seminar Zit gezondheid in de genen?

1. Gut microbiota and health
Sasha Zhernakova
Department of Genetics
University Medical Center
Groningen, The Netherlands

2. Sender et al. Cell 2016
A ’reference man'
70 kilograms
20–30 years old
1.7 metres tall
39 trillion
30 trillion

3. Collection of bacteria, archaea, fungi, protozoa and viruses on various body sites
Most focus on gut microbiome; bacteria are mostly studied

4. Microbiome is a hype, but not new
Ilya Metchnikov
(1845-1916)
Minoru Shirota (1899-
1982)
Since
1935

5. New methods of microbiome studies
In the past:
culturing methods
Now: also next
generation sequencing

6. Fecal transplantation

7. Fecal transplantation in clinical practice
Rates of Cure without Relapse for
Recurrent Clostridium difficile Infection.
van Nood E et al. N Engl J Med 2013;368:407-415.

8. FMT suggested for many diseases

9. Vrieze, Gastroenterology, 2012
Ridaura, Science, 2013,
Turnbaugh, Nature, 2006
Fecal transplantation
Healthy donors Patients
Casual role of microbiota in obesity and metabolic syndrome
Improvement of metabolic
syndrome in 6 weeks
Obese mice Lean mice
Fecal transplantation

10. Patients initiative
Poop Power: do-it-yourself guide from a man who
cured himself of Ulcerative Colitis

11. Several parameters are commonly studied in microbiome analysis
• Diversity of bacteria in every individual
• Composition of microbiome in general
• Abundance of particular bacteria
• Abundance of functional categories (group of
bacterial genes)

12. Richness and diversity
Sample 1
Less different bacteria
Sample 2
More different bacteria
Richness: the number of species in the sample
Richness sample 1 < richness sample 2
Diversity: richness and evenness (how many species and
how evenly are they distributed)

13. Overall composition of microbiome in the population (Bray-Curtis distance matrix)
Compositional dissimilarity between samples
Example of microbiome composition in Belgian population (Falony et al, Science, 2016)

14. Example of PCA plot of IBD patients vs controls, based on 16S data
Imhann et al, Gut, 2015

15. Abundance of bacteria
Sample 1 Sample 2 Sample 3

16. Abundance of functional groups
Bacteria 1 Bacteria 2 Bacteria 3
6 “green” genes in this population of bacteria
Types of “green” genes can be different, for example:
Antibiotic resistance,
Bacterial pathways,
Virulence factors,
Etc

17. How do we perform microbiome analysis

18. 2. DNA isolation
DNA isolation methods can greatly influence results!

19. From sequence reads to bacteria

20. Microbiome composition is highly variable across individuals
Figure from io9.gizmondo.com
We do not know all factors that influence these variations

21. Microbiome composition is highly variable across individualsgenera
> 1000 samples
LifeLines Deep, Netherlands

22. Population
Cohorts
BIOBANKS
Clinical cohorts
Intervention
and
functional
studies

23. Lifelines DEEP: ~1500 random participants with detailed multi-‘omics’ data
Age: 18-86
636 903
Tigchelaar et al., BMJ Open 2015
RNAseq on PBMCs
Illumina 450k arrays
Exhaled air (VOCs)
Plasma (lipidomics)
16S rRNA
Metagenomics WGS
Olink, Cytokines
8 mi imputed SNP genotypes

24. Analysis of multiple factors in relation to microbiome composition
44
39
41
5
68
Included 207 intrinsic and external factor analysis in
populational cohort of 1135 individuals
Zhernakova, Science, 2016

25. Zhernakova et al. Science 2016

26. Belgium cohort: similar results, 92% replication
Falony et al. (2016) Science 352;6285: 560-56416S rRNA based data

27. Proton Pump Inhibitors massively change the microbiota composition
44 categories
of drugs

28. PPI: Significant changes in abundance of 92 bacteria; replicated in 3 cohorts
General
population
IBD
patients
IBS case
control
Meta-
analysis
Direction
Red: up
Blue: down
Imhann, et al, Gut, 2015

29. More abundant
in oral
microbiome
Increase in
proteobacteria
C-dif risk profile
Imhann, et al, Gut, 2015

30. Medication and microbiome

31. Antibiotics and microbiome
13 out of 1135 participants used
antibiotics prior or at the
moment of sample collection

32. Strong correlation of antibiotics with several species
NO
1122
YES
13
Bifidobacteriumlongum
p=1.97e−05
q=0.0036
Bifidobacterium longum and Roseburia inulinivorans are
decreased in antibiotic-users
Roseburia_inulinivorans
NO
1122
YES
13
p=0.00171
q=0.103

