A system-level view on the function of natural eukaryotic biomes through taxonomically resolved metabolic pathway profiling

Abstract

AbstractHigh-throughput sequencing of environmental samples has dramatically improved our understanding of the molecular activities of complex microbial communities in their natural environments. For instance, by enabling taxonomic profiling and differential gene expression analysis, microbiome studies have revealed intriguing associations between community structure and ecosystem functions. However, the effectiveness of sequence data analysis to characterize the functioning of microbial ecosystems at the systems level (e.g. metabolic pathways) and at high taxonomic resolution has thus far been limited by the quality and scope of reference sequence databases. In this work, we applied state of the art bioinformatics tools to leverage publicly available genome/gene sequences for a wide array of (mostly eukaryotic) planktonic organisms to build a customized protein sequence database. Based on this, our goal is to conduct a systems-level interrogation of environmental samples, which can effectively augment the insights obtained through traditional gene-centric analysis (i.e. analysis of single gene expression profiles at the genome-wide level). To achieve this, we utilized the popular HUMAnN pipeline, which has proven effective at delineating taxon-specific metabolic pathways that may be actively contributing to the overall functioning of a microbiome. To test the efficacy of our database customization for mapping metabolic pathway activities in complex planktonic ecosystems, we reanalyzed previously published metatranscriptome datasets derived from different marine environments. Our results demonstrate that database customization can substantially improve our ability to quantitatively assess core metabolic processes across taxonomically diverse marine microbiomes, which have so far remained largely uncharacterized at the systems level. By further expanding on the taxonomic and functional complexity of our database with newly released high-quality genome assemblies and gene catalogs for marine microbes, we aim to improve our ability to map the molecular traits that drive changes in the composition and functioning of marine planktonic networks through space and time.