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A proteomics-based method for identifying antigens within immune complexes.

ABSTRACT: A novel approach to recover and identify immune complexes (ICs) was developed using size exclusion chromatography (SEC) and affinity chromatography on immunoglobulin binding columns (HiTrap Protein G). The purification process was monitored by 1D SDS-PAGE, protein staining, Western blotting and, finally, liquid chromatography tandem mass spectrometry (LC MS/MS) was used to identify the recovered antigens. This approach was applied to serum with artificially created immune complexes (ICs) comprising vaccine antigen (influenza) and antibody, which led to recovery and identification of influenza peptides within the recovered ICs. This approach was compared with the established method for IC detection and recovery, polyethylene glycol (PEG) precipitation, followed by LC MS/MS. Both approaches successfully enabled capture, recovery and characterization of immunoglobulins and influenza antigen(s) in complex with the immunoglobulins. However, PEG precipitation has the advantage of simplicity and is more suited for large scale studies.

SUBMITTER: Menikou S

PROVIDER: S-EPMC7757895 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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A proteomics-based method for identifying antigens within immune complexes.

Menikou Stephanie S McArdle Andrew J AJ Li Ming-Shi MS Kaforou Myrsini M Langford Paul R PR Levin Michael M

PloS one 20201223 12

A novel approach to recover and identify immune complexes (ICs) was developed using size exclusion chromatography (SEC) and affinity chromatography on immunoglobulin binding columns (HiTrap Protein G). The purification process was monitored by 1D SDS-PAGE, protein staining, Western blotting and, finally, liquid chromatography tandem mass spectrometry (LC MS/MS) was used to identify the recovered antigens. This approach was applied to serum with artificially created immune complexes (ICs) compris ...[more]

PMID: 33362259

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Project description:BackgroundProtein complexes formed by non-covalent interaction among proteins play important roles in cellular functions. Computational and purification methods have been used to identify many protein complexes and their cellular functions. However, their roles in terms of causing disease have not been well discovered yet. There exist only a few studies for the identification of disease-associated protein complexes. However, they mostly utilize complicated heterogeneous networks which are constructed based on an out-of-date database of phenotype similarity network collected from literature. In addition, they only apply for diseases for which tissue-specific data exist.MethodsIn this study, we propose a method to identify novel disease-protein complex associations. First, we introduce a framework to construct functional similarity protein complex networks where two protein complexes are functionally connected by either shared protein elements, shared annotating GO terms or based on protein interactions between elements in each protein complex. Second, we propose a simple but effective neighborhood-based algorithm, which yields a local similarity measure, to rank disease candidate protein complexes.ResultsComparing the predictive performance of our proposed algorithm with that of two state-of-the-art network propagation algorithms including one we used in our previous study, we found that it performed statistically significantly better than that of these two algorithms for all the constructed functional similarity protein complex networks. In addition, it ran about 32 times faster than these two algorithms. Moreover, our proposed method always achieved high performance in terms of AUC values irrespective of the ways to construct the functional similarity protein complex networks and the used algorithms. The performance of our method was also higher than that reported in some existing methods which were based on complicated heterogeneous networks. Finally, we also tested our method with prostate cancer and selected the top 100 highly ranked candidate protein complexes. Interestingly, 69 of them were evidenced since at least one of their protein elements are known to be associated with prostate cancer.ConclusionsOur proposed method, including the framework to construct functional similarity protein complex networks and the neighborhood-based algorithm on these networks, could be used for identification of novel disease-protein complex associations.

Project description:BackgroundSporothrix schenckii and associated species are agents of human and animal sporotrichosis that cause large sapronoses and zoonoses worldwide. Epidemiological surveillance has highlighted an overwhelming occurrence of the highly pathogenic fungus Sporothrix brasiliensis during feline outbreaks, leading to massive transmissions to humans. Early diagnosis of feline sporotrichosis by demonstrating the presence of a surrogate marker of infection can have a key role for selecting appropriate disease control measures and minimizing zoonotic transmission to humans.MethodologyWe explored the presence and diversity of serum antibodies (IgG) specific against Sporothrix antigens in cats with sporotrichosis and evaluated the utility of these antibodies for serodiagnosis. Antigen profiling included protein extracts from the closest known relatives S. brasiliensis and S. schenckii. Enzyme-linked immunosorbent assays and immunoblotting enabled us to characterize the major antigens of feline sporotrichosis from sera from cats with sporotrichosis (n = 49), healthy cats (n = 19), and cats with other diseases (n = 20).Principal findingsEnzyme-linked immunosorbent assay-based quantitation of anti-Sporothrix IgG exhibited high sensitivity and specificity in cats with sporotrichosis (area under the curve, 1.0; 95% confidence interval, 0.94-1; P<0.0001) versus controls. The two sets of Sporothrix antigens were remarkably cross-reactive, supporting the hypothesis that antigenic epitopes may be conserved among closely related agents. One-dimensional immunoblotting indicated that 3-carboxymuconate cyclase (a 60-kDa protein in S. brasiliensis and a 70-kDa protein in S. schenckii) is the immunodominant antigen in feline sporotrichosis. Two-dimensional immunoblotting revealed six IgG-reactive isoforms of gp60 in the S. brasiliensis proteome, similar to the humoral response found in human sporotrichosis.ConclusionsA convergent IgG-response in various hosts (mice, cats, and humans) has important implications for our understanding of the coevolution of Sporothrix and its warm-blooded hosts. We propose that 3-carboxymuconate cyclase has potential for the serological diagnosis of sporotrichosis and as target for the development of an effective multi-species vaccine against sporotrichosis in animals and humans.

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A proteomics-based method for identifying antigens within immune complexes.

Publications

A proteomics-based method for identifying antigens within immune complexes.

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