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Summary paragraph Plankton are essential in marine ecosystems. However, our knowledge of overall community structure is sparse due to inconsistent sampling across their very large organismal size range. Here we use diverse imaging methods to establish complete plankton inventories of organisms spanning five orders of magnitude in size. Plankton community size and trophic structure variation validate a long-held theoretical link between organism size-spectra and ecosystem trophic structures. We found that predator/grazer biomass and biovolume unexpectedly exceed that of primary producers at most (55%) locations, likely due to our better quantification of gelatinous organisms. Bottom- heavy ecosystems (the norm on land) appear to be rare in the ocean. Collectively, gelatinous organisms represent 30% of the total biovolume (8-9% of carbon) of marine plankton communities from tropical to polar ecosystems. Communities can be split into three extreme typologies: diatom/copepod-dominated in eutrophic blooms, rhizarian/chaetognath-dominated in oligotrophic tropical oceans, and gelatinous-dominated elsewhere. While plankton taxonomic composition changes with latitude, functional and trophic structures mostly depend on the amount of prey available for each trophic level. Given future projections of oligotrophication of marine ecosystems, our findings suggest that rhizarian and gelatinous organisms will increasingly dominate the apex position of planktonic ecosystems, leading to significant changes in the ocean’s carbon cycle.

Due to the scarcity of organic matter (OM) sources in the bathypelagic (1000–4000 m depth), prokaryotic metabolism is believed to be concentrated on particles originating from the surface. However, the structure of active bathypelagic prokaryotic communities and how it changes across environmental gradients remains unexplored. Using a combination of 16S rRNA gene and transcripts sequencing, metagenomics, and substrate uptake potential measurements, here we aimed to explore how water masses aging and the quality of OM influence the structure of the active microbiome, and the potential implications for community function. We found that the relative contribution of taxa with a free-living lifestyle to the active microbiome increased in older water masses that were enriched in recalcitrant OM, suggesting that these prokaryotes may also play a substantial role in the bathypelagic metabolism of vast areas of the ocean. In comparison to particle-associated prokaryotes, free-living prokaryotes exhibited lower potential metabolic rates, and harbored a limited number of two-component sensory systems, suggesting they have less ability to sense and respond to environmental cues. In contrast, particle-associated prokaryotes carried genes for particle colonization and carbohydrate utilization that were absent in prokaryotes with a free-living lifestyle. Consistently, we observed that prokaryotic communities inhabiting older waters displayed reduced abilities to colonize particles, and higher capabilities to use complex carbon sources, compared to communities in waters with a higher proportion of labile OM. Our results provide evidence of regionalization of the bathypelagic active prokaryotic microbiome, unveiling a niche partitioning based on the quality of OM

Marine sediments impacted by urban and industrial pollutants are typically exposed to reducing conditions and represent major reservoirs of toxic mercury species. Mercury methylation mediated by anaerobic microorganisms is favored under such conditions, yet little is known about potential microbial mechanisms for mercury detoxification. We used culture-independent (metagenomics, metabarcoding) and culture-dependent approaches in anoxic marine sediments to identify microbial indicators of mercury pollution and analyze the distribution of genes involved in mercury reduction (merA) and demethylation (merB). While none of the isolates featured merB genes, 52 isolates, predominantly affiliated with Gammaproteobacteria, were merA positive. In contrast, merA genes detected in metagenomes were assigned to different phyla, including Desulfobacterota, Actinomycetota, Gemmatimonadota, Nitrospirota, and Pseudomonadota. This indicates a widespread capacity for mercury reduction in anoxic sediment microbiomes. Notably, merA genes were predominately identified in Desulfobacterota, a phylum previously associated only with mercury methylation. Marker genes involved in the latter process (hgcAB) were also mainly assigned to Desulfobacterota, implying a potential central and multifaceted role of this phylum in the mercury cycle. Network analysis revealed that Desulfobacterota were associated with anaerobic fermenters, methanogens and sulfur-oxidizers, indicating potential interactions between key players of the carbon, sulfur and mercury cycling in anoxic marine sediments

