BARCODED PYROSEQUENCING
FOR DE NOVO CHARACTERIZATION
OF A MICROBIOTA
Thursday, February 20, 2014
NIZO Microbiota Characterization Platform
prior knowledge
targeted / profiling
quantitative
selectivity
before you start
sensitivity
phylogeny information
depth of phylogeny
throughput
costs / sample
FISH
qPCR
t-RFLP
TGGE / DGGE
X-IT-CHIP
pyro-sequencing
yes
targeted
+++
+++
probe
1%
yes
taxon to genus
low
high (laborious)
yes
targeted
+++
+++
primer & probe
0.001 %
yes
phylum to OTU
high
low (broad analysis is
costly)
no
profiling
+
+
no
0.1 %
no
phylum
low to medium
medium
no
profiling
+
++
no (group primers)
1%
no
phylum to OTU
low to medium
medium
yes
profiling
++
+
no
0.05 %
yes (prediction)
phylum to OTU
high
high
no
profiling
+++
++
no
0.1 %
yes
phylum to genus
high
low
taxon > phylum > genus > species (OTU)
 Depending on scientific question, choice of technology is determined
 targeted or profiling
 composition or relative proportion (quantitative values)
 composition pattern or phylogeny (phylogenetic information)
 ambition of detection depth
 prior knowledge or de novo approach
 available budget……..
NIZO Microbiota Characterization Platform
Quantification of bacterial groups
Real-time qPCR
Microbiota profiling
Micro-array technology (HITCHIP)
Detector
Tube
Thermal
Cycler Base
FISH combined with Flow Cytometry
Barcoded pyrosequencing
Substantiation of EFSA health claims:
Molecular quantification platform (potential pathogens)
Quantitative RT-PCR
Count bacterial groups by
specific primers & probes
(DNA quantification)
FISH-FACS
Count bacterial groups
by specific fluorescence tags
(cell quantification)
Luminex xMAP technology
Quantification
of multiple targets
(bacteria/viral/parasite)
Detector
xMAP-technology
Tube
Thermal
Cycler
Base
Barcoded pyrosequencing
SAMPLE
-intestinal content
- biopsy
-faeces
-skin
LYSIS
1. Optimization
DNA isolation
protocol
ISOLATE
DNA
FRAGMENTS
DNA
LIBRARY
PREPARATION
DNA
SEQUENCER
REMOVE
CHIMERAS
RDP 10
CLASSIFIER
VISUALIZATION
MICROBIOTA
2. Primer design &
PCR optimalisation
3. Bioinformatics
tool for
classification,
visualization &
statistical
DNA isolation
16S
rRNA
gene
variable
B
conserved
Sample specific
sequence tag
conserved
PCR amplification of
mixed 16s population
~ 750 bp amplicon
A
Primer design:
Balancing phylogenetic coverage vs resolution
PHYLOGENETIC COVERAGE
Primer pair should target highly conserved sequence domain
16S rRNA
V6
V4
V7
V5
V3
V8
V1
V9
V2
PHYLOGENETIC RESOLUTION / DEPTH OF PHYLOGENY
Primer pair should amplify highly variable sequence domain
Primer coverage universal probes
selected primers for amplifying V3-V6
% coverage for different groups
Domain
Phylum
Proteo Firmi
bacteria archaea bacteria cutes
primer name
p338Forward 357
NIZO F2
detlefsen et al plos biology (2008) vol 6(11)
Reverse
1061RR4
F. Andersson Plos ONE (2008) vol 3(8)
NIZO
Reverse
520 F F9
Cleasson et al Plos ONE (2009) vol 4(8)
NIZO
Class
suborder
Family
Gamma Propioni Coryne
Staphylo
Actino Bacteroi proteo bacterine bacterine Enteroco Streptoc coccacea Lacto
Bifidobact
bacteria detes
bacteria ae
ae
c caceae oc caceae e
bacillales er iaceae
93,54%
0,01%
97,05%
93,98%
96,97%
96,96%
96,99%
97,74%
97,70%
97,75%
97,39%
97,66%
97,19%
96,30%
97,67%
2,46%
98,05%
97,83%
98,29%
97,96%
98,18%
98,19%
98,43%
98,77%
97,50%
98,09%
97,92%
96,71%
97,32%
14,90%
97,27%
97,50%
98,01%
97,54%
97,88%
97,95%
98,08%
97,07%
97,42%
97,77%
97,46%
97,33%
79,87%
70,24%
86,89%
81,20%
82,08%
79,86%
84,13%
69,27%
81,32%
41,64%
82,27%
60,75%
86,97%
69,10%
80,97%
89,25%
87,46%
88,65%
70,04%
93,32%
85,61%
88,61%
64,25%
92,06%
46,53%
79,49%
73,26%
69,96%
63,75%
84,48%
59,17%
76,70%
35,22%
79,82%
64,71%
85,62%
79,84%
88,22%
73,75%
72,89%
66,78%
78,96%
64,63%
89,80%
70,31%
69,51%
45,38%
84,76%
reference papers
Forward 8F
Gao et al.
