Bacteria transform nutrients and degrade organic matter, making them an essential part of healthy ecosystems. By assaying bacterial physiology within a complex system, the status of the whole ecosystem can be investigated. Proteins are the dynamic molecules that control essential bacterial physiological responses and those of every organism; characterizing an organism’s proteome can therefore provide information on its interaction with the environment. Data dependent proteomic analysis (DDA) is a global approach to assay the entire proteome, but sample complexity and the stochastic nature of mass spectrometry can make it difficult to detect low abundance proteins. We explored the development of targeted proteomic (selected reaction monitoring, SRM) assays in complex ocean samples in order to detect specific bacterial proteins of interest and to assess new tools for mixed community metaproteomic exploration. A mixed community was created from a dilution series of an isolated cultures of bacteria (Reugeria pomeroyi) and phytoplankton (Thalassiosira pseudonana). Using SRM, we were able to select and detect bacterial peptides from the community that were undetectable with the standard DDA approach. We demonstrate benefits and drawbacks of different proteomic approaches that can be used to probe for and resolve nuances of bacterial physiological processes in complex environmental systems.

To mimic a complex marine sample, a dilution series of R. pomeroyi and Thalasiosira pseudonana was created at different cellular ratios. These Rpom:Thaps mixtures were filtered onto 47 mm, 0.2 um Nucleopore polycarbonate filters from which proteins were extracted and digested. Samples were prepared so that 1 µg of protein (sample + QC mix) was injected per 3 µl injection. A C18 trap (2 cm) and C18 analytical columns (27.5 cm) were used and each sample was analyzed in 2 MS experiments to cover the entire peptide transition list (n=334). Raw data can be accessed in the PeptideAtlas (http://www.peptideatlas.org/PASS/PASS00917) under accession PASS00917.