Download Distribution in percentage Inter-environmental

the ‘omic generation
To remember
mRNA
Protein
mRNA
Protein
 The genome is the base of everything (long evolution)
 About 25 000 genes in P. antarctica
 “Universal” genes can be used to look at evolution
 Many genes are unique to Phaeocystis
 Only a fraction of the genes will be used at a given time
 mRNA /proteins useful to look at activity
Introducing the ‘omic generation
Culture:
Genomic
Transcriptomic
Proteomic
Environment:
Metagenomic
Metatranscriptomic
Metaproteomic
Different methods for each question
 The question drives the method
 Want to know the overall productivity of the Southern Ocean?
 Interpret satellite data
Different methods for each question
 The question drives the method
 Want to know the overall productivity of the Southern Ocean?
 Interpret satellite data
 Want to know the physiology of a microbial community?
 Isolates
 Direct observation (e.g., FlowCam)
Different methods for each question
 The question drives the method
 Want to know the overall productivity of the Southern Ocean?
 Interpret satellite data
 Want to know the physiology of a microbial community?
 Isolates
 Direct observation (e.g., FlowCam)
 Want to know the diversity of a microbial community?
 FlowCan for physiology (limited to a few parameters)
 Pigments analysis (differentiate phylum only–takes years )
 Sequencing a biomarker of diversity (e.g., 16S/18S)
 Very sensitive: detect thousands of lowly abundant taxa
Different methods for each question
 The question drives the method
 Want to know the overall productivity of the Southern Ocean?
 Interpret satellite data
 Want to know the physiology of a microbial community?
 Isolates
 Direct observation (e.g., FlowCam)
 Want to know the diversity of a microbial community?
 FlowCan for physiology (limited to a few parameters)
 Pigments analysis (differentiate phylum only–takes years )
 Sequencing a biomarker of diversity (e.g., 16S/18S)
 Very sensitive: detect thousands of lowly abundant taxa
 Want to know the functionality of a microbial community?
 Sequence all the extracted DNA and annotate reads
Different methods for each question
 The question drives the method
 Want to know the overall productivity of the Southern Ocean?
 Interpret satellite data
 Want to know the physiology of a microbial community?
 Isolates
 Direct observation (e.g., FlowCam)
 Want to know the diversity of a microbial community?
 FlowCan for physiology (limited to a few parameters)
 Pigments analysis (differentiate phylum only–takes years )
 Sequencing a biomarker of diversity (e.g., 16S/18S)
 Very sensitive: detect thousands of lowly abundant taxa
 Want to know the functionality of a microbial community?
 Sequence all the extracted DNA and annotate reads
 Want to know who is doing what in your microbial community?
 Isolate individuals  culture genome sequencing
 Assemble a metagenome into different genomes
My questions regarding the Amundsen Sea bacterial communities:
1/Who is dominating the Amundsen polynya surface?
2/What can they do (e.g., related to the cycles of C, N, Fe)?
3/What are they doing? Bacteria-alga activity interactions?