Reverse 1510R Gao et al.
PLoS ONE (2008) Vol 3(7)
PLoS ONE (2008) Vol 3(7)
83,02%
71,27%
Forward 8F
Grice et al.
Reverse 1391R Grice et al.
Science 2009; 324, 1190
Science 2009; 324, 1190
60,17%
76,30%
71,72%
Data processing: NIZO’s Bio-informatics pipeline
I
N
P
U
T
RAW microbiota sequences
Pyronoise
metadata
0
Chimeras
PCA
Statistics
Visualization
tools
5. Chimera removal
-Chimera slayer
-Wigeon
-Mallard
Dynamics of the microbiome after skin damage
Skin damage by repetitive
tape-stripping of skin
microbial diversity (phylogenetic distance) of the skin microbiota
in different niches
8/9 Lowerback
3 inner-elbow
1 Fronthead
inner-elbow
Armpit
Fronthead
Fronthead
~ P. acnes
CCA analyses of the microbiota composition of the lowerback
The impact of the different variables:
Volunteer, gender and stripping depth
Clustering and microbial community composition of different
volunteers and epidermal layers (weighted UNIFRAC)
Large inter-individual differences between all the sampled skin layers
Tape stripping of skin epidermal layers
Phylogenetic visualisation human skin microbiota
example lower back
How to compare for e.g. treatment effects?
Visualization of all taxa being significantly
increased or decreased in abundance between
Halithosis versus healthy subjects
- Size of node = size of bacterial group
- Color of node = fold increase (red) or fold-decrease (blue) between groups
- Thickness and color of node bored = how well supported is difference by all
individual members within the groups being compared
Jos boekhorst
Sacha van Hijum
Difference in microbiota composition
between the genders
The Females
versus
The males
Recolonization patterns of the skin microbiome
The developing ‘neo-microbiome’ was more similar to that of the deeper stratum corneum layers
Can the microbiome of the deeper layers be regarded as the host indigenous microbiome?
Stronger statistical tools to indentify OTUs/bacterial
taxa (biomarkers) specific for treatment, health
condition, inflammatory status etc.
Random Forest modelling & multivariate statistics
Dorsal tongue microbiota of 3 healthy volunteers
Mouse Instance
Elephant Instance
Class
overlap
Class Mouse
Class Elephant
Technology for your success
NOP
OP
NOP
OP
NOP
Visual analytics – browsing your phylogenetic
data with personalized NIZO-tools (BIOTAVIZ)
Skin microbiota:
P. acnes * S. epidermidis * the rest….
Acknowledgements
Kasper Dinkla
Michel Westenberg
Iris van Swam
Jos Boekhorst
Esther van der Meulen
Esther Floris
Erik Smit
Peter Bron
Sacha van Hijum
Koos Oosterhaven
Michiel Kleerebezem
Tim te Beek
Marc van Driel
Joost van Schalkwijk
Patrick Zeeuwen