Metabolic pathways inferred from a bacterial marker gene illuminate ecological changes across South Paci�c frontal boundaries

Global oceanographic monitoring initiatives started by measuring abiotic essential ocean variables but are currently incorporating biological and metagenomic sampling. There is, however, a large gap between the taxonomic information produced by bacterial genomic analyses and information on bacterial functions, which is sought by biogeochemists, ecologists, and modellers. Here, we provide a mechanistic understanding of how a bacterial marker gene (16S rRNA) can be used to derive latitudinal trends for core metabolic pathways and, ultimately, be used for mapping ecosystem function change in global monitoring campaigns. From a transect spanning 7000 km in the South Paci�c Ocean we identi�ed ten metabolic pathways, which were related to ecological processes of primary productivity, temperature-regulated growth, coping strategies for nutrient limitation, energy metabolism, and degradation. We compared and contrasted these metabolic pathways with measured physico-biochemical parameters within and between oceanographic provinces, and found that functional diversity is as affected by oceanographic boundaries as is taxonomic composition. This study demonstrates that bacterial marker gene data, sampled and analysed with low costs and high throughput, can be used to infer on metabolic changes at the community scale. Such analyses may provide insight into the drivers of ecological changes and, overall, into the effects of biodiversity on marine ecosystem functioning.

[1]  T. Trull,et al.  Oceanographic Fronts Shape Phaeocystis Assemblages: A High-Resolution 18S rRNA Gene Survey From the Ice-Edge to the Equator of the South Pacific , 2020, Frontiers in Microbiology.

[2]  M. Charleston,et al.  Mainstreaming Microbes across Biomes , 2020 .

[3]  Gavin M Douglas,et al.  PICRUSt2 for prediction of metagenome functions , 2020, Nature Biotechnology.

[4]  B. Eyre,et al.  N2 Fixation and New Insights Into Nitrification From the Ice-Edge to the Equator in the South Pacific Ocean , 2020, Frontiers in Marine Science.

[5]  C. Duarte,et al.  The Ocean Genome: Conservation and the Fair, Equitable and Sustainable Use of Marine Genetic Resources. , 2020 .

[6]  R. Colwell,et al.  Microbial resolution of whole genome shotgun and 16S amplicon metagenomic sequencing using publicly available NEON data , 2020, PloS one.

[7]  A. Fodor,et al.  Inference-based accuracy of metagenome prediction tools varies across sample types and functional categories , 2020, Microbiome.

[8]  K. Petrou,et al.  Dimethylated sulfur production in batch cultures of Southern Ocean phytoplankton , 2019, Biogeochemistry.

[9]  Luis Pedro Coelho,et al.  Gene Expression Changes and Community Turnover Differentially Shape the Global Ocean Metatranscriptome , 2019, Cell.

[10]  P. Bown,et al.  Diversity decoupled from ecosystem function and resilience during mass extinction recovery , 2019, Nature.

[11]  M. Moran,et al.  Sulfur metabolites that facilitate oceanic phytoplankton–bacteria carbon flux , 2019, The ISME Journal.

[12]  T. Hackl,et al.  Marine microbial metagenomes sampled across space and time , 2018, Scientific Data.

[13]  Mark V Brown,et al.  Oceanographic boundaries constrain microbial diversity gradients in the South Pacific Ocean , 2018, Proceedings of the National Academy of Sciences.

[14]  A. Waite,et al.  Hard and soft plastic resin embedding for single‐cell element uptake investigations of marine‐snow‐associated microorganisms using nano‐scale secondary ion mass spectrometry , 2018, Limnology and Oceanography: Methods.

[15]  Jason Koval,et al.  Systematic, continental scale temporal monitoring of marine pelagic microbiota by the Australian Marine Microbial Biodiversity Initiative , 2018, Scientific Data.

[16]  E. Achterberg,et al.  Nutrient co-limitation at the boundary of an oceanic gyre , 2017, Nature.

[17]  R. Braakman,et al.  Metabolic evolution and the self-organization of ecosystems , 2017, Proceedings of the National Academy of Sciences.

[18]  Carla M. Zammit,et al.  Introducing BASE: the Biomes of Australian Soil Environments soil microbial diversity database , 2016, GigaScience.

[19]  J. Brandsma,et al.  Lipid remodelling is a widespread strategy in marine heterotrophic bacteria upon phosphorus deficiency , 2015, The ISME Journal.

[20]  Luis Pedro Coelho,et al.  Structure and function of the global ocean microbiome , 2015, Science.

[21]  R. Davis,et al.  The Southwest Pacific Ocean circulation and climate experiment (SPICE) , 2014 .

[22]  D. Karl,et al.  Microbial oceanography and the Hawaii Ocean Time-series programme , 2014, Nature Reviews Microbiology.

[23]  M. Cottrell,et al.  Metagenomic analysis of organic matter degradation in methane‐rich Arctic Ocean sediments , 2014 .

[24]  Daniel H. Buckley,et al.  A comprehensive aligned nifH gene database: a multipurpose tool for studies of nitrogen-fixing bacteria , 2014, Database J. Biol. Databases Curation.

