tom delmont: from the terragenome project to global metagenomic comparisons: implications for the...
DESCRIPTION
Tom O Delmont's talk from the Earth Microbiome Project meeting in ShenzhenTRANSCRIPT
Earth Microbiome Project and
Global Metagenomic Comparisons
Tom O. Delmont Emmanuel Prestat Pascal Simonet Timothy M. Vogel
15.16%
8.10%
Environmental Microbial Genomics GroupLaboratoire Ampère . Ecole Centrale de Lyon . Université de Lyon
Functional subsystems distribution among 77
metagenomes
Earth Microbiome Project and
Global Metagenomic Comparisons
Tom O. Delmont Emmanuel Prestat Pascal Simonet Timothy M. Vogel
15.16%
8.10%
Metasoil project(Terragenome) Global Ocean
Survey
Environmental Microbial Genomics GroupLaboratoire Ampère . Ecole Centrale de Lyon . Université de Lyon
Functional subsystems distribution among 77
metagenomes
1/ Metasoil project (Terragenome consortium)
To sequence as in depth as possible the Rothamsted soil metagenome
A 2 million fosmid library was constructed (Libragen Company)
90 Titanium pyrosequencing runs and some HiSeq are being generated by
varying different parameters
To sequence as in depth as possible the Rothamsted soil metagenome
A 2 million fosmid library was constructed (Libragen Company)
90 Titanium pyrosequencing runs and some HiSeq are being generated by
varying different parameters
-Time (years/seasons)
-Spatial variations (e.g. depth)
-Methodology (DNA extraction approaches)
To maximize the natural and methodological fluctuations of this soil metagenome (Delmont et al., 2011, AEM)
Our strategy to sequence a new environment:Five dimensions:
1 for time3 for space
1 for methodology
1/ Metasoil project (Terragenome consortium)
Seasonal \ Sampling effect Cell lysis stringency effectDepth effect
0
1
2
3
4
5
6
7
8 Species distribution (SEED annotation) using four lyses
Re
lati
ve d
istr
ibu
tio
n (
%)
Species
Important DNA extraction biases
Concept of standard deviation of distribution
Re
lati
ve f
un
ctio
nal
dis
trib
uti
on
(%
)
Comparison of functional distributions among metagenomes (1million reads) using MG RAST and STAMP
1/ Metasoil project (Terragenome consortium)
0
2
4
6
8
10Rothamsted soil
Seasonal \ Sampling effect Cell lysis stringency effectDepth effect
Species distribution
Re
lati
ve d
istr
ibu
tio
n (
%)
Species
Important methodological fluctuations
Concept of metagenomic variance
Re
lati
ve f
un
ctio
nal
dis
trib
uti
on
(%
)1/ Metasoil project (Terragenome consortium)
0
2
4
6
8
10
12 Rothamsted soilPuerto-Rico forest soil
Seasonal \ Sampling effect Cell lysis stringency effectDepth effect
Species distribution
Re
lati
ve d
istr
ibu
tio
n (
%)
Species
Re
lati
ve f
un
ctio
nal
dis
trib
uti
on
(%
)1/ Metasoil project (Terragenome consortium)
The other metagenomic variance is lacking
Comparison difficult
actu
cd
ef
gh
ij
kl
mn
op
qr
st
uv
wx
yz
bb
0 5 10 15
Clustering-based subsystems
Carbohydrates
Amino Acids and Derivatives
Protein Metabolism
Cofactors, Vitamins, Prosthetic Groups, Pigments
Cell Wall and Capsule
Unclassified
Virulence
DNA Metabolism
RNA Metabolism
Respiration
Nucleosides and Nucleotides
Membrane Transport
Cell Division and Cell Cycle
Stress Response
Phosphorus Metabolism
Fatty Acids and Lipids
Motility and Chemotaxis
Sulfur Metabolism
Metabolism of Aromatic Compounds
Regulation and Cell signaling
Nitrogen Metabolism
Miscellaneous
Potassium metabolism
Photosynthesis
Macromolecular Synthesis
Secondary Metabolism
Prophage
Dormancy and Sporulation
Relative distribution in percentage36 metagenomes from the GOS (coastal and open oceans)
2/ Lessons from the Global ocean survey
Different timesDifferent locations
Only one method used
Do these datasets represent this environment?
If not DNA extraction,cells filtration effect?