The global ocean genome (the pool of genes in marine organisms and the functional information they encode) is a major, untapped resource for science and society with a growing range of biotechnology applications in sectors such as biomedicine, energy, and food. Shotgun sequencing and metagenomics can now be used to catalog the diversity of ocean microbial life and to explore its functional potential, but has been limited by sample coverage, access to suitable sequencing platforms, and computational capacity. Here we provide a novel synthesis of the global ocean genome based on analysis of 2,102 sampled ocean metagenomes, with gene assembly and annotation via the KAUST Metagenome Analysis Platform (KMAP) Global Ocean Gene Catalog 1.0 containing ~317.5 million gene clusters. Taxonomically, we report the distribution of marine genes across the tree of life and different ocean basins and depth zone biomes. Functionally, we map its relationship to protein families and biogeochemical processes, including the major microbial metabolic pathways that process three elements that play fundamental roles in biogeochemical cycles and are relevant to climate change. These data extend our understanding of the complex, dynamic nature of the ocean microbiome and its metabolic capabilities. Further research is of critical global importance both to unlock the potential of the ocean genome and to understand and predict the effects of human-induced changes, including pollution and climate change. Further hypothesis-driven research should target under-sampled deep sea and benthic microbial communities using enhanced metagenomic methods, to better understand marine ecosystem functioning. Investment in the necessary computational capacity is essential, as are suitable intellectual property frameworks

The Ocean microbiome has a crucial role in Earth’s biogeochemical cycles. During the last decade, global cruises such as Tara Oceans and the Malaspina Expedition have expanded our understanding of the diversity and genetic repertoire of marine microbes. Nevertheless, there are still knowledge gaps regarding their diversity patterns throughout depth gradients ranging from the surface to the deep ocean. Here we present a dataset of 76 microbial metagenomes (MProfile) of the picoplankton size fraction (0.2–3.0 µm) collected in 11 vertical profiles covering contrasting ocean regions sampled during the Malaspina Expedition circumnavigation (7 depths, from surface to 4,000 m deep). The MProfile dataset produced 1.66 Tbp of raw DNA sequences from which we derived: 17.4 million genes clustered at 95% sequence similarity (M-GeneDB-VP), 2,672 metagenome-assembled genomes (MAGs) of Archaea and Bacteria (Malaspina-VP-MAGs), and over 100,000 viral genomic sequences. This dataset will be a valuable resource for exploring the functional and taxonomic connectivity between the photic and bathypelagic tropical and sub-tropical ocean, while increasing our general knowledge of the Ocean microbiome

Microbial interactions are vital in maintaining ocean ecosystem function, yet their dynamic nature and complexity remain largely unexplored. Here, we use association networks to investigate possible ecological interactions in the marine microbiome among archaea, bacteria, and picoeukaryotes throughout different depths and geographical regions of the tropical and subtropical global ocean. Our findings reveal that potential microbial interactions change with depth and geographical scale, exhibiting highly heterogeneous distributions. A few potential interactions were global, meaning they occurred across regions at the same depth, while 11-36% were regional within specific depths. The bathypelagic zone had the lowest proportion of global associations, and regional associations increased with depth. Moreover, we observed that most surface water associations do not persist in deeper ocean layers despite microbial vertical dispersal. Our work contributes to a deeper understanding of the tropical and subtropical global ocean interactome, which is essential for addressing the challenges posed by global change