[25]  Jesse R. Zaneveld,et al.  Predictive functional profiling of microbial communities using 16S rRNA marker gene sequences , 2013, Nature Biotechnology.

[26]  C. Parrish.  Lipids in Marine Ecosystems , 2013 .

[27]  Susan Holmes,et al.  phyloseq: An R Package for Reproducible Interactive Analysis and Graphics of Microbiome Census Data , 2013, PloS one.

[28]  Elena Litchman,et al.  A Global Pattern of Thermal Adaptation in Marine Phytoplankton , 2012, Science.

[29]  Matthew Z. DeMaere,et al.  Global biogeography of SAR11 marine bacteria , 2012, Molecular systems biology.

[30]  Katherine H. Huang,et al.  A framework for human microbiome research , 2012, Nature.

[31]  Hadley Wickham,et al.  The Split-Apply-Combine Strategy for Data Analysis , 2011 .

[32]  F. Azam,et al.  Major Role of Microbes in Carbon Fluxes during Austral Winter in the Southern Drake Passage , 2009, PloS one.

[33]  J Elith,et al.  A working guide to boosted regression trees. , 2008, The Journal of animal ecology.

[34]  Sonya T. Dyhrman,et al.  Microbes and the marine phosphorus cycle , 2007 .

[35]  R. Amann,et al.  Potential Interactions of Particle-Associated Anammox Bacteria with Bacterial and Archaeal Partners in the Namibian Upwelling System , 2007, Applied and Environmental Microbiology.

[36]  M. Chytrý,et al.  Statistical determination of diagnostic species for site groups of unequal size , 2006 .

[37]  Maureen L. Coleman,et al.  Phosphate acquisition genes in Prochlorococcus ecotypes: Evidence for genome-wide adaptation , 2006, Proceedings of the National Academy of Sciences.

[38]  Gabrielle Rocap,et al.  Sulfolipids dramatically decrease phosphorus demand by picocyanobacteria in oligotrophic marine environments. , 2006, Proceedings of the National Academy of Sciences of the United States of America.

[39]  H. Hoppe,et al.  Bacterial growth and primary production along a north–south transect of the Atlantic Ocean , 2002, Nature.

[40]  Milan Chytrý,et al.  Determination of diagnostic species with statistical fidelity measures , 2002 .

[41]  H. Ploug.  Small‐scale oxygen fluxes and remineralization in sinking aggregates , 2001 .

[42]  L. Legendre,et al.  Biogenic carbon cycling in the upper ocean: effects of microbial respiration. , 2001, Science.

[43]  Andrew J. Watson,et al.  A mesoscale phytoplankton bloom in the polar Southern Ocean stimulated by iron fertilization , 2000, Nature.

[44]  A. Longhurst.  Ecological Geography of the Sea , 1998 .

[45]  P. Nichols,et al.  LIPIDS AND TROPHODYNAMICS OF ANTARCTIC ZOOPLANKTON , 1998 .

[46]  Paul G. Falkowski,et al.  Evolution of the nitrogen cycle and its influence on the biological sequestration of CO2 in the ocean , 1997, Nature.

[47]  H. Berresheim.  Biogenic sulfur emissions from the Subantarctic and Antarctic Oceans , 1987 .

[48]  N. Pace,et al.  Rapid determination of 16S ribosomal RNA sequences for phylogenetic analyses. , 1985, Proceedings of the National Academy of Sciences of the United States of America.

[49]  G. Kattner,et al.  Development of lipids during a spring plankton bloom in the northern North Sea: II. Dissolved lipids and fatty acids , 1983 .

[50]  D. Jones,et al.  Microbiological and zooplankton activity at a front in Liverpool Bay , 1981, Nature.

[51]  B. Peterson,et al.  Particulate organic matter flux and planktonic new production in the deep ocean , 1979, Nature.

[52]  J. Goering,et al.  UPTAKE OF NEW AND REGENERATED FORMS OF NITROGEN IN PRIMARY PRODUCTIVITY1 , 1967 .

[53]  Isabel Ferrera,et al.  Recommendations for plankton measurements on the GO-SHIP program with relevance to other sea-going expeditions. SCOR Working Group 154 GO-SHIP Report. , 2020 .

[54]  R. Beiko,et al.  Predicting the Functional Potential of the Microbiome from Marker Genes Using PICRUSt. , 2018, Methods in molecular biology.

[55]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[56]  Matthew Z. DeMaere,et al.  Global biogeography of SAR 11 marine bacteria , 2012 .

[57]  W. Hagen,et al.  Seasonal adaptations and the role of lipids in oceanic zooplankton. , 2001, Zoology.

[58]  E. Stackebrandt,et al.  Nucleic acid techniques in bacterial systematics , 1991 .

[59]  D. Lane.  16S/23S rRNA sequencing , 1991 .

[60]  W. Richard,et al.  TEMPERATURE AND PHYTOPLANKTON GROWTH IN THE SEA , 1972 .