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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!!!