33. Intrinsic factors
41 intrinsic
factors

34. Major effect of CgA

35. Biomarkers for gut health
Citrulline
Calprotectin
IL-1β and IL-6
IL-12p70 and TNF
function
HBD-2
Chromogranin A
Adapted from Bischoff, BMC Medicine, 2011

36. 41 intrinsic
factors
Intrinsic factors

37. Intrinsic factors

38. Stool frequency and stool composition have major effect on microbiome
BM
p.Firmicutes
k. Archaea
Average number bowel movements per day
Bacterialabundance
Firmicutes
BM Archaea
Tigchelaar et al, Gut, 2015

39. Jankipersadsing et al., Gut 2016
LifeLines-Deep (N=1546) and PopCol Sweden (N=284):
1. Evidence for roles for xenobiotic metabolism (CYP8B1; AHR;
ALDH1A1, etc)
2. Ion channel activity pathways (ion channels as anti-IBS drugs!)
Stool frequency is determined by genetics
0.00
10.00
20.00
30.00
40.00
50.00
60.00
<1 per day 1 per day 1-2 per day >2 per day
Percentage
Stool frequency in LifeLines-Deep study
All
Female
Male
With Mauro D’Amato

40. Jankipersadsing et al., Gut 2016
LifeLines-Deep (N=1546) and PopCol Sweden (N=284):
1. Evidence for roles for xenobiotic metabolism (CYP8B1; AHR;
ALDH1A1, etc)
2. Ion channel activity pathways (ion channels as anti-IBS drugs!)
Stool frequency is determined by genetics

41. 41 intrinsic
factors
Intrinsic factors

42. Microbiome can explain substantial proportion of blood lipid levels
Fu et al, Circ. Research, 2015

43. Diet: a major modulator of gut microbiome variation

44. Diet is a major modulator of
gut microbiome variation
Some enrich diversity (green)
Others decrease diversity (red)
carbohydrates
fruit
beer
kcal
coffee
soda/sugar
butter milk
whole-fat milk
tea
yogurt
vegetables
red wine

46. Examples of food effect on specific bacteria
Increased
abundance of
Eubacterium
eligens
Beneficial role in maintaining the
normal gut ecology, butyrate production

47. Soda: reduced microbiome diversity
Suez et al, Nature, 2014

48. Specific effect of milk and buttermilk

49. Strong correlation of buttermilk with Leuconostoc abundance
Q = 9.1x10-46
Fold changes Leuconostoc

50. Adult type lactose intolerance: LCT locus

51. Interaction of LCT genotype with milk intake and microbiome
Discovery:
Lifelines-DEEP
N=984
Replication:
Two cohorts
N=529
Meta-analysis:
N=1,514
40 hits (5x10-8)

52. Example of effect of LCT polymorphism on microbiome composition
People with genetic lactase deficiency
have increased abundance of
bifidobacteria
People with genetic lactase deficiency
consume same amount of dairy
products
Bonder, Kurilshikov et al, Nat.Genetics, 2016

53. Interaction analysis in LCT locus
Interaction of consumption of diary products and genetic lacatase
deficiency lead to high abundance of bifidobacteria.
Bifidobacteria can have lactase function and help digesting milk

54. Genetics of gut microbiome: 4 GWAS studies in cohorts of >1000 individuals
N=1,812
N=1,561
N=1,514
N=2,139

55. Genetics of gut microbiome: 4 GWAS studies in cohorts of >1000 individuals
N=1,812
N=1,561
N=1,514
N=2,139

56. Current studies in genetics of gut microbiome
Kurilshikov, Trends Imm, 2017
Wang et al, submitted
LCT gene is associated with
bifidobacteria abundance in
Dutch and TwinUK GWAS
Innate immune receptors are
enriched in results

57. MiBioGen consortium: meta-analysis of miQTL studies
If you have samples with genetics and microbiome please contact me: Sasha.zhernakova@gmail.com
Cohort name Ethnicity variable region N subjects
SHIP / SHIP-TREND German V1-V2 1904
PopGen German V1-V2 912
FoCus German V1-V2 1555
Compuls Dutch V1-V2 153
CARDIA USA V3-V4 282
Rotterdam Study Dutch V3-V4 1427
Generation R Multi-ethnic V3-V4 2111
Pers.Nutrition Israel V3-V4 1066
KSCS South Korea V3-V4 833
TwinsUK UK V4 1793
COPSAC2010 Danish V4 424
GEM Canada, Israel, US, UK V4 1543
LifeLines-DEEP Dutch V4 1089
METSIM Finnish V4 531
MIBS_Co Dutch V4 111
NTR Dutch V4 499
FGFP Flemish V4 2482
Total samples 18715