*When comparing samples from the same environment:we use (in general subjectively) the same method
To summarize
*When comparing samples from the same environment:we use (in general subjectively) the same method
*When studying a new environment: use different approaches metagenomic variance (represents a global picture)
Temporal, spatial and methodological variations
To summarize
DNA extraction dilemma
Until the inverse is proved, we should consider that DNA biases are different between and among environments
Problem when using one single method
0
10
20
30
40
50
60
70
80
90
100
0 2000 4000 6000 8000 10000 12000 14000 16000 18000 20000
Number of probes
% o
f p
osi
tiv
e p
hy
log
en
eti
cp
rob
es
One DNA extraction approach (<40%)
15 DNA extraction approaches (>99%)
The diversity is highly underestimated whenusing only one DNA extraction approach
% o
f p
osi
tive
ph
ylo
gen
etic
pro
be
s
(Delmont et al., 2011, AEM)
DNA extraction dilemma alternative
Until the inverse is proved, we should consider that DNA biases are different between and among environments
1/ We cannot know how cosmopolitan are taxa with one method2/ Replicates are insufficient (biases are hidden behind strong reproducibilities)
The most protocols we use,The most species will be detected
The better the global picture will be
DNA extraction dilemma alternative
Until the inverse is proved, we should consider that DNA biases are different between and among environments
1/ We cannot know how cosmopolitan are taxa with one method2/ Replicates are insufficient (biases are hidden behind strong reproducibilities)
The most protocols we use,The most species will be detected
Proposition: MoBio for all samples (referential protocol)-Microbial ecologists send DNA samples that represent, in their
point of view, the environment they study since years
The “tricky” DNA extraction effort could be shared by laboratories involved in EMP
*When comparing samples from the same environment:we use (in general subjectively) the same method
*When studying a new environment: use different approaches metagenomic variance (represents a global picture)
Temporal, spatial and methodological variations
*When performing global metagenomic comparisons:metagenomic variance for all environments
the experimental design depends on the environmentneed to be flexible and adapted to specific problems
To summarize
To summarize
Possible metagenomic definition of ecosystem boundaries:
When inter-environmental distribution differences are globally stronger than intra-environmental fluctuations (natural OR
methodological)
Need to define environments at the microorganism level
Global sampling grid is not coherent
Carbohydrates CELLwall Regulation Fatty Membrane dormancy
05
1015
20
Relative distribution of the function x among n ecosystems
Dis
trib
uti
on
in p
erc
en
tage
Ecos
yste
m 1
Ecos
yste
m 2
Ecos
yste
m 3
Ecos
yste
m 4
Ecos
yste
m 5
Ecos
yste
m 6
Ecos
yste
m 7
Ecos
yste
m 8
Ecos
yste
m 9
Ecos
yste
m 1
0
Ecos
yste
m 1
1
Ecos
yste
m 1
2
Ecos
yste
m 1
3
Ecos
yste
m 1
4
Ecos
yste
m 1
5
Ecos
yste
m 1
6
Ecos
yste
m 1
7
Ecos
yste
m 1
8
Ecos
yste
m 1
9
Ecos
yste
m 2
0
Ecos
yste
m 2
1
Ecos
yste
m 2
2
Ecos
yste
m 2
3
Ecos
yste
m 2
4
Ecos
yste
m 2
5
Ecos
yste
m 2
6
Ecos
yste
m 2
7
Ecos
yste
m 2
8
Ecos
yste
m 2
9
As a perspective for EMP
Carbohydrates CELLwall Regulation Fatty Membrane dormancy
05
1015
20
Relative distribution of the function x among n ecosystems
Dis
trib
uti
on
in p
erc
en
tage
Ecos
yste
m 1
Ecos
yste
m 2
Ecos
yste
m 3
Ecos
yste
m 4
Ecos
yste
m 5
Ecos
yste
m 6
Ecos
yste
m 7
Ecos
yste
m 8
Ecos
yste
m 9
Ecos
yste
m 1
0
Ecos
yste
m 1
1
Ecos
yste
m 1
2
Ecos
yste
m 1
3
Ecos
yste
m 1
4
Ecos
yste
m 1
5
Ecos
yste
m 1
6
Ecos
yste
m 1
7
Ecos
yste
m 1
8
Ecos
yste
m 1
9
Ecos
yste
m 2
0
Ecos
yste
m 2
1
Ecos
yste
m 2
2
Ecos
yste
m 2
3
Ecos
yste
m 2
4
Ecos
yste
m 2
5
Ecos
yste
m 2
6
Ecos
yste
m 2
7
Ecos
yste
m 2
8
Ecos
yste
m 2
9
As a perspective for EMP
My vision of EMP is a concerted and flexible experimental design constructed
with the expertise of all microbial ecologists to represent for the best
microbial communities
What should be the next sensational “omic” project?
What should be the next sensational “omic” project?
Colonizing Mars and waiting for a Martian microbiome project?
Or sequencing an alien gut (with metadata of course)
What should be the next sensational “omic” project?