The Marine Microbiome Initiative
Justin Seymour, Martin Ostrowski, Mark Brown, Lev Bodrossy, Jodie van de Kamp, Andrew Bissett, Ana Lara-Lopez Consortium of > 50 researchers from 10 universities and research institutes
Contributed $910K Bioinformatic position: $560,000 DNA extraction: $90,000 Coastal microbial observatory support: $260,000 Marine Microbes now an IMOS Facility! Seymour 2014 From Anthony Richardson’s talk on Wednesday:
EOVs: Slides from Pier Buttigieg (via Ana Lara-Lopez) Emerging EOV: Microbial diversity and biomass
Slides from Pier Buttigieg (via Ana Lara-Lopez) Evolution of the Marine Microbiome Initiative 2012
Australian Marine Microbe Biodiversity Initiative (AMMBI)
NSI
PHB
MAI Evolution of the Marine Microbiome Initiative 2012 2014 Australian Marine Microbe BPA Marine Microbes Project Biodiversity Initiative (AMMBI) $1M
DAR
YON
NSI NSI
ROT PHB PHB KAI
MAI MAI Evolution of the Marine Microbiome Initiative 2012 2014 2018 Australian Marine Microbe BPA Marine Microbes Project Biodiversity Initiative (AMMBI)
DAR
YON
NSI NSI ROT
PHB PHB
KAI
MAI MAI Evolution of the Marine Microbiome Initiative 2012 2014 2018 Australian Marine Microbe BPA Marine Microbes Project Biodiversity Initiative (AMMBI)
Marine Microbes Project + Biomes of Australian DAR Soil Environments
YON
NSI NSI ROT
PHB PHB
KAI
MAI MAI Evolution of the Marine Microbiome Initiative 2012 2014 2018 Australian Marine Microbe BPA Marine Microbes Project Biodiversity Initiative (AMMBI)
DAR 2019
YON Marine Microbiome Facility
NSI NSI ROT
PHB PHB
KAI
MAI MAI Evolution of the Marine Microbiome Initiative 2012 2014 2018 Australian Marine Microbe BPA Marine Microbes Project Biodiversity Initiative (AMMBI)
DAR 2019
YON Marine Microbiome Facility
NSI NSI Sample processing ROT
PHB PHB DNA extractions
KAI Data analysis
MAI MAI The Australian Microbiome dataset contains ~5,000 marine samples from 7 NRS and 13 research voyages,
Martin Ostrowski The Australian Microbiome dataset contains ~5,000 marine samples from 7 NRS and 13 research voyages,
NSI
DAR PH Dark Ocean ROT KAI MAI
Martin Ostrowski > 5,000 samples 169,635 bacterial zOTUs (“species”) 265,910 eukaryote zOTUs (“species”)
Figures courtesy M. Ostrowski (Macquarie)
Microbial Indicators (EOVs?)
Nicole Webster David Bourne “Keystone Microbes” (EOVs?) Prochlorococcus SAR11 Synechococcus Roseobacter
Most abundant Most abundant Important bacteria in microbe in ocean phototsynthetic productive coastal (most abundant organisms in the environments organism on earth) ocean Links to phytoplankton Dominates blooms oligotrophic ocean Keystone Microbes NRS Data: Two SAR11 sequence variants (1 DNA base-pair difference in 16S gene) Port Hacking Mark Brown
30
25
20 SAR11 15
10
5
0 0 5 10 15 20 25 30 35 40
Different dynamics explained by different capacity to compete in low organic nutrient conditions → Indicator of nutrient status of S.W.? Keystone Microbes NRS Data: Prochlorococcus and Synechococcus ecotypes display clear Martin Ostrowski regional patterns:
Prochlorococcus Synechococcus
Patterns linked to temperature Keystone Microbes NRS: Very closely related Roseobacter strains show divergent spatial and temporal patterns James O’Brien
Differences explained by links to different phytoplankton taxa Roseobacter Sentinels for ecosystem shifts? (EOVs?)
Organic nutrients Temperature Phytoplankton dynamics Microbial Function (An EOV?)
459 Pelagic metagenomes 4,261 Gbp 56,000,000 unique functional genes
Look for ”indicator genes”? Microbial Function (An EOV?)
Ric Carney
The class 1 integron-integrase gene (IntI1) : potential signal for anthropogenic pollution1:
Linked to antibiotic, disinfectant and heavy metal resistance
1Gillings et al. (2015) ISME J Microbial Function
“Re-assembling complete microbial genomes from metagenomes is like doing a 648 billion-piece jigsaw puzzle” – Martin Ostrowski
459 Pelagic metagenomes 4,261 Gbp Assemble key genomes 162 Gbp assembled (~74,000 genomes)
Martin Ostrowski Microbial Function
459 Pelagic metagenomes 4,261 Gbp 162 Gbp assembled (~74,000 genomes)
The genomic composition of the different species may provide clues Daniel Vaulot SBR Roscoff to the health and productivity of the system
Martin Ostrowski Modelling Keystone Microbes Huge AMI data-set and comprehensive suite of environmental metadata unprecedented platform for modelling regional and global dynamics of keystone microbes (e.g.): Hind-casting cyanobacterial community at MAI PHB 1946-
Species Distribution Modelling: Next steps: Moving into the coasts Coasts are key interface for marine microbes and human population Increasing coastal presence future ambition for IMOS
2018: Consultative process to include a network of coastal microbial observatories in the AMI Next steps: Integrating Across IMOS e.g. Linking physical and microbial oceanography
Michael Doane
Linking lower trophic levels ⟷
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