58. Population
Cohorts
BIOBANKS
Clinical cohorts
(Inflammatory
Bowel Disease,
Irritable Bowel
Syndrome)
Intervention
and
functional
studies

59. 3. Gut health is a continuum: link with microbiome
Healthy
No gut complaints
FGID IBS IBD
Research aims:
 Identify biomarkers for diagnostic of IBS,
IBD and other gut diseases
 Novel treatment and prevention of IBS and IBD
controls
N=1025
Pop-IBS
N=218
Clin-IBS
N=182
IBD
N=355
>200 clinical phenotypes: diet, medication, clinical characteristics, serology, etc

60. Microbiome composition
Actinobacteria Bacteroides Firmicutes
−0.50
−0.25
0.00
0.25
−0.4 −0.2 0.0 0.2 0.4
PCoA1
PCoA2
0.0
0.2
0.4
0.6
Bacteroidetes
- +

61. Microbiome composition
Actinobacteria Bacteroides Firmicutes
−0.50
−0.25
0.00
0.25
−0.4 −0.2 0.0 0.2 0.4
PCoA1
PCoA2
0.0
0.2
0.4
0.6
Bacteroidetes
- +

62. Taxonomical analysis: Large dysbiosis
Crohn vs Controls:
134 species
UC vs Controls:
58 species
IBS vs Controls:
37 species

63. Taxonomical analysis: Dynamics and within-species richness
In IBD/IBS patients:
increased diversity of some pathogenic bacteria (E. coli)
Decreased diversity of beneficial bacteria (F. prausnitzii)
Vich Vila, et al, submitted

64. Pathway implication
L-arginine
- Decreased in CD, ileal surgery
- Involved in wound healing
Tryptophan (serotonin
precursor)
- Increased in IBS, especially
IBS-D
- Serotonin can increase gut
motility and visceral pain
Vitamin B2
- Decreased in CD / antioxidant
Kennedy et al, WJG, 2014
Dozens of pathways and virulence factors are associated to IBD
and/or IBS – link to function

65. Genetic risk score for IBD genes change the microbiota composition in controls
• Two risk scores composed
1 - All 200 IBD-associated variants
2 - Hypothesis-based: 11 functional variants
• 5 genes (NOD2, CARD9, FUT2, ATG16L1, IRGM)
- Related to IBD
- Function
- Literature: related to microbiome changes

66. Genetic IBD risk in healthy individuals associated with decreased Roseburia
- Healthy individuals
- Without gut complaints
- Higher IBD genetic risk
- Differences microbiome
Roseburia is decreased in IBD
patients
IBD genetic risk – pre-IBD
microbiome changes.
FDR=0.017
Co-factors: age, sex, BMI, antibiotics, PPI, read depth
Imhann et al, Gut, 2016

67. 10-fold cross validation:
90% training set
10% testing set
Calprotectin levels (AUC=0.79)
Microbiome (AUC= 0.91)
Top-20 taxa + Calprotectin
(AUC=0.93)
Microbiome-based prediction model to distinguish between IBD/IBS

68. Diet and gut microbiome
Healthy
No gut complaints
FGID IBS IBD
 Clustering of food items and microbial
features
 Analysis per cohort -> meta-analysis
 Significant association of 25 clusters of food
with 29 clusters of species (FDR<0.05)
controls
N=1025
Pop-IBS
N=218
Clin-IBS
N=182
IBD
N=355
Laura Bolte

69. Inverse Effects of Plant vs. Animal Protein
-
+
SCFA producers
Bifidobacteria
Lactic bacteria
F. prausnitzii
Eubacteria
Bacteroides
Carbohydrate
fermentation
Pyruvate, fructose to
propanoate,
butanoate, acetate,
lactate
Starch degradation
Plant protein
Animal protein

