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A Label-free Mass Spectrometry Method to Predict Endogenous Protein Complex Composition.

ABSTRACT: Information on the composition of protein complexes can accelerate mechanistic analyses of cellular systems. Protein complex composition identifies genes that function together and provides clues about regulation within and between cellular pathways. Cytosolic protein complexes control metabolic flux, signal transduction, protein abundance, and the activities of cytoskeletal and endomembrane systems. It has been estimated that one third of all cytosolic proteins in leaves exist in an oligomeric state, yet the composition of nearly all remain unknown. Subunits of stable protein complexes copurify, and combinations of mass-spectrometry-based protein correlation profiling and bioinformatic analyses have been used to predict protein complex subunits. Because of uncertainty regarding the power or availability of bioinformatic data to inform protein complex predictions across diverse species, it would be highly advantageous to predict composition based on elution profile data alone. Here we describe a mass spectrometry-based protein correlation profiling approach to predict the composition of hundreds of protein complexes based on biochemical data. Extracts were obtained from an intact organ and separated in parallel by size and charge under nondenaturing conditions. More than 1000 proteins with reproducible elution profiles across all replicates were subjected to clustering analyses. The resulting dendrograms were used to predict the composition of known and novel protein complexes, including many that are likely to assemble through self-interaction. An array of validation experiments demonstrated that this new method can drive protein complex discovery, guide hypothesis testing, and enable systems-level analyses of protein complex dynamics in any organism with a sequenced genome.

SUBMITTER: McBride Z

PROVIDER: S-EPMC6683005 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundInflammatory arthritis (IA) is an immunological disorder in which loss of immune tolerance to endogenous self-antigens perpetuates synovitis and eventual destruction of the underlying cartilage and bone. Pathological changes in the joint are expected to be represented by synovial fluid (SF) proteins and peptides. In the present study, a mass spectrometry-based approach was utilized for the identification of key protein and peptide mediators of IA.MethodsAge-matched SF samples from 10 rheumatoid arthritis patients, 10 psoriatic arthritis patients and 10 cadaveric controls were subjected to an integrated proteomic and peptidomic protocol using liquid chromatography tandem mass spectrometry. Significant differentially abundant proteins and peptides were identified between cohorts according to the results of a Mann-Whitney U test coupled to the Benjamini-Hochberg correction for multiple hypothesis testing. Fold change ratios were computed for each protein and peptide according to their log-transformed extracted ion current. Pathway analysis and antimicrobial peptide (AMP) prediction were conducted to clarify the pathophysiological relevance of identified proteins and peptides to IA.ResultsWe determined that 144 proteins showed significant differential abundance between the IA and control SF proteomes, of which 11 protein candidates were selected for future follow-up studies. Similar analyses applied to our peptidomic data identified 15 peptide sequences, originating from 4 protein precursors, to have significant differential abundance in IA compared to the control SF peptidome. Pathway enrichment analysis of the IA SF peptidome along with AMP prediction suggests a possible mechanistic role of microbes in eliciting an immune response which drives the development of IA.ConclusionsThe discovery-phase data generated herein has provided a basis for the identification of candidates with the greatest potential to serve as novel serum biomarkers specific to inflammatory arthritides. Moreover, these findings facilitate the understanding of possible disease mechanisms specific to each subtype.

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A Label-free Mass Spectrometry Method to Predict Endogenous Protein Complex Composition.

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