Integrated metagenomic and metaproteomic analyses of marine biofilm communities

Abstract

Metagenomic and metaproteomic analyses were utilized to determine the composition and function of complex air–water interface biofilms sampled from the hulls of two US Navy destroyers. Prokaryotic community analyses using PhyloChip-based 16S rDNA profiling revealed two significantly different and taxonomically rich biofilm communities (6,942 taxa) in which the majority of unique taxa were ascribed to members of the Gammaproteobacteria, Alphaproteobacteria and Clostridia. Although metagenomic sequencing indicated that both biofilms were dominated by prokaryotic sequence reads (> 91%) with the majority of the bacterial reads belonging to the Alphaproteobacteria, the Ship-1 metagenome harbored greater organismal and functional diversity and was comparatively enriched for sequences from Cyanobacteria, Bacteroidetes and macroscopic eukaryotes, whereas the Ship-2 metagenome was enriched for sequences from Proteobacteria and microscopic photosynthetic eukaryotes. Qualitative liquid chromatography-tandem mass spectrometry metaproteome analyses identified 678 unique proteins, revealed little overlap in species and protein composition between the ships and contrasted with the metagenomic data in that ~80% of classified and annotated proteins were of eukaryotic origin and dominated by members of the Bacillariophyta, Cnidaria, Chordata and Arthropoda (data deposited to the ProteomeXchange, identifier PXD000961). Within the shared metaproteome, quantitative 18O and iTRAQ analyses demonstrated a significantly greater abundance of structural proteins from macroscopic eukaryotes on Ship-1 and diatom photosynthesis proteins on Ship-2. Photosynthetic pigment composition and elemental analyses confirmed that both biofilms were dominated by phototrophic processes. These data begin to provide a better understanding of the complex organismal and biomolecular composition of marine biofilms while highlighting caveats in the interpretation of stand-alone environmental ‘-omics’ datasets.