03/11/2014
Intestinal microbiota,
diet and health.
Kieran Tuohy
Alimentazione
Agricoltura
Fondazione Edmund Mach, TN
Migliore output con minore input
Fondazione Edmund Mach
Research & Innovation Centre
The human gut microbiota
Up to 1000 species
Interactions with;
•Diet
•Drugs
70% unculturable
•Immune system
•Gut physiology
•Bile acids/liver •Systemic metabolism
Closely co‐evolved microbial partners
•Adipose tissue
•Brain development & function
03/11/2014
Impact of Western style diet on colonic fermentation
Proximal colon
~ saccharolytic
Distal colon
~ proteolytic
SCFA
Acetate
Propionate
Butyrate
Amines
Indoles
Ammonia
Sulphides
N‐nitroso
Energy source
Apoptosis
Differentiation
Epigenetics
Gene expression
Gut hormones
Gut permeability
DNA damage
Tumours
Cytotoxicity
Leaky gut
Liver disease
Altered mucosal permeability and systemic inflammation?
Modified from Geroge Macfarlane
03/11/2014
Impact of traditional diets rich in fiber, polyphenols on colonic fermentation
Proximal colon
~ saccharolytic
Distal colon
~ proteolytic
SCFA
Acetate
Propionate
Butyrate
Amines
Indoles
Ammonia
Sulphides
N‐nitroso
Energy source
Apoptosis
DNA damage
Tumours
Differentiation
Epigenetics
Cytotoxicity
Leaky gut
Gene expression
Liver disease
Gut hormones
Gut permeability High fiber diets, Paleolitic, Mediterranean, rural African and Asian
Enhanced mucosal barrier function and immune homeostasis
Modified from Geroge Macfarlane
Koeth et al 2013 Nature Medicine
•TMA/TMAO confirmed strong link with CVD in patients
•confirmed microbiota metabolism of L-carnitine/choline → TMA→TMAO
•TMA not produced in vegans
•confirmed inflammatory activity & linked to macrophages reverse cholesterol transport
•TMAO reduced bile acid pool
Log count of bacteria /g feces
03/11/2014
12
Bacteroids, Eubacterium,
Peptococooccae
10
Bifidobacterium
Group I
8
Escherichia coli, Streptococcus
6
Lactobacillus
4
Group II
Clostridium perfringens
Group III
2
birth
babies
weanings
infants
adults
aged
Human diet shaped our closely coevolved human:microbe ecosystem
Human microbiome evolution
Dietary evolution
•Neolithic times: ~10,000
yrs BP (birth of
agriculture)
•Agricultural/Industrial
revolutions: Late 18th
and early 19th century
•Recent changes: Over
the last 50 yrs (Westernstyle diet)
03/11/2014
Estimated daily fiber intake in Palaeolithic
/Traditional diets and Modern diet
Dietary pattern
Fiber content
Palaeolithic diet first reported in 1985 (Eaton SB)
45.7g
Palaeolithic diet modified in 1990 (Eaton SB)
>100g
Palaeolithic diet reported in 1996/1997 (Eaton SB)
104g
Rural Chinese diet
77g
Rural African diet
120g
Current US diet
10-20g
Recommended fiber content in US
25-38g
Current UK diet
12g
Recommended fiber content in UK
18g
(Tuohy et al. Current Pharmaceutical Design, 2009)
Total polyphenols (catechin equivalents, mg/100 g)
800
WILD
BERRIES
CULTIVATED
BERRIES
645
641
600
475
424 408
411
400
330
FRUITS
294 280
291
292
231
211 211
193 179
200
Mango
28
27
21
Fig
Peach
38
Banana
54 40
Papaia
54
Persimmon
57
Pear (n=3)
111 98
Kiwi
Pomegranate
Apple (n=28)
Cherry (n=7)
Sour cherry
Plum (n=6)
Raspberry (n=5)
Strawberry (n=9)
Blueberry (n=9)
Gooseberry, white
White currant
Blackberry (n=3)
Red currant
Wild strawberry
Black currant
Elderberry
Wild raspberry
Wild bilberry
Wild blackberry
0
Redrawn from: Mattivi F., Dietas Mediterráneas: La evidencia científica, 2004, 99-111
03/11/2014
Gut microbiota differs between children following
Western-style diet in Italy and children in rural Africa
following traditional diet.
De Filippo et al., PNAS (2010)
Increasing
De Filippo et al., PNAS (2010)
03/11/2014
Aberrant gut microbiota associated
with Western-style diet
British Journal of Nutrition 2011
•SCFA about 3-4 fold higher in African children than Italian children
•Abundance of Enterobacterial groups commonly associated with gastrointestinal
disease higher in EU/Italian children
De Filippo et al., PNAS (2010)
Gut microbiota differs between children following
Western-style diet in Italy and children in rural Africa
following traditional diet.
