Metagenome-assembled genomes of phytoplankton microbiomes from the Arctic and Atlantic Oceans

Abstract

Background Phytoplankton communities significantly contribute to global biogeochemical cycles of elements and underpin marine food webs. Although their uncultured genomic diversity has been estimated by planetary-scale metagenome sequencing and subsequent reconstruction of metagenome-assembled genomes (MAGs), this approach has yet to be applied for complex phytoplankton microbiomes from polar and non-polar oceans consisting of microbial eukaryotes and their associated prokaryotes. Results Here, we have assembled MAGs from chlorophyll a maximum layers in the surface of the Arctic and Atlantic Oceans enriched for species associations (microbiomes) with a focus on pico- and nanophytoplankton and their associated heterotrophic prokaryotes. From 679 Gbp and estimated 50 million genes in total, we recovered 143 MAGs of medium to high quality. Although there was a strict demarcation between Arctic and Atlantic MAGs, adjacent sampling stations in each ocean had 51–88% MAGs in common with most species associations between Prasinophytes and Proteobacteria. Phylogenetic placement revealed eukaryotic MAGs to be more diverse in the Arctic whereas prokaryotic MAGs were more diverse in the Atlantic Ocean. Approximately 70% of protein families were shared between Arctic and Atlantic MAGs for both prokaryotes and eukaryotes. However, eukaryotic MAGs had more protein families unique to the Arctic whereas prokaryotic MAGs had more families unique to the Atlantic. Conclusion Our study provides a genomic context to complex phytoplankton microbiomes to reveal that their community structure was likely driven by significant differences in environmental conditions between the polar Arctic and warm surface waters of the tropical and subtropical Atlantic Ocean.

Bio-Organic Fertilizer Promotes Pear Yield by Shaping the Rhizosphere Microbiome Composition and Functions

“…Metagenomics can provide taxonomic, genomic, and functional profiles of microbiomes for building better connections between potential microbiomes, functional genes, and yield ( 24 ). These advanced metagenomic studies have been well established for human ( 25 ), oceanic ( 26 ), soil ( 27 ), and rhizosphere microbiomes in model and crop plants ( 28 ), but rarely for fruit trees ( 29 ), particularly pear ( 21 ). Thus, metagenomic analyses would enable a deeper understanding of the impacts of the functional microbiome, including community composition and functions, on fruit production.…”

Multiomics in the central Arctic Ocean for benchmarking biodiversity change

“…Moreover, accelerating environmental change in the Arctic has focused geopolitical interests due to increased access to biological and geological resources. The Arctic also provides significant ecosystem services based in part on its biodiversity, which is largely unknown [ 3 , 4 ]. In particular, marine microbes in sea ice and seawater are a cornerstone of this ecosystem and have pivotal roles in climate feedbacks and in sustaining food webs, which are central for conservation and ecosystem services.…”

“…Benchmarking these biotic responses requires us to catalogue as many of the microbial taxa in the central Arctic Ocean as possible. This is being addressed by a combination of barcoding sequencing based on phylogenetic marker genes (e.g., 16 and 18S rDNA), eDNA, and metagenome-assembled genomes (MAGs) ( Fig 2 ) [ 4 ]. Preliminary results based on 18S-metabarcoding data and pilot metagenome sequencing have provided first evidence of habitat filtering in the Arctic Ocean likely caused by significantly different habitat characteristics ( Fig 2B–2D ) [ 10 ].…”

Differential global distribution of marine picocyanobacteria gene clusters reveals distinct niche-related adaptive strategies

“…These can be used to generate metagenome-assembled genomes (MAGs) that can fill the gaps for yet uncultured microbial taxa as well as to identify niche-specific genes, i.e. genes enriched in specific spatial and/or temporal environmental conditions, by recruiting metagenomic reads onto reference genomes [10][11][12][13][14][15][16].…”