70. Polyphenols and Omega-3 rich foods boost SCFA-producers
+
SCFA producers,
mucin producers
Eubacterium spp
Bifidobacterium spp
F. prausnitzii
R. hominis
A. muciniphila
Carbohydrate
fermentation
Pyruvate, fructose to
propanoate,
butanoate, acetate,
lactate, …
Starch degradation
Foods rich in
polyphenols or
n-3 FA
- Potentially
harmful species
H. parainfluenzae
E. Coli
B. Fragilis

71. Probiotics effective in 10k metagenome project DAG-3
Ongoing: effect of probiotics on gut microbiome in 10 K metagenome project DAG-3.
Analysis of small pilot data set.
Probiotic drinks
-
Dorea spp.
• Gas producing bacteria
• Increased in IBS patients

72. Population
Cohorts
BIOBANKS
Clinical cohorts
(Inflammatory
Bowel Disease,
Irritable Bowel
Syndrome)
Intervention
and
functional
studies

73. GFD is one of the most common diet
No wheat, barley and rye
10% australians are of GFD (5% males, 15% females)
Only treatment for celiac disease
Helpful by gluten intolerance
Helpful by IBS
Catassi et al, Nutrients, 2013

74. Study design
Time →
Habitual diet
Gluten free diet (4 weeks)
Wash out period (5 weeks)
T=0 T=1 T=2 T=3 T=4 T=5 T=6 T=7 T=8
Stool sample
Exhaled breath sample
Blood sample
Permeability test
5 random days food record
21 individuals, 9 time points

75. Intraindividual variability is higher than effect of GFDComponent
2
Component 1

76. (Weak) split for 2 groups based on richness
One sample in between groups
No difference in GFD effect in high and low richness group

77. Strongest associations on taxonomic level – Veillonellacea family
• Strongest association:
Decrease in Veillonellacea on
GFD
• Pro-inflammatory bacteria
• Maybe linked with
“improvement” of gut
symptoms on GFD
• Replication: also decreased in
autistic patients (many on
GFD)
• Overall moderate effect of GFD
on gut microbiome
Veillonellaceae

78. Ongoing projects and future plans

79. LifeLines 10K Metagenome Project

80. Lifelines-NEXT cohort: genetics, exposome, microbiome, and health

81. Other opportunities and research questions of LifeLines-NEXT
Oral microbiome and saliva in
mothers and babies
(pregnancy complications,
allergies, strain stability across
body sites…)
The role of vaginal microbiome
Long term effect of microbiome
on health
Genetics of microbiome
…..

82. Oral bacteria might protect against allergies
Hesselmar et al (2013) (Slide from E.Zaura)
Parental sucking of their infant’s pacifier was associated
with a reduced risk of allergy development

83. Organs on a chip: how gut bacteria communicate with the host

84. Examples of organs on a chip plans

85. Take home messages
• Gut microbiome varies greatly among individuals
• Gut microbiome is influenced by food, medication,
lifestyle, genetics, etc
• Gut microbiome is associated to many diseases, the
causal role is proven for several diseases
• Power of cohorts studies to identify gene-
microbiome-environmental interactions and
relations to health

86. Thank you!

87. Questions??

88. Acknowledgements
UMCG Groningen
Cisca Wijmenga
Jingyuan Fu
Marc Jan Bonder
Arnau Vich Villa
Ettje Tigchelaar
Soesma Medema-
jankipersadsing
Alexandr Kurilshikov
Rinse Weersma
Floris Imhann
Eelke Brandsma
Marten Hofker
Jackie Dekens
University medical center
Maastricht
Daisy Jonkers
Zlatan Mujagic
Radboud University
Mihai Netea
Sanne Smeekens
Marije Oosting
Leo Joosten
Broad Institute/MHG
Boston
Melanie Schirmer
Ramnik Xavier
Tommi Vatanen
Curtis Huttenhower
MiBioGen consortium

89. Acknowledgements
UMCG Groningen
Cisca Wijmenga
Jingyuan Fu
Marc Jan Bonder
Arnau Vich
Ettje Tigchelaar
Soesma Medema-
jankipersadsing
Alexandr Kurilshikov
Rinse Weersma
Floris Imhann
Eelke Brandsma
Marten Hofker
Jackie Dekens
University medical center
Maastricht
Daisy Jonkers
Zlatan Mujagic
Frederik-Jan van
Schooten
Agnieszka Smolinska
Broad Institute/MHG
Boston
Melanie Schirmer
Ramnik Xavier
Wageningen University
Erwin Zoetendal
Hauke Smidt
Looking for PhD students and postdocs
in the microbiome group!
Sasha.zhernakova@gmail.com