Borrelia PeptideAtlas: A proteome resource of common Borrelia burgdorferi isolates for Lyme research.
Panga Jaipal ReddyZhi SunHelisa H WippelSeamus R MorroneKristian E SwearingenDavid D ShteynbergMukul K MidhaMelissa J CaimanoKlemen StrleYongwook ChoiYongwook ChoiNicholas J SchorkRobert L MoritzPublished in: bioRxiv : the preprint server for biology (2023)
Lyme disease, caused by an infection with the spirochete Borrelia burgdorferi , is the most common vector-borne disease in North America. B. burgdorferi strains harbor extensive genomic and proteomic variability and further comparison is key to understanding the spirochetes infectivity and biological impacts of identified sequence variants. To achieve this goal, both transcript and mass spectrometry (MS)-based proteomics was applied to assemble peptide datasets of laboratory strains B31, MM1, B31-ML23, infective isolates B31-5A4, B31-A3, and 297, and other public datasets, to provide a publicly available Borrelia PeptideAtlas ( http://www.peptideatlas.org/builds/borrelia/ ). Included is information on total proteome, secretome, and membrane proteome of these B. burgdorferi strains. Proteomic data collected from 35 different experiment datasets, with a total of 855 mass spectrometry runs, identified 76,936 distinct peptides at a 0.1% peptide false-discovery-rate, which map to 1,221 canonical proteins (924 core canonical and 297 noncore canonical) and covers 86% of the total base B31 proteome. The diverse proteomic information from multiple isolates with credible data presented by the Borrelia PeptideAtlas can be useful to pinpoint potential protein targets which are common to infective isolates and may be key in the infection process.
Keyphrases
- mass spectrometry
- escherichia coli
- genetic diversity
- rna seq
- liquid chromatography
- label free
- high performance liquid chromatography
- healthcare
- gas chromatography
- high resolution
- capillary electrophoresis
- multiple sclerosis
- single cell
- big data
- amino acid
- emergency department
- health information
- mental health
- dna methylation
- risk assessment
- machine learning
- binding protein
- human health