De Filippo et al., PNAS (2010)
03/11/2014
Total daily food intake in relation to the average of maximum quantity ingested per day
300
BF rural
Daily Intake
250
BF
urban
b
.
 EU
200
g/day
a
.
150
c
.
d
.
e
.
f
.
100
50
0
Protein
Fat
Carbohydrate
(fiber
included)
Fiber
The diet of ‐BF rural children is low in fat and rich in fibers and plant‐polysaccharides and predominantly vegetarian
‐BF urban children maintain the consumption of cereals and legumes but introduces milk, meat, fish, egg and peanuts.
‐ EU is a a typical western diet high in animal protein, sugar, starch, and fat and low in fiber.
a) Millet; b) Millet flour; c‐d) black‐
eyed peas, Niebè, e) Parkia
biglobosa tree (Néré); f) Soumbalà ,
Nerè fruits fermented.
Nutritional composition of foods is available from http://www.inran.it for EU and http://www.fao.org for BF
Quantification of SCFAs in fecal samples from BF and EU populations by SPME‐GC‐MS.
ACETIC
*
60,0
PROPANOIC
35,0
*
50,0
µmol/g
30,0
20,0
20,0
15,0
10,0
10,0
5,0
0,0
0,0
Rural BF
Urban BF
EU
Rural BF
*
14,0
Urban BF
EU
PENTANOIC
BUTANOIC
16,0
**
*
1,8
1,6
1,4
12,0
1,2
10,0
µmol/g
µmol/g
**
25,0
40,0
µmol/g
*
30,0
**
8,0
6,0
1,0
0,8
0,6
4,0
0,4
2,0
0,2
0,0
0,0
Rural BF
Urban BF
EU
Rural BF
Urban BF
EU
03/11/2014
Gut microbiota….. but not as we know it!
OBESITY EPIDEMIC
•
Currently 300 million people obese worldwide
•
Obese adults are up to 80 times more
likely to develop type 2 diabetes than nonobese adults
•
Obese adults are 2-3 times more likely to
develop heart disease
•
Obese adults have a 40% increased risk of
dieing from cancer
03/11/2014
The 3Ps: Probiotics, Prebiotics &
Polyphenols
• PROBIOTICS....“live microorganisms which when administered in adequate amount confer a health benefit on the host” (FAO, 2001). -
Lactobacillus
Bifidobacterium
Escherichia coli Nissle 1917, Bacillus sporogenes, Enteorcoccus faecium, Clostridium butyricum, Saccharomyces ceriviseae
• PREBIOTICS…. a selectively fermented ingredient that results in specific changes, in the composition and/or activity of the gastrointestinal microbiota, thus conferring benefit(s) upon host health. Gibson et al (2010)
– Inulin, oligofructose, fructooligosaccharides, galactooligosaccharides, lactulose, arabinogalactan, arabinoxylan, pectic‐oligosaccharides, glucooligosaccharides
– Resistant starch and certain whole plant foods including whole grain wheat, whole grain oats
• POLYPHENOLS….. 90% resistant to digestion and reach the colon, plant secondary metabolites, usually antioxidant, antimicrobial activities, enzyme/nutrient binding properties and possibly prebiotic type properties, e.g. red‐wine polyphenols, apple tannins
Gut microbiota and systemic health
Cancer (CRC)
Obesity
Immune function
IBD
Probiotics
Blood glucose
Satiety
Diarrhoea/IBS
Lipid metabolism
Laxation
Mineral absorption
03/11/2014
• Lb. reuteri selected for Bile Salt Hydrolase activity (2 capsules/day
at 2 x 109 CFU/capsule) for 9 weeks
• Randomized, double-blind, placebo-controlled, parallel-arm,
multicenter study
• N=127 hypercholesterolemic patients
• Probiotic reduced plasma
–
–
–
–
–
TC by 9.14%
LDL-C by 11.64%
LDL-C/HDL-C ratio by 13.39%
Non-cholesterol plant sterols
Increased circulating deconjugated bile acids
• Proposed new cholesterol lowering activity of probiotics via modified
absorption of lipids from the gut
Journal of Hepatology (2011)
Enterohepatic BA circulation
microbiota
(deconjugation, 1°→ 2°)
FXRα
BA, fat & glucose
homeostasis
Inflammation (NF-κB)
VDR
PXR
Table 1. Cellular actions described for TGR5 in different cell types. ∗Macrophages include alveolar macrophages,
Kupffer cells and THP-1 cells.
