# Genus, Species, and Subspecies Classification of _Salmonella_ Isolates by Proteomics
## 1 Introduction  
Despite decades of efforts to control _Salmonella_ contamination, this pathogen remains the most common foodborne bacteria in the U.S. [1]. Each year more than one million human _Salmonella_ infections occur in the United States, with non-typhoidal _Salmonella_ alone accounting for more than three billion dollars in medical expenses [2, 3]. Subtyping of _Salmonella_, i.e., identification beyond the species level, is essential for tracking and control of this pathogen and provides information regarding potential pathogenicity and antibiotic resistance. However, full characterization of a _Salmonella_ isolate for rapid outbreak response is challenging due to its tremendous complexity and diversity, with more than 2600 servovars identified. In fact, new serotypes continue to be discovered all the time [4]. While _polymense chain reaction (PCR)_ methods have become common, they rely on the pre-selection of a set of markers [5]. Whole genome sequencing (WGS) has emerged as a typing tool for foodborne pathogens, including _Salmonella_[6, 7]. However, it is not yet adopted for routine use in most diagnostic laboratories [7, 8]. Mass spectrometry (MS) provides a complementary means for identification of bacteria. Two common approaches have been recognized: (1) biomarkers (targeted methods) [9, 10, 11] and (2) spectral profiles (non-targeted methods) [12, 13]. Biomarker-based approaches rely on selection and detection of a number of characteristic peptides or proteins that are predictive of a pathogen. In the case of _Salmonella_, due to high sequence homology below the species level, identification of markers is challenging [14, 11]. For example, an analysis of 4893 _Salmonella enterica_ genomes revealed that genomic markers for identification ofSalmonella enterica_ subsp. _ enterica_ were few, and no genomic regions were useful for definitive identification of a _Salmonella_ serovar [14]. An informatics pipeline recently developed by our group for selection of _Salmonella_ taxon markers based on shotgun proteomics and BLAST searches also suggested a lack of representative subspecies and serovar peptide markers. Multistep classification with multiple peptide sequences was required to define subspecies and serovar specificity [11]. Nontargeted approaches, on the other hand, provide a global view of overall analyte content, serving as a mass fingerprint used for comparison of multiple features with existing databases generated by reference strains to aid identification [12]. Among these approaches, whole-cell matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) MS has been used extensively and approved by the U.S. Food and Drug Administration for bacterial identification for clinical use. This approach is, however, largely focused on discrimination at the genus and species level due to a limited number of detectable peaks, typically a few dozen peaks per profile [15; 16]. Consequently, whole-cell MALDI-TOF MS has not been effective for routine subtyping of _Salmonella_[17; 18], although a few studies have reported identification of a limited number of subspecies and serovars with advanced statistical analyses [17; 19; 20; 21]. Advances in MS hardware and the combination with liquid chromatography (LC) have made deep proteome analysis possible, and thousands of proteins are typically identified in a single experiment. This facilitates the detection of a large portion of a bacterial proteome, offering greater opportunities to discover small differences expressed by closely related bacteria [11; 16; 22; 23; 24; 25; 26; 27; 28; 29]. Several groups proposed the use of LC-MS/MS for classification/identification by matching tryptic peptides derived from bacterial proteins against a set of reference genomes or translated proteomes. In most cases, the number of matching peptides per genome was compared [22; 23]. In some cases, data analyses were further modified to include the number of unique tryptic peptides to aid identification [22; 23; 24; 25].