /0
S
H 0
N 28
A /0
S
H 0
N 16
A /0
S
H 1
N 27
A /0
S
H 1
1
N 9/0
A
S 2
H
N 9
A /0
S
2
H
1
N 0/0
A
S 2
H
N 7
A /0
S
2
H
N 15
/
A
S 03
H
1
N 4/0
A
S 3
H
N 5
A /0
S
3
H
N 25/
A
0
S
4
H
1
N 2/0
A
S 4
H
N 8/0
A
S 5
H
N 6
A /0
S
5
H
N 29
A /0
S
H 5
N 24
A /0
S
H 6
23
/0
6
0
N
ppm
Aldehydes in
particular:
•hexanal
•octanal
•nonanal
•benzaldehyde
•benzeneacetaldeh
yde
•4-methyl
benzaldehyde
7000
samples
1998
From Del Chierico et al., manuscript in preparation,. 2014
2006
50
GC-MS/SPME results
//
//
80000
3 methyl phenol
2-methyl phenol
70000
4 methyl phenol/p-cresol
phenol
Phenols in
particular:
•phenol
•p-cresol
•2-methyl phenol
•3 methyl phenol
60000
ppm
50000
40000
30000
20000
10000
S
N
A
N
A
S
H
3/
98
H
26
/9
N
8
A
S
H
30
/9
N
8
A
S
H
4/
9
N
9
A
S
H
11
/
9
N
9
A
S
H
20
/9
N
9
A
S
H
2/
00
N
A
S
H
17
/0
N
0
A
S
H
28
/
N
0
0
A
S
H
16
/0
N
1
A
S
H
27
/0
N
1
A
S
H
19
/0
2
N
A
S
H
9/
N
02
A
S
H
10
/0
2
N
A
S
H
7/
02
N
A
S
H
15
/0
N
3
A
S
H
14
/0
3
N
A
S
H
5/
03
N
A
S
H
25
/0
N
4
A
S
H
12
/0
4
N
A
S
H
8/
05
N
A
S
H
6/
N
05
A
S
H
29
/0
N
5
A
S
H
24
/0
N
6
A
S
H
23
/0
6
0
samples
2006
1998
//
12000
11000
2-nonanone
6 m ethyl 5 hepten 2 one
10000
2-octanone
2,6-dim ethyl 4 heptanone
2-hexanone
9000
4 m ethyl 2 pentanone
2-pentanone
8000
2,3 butanedione
2-butanone
2-propanone/acetone
6000
5000
4000
3000
2000
1000
/0
6
/0
6
H
23
N
A
S
H
24
N
A
S
H
29
/0
5
H
6/
05
S
S
A
N
A
S
A
N
N
/0
4
H
8/
05
/0
4
H
12
S
H
25
S
A
N
A
S
A
N
N
/0
3
H
5/
03
/0
3
H
14
S
H
15
A
S
A
N
N
/0
2
H
7/
02
A
S
H
10
N
A
S
S
A
N
N
/0
2
H
9/
02
/0
1
N
A
S
H
19
/0
1
N
A
S
H
27
/0
0
N
A
S
H
16
/0
0
H
28
S
H
17
A
S
A
N
N
/9
9
H
2/
00
S
A
N
N
A
S
H
20
/9
9
4/
99
S
A
N
N
A
S
H
H
11
/9
8
/9
8
H
30
H
26
S
A
N
S
A
A
S
H
3/
98
0
N
ppm
7000
N
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
samples
1998
From Del Chierico et al., manuscript in preparation,. 2014
2006
51
GC-MS/SPME results
//
//
3500
//
3000
beta caryophyllene
trans alpha bergamotene
alpha terpinolene
2500
para cimene
gamma terpinene
delta 3-carene
beta pinene
ppm
2000
1500
1000
500
9
/9
N
A
S
H
2/
00
N
A
S
H
17
/0
N
0
A
S
H
28
/
0
N
0
A
S
H
16
/0
N
1
A
S
H
27
/
0
N
1
A
S
H
19
/0
2
N
A
S
H
9/
02
N
A
S
H
10
/0
2
N
A
S
H
7/
02
N
A
S
H
15
/0
N
3
A
S
H
14
/0
3
N
A
S
H
5/
0
N
3
A
S
H
25
/
0
N
4
A
S
H
12
/0
4
N
A
S
H
8/
05
N
A
S
H
6/
05
N
A
S
H
29
/0
N
5
A
S
H
24
/0
N
6
A
S
H
23
/0
6
9
/9
H
20
H
11
S
S
N
A
N
A
8
/9
4/
99
H
S
N
A
/9
H
30
S
S
H
26
N
A
N
A
N
A
S
H
3/
98
8
0
Terpenes in
particular:
•beta pinene
•delta 3-carene
•gamma terpinene