Continental Shelf
Break
Polynya
The Amundsen
Sea
1
Dotson
Glacier
The Amundsen Sea diversity
 First, sequence a biomarker  16S rRNA gene amplicons
 Provides the community structure of the different locations
SAR92
SAR86
>10%
1 - 10%
<1%
16.57%
SAR116 Rhodobacteraceae
Methylophilaceae
Thalassobacter
SAR86
Gammaproteobacteria
Pelagibacter
Owenweeksia
Pseudospirillum
Piscirickettsiaceae
31.66%
Oceanospirillales
Rhodospirillaceae
SAR92
SAR406
SAR11
Colwellia
Cryomorphaceae
Oceanospirillum-like
Ulvibacter
Roseobacter
Flavobacteriaceae
Prochlorococcus
Lutibacter
Pseudoalteromonas
Crocinitomix
SAR324
Nitrospina
Methylophaga
Salinisphaeraceae Verrucomicrobia
Shewanella
Acidimicrobiales
Psychrobacter
A)
B)
D)
C)
E)
In situ spectral Imaging of Phaeocystis
Looking for attached cells
and possible symbioses
Valm et al., 2011
By targeting specific taxa (e.g., SAR92)
The Amundsen Sea functionality
 Secondly, sequence
and assemble the
metagenome
 Then annotate and
compare the
dominant genomes
24.15%
Uni, Sym, and Antiporters
NAD and NADP
DNA recombination
Dormancy and Sporulation
Iron acquisition and metabolism
Checkpoint control Heat shock
ATP synthases
Cold shock
Invasion and intracellular resistance
Electron transport and photophosphorylation
Isoprenoids
Pyrimidines
Cell Wall and Capsule
Lipoic acid
Inorganic sulfur assimilation
Protein folding
Protein biosynthesis
Pyridoxine Coenzyme F420
Selenoproteins
Cation transporters
Tetrapyrroles
30.68%
Oxidative stress
Resistance to antibiotics and toxic compounds
Nitrogen Metabolism
One,carbon Metabolism
Nucleosides and Nucleotides
DNA repair
Monosaccharides
Protein secretion system, Type VI
Denitrification
PCA based on the
relative distribution of
105 functional
subcategories (RAST) in
8 bacterial genomes
DNA uptake, competence Metabolism of Aromatic Compounds
Sugar alcohols Fatty Acids, Lipids, and Isoprenoids
Carbohydrates
Fermentation
ABC transporters
Sulfur Metabolism Organic sulfur assimilation
Fatty acids
Electron accepting reactions
Osmotic stress
Motility and Chemotaxis
The Amundsen Sea functionality
Nitrogen metabolism
Nitrite to Ammonium Urea to
Ammonia transporter Ammonia
Sulfate metabolism
Iron metabolism
Sulfure
DMSP acyl
DMSP
Siderophore
oxidation
CoA
demethylase
biosynthesis
(SOX system) transferase
(DmdA)
Ferric
Protoporphyrin
siderophore
to Heme
transport
Proteobacteria
Oceanospirillum
SAR92
Rhodobacteraceae
No
No
No
Yes
Yes
Yes
No
No
No
No
No
Yes
No
Yes
No
Yes
No
Yes
No
No
No
No
Yes
No
Yes
Yes
Yes
Bacteroidetes
Cryomorphaceae
Polaribacter 2.9
Flavobacteriaceae 2.5
Yes
Yes
Yes
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
Yes
Yes
Yes
No
Yes
Yes
Yes
Yes
No
Pico-eukaryota
Chlorophyta
Yes
Yes
Yes
No
No
No
Yes
No
Yes
Metagenomic assembly possible
only when low diversity and simple genomes!
