Metagenomic Insights Into the Microbial Community and Nutrient Cycling in the Western Subarctic Pacific Ocean

Abstract

The composition and metabolic functions of prokaryotic communities in the western subarctic Pacific (WSP), where strong mixing of waters from the Sea of Okhotsk and the East Kamchatka Current result in transfer to the Oyashio Current, were investigated using a shotgun metagenome sequencing approach. Functional metabolic genes related to nutrient cycling of nitrogen, sulfur, carbohydrates, iron and amino acids were differently distributed between the surface and deep waters of the WSP. Genes related to nitrogen metabolism were mainly found in deep waters, where Thaumarchaeaota, Sphingomonadales, and Pseudomonadales were closely associated and performing important roles in ammonia oxidation, assimilatory nitrate reduction, and dissimilatory nitrate reduction processes, respectively. In addition, orders affiliated to Spingobacteria and Alphaproteobacteria were crucial for sulfate reduction and abundant at 3000 m, whereas orders affiliated to Gammaproteobacteria, which harbored the most sulfate reduction genes, were abundant at 1000 m. Additionally, when compared with the East Kamchatka Current, the prokaryotes in the Oyashio Current were likely to consume more energy for synthesizing cellular components. Also, genes encoding iron transport and siderophore biosynthesis proteins were in low abundance, indicating that the iron was not a limiting factor in the Oyashio current. In contrast, in the East Kamchatka Current, prokaryotes were more likely to directly utilize the amino acids and absorb iron from the environment. Overall, our data indicated that the transformation from the East Kamchatka Current to the Oyashio Current reshapes not only the composition of microbial community, but also the function of the metabolic processes. These results extended our knowledge of the microbial composition and potential metabolism in the WSP.

Under the Christmas Tree: Belowground Bacterial Associations With Abies nordmanniana Across Production Systems and Plant Development

“…However, the increases in denitrification genes in the greenhouse nursery could not be related to dynamics in the above taxa. It might be related to the increase with plant age in the relative abundance of Pseudomonas and Sphingomonas, as these bacterial genera contain members with denitrification characteristics (Deng et al, 2014;Hester et al, 2018;Li et al, 2018). It has been reported before that the C:N ratio influences denitrification process, as the abundance of denitrifiers positively responds to increased C availability in the soil environment (Miller et al, 2008(Miller et al, , 2009Henderson et al, 2010).…”

“…These studies have revealed the importance of soil characteristics, management, and plant developmental stage on the assembly of the rhizosphere microbiome. Fewer studies have addressed the rhizosphere microbiota in perennial plants, where each growing season represents a small part of the plant's lifetime, and in particular, the bacterial communities are understudied (Wagner et al, 2016;Haas et al, 2018;Li et al, 2018). Perennial tree crops, including several conifers, are a source of products of economic importance, for example, wood, paper, and Christmas trees.…”

Nutrient-imbalanced conditions shift the interplay between zooplankton and gut microbiota

“…According to the barcode, the sequencing data were assigned to nine experimental groups (Control, N-limitation and P-limitation). The quality control of sequenced reads was performed as described in previous reports (Gong et al 2018, Li et al 2018. In addition, the reads that belong to different parts of the holobiont (i.e., D. magna and its gut microbiota) were separated by applying a previously reported method (Meng et al 2018).…”

Nutrient-imbalanced Conditions Shift the Interplay Between Zooplankton and Gut Microbiota

“…Chimeras were identi ed and removed using UCHIME (Edgar et al 2011). The remaining high-quality sequences were merged according to the experimental treatments, and the taxonomically assignment was processed with Silva database (version 123) by following the previous reported method (Li et al 2018). Finally, sequences were clustered into OTUs with a 97% sequence similarity cutoff.…”

“…According to the barcode, the sequencing data were assigned to nine experimental groups (Normal, Nlimitation and P-limitation). The quality control of sequenced reads was performed as described in previous reports (Gong et al 2018, Li et al 2018. In addition, the reads that belong to different parts of the holobiont (i.e., D. magna and its gut microbiota), were separated by applying a previously reported method (Meng et al 2018).…”