A scalable practice for deep-sea metagenomic studies

Abstract

Abstract Background Metagenomic sequencing protocols are increasingly employed in research on deep-sea microbial communities for the purpose of examining their taxonomic makeup and metabolic capabilities. Despite the development and testing of various experimental techniques, commercial kits, and analytical software on mock communities and stool samples, a noticeable gap remains in the systematic evaluation of the entire procedure across a range of diverse deep-sea habitats. Moreover, the growing scale of studies raises a need for an efficient and resource-friendly automated approach to accommodate the increasing demand for throughput. Results We conducted evaluations of the entire metagenomic investigation process using samples obtained from three distinct habitats: open ocean water, trench sediments, and cold seep sediments. Our findings revealed that employing automated DNA extraction with a small sample size, along with enzymatic fragmentation-based library construction methods requiring minimal DNA input, could generate high-quality and representative metagenomic sequencing data for samples of different habitats. Notably, k-mer-based profiling tools such as Kraken2 effectively characterized the microbiota composition across all three habitats, demonstrating particular efficacy in the understudied trench sediments. Additionally, integrating various binners, particularly those utilizing co-binning (binning by group-of-samples) algorithms, significantly enhanced the recovery of metagenome-assembled genomes (MAGs). Through this approach, we identified distinct habitat-specific variations in the metabolic potential of a deep-sea Bacteroidia clade. Conclusions We established and evaluated an automated method for conducting metagenomic studies in deep-sea environments, designed to be adjustable in resource-constrained settings. This approach is adaptable to various habitats and has demonstrated effectiveness in facilitating taxonomic and functional analysis. Its implementation has the potential to significantly enhance our comprehension of the deep-sea ecosystem.