The 6 bacteria  50% bacterial population
Not possible to reconstruct sub-dominant genomes…
Metagenomic is strongly evenness dependent
Relative distribution of
genetic structures
No signal
Genomic units
Direct
sequencing
detection
Genomic reconstruction when >1% of the community
104 -109
Legend:
High sequencing redondancy
Medium sequencing redondancy
Low sequencing redondancy
Inter-environmental metagenomic comparisons
PCA based on the
distribution in
percentage of
general functional
subsystems (MG
RAST) among 31
metagenomes
31.59%
Cofactors. Vitamins. Prosthetic
Groups. Pigments
Amino Acids and Derivatives
Photosynthesis
Unclassified
Protein Metabolism
RNA Metabolism
Nucleosides and
Nucleotides
Fatty Acids and Lipids
Macromolecular Synthesis
Respiration
Metabolism of Aromatic
Compounds
41.62%
Nitrogen Metabolism
Secondary Metabolism
Motility and Chemotaxis
Cell Division and Cell Cycle
DNA Metabolism
Cell Wall and Capsule
Sulfur Metabolism
Clustering.based subsystems
Regulation and Cell signaling
Potassium metabolism
Dormancy and
Sporulation
Membrane Transport
Carbohydrates
Stress Response
Phosphorus Metabolism
Miscellaneous
Prophage
Virulence
Inter-environmental metagenomic comparisons
20.00%
PCA based on the
distribution in
percentage of
general functional
subsystems (MG
RAST) among 77
metagenomes
Prophage
Phosphorus Metabolism
Miscellaneous
Virulence
Cell Division and Cell Cycle
Dormancy and Sporulation
Cell Wall and Capsule
Sulf ur Metabolism
DNA Metabolism
Similar
Carbohydrates
comparison done
by Dinsdale and
31.34%
colleagues in
2008
Limited to
general functions
Only general
tendencies
observed
Regulation and Cell signaling
Stress Response
Potassium metabolism
Motility and Chemotaxis
Clustering.based subsystems
Membrane Transport
Nitrogen Metabolism
Secondary Metabolism
Metabolism of Aromatic Compounds
Respiration
Fatty Acids and Lipids
Macromolecular Synthesis
RNA Metabolism
Protein Metabolism
Nucleosides and Nucleotides
Photosynthesis
Cof actors, Vitamins,
Unclassif ied
Prosthetic Groups Pigments
Amino Acids and Derivatives
Inter-environmental metagenomic comparisons
MG-RAST, E-value<10-5
0.2
DMSP breakdown
0.18
0.14
0.12
0.1
0.08
0.06
0.04
Human
feces
Cow
Sediments
Mouse
Soil
Chicken
Antarctic
lakes
Mine
Air
Deep
Oceans
MFC
Oceans
Sludg.
0
Arctic
0.02
Coral
Distribution in percentage
0.16
Inter-environmental metagenomic comparisons
MG-RAST, E-value<10-5
3
Sucrose Metabolism
Obese mouse
Lean mouse
2
1.5
1
Human
feces
Cow
Sediments
Mouse
Soil
Chicken
Antarctic
lakes
Mine
Air
Deep
Oceans
MFC
Oceans
Sludg.
0
Arctic
0.5
Coral
Distribution in percentage
2.5
Inter-environmental metagenomic comparisons
MG-RAST, E-value<10-5
1.2
Tetracycline resistance, ribosome protection type
0.8
0.6
0.4
Human
feces
Cow
Sediments
Mouse
Soil
Chicken
Antarctic
lakes
Mine
Air
Deep
Oceans
MFC
Oceans
Sludg.
0
Arctic
0.2
Coral
percentage
inin
Distribution
percentage
Distribution
1
Inter-environmental metagenomic comparisons
MG-RAST, E-value<10-5
1.2
Tetracycline resistance, ribosome protection type
0.8
0.6
Only functional potential
Not activity!!!
0.4
Human
feces
Cow
Sediments
Mouse
Soil
Chicken
Antarctic
lakes
Mine
Air
Deep
Oceans
MFC
Oceans
Sludg.
0
Arctic
0.2
Coral
percentage
inin
Distribution
percentage
Distribution
1
Transcriptomic
 What we learn from the mRNA is the activity
E.g., We want to know how Phaeocystis reacts to light variation, so
you sequence extracted mRNA from cultures at different light
All proteins
Cytoskeleton
related proteins
Transcriptomic
 What we learn from the mRNA is the activity
E.g., We want to know how Phaeocystis reacts to light variation, so
you sequence extracted mRNA from cultures at different light
A strength for the Phantastic expedition
A strength for the Phantastic expedition
Satellite
data
FlowCam
High throughput physiology
Cell isolation
Macromolecules
DNA / proteins
Molecules
mRNA / DMS / H2O2 /
Chlorophyll A, etc.
Elements
N / Fe
We are about to connect
different scales,
for example by linking
satellite data, P.
antarctica physiology,
transcription processes
and DMSP/Iron/HOOH fluxes
This is going to be
Phantastic!!!