Metagenome-assembled genomes of phytoplankton communities across the Arctic Circle

Abstract

18Phytoplankton communities significantly contribute to global biogeochemical cycles of elements 19 and underpin marine food webs. Although their uncultured genetic diversity has been estimated by 20 planetary-scale metagenome sequencing and subsequent reconstruction of metagenome-assembled 21 genomes (MAGs), this approach has yet to be applied for eukaryote-enriched polar and non-polar 22 phytoplankton communities. Here, we have assembled draft prokaryotic and eukaryotic MAGs 23 from environmental DNA extracted from chlorophyll a maximum layers in the surface ocean across 24 the Arctic Circle in the Atlantic. From 679 Gbp and estimated 50 million genes in total, we 25 recovered 140 MAGs of medium to high quality. Although there was a strict demarcation between 26 polar and non-polar MAGs, adjacent sampling stations in each environment on either side of the 27 Arctic Circle had MAGs in common. Furthermore, phylogenetic placement revealed eukaryotic 28MAGs to be more diverse in the Arctic whereas prokaryotic MAGs were more diverse in the 29 Atlantic south of the Arctic Circle. Approximately 60% of protein families were shared between 30 polar and non-polar MAGs for both prokaryotes and eukaryotes. However, eukaryotic MAGs had 31 more protein families unique to the Arctic whereas prokaryotic MAGs had more families unique to 32 south of the Arctic circle. Thus, our study enabled us to place differences in functional plankton 33 diversity in a genomic context to reveal that the evolution of these MAGs likely was driven by 34 significant differences in the seascape on either side of an ecosystem boundary that separates polar 35 from non-polar surface ocean waters in the North Atlantic. 36 37 carbon cycle and marine food webs [3]. Furthermore, the significance of organism interactions and 48 specifically symbiosis for cycling of energy and matter was revealed, with viral-host dynamics as 49 the most impactful form of these biotic interactions [4,5]. 50 51Linking functional microbial diversity, estimated by metagenomics and metatranscriptomics, with 52 microbial activity as part of physico-chemical ecosystem properties shed light on how different 53 microbial groups contribute to biogeochemical cycling of elements [1, 6, 7]. These results built the 54 foundation for estimating how changing oceans due to global warming might impact the diversity 55 and activity of ocean microbes [7, 8]. However, to fully explore the role of microbes and their 56 interactions in changing environmental conditions, we must understand their metabolic capabilities 57 in an evolutionary context [9, 10]. As the majority of marine microbes are unculturable and because 58 genomic information is required to reconstruct their metabolic evolution, metagenome-assembled 59 genomes (MAGs) offer a solution [11,12]. Although most MAGs are not at the level of quality 60 achieved through sequencing cultures of isolated strains, they provide genome-level insights into 61 the microbial diversity of natural ecosystems. Due to their small size and structural s...

Functional repertoire convergence of distantly related eukaryotic plankton lineages revealed by genome-resolved metagenomics

“…This approach may thus be appropriate also to characterize the genomes of the most abundant eukaryotic plankton. However, very few eukaryotic genomes have been resolved from metagenomes thus far 27,[33][34][35][36] , in part due to their complexity (e.g., high density of repeats 37 ) and extended size 38 that might have convinced many of the unfeasibility of such a methodology. With the notable exception of some photosynthetic eukaryotes 27,33,36 , metagenomics is lagging far behind cultivation for eukaryote genomics, contrasting with the two other domains of life.…”

“…However, very few eukaryotic genomes have been resolved from metagenomes thus far 27,[33][34][35][36] , in part due to their complexity (e.g., high density of repeats 37 ) and extended size 38 that might have convinced many of the unfeasibility of such a methodology. With the notable exception of some photosynthetic eukaryotes 27,33,36 , metagenomics is lagging far behind cultivation for eukaryote genomics, contrasting with the two other domains of life. Here we fill this critical gap using hundreds of billions of metagenomic reads generated from the eukaryotic plankton size fractions of Tara Oceans and demonstrate that genomeresolved metagenomics is well suited for marine eukaryotic genomes of substantial complexity and length exceeding the emblematic gigabase.…”

Tiara: Deep learning-based classification system for eukaryotic sequences

“…In contrast to these methods, the utilization of the metagenomic approaches was hampered by the complexity and size of eukaryotic genomes, as well as a limited number of reference databases allowing further taxonomical or functional annotation. With a few exceptions, such as phytoplankton (Delmont et al, 2015;Duncan et al, 2020) or human microbiome (Olm et al, 2019) studies, eukaryotes were neglected in metagenomic studies. Only recently the metagenomic datasets from large sampling projects, such as the Tara Oceans expedition (Pesant et al, 2015) or Ocean Sampling Day (Kopf et al, 2015), were exploited to uncover the eukaryotic plankton biogeography (Richter et al, 2019;Leconte et al, 2020), taxonomy (Obiol et al, 2020), and functional diversity (Delmont et al, 2020).…”

“…However, organellar data are mostly unexplored in the metagenomes, since they are often classified as bacterial sequences, and are thus removed from the eukaryotic genome assemblies (Delmont et al, 2020;Duncan et al, 2020).…”

“…In the first approach, the assembled contigs are binned, visualized, and manually refined using Anvio'O (Eren et al, 2015;Delmont and Eren, 2016), whereas the second approach, used in EukRep, assumes initial separation of contigs into two domains (Prokarya and Eukarya), and then binning within those two groups independently (West et al, 2018). Both approaches have been successfully used for obtaining partial nuclear eukaryotic genomes but failed to correctly classify the organellar fraction in the metagenomic data (Duncan et al, 2020;Delmont et al, 2020). Only one tool, MitoZ, was designed explicitly for the organellar data, but it is only applicable for the assembly, identification and analysis of the animals' mitochondrial genomes (Meng et al, 2019).…”