03/11/2014
Influencing the Gut:brain axis
03/11/2014
03/11/2014
GABA….. An effective immunomodulatory molecule
•GABA receptors also on human PBMC, monocytes and neutraphils
GABA treatment improves IN sensitivity
03/11/2014
Lactobacillus plantarum/brevis FEM 1874: GABA producer and BSH positive
140
High producers of GABA GABA PRODUCTION (mg/L)
120
100
80
60
40
20
0
Not all cheeses are equal.
03/11/2014
Smelly cheeses – live cheeses!
Anti‐pathogen activity of Trentino cheese lactic acid bacteria
Escherichia
coli DSM 1103
Listeria
monocitogenes
309
Listeria
monocitogenes
306
Staphilococcus
aureus
ATCC 25923
Bacillus
cereus
DSM 345
Candida
albicans
ATCC 14053
Salmonella
enterica
DSM 14221
NI
NI
NI
NI
+++++
+++++
NI
+++++
NI
+++++
+++++
+++++
NI
+++++
NI
NI
NI
NI
NI
+++++
NI
Lactobacillus
paracasei P210 (1M)
+++++
+++++
+++++
+++++
+++++
+++++
+++++
Lactobacillus
cas/par/rham
St 2 (1M)
+++++
+++++
+++++
+++++
+++++
NI
+++++
+++++
+++++
+++++
+++++
+++++
NI
+++++
+++
+++
NI
NI
NI
NI
NI
+++++
++++
++++
++++
+++++
NI
+++++
+
+
+
+
+
NI
+
LAB Strain
Lactobacillus
paracasei
Vi44 (6M)
Lactobacillus
rhamnosus
2360
Lactobacillus
delbrueckii
P243
Enterococcus
faecalis
Vi4 (3M)
Lactobacillus
paracasei 2689
Lactobacillus
cas/par/rham
S43 (2M)
Leuconostoc
mesenteroides
Vi 47 (3M)
03/11/2014
Acid (pH 2) and bile tolerance of Trentino cheese lactic acid bacteria
pH 2 incubation
1,E+08
Log 10 CFU/ml
1,E+07
1,E+06
1,E+05
1,E+04
1,E+03
1,E+02
0,35
0%
0.2 %
0.4 %
0,30
0,25
Growth Rate (h‐1)
Lb paracasei P210
Lc mesenteroides Vi47
Lb cas/par/rham Sp43
E. faecalis V4
Lb cas/par/rham St2
Lb paracasei 2689
Lb paracasei 2360
Lb paracasei Vi44
Lb delbrueckii P243
1,E+09
0,20
0,15
0,10
0,05
1,E+01
0,00
1,E+00
0
20
40
60
80
100
120
140
Minutes
P210
2689
2360
Sp43
St2
V4
Vi44
‐0,05
Strain
Survival of selected Trentino cheese lactic acid bacteria under simulated
gastrointestinal conditions of pH and bile
– a first screen for potential as probiotics
TrentinoGut
Lorenza Conterno and Alice de Angelis
In vitro Digestion
Mouth
Food ground in buffer
Stomach
 pH to 2.5
Add pepsin gastric and gastric lipase
37 °C/ 2h.
pH (6N NaOH) to 6.5
Add bile salts
Liver &
Pancreas
Amylase, trypsin, chymotrypsin, colipase
Large intestine
37 °C/ 1h
Vi47
P243
03/11/2014
Selection of putative probiotics from lactic acid bacteria isolated from Trentino cheeses
•….
•Aim: to select putative probiotics suitable for
cheese applications from Trnetino dairy
Microbiome
•TrentinoGut - Lorenza Conterno
•University of Camerino,
Stefania Silvi and Alice de Angelis
•Strains kindly isolated and provided by
Elena Franciosi
Putative Trentino probiotic survival under gastrointestinal conditions in presence and absernce of cheese matrix
CultureBroth
CultureinModelCheese
70
60
Survival%
50
40
30
20
10
0
2689
St2
2360
Strain
2689
St2
2360
=
=
=
TrentinoGut
Lactobacillus paracasei
Lactobacillus casei paracasei rhamnosus
Lactobacillus rhamnosus
Lorenza Conterno and Alice de Angelis
03/11/2014
The 3Ps: Probiotics, Prebiotics &
Polyphenols
• PROBIOTICS....“live microorganisms which when administered in adequate amount confer a health benefit on the host” (FAO, 2001). -
Lactobacillus
Bifidobacterium
Escherichia coli Nissle 1917, Bacillus sporogenes, Enteorcoccus faecium, Clostridium butyricum, Saccharomyces ceriviseae
• PREBIOTICS…. a selectively fermented ingredient that results in specific changes, in the composition and/or activity of the gastrointestinal microbiota, thus conferring benefit(s) upon host health. Gibson et al (2010)
– Inulin, oligofructose, fructooligosaccharides, galactooligosaccharides, lactulose, arabinogalactan, arabinoxylan, pectic‐oligosaccharides, glucooligosaccharides
– Resistant starch and certain whole plant foods including whole grain wheat, whole grain oats
• POLYPHENOLS….. 90% resistant to digestion and reach the colon, plant secondary metabolites, usually antioxidant, antimicrobial activities, enzyme/nutrient binding properties and possibly prebiotic type properties, e.g. red‐wine polyphenols, apple tannins
CCC
03/11/2014
CCC
Gut microbiota and systemic health
Cancer (CRC)
Obesity
Immune function
IBD
Prebiotics
Blood glucose
Satiety
Diarrhoea/IBS
Lipid metabolism
Laxation
Mineral absorption
03/11/2014
Delaying the progression of obesity with
fermentable carbohydrates and prebiotics
• Does dietary supplementation with prebiotics or
fermentable CHO/fiber reduce body weight through
enhanced satiety
• High fat fed animals (control)
• High fat supplemented with Inulin (Synergy 1 (10% w/w)
• High fat supplemented with β-glucan (10% w/w)
– Diets were isoenergetic with cellulose used to reduce calorie
load of control, high fat diet.
