title: Quantitative proteogenomics of human pathogens using DIA-MS.
abstract: The increasing number of bacterial genomes in combination with reproducible quantitative proteome measurements provides new opportunities to explore how genetic differences modulate proteome composition and virulence. It is challenging to combine genome and proteome data as the underlying genome influences the proteome. We present a strategy to facilitate the integration of genome data from several genetically similar bacterial strains with data-independent analysis mass spectrometry (DIA-MS) for rapid interrogation of the combined data sets. The strategy relies on the construction of a composite genome combining all genetic data in a compact format, which can accommodate the fusion with quantitative peptide and protein information determined via DIA-MS. We demonstrate the method by combining data sets from whole genome sequencing, shotgun MS and DIA-MS from 34 clinical isolates of Streptococcus pyogenes. The data structure allows for fast exploration of the data showing that undetected proteins are on average more amenable to amino acid substitution than expressed proteins. We identified several significantly differentially expressed proteins between invasive and non-invasive strains. The work underlines how integration of whole genome sequencing with accurately quantified proteomes can further advance the interpretation of the relationship between genomes, proteomes and virulence. This article is part of a Special Issue entitled: Computational Proteomics.
authors.affiliation: S3IT, University of Zurich, Zurich, Switzerland. Electronic address: lars.malmstroem@uzh.ch.
authors.forename: Lars
authors.initials: L
authors.lastname: Malmström
authors.affiliation: Division of Infection Medicine, Department of Clinical Sciences Lund, Lund University, Lund, Sweden.
authors.forename: Anahita
authors.initials: A
authors.lastname: Bakochi
authors.affiliation: Division of Infection Medicine, Department of Clinical Sciences Lund, Lund University, Lund, Sweden.
authors.forename: Gabriel
authors.initials: G
authors.lastname: Svensson
authors.affiliation: Division of Infection Medicine, Department of Clinical Sciences Lund, Lund University, Lund, Sweden.
authors.forename: Ola
authors.initials: O
authors.lastname: Kilsgård
authors.affiliation: Department of Medical Biochemistry and Microbiology/BILS, Uppsala University, Uppsala, Sweden.
authors.forename: Henrik
authors.initials: H
authors.lastname: Lantz
authors.affiliation: Department of Clinical Microbiology, Division of Laboratory Medicine, Region Skåne, Lund, Sweden.
authors.forename: Ann Cathrine
authors.initials: AC
authors.lastname: Petersson
authors.affiliation: Division of Infection Medicine, Department of Clinical Sciences Lund, Lund University, Lund, Sweden.
authors.forename: Simon
authors.initials: S
authors.lastname: Hauri
authors.affiliation: Division of Infection Medicine, Department of Clinical Sciences Lund, Lund University, Lund, Sweden.
authors.forename: Christofer
authors.initials: C
authors.lastname: Karlsson
authors.affiliation: Division of Infection Medicine, Department of Clinical Sciences Lund, Lund University, Lund, Sweden.
authors.forename: Johan
authors.initials: J
authors.lastname: Malmström
mesh_terms: D001426:Bacterial Proteins / Q000235:genetics*; D002874:Chromosome Mapping / Q000379:methods; D016680:Genome, Bacterial / Q000235:genetics*; D006801:Humans; D013058:Mass Spectrometry; D020543:Proteome / Q000235:genetics*; D040901:Proteomics / Q000379:methods*; D017422:Sequence Analysis, DNA / Q000379:methods*; D013297:Streptococcus pyogenes / Q000235:genetics*
chemical_list: D001426:Bacterial Proteins; D020543:Proteome
keywords: DIA; Data integration; Proteogenomics; Quantitative mass spectrometry; Streptococcus pyogenes