•para cimene
•alpha terpinolene
•trans alpha
bergamotene
•beta
caryophyllene
samples
2006
1998
3 methyl indole
2 methyl 1H indole
4000
3 methyl 1H indole
7-methyl indole
Indole
ppm
3000
1H-indole
5-methyl-2phenyl-1H-Indole
2000
1000
N
AS
H
AS 3/9
H 8
N 26
AS /9
H 8
N 30/
AS 98
N H4
AS /9
H 9
N 11
AS /9
H 9
2
N 0/9
AS 9
N H2
AS /0
H 0
N 17
AS /0
H 0
N 28
AS /0
H 0
N 16
AS /0
H 1
N 27
AS /0
H 1
1
N 9/0
AS 2
N H9
AS /0
H 2
1
N 0/0
AS 2
N H7
AS /0
H 2
N 15
AS /0
H 3
1
N 4/0
AS 3
N H5
AS /0
H 3
N 25
AS /0
H 4
1
N 2/0
AS 4
H
N 8/0
AS 5
N H6
AS /0
H 5
N 29
AS /0
H 5
N 24
AS /0
H 6
23
/0
6
0
N
Indoles in
particular:
•5-methyl2phenyl-1H-Indole
•1H-indole
•Indole
•7-methyl indole
•3 methyl 1H
indole
•2 methyl 1H
indole
•3 methyl indole
//
5000
sam ples
1998
From Del Chierico et al., manuscript in preparation,. 2014
2006
52
CONCLUSIONS
BIOMARKER SEARCHING WITHIN MICROBIOME OF
HARMFUL AND HARMELESS BACTERIA
BIOMARKER SEARCHING OF PROTEINS AND COGS
BIOMARKERS SEARCHING OF METABOLITES
SYMBIOSIS
IN THE GUT
DYSBIOSIS
IN THE GUT
•LABORATORY MEDICINE
•AP TECHNOLOGY
•PUBLIC HEALTH
INTESTINAL
DISEASES
LOCAL AND PUBLIC DATA REPOSITORY
53
ACKNOWLEDGEMENTS
54
1. Grant 201103X002675 "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 201302P002991) Bambino Gesù Hospital and Research Institute (OBG) to L. Putignani/OPBG
4. Ricerca Corrente (RC 201302G003050) 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, UNIBO/OPBG
6. OPBG-DICOFARM: “The protein high mobility group box1, HMGB1, as new intestinal marker in neonates affected by
NEC”
7. CONTO CAPITALE 2012 Studio integrato del metagenoma e del metaproteoma in patologie pediatriche associate ad
alterazioni del microbiota intestinale “ to Dallapiccola/Putignani OPBG
8.
SEVENTH FRAMEWORK PROGRAMME,
Dallapiccola/Putignani 2013
9. FONDAZIONE LUCA BARBARESCHI
ICT-2011.5.2,
MD-PEDIGREE,
GRANT
NUMBER 600932,
to
THE OPBG SCIENTIFIC AND CLINICAL CONSORTIUM
META-OMICS COLLABORATIONS
ANDREA URBANI
“GENOMICS GROUP”
AXEL KARGER
“PROTEOMICS GROUP”
WILLEM DE VOS
DANILO
ERCOLINI
STEFANO LEVI MORTERA
CAROLIN KOLMEDER
“METABOLOMICS GROUP”
ME GUERZONI
CESARE MANETTI
ROSALBA LANCIOTTI
ALFREDO MICCHELI
CRISTINA VALERIO
LUCA CASADEI
57
ROME CONSORTIUM MICROBIOTA
SALVATORE CUCCHIARA
MICHELE CICALA
ANTONIO GASBARINI
LAURA STRONATI
MARINA LOI
ANNAMARIA ALTOMARE
MICHELE GUARINO
58
OUR META-OMICS OPBG GROUP
LORENZA PUTIGNANI
FEDERICA DEL CHIERICO
STEFANO GARRONE
ANDREA PETRUCCA
PAMELA VERNOCCHI

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