– Measures: magnetic resonance imaging (whole body fat
deposition and stimulation of hypothalamus appetite centres),
PYY, gut microbiota and caecal/faecal metabolites
Tulika Arora, Gary Frost et al. PLoS One 2012
10
8
6
*
*
*
**
**
***
Control
Beta glucan
Inulin
4
2
0
1
2
3
4
5
6
7
8
Cumulative food intake (g)
Cumulative BW gain (g)
Inulin and β-glucan reduce body weight gain
***
200
175
150
***
125
***
100
50
25
0
1
Weights (in g)
*
1.5
***
**
1.0
0.5
0.0
Liv er
Epididymal
Adipose tissue
Cae cum
Tissue
Colon
2
3
*
25
% Body adiposity
Control
Beta glucan
Inulin
*
*** *
*
75
Week
2.0
***
Control
Beta glucan
Inulin
4
5
6
7
8
Week
Control
20
Beta glucan
Inulin
15
10
5
0
Control
Inulin
Groups
Beta glucan
03/11/2014
Effect of inulin and β-glucan supplementation on adiposity
parameters and PYY level in high fat fed mice.
HFD-C
HFD-I
HFD-BG
Epididymal adipose tissue
(g)
1.14±0.16a
0.59±0.10b
0.77±0.10a
Whole body adiposity (%)
18.03±2.72a
8.95±1.66b
12.17±1.92a
Liver lipid content (%)
6.30±1.62a
6.02±1.97a
6.02±1.36a
Muscle lipid content (%)
0.96±0.149a
0.72±0.05a
1.29±0.57a
Visceral fat (g)
2.17±0.46a
1.23±0.17a
1.49±0.27a
Subcutaneous fat (g)
3.40±0.53a
2.08±0.13a
2.44±0.28a
Adipocyte size (μm)
122.25±10.2a
72.95±8.72
111.19±4.03 ac
b
Adipocyte number (x107)
1.43E+08a
1.31E+08a
1.86E+08a
Liver size (g)
1.43±0.13a
1.23±0.15a
1.40±0.06a
Caecum (g)
0.21±0.01a
0.69±0.05b
0.49±0.03c
Colon (g)
0.13±0.01a
0.19±0.02a
0.14±0.02a
PYY (pmol/ml)
0.10±0.012a
0.10±0.008
0.13±0.016a
a
Colonic PYY
27.3 3.7
22.8 5.3
19.9 1.6
The values with different superscripts letters are significantly different from each other
Effect of inulin and β-glucan supplementation on changes in
signal intensity in the appetite centres of the brain measured by
MRI
Control
Inulin
Beta glucan
Arcuate nucleus
60
*
50
40
30
20
10
0
-10
10 20
40
60
80
Time (in mins)
100
120
140
Normalized percent enhancement
Normalize d perce nt e nhance me nt
80
70
Control
Inulin
Beta glucan
Ventromedial hypothalamic
nucleus
60
50
***
40
30
20
10
0
-10
10 20
40
60
80
100
120
140
Time (in mins)
The arrow shows the start of Mn2+ infusion and grey bar represents the duration of Mn2+ infusion.
