Proteomic Profiling of Salmonella enterica Strains from Environmental and Laboratory Sources: Comparative Analysis of Wastewater Isolates and Lab Strain UK-1

This dataset represents a comprehensive proteomic analysis of three Salmonella enterica strains, specifically two wastewater isolates—Salmonella enterica serovar Diarizonae (61:c:1,5,7) and S. enterica serovar Enteritidis (9:g,m:-)—alongside the well-characterized S. Typhimurium UK-1 lab strain. The preparation and analysis methods were carefully optimized to provide high-quality protein identifications and quantifications across these distinct strains, allowing for comparative proteomic analysis.

To generate the dataset, stationary-phase cultures of each strain were processed meticulously. Cultures were centrifuged at high speed (13,000 x g) at 4ºC to obtain cell pellets, which were washed twice—initially with PBS containing 5 mM EDTA to eliminate metal ion contaminants and then with PBS alone to enhance sample purity. The cells were lysed using a Sonifier Cell Disruptor, a sonication-based technique that preserves protein integrity, and then centrifuged again to yield a protein-rich supernatant, which was subsequently filter-sterilized using a 0.22-µm PES filter.

For protein separation, 25 µg of protein from each sample was loaded onto SDS-PAGE gels, with three biological replicates for each strain, ensuring reproducibility and robustness in measurements. Each gel lane was sectioned, and in-gel digestion with trypsin was performed to produce peptides suitable for mass spectrometry. The resulting peptide samples were analyzed using a 250-mm Ultrahigh-Performance Liquid Chromatography (UHPLC) system coupled to an Orbitrap Fusion mass spectrometer. This setup enabled high-resolution detection and precise mass spectrometry data collection.

Mass spectrometry data were analyzed using Sequest and X! Tandem, which compared spectral data against a Salmonella Uniprot database, assuming trypsin digestion specificity. Scaffold software validated peptide and protein identifications using a stringent probability threshold (>95.0%) through the Scaffold Local FDR algorithm. Additionally, protein probabilities were refined with the Protein Prophet algorithm, requiring at least two peptides for protein identification. Proteins with overlapping peptide sequences were grouped to streamline interpretation, with the top-scoring hit reported.

The dataset achieved a peptide-level false discovery rate (FDR) of 0.35%, well below the commonly accepted threshold of 1%, and a protein FDR of 3.3%, within the standard 1-5% range, ensuring high reliability in protein identifications. Quantification across strains was performed using normalized spectral counts, and statistical significance was evaluated using t-test.

This dataset provides a resource for understanding the proteomic landscape across two environmentally derived Salmonella wastewater isolates and compared to a lab strain.