Characterization of rubella-specific humoral immunity following two doses of MMR vaccine using proteome microarray technology

Abstract

Introduction//BackgroundThe lack of standardization of the currently used commercial anti-rubella IgG antibody assays leads to frequent misinterpretation of results for samples with low/equivocal antibody concentration. The use of alternative approaches in rubella serology could add new information leading to a fuller understanding of rubella protective immunity and neutralizing antibody response after vaccination.MethodsWe applied microarray technology to measure antibodies to all rubella virus proteins in 75 high and 75 low rubella virus-specific antibody responders after two MMR vaccine doses. These data were used in multivariate penalized logistic regression modeling of rubella-specific neutralizing antibody response after vaccination.ResultsWe measured antibodies to all rubella virus structural proteins (i.e., the glycoproteins E1 and E2 and the capsid C protein) and to the non-structural protein P150. Antibody levels to each of these proteins were: correlated with the neutralizing antibody titer (p<0.006); demonstrated differences between the high and the low antibody responder groups (p<0.008); and were components of the model associated with/predictive of vaccine-induced rubella virus-specific neutralizing antibody titers (misclassification error = 0.2).ConclusionOur study supports the use of this new technology, as well as the use of antibody profiles/patterns (rather than single antibody measures) as biomarkers of neutralizing antibody response and correlates of protective immunity in rubella virus serology.

Current advances in antibody‐based serum biomarker studies: From protein microarray to phage display

“…In addition, the huge consumption of IVTT reagents for the second route should not be ignored. Streptococcus pneumoniae >2000 IVTT b [41] Rubella virus Unknown IVTT [42] Leptospira interrogans Unknown IVTT [43] Mycobacterium tuberculosis 3963 IVTT [44] Chlamydia trachomatis 908 Purified [45] Mycobacterium tuberculosis 4045 IVTT [19] Chlamydia trachomatis 898 IVTT [46] Shigella spp. 2133 IVTT [47] Plasmodium falciparum 1266 IVTT [48] Plasmodium falciparum 7455 IVTT [49] Helicobacter pylori 1440 IVTT [22] a Proteome microarrays fabricated in recent years.…”

Role of Multiomics Data to Understand Host–Pathogen Interactions in COVID-19 Pathogenesis

“… 16 On one hand, in-depth proteome analysis using mass spectrometry (MS)-based proteomics has come of age and has previously allowed disease pathways of the Ebola virus, 17 Zika virus, 18 Chikungunya virus, 19 and dengue 20 and influenza virus infection to be unraveled. 21 Also, technologies such as proteome arrays 22 have been used to assess host responses in other viral diseases such as papillomavirus, 23 rubella virus, 24 herpes simplex virus 1 and 2, 25 and Zika virus. 26 Equally, MS-based metabolomics has been used by various researchers in search of potential diagnostics biomarker candidates in various diseases, namely autism spectrum disorders in children, 27 oncometabolite in cancer, 28 , 29 infectious diseases such as tuberculosis, 30 and Lyme disease.…”

Transcriptional signatures associated with rubella virus‐specific humoral immunity after a third dose of MMR vaccine in women of childbearing age

“…Protection against rubella is conferred predominantly via neutralizing antibodies directed against the two surface rubella virus glycoproteins E1 and E2 (most notably, against E1, which is suggested to be the immunodominant antigen) [2]. The current vaccine is considered highly effective in most individuals (95% effectiveness) and confers sufficient immunity (including neutralizing antibodies) to protect against disease [3].…”

“…Coefficients from the univariate linear regressions between neutralizing antibody response and the log 2 fold-change of the rubella virus-stimulated gene expression relative to the unstimulated gene expression (standardized) 2. Coefficients from multivariate linear regression between neutralizing antibody response and the log 2 fold-change of the rubella virus-stimulated gene expression relative to the unstimulated gene expression (standardized), incorporating all genes listed in the table in the model.…”