03/11/2014
Effect of inulin and β-glucan supplementation on changes in
signal intensity in the appetite centres of the brain measured by
MRI
Control
Inulin
Beta glucan
Paraventricular nucleus
Normalized percent enhancement
8
Normalized percent enhanceme nt
40
**
30
20
10
0
-10
10
20
40
60
80
100
120
140
Time (in mins)
Control
Inulin
Beta glucan
Periventricular nucleus
70
Normalized percent enhancement
Control
Inulin
Beta glucan
Nucleus of tractus solitarius
50
7
6
5
4
*
3
2
1
0
-1
-2
-3
-4
10 20
40
60
60
80
100
120
140
Time (in mins)
50
40
**
***
30
The arrow shows the start of Mn2+ infusion and
grey bar represents the duration of Mn2+ infusion.
20
10
0
-10
10
20
40
60
80
100
120
140
Time (in mins)
11.0
11.0
10.5
10.0
9.5
Control
Inulin
Beta glucan
10.5
10.0
9.5
9.0
A: Total bacteria
Control
Inulin
B: Mouse Intestinal Bacteria
9
8
7
Control
Inulin
D: Lactobacillus
Beta glucan
Log10 cells/gfaeces
Log10 cells/g faeces
***
10.0
9.5
9.0
Control
Inulin
Control week 0
Control week 4
Control week 8
9
Inulin week 0
Inulin week 4
8
Inulin week 8
Beta Glucan week 0
Beta Gllucan week 4
7
Beta Glucan week 8
Control
Inulin
Beta glucan
C: Eubacterium rectale-Clostridium coccoides
***
10
***
*
10.5
8.5
Beta glucan
***
10
**
11.0
***
Log 10cells/g faeces
***
11.5
Log10 cells/g faeces
Log10 cells/g faeces
Effect of inulin and β-glucan supplementation on murine gut
microbiota compared to high fat diet supplemented with cellulose
Beta glucan
E: Bifidobacterium
Similar findings observed for caecal contents at week 8.
03/11/2014
Effect of inulin and β-glucan supplementation on murine faecal
metabolite profiles (NMR) compared to high fat diet
supplemented with cellulose
NMR based metabolomics separates cellulose
from inulin or β-glucan supplemented animals on
high fat diets
PCA scores plot of fecel metabolite profiles showing clear clustering patterns
for mice fed with HFD-C, HFD-BG and HFD-I groups.
03/11/2014
Fermentable fibers/prebiotics reduce
body weight but by different mechanisms
• β‐glucan reduced cumulative body weight apparantly through reduced stimulation of hypothalamic appetite centres, increased satiety and reduced food intake.
• Inulin appeared to reduce cumulative body weight gain through reduced adipocyte size and whole body adiposity
• SCFA concentrations in the caecum β‐glucan > inulin > high fat control
• Inulin gave increased caecum weight
• β‐glucan had higher excretion of glucose in faeces while high‐fat control had higher excretion of butyrate and propionate
Arora et al. PLoS One 2012
Whole grain oats vs non-whole grain breakfast cereal
dietary intervention in subjects “at risk” of developing
the metabolic syndrome
•Randomized, crossover study, 30 volunteers, male and female with slightly elevated
levels of either total cholesterol or fasting glucose at risk of developing metabolic
disorders
Run-in
WGO
Wash out
NWG
Follow up
Run-in
NWG
Wash out
WGO
Follow up
2 weeks
6 weeks
4 weeks
6 weeks
4 weeks
•Two 6 week treatment periods separated by 4 week washout periods.
•Whole oat grain (WGO) vs non-whole grain cereal (NWG)
•Samples collected before and after cereal consumption and then 4 weeks following
end of consumption.
•Blood (fasted), 24 hour urine, saliva and fecal samples
Connolly et al. In preparation
Supported by Jordans Cereals
03/11/2014
Whole grain oats modified gut microbiota in
beneficial manner compared to non-whole grain
cereal
WGO
WGO
Whole grain oats significantly increased faecal bifidobacteria and lactobacilli
but no other bacterial groups measured.
Whole grain oats improved blood
cholesterol profiles
WGO
•Whole grain oats significantly reduced LDL and total cholesterol,
reversing a trend towards elevated LDL and TC in the non-whole grain
breakfast cereal treatment.
