A peptide identification-free, genome sequence-independent shotgun proteomics workflow for strain-level bacterial differentiation

Abstract

Shotgun proteomics is an emerging tool for bacterial identification and differentiation. However, the identification of the mass spectra of peptides to genome-derived peptide sequences remains a key issue that limits the use of shotgun proteomics to bacteria with genome sequences available. In this proof-of-concept study, we report a novel bacterial fingerprinting method that enjoys the resolving power and accuracy of mass spectrometry without the burden of peptide identification (i.e. genome sequence-independent). This method uses a similarity-clustering algorithm to search for mass spectra that are derived from the same peptide and merge them into a unique consensus spectrum as the basis to generate proteomic fingerprints of bacterial isolates. In comparison to a traditional peptide identification-based shotgun proteomics workflow and a PCR-based DNA fingerprinting method targeting the repetitive extragenic palindromes elements in bacterial genomes, the novel method generated fingerprints that were richer in information and more discriminative in differentiating E. coli isolates by their animal sources. The novel method is readily deployable to any cultivable bacteria, and may be used for several fields of study such as environmental microbiology, applied microbiology, and clinical microbiology.