PNNL

Abstract

While genomic approaches have been applied to the detection and identification of individual bacteria within microbial communities, analogous proteomics approaches have been effectively precluded due to the inherent complexity. An in silico assessment of peptides derived from artificial simple and complex communities was performed to evaluate the effect of proteome complexity on species detection. Detection and validation of predicted peptides initially identified as distinctive within the simple community was experimentally performed using a mass spectrometry-based proteomics approach. An assessment of peptide distinctiveness and the potential for mapping to a particular bacterium within a community was made throughout each step of the study. A second assessment performed in silico of peptide distinctiveness for a complex community of 25 microorganisms was also conducted. The experimental data for a simple community, and the in silico data for a complex community revealed that it is feasible to predict, observe, and quantify distinctive peptides from one organism in the presence of at least a 100-fold greater abundance of another, thus yielding putative markers for the identification of a bacterium of interest. This work represents a first step towards quantitative proteomic characterization of complex microbial communities.