Satellite remote sensing is a powerful tool to monitor the global dynamics of marine plankton. Previous research has focused on developing models to predict the size or taxonomic groups of phytoplankton. Here, we present an approach to identify community types from a global plankton network that includes phytoplankton and heterotrophic protists and to predict their biogeography using global satellite observations. Six plankton community types were identified from a co-occurrence network inferred using a novel rDNA 18 S V4 planetary-scale eukaryotic metabarcoding dataset. Machine learning techniques were then applied to construct a model that predicted these community types from satellite data. The model showed an overall 67% accuracy in the prediction of the community types. The prediction using 17 satellite-derived parameters showed better performance than that using only temperature and/or the concentration of chlorophyll a. The constructed model predicted the global spatiotemporal distribution of community types over 19 years. The predicted distributions exhibited strong seasonal changes in community types in the subarctic–subtropical boundary regions, which were consistent with previous field observations. The model also identified the long-term trends in the distribution of community types, which suggested responses to ocean warming.

Mercury (Hg) methylation genes (hgcAB) mediate the formation of the toxic methylmercury and have been identified from diverse environments, including freshwater and marine ecosystems, Arctic permafrost, forest and paddy soils, coal-ash amended sediments, chlor-alkali plants discharges and geothermal springs. Here we present the first attempt at a standardized protocol for the detection, identification and quantification of hgc genes from metagenomes. Our Hg-cycling microorganisms in aquatic and terrestrial ecosystems (Hg-MATE) database, a catalogue of hgc genes, provides the most accurate information to date on the taxonomic identity and functional/metabolic attributes of microorganisms responsible for Hg methylation in the environment. Furthermore, we introduce “marky-coco”, a ready-to-use bioinformatic pipeline based on de novo single-metagenome assembly, for easy and accurate characterization of hgc genes from environmental samples. We compared the recovery of hgc genes from environmental metagenomes using the marky-coco pipeline with an approach based on coassembly of multiple metagenomes. Our data show similar efficiency in both approaches for most environments except those with high diversity (i.e., paddy soils) for which a coassembly approach was preferred. Finally, we discuss the definition of true hgc genes and methods to normalize hgc gene counts from metagenomes.

Metagenomes encode an enormous diversity of proteins, reflecting a multiplicity of functions and activities1,2. Exploration of this vast sequence space has been limited to a comparative analysis against reference microbial genomes and protein families derived from those genomes. Here, to examine the scale of yet untapped functional diversity beyond what is currently possible through the lens of reference genomes, we develop a computational approach to generate reference-free protein families from the sequence space in metagenomes. We analyse 26,931 metagenomes and identify 1.17 billion protein sequences longer than 35 amino acids with no similarity to any sequences from 102,491 reference genomes or the Pfam database3. Using massively parallel graph-based clustering, we group these proteins into 106,198 novel sequence clusters with more than 100 members, doubling the number of protein families obtained from the reference genomes clustered using the same approach. We annotate these families on the basis of their taxonomic, habitat, geographical and gene neighbourhood distributions and, where sufficient sequence diversity is available, predict protein three-dimensional models, revealing novel structures. Overall, our results uncover an enormously diverse functional space, highlighting the importance of further exploring the microbial functional dark matter.

Metagenomes encode an enormous diversity of proteins, reflecting a multiplicity of functions and activities1,2. Exploration of this vast sequence space has been limited to a comparative analysis against reference microbial genomes and protein families derived from those genomes. Here, to examine the scale of yet untapped functional diversity beyond what is currently possible through the lens of reference genomes, we develop a computational approach to generate reference-free protein families from the sequence space in metagenomes. We analyse 26,931 metagenomes and identify 1.17 billion protein sequences longer than 35 amino acids with no similarity to any sequences from 102,491 reference genomes or the Pfam database3. Using massively parallel graph-based clustering, we group these proteins into 106,198 novel sequence clusters with more than 100 members, doubling the number of protein families obtained from the reference genomes clustered using the same approach. We annotate these families on the basis of their taxonomic, habitat, geographical and gene neighbourhood distributions and, where sufficient sequence diversity is available, predict protein three-dimensional models, revealing novel structures. Overall, our results uncover an enormously diverse functional space, highlighting the importance of further exploring the microbial functional dark matter