WGO
03/11/2014
Impact of wheat bran fibre (WBF) on gut microbiota &
markers of CVD in overweight adults
Run-in
WBF (bread, biscuits, breakfast cereals)
Run-in
Cellulose (bread, biscuits, breakfast cereals)
2 weeks
8 weeks
•Subjects: n=80, BMI > 27
•FEM & Santa Chiara Hospital (Dr Carlo Pedrolli), APSS, Trento
•Biomarkers of CVD risk
•Gut microbiota (454-pyrosequencing, FISH, qPCR)
•MS based metabolomics (targeted and untargeted)
The 3Ps: Probiotics, Prebiotics &
Polyphenols
• PROBIOTICS....“live microorganisms which when administered in adequate amount confer a health benefit on the host” (FAO, 2001). -
Lactobacillus
Bifidobacterium
Escherichia coli Nissle 1917, Bacillus sporogenes, Enteorcoccus faecium, Clostridium butyricum, Saccharomyces ceriviseae
• PREBIOTICS…. a selectively fermented ingredient that results in specific changes, in the composition and/or activity of the gastrointestinal microbiota, thus conferring benefit(s) upon host health. Gibson et al (2010)
– Inulin, oligofructose, fructooligosaccharides, galactooligosaccharides, lactulose, arabinogalactan, arabinoxylan, pectic‐oligosaccharides, glucooligosaccharides
– Resistant starch and certain whole plant foods including whole grain wheat, whole grain oats
• POLYPHENOLS….. 90% resistant to digestion and reach the colon, plant secondary metabolites, usually antioxidant, antimicrobial activities, enzyme/nutrient binding properties and possibly prebiotic type properties, e.g. red‐wine polyphenols, apple tannins
03/11/2014
Gut microbiota and systemic health
Cancer (CRC)
Obesity
Immune function
Blood glucose
Polyphenols
IBD
Satiety
Diarrhoea/IBS
Lipid metabolism
Laxation
Mineral absorption
14
10
*
*
8
*
*
*
12
*
*
*
*
Basline
De-alcoholized red wine
Red wine
Gin
6
4
2
nt
a
lla
le
te
riu
m
la
Eg
ge
r
th
e
ac
ob
Bi
fid
Pr
ev
ot
el
ifo
rm
is
de
s
un
de
s
ct
er
oi
gr
ou
ct
er
oi
Ba
Ba
ac
te
r
iu
m
re
ct
al
e
yt
ic
um
st
ol
hi
m
iu
tia
co
cc
oi
de
sEu
b
lo
st
rid
C
Bl
au
p
ou
p
gr
iu
m
illu
s
lo
st
rid
C
ac
ct
ob
La
te
ro
co
cc
u
s
0
En
16S rRNA gene copies/g faeces, mean +/- SD, n=10
American Journal of Clinical Nutrition, 2012
03/11/2014
(2011) 93:62-72.
03/11/2014
Measuring the impact of raspberries of different
polyphenol content on gut microbiota
•Raspberries digested and fermented
•Alpen Gold (yellow), Tulameen (red), Anne (yellow),
Sugana Yellow (yellow), Sugana Red (red)
•Controls (inulin and cellulose)
qPCR (total, Enterobacteriaceae, LAB, Bifidobacterium
spp)
FISH (totals, Bifidobacterium spp., Bacteroidaceae e
Prevotellaceae (Bac303), Clostridium hystoliticum
(Chis), Clostridium coccoides-Eubacterium rectale
(Erec), Faecalibacterium prausnitzii (Fpra)
ARISA
Targeted Metabolomic:
Anthocyanins and Ellagitannins (UPLC-MS)
SCFA (GC-MS)
03/11/2014
Significant increase in bifidobacterial abundance as measured by FISH
Log10 cells/mL
8,2
a
a
a
8,4
a
a
a
8
7,8
0
5
10
24
7,6
7,4
7,2
7
HOURS
6,8
6,6
os
e
C
el
lu
l
In
ul
in
e
R
ed
Su
ga
na
An
ne
S
ug
an
aY
el
lo
w
Tu
la
m
ee
n
A
lp
en
G
ol
d
6,4
a: average of five significantly higher
Total anthocyanins and anthocyanins profile during
fermentation for Tulameen or Sugana Red
T0
T5
T10
T24
6000
5000
pg: pelargonidin
cy: cyanidin
ng/ml
4000
glu: glucoside
gal: galactoside
samb: sambubioside
rut: rutinoside
soph: sophoroside
3000
2000
1000
0
Alpen Gold Tulameen
Anne
Sugana
Yellow
Sugana
Red
Inuline
4000
4000
ng/ml
3000
2000
0
5
10
24
5000
0
5
10
24
5000
ng/ml
Cellulose
3000
2000
1000
1000
0
0
pg3glu
cy3glu
cy3gal
cy3samb
anthocyanin
cy3rut
cy3soph
pg3glu
cy3glu
cy3gal
cy3samb
anthocyanin
cy3rut
cy3soph
03/11/2014
Ellagitannins measured during raspberry
fermentation
Lambertianin C
T 0
T 5
T 10
T 24
ng/ml
0,15
Casuarictin: monomer
Sanguiin: dimer
Lambertianin: trimer
0,10
0,05
0,00
Alpen Tulameen Anne
Gold
Sugana Sugana Inuline Cellulose
Yellow
Red
Increasing fruit and vegetable intake in vivo
– FLAVURS project
Flavonoid‐rich F&V
Flavonoid‐poor F&V
+2
+ 4
+ 6
+2
+ 4
+ 6
?
Habitual diet
Wk 0
Wk 6
Wk 12
Wk 18
Visit 1
Visit 2
Visit 3
Visit 4
03/11/2014
High Flavonoid group
Apple crumble
Dried cranberries/ blueberries
Fruit smoothies
(Strawberry and raspberry/
Blackberry and blueberry)
Fruit juices
(Blackcurrant /apple/cranberry
/orange )
Roasted peppers
Pepperdew cherry peppers
All fruits and vegetables contain ≥ 15mg/100g of flavonoids
Low Flavonoid group
Rhubarb crumble
Dried fruits (raisins, currants,
mango)
Fruit smoothies (tropical mix)
Fruit juices (mango/
pineapple)
Guacamole
Houmous
Soups
(Carrot & coriander/broccoli &
stilton)
All fruits and vegetables contain
< 5mg/100g of flavonoids
Canned chopped tomatoes
03/11/2014
FLAVONOIDS
+2
+4
+6
Additional F&V portions
Dietary intake: HF dose dependent increase
HF higher vs LF & CT +2,+4,+6
Time x treatment (P=0.006)
+2
+4
+6
Additional F&V portions
Biomarker : 24h urinary flavonoid & metabolites
HF dose dependent increase
HF higher vs LF & CT +2, +4, +6
Time x treatment (P=0.0001)
VITAMIN C
+2
+4
+6
Additional F&V portions
Dietary intake: HF & LF dose increase
HF & LF vs CT higher +2, +4, +6
Time x treatment (P=0.0001)
+2
+4
+6
Additional F&V portions
Biomarker: Plasma vitamin C
HF & LF dose increase
HF & LF vs CT higher +2, +4, +6
Time x treatment (P=0.0001)
03/11/2014
Dietary carotene
(µg/day)
CAROTENOIDS
+2
+4
+6
Additional F&V portions
Dietary intake : LF dose dependent increase
HF & LF higher CT all points
Time x treatment (P=0.001)
+2
+4
+6
Additional F&V portions
Biomarker : Total plasma carotenoids
LF dose dependent increase
HF & LF higher CT all points
Time x treatment (P=0.0001)
Non-starch polysaccharide (NSP) changes
HF & LF higher than the CT all time points
LF dose dependent increase
Time x treatment interaction (P=0.0001).
03/11/2014
F&V impact on arterial stiffness measured
by PWA
HF and LF attenuated increase shown in CT group
Time x treatment P=0.009 when standardised for HR75 P=0.03
Other blood parameters
+2
+4
+6
Additional F&V portions
Total plasma nitrate/nitrite
HF higher than LF & CT +6
Time x treatment (p=0.03)
+2
+4
+6
Additional F&V portions
Plasma FRAP
HF dose dependent increase
LF higher +4 & +6 vs baseline
Time x treatment (P=0.009)
03/11/2014
High fruit and veg diet appears to modulate gut microbiota in “benificial”manner
Lactobacilli

9,60
9,50
9,40
9,30
9,20
9,10
9,00
8,90
0,00
2,00
4,00
Log10 cells/g faeces (mean +/- SEM)
Log10 cells/g faeces (mean +/- SEM)
Bifidobacteria
9,70

9,00
8,90
8,70
•In “right”direction
8,60
8,50
8,40
•HF  Eu. rectale
8,30
8,20
8,10
0,00


9,70
9,60
9,50
9,40
9,30
9,20
0,00
2,00
4,00
2,00
4,00
•LF  F. prausnitzii
C. perfringens/histolyticum
Log10 cells/g faeces (mean +/- SEM)
Log10 cells/g faeces (mean +/- SEM)
Atopobium
9,80
•Small changes
8,80

9,20
•LF  Bacteroides
9,10
9,00
 HF
 LF
▲ CT
8,90
8,80
8,70
8,60
8,50
0,00
2,00
4,00
 P<0.05 Sig diff to wk 0 Wilcoxon Test;  P<0.05 sig diff to CT Kruskal-Wallis Test
UNTARGETED METABOLOMIC ANALYSIS OF URINE
• Urine dilution 1:5
• HPLC Analysis on RP column in
positive and negative ionization mode
• XL Orbitrap in Full Scan MS and MS/MS
within high resolution and mass accuracy
Approaches
• Substances considered as biomarkers
when p<0.005 (t-test)
• Annotation of metabolites:
− Mass accuracy of precursor ion [M+H]+ (< 3 ppm error)
− Isotopic pattern distribution
• Databases used for annotation: In-house data base, Human Metabolome Database,
Metlin, MAssBank, LipidMaps
03/11/2014
Metabolomics workflow
F
+
C
N
H3C
Samples: urine,
plasma, fecal water
Biomarker identification
N
NH
O
CH3
Separation on
Sample preparation:
extraction of all analytes LC column
Statistic
analysis
Untargeted analysis with
HR mass spectrometer
ALLIGNMENT OF CHROMATOGRAMS, BATCH CORRECTIONS, PEAK PICKING
UNIVARIATE ANALYSIS with XCMS
Data processing - XCMS using the “matchedFilter” peak picking method with Spectra
Filter Window Mower function.
For each mass feature two linear mixed models were fitted, diet-time interaction and
time alone.
Both models were adjusted for baseline. p values for all features were corrected for
multiple testing according to the two-stage Benjamini and Hochberg step-up false
discovery rate (FDR).
03/11/2014
Rt
1.10
Annotation; Elemental Composition, MW, adjusted p value
ProlineBetaine; MMW: 143.0946, p 0.002 ↑Diet A;
2.20
N-acetyl-S-(2-hydroxypropyl) cysteine, MMW: C8H15NO4S; p
3.80
Hydroxy Hippuric Acid (isomer); MMW: 195.0531, p 0.02 ↑Diet A;
4.40 Hydroxy Hippuric Acid (isomer); MMW: 195.0531, p 0.002 ↑Diet A,
4.82 Vanilloylglycine, MMW: 225.0637; p.0.03 ↑ Diet A, B;
5.70
Hippuric Acid, MMW: 179.0582; p 0.002 ↑Diet A
5.89
Phenylacetylglutamine, MMW: 264.1110, p 0.04 ↑Diet A;
6.15
FerulicAcid Sulfate , MMW: 274.0731, p 0.04; ↑Diet B
6.26
Dihydroxyphenyl-γ-valerolactone-O-sulphate MMW:288.0306 p 0.0003 ↑Diet A;
6.56
Dihydroxyphenyl-γ-valerolactone-O-methyl-O-GLC, p 0.01 ↑ Diet A;
7.14 Cresol-Glucuronide, MMW: 284.0896; p 0.001 ↓Diet A;
7.35
Hydroxy Hippuric Acid (isomer), MMW: 195.0531, p 0.01 ↑ Diet A;
7.76
Hydroxy-tridecenoic acid GLC, MMW: 404.2046, p 0.001 ↑ Diet A;
12.38 Iberin N-acetyl-cysteine MMW: p 0.0001 ↑ Diet A & p 0.001↑ Diet B
Nutrition and Nutrigenomics
Alimentazione
Apples Gut Microbiota Modulation
Adding value to the food chain
FOOD QUALITY AND NUTRITION DEPARTMENT Pre ‐
treatment
Group 1
Whole Apple (WA)
Wash out
Apple Juice (AJ)
Apple Juice (AJ)
Wash out Whole Apple (WA)
2 weeks
Habitual diet
Group
2
8 weeks
Visit 1 (V1)
0 week
8 weeks
4 weeks
Visit 2 (V2)
8 week
Visit 3 (V3)
Visit 4 (V4)
13 week
21 week
Biological samples: Blood, Urine and Faecal samples
Measurements
•% body fat composition, DEXA
•blood pressure, •vascular stiffness (pulse wave analysis, PWA) •and vascular reactivity (laser Doppler imaging, LDI)
•Gut microbiota (454‐pyrosequencing)
•Untargeted metabolomics. 03/11/2014
“Conslusions: Adherence to an MD pattern is associated with better HRQL. The
association is stronger with mental health than with physical health. Dietary total
antioxidant and fibre content independently explain this relationship”.
Dietary patterns – Mediterranean diet & Gut Microbiome “ecosystem support”
CHRONIC DISEASE PYRAMID
GUT MICROBIOTA PYRAMID
INRAN, FAO Double Pyramid
Barilla Centre for Food Nutrition: Double Pyramid: healthy food for people,
sustainable food for the planet
http://www.barillacfn.com/en/position-paper/pp-doppia-piramide-alimentazione/
03/11/2014
Fondazione Edmund Mach
•Thank you: SINU, Professor Brighenti
•Fulvio Mattivi, Duccio Cavalieri and Roberto Viola, FEM-IASMA
•NN Group: Lorenza Conterno, Francesca Fava, Elena Franciosi, Carlotta de Filippo,
Athanasios Koutsos, Ilaria Caraffa, Florencia Ceppa, Andrea Manchini
•University of Reading, Glenn Gibson, Bob Rastall, Julie Lovegrove, Parveen Yaqoob,
Christine Williams, Ian Rowland, Michael Connolly
•Gary Frost, Imperial College London, Daniele